query: A 0.21-V minimum input, 73.6% maximum efficiency, fully integrated voltage boost converter with MPPT for low-voltage energy harvesters

positive: Design of charge pump circuit with consideration of gate-oxide reliability in low-voltage CMOS processes A new charge pump circuit with consideration of gateoxide reliability is designed with two pumping branches in this paper. The charge transfer switches in the new proposed circuit can be completely turned on and turned off, so its pumping efficiency is higher than that of the traditional designs. Moreover, the maximum gate-source and gate-drain voltages of all devices in the proposed charge pump circuit do not exceed the normal operating power supply voltage (VDD). Two test chips have been implemented in a 0.35m 3.3-V CMOS process to verify the new proposed charge pump circuit. The measured output voltage of the new proposed four-stage charge pump circuit with each pumping capacitor of 2 pF to drive the capacitive output load is around 8.8 V under 3.3-V power supply (VDD = 3 3 V), which is limited by the junction breakdown voltage of the parasitic pn-junction in the given process. The new proposed circuit is suitable for applications in low-voltage CMOS processes because of its high pumping efficiency and no overstress across the gate oxide of devices.

positive: 23.8 A 34V charge pump in 65nm bulk CMOS technology Recent advances in MEMS-based oscillators have resulted in their proliferation in timing applications that were once exclusive to quartz-based devices [1]. For applications requiring low phase noise - e.g., cellular, GPS and high-speed serial links - one possible approach is to bias the MEMS resonator at a higher DC voltage to reduce its motional impedance and increase signal energy [2]. Realizing high-voltage charge pumps in bulk CMOS technology is limited by the breakdown voltage of the well/substrate diodes shown in Fig. 23.8.1(a) and Fig. 23.8.1(b). This breakdown limit is even lower with technology scaling and is <;10V in a 22nm CMOS node. Systems with high-voltage requirements often resort to older, high-voltage-tolerant nodes or exotic technologies that limit MEMS integration into SoCs. This work demonstrates a charge pump design in 65nm technology with a three-fold increase in the output voltage range. Highvoltage tolerance is enabled by the proposed well-biasing arrangement and oxide isolation. The pump achieves 34V output by using three different charge pump cells that tradeoff achievable voltage range and power efficiency to achieve a peak efficiency of 38%. Additionally, finger capacitors are optimized to ensure reliability while maintaining efficiency.

positive: Ultra-High-Voltage Charge Pump Circuit in Low-Voltage Bulk CMOS Processes With Polysilicon Diodes An on-chip ultra-high-voltage charge pump circuit realized with the polysilicon diodes in the low-voltage bulk CMOS process is proposed in this work. Because the polysilicon diodes are fully isolated from the silicon substrate, the output voltage of the charge pump circuit is not limited by the junction breakdown voltage of MOSFETs. The polysilicon diodes can be implemented in the standard CMOS processes without extra process steps. The proposed ultra-high-voltage charge pump circuit has been fabricated in a 0.25-mum 2.5-V standard CMOS process. The output voltage of the four-stage charge pump circuit with 2.5-V power-supply voltage (VDD=2.5 V) can be pumped up to 28.08 V, which is much higher than the n-well/p-substrate breakdown voltage (~18.9 V) in a 0.25-mum 2.5-V bulk CMOS process

positive: Complex thermoelectric materials. Thermoelectric materials, which can generate electricity from waste heat or be used as solid-state Peltier coolers, could play an important role in a global sustainable energy solution. Such a development is contingent on identifying materials with higher thermoelectric efficiency than available at present, which is a challenge owing to the conflicting combination of material traits that are required. Nevertheless, because of modern synthesis and characterization techniques, particularly for nanoscale materials, a new era of complex thermoelectric materials is approaching. We review recent advances in the field, highlighting the strategies used to improve the thermopower and reduce the thermal conductivity.

negative: AVANT-GUARD: scalable and vigilant switch flow management in software-defined networks Among the leading reference implementations of the Software Defined Networking (SDN) paradigm is the OpenFlow framework, which decouples the control plane into a centralized application. In this paper, we consider two aspects of OpenFlow that pose security challenges, and we propose two solutions that could address these concerns. The first challenge is the inherent communication bottleneck that arises between the data plane and the control plane, which an adversary could exploit by mounting a "control plane saturation attack" that disrupts network operations. Indeed, even well-mined adversarial models, such as scanning or denial-of-service (DoS) activity, can produce more potent impacts on OpenFlow networks than traditional networks. To address this challenge, we introduce an extension to the OpenFlow data plane called "connection migration", which dramatically reduces the amount of data-to-control-plane interactions that arise during such attacks. The second challenge is that of enabling the control plane to expedite both detection of, and responses to, the changing flow dynamics within the data plane. For this, we introduce "actuating triggers" over the data plane's existing statistics collection services. These triggers are inserted by control layer applications to both register for asynchronous call backs, and insert conditional flow rules that are only activated when a trigger condition is detected within the data plane's statistics module. We present Avant-Guard, an implementation of our two data plane extensions, evaluate the performance impact, and examine its use for developing more scalable and resilient SDN security services.

negative: Less-structured time in children's daily lives predicts self-directed executive functioning Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

negative: SECURITY ENHANCEMENT OF ADVANCED ENCRYPTION STANDARD (AES) USING TIME-BASED DYNAMIC KEY GENERATION Login is the first step which is conducted every time we access to a system and only granted to those who are entitled. Login is very important and it is a part of the security of a system. Registered user or guest and administrator are two kinds of users that have the privilege and access. Nowadays, wrongdoers are always on the dark side and frequently try to gain access to the system. To provide a better security, it is important to enhance the access mechanism and to evaluate the authentication process. Message-Digest 5 (MD5) is one of the algorithms that commonly used in the login system. Although it has been so popular, but the algorithm is still vulnerable to dictionary attacks and rainbow tables. In addition to the hash function algorithm, Advanced Encryption Standard (AES) algorithm alternatively can be the best choice in the login authentication process. This study is proposed to develop a dynamic key generation on the AES algorithm using the function of time. The experimental results obtained that AES key can be generated at random based on the value of the time when a user logs in with a particular active period. In this implementation, the security authentication becomes stronger because of changes in key generating chipertext changes for each encryption process. Based on the time as a valuable benchmark, the result shown that AES encryption-decryption process is relatively fast with a time average about 0.0023 s.

negative: Efficiently solving dynamic Markov random fields using graph cuts In this paper, we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP estimates for dynamically changing MRF models of labeling problems in computer vision, such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, we show how to efficiently compute the maximum flow in a modified version of the graph. Our experiments showed that the time taken by our algorithm is roughly proportional to the number of edges whose weights were different in the two graphs. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video and compare it with the best known st-mincut algorithm. The results show that the dynamic graph cut algorithm is much faster than its static counterpart and enables real time image segmentation. It should be noted that our method is generic and can be used to yield similar improvements in many other cases that involve dynamic change in the graph

negative: Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD Majority of the existing copy-move forgery detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy-move forgery using stationary wavelet transform (SWT) which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD) of an image. Also, the concept of colour-based segmentation used in this work helps to achieve blur invariance. The authors’ experimental results prove the efficiency of the proposed method in detection of copy-move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state-of-theart.

negative: Automatic Service Discovery of IP Cameras over Wide Area Networks with NAT Traversal A novel framework for remote service discovery and access of IP cameras with Network address Translation (NAT) traversal is presented in this paper. The proposed protocol, termed STDP (Service Trader Discovery Protocol), is a hybrid combination of Zeroconf and SIP (Session Initial Protocol). The Zeroconf is adopted for the discovery and/or publication of local services; whereas, the SIP is used for the delivery of local services to the remote nodes. In addition, both the SIP-ALG (Application Layer Gateway) and UPnP (Universal Plug and Play)-IGD (Internet Gateway Device) protocols are used for NAT traversal. The proposed framework is well-suited for high mobility applications where the fast deployment and low administration efforts of IP cameras are desired.

negative: Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build truly, high-performance and highly-flexible big data management systems. We report on state-of-the-art approaches, methodologies and trends, and finally conclude by providing open problems and challenging research directions to be considered by future efforts.

negative: Impact of Technostress on End-User Satisfaction and Performance Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. recent literature has encapsulated these cognitions in 304 TarafDar, Tu, aND ragu-NaThaN the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users’ satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual’s satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users’ appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users’ satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use–related outcomes. Key wordS and phraSeS: end-user performance, end-user satisfaction, ICT use, information overload, survey research, technostress, user involvement. organizaTional uSe of inforMaTion and coMMunicaTionS TechnologieS (icT) has become complex, real-time, ubiquitous, and functionally pervasive, often requiring users to process information simultaneously and continually from different applications and devices. Consequently, ICT users deal with a surfeit of information, experience frequent interruptions from different computing devices and applications, and engage in multitasking on them. further, they are increasingly frustrated and overwhelmed by continual efforts required to master the frequent introduction of new ICT. In recent times, therefore, managers have experienced negative cognitions toward ICT. recent academic literature has encapsulated these cognitions in the concept of technostress [65], which is stress caused by an inability to cope with the demands of organizational computer usage. Technostress describes the stress that users experience as a result of application multitasking, constant connectivity, information overload, frequent system upgrades and consequent uncertainty, continual relearning and consequent job-related IMpaCT Of TEChNOSTrESS ON END-uSEr SaTISfaCTION aND pErfOrMaNCE 305 insecurities, and technical problems associated with the organizational use of ICT. from the perspective of psychological outcomes, technostress reduces individuals’ job satisfaction and commitment to their organization [65]. from the point of view of behavioral outcomes, it reduces individuals’ productivity at work [75]. What is the potential effect of these negative cognitions on outcomes relating to the individual’s use of ICT? There is research evidence [8, 35, 63] that in spite of continuing sophistication in the functional capabilities of ICT, technology overload and ICT-mediated interruptions reduce the satisfaction of users with the ICT they employ for their tasks and their ability to benefit from them. Emerging practitioner perspectives [22, 78] reinforce these findings, suggesting that excessive information, frequent upgrades, and blurring of work–home boundaries induced by pervasive connectivity result in inaccurate information processing and poor task-related decision making using ICT and in dissatisfaction with ICT. at the same time, organizational computing environments have an important role for the end user in generating, accessing, analyzing, and using business information and in accomplishing application-enabled workflows. In such environments, it is critical that end users be satisfied with the applications and systems they interact with and work on and be able to effectively use them to enhance the quality and efficiency of their work tasks [16, 45, 81]. understanding how these “negative” or “unconstructive” aspects of ICT and the negative cognitions associated with them affect user satisfaction and ICT-mediated task performance is therefore clearly an important step in managing and appropriating benefits from current commuting environments. research in this area is emerging and scarce, and there is absence of (1) a theoretical framework for understanding and (2) systematic empirical investigation for demonstrating how these aspects impact the user in his or her day-to-day application of ICT to organizational tasks. recognizing that the concept of technostress encompasses user-perceived negative cognitions about ICT, we apply the technostress lens to this research gap and enunciate a twofold objective of this paper. The first objective is to understand the (adverse) effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work. Second, we identify mechanisms that can reduce these effects. Specifically, we draw from the end-user computing and organizational stress literature to develop and validate a model that analyzes the effects of factors that create technostress on end-user satisfaction and end-user performance. We further study how stress-alleviating factors, such as mechanisms that facilitate user involvement in ICT development and those that support innovation, can be used to weaken technostresscreating conditions and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create techno stress reduce users’ satisfaction with the ICT they use and the extent to which they utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use diminish the factors that create technostress for them and increase their satisfaction with the ICT they use. These mechanisms also have a positive effect on users’ appropriation of ICT for productivity and innovation in their tasks. The paper 306 TarafDar, Tu, aND ragu-NaThaN contributes to emerging literature on “negative” outcomes of organizational ICT use by (1) highlighting the importance of technostress on users’ satisfaction with ICT and on their performance—that is, their productivity and innovation—in ICT-mediated tasks, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and outcomes. Next, we provide a theoretical background by reviewing studies on stress and technostress, and then we develop the research model and hypotheses. In the fourth section, we describe methods and validate the model. The paper closes with a discussion of the findings and research and managerial implications. Theoretical Background Organizational Stress STreSS iS a cogniTive STaTe experienced by an individual when there is an “environmental situation that is perceived as presenting a demand which threatens to exceed the person’s capabilities and resources for meeting it, under conditions where he or she expects a substantial differential in the rewards and costs from meeting the demand versus not meeting it” [56, p. 1351]. It is a reaction to the perceived imbalance between a person and the environment [9, 19], borne out of anticipation of an inability to adequately respond to demand from the imbalance and accompanied by the expectation of negative consequences for inadequate response [56]. as shown in figure 1, the phenomenon of stress consists of three aspects—stressors, strain, and situational variables [52]. Stressors represent factors or conditions that create stress. These conditions could be due to the individual’s role [50] and task [56]. role stress and task stress have been widely studied. More recently, technology in general and ICT in particular have Figure 1. general relationships among Stressors, Strain, and Situational Variables IMpaCT Of TEChNOSTrESS ON END-uSEr SaTISfaCTION aND pErfOrMaNCE 307 emerged as conditions for the cause of stress [20]; this is the focus of technostress. Strain represents the outcome of stress. Individuals experience strain as a result of being ex

negative: WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and accoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awarenss to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives. J. White and H. Turner Dept. of Electrical and Computer Engineering Virginia Tech E-mail: {julesw,hturner0}@vt.edu C. Thompson, B. Dougherty, and D.C. Schmidt Dept. of Electrical Engineering and Computer Science Vanderbilt University E-mail: {cthompson,briand,schmidt}@dre.vanderbilt.edu

negative: Asynchronous Large-Scale Graph Processing Made Easy Scaling large iterative graph processing applications through parallel computing is a very important problem. Several graph processing frameworks have been proposed that insulate developers from low-level details of parallel programming. Most of these frameworks are based on the bulk synchronous parallel (BSP) model in order to simplify application development. However, in the BSP model, vertices are processed in fixed rounds, which often leads to slow convergence. Asynchronous executions can significantly accelerate convergence by intelligently ordering vertex updates and incorporating the most recent updates. Unfortunately, asynchronous models do not provide the programming simplicity and scalability advantages of the BSP model. In this paper, we combine the easy programmability of the BSP model with the high performance of asynchronous execution. We have designed GRACE, a new graph programming platform that separates application logic from execution policies. GRACE provides a synchronous iterative graph programming model for users to easily implement, test, and debug their applications. It also contains a carefully designed and implemented parallel execution engine for both synchronous and user-specified built-in asynchronous execution policies. Our experiments show that asynchronous execution in GRACE can yield convergence rates comparable to fully asynchronous executions, while still achieving the near-linear scalability of a synchronous BSP system.

negative: An IT Balance Scorecard Design under Service Management Philosophy The present study proposes a design for an Information Technology Balanced Scorecard (IT BSC) that integrates with business and environment and balances and optimizes deployment and control of the Information Technology strategy. This Strategic and Tactical IT Planning model enhances IT's role in obtaining and measuring its contribution to business value: optimizing IT operational efficiency or proposing new business ideas with a high IT component. The proposed model stimulates innovation in each of the parts of the IT Services lifecycle, is based on IT Service Management best practices and uses ideas from the ITIL v3 standard.

negative: Clustering Based Approach for Customers ’ Classification from Electrical Distribution Systems The paper presents a clustering based approach for consumers’ classification in representative categories characterized by typical load profiles. For determination of the consumption categories, every customer must be characterized by the following primary information: daily (monthly) energy consumption, average load and peak load. The used database contains the daily load curves corresponding to the small customers from a rural distribution system from Romania. The obtained results it demonstrated that the proposed approach can be used with the success in building specific tariff structures of the customers or in the optimal operation and planning of distribution systems.

negative: The Media ' s Influence on Body Image Disturbance and Eating Disorders : We ' ve Reviled Them , Now Can We Rehabilitate Them ? Survey, correlational, randomized control, and covariance structure modeling investigations indicate that the media are a significant factor in the development and maintenance of eating and shape-related disorders. One specific individual difference variable, intemalization of societal pressures regarding prevailing standards of attractiveness, appears to moderate or even mediate the media's effects on women's body satisfaction and eating dysfunction. Problematic media messages inherent in existing media portrayals of eating disorders are apparent, leading researchers to pinpoint intervention strategies that might counteract such viewpoints. Social activism and social marketing approaches are suggested as methods for fighting negative media messages. The media itself is one potential vehicle for communicating productive, accurate, and deglamorized messages about eating and shape-related disorders.

negative: Statistical Edge Detection: Learning and Evaluating Edge Cues We formulate edge detection as statistical inference. This statistical edge detection is data driven, unlike standard methods for edge detection which are model based. For any set of edge detection filters (implementing local edge cues) we use pre-segmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge. Edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses. This approach emphasizes the necessity of modeling the image background (the off-edges). We represent the conditional probability distributions non-parametrically and learn them on two different datasets of 100 (Sowerby) and 50 (South Florida) images. Multiple edges cues, including chrominance and multiple-scale, are combined by using their joint distributions. Hence this cue combination is optimal in the statistical sense. We evaluate the effectiveness of different visual cues using the Chernoff information and Receiver Operator Characteristic (ROC) curves. This shows that our approach gives quantitatively better results than the Canny edge detector when the image background contains

negative: Single-Fed Low Profile Broadband Circularly Polarized Stacked Patch Antenna A simple technique is developed in this communication to increase the axial ratio (AR) bandwidth and achieve good impedance matching of a single-fed low profile circularly polarized (CP) stacked patch antenna. The proposed antenna is composed of a driven patch layer and a parasitic patch layer. The driven patch layer consists of a truncated main patch, a parasitic patch and a probe feeding structure while the stacked patch layer is comprised of five patches. The proposed antenna combines the attractive features such as low profile, wide impedance and AR bandwidths, high gain as well as easiness of design, manufacture and integration. The antenna operating at 6 GHz band is designed and fabricated on an FR4 substrate and the overall volume is 0.8 λ0 ×0.8 λ0 ×0.09 λ0. Measured results show that the antenna achieves an impedance bandwidth of more than 30% for dB, a 3-dB AR bandwidth of about 20.7%, and a gain level of over 7.9 dBi within the 3-dB AR bandwidth.

negative: Man-At-The-End attacks: Analysis, taxonomy, human aspects, motivation and future directions Man-At-The-End (MATE) attacks and fortifications are difficult to analyze, model, and evaluate predominantly for three reasons: firstly, the attacker is human and, therefore, utilizes motivation, creativity, and ingenuity. Secondly, the attacker has limitless and authorized access to the target. Thirdly, all major protections stand up to a determined attacker till a certain period of time. Digital assets range from business to personal use, from consumer devices to home networks, the public Internet, the cloud, and the Internet of Things – where traditional computer and network security are inadequate to address MATE attacks. MATE is fundamentally a hard problem. Much of the extant focus to deal with MATE attacks is purely technical; though security is more than just a technical issue. The main objective of the paper is to mitigate the consequences of MATE attacks through the human element of security and highlight the need for this element to form a part of a holistic security strategy alongside the necessary techniques and technologies. This paper contributes by taking software protection (SP) research to a new realm of challenges. Moreover, the paper elaborates the concept of MATE attacks, the different forms, and the analysis of MATE versus insider threats to present a thematic taxonomy of a MATE attack. The ensuing paper also highlights the fundamental concept of digital assets, and the core protection mechanisms and their qualitative comparison against MATE attacks. Finally, we present state-of-the-art trends and cutting-edge future research directions by taking into account only the human aspects for young researchers and professionals. & 2014 Elsevier Ltd. All rights reserved.

negative: A multiperiod set covering location model for dynamic redeployment of ambulances Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided. 2006 Elsevier Ltd. All rights reserved.

negative: Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

negative:
Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks. BACKGROUND AND PURPOSE
Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA).

METHODS
We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors.

RESULTS
BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ratios for every successive tertile were greater than that of the previous one (2nd tertile: 2.78, 1.57 to 4.91; 3rd tertile: 7.69, 4.53 to 13.03). Significant associations with the risk of stroke/TIA were also found for waist circumference and waist-to-stature ratio (odds ratio 4.25, 2.65 to 6.84 and odds ratio 4.67, 2.82 to 7.73, top versus bottom tertile after risk adjustment, respectively).

CONCLUSIONS
Markers of abdominal adiposity showed a graded and significant association with risk of stroke/TIA, independent of other vascular risk factors. Waist circumference and related ratios can better predict cerebrovascular events than BMI.

negative: Sugar: Secure GPU Acceleration in Web Browsers Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

negative: Firmaster: Analysis Tool for Home Router Firmware As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.

negative: An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews Opinion mining is a challenging task to identify the opinions or sentiments underlying user generated contents, such as online product reviews, blogs, discussion forums, etc. Previous studies that adopt machine learning algorithms mainly focus on designing effective features for this complex task. This paper presents our approach based on tree kernels for opinion mining of online product reviews. Tree kernels alleviate the complexity of feature selection and generate effective features to satisfy the special requirements in opinion mining. In this paper, we define several tree kernels for sentiment expression extraction and sentiment classification, which are subtasks of opinion mining. Our proposed tree kernels encode not only syntactic structure information, but also sentiment related information, such as sentiment boundary and sentiment polarity, which are important features to opinion mining. Experimental results on a benchmark data set indicate that tree kernels can significantly improve the performance of both sentiment expression extraction and sentiment classification. Besides, a linear combination of our proposed tree kernels and traditional feature vector kernel achieves the best performances using the benchmark data set.

negative: Postural hand synergies for tool use. Subjects were asked to shape the right hand as if to grasp and use a large number of familiar objects. The chosen objects typically are held with a variety of grips, including "precision" and "power" grips. Static hand posture was measured by recording the angular position of 15 joint angles of the fingers and of the thumb. Although subjects adopted distinct hand shapes for the various objects, the joint angles of the digits did not vary independently. Principal components analysis showed that the first two components could account for >80% of the variance, implying a substantial reduction from the 15 degrees of freedom that were recorded. However, even though they were small, higher-order (more than three) principal components did not represent random variability but instead provided additional information about the object. These results suggest that the control of hand posture involves a few postural synergies, regulating the general shape of the hand, coupled with a finer control mechanism providing for small, subtle adjustments. Because the postural synergies did not coincide with grip taxonomies, the results suggest that hand posture may be regulated independently from the control of the contact forces that are used to grasp an object.

negative: An evaluation of educational values of YouTube videos for academic writing The aim is to assess the impact of YouTube videos about academic writing and its skills on the writing performance of students. Theoretical perspectives from constructivism and associated learning models are used to inform the purpose of the research. The contextual setting is matriculation students awaiting admission to higher institutions. The population is 40 students belonging to a class aimed at assisting disadvantaged students in their academic writing in Scottsville, Province of KwaZulu-Natal, South Africa. The students are broken into two groups – control/traditional teaching and the treatment/YouTube facilitated groups. Consequently, a dominant qualitative approach is adopted using focus group discussion, interviews and tests to identify underlying patterns, methods and approaches to best fit academic writing guides and videos to improve user experiences of the media for academic writing. The fundamental results show that positive characterisations of user experiences include innovation, surprise, playfulness and stimulation whereas the narratives that are not satisfying are categorised as dissatisfaction, frustration, dissolution, disappointment, anger, confusion and irritation. Ultimately, the major findings of the research have the potential to improve user experiences on the platform by highlighting how and when the positive and negative experiences of users occur and a mapping of the differences in the academic writing performance between the two groups and best practices. Finally, the results have implications for pedagogy the fitting of YouTube videos to academic writing instruction.

negative: BGP-RCN: improving BGP convergence through root cause notification This paper presents a new mechanism, called BGP with root cause notification (BGP-RCN), that provides an upper bound of O(d) on routing convergence delay for BGP, where d is the network diameter as measured by the number of AS hops. BGP-RCN lets each routing update message carry the information about the specific cause which triggered the update message. Once a node v receives the first update message triggered by a link failure, v can avoid using any paths that have been obsoleted by the same failure. The basic approach in BGP-RCN is applicable to path vector routing protocols in general. Our analysis and simulation show that BGP-RCN can achieve substantial reduction in both BGP convergence time and the total number of intermediate route changes. 2004 Elsevier B.V. All rights reserved.

negative: Technical Committees Switched-Capacitor Power Electronics Circuits By One of the main orientations in power electronics in the last decade has been the development of switching-mode converters without inductors and transformers. Light weight, small size and high power density are the result of using only switches and capacitors in the power stage of these converters. Thus, they serve as ideal power supplies for mobile electronic systems (e.g. cellular phones, personal digital assistants, and so forth). Switched-capacitor (SC) converters, with their large voltage conversion ratio, promise to be a response to such challenges of the 21st century as high-efficiency converters with low EMI emissions and the ability to realize steep step-down of the voltage (to 3V or even a smaller supply voltage for integrated circuits) or steep step-up of the voltage for automotive industry or internet services in the telecom industry. This paper is a tutorial of the main results in SC-converter research and design. Switched-Capacitor Converters— A Typical Power Electronics Contribution of the CAS Society From all of the research subjects in the Power Systems and Power Electronics Circuits Technical Committee—the stability of power systems, analysis of power electronics circuits (which are time-variable circuits with internally-controlled switches), chaos, modeling and simulation of converters, hardswitching and soft-switching converters, and so forth—the SCconverter is probably the most pertinent contribution of the CAS Society to power electronics. In fact, most of the researchers in this field belong to our Society’s technical committee and most of the breakthrough contributions on this subject have appeared in CAS publications. The reason for this is clear. Pursuing small size filters in the 1950’s, the circuit theory community found a solution in the elimination of bulky inductors from the structure of a passive filter. Active and then switched-capacitor filters showed the possibility of implementing the filtering function without using magnetic devices. The power electronics designer faced a similar challenge: the request for miniaturization of the power supply. This could be achieved only by eliminating the inductors and transformers. In a converter, the inductor serves the two purposes of processing energy and filtering the output voltage. The use of a switched-capacitor circuit for processing energy seemed doomed to failure because it is known that charging a capacitor from zero is achieved at a 50% efficiency. Ten years of research were needed to overcome the efficiency dilemma and to develop high-efficiency switched-capacitor energy-processing circuits. First SC Converters and Basic Principles The primary goal of any switching-mode power converter is to provide a constant (DC or AC) output voltage at its load, despite variations in the input voltage or load. A control element, therefore, has to be introduced in the process of transmitting energy so that the converter (power stage) changes its topology cyclically, and the durations of the switching topologies are adjusted for regulation purposes. The first SC converters were developed by a group of researchers from Kumamoto, Japan, who processed a DC unregulated voltage toward a DC regulated voltage [1, 2, 4]. These DC-DC converters were soon followed by AC-DC converters [3], DC-AC inverters [5] and AC-AC transformers [6]. By avoiding magnetic elements, these circuits, realized in a hybrid technology, featured a high power density (23W/inch). Figure 1(a). Basic SC step-down DC-DC converter. *A. Ioinovici is with the Department of Electrical and Electronics Engineering, Holon Academic Institute of Technology, Israel. R V S

negative: Analysis and Comparison of a Fast Turn-On Series IGBT Stack and High-Voltage-Rated Commercial IGBTS High-voltage-rated solid-state switches such as insulated-gate bipolar transistors (IGBTs) are commercially available up to 6.5 kV. Such voltage ratings are attractive for pulsed power and high-voltage switch-mode converter applications. However, as the IGBT voltage ratings increase, the rate of current rise and fall are generally reduced. This tradeoff is difficult to avoid as IGBTs must maintain a low resistance in the epitaxial or drift region layer. For high-voltage-rated IGBTs with thick drift regions to support the reverse voltage, the required high carrier concentrations are injected at turn on and removed at turn off, which slows the switching speed. An option for faster switching is to series multiple, lower voltage-rated IGBTs. An IGBT-stack prototype with six, 1200 V rated IGBTs in series has been experimentally tested. The six-series IGBT stack consists of individual, optically isolated, gate drivers and aluminum cooling plates for forced air cooling which results in a compact package. Each IGBT is overvoltage protected by transient voltage suppressors. The turn-on current rise time of the six-series IGBT stack and a single 6.5 kV rated IGBT has been experimentally measured in a pulsed resistive-load, capacitor discharge circuit. The IGBT stack has also been compared to two IGBT modules in series, each rated at 3.3 kV, in a boost circuit application switching at 9 kHz and producing an output of 5 kV. The six-series IGBT stack results in improved turn-on switching speed, and significantly higher power boost converter efficiency due to a reduced current tail during turn off. The experimental test parameters and the results of the comparison tests are discussed in the following paper

negative: A Compact Fractal Loop Rectenna for RF Energy Harvesting This letter presents a compact fractal loop rectenna for RF energy harvesting at GSM1800 bands. First, a fractal loop antenna with novel in-loop ground-plane impedance matching is proposed for the rectenna design. Also, a high-efficiency rectifier is designed in the loop antenna to form a compact rectenna. Measured results show that an efficiency of 61% and an output dc voltage of 1.8 V have been achieved over 12-kΩ resistor for 10 μW/cm2 power density at 1.8 GHz. The rectenna is able to power up a battery-less LCD watch at a distance of 10 m from the cell tower. The proposed rectenna is compact, easy to fabricate, and useful for various energy harvesting applications.


query: Convex Color Image Segmentation with Optimal Transport Distances

positive: Fast Computation of Wasserstein Barycenters We present new algorithms to compute the mean of a set of empirical probability measures under the optimal transport metric. This mean, known as the Wasserstein barycenter, is the measure that minimizes the sum of its Wasserstein distances to each element in that set. We propose two original algorithms to compute Wasserstein barycenters that build upon the subgradient method. A direct implementation of these algorithms is, however, too costly because it would require the repeated resolution of large primal and dual optimal transport problems to compute subgradients. Extending the work of Cuturi (2013), we propose to smooth the Wasserstein distance used in the definition of Wasserstein barycenters with an entropic regularizer and recover in doing so a strictly convex objective whose gradients can be computed for a considerably cheaper computational cost using matrix scaling algorithms. We use these algorithms to visualize a large family of images and to solve a constrained clustering problem.

positive: Proximal Splitting Methods in Signal Processing ∗ The proximity operator of a convex function is a natural exte nsion of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex opt imization problems, has recently been introduced in the arena of inverse problems an d, especially, in signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. The e proximal splitting methods are shown to capture and extend several well-known a lgorithms in a unifying framework. Applications of proximal methods in signal r ecovery and synthesis are discussed.

positive: Super-Samples from Kernel Herding We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting “kernel herding” algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rateO(1/T )which is much faster than the usual O(1/ √ T ) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.

positive: Clustering with Bregman Divergences A wide variety of distortion functions, such as squared Eucl idean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been use d for clustering. In this paper, we propose and analyze parametric hard and soft clustering algori thms based on a large class of distortion functions known as Bregman divergences. The proposed algor ithms unify centroid-based parametric clustering approaches, such as classical kmeans, the Linde-Buzo-Gray (LBG) algorithm and information-theoretic clustering, which arise by special hoices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the c lassicalkmeans algorithm, while generalizing the method to a large class of clustering loss functi o s. This is achieved by first posing the hard clustering problem in terms of minimizing the loss i n Bregman information, a quantity motivated by rate distortion theory, and then deriving an it erative algorithm that monotonically decreases this loss. In addition, we show that there is a biject ion between regular exponential families and a large class of Bregman divergences, that we call regula r Bregman divergences. This result enables the development of an alternative interpretation o f a efficient EM scheme for learning mixtures of exponential family distributions, and leads to a si mple soft clustering algorithm for regular Bregman divergences. Finally, we discuss the connection be tween rate distortion theory and Bregman clustering and present an information theoretic analys is of Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman information.

positive: Fast Image Recovery Using Variable Splitting and Constrained Optimization We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.

negative: Needs, affect, and interactive products - Facets of user experience Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human–Computer Interaction look for ways to broaden their understanding of what constitutes ‘‘pleasurable experiences” with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as ‘‘motivator” and pragmatic quality as ‘‘hygiene factor.” Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution). 2010 Elsevier B.V. All rights reserved.

negative: MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.

negative: An integrated 122GHz differential frequency doubler with 37GHz bandwidth in 130 nm SiGe BiCMOS technology This paper describes an integrated frequency multiplier, implemented as a Gilbert cell based frequency doubler in a 130 nm SiGe BiCMOS technology. The circuit demonstrates a 3 dB bandwidth of 97–134GHz with peak output power of 1 dBm for 1 dBm input power. The fundamental suppression, measured at the single-ended output, is better than 21 dBc while the frequency doubler consumes 69mW from a 3.3V supply. The doubler is preceded by a differential amplifier functioning as an active balun to generate a differential signal for the Gilbert cell.

negative: Survey on Fraud Detection Techniques Using Data Mining Now a days, fraud is a million dollar business and every year it is increasing more and more. In a crime survey in 2009, tells that close to 30% of the company’s worldwide reported falling victim to fraud in the past year. Fraud involves individual or in a form of groups who intentionally act secretly to rob another of something of worth, for their own profit. Fraud is as against the humanity and it is unlimited variety of different forms. So in this paper, we introduce different methods and techniques to tackle the fraud and how to prevent the fraud into the organization, businesses, and hospital and insurance companies. Fraud prevention is a continuing battle. Many website owners must continually fight against many kinds of crime ranging from stealing identity theft and credit cards and many other types of fraud related to that. Internet Fraud is also very popular, where fraudster can steal the credit card number and buy thing from the different websites.

negative: Disease recognition in Mango crop using modified rotational kernel transform features Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT based scheme is used to calculate the directional features and histograms for plant parts like leaf and fruit of Mango crop. These histograms and the directional feature set with use of artificial neural network lead to a better recognition technique of Anthracnose disease which occurs in form of black spots on fruits and leaves of mango plant. The results obtained using proposed MRKT directional feature set have shows the better results with accuracy up to 98%.

negative: A 12-bit 1.6 GS/s interleaved SAR ADC with dual reference shifting and interpolation achieving 17.8 fJ/conv-step in 65nm CMOS A 12-bit SAR ADC architecture with dual reference shifting and interpolation technique has been proposed and implemented with 8-way time interleaving in 65nm CMOS. The proposed technique converts 4 bits per SAR conversion cycle with reduced overhead, which is a key to achieve both high speed and resolution while maintaining low power consumption. The measured peak SNDR is 72dB and remains above 65.3dB at 1-GHz input frequency at sample rate of 1.6 GS/s. It achieves a record power efficiency of 17.8fJ/conv-step among the recently published high-speed/resolution ADCs.

negative: Deep Clustering with Convolutional Autoencoders Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.

negative: Oblivious Dynamic Searchable Encryption on Distributed Cloud Systems Dynamic Searchable Symmetric Encryption (DSSE) allows search/update operations over encrypted data via an encrypted index. However, DSSE has been shown to be vulnerable to statistical inference attacks, which can extract a significant amount of information from access patterns on encrypted index and files. While generic Oblivious Random Access Machine (ORAM) can hide access patterns, it has been shown to be extremely costly to be directly used in DSSE setting. By exploiting the distributed cloud infrastructure, we develop a series of Oblivious Distributed DSSE schemes called ODSE, which enable oblivious access on the encrypted index with a high security and improved efficiency over the use of generic ORAM. Specifically, ODSE schemes are 3×-57× faster than applying the state-of-the-art generic ORAMs on encrypted dictionary index in real network settings. One of the proposed ODSE schemes offers desirable security guarantees such as informationtheoretic security with robustness against malicious servers. These properties are achieved by exploiting some of the unique characteristics of searchable encryption and encrypted index, which permits us to harness the computation and communication efficiency of multi-server PIR and Write-Only ORAM simultaneously. We fully implemented ODSE and have conducted extensive experiments to assess the performance of our proposed schemes in a real cloud environment.

negative: Automatic categorization of figures in scientific documents Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose an architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for realworld use. Our tools will be integrated into a scientific document digital library.

negative: Persistence Diagrams with Linear Machine Learning Models Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features of persistence diagrams is significantly important for practical applications. In this paper, we propose a unified method for the inverse analysis by combining linear machine learning models with persistence images. The method is applied to point clouds and cubical sets, showing the ability of the statistical inverse analysis and its advantages. This work is partially supported by JSPS KAKENHI Grant Number JP 16K17638, JST CREST Mathematics15656429, JST “Materials research by Information Integration” Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, Structural Materials for Innovation Strategic Innovation Promotion Program D72, and New Energy and Industrial Technology Development Organization (NEDO). Ippei Obayashi Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan Tel.: +81-22-217-6320 Fax: +81-22-217-5129 E-mail: ippei.obayashi.d8@tohoku.ac.jp Y. Hiraoka Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. Center for Materials research by Information Integration (CMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS). E-mail: hiraoka@tohoku.ac.jp

negative:
The impact of modified Hatha yoga on chronic low back pain: a pilot study. PURPOSE
The purpose of this randomized pilot study was to evaluate a possible design for a 6-week modified hatha yoga protocol to study the effects on participants with chronic low back pain.

PARTICIPANTS
Twenty-two participants (M = 4; F = 17), between the ages of 30 and 65, with chronic low back pain (CLBP) were randomized to either an immediate yoga based intervention, or to a control group with no treatment during the observation period but received later yoga training.

METHODS
A specific CLBP yoga protocol designed and modified for this population by a certified yoga instructor was administered for one hour, twice a week for 6 weeks. Primary functional outcome measures included the forward reach (FR) and sit and reach (SR) tests. All participants completed Oswestry Disability Index (ODI) and Beck Depression Inventory (BDI) questionnaires. Guiding questions were used for qualitative data analysis to ascertain how yoga participants perceived the instructor, group dynamics, and the impact of yoga on their life.

ANALYSIS
To account for drop outs, the data were divided into better or not categories, and analyzed using chi-square to examine differences between the groups. Qualitative data were analyzed through frequency of positive responses.

RESULTS
Potentially important trends in the functional measurement scores showed improved balance and flexibility and decreased disability and depression for the yoga group but this pilot was not powered to reach statistical significance. Significant limitations included a high dropout rate in the control group and large baseline differences in the secondary measures. In addition, analysis of the qualitative data revealed the following frequency of responses (1) group intervention motivated the participants and (2) yoga fostered relaxation and new awareness/learning.

CONCLUSION
A modified yoga-based intervention may benefit individuals with CLB, but a larger study is necessary to provide definitive evidence. Also, the impact on depression and disability could be considered as important outcomes for further study. Additional functional outcome measures should be explored. This pilot study supports the need for more research investigating the effect of yoga for this population.

negative: GestureGAN for Hand Gesture-to-Gesture Translation in the Wild Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator G and a discriminator D, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients. In addition, we present the Frechet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at https://github.com/Ha0Tang/GestureGAN.

negative: A review of optical Braille recognition The process to take a Braille document image and convert its content into its equivalent natural language characters, is called Optical Braille Recognition (OBR). It involves two main consecutive steps: Braille cell recognition and Braille cell transcription. Braille cell recognition contains few steps including: Image acquisition, Image De-skewing, Image pre-processing, dot recognition, cell recognition and segmentation. Image transcription aims to convert the segmented Braille cell, into its equivalent natural language characters. In this survey we aim to study the earlier works done by other researchers on both Braille cell recognition and transcription.

negative: Towards Zero Unknown Word in Neural Machine Translation Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.

negative: Memory traces in dynamical systems. To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed square root N. This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence.

negative: Webcam-Based Eye Movement Analysis Using CNN Due to its low price, webcam has become one of the most promising sensors with the rapid development of computer vision. However, the accuracies of eye tracking and eye movement analysis are largely limited by the quality of the webcam videos. To solve this issue, a novel eye movement analysis model is proposed based on five eye feature points rather than a single point (such as the iris center). First, a single convolutional neural network (CNN) is trained for eye feature point detection, and five eye feature points are detected for obtaining more useful eye movement information. Subsequently, six types of original time-varying eye movement signals can be constructed by feature points of each frame, which can reduce the dependency of the iris center in low quality videos. Finally, behaviors-CNN can be trained by the time-varying eye movement signals for recognizing different eye movement patterns, which is capable of avoiding the influence of errors from the basic eye movement type detection and artificial eye movement feature construction. To validate the performance, a webcam-based visual activity data set was constructed, which contained almost 0.5 million frames collected from 38 subjects. The experimental results on this database have demonstrated that the proposed model can obtain promising results for natural and convenient eye movement-based applications.

negative: Experimental study of desalination using direct contact membrane distillation : a new approach to flux enhancement New membrane distillation configurations and a new membrane module were investigated to improve water desalination. The performances of three hydrophobic microporous membranes were evaluated under vacuum enhanced direct contact membrane distillation (DCMD) with a turbulent flow regime and with a feed water temperature of only 40 ◦C. The new configurations provide reduced temperature polarization effects due to better mixing and increased mass transport of water due to higher permeability through the membrane and due to a total pressure gradient across the membrane. Comparison with previously reported results in the literature reveals that mass transport of water vapors is substantially improved with the new approach. The performance of the new configuration was investigated with both NaCl and synthetic sea salt feed solutions. Salt rejection was greater than 99.9% in almost all cases. Salt concentrations in the feed stream had only a minor effect on water flux. The economic aspects of the enhanced DCMD process are briefly discussed and comparisons are made with the reverse osmosis (RO) process for desalination. © 2003 Elsevier B.V. All rights reserved.

negative: An efficient active ripple filter for use in DC-DC conversion When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.

negative: Revealing the problems with 802.11 medium access control protocol in multi-hop wireless ad hoc networks The IEEE 802.11 medium access control (MAC) protocol is a standard for wireless LANs, it is also widely used in almost all test beds and simulations for the research in wireless mobile multi-hop ad hoc networks. However, this protocol was not designed for multi-hop networks. Although it can support some ad hoc network architecture, it is not intended to support the wireless mobile ad hoc network, in which multi-hop connectivity is one of the most prominent features. In this paper, we focus on the following question: can IEEE 802.11 MAC protocol function well in multi-hop networks? By presenting several serious problems encountered in transmission control protocol (TCP) connections in an IEEE 802.11 based multi-hop network, we show that the current TCP protocol does not work well above the current 802.11 MAC layer. The relevant problems include the TCP instability problem found in this kind of network, the a b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

negative: Electricity Generation Using an Air-Cathode Single Chamber Microbial Fuel Cell in the Presence and Absence of a Proton Exchange Membrane Microbial fuel cells (MFCs) are typically designed as a two-chamber system with the bacteria in the anode chamber separated from the cathode chamber by a polymeric proton exchange membrane (PEM). Most MFCs use aqueous cathodes where water is bubbled with air to provide dissolved oxygen to electrode. To increase energy output and reduce the cost of MFCs, we examined power generation in an air-cathode MFC containing carbon electrodes in the presence and absence of a polymeric proton exchange membrane (PEM). Bacteria present in domestic wastewater were used as the biocatalyst, and glucose and wastewater were tested as substrates. Power density was found to be much greater than typically reported for aqueous-cathode MFCs, reaching a maximum of 262 ( 10 mW/m2 (6.6 ( 0.3 mW/L; liquid volume) using glucose. Removing the PEM increased the maximum power density to 494 ( 21 mW/m2 (12.5 ( 0.5 mW/L). Coulombic efficiency was 40-55% with the PEM and 9-12% with the PEM removed, indicating substantial oxygen diffusion into the anode chamber in the absence of the PEM. Power output increased with glucose concentration according to saturation-type kinetics, with a half saturation constant of 79 mg/L with the PEM-MFC and 103 mg/L in the MFC without a PEM (1000 Ω resistor). Similar results on the effect of the PEM on power density were found using wastewater, where 28 ( 3 mW/m2 (0.7 ( 0.1 mW/L) (28% Coulombic efficiency) was produced with the PEM, and 146 ( 8 mW/m2 (3.7 ( 0.2 mW/L) (20% Coulombic efficiency) was produced when the PEM was removed. The increase in power output when a PEM was removed was attributed to a higher cathode potential as shown by an increase in the open circuit potential. An analysis based on available anode surface area and maximum bacterial growth rates suggests that mediatorless MFCs may have an upper order-of-magnitude limit in power density of 103 mW/m2. A cost-effective approach to achieving power densities in this range will likely require systems that do not contain a polymeric PEM in the MFC and systems based on direct oxygen transfer to a carbon cathode. Introduction Bacteria can be used to catalyze the conversion of organic matter into electricity (1-7). Fuel cells that use bacteria are loosely classified here as two different types: biofuel cells that generate electricity from the addition of artificial electron shuttles (mediators) (8-12) and microbial fuel cells (MFCs) that do not require the addition of a mediator (5, 7, 13-15). It has recently been shown that certain metal-reducing bacteria, belonging primarily to the family Geobacteraceae, can directly transfer electrons to electrodes using electrochemically active redox enzymes such as cytochromes on their outer membrane (16-18). These so-called mediatorless MFCs are considered to have more commercial application potential than biofuel cells because the mediators used in biofuel cells are expensive and toxic to the microorganisms (7). MFCs typically produce power at a density of less than 50 mW/m2 (normalized to anode projected surface area) (7, 13, 19). In a MFC, two electrodes (anode and cathode) are each placed in water in two chambers joined by a proton exchange membrane (PEM). The main disadvantage of a twochamber MFC is that the solution cathode must be aerated to provide oxygen to the cathode. It is known that the power output of an MFC can be improved by increasing the efficiency of the cathode. For example, power is increased by adding ferricyanide (20) to the cathode chamber. It is possible, however, to design a MFC that does not require that the cathode be placed in water. In hydrogen fuel cells, the cathode is bonded directly to the PEM so that oxygen in the air can directly react at the electrode (21). This technique was successfully used to produce electricity from wastewater in a single chamber MFC by Liu et al. (15). Park and Zeikus (20) produced a maximum of 788 mW/m2 using a unique system with a Mn4+ graphite anode and a direct-air Fe3+ graphite cathode (20). Because the power output of MFCs is low relative to other types of fuel cells, reducing their cost is essential if power generation using this technology is to be an economical method of energy production. Most studies have used relatively expensive solid graphite electrodes (7, 19), but graphite-felt (14) and carbon cloth (15) can also be used. The use of air-driven cathodes can reduce MFC costs because passive oxygen transfer to the cathode using air does not require energy intensive air sparging of the water. Finally, PEMs such as Nafion are quite expensive. We wondered if this material was essential for power production in an MFC. We therefore designed and constructed a carbon-cloth, aircathode fuel cell to try to increase power density to levels not previously achieved with aqueous cathode systems. To study the effect of the PEM on power production, we compared power density for glucose and wastewater feeds with the system in the presence and absence of a polymeric PEM.

negative: Advanced Machine Learning Technologies and Applications The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition. In our work we propose a feature selection approach based genetic algorithm to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLP).

negative: Effects of birth spacing on maternal, perinatal, infant, and child health: a systematic review of causal mechanisms. This systematic review of 58 observational studies identified hypothetical causal mechanisms explaining the effects of short and long intervals between pregnancies on maternal, perinatal, infant, and child health, and critically examined the scientific evidence for each causal mechanism hypothesized. The following hypothetical causal mechanisms for explaining the association between short intervals and adverse outcomes were identified: maternal nutritional depletion, folate depletion, cervical insufficiency, vertical transmission of infections, suboptimal lactation related to breastfeeding-pregnancy overlap, sibling competition, transmission of infectious diseases among siblings, incomplete healing of uterine scar from previous cesarean delivery, and abnormal remodeling of endometrial blood vessels. Women's physiological regression is the only hypothetical causal mechanism that has been proposed to explain the association between long intervals and adverse outcomes. We found growing evidence supporting most of these hypotheses.

negative: Paternal filicide in Québec. In this retrospective study, relevant demographic, social, and clinical variables were examined in 77 cases of paternal filicide. Between 1991 and 2001, all consecutive coroners' files on domestic homicide in Québec, Canada, were reviewed, and 77 child victims of 60 male parent perpetrators were identified. The results support data indicating that more fathers commit filicide than do mothers. A history of family abuse was characteristic of a substantial number of cases, and most of the cases involved violent means of homicide. Filicide was frequently (60%) followed by the suicide of the perpetrator and more so (86%) in cases involving multiple sibling victims. The abuse of drugs and alcohol was rare. At the time of the offense, most of the perpetrators were suffering from a psychiatric illness, usually depressive disorder. Nearly one-third were in a psychotic state. The proportion of fatal abuse cases was comparatively low. Many of the perpetrators had had contact with health professionals prior to the offense, although none had received treatment for a psychiatric illness.

negative: Adaptive Comparator Bias-Current Control of 0.6 V Input Boost Converter for ReRAM Program Voltages in Low Power Embedded Applications A novel method adaptively controls the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM). ReRAM is ideal for low-power sensors because it is faster and lower voltage than NAND flash. However, ReRAM's voltage design needs to consider program current variation due to data pattern dependency and cell reliability is sensitive to small voltage fluctuations. The proposed control method of the boost converter architecture has three proposals. First, low program voltage ripple (VRIPPLE) and high program energy efficiency is obtained with an adaptive 2 to 8 μA comparator bias-current (ICMP) control method. ICMP is adjusted depending on the number of program bits (NBITS) in the SET data. In 2 cases of NBITS = 1 and 16, and compared to a conventional scheme (ICMP = 8 μA and 2 μA), respectively, VRIPPLE decreases by 41.2% and energy efficiency improves by 6.6%, respectively. Secondly, as NBITS decrease during SET verify, ICMP also decreases, which further reduces SET energy by 8.9 pJ. Thirdly, although the conventional boost converter fails in the `SS' process condition, an additional cross-coupled charge-pump boosts the buffer circuit, which provides full functional operation over the input range of 0.6 to 1.0 V.

negative: ECOFEN: An End-to-end energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.

negative: Automatic Segmentation of Hair in Images Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.

negative: A License Plate-Recognition Algorithm for Intelligent Transportation System Applications In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low

negative: A Neural Network Color Classifier in HSV Color Space In this paper, a neural network approach for color classification in HSV color space based on color JND (just noticeable difference) is presented. The HSV color samples are generated using an interactive tool based on JND concept and these samples are used for supervised training. A four layer feed forward neural network is trained for classifying a given color pair as similar or dissimilar pair. An interactive tool for color pixel comparison is developed for testing the color classifier on real life images. Research shows that neural network classifier in HSV color space works better than RGB classifier in terms of efficiency for segmentation purposes. Thus the experimentation results can be used for segmenting real life images.

negative: Multitarget miss distance via optimal assignment The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.

negative: Supervised tensor learning Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem.

negative: Human Face Detection by using Skin Color Segmentation, Face Features and Regions Properties The goal of face detection is to locate all regions that contain a face. This paper has a simple face detection procedure which has two major steps, first to segment skin region from an image, and second, to decide these regions contain human face or not. Our procedure is based on skin color segmentation and human face features (knowledge-based approach). In this paper, we used RGB, YCbCr, CEILAB (L*a*b) and HSV color models for skin color segmentation. These color models with thresholds, help to remove non skin like pixels from an image. We tested each skin region, that skin region is actually represents a human face or not, by using human face features based on knowledge of geometrical properties of human face. The experiment result shows that, the algorithm gives hopeful results. At last, we concluded this paper and proposed future work.

negative: ANALYSIS OF SEGMENTATION PARAMETERS IN ECOGNITION SOFTWARE USING HIGH RESOLUTION QUICKBIRD MS IMAGERY For objectoriented classification approaches, main step is the segmentation part of the imagery. In eCognition v.4.0.6 software, with the segmentation process, meaningful objects can be created for following steps. In the experimental imagery with 2.4m ground sampling distance (GSD) has been used and several different parameters e.g. scale, color/shape and smoothness/compactness parameters have been tested accordingly. Additionally, segmentation parameters were set to low and high values and thus, dissimilarity of segmentation results were examined.