query: The fully informed particle swarm: simpler, maybe better

positive: On computable numbers, with an application to the Entscheidungsproblem The "computable" numbers may be described briefly as the real numbers whose expressions as a decimal are calculable by finite means. Although the subject of this paper is ostensibly the computable numbers. it is almost equally easy to define and investigate computable functions of an integral variable or a real or computable variable, computable predicates, and so forth. The fundamental problems involved are, however, the same in each case, and I have chosen the computable numbers for explicit treatment as involving the least cumbrous technique. I hope shortly to give an account of the relations of the computable numbers, functions, and so forth to one another. This will include a development of the theory of functions of a real variable expressed in terms of computable numbers. According to my definition, a number is computable if its decimal can be written down by a machine.

positive: The particle swarm - explosion, stability, and convergence in a multidimensional complex space The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. Even though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some undesirable dynamical properties, notably the particles’ velocities needed to be limited in order to control their trajectories. The present paper analyzes a particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

positive: No free lunch theorems for optimization A framework is developed to explore the connection between e ective optimization algorithms and the problems they are solving A number of no free lunch NFL theorems are presented that establish that for any algorithm any elevated performance over one class of problems is exactly paid for in performance over another class These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem Applications of the NFL theorems to information theoretic aspects of optimization and benchmark measures of performance are also presented Other issues addressed are time varying optimization problems and a priori head to head minimax distinctions between optimization algorithms distinctions that can obtain despite the NFL theorems enforcing of a type of uniformity over all algorithms

positive: No Free Lunch Theorems for Search We show that all algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions In particular if algorithm A outperforms algorithm B on some cost functions then loosely speaking there must exist exactly as many other functions where B outperforms A Starting from this we analyze a number of the other a priori characteristics of the search problem like its geometry and its information theoretic aspects This analysis allows us to derive mathematical benchmarks for assessing a particular search algo rithm s performance We also investigate minimax aspects of the search problem the validity of using characteristics of a partial search over a cost function to predict future behavior of the search algorithm on that cost function and time varying cost functions We conclude with some discussion of the justi ability of biologically inspired search methods

positive: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report on a large scale experiment over half a million runs of C and a Naive Bayes algorithm to estimate the e ects of di erent parameters on these al gorithms on real world datasets For cross validation we vary the number of folds and whether the folds are strati ed or not for boot strap we vary the number of bootstrap sam ples Our results indicate that for real word datasets similar to ours the best method to use for model selection is ten fold strati ed cross validation even if computation power allows using more folds

negative: Information Retrieval from Neurophysiological Signals One of the ultimate goals of neuroscience is decoding someone’s intentions directly from his/her brain activities. In this thesis, we aim at pursuing this goal in different scenarios. Firstly, we show the possibility of creating a user-centric music/movie recommender system by employing neurophysiological signals. Regarding this, we employed a brain decoding paradigm in order to classify the features extracted from brain signals of participants watching movie/music video clips, into our target classes (two broad music genres and four broad movie genres). Our results provide a preliminary experimental evidence towards user-centric music/movie content retrieval by exploiting brain signals. Secondly, we addressed one of the main issue of the applications of brain decoding algorithms. Generally, the performance of such algorithms suffers from the constraint of having few and noisy samples, which is the case in most of the neuroimaging datasets. In order to overcome this limitation, we employed an adaptation paradigm in order to transfer knowledge from another domain (e.g. large-scale image domain) to the brain domain. We experimentally show that such adaptation procedure leads to improved results. We performed such adaptation pipeline on different tasks (i.e. object recognition and genre classification) using different neuroimaging modalities (i.e. fMRI, EEG, and MEG). Thirdly, we aimed at one of the fundamental goals in brain decoding which is reconstructing the external stimuli using only the brain features. Under this scenario, we show the possibility of regressing the stimuli spectrogram using time-frequency analysis of the brain signals. Finally, we conclude the thesis by summarizing our contributions and discussing the future directions and applications of our research.

negative: An energy framework for the network simulator 3 (NS-3) The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or in the overall network, while running network simulations. This requires the underlying simulator to support energy consumption and energy source modeling. Currently however, ns-3 does not provide any support for modeling energy consumption or energy sources. In this paper, we introduce an integrated energy framework for ns-3, with models for energy source as well as energy consumption. We present the design and implementation of the overall framework and the specific models therein. Further, we show how the proposed framework can be used in ns-3 to simulate energy-aware protocols in a wireless network.

negative:
Differentiating optic disc edema from optic nerve head drusen on optical coherence tomography. OBJECTIVE
To assess optical coherence tomography in differentiating optic disc edema (ODE) due to papilledema and other optic neuropathies from optic nerve head drusen (ONHD).

METHODS
Optical coherence tomographic images from 60 subjects (20 with ODE, 20 with ONHD, and 20 control subjects) were assessed qualitatively and quantitatively. Qualitative criteria for ODE included an elevated optic nerve head with smooth internal contour and subretinal hyporeflective space (SHYPS) with recumbent "lazy V" pattern. Optic nerve head drusen displayed a "lumpy-bumpy" internal optic nerve contour and a rapid decline in SHYPS thickness. Quantitative comparisons included retinal nerve fiber layer and SHYPS thickness.

RESULTS
Optical coherence tomography differentiated ODE from ONHD qualitatively (sensitivity, 63%; specificity, 63%) and quantitatively (sensitivity, 80%; specificity, 90%). Respective differences in mean retinal nerve fiber layer thickness between ODE and ONHD were significant (P < .002) superiorly (206.8 vs 121.7 microm), nasally (176.3 vs 78.6 microm), inferiorly (247.2 vs 153.8 microm), and temporally (180.0 vs 85.5 microm). Respective differences in mean SHYPS thickness between ODE and ONHD were significant (P < .001) at radii of 0.75 mm (512.1 vs 274.4 microm), 1.5 mm (291.4 vs 103.0 microm), and 2.0 mm (145.5 vs 60.7 microm).

CONCLUSION
Optical coherence tomography can differentiate ODE from ONHD, particularly when the nasal retinal nerve fiber layer and SHYPS thickness at the 2.0-mm radius are greater than 86 microm and 127 microm, respectively.

negative: Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme Ball and plate system is the extension of traditional ball and beam system. In this paper, a trajectory planning and tracking problem of ball and plate system is put forward to proof-test diverse control schemes. Firstly, we derive the mathematical model of the ball and plate system in detail. Then a hierarchical fuzzy control scheme is proposed to deal with this problem. Our scheme is composed of three levels. The lowest level is a TS type fuzzy tacking controller; the middle level is a fuzzy supervision controller that takes actions in extreme situations; and the top level is a fuzzy planning controller that determines the desired trajectory. In order to optimize the ball’s moving trajectory, the genetic algorithm is introduced to adjust the output membership functions of the fuzzy planning controller. Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time. c © 2003 Elsevier B.V. All rights reserved.

negative: Efficacy and safety of Meriva®, a curcumin-phosphatidylcholine complex, during extended administration in osteoarthritis patients. In a previous three-month study of Meriva, a proprietary curcumin-phosphatidylcholine phytosome complex, decreased joint pain and improvement in joint function were observed in 50 osteoarthritis (OA) patients. Since OA is a chronic condition requiring prolonged treatment, the long-term efficacy and safety of Meriva were investigated in a longer (eight months) study involving 100 OA patients. The clinical end points (Western Ontario and McMaster Universities [WOMAC] score, Karnofsky Performance Scale Index, and treadmill walking performance) were complemented by the evaluation of a series of inflammatory markers (interleukin [IL]-1beta, IL-6, soluble CD40 ligand [sCD40L], soluble vascular cell adhesion molecule (sVCAM)-1, and erythrocyte sedimentation rate [ESR]). This represents the most ambitious attempt, to date, to evaluate the clinical efficacy and safety of curcumin as an anti-inflammatory agent. Significant improvements of both the clinical and biochemical end points were observed for Meriva compared to the control group. This, coupled with an excellent tolerability, suggests that Meriva is worth considering for the long-term complementary management of osteoarthritis.

negative: Anomaly detection and monitoring in Internet of Things communication The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.

negative: Fractional Order ControlA Tutorial Many real dynamic systems are better characterized using a non-integer order dynamic model based on fractional calculus or, differentiation or integration of noninteger order. Traditional calculus is based on integer order differentiation and integration. The concept of fractional calculus has tremendous potential to change the way we see, model, and control the nature around us. Denying fractional derivatives is like saying that zero, fractional, or irrational numbers do not exist. In this paper, we offer a tutorial on fractional calculus in controls. Basic definitions of fractional calculus, fractional order dynamic systems and controls are presented first. Then, fractional order PID controllers are introduced which may make fractional order controllers ubiquitous in industry. Additionally, several typical known fractional order controllers are introduced and commented. Numerical methods for simulating fractional order systems are given in detail so that a beginner can get started quickly. Discretization techniques for fractional order operators are introduced in some details too. Both digital and analog realization methods of fractional order operators are introduced. Finally, remarks on future research efforts in fractional order control are given.

negative: Machine Learning Approaches for Catchphrase Extraction in Legal Documents The purpose of this research was to automatically extract catchphrases given a set of Legal documents. For this task, our focus was mainly on the Machine learning approaches: a comparative approach was used between the unsupervised and supervised approaches. The idea was to compare the different approaches to see which one of the two was comparatively better for automatic catchphrase extraction given a dataset of Legal documents. To perform this, two open source text mining software were used; one for the unsupervised approach while another one was used for the supervised approach. We then fine tuned some parameters for each tool before extracting catchphrases. The training dataset was used when fine tuning parameters in order to find optimal parameters that were then used for generating the final catchphrases. Different metrics were used to evaluate the results. We used the most common measures in Information Extraction which include Precision and Recall and the results from the two Machine learning approaches were compared. In general our results showed that the supervised approach performed far much better than the unsupervised approach.

negative:
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task.
 In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers' true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility.

negative: A hierarchical framework for evaluating simulation software In simulation software selection problems, packages are evaluated either on their own merits or in comparison with other packages. In either method, a comprehensive list of criteria for evaluation of simulation software is essential for proper selection. Although various simulation software evaluation checklists do exist, there are dierences in the lists provided and the terminologies used. This paper presents a hierarchical framework for simulation software evaluation consisting of seven main groups and several subgroups. An explanation for each criterion is provided and an analysis of the usability of the proposed framework is further discussed. Ó 1999 Elsevier Science B.V. All rights reserved.

negative: Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks We use an autoencoder composed of stacked restricted Boltzmann machines to extract features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period versus 10.53% for basic momentum.

negative: A New Method for Optimization of Dynamic Ride Sharing System Dynamic ridesharing is a profitable way to reduce traffic and carbon emissions by providing an opportunity for a flexible and affordable service that utilizes vehicle seating space. Matching of ride seeker requests with the rides, distributed over the roads is a tedious work. While fulfilling the request of all passengers, the total travel distance of the trip may get increased. Therefore, this article proposes optimal dynamic ridesharing system which matches rides and requests in real time by satisfying multiple participant constraints (e.g. time bounds, availability of empty seat, maximum allowed deviation distance and minimized route ride) to minimize the total travel distance. To efficiently match ride givers and riders we are proposing a novel dynamic ride matching algorithm MRB (Minimal route bisearching algorithm) considering all above mentioned constraints. We demonstrate working of our algorithm by developing a prototype and evaluated our system on GPS (Global positioning system) trajectories of Lahore city dataset. Evaluated results are compared with existing algorithms which shows that our system significantly reduces the travel distance and computation cost in comparison with other recent ride searching methods to maximize efficiency.

negative: Multi-domain spoken language understanding with transfer learning This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domaindependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems. 2009 Elsevier B.V. All rights reserved.

negative: On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.

negative: Compact 4-element MIMO antenna with isolation enhancement for 4G LTE terminals In this work, a wide-band, planar, printed inverted-F antenna (PIFA) is proposed with multiple-input-multiple-output (MIMO) antenna configuration. The MIMO antenna system consists of 4-elements operating at 2.1 GHz frequency band for 4G LTE applications. The proposed design is compact, low profile and suitable for wireless handheld devices. The MIMO antenna is fabricated on commercially available FR4 substrate with εr equal to 4.4. The dimensions of single element are 26×6 mm2 with board volume equal to 100×60×0.8 mm3. Isolation is improved by 5 dB in the proposed design using ground slots. Characteristics mode analysis (CMA) is used to analyze the behaviour of the antenna system.

negative: LIMERIC: a linear message rate control algorithm for vehicular DSRC systems Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high density scenarios.

negative: Measuring cultural awareness in nursing students. Recognizing the need for a valid and reliable way to measure outcomes of a program to promote multicultural awareness among nursing faculty and students, the authors developed a cultural awareness scale. In the first phase of the study, a scale consisting of 37 items was generated from a literature review on cultural awareness, sensitivity, and competence in nursing. A Cronbach's alpha reliability coefficient of .91 was obtained from a sample of 72 student nurses. In the second phase, the items were presented to a panel of experts in nursing and culture to determine content validity. A content validity index of .88 was calculated, and the total number of items on the scale was reduced to 36. The scale then was administered to 118 nursing students. Data from the two samples then were combined, and factor analysis was conducted to support construct validity. Cronbach's alpha for the combined samples was .82.

negative: CORPORATE SOCIAL RESPONSIBILITY IN EMERGING MARKETS Purpose: This paper analyses CSR initiatives in emerging markets from developed-countriesbased multinational companies (MNCs). Methods: The analysis is based on eight case studies with data collected through in-depth interviews with senior managers of the companies representing 85% of the Spanish foreign investments in Latin America. Results: The results show that CSR initiatives from these companies seem to be guided by Instrumental Theories as they use these initiatives as a strategic tool to achieve economic objectives, seek a positive relation between them and their financial performance, and use them to strengthen their reputation. Conclusions: The findings tend to indicate that Instrumental Theories of CSR seem to apply for Western MNCs operating in emerging markets.

negative: Thin skin elastodynamics We present a novel approach for simulating thin hyperelastic skin. Real human skin is only a few millimeters thick. It can stretch and slide over underlying body structures such as muscles, bones, and tendons, revealing rich details of a moving character. Simulating such skin is challenging because it is in close contact with the body and shares its geometry. Despite major advances in simulating elastodynamics of cloth and soft bodies for computer graphics, such methods are difficult to use for simulating thin skin due to the need to deal with non-conforming meshes, collision detection, and contact response. We propose a novel Eulerian representation of skin that avoids all the difficulties of constraining the skin to lie on the body surface by working directly on the surface itself. Skin is modeled as a 2D hyperelastic membrane with arbitrary topology, which makes it easy to cover an entire character or object. Unlike most Eulerian simulations, we do not require a regular grid and can use triangular meshes to model body and skin geometry. The method is easy to implement, and can use low resolution meshes to animate high-resolution details stored in texture-like maps. Skin movement is driven by the animation of body shape prescribed by an artist or by another simulation, and so it can be easily added as a post-processing stage to an existing animation pipeline. We provide several examples simulating human and animal skin, and skin-tight clothes.

negative: Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.

negative: OpenHSM: An Open Key Life Cycle Protocol for Public Key Infrastructure's Hardware Security Modules The private keys used in a PKI are its most important asset. Protect these keys from unauthorised use or disclosure is essential to secure a PKI. Relying parties need assurances that the private key used to sign their certificates is controlled and managed following pre-defined statement policy. Hardware Security Modules (HSM) offer physical and logical protection and should be considered for any PKI deployment. The software that manages keys inside an HSM should control all life cycle of a private key. Normally this kind of equipment implements a embedded key management protocol and this protocols are not available to public scrutiny due to industrial interests. Other important issue is that HSMs are targeted in their development to the Bank industry and not to PKI, making some important PKI issues, like, strict key usage control and a secure auditing trail, play a secondary role. This paper presents an open protocol to securely manage private keys inside HSMs. The protocol is described, analysed and discussed.

negative: Knowledge Begets Knowledge: Steps towards Assisted Knowledge Acquisition in Cyc The Cyc project is predicated on the idea that, in order to be effective and flexible, computer software must have an understanding of the context in which its tasks are performed. We believe this context is what is known informally as “common sense.” Over the last twenty years, sufficient common sense knowledge has been entered into Cyc to allow it to more effectively and flexibly support an important task: increasing its own store of world knowledge. In this paper, we describe the Cyc knowledge base and inference system, enumerate the means that it provides for knowledge elicitation, including some means suitable for use by untrained or lightly trained volunteers, review some ways in which we expect to have Cyc assist in verifying and validating collected knowledge, and describe how we expect the knowledge acquisition process to accelerate in the future.

negative: Trends in computer science research Keywords in the ACM Digital Library and IEEE Xplore digital library and in NSF grants anticipate future CS research.

negative: Spinal and supraspinal factors in human muscle fatigue. Muscle fatigue is an exercise-induced reduction in maximal voluntary muscle force. It may arise not only because of peripheral changes at the level of the muscle, but also because the central nervous system fails to drive the motoneurons adequately. Evidence for "central" fatigue and the neural mechanisms underlying it are reviewed, together with its terminology and the methods used to reveal it. Much data suggest that voluntary activation of human motoneurons and muscle fibers is suboptimal and thus maximal voluntary force is commonly less than true maximal force. Hence, maximal voluntary strength can often be below true maximal muscle force. The technique of twitch interpolation has helped to reveal the changes in drive to motoneurons during fatigue. Voluntary activation usually diminishes during maximal voluntary isometric tasks, that is central fatigue develops, and motor unit firing rates decline. Transcranial magnetic stimulation over the motor cortex during fatiguing exercise has revealed focal changes in cortical excitability and inhibitability based on electromyographic (EMG) recordings, and a decline in supraspinal "drive" based on force recordings. Some of the changes in motor cortical behavior can be dissociated from the development of this "supraspinal" fatigue. Central changes also occur at a spinal level due to the altered input from muscle spindle, tendon organ, and group III and IV muscle afferents innervating the fatiguing muscle. Some intrinsic adaptive properties of the motoneurons help to minimize fatigue. A number of other central changes occur during fatigue and affect, for example, proprioception, tremor, and postural control. Human muscle fatigue does not simply reside in the muscle.

negative: A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.

negative: Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pheromone update by particle swarm optimization. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm. Keywordssocial network analysis; clique problem; ACO; PSO.

negative: Catfish Binary Particle Swarm Optimization for Feature Selection The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (“catfish particles”), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-oneout cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to six classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.

negative: Swarm Intelligence Approaches for Grid Load Balancing With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.

negative: On the control of planar cable-driven parallel robot via classic controllers and tuning with intelligent algorithms This paper presents different classical control approaches for planar cable-driven parallel robots. For the proposed robot, PD and PID controllers are designed based on the concept of pole placement method. In order to optimize and tune the controller parameters of planar cable-driven parallel robot, Differential Evaluation, Particle Swarm Optimization and Genetic algorithms are applied as optimization techniques. The simulation results of Genetic algorithm, Particle Swarm Optimization and Differential Evaluation algorithms reveal that the output results of tunes controllers with Particle Swarm Optimization and Differential Evaluation algorithms are more similar than Genetic algorithm and the processing time of Differential Evaluation is less than Genetic algorithm and Particle Swarm Optimization. Moreover, performance of the Particle Swarm Optimization and Differential Evaluation algorithms are better than Genetic algorithm for tuning the controllers parameters.

negative: A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.

negative: Extensive Analysis and Prediction of Optimal Inventory levels in supply chain management based on Particle Swarm Optimization Algorithm Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain. The dynamic nature of the excess stock level and shortage level from one period to another is a serious issue. In addition, consideration of multiple products and more supply chain members leads to very complex inventory management process. Moreover, the supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. A better optimization methodology would consider all these factors in the prediction of the optimal stock levels to be maintained in order to minimize the total supply chain cost. Here, we are proposing an optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, to overcome the impasse in maintaining the optimal stock levels at each member of the supply chain.

negative: A particle filter for monocular vision-aided odometry We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.


query: Detecting Webshell Based on Random Forest with FastText

positive: Bag of Tricks for Efficient Text Classification This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.

positive: Convolutional Neural Networks for Sentence Classification We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

positive: A Matrix Decomposition based Webshell Detection Method WebShell is a web based network backdoor. With the help of WebShells, the hacker can take any control of the web services illegally. The current method of detecting WebShells is just matching the eigenvalues or detecting the produced flow or services, which is hard to find new kinds of WebShells. To solve these problems, this paper analyzes the different features of a page and proposes a novel matrix decomposition based WebShell detection algorithm. The algorithm is a supervised machine learning algorithm. By analyzing and learning features of known existing and non-existing WebShell pages, the algorithm can make predictions on the unknown pages. The experimental results show that, compared with traditional detection methods, this algorithm spends less time, has higher accuracy and recall rate, and can detect new kinds of WebShells with a certain probability, overcoming the shortcomings of the traditional feature matching based method, improving the accuracy and recalling rate of WebShell detection.

positive: Classification and Regression by randomForest Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.

positive: Webshell detection techniques in web applications With widely adoption of online services, malicious web sites have become a malignant tumor of the Internet. Through system vulnerabilities, attackers can upload malicious files (which are also called webshells) to web server to create a backdoor for hackers' further attacks. Therefore, finding and detecting webshell inside web application source code are crucial to secure websites. In this paper, we propose a novel method based on the optimal threshold values to identify files that contain malicious codes from web applications. Our detection system will scan and look for malicious codes inside each file of the web application, and automatically give a list of suspicious files and a detail log analysis table of each suspicious file for administrators to check further. The Experimental results show that our approach is applicable to identify webshell more efficient than some other approaches.

negative:
Countering kernel rootkits with lightweight hook protection Kernel rootkits have posed serious security threats due to their stealthy manner. To hide their presence and activities, many rootkits hijack control flows by modifying control data or hooks in the kernel space. A critical step towards eliminating rootkits is to protect such hooks from being hijacked. However, it remains a challenge because there exist a large number of widely-scattered kernel hooks and many of them could be dynamically allocated from kernel heap and co-located together with other kernel data. In addition, there is a lack of flexible commodity hardware support, leading to the socalled protection granularity gap -- kernel hook protection requires byte-level granularity but commodity hardware only provides page level protection.
 To address the above challenges, in this paper, we present HookSafe, a hypervisor-based lightweight system that can protect thousands of kernel hooks in a guest OS from being hijacked. One key observation behind our approach is that a kernel hook, once initialized, may be frequently "read"-accessed, but rarely write"-accessed. As such, we can relocate those kernel hooks to a dedicated page-aligned memory space and then regulate accesses to them with hardware-based page-level protection. We have developed a prototype of HookSafe and used it to protect more than 5,900 kernel hooks in a Linux guest. Our experiments with nine real-world rootkits show that HookSafe can effectively defeat their attempts to hijack kernel hooks. We also show that HookSafe achieves such a large-scale protection with a small overhead (e.g., around 6% slowdown in performance benchmarks).

negative:
Social Collaborative Viewpoint Regression with Explainable Recommendations A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction.
 In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.

negative: Feature hashing for large scale multitask learning Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.

negative: PERFORMANCE ANALYSIS OF CHAOTIC CHIRP SPREAD SPECTRUM SYSTEM IN MULTIPATH ENVIRONMENT Wireless channels often include multipath propagation, in which the signal has more than one path from the transmitter to the receiver. Atmospheric reflection or refraction, as well as reflection from the ground or from objects such as buildings may cause this phenomena. The aim of this research is to study the effects of multipath environment on the performance of a proposed chaotic chirp spread spectrum system (CCSSS). The benchmark performance of the proposed system is investigated in term of bit error rate (BER) under multipath environment. A Quadrature Phase Shift Keying (QPSK) modulation scheme and a matched filter based on adaptive threshold decision were used in this work. To assess the performance of this system, simulations were performed to compare the proposed system with the traditional frequency modulation differential chaotic shift keying (DCSK) spread spectrum system. The proposed system may be considered as a well qualified communication system in multipath environment.

negative: Knowledge Technologies Several technologies are emerging that provide new ways to capture, store, present and use knowledge. This book is the first to provide a comprehensive introduction to five of the most important of these technologies: Knowledge Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and Semantic Webs. For each of these, answers are given to a number of key questions (What is it? How does it operate? How is a system developed? What can it be used for? What tools are available? What are the main issues?). The book is aimed at students, researchers and practitioners interested in Knowledge Management, Artificial Intelligence, Design Engineering and Web Technologies. During the 1990s, Nick worked at the University of Nottingham on the application of AI techniques to knowledge management and on various knowledge acquisition projects to develop expert systems for military applications. In 1999, he joined Epistemics where he worked on numerous knowledge projects and helped establish knowledge management programmes at large organisations in the engineering, technology and legal sectors. He is author of the book"Knowledge Acquisition in Practice", which describes a step-by-step procedure for acquiring and implementing expertise. He maintains strong links with leading research organisations working on knowledge technologies, such as knowledge-based engineering, ontologies and semantic technologies.

negative: From goals to components: a combined approach to self-management Autonomous or semi-autonomous systems are deployed in environments where contact with programmers or technicians is infrequent or undesirable. To operate reliably, such systems should be able to adapt to new circumstances on their own. This paper describes our combined approach for adaptable software architecture and task synthesis from high-level goals, which is based on a three-layer model. In the uppermost layer, reactive plans are generated from goals expressed in a temporal logic. The middle layer is responsible for plan execution and assembling a configuration of domain-specific software components, which reside in the lowest layer. Moreover, the middle layer is responsible for selecting alternative components when the current configuration is no longer viable for the circumstances that have arisen. The implementation demonstrates that the approach enables us to handle non-determinism in the environment and unexpected failures in software components.

negative: Support Vector Machines and Kernel Methods: The New Generation of Learning Machines algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems. Successful applications range from text categorization to handwriting recognition to classification of geneexpression data.

negative: A survey on unmanned aerial vehicle collision avoidance systems Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.

negative: No-reference perceptual quality assessment of JPEG compressed images Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signalto-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance. A Matlab implementation of the proposed method is available at http://anchovy.ece.ute xas.edu/ ̃zwang/research/nr jpeg quality/index.html .

negative: Binarization-free OCR for historical documents using LSTM networks A primary preprocessing block of almost any typical OCR system is binarization, through which it is intended to remove unwanted part of the input image, and only keep a binarized and cleaned-up version for further processing. The binarization step does not, however, always perform perfectly, and it can happen that binarization artifacts result in important information loss, by for instance breaking or deforming character shapes. In historical documents, due to a more dominant presence of noise and other sources of degradations, the performance of binarization methods usually deteriorates; as a result the performance of the recognition pipeline is hindered by such preprocessing phases. In this paper, we propose to skip the binarization step by directly training a 1D Long Short Term Memory (LSTM) network on gray-level text lines. We collect a large set of historical Fraktur documents, from publicly available online sources, and form train and test sets for performing experiments on both gray-level and binarized text lines. In order to observe the impact of resolution, the experiments are carried out on two identical sets of low and high resolutions. Overall, using gray-level text lines, the 1D LSTM network can reach 24% and 19% lower error rates on the low- and high-resolution sets, respectively, compared to the case of using binarization in the recognition pipeline.

negative: A3: a coding guideline for HCI+autism research using video annotation Due to the profile of strengths and weaknesses indicative of autism spectrum disorders (ASD), technology may play a key role in ameliorating communication difficulties with this population. This paper documents coding guidelines established through cross-disciplinary work focused on facilitating communication development in children with ASD using computerized feedback. The guidelines, referred to as A3 (pronounced A-Cubed) or Annotation for ASD Analysis, define and operationalize a set of dependent variables coded via video annotation. Inter-rater reliability data are also presented from a study currently in-progress, as well as related discussion to help guide future work in this area. The design of the A3 methodology is well-suited for the examination and evaluation of the behavior of low-functioning subjects with ASD who interact with technology.

negative: “Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images The role of anomaly detection in X-ray security imaging, as a supplement to targeted threat detection, is described, and a taxonomy of anomaly types in this domain is presented. Algorithms are described for detecting appearance anomalies of shape, texture, and density, and semantic anomalies of object category presence. The anomalies are detected on the basis of representations extracted from a convolutional neural network pre-trained to identify object categories in photographs, from the final pooling layer for appearance anomalies, and from the logit layer for semantic anomalies. The distribution of representations in normal data is modeled using high-dimensional, full-covariance, Gaussians, and anomalies are scored according to their likelihood relative to those models. The algorithms are tested on X-ray parcel images using stream-of-commerce data as the normal class, and parcels with firearms present the examples of anomalies to be detected. Despite the representations being learned for photographic images and the varied contents of stream-of-commerce parcels, the system, trained on stream-of-commerce images only, is able to detect 90% of firearms as anomalies, while raising false alarms on 18% of stream-of-commerce.

negative: Smart home design based on ZigBee wireless sensor network This work is based on the Internet of Things and ZigBee wireless sensor network technology. A kind of smart home design based on ZigBee wireless sensor network was proposed in this paper. Texas Instruments MCU device LM3S9B96, which is the ARM Cortex-M3 based controllers, was used in this system. The entire system is running on the μC/OS-II embedded real-time multitasking operating system. Users can access this system by a dynamic webpage of LwIP TCP/IP protocol stack or GSM SMS. Using this system users can conveniently know the environment parameters of home, such as temperature, humidity, meter readings, light, and control the home electronic equipments, such as light, aircondition, heater, by ZigBee wireless sensor network.

negative: Aiding in the Treatment of Low Back Pain by a Fuzzy Linguistic Web System Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a novel fuzzy linguistic Web system that uses a recommender system to provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention. This system may be useful to reduce the economic impact of low back pain, help professionals to assist patients, and inform users on low back pain prevention measures. A strong part of TPLUFIB-WEB is that it satisfies the Web quality standards proposed by the Health On the Net Foundation (HON), Official College of Physicians of Barcelona, and Health Quality Agency of the Andalusian Regional Government, endorsing the health information provided and warranting the trust of users.

negative: Revisiting Batch Normalization For Practical Domain Adaptation Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain. Recent study (Tommasi et al. 2015) shows that a DNN has strong dependency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without finetuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN. By modulating the statistics in all Batch Normalization layers across the network, our approach achieves deep adaptation effect for domain adaptation tasks. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameter-free. It archives stateof-the-art performance despite its surprising simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance.

negative: Behavior of Machine Learning Algorithms in Adversarial Environments Behavior of Machine Learning Algorithms in Adversarial Environments

negative: Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.

negative: Diffusion of innovations in service organizations: systematic review and recommendations. This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and organization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a systematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically reviewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.

negative: Ultra-low on-resistance LDMOS implementation in 0.13µm CD and BiCD process technologies for analog power IC's Toshiba's 5th generation BiCD/CD-0.13 is a new process platform for analog power applications based on 0.13µm CMOS technology. The process platform has six varieties of rated voltage, 5V, 6V, 18V, 25V, 40V, and 60V. 5 to 18V CD-0.13 process use P-type silicon substrate. 25 to 60V BiCD-0.13 process use N-Epi wafer with N+/P+ buried layer on P type silicon substrate. Each LDMOS recode ultra-low on-resistance compared with that of previous papers, and we will realize the highest performance analog power IC's using this technology.

negative:
Going Wild: Large-Scale Classification of Open DNS Resolvers Since several years, millions of recursive DNS resolvers are-deliberately or not-open to the public. This, however, is counter-intuitive, since the operation of such openly accessible DNS resolvers is necessary in rare cases only. Furthermore, open resolvers enable both amplification DDoS and cache snooping attacks, and can be abused by attackers in multiple other ways. We thus find open recursive DNS resolvers to remain one critical phenomenon on the Internet.
 In this paper, we illuminate this phenomenon by analyzing it from two different angles. On the one hand, we study the landscape of DNS resolvers based on empirical data we collected for over a year. We analyze the changes over time and classify the resolvers according to device type and software version. On the other hand, we take the viewpoint of a client and measure the response authenticity of these resolvers. Besides legitimate redirections (e.g., to captive portals or router login pages), we find millions of resolvers to deliberately manipulate DNS resolutions (i.e., return bogus IP address information). To understand this threat in more detail, we systematically analyze non-legitimate DNS responses and reveal open DNS resolvers that manipulate DNS resolutions to censor communication channels, inject advertisements, serve malicious files, perform phishing, or redirect to other kinds of suspicious or malicious activities.

negative: The evolutionary significance of polyploidy Polyploidy, or the duplication of entire genomes, has been observed in prokaryotic and eukaryotic organisms, and in somatic and germ cells. The consequences of polyploidization are complex and variable, and they differ greatly between systems (clonal or non-clonal) and species, but the process has often been considered to be an evolutionary 'dead end'. Here, we review the accumulating evidence that correlates polyploidization with environmental change or stress, and that has led to an increased recognition of its short-term adaptive potential. In addition, we discuss how, once polyploidy has been established, the unique retention profile of duplicated genes following whole-genome duplication might explain key longer-term evolutionary transitions and a general increase in biological complexity.

negative:
Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults. OBJECTIVE
Rising obesity rates have been linked to the consumption of energy-dense diets. We examined whether dietary energy density was associated with obesity and related disorders including insulin resistance and the metabolic syndrome.

RESEARCH DESIGN AND METHODS
We conducted a cross-sectional study using nationally representative data of U.S. adults > or =20 years of age from the 1999-2002 National Health and Nutrition Examination Survey (n = 9,688). Dietary energy density was calculated based on foods only. We used a series of multivariate linear regression models to determine the independent association between dietary energy density, obesity measures (BMI [in kilograms per meters squared] and waist circumference [in centimeters]), glycemia, or insulinemia. We used multivariate Poisson regression models to determine the independent association between dietary energy density and the metabolic syndrome as defined by the National Cholesterol and Education Program (Adult Treatment Panel III).

RESULTS
Dietary energy density was independently and significantly associated with higher BMI in women (beta = 0.44 [95% CI 0.14-0.73]) and trended toward a significant association in men (beta = 0.37 [-0.007 to 0.74], P = 0.054). Dietary energy density was associated with higher waist circumference in women (beta = 1.11 [0.42-1.80]) and men (beta = 1.33 [0.46-2.19]). Dietary energy density was also independently associated with elevated fasting insulin (beta = 0.65 [0.18-1.12]) and the metabolic syndrome (prevalence ratio = 1.10 [95% CI 1.03-1.17]).

CONCLUSIONS
Dietary energy density is an independent predictor of obesity, elevated fasting insulin levels, and the metabolic syndrome in U.S. adults. Intervention studies to reduce dietary energy density are warranted.

negative: Performance Evaluation and Optimization of Communication Infrastructure for the Next Generation Air Transportation System Automatic dependent surveillance-broadcast (ADS-B) is one of the fundamental surveillance technologies to improve the safety, capacity, and efficiency of the national airspace system. ADS-B shares its frequency band with current radar systems that use the same 1,090 MHz band. The coexistence of radar systems and ADS-B systems is a key issue to detect and resolve conflicts in the next generation air transportation system (NextGen). This paper focuses on the performance evaluation of ADS-B with existing radar systems and performance optimization of ADS-B systems to improve the safety and efficiency of conflict detection and resolution in NextGen. We have developed a simulation environment which models the complex interplay among the air traffic load, the radar systems, the ADS-B systems, and the wireless channel. A simple model is used to derive an analytical expression for a performance metric of ADS-B. This model is then used to design an adaptive ADS-B protocol for maximizing the information coverage while guaranteeing reliable and timely communication in air traffic surveillance networks. Simulation results show that the effect of ADS-B interference on the current radar system is negligible. The operational ability of ADS-B meets the performance requirements of conflict detection and resolution in air traffic control. However, upgrades are required in the current radar system for operation within an ADS-B environment since the current radars can significantly degrade the ADS-B performance. Numerical results indicate that the proposed adaptive protocol has the potential to improve the performance of conflict detection and resolution in air traffic control.

negative: Cross-Validation and the Estimation of Conditional Probability Densities Many practical problems, especially some connected with forecasting, require nonparametric estimation of conditional densities from mixed data. For example, given an explanatory data vector X for a prospective customer, with components that could include the customer’s salary, occupation, age, sex, marital status, and address, a company might wish to estimate the density of the expenditure, Y , that could be made by that person, basing the inference on observations of (X,Y) for previous clients. Choosing appropriate smoothing parameters for this problem can be tricky, not in the least because plug-in rules take a particularly complex form in the case of mixed data. An obvious difficulty is that there exists no general formula for the optimal smoothing parameters. More insidiously, and more seriously, it can be difficult to determine which components of X are relevant to the problem of conditional inference. For example, if the jth component of X is independent of Y , then that component is irrelevant to estimating the density of Y given X, and ideally should be dropped before conducting inference. In this article we show that cross-validation overcomes these difficulties. It automatically determines which components are relevant and which are not, through assigning large smoothing parameters to the latter and consequently shrinking them toward the uniform distribution on the respective marginals. This effectively removes irrelevant components from contention, by suppressing their contribution to estimator variance; they already have very small bias, a consequence of their independence of Y . Cross-validation also yields important information about which components are relevant; the relevant components are precisely those that cross-validation has chosen to smooth in a traditional way, by assigning them smoothing parameters of conventional size. Indeed, cross-validation produces asymptotically optimal smoothing for relevant components, while eliminating irrelevant components by oversmoothing. In the problem of nonparametric estimation of a conditional density, cross-validation comes into its own as a method with no obvious peers.

negative: How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures ACL 2018 • Baseline: 6 layer Transformer, 512 hidden units • Data sets: IWSLT'16, WMT'17 • Metrics: BLEU (and METEOR in the paper) • 3 runs, reporting mean and standard deviation Encoder/Decoder Combinations • Architecture Definition Language • Allows easy experimentation of Neural Machine Translation (NMT) architecture variations • Detailed analysis of NMT architecture combinations • Multiple source attention layers and residual feedforward layer are key • Self-attention more important on the source side Introduction

negative: Cascaded Random Forest for Fast Object Detection A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.

negative:
Malicious PDF detection using metadata and structural features Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware.
 Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set.

negative: Traffic sign detection and recognition based on random forests In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI raffic sign detection raffic sign recognition olor segmentation andom forests upport vector machines (SVMs) istogram of oriented gradients (HOG) color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods. © 2016 Elsevier B.V. All rights reserved. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 ocal self-similarity (LSS)

negative: Web-based statistical fact checking of textual documents User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.

negative: A Hybrid Approach to Detect and Localize Texts in Natural Scene Images Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

negative: Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE). Keywords— IDS, DDoS, MLP, Naïve Bayes, Random Forest

negative: Discovering Parallel Text from the World Wide Web Parallel corpus is a rich linguistic resource for various multilingual text management tasks, including crosslingual text retrieval, multilingual computational linguistics and multilingual text mining. Constructing a parallel corpus requires effective alignment of parallel documents. In this paper, we develop a parallel page identification system for identifying and aligning parallel documents from the World Wide Web. The system crawls the Web to fetch potentially parallel multilingual Web documents using a Web spider. To determine the parallelism between potential document pairs, two modules are developed. First, a filename comparison module is used to check filename resemblance. Second, a content analysis module is used to measure the semantic similarity. The experiment conducted to a multilingual Web site shows the effectiveness of the system.