query: RBFOpt: an open-source library for black-box optimization with costly function evaluations

positive: Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods Direct search methods are best known as unconstrained optimization techniques that do not explicitly use derivatives. Direct search methods were formally proposed and widely applied in the 1960s but fell out of favor with the mathematical optimization community by the early 1970s because they lacked coherent mathematical analysis. Nonetheless, users remained loyal to these methods, most of which were easy to program, some of which were reliable. In the past fifteen years, these methods have seen a revival due, in part, to the appearance of mathematical analysis, as well as to interest in parallel and distributed computing. This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited. Our focus then turns to a broad class of methods for which we provide a unifying framework that lends itself to a variety of convergence results. The underlying principles allow generalization to handle bound constraints and linear constraints. We also discuss extensions to problems with nonlinear constraints.

positive: On automatic differentiation During these last years, the environmental problems have acquired a growing place in our society. It becomes necessary to find a lasting way to the nuclear waste storage. The waste storage is in relation to the characteristics of wastes according to their activity and the life of the radionuclide. After many studies on the topic, led in particular by ANDRA, the storage in deep level is considered as the reference strategy for wastes with high or medium activity and long-lived. We have to make simulations of the radionucleide transport in underground in order to determine the impact of a possible propagation of radioelements. The modelling of the flow in porous media around the storage requires to know the physical parameters of the different geological layers. Those parameters (porosity and diffusion) are not directly accessible by measurements, hence we have to solve an inverse problem to recover them.

positive: Equation of State Calculations by Fast Computing Machines 1087 instead, only water molecules with different amounts of excitation energy. These may follow any of three paths: (a) The excitation energy is lost without dissociation into radicals (by collision, or possibly radiation, as in aromatic hydrocarbons). (b) The molecules dissociate, but the resulting radicals recombine without escaping from the liquid cage. (c) The molecules dissociate and escape from the cage. In this case we would not expect them to move more than a few molecular diameters through the dense medium before being thermalized. paths (a) and (b) can be designated H 2 0* and those following path (c) can be designated H 2 0t. It seems reasonable to assume for the purpose of these calculations that the ionized H 2 0 molecules will become the H 20 t molecules, but this is not likely to be a complete correspondence. In conclusion we would like to emphasize that the qualitative result of this section is not critically dependent on the exact values of the physical parameters used. However, this treatment is classical, and a correct treatment must be wave mechanical; therefore the result of this section cannot be taken as an a priori theoretical prediction. The success of the radical diffusion model given above lends some plausibility to the occurrence of electron capture as described by this crude calculation. Further work is clearly needed. A general method, suitable for fast computing machines, for investigatiflg such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two-dimensional rigid-sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four-term virial coefficient expansion.

positive: Efficient Global Optimization of Expensive Black-Box Functions In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.

positive: A theoretical framework for simulated annealing Simulated Annealing has been a very successful general algorithm for the solution of large, complex combinatorial optimization problems. Since its introduction, several applications in different fields of engineering, such as integrated circuit placement, optimal encoding, resource allocation, logic synthesis, have been developed. In parallel, theoretical studies have been focusing on the reasons for the excellent behavior of the algorithm. This paper reviews most of the important results on the theory of Simulated Annealing, placing them in a unified framework. New results are reported as well.

negative: Evidence of Time-Dependent Vertical Breakdown in GaN-on-Si HEMTs This paper demonstrates and investigates the time-dependent vertical breakdown of GaN-on-Si power transistors. The study is based on electrical characterization, dc stress tests and electroluminescence measurements. We demonstrate the following original results: 1) when submitted to two-terminal (drain-to-substrate) stress, the AlGaN/GaN transistors show a time-dependent degradation process, which leads to the catastrophic failure of the devices; 2) time-to-failure follows a Weibull distribution and is exponentially dependent on stress voltage; 3) the degradation mechanism is strongly field dependent and weakly thermally activated, with an activation energy of 0.25 eV; and 4) emission microscopy suggests that vertical current flows under the whole drain area, possibly through extended defects. The catastrophic failure occurs at random positions under the drain contact. The time-dependent failure is ascribed to a percolation process activated by the high-electric field that leads to the generation of localized shunt paths between drain and substrate.

negative: Document clustering algorithms, representations and evaluation for information retrieval Digital collections of data continue to grow exponentially as the information age continues to infiltrate every aspect of society. These sources of data take many different forms such as unstructured text on the world wide web, sensor data, images, video, sound, results of scientific experiments and customer profiles for marketing. Clustering is an unsupervised learning approach that groups similar examples together without any human labeling of the data. Due to the very broad nature of this definition, there have been many different approaches to clustering explored in the scientific literature. This thesis addresses the computational efficiency of document clustering in an information retrieval setting. This includes compressed representations, efficient and scalable algorithms, and evaluation with a specific use case for increasing efficiency of a distributed and parallel search engine. Furthermore, it addresses the evaluation of document cluster quality for large scale collections containing millions to billions of documents where complete labeling of a collection is impractical. The cluster hypothesis from information retrieval is also tested using several different approaches throughout the thesis. This research introduces novel approaches to clustering algorithms, document representations, and clustering evaluation. The combination of document clustering algorithms and document representations outlined in this thesis are able to process large scale web search data sets. This has not previously been reported in the literature without resorting to sampling and producing a small number of clusters. Furthermore, the algorithms are able to do this on a single machine. However, they can also function in a distributed and parallel setting to further increase their scalability. This thesis introduces new clustering algorithms that can also be applied to problems outside the information retrieval domain. There are many large data sets being produced from advertising, social networks, videos, images, sound, satellites, sensor data, and a myriad of other sources. Therefore, I anticipate these approaches will become applicable to more domains, as larger data sets become available.

negative: Data augmentation for deep convolutional neural network acoustic modeling This paper investigates data augmentation based on label-preserving transformations for deep convolutional neural network (CNN) acoustic modeling to deal with limited training data. We show how stochastic feature mapping (SFM) can be carried out when training CNN models with log-Mel features as input and compare it with vocal tract length perturbation (VTLP). Furthermore, a two-stage data augmentation scheme with a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Improved performance has been observed in experiments conducted on the limited language pack (LLP) of Haitian Creole in the IARPA Babel program.

negative: A Personalized Company Recommender System for Job Seekers Our team intends to develop a recommendation system for job seekers based on the information of current employees in big companies. Several models are implemented to achieve over 60% success rate in classifying employees, and we use these models to help job seekers identify their best fitting company.

negative: Spaceborne bi-and multistatic SAR : potential and challenges Biand multistatic synthetic aperture radar (SAR) operates with distinct transmit and receive antennas that are mounted on separate platforms. Such a spatial separation has several operational advantages, which will increase the capability, reliability and flexibility of future SAR missions. Various spaceborne biand multistatic SAR configurations are introduced, and their potential for different applications such as frequent monitoring, wide-swath imaging, scene classification, single pass cross-track interferometry and resolution enhancement is compared. Furthermore, some major challenges such as phase and time synchronisation, biand multistatic SAR processing, satellite orbit selection and relative position sensing are addressed.

negative: Joint Multimodal Learning with Deep Generative Models We investigate deep generative models that can exchange mul tiple modalities bidirectionally, e.g., generating images from correspondin g texts and vice versa. Recently, some studies handle multiple modalities on deep gen erative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we sh ould extract a joint representation that captures high-level concepts among al l modalities and through which we can exchange them bi-directionally. As described h rein, we propose a joint multimodal variational autoencoder (JMVAE), in whicall modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to gene rate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence betwee n JMVAE’s encoder and prepared networks of respective modalities. Our experi ments show that our proposed method can obtain appropriate joint representati o from multiple modalities and that it can generate and reconstruct them more prop erly than conventional VAEs. We further demonstrate that JMVAE can generate multip le modalities bidirectionally.

negative: Virtual garments : A Fully Geometric Approach for Clothing Design Modeling dressed characters is known as a very tedious process. It usually requires specifying 2D fabric patterns, positioning and assembling them in 3D, and then performing a physically-based simulation. The latter accounts for gravity and collisions to compute the rest shape of the garment, with the adequate folds and wrinkles. This paper presents a more intuitive way to design virtual clothing. We start with a 2D sketching system in which the user draws the contours and seam-lines of the garment directly on a virtual mannequin. Our system then converts the sketch into an initial 3D surface using an existing method based on a precomputed distance field around the mannequin. The system then splits the created surface into different panels delimited by the seam-lines. The generated panels are typically not developable. However, the panels of a realistic garment must be developable, since each panel must unfold into a 2D sewing pattern. Therefore our system automatically approximates each panel with a developable surface, while keeping them assembled along the seams. This process allows us to output the corresponding sewing patterns. The last step of our method computes a natural rest shape for the 3D garment, including the folds due to the collisions with the body and gravity. The folds are generated using procedural modeling of the buckling phenomena observed in real fabric. The result of our algorithm consists of a realistic looking 3D mannequin dressed in the designed garment and the 2D patterns which can be used for distortion free texture mapping. As we demonstrate, the patterns we create allow us to sew real replicas of the virtual garments.

negative: Synthetic Population Generation Without a Sample

negative: A Low-energy, Multi-copy Inter-contact Routing Protocol for Disaster Response Networks This paper presents a novel multi-copy routing protocol for disruption-tolerant networks whose objective is to minimize energy expended on communication. The protocol is designed for disaster-response applications, where power and infrastructure resources are disrupted. Unlike other delay-tolerant networks, energy is a vital resource in post-disaster scenarios to ensure availability of (disruption-tolerant) communication until infrastructure is restored. Our approach exploits naturally recurrent mobility and contact patterns in the network, formed by rescue workers, volunteers, survivors, and their (possibly stranded) vehicles to reduce the number of message copies needed to attain an adequate delivery ratio in the face of disconnection and intermittent connectivity. A new notion of inter-contact routing is proposed that allows estimating route delays and delivery probabilities, identifying more reliable routes and controlling message replication and forwarding accordingly. We simulate the scheme using a mobility model that reflects recurrence inspired by disaster scenarios, and compare our results to previous DTN routing techniques. The evaluation shows that the new approach reduces the resource overhead per message over previous approaches while maintaining a comparable delivery ratio at the expense of a small (bounded) increase in latency.

negative: Emotion detection in suicide notes 0957-4174/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.05.050 ⇑ Corresponding author at: LT3 Language and Translation Technology Team, University College Ghent, Groot-Brittanniëlaan 45, 9000 Ghent, Belgium. Tel.: +32 9 224 97 53. E-mail addresses: bart.desmet@hogent.be (B. Desmet), veronique.hoste@ hogent.be (V. Hoste). Bart Desmet a,b,⇑, Véronique Hoste a,c

negative: An improved MPPT technique for high gain DC-DC converter using model predictive control for photovoltaic applications This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.

negative: Islanding detection of active distribution system with parallel inverters Average absolute frequency deviation value (AFDVavg) based active islanding detection technique is recently introduced for islanding detection of active distribution system with a single active DG. This paper implements, evaluates and analyze the performance of AFDVavg technique for active distribution system with parallel inverter. The main focus is on islanding detection of an ADN with parallel inverters including the worst case i.e. zero power imbalance condition. The case study is done on an ADN which is energized by two parallel inverter based DGs. Worst case of islanding has been simulated successfully by adjusting the equivalent load of ADN to match the total generation. The resultant quality factor of the equivalent load is kept 1 for testing islanding. Computer simulations are done in MATLAB.

negative: Integration of open source platform duckietown and gesture recognition as an interactive interface for the museum robotic guide In recent years, population aging becomes a serious problem. To decrease the demand for labor when navigating visitors in museums, exhibitions, or libraries, this research designs an automatic museum robotic guide which integrates image and gesture recognition technologies to enhance the guided tour quality of visitors. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving based on the function of lane-following via image recognition. This enables the robot to lead guests to visit artworks following the preplanned route. In conjunction with the vocal service about each artwork, the robot can convey the detailed description of the artwork to the guest. We also design a simple wearable device to perform gesture recognition. As a human machine interface, the guest is allowed to interact with the robot by his or her hand gestures. To improve the accuracy of gesture recognition, we design a two phase hybrid machine learning-based framework. In the first phase (or training phase), k-means algorithm is used to train historical data and filter outlier samples to prevent future interference in the recognition phase. Then, in the second phase (or recognition phase), we apply KNN (k-nearest neighboring) algorithm to recognize the hand gesture of users in real time. Experiments show that our method can work in real time and get better accuracy than other methods.

negative: Stochastic policy gradient reinforcement learning on a simple 3D biped We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blank-slate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a state-based estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks.

negative:
Assessment of exposure to sexually explicit materials and factors associated with exposure among preparatory school youths in Hawassa City, Southern Ethiopia: a cross-sectional institution based survey BACKGROUND
According to the 2007 Ethiopian census, youths aged 15-24 years were more than 15.2 million which contributes to 20.6% of the whole population. These very large and productive groups of the population are exposed to various sexual and reproductive health risks. The aim of this study was to assess exposure to Sexually Explicit Materials (SEM) and factors associated with exposure among preparatory school students in Hawassa city, Southern Ethiopia.

METHODOLOGY
A cross-sectional institution based study involving 770 randomly selected youth students of preparatory schools at Hawassa city. Multi stage sampling technique was used to select study subjects. Data was collected using pre-tested and self-administered questionnaire. Data was entered by EPI INFO version 3.5.1 and analyzed using SPSS version 20.0 statistical software packages. The result was displayed using descriptive, bivariate and multivariate analysis. Statistical association was done for independent predictors (at p < 0.05).

RESULT AND DISCUSSION
About 750 students were participated in this study with a response rate of 97.4%. Among this, about 77.3% of students knew about the presence of SEM and most of the respondents 566(75.5%) were watched SEM films/movies and 554(73.9%) were exposed to SE texts. The overall exposure to SEM in school youths was 579(77.2%). Among the total respondents, about 522(70.4%) claimed as having no open discussion on sexual issues with in their family. Furthermore, About 450 (60.0%) respondents complained for having no sexual and reproductive health education at their school. Male students had faced almost two times higher exposure to SEM than female students (95 % CI: AOR 1.84(C.I = 1.22, 2.78). Students who attended private school were more than two times more likely exposed to SEM than public schools (95 % CI: AOR 2.07(C.I = 1.29, 3.30). Students who drink alcohol and labelled as 'sometimes' were two times more likely exposed to SEM than those who never drink alcohol (95 % CI = AOR 2.33(C.I = 1.26, 4.30). Khat chewers who labelled "rarely", "sometimes" and "often" had shown higher exposure (95 % CI: AOR 3.02(CI = 1.65, 5.52), (95 % CI: AOR 3.40(CI = 1.93, 6.00) and (95 % CI: AOR 2.67(CI = 1.46, 4.86) than those who never chew khat, respectively. Regarding SEM access, school youths with label 'easy access were exposed in odds of six folds than youths of no access (95 % CI: AOR 5.64(C.I = 3.56, 8.9).

CONCLUSION
High number of students was exposed to sexually explicit materials. Sex, school type, substance use and access to SEM were observed independent predictors of exposure to SEM.

MOTIVATION
The current generation of young people is the healthiest, most educated, and most urbanized in history. However, there still remain some serious concerns. Most people become sexually active during adolescence. Premarital sexual activity is common and is on the rise worldwide. Rates are highest in sub Saharan Africa, where more than half of girls aged 15-19 are sexually experienced. Millions of adolescents are bearing children, in sub-Saharan Africa. More than half of women give birth before age 20. The need for improved health and social services aimed at adolescents, including reproductive health services, is being increasingly recognized throughout the world. Approximately 85 % of world adolescents live in developing countries. Each year, up to 100 million becomes infected with a curable sexually transmitted disease (STI). About 40 % of all new global human immunodeficiency virus (HIV) infections occur among 15-24 year olds; with recent estimates of 7000 infected each day. These health risks are influenced by many interrelated factors, such as expectations concerning early marriage and sexual relationships, access to education and employment, gender inequities, sexual violence, and the influence of mass media and popular culture. Furthermore, many adolescents lack strong stable relationships with parents or other adults whom they can talk to about their reproductive health concerns. Despite these challenges, programs that meet the information and service needs of adolescents can make a real difference. Successful programs help young people develop life-planning skill, respect the needs and concerns of young people, involve communities in their efforts, and provide respectful and confidential clinical services. Accordingly, the government of Ethiopia now works on improving adolescent's health as one part of MDG (Goal VI-halting transmission of HIV/AIDS, STI, and other communicable diseases) with a focus on adolescents, since they are most affected population. This finding, therefore, will benefit the government to partly evaluate the goal achieving through adolescents exposure status to sexually explicit materials and improvement of sexual issues free talk with in school with class mates and their family at home. For that matter, we authors decided to publish this finding in BMC Reproductive Health Journal so that on line access will be easy to all governing bodies that they use to re-plan their strategies for better product of plan. Moreover, Researchers, Practitioners, policy makers, Students, school leaders and professionals will also benefit from this finding for their future researches references, knowledge gain and practice.

negative: Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be in- flated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and informa- tion retrieval baseline that incorporates two novel contributions: a one-to-many alignment between query and document terms and negative alignment as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47% P@1 on an 8th grade Science QA dataset, 32.9% P@1 on a Yahoo! answers QA dataset and 64% MAP on WikiQA.

negative: MPPT of photovoltaic systems using extremum - seeking control A stability analysis for a maximum power point tracking (MPPT) scheme based on extremum-seeking control is developed for a photovoltaic (PV) array supplying a dc-to-dc switching converter. The global stability of the extremum-seeking algorithm is demonstrated by means of Lyapunov's approach. Subsequently, the algorithm is applied to an MPPT system based on the "perturb and observe" method. The steady-state behavior of the PV system with MPPT control is characterized by a stable oscillation around the maximum power point. The tracking algorithm leads the array coordinates to the maximum power point by increasing or decreasing linearly with time the array voltage. Off-line measurements are not required by the control law, which is implemented by means of an analog multiplier, standard operational amplifiers, a flip-flop circuit and a pulsewidth modulator. The effectiveness of the proposed MPPT scheme is demonstrated experimentally under different operating conditions.

negative: Systematic Risk in Supply Chain Networks I production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from multiple customers in a supply chain network, and aggregation of orders over time. We find that the first effect does not amplify the correlation; however, the latter two intensify correlation and result in the amplification of correlation upstream in supply networks. We demonstrate three managerial implications of this phenomenon: implications for the cost of capital, for the risk-adjusted valuation of supply chain improvement projects, and for supplier selection and risk. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2187.

negative: Planning-based prediction for pedestrians We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

negative: The Weighted Majority Algorithm We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective method based on weighted voting is introduced for constructing a compound algorithm in such a circumstance We call this method the Weighted Majority Algorithm We show that this algorithm is robust in the presence of errors in the data We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool For example given a sequence of trials if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c log jAj m mistakes on that sequence where c is xed constant

negative: A Novel Approach for Network Attack Classification Based on Sequential Questions With the development of incipient technologies, user devices becoming more exposed and ill-used by foes. In upcoming decades, traditional security measures will not be sufficient enough to handle this huge threat towards distributed hardware and software. Lack of standard network attack taxonomy has become an indispensable dispute on developing a clear understanding about the attacks in order to have an operative protection mechanism. Present attack categorization techniques protect a specific group of threat which has either messed the entire taxonomy structure or ambiguous when one network attacks get blended with few others attacks. Hence, this raises concerns about developing a common and general purpose taxonomy. In this study, a sequential question-answer based model of categorization is proposed. In this article, an intrusion detection framework and threat grouping schema are proposed on the basis of four sequential questions (“Who”, “Where”, “How” and “What”). We have used our method for classifying traditional network attacks in order to identify initiator, source, attack style and seriousness of an attack. Another focus of the paper is to provide a preventive list of actions for network administrator as a guideline to reduce overall attack consequence. Recommended taxonomy is designed to detect common attacks rather than any particular type of attack which can have a practical effect in real life attack classification. From the analysis of the classifications obtained from few infamous attacks, it is obvious that the proposed system holds certain benefits related to the prevailing taxonomies. Future research directions have also been well acknowledged.

negative: Community Detection in Temporal Networks Many complex systems in nature, society and technologyfrom the online social networks to the internet from the nervous system to power gridscan be represented as a graph of vertices interconnected by edges. Small world network, Scale free network, Community detection are fundamental properties of complex networks. Community is a sub graph with densely connected nodes typically reveals topological relations, network structure, organizational and functional characteristics of the underlying network, e.g., friendships on Facebook, Followers of a VIP account on Twitter, and interaction with business professionals on LinkedIn. Online social networks are dynamic in nature, they evolve in size and space rapidly. In this paper we are detecting incremental disjoint communities using dyanamic multi label propagation algorithm. It tackles the temporal event changes, i.e., addition, deletion of an edge or vertex in a sub graph for every timestamp. The experimental results on Enron real network dataset shows that the proposed method is quite well in identifying communities.

negative:
GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs.
 Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.

negative: RN to BSN Transition: A Concept Analysis. Over 670,000 ADN- and diploma-prepared nurses will need to complete their BSN degrees to meet the Institute of Medicine's recommendation that at least 80% of registered nurses (RNs) be BSN-prepared by year 2020. Understanding motivators, barriers, and the transition experience for RNs to advance their degree will help educators and nurse leaders understand the importance of a partnership to educate and mentor RNs to pursue a BSN degree.

negative: Design Considerations of Charge Pump for Antenna Switch Controller With SOI CMOS Technology An enhanced charge pump for the antenna switch controller using the silicon-on-insulator (SOI) CMOS technology is presented in this brief. The pseudo cross-coupled technique is proposed to reduce parasitic capacitances at charging/discharging nodes through charge transferring paths, which improves the current drive capability and provides better accuracy of the output voltage. Furthermore, the codesign between the gate control voltages of power MOS transistors and the clock drive signals of pumping capacitors has been investigated to eliminate the reversion loss and reduce the ripple voltage. The pseudo cross-coupled charge pump has been fabricated in the 0.18-  $\mu\text{m}$ SOI CMOS technology with an area of 0.065 mm2. According to the comparison results of the conventional and enhanced charge pumps, the start-up time and the recovery time are typically shortened by 71.4% and 21.7%, owing to the improvement of the current drive capability, and the ripple voltage at no-load condition is greatly reduced by 46.1%.

negative: Static test case prioritization using topic models Software development teams use test suites to test changes to their source code. In many situations, the test suites are so large that executing every test for every source code change is infeasible, due to time and resource constraints. Development teams need to prioritize their test suite so that as many distinct faults as possible are detected early in the execution of the test suite. We consider the problem of static black-box test case prioritization (TCP), where test suites are prioritized without the availability of the source code of the system under test (SUT). We propose a new static black-box TCP technique that represents test cases using a previously unused data source in the test suite: the linguistic data of the test cases, i.e., their identifier names, comments, and string literals. Our technique applies a text analysis algorithm called topic modeling to the linguistic data to approximate the functionality of each test case, allowing our technique to give high priority to test cases that test different functionalities of the SUT. We compare our proposed technique with existing static black-box TCP techniques in a case study of multiple real-world open source systems: several versions of Apache Ant and Apache Derby. We find that our static black-box TCP technique outperforms existing static black-box TCP techniques, and has comparable or better performance than two existing execution-based TCP techniques. Static black-box TCP methods are widely applicable because the only input they require is the source code of the test cases themselves. This contrasts with other TCP techniques which require access to the SUT runtime behavior, to the SUT specification models, or to the SUT source code.

negative: An Analysis of Black-Box Web Application Security Scanners against Stored SQL Injection Web application security scanners are a compilation of various automated tools put together and used to detect security vulnerabilities in web applications. Recent research has shown that detecting stored SQL injection, one of the most critical web application vulnerabilities, is a major challenge for black-box scanners. In this paper, we evaluate three state of art black-box scanners that support detecting stored SQL injection vulnerabilities. We developed our custom test bed that challenges the scanners capability regarding stored SQL injections. The results show that existing vulnerabilities are not detected even when these automated scanners are taught to exploit the vulnerability. The weaknesses of black-box scanners identified reside in many areas: crawling, input values and attack code selection, user login, analysis of server replies, miss-categorization of findings, and the automated process functionality. Because of the poor detection rate, we discuss the different phases of black-box scanners' scanning cycle and propose a set of recommendations that could enhance the detection rate of stored SQL injection vulnerabilities.

negative: Comparing White-Box and Black-Box Test Prioritization Although white-box regression test prioritization has been well-studied, the more recently introduced black-box prioritization approaches have neither been compared against each other nor against more well-established white-box techniques. We present a comprehensive experimental comparison of several test prioritization techniques, including well-established white-box strategies and more recently introduced black-box approaches. We found that Combinatorial Interaction Testing and diversity-based techniques (Input Model Diversity and Input Test Set Diameter) perform best among the black-box approaches. Perhaps surprisingly, we found little difference between black-box and white-box performance (at most 4% fault detection rate difference). We also found the overlap between black- and white-box faults to be high: the first 10% of the prioritized test suites already agree on at least 60% of the faults found. These are positive findings for practicing regression testers who may not have source code available, thereby making white-box techniques inapplicable. We also found evidence that both black-box and white-box prioritization remain robust over multiple system releases.

negative:
PESTO: Parameter EStimation TOolbox Summary
PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.

Availability and implementation
PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.

Contact
jan.hasenauer@helmholtz-muenchen.de.

Supplementary information
Supplementary data are available at Bioinformatics online.

negative: Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX Intel SGX hardware enables applications to protect themselves from potentially-malicious OSes or hypervisors. In cloud computing and other systems, many users and applications could benefit from SGX. Unfortunately, current applications will not work out-of-the-box on SGX. Although previous work has shown that a library OS can execute unmodified applications on SGX, a belief has developed that a library OS will be ruinous for performance and TCB size, making application code modification an implicit prerequisite to adopting SGX. This paper demonstrates that these concerns are exaggerated, and that a fully-featured library OS can rapidly deploy unmodified applications on SGX with overheads comparable to applications modified to use “shim” layers. We present a port of Graphene to SGX, as well as a number of improvements to make the security benefits of SGX more usable, such as integrity support for dynamically-loaded libraries, and secure multi-process support. Graphene-SGX supports a wide range of unmodified applications, including Apache, GCC, and the R interpreter. The performance overheads of GrapheneSGX range from matching a Linux process to less than 2× in most single-process cases; these overheads are largely attributable to current SGX hardware or missed opportunities to optimize Graphene internals, and are not necessarily fundamental to leaving the application unmodified. Graphene-SGX is open-source and has been used concurrently by other groups for SGX research.

negative: On Best-Possible Obfuscation An obfuscator is a compiler that transforms any program (which we will view in this work as a boolean circuit) into an obfuscated program (also a circuit) that has the same input-output functionality as the original program, but is “unintelligible”. Obfuscation has applications for cryptography and for software protection. Barak et al. (CRYPTO 2001, pp. 1–18, 2001) initiated a theoretical study of obfuscation, which focused on black-box obfuscation, where the obfuscated circuit should leak no information except for its (black-box) input-output functionality. A family of functionalities that cannot be obfuscated was demonstrated. Subsequent research has showed further negative results as well as positive results for obfuscating very specific families of circuits, all with respect to black box obfuscation. This work is a study of a new notion of obfuscation, which we call best-possible obfuscation. Best possible obfuscation makes the relaxed requirement that the obfuscated program leaks as little information as any other program with the same functionality (and of similar size). In particular, this definition allows the program to leak information that cannot be obtained from a black box. Best-possible obfuscation guarantees that any information that is not hidden by the obfuscated program is also not hidden by any other similar-size program computing the same functionality, and thus the obfuscation is (literally) the best possible. In this work we study best-possible obfuscation and its relationship to previously studied definitions. Our main results are: (1) A separation between black-box and best-possible obfuscation. We show a natural obfuscation task that can be achieved under the best-possible definition, but cannot be achieved under the black-box definition. (2) A hardness result for best-possible obfuscation, showing that strong (information-theoretic) best-possible obfuscation implies a collapse in the Polynomial-Time Hierarchy. (3) An impossibility result for efficient best-possible (and black-box) obfuscation in the presence of random oracles. This impossibility result uses a random oracle to construct hard-to-obfuscate circuits, and thus it does not imply impossibility in the standard model.

negative: MAGIX: Model Agnostic Globally Interpretable Explanations Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.


query: NGUARD: A Game Bot Detection Framework for NetEase MMORPGs

positive: Semi-supervised Sequence Learning We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a “pretraining” step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.

positive: Crime Scene Reconstruction: Online Gold Farming Network Analysis Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

positive: Learning methods for generic object recognition with invariance to pose and lighting We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13% for SVM and 7% for convolutional nets. On a segmentation/recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16/7% error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.

positive: A discriminatively trained, multiscale, deformable part model This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

positive: Learning to Forget: Continual Prediction with LSTM Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive forget gate that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.

negative:
The anatomy of the posterior aspect of the knee. An anatomic study. BACKGROUND
The orthopaedic literature contains relatively little quantitative information regarding the anatomy of the posterior aspect of the knee. The purpose of the present study was to provide a detailed description of, and to propose a standard nomenclature for, the anatomy of the posterior aspect of the knee.

METHODS
Detailed dissection of twenty nonpaired, fresh-frozen knees was performed. Posterior knee structures were measured according to length, width, and/or distance to reproducible osseous landmarks.

RESULTS
The semimembranosus tendon had eight attachments distal to the main common tendon. The main components were a lateral expansion to the oblique popliteal ligament; a direct arm, which attached to the tibia; and an anterior arm. The oblique popliteal ligament, the largest posterior knee structure, formed a broad fascial sheath over the posterior aspect of the knee and measured 48.0 mm in length and 9.5 mm wide at its medial origin and 16.4 mm wide at its lateral attachment. It had two lateral attachments, one to the meniscofemoral portion of the posterolateral joint capsule and one to the tibia, along the lateral border of the posterior cruciate ligament facet. The semimembranosus also had a distal tibial expansion, which formed a posterior fascial layer over the popliteus muscle. A thickening of the posterior joint capsule, the proximal popliteus capsular expansion, which in this study averaged 40.5 mm in length, connected the posteromedial knee capsule at its attachment at the intercondylar notch to the medial border of the popliteus musculotendinous junction. The plantaris muscle, popliteofibular ligament, fabellofibular ligament, and semimembranosus bursa were present in all specimens.

CONCLUSIONS
The anatomy of the posterior aspect of the knee is quite complex. This study provides information that can lead to further biomechanical, radiographic imaging, and clinical studies of the importance of these posterior knee structures.

negative: The discrete wavelet transform: wedding the a trous and Mallat algorithms

negative: Benefit of Direct Charge Measurement (DCM) on Interconnect Capacitance Measurement This paper discusses application of direct charge measurement (DCM) on characterizing on-chip interconnect capacitance. Measurement equipment and techniques are leveraged from Flat Panel Display testing. On-chip active device is not an essential necessity for DCM test structure and it is easy to implement parallel measurements. Femto-Farad measurement sensitivity is achieved without having on-chip active device. Measurement results of silicon and glass substrates, including parallel measurements, are presented.

negative: Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. Potential privacy risks) and incentives of sharing (i.e. Potential social benefits), and quantifies users' requirements as a trade-off between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.

negative: Evaluating Reinforcement Learning Algorithms in Observational Health Settings Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez Paulson School of Engineering and Applied Sciences, Harvard University Institute for Medical Engineering and Science, MIT T.H. Chan School of Public Health, Harvard University Department of Statistics, Harvard University Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT Department of Surgery and Cancer, Faculty of Medicine, Imperial College London Department of Bioengineering, Imperial College London Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center MIT Critical Data

negative: Personality traits and group-based information behaviour: an exploratory study Although the importance of individual characteristics and psychological factors has been conceptualized in many models of information seeking behaviour (e.g., Case 2007; Wilson 1999) research into personality issues has only recently attracted attention in information science. This may be explained by the work by Heinström (2002; 2003; 2006a; 2006b; 2006c), but may also be explained by the emerging interest and research into affective dimensions of information behaviour (Nahl and Bilal 2007). Hitherto, the research focus in information science VOL. 14 NO. 2, JUNE, 2009

negative: Design of Neuro-Fuzzy System Controller for DC ServomotorBased Satellite Tracking System A parabolic dish antenna positioning system is a control unit which directs the antenna to desired target automatically. In all its aspects, automation has become widespread and promises to represent the future for satellite tracking antenna systems. The term tracking is used to mean that the control system should utilize suitable algorithms to automate the process of pointing the antenna to a selected satellite thereby establishing the desired line of sight (LOS). This paper aims to present and discuss the results obtained from the development of a DC servomotor-based Neuro-Fuzzy System Controller (NFSC) algorithm which can be applied in the positioning of the parabolic reflector antennas. The advantage of using NFSC method is that it has a high degree of nonlinearity tolerance, learning ability and solves problems that are difficult to address with the conventional techniques such as Proportional Integral Derivative (PID) strategy. The architecture of the proposed antenna control system employs Adaptive Neuro-Fuzzy Inference System (ANFIS) design environment of MATLAB/SIMULINK package. The results obtained indicated that the proposed NFSC was able to achieve desired output DC motor position with reduced rise time and overshoot. Future work is to apply the controller in real time to achieve automatic satellite tracking with parabolic antenna using data derived from the signal strength.

negative: Google effects on memory: cognitive consequences of having information at our fingertips. The advent of the Internet, with sophisticated algorithmic search engines, has made accessing information as easy as lifting a finger. No longer do we have to make costly efforts to find the things we want. We can "Google" the old classmate, find articles online, or look up the actor who was on the tip of our tongue. The results of four studies suggest that when faced with difficult questions, people are primed to think about computers and that when people expect to have future access to information, they have lower rates of recall of the information itself and enhanced recall instead for where to access it. The Internet has become a primary form of external or transactive memory, where information is stored collectively outside ourselves.

negative: Rigid scene flow for 3D LiDAR scans The perception of the dynamic aspects of the environment is a highly relevant precondition for the realization of autonomous robot system acting in the real world. In this paper, we propose a novel method for estimating dense rigid scene flow in 3D LiDAR scans. We formulate the problem as an energy minimization problem, where we assume local geometric constancy and incorporate regularization for smooth motion fields. Analyzing the dynamics at point level helps in inferring the fine-grained details of motion. We show results on multiple sequences of the KITTI odometry dataset, where we seamlessly estimate multiple motions pertaining to different dynamic objects. Furthermore, we test our approach on a dataset with pedestrians to show how our method adapts to a case with non-rigid motion. For comparison we use the ground truth from KITTI and show how our method outperforms different ICP-based methods.

negative: Dialogue-act-driven Conversation Model : An Experimental Study The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced DailyDialog dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialogue act information.

negative: Evaluation of Objective Quality Measures for Speech Enhancement In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.

negative: A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.

negative: Compact slow-wave millimeter-wave bandpass filter (BPF) using open-loop resonator A compact slow-wave millimeter-wave bandpass filter for wireless communication systems is developed and proposed in this paper. The filter is based on an open-loop resonator that is composed of a microstrip line with both ends loaded with folded open stubs. The folded arms of the open stubs are utilized to increase the loading capacitance to ground. In addition, the folded arms of the open stubs are also used to introduce cross couplings. The cross couplings can create transmission zeros at the edges of the desired passband. As a result, the filter can exhibit a passband with sharp rejection skirts. The filter is designed to have a fractional bandwidth of about 3.3% at a center frequency of 30.0 GHz. The filter design is realized using Rogers RT6002 substrate with a dielectric constant of 2.94 and a thickness of 0.127 mm. The filter design is successfully fabricated using the printed circuit board (PCB) technology. The fabricated filter is measured and an excellent agreement between the simulated and measured results is achieved. The fabricated filter has the advantages of compact size and excellent performance.

negative: RDF-4X: a scalable solution for RDF quads store in the cloud Resource Description Framework (RDF) represents a flexible and concise model for representing the metadata of resources on the web. Over the past years, with the increasing amount of RDF data, efficient and scalable RDF data management has become a fundamental challenge to achieve the Semantic Web vision. However, multiple approaches for RDF storage have been suggested, ranging from simple triple stores to more advanced techniques like vertical partitioning on the predicates or centralized approaches. Unfortunately, it is still a challenge to store a huge quantity of RDF quads due, in part, to the query processing for RDF data. This paper proposes a scalable solution for RDF data management that uses Apache Accumulo. We focus on introducing storage methods and indexing techniques that scale to billions of quads across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases our approach works well against large RDF datasets.

negative: Global, Dense Multiscale Reconstruction for a Billion Points We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with nonuniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently with a lookup table which allows to map octree cells to the nodes of the finite elements. We optimize memory efficiency by data aggregation, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.

negative: Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining

negative: Parsimonious module inference in large networks. We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obtain general bounds on the detectability of any prescribed block structure, given the number of nodes and edges in the sampled network. We also obtain that the maximum number of detectable blocks scales as sqrt[N], where N is the number of nodes in the network, for a fixed average degree ⟨k⟩. We also show that the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm with a complexity of O(τNlogN), if the number of blocks is unknown, and O(τN) if it is known, where τ is the mixing time of the Markov chain. We illustrate the application of the method on a large network of actors and films with over 10(6) edges, and a dissortative, bipartite block structure.

negative: Discriminative Optimization: Theory and Applications to Computer Vision Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: designing a cost function with a local optimum at an acceptable solution, and developing an efficient numerical method to search for this optimum. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, we propose Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to the desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 3D registration, camera pose estimation, and image denoising. We show that DO outperformed or matched state-of-the-art algorithms in terms of accuracy, robustness, and computational efficiency.

negative: Pervasive Motion Tracking and Muscle Activity Monitor This paper introduces a novel human activity monitoring system combining Inertial Measurement Units (IMU) and Mechanomyographic (MMG) muscle sensing technology. While other work has recognised and implemented systems for combined muscle activity and motion recording, they have focused on muscle activity through EMG sensors, which have limited use outside controlled environments. MMG is a low frequency vibration emitted by skeletal muscle whose measurement does not require gel or adhesive electrodes, potentially offering much more efficient implementation for pervasive use. We have developed a combined IMU and MMG sensor the fusion of which provides a new dimension in human activity monitoring and bridges the gap between human dynamics and muscle activity. Results show that synergy between motion and muscle detection is not only viable, but straightforward, inexpensive, and can be packaged in a lightweight wearable package.

negative: Human Activity Recognition Using Recurrent Neural Networks Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.

negative: Recognition for Handwritten English Letters : A Review Character recognition is one of the most interesting and challenging research areas in the field of Image processing. English character recognition has been extensively studied in the last half century. Nowadays different methodologies are in widespread use for character recognition. Document verification, digital library, reading bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance.

negative: Anomaly Detection for Astronomical Data Modern astronomical observatories can produce massive amount of data that are beyond the capability of the researchers to even take a glance. These scientific observations present both great opportunities and challenges for astronomers and machine learning researchers. In this project we address the problem of detecting anomalies/novelties in these large-scale astronomical data sets. Two types of anomalies, the point anomalies and the group anomalies, are considered. The point anomalies include individual anomalous objects, such as single stars or galaxies that present unique characteristics. The group anomalies include anomalous groups of objects, such as unusual clusters of the galaxies that are close together. They both have great values for astronomical studies, and our goal is to detect them automatically in un-supervised ways. For point anomalies, we adopt the subspace-based detection strategy and proposed a robust low-rank matrix decomposition algorithm for more reliable results. For group anomalies, we use hierarchical probabilistic models to capture the generative mechanism of the data, and then score the data groups using various probability measures. Experimental evaluation on both synthetic and real world data sets shows the effectiveness of the proposed methods. On a real astronomical data sets, we obtained several interesting anecdotal results. Initial inspections by the astronomers confirm the usefulness of these machine learning methods in astronomical research.

negative: Graph SLAM based mapping for AGV localization in large-scale warehouses The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.

negative: Evaluation of output embeddings for fine-grained image classification Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due to the annotation cost of large numbers of fine-grained categories. This project shows that compelling classification performance can be achieved on such categories even without labeled training data. Given image and class embeddings, we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones; zero-shot classification of an image proceeds by finding the label yielding the highest joint compatibility score. We use state-of-the-art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora. We establish a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate that purely unsupervised output embeddings (learned from Wikipedia and improved with finegrained text) achieve compelling results, even outperforming the previous supervised state-of-the-art. By combining different output embeddings, we further improve results.

negative: Advice weaving in AspectJ This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's advice weaver is to statically transform a program so that at runtime it will behave according to the AspeetJ language semantics. This paper describes the 1.1 implementation which is based on transforming bytecode. We describe how AspectJ's join points are mapped to regions of bytecode, how these regions are efficiently matched by AspectJ's pointcuts, and how pieces of advice are efficiently implemented at these regions. We also discuss both run-time and compile-time performance of this implementation.

negative: Social Interactions in Online Gaming This paper briefly overviews five studies examining massively multiplayer online role-playing games (MMORPGs). The first study surveyed 540 gamers and showed that the social aspects of the game were the most important factor for many gamers. The second study explored the social interactions of 912 MMORPG players and showed they created strong friendships and emotional relationships. A third study examined the effect of online socializing in the lives of 119 online gamers. Significantly more male gamers than female gamers said that they found it easier to converse online than offline, and 57% of gamers had engaged in gender swapping. A fourth study surveyed 7,069 gamers and found that 12% of gamers fulfilled at least three diagnostic criteria of addiction. Finally, an interview study of 71 gamers explored attitudes, experiences, and feelings about online gaming. They provided detailed descriptions of personal problems that had arisen due to playing MMORPGs. an individualistic character (Griffiths, Davies, & Chappell, 2004). This is the only setting where millions of users voluntarily immerse themselves in a graphical virtual environment and interact with each other through avatars on a daily basis (Yee, 2007). Research suggests that the game play within these virtual worlds is enhanced because players use them as traditional games as well as arenas in which to explore DOI: 10.4018/ijgbl.2011100103 International Journal of Game-Based Learning, 1(4), 20-36, October-December 2011 21 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. new relationships, new places and themselves (Krotoski, 2004). Despite the massive amounts of money spent on online gaming, very little research has been carried out regarding the positive social aspects of these games. Much of the debate over the last 30 years has focused on the dangers of computer gaming in the adolescent population, including increased aggression and addiction. Research has also been carried out examining the potentially harmful effects playing computer games may have on social development, self-esteem, social inadequacy, and social anxiety. MMORPGs are very (virtually) socially interactive but little social interaction in the real world is needed when playing them as only one person can play them at any one time from a single computer, unlike some popular two-player console games such as Mortal Kombat. Yee (2001, 2006, 2007) has carried out research into MMORPGs and notes that they allow new forms of social identity and social interaction. Yee’s research has shown that MMORPGs appeal to adults and teenagers from a wide range of backgrounds, and they spend on average more than half a working week in these environments. In a study by Utz (2000), it was found that 77% of respondents reported that they had some sort of relation with other Multi-User Dungeon (MUD) Gamers. It has also been suggested that college students can develop compulsions to play MMORPGs leading to social isolation, poor academic performance, and sleep deprivation. In 2004, a survey of over 54,000 American students found 11% of females and 20% of males said their recreational computer use had significantly hindered their performance at college and University (American College Health Association, 2005). Players can become fixated on their virtual characters, striving to obtain the best armour, experience and reputation in the game, ignoring the fact that their grades are dropping and their friends have drifted apart. It is clear to see that computer games appear to play a role in the socialisation of heavy game players particularly for those who play MMORPGs. Krotoski (2004) maintains that MMORPGs encourage group interaction and involvement, flexibility and mastery, resulting in significant friendships and personal empowerment. It is important to realise that gaming has shown elements of being a compulsive behaviour, with players feeling addicted, experiencing a strong impulse to play the games and finding it hard to resist the games (Griffiths & Davies, 2005). Positive social interaction is paramount in MMORPGs because they require a large number of players to cooperate together and work as a team at the same time. MMORPGs also have multiple tasks that require different characters with different skills in order to complete a challenge or quest. This teaches gamers to be dependent on one another that reinforce their relationships, providing a good understanding of teamwork. The purpose of the research here is to examine the social interactions that occur both within and outside of MMORPGs. The development of virtual friendships can be very enjoyable for gamers, and anecdotal evidence has suggested they sometimes develop into serious real-life friendships and relationships. Not only do MMORPGs facilitate formation of relationships, they are also windows into and catalysts in existing relationships. To date, there has been relatively little research into massively multiplayer online roleplaying games (MMORPGs). This paper briefly overviews five studies by the authors (Griffiths, Davies & Chappell, 2004; Cole & Griffiths, 2007; Hussain & Griffiths, 200, 2009; Grüsser, Thalemann, & Griffiths, 2007). In all studies outlined below, participants were informed that participation was entirely voluntary and that the research was conducted according to the British Psychological Society’s Ethical Code of Conduct for Psychologists. In the case of the surveys, if participants no longer wished to take part they simply had to close the Internet browser. All players were guaranteed anonymity and confidentiality. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/social-interactions-onlinegaming/60132?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Library Science, Information Studies, and Education, InfoSci-Select, InfoSci-Select, InfoSciCommunications, Online Engagement, and Media eJournal Collection, InfoSci-Educational Leadership, Administration, and Technologies eJournal Collection, InfoSci-Select, InfoSci-Select, InfoSci-Journal Disciplines Communications and Social Science. Recommend this product to your

negative: Autonomous Self-Assembly in Swarm-Bots In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly

negative: Development and Implementation of a Chat Bot in a Social Network This document describes how to implement a Chat Bot on the Twitter social network for entertainment and viral advertising using a database and a simple algorithm. Having as a main theme a successfully implementation of a Chat Bot preventing people classify it as SPAM, as a result of this a Twitter account (@DonPlaticador) that works without the intervention of a person and every day earns more followers was obtained.

negative: A Theoretical Framework for Serious Game Design: Exploring Pedagogy, Play and Fidelity and their Implications for the Design Process It is widely acknowledged that digital games can provide an engaging, motivating and “fun” experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design (Kiili, 2005; Seeney & Routledge, 2009). Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore a triadic theoretical framework for serious game design comprising play, pedagogy and fidelity. It will outline underpinning theories, review key literatures and identify challenges and issues involved in balancing these elements in the process of serious game design. DOI: 10.4018/ijgbl.2012100103 42 International Journal of Game-Based Learning, 2(4), 41-60, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. game design (Kiili, 2005; Seeney & Routledge, 2009). While a sound pedagogical framework is considered essential to their effectiveness as learning tools, equally important is the integration of game play elements which harness and sustain player engagement. Additionally, with the advent of sophisticated and immersive technologies, as exemplified in the virtual worlds of contemporary games, and increasing interest in the opportunities for constructivist learning offered by these worlds, concepts of fidelity, and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore this triadic theoretical framework for serious game design, outlining underpinning theories, reviewing key literatures and identifying associated challenges and issues (Figure 1). The paper begins by reflecting on pedagogical theories commonly utilised to conceptualise game-based learning, focusing on three constructivist theories. Following this, attention switches to theories used to conceptualise players’ engagement with digital games, and thus inform effective, engaging and “fun” game design. As a key component of engaging and pedagogically effective game design, the concept of fidelity, and how it relates to game design and game-based learning, is discussed. The paper will conclude by reflecting on issues and challenges involved in balancing these components when designing

negative: Personality-based Adaptation for Teamwork in Game Agents This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted to-

negative: Analysis of level design 'push & pull' within 21 games This paper investigates the differences between 3D level designs in 21 popular games. We have developed a framework to analyze 3D level designs based on patterns extracted from level designers and game play sessions. We then use these patterns to analyze several game play sessions. Results of this analysis reveal methods by which designers push and pull players through levels. We discuss an analysis of these patterns in terms of three level affordance configurations, combat, environmental resistance, and mixed goals in 21 different games using one walkthrough play session per game. By looking at the variety of games, we can further explore the level similarities and differences between games.

negative: Gore Galore: Literary Theory and Computer Games Computer games have not been adequately theorized within the humanities. In this paper a brief history of computer games is presented as a starting point for developing a topology of games and a theory of computer games as rhetorical artifacts suitable for critical study. The paper addresses the question of why games should be treated seriously and suggests a theoretical approach based on Backhtin’s poetics of the novel where the experience of time and space (the chronotope) provides a framework of questions for discussing computer games.