query: Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms

positive: Differential Evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces by A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.

positive: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.

positive: Hybrid Particle Swarm Optimiser with breeding and subpopulations In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models. This is done to illustrate that PSOs with breeding strategies have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe how to make the hybrids benefit from genetic methods and to test their potential and competetiveness on function optimisation.

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

positive: Simulated annealing: Practice versus theory Simulated annealing (SA) presents an optimization technique with several striking positive and negative features. Perhaps its most salient feature, statistically promising to deliver an optimal solution, in current practice is often spurned to use instead modified faster algorithms, “simulated quenching” (SQ). Using the author’s Adaptive Simulated Annealing (ASA) code, some examples are given which demonstrate how SQ can be much faster than SA without sacrificing accuracy.

negative: Deep Image Segmentation by Quality Inference Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations.

negative: 1 ACO Algorithms for the Traveling Salesman Problemy Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect communication of a colony of agents, called (artificial) ants, based on pheromone trails (pheromones are also used by real ants for communication). The (artificial) pheromone trails are a kind of distributed numeric information which is modified by the ants to reflect their experience while solving a particular problem. Recently, the Ant Colony Optimization (ACO) metaheuristic has been proposed which provides a unifying framework for most applications of ant algorithms [15, 16] to combinatorial optimization problems. In particular, all the ant algorithms applied to the TSP fit perfectly into the ACO meta-heuristic and, therefore, we will call these algorithms also ACO algorithms. The first ACO algorithm, called Ant System (AS) [18, 14, 19], has been applied to the Traveling Salesman Problem (TSP). Starting from Ant System, several improvements of the basic algorithm have been proposed [21, 22, 17, 51, 53, 7]. Typically, these improved algorithms have been tested again on the TSP. All these improved versions of AS have in common a stronger exploita-

negative: Variational shape approximation A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.

negative: End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons. * Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence © 2012 Elsevier B.V. All rights reserved.

negative: On the Kinematic Analysis of Robotic Mechanisms The kinematic analyses, of manipulators and other robotic devices composed of mechanical links, usually depend on the solution of sets of nonlinear equations. There are a variety of both numerical and algebraic techniques available to solve such systems of equations and to give bounds on the number of solutions. These solution methods have also led to an understanding of how special choices of the various structural parameters of a mechanism influence the number of solutions inherent to the kinematic geometry of a given structure. In this paper, results from studying the kinematic geometry of such systems are reviewed, and the three most useful solution techniques are summarized. The solution techniques are polynomial continuation, Gröbner bases, and elimination. We then discuss the results that have been obtained with these techniques in the solution of two basic problems, namely, the inverse kinematics for serial-chain manipulators, and the direct kinematics of in-parallel platform devices. KEY WORDS—inverse kinematics, direct kinematics, manipulators, in-parallel manipulators, series manipulators, polynomial continuation, characteristic polynomial

negative: The spread of alcohol consumption behavior in a large social network. Context A person's alcohol use might mirror that of his or her social contacts. Contribution Using the same group of Framingham Heart Study participants who helped to define the associations between social networks and other health behaviors, the researchers found that alcohol use was similar among individuals in a social cluster. Furthermore, changes in a person's alcohol intake over time followed changes in the alcohol intake of their social contacts. Caution Alcohol use was self-reported, and the researchers did not have access to social contacts who were not participating in the study. Implication Changing alcohol use may require intervening with social groups as well as with individuals. The Editors Alcohol use is common in the United States. In 2002, 55% of adults reported having had at least 1 drink in the previous month, and the prevalence of past-month alcohol consumption was somewhat higher for men (62%) than for women (48%) (1). The lifetime prevalence of alcohol use disorders has been measured at 14.6% (1). Excessive alcohol use, either in the form of heavy drinking or binge drinking, increases the risk for numerous health and social problems (2, 3), and approximately 75000 deaths in 2001 were attributable to excessive alcohol use, which makes it the third-leading lifestyle-related cause of death (3). Alcohol consumption behavior has many determinants. Previous studies (3, 4) suggest that biological factors have a significant effect on the progression from experimentation to regular use and that social and cultural factors play a critical role in experimentation with alcohol and the development of drinking patterns over time. Given the social nature of this behavior, it is not surprising that previous work has identified interactions with friends and family members as key factors (48). Although this literature primarily focused on cross-sectional panels, some studies (68) have attempted to test whether social influences act over time. These studies, which focused on peer influence among college students, showed inconsistent results and tended to focus just on pairs of connected persons. The study of social influences on behavior has expanded in recent years to the study of networks of linked individuals over time (9). Recent work in this area has shown that various health-related phenomena, ranging from sexually transmitted diseases to obesity, smoking, and even suicide, may travel along and within social networks (1015). Using a longitudinal, dynamic network of 12067 persons, we analyzed the role of social networks in alcohol use, focusing on 1) whether clusters of heavy drinkers and abstainers existed within the network; 2) whether a person's alcohol consumption behavior was associated with that of his or her social contacts; 3) the extent to which such associations depended on the nature and direction of the social ties (for example, friends of different kinds, siblings, spouses, coworkers, or neighbors); and 4) whether gender affected the spread of alcohol consumption across social ties. Methods Source Data We used data from participants in the Framingham Heart Study (FHS). The FHS is a population-based, longitudinal, observational cohort study that was initiated in 1948 to prospectively investigate risk factors for cardiovascular disease. Four cohorts, who mostly represent different generations linked to an original cohort, are included in the entire FHS. Participant data, collected every 2 to 4 years, includes physical examinations, laboratory tests, noninvasive cardiac and vascular testing, battery testing (such as the Mini-Mental State Examination), questionnaire results, and basic demographic information. For our analyses, we aligned the examination waves for the original cohort with those of the second-generation offspring cohort, which allowed us to treat all participants as having been examined in 7 waves. The offspring cohort, initiated in 1971, is the source of our study's principals, or focal individuals in the network (16). However, we included other FHS participants whom the principals listed as social contacts and refer to them here as contacts. Therefore, even though principals come only from the offspring cohort, contacts are drawn from the entire set of both the original and offspring cohorts. To ascertain social network ties, we created a separate data set that linked individuals through self-described social ties, collected in each of the 7 waves of the study. We could then detect relationships between participants (for example, spouse, sibling, friend, coworker, or neighbor) and observe changes in these ties across time. Either party to a link between 2 people might identify his or her link to the other. This is most relevant to the friend link, which could exist if A nominated B or B nominated A as a friend. We also used complete records of participants' and their contacts' address in each wave since 1971 in our analyses, although we have no information about relationships that participants did not report. For each wave, we could determine who is whose neighbor and the geographic distance between persons (10, 17). Table 1 provides descriptive statistics for the 5124 principals in our sample. Table 1. Summary Statistics for Principals Measures Alcohol consumption was self-reported in all studied waves, with participants reporting their average number of drinks per week over the past year as well as the number of days within the past week during which they consumed alcohol (beer, wine, and liquor). Self-reported data are generally considered a valid and reliable source when assessing alcohol consumption, although recall measures, such as those used in this study, can be subject to recall bias from participants (18). We treated alcohol consumption as a continuous variable in some analyses (for example, number of drinks per day, calculated from participant responses) but conducted others with dichotomous cut-points, defining heavy drinkers as those who averaged more than 1 (for women) or 2 (for men) drinks per day; moderate drinkers as those whose alcohol consumption was less than the cutoff values for heavy drinkers; and abstainers as those who reported no alcohol consumption. We did not use self-reported number of days drinking in the past week as a measure in and of itself but rather as a means to calculate average number of drinks in a day. (These labels do not reflect clinical definitions of alcohol abuse or dependence.) Table 2 shows averages for the study population across time, including age, alcohol consumption, and percentages of abstainers and drinkers. Although the differences in how we measured heavy drinking made it difficult to compare our results with those for other population samples, the other averages for the mean-age groups in each year of the given waves are roughly similar to national averages of alcohol consumption behavior (1, 19, 20). Table 2. Average Age and Alcohol Consumption Behavior, by Examination Statistical Analysis Our first goal was to evaluate whether a person's alcohol consumption behavior was associated with that of his or her social network ties at various degrees of separation. To test this hypothesis, we took an observed clustering of persons (and their alcohol consumption behavior) within the whole network and compared them with 1000 simulated networks with the same topology and overall prevalence of drinking as the observed network, but with the incidence of drinking (for example, at least 1 drink per day) randomly distributed across the nodes (random drinking networks). If clustering occurs in drinking behavior, then the probability that a contact is a drinker given that a principal is a drinker should be higher in the observed network than in the random drinking networks (21). We used the KamadaKawai algorithm, which iteratively repositions nodes to reduce the number of ties that cross each other, to draw the networks (22). Our second goal was to examine the possible determinants of any clustering in alcohol consumption behavior. We considered 3 explanations for nonrandom clustering of alcohol consumption behavior in the network: principals might choose to associate with like contacts (homophily) (23, 24); principals and contacts might share attributes or jointly experience unobserved contemporaneous events that cause their alcohol consumption behavior to covary (omitted variables or confounding); and contacts might exert social influence or peer effects on principals (induction). The availability of dynamic, longitudinal data on both network connections and drinking behavior allowed us to distinguish between interpersonal induction of drinking and homophily (25). Our basic statistical approach involved specifying longitudinal logistic regression models in which a principal's drinking status at time t + 1 is a function of his or her various attributes, such as age, sex, and education; his or her drinking status at time t; and the drinking status of his or her contacts at times t and t + 1 (25). We used generalized estimating equation procedures to account for multiple observations of the same principal across both waves and principalcontact pairings (26). We assumed an independent working correlation structure for the clusters (27). By using a time-lagged dependent variable (lagged to the previous examination) for alcohol consumption, we eliminated serial correlation in the errors (28) (evaluated with a Lagrange multiplier test) and substantially controlled for the principal's genetic endowment and any intrinsic, stable predilection to drink. In addition, the lagged independent variable for a contact's drinking status substantially controlled for homophily (25, 29). The key variable of interest is a contact's alcohol consumption behavior at time t + 1. A significant coefficient on this variable would suggest either that a contact's drinking affects a principal's drinking or that a principal and a contact experience contemporaneous

negative: Chapter 2 : Game Analytics – The Basics Take Away Points:  Overview of important key terms in game analytics  Introduction to game telemetry as a source of business intelligence.  In-depth description and discussion of user-derived telemetry and metrics.  Introduction to feature selection in game analytics  Introduction to the knowledge discovery process in game analytics  References to essential further reading. 1 Analytics – a new industry paradigm Developing a profitable game in today's market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players' time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment. Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The meth-odological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predic-tive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007). Analytics typically relies on computational modeling. There are several branches or domains of analytics, e.g. marketing analytics, risk analytics, web analytics – and game analytics. Importantly, analytics is not the same thing as data analysis. Analytics is an umbrella term, covering the entire methodology of finding and communicating patterns in data, whereas analysis is used for individual applied instances, e.g. running

negative: [POSTER] Realtime Shape-from-Template: System and Applications An important yet unsolved problem in computer vision and Augmented Reality (AR) is to compute the 3D shape of nonrigid objects from live 2D videos. When the object's shape is provided in a rest pose, this is the Shape-from-Template (SfT) problem. Previous realtime SfT methods require simple, smooth templates, such as flat sheets of paper that are densely textured, and which deform in simple, smooth ways. We present a realtime SfT framework that handles generic template meshes, complex deformations and most of the difficulties present in real imaging conditions. Achieving this has required new, fast solutions to the two core sub-problems: robust registration and 3D shape inference. Registration is achieved with what we call Deformable Render-based Block Matching (DRBM): a highly-parallel solution which densely matches a time-varying render of the object to each video frame. We then combine matches from DRBM with physical deformation priors and perform shape inference, which is done by quickly solving a sparse linear system with a Geometric Multi-Grid (GMG)-based method. On a standard PC we achieve up to 21fps depending on the object. Source code will be released.

negative: Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

negative: Small form factor PIFA antenna design at 28 GHz for 5G applications This paper presents the design and analysis of lowest form factor planar inverted-F Antenna at 28GHz for 5G Mobile applications. Feeding and shorting of the antenna is done using metallic strips. The Important properties of this antenna are its small footprint (0.25λg), good gain of 4.5dBi, 10dB impedance bandwidth of 1.55 GHz and radiation efficiency of 94%. The total size of the PIFA antenna is 0.25λg. PIFA antenna is one of the antennas whose performance greatly depends on ground plane size and its position on the ground plane. It shows good radiation pattern when placed at the corner or edge.

negative: Amharic-English Speech Translation in Tourism Domain This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and AmharicEnglish Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model (AM). Likewise, SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4% of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4% word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42%. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95%) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.

negative: Optimal management of shoulder impingement syndrome Shoulder impingement is a progressive orthopedic condition that occurs as a result of altered biomechanics and/or structural abnormalities. An effective nonoperative treatment for impingement syndrome is aimed at addressing the underlying causative factor or factors that are identified after a complete and thorough evaluation. The clinician devises an effective rehabilitation program to regain full glenohumeral range of motion, reestablish dynamic rotator cuff stability, and implement a progression of resistive exercises to fully restore strength and local muscular endurance in the rotator cuff and scapular stabilizers. The clinician can introduce stresses and forces via sport-specific drills and functional activities to allow a return to activity.

negative: Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime Purpose: The present case study investigates the usefulness of altmetrics for measuring the broad impact of research. Methods: This case study is based on a sample of 1,082 PLOS (the Public Library of Science) journal articles recommended in F1000. The dataset includes altmetrics which were provided by PLOS. The F1000 dataset contains tags on papers which were assigned by experts to characterise them. Findings: Results from the Facebook and Twitter models show higher predicted numbers of counts for "good for teaching" papers than for those papers where the tag is not set. Further model estimations show that saves by Mendeley users are particularly to be expected when a paper introduces a new practical/ theoretical technique (tag: "technical advance"). The tag "New finding" is statistically significant in the model with which the Facebook counts are evaluated. Conclusions: The "good for teaching" is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialized area. Thus, the results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the social impact of research.

negative: Introduction of the automated assessment of homework assignments in a university-level programming course Modern teaching paradigms promote active student participation, encouraging teachers to adapt the teaching process to involve more practical work. In the introductory programming course at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, homework assignments contribute approximately one half to the total grade, requiring a significant investment of time and human resources in the assessment process. This problem was alleviated by the automated assessment of homework assignments. In this paper, we introduce an automated assessment system for programming assignments that includes dynamic testing of student programs, plagiarism detection, and a proper presentation of the results. We share our experience and compare the introduced system with the manual assessment approach used before.

negative: Sediment quality criteria in use around the world There have been numerous sediment quality guidelines (SQGs) developed during the past 20 years to assist regulators in dealing with contaminated sediments. Unfortunately, most of these have been developed in North America. Traditionally, sediment contamination was determined by assessing the bulk chemical concentrations of individual compounds and often comparing them with background or reference values. Since the 1980s, SQGs have attempted to incorporate biological effects in their derivation approach. These approaches can be categorized as empirical, frequency-based approaches to establish the relationship between sediment contamination and toxic response, and theoretically based approaches that attempt to account for differences in bioavailability through equilibrium partitioning (EqP) (i.e., using organic carbon or acid volatile sulfides). Some of these guidelines have been adopted by various regulatory agencies in several countries and are being used as cleanup goals in remediation activities and to identify priority polluted sites. The original SQGs, which compared bulk chemical concentrations to a reference or to background, provided little insight into the ecosystem impact of sediment contaminants. Therefore, SQGs for individual chemicals were developed that relied on field sediment chemistry paired with field or laboratory-based biological effects data. Although some SQGs have been found to be relatively good predictors of significant site contamination, they also have several limitations. False positive and false negative predictions are frequently in the 20% to 30% range for many chemicals and higher for others. The guidelines are chemical specific and do not establish causality where chemical mixtures occur. Equilibrium-based guidelines do not consider sediment ingestion as an exposure route. The guidelines do not consider spatial and temporal variability, and they may not apply in dynamic or larger-grained sediments. Finally, sediment chemistry and bioavailability are easily altered by sampling and subsequent manipulation processes, and therefore, measured SQGs may not reflect in situ conditions. All the assessment tools provide useful information, but some (such as SQGs, laboratory toxicity and bioaccumulation, and benthic indices) are prone to misinterpretation without the availability of specific in situ exposure and effects data. SQGs should be used only in a “screening” manner or in a “weight-of-evidence” approach. Aquatic ecosystems (including sediments) must be assessed in a “holistic” manner in which multiple components are assessed (e.g., habitat, hydrodynamics, resident biota, toxicity, and physicochemistry, including SQGs) by using integrated approaches.

negative: Mapping science through bibliometric triangulation: An experimental approach applied to water research The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question what can be learned from a combination – triangulation – of these different science maps. In this paper, we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method does individually. The outcomes from the three different approaches are associated with each other and can be systematically interpreted, and provide insights into the complex multidisciplinary structure of the field of water

negative: Iterative Document Representation Learning Towards Summarization with Polishing In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.

negative: TouchIn: Sightless two-factor authentication on multi-touch mobile devices Mobile authentication is indispensable for preventing unauthorized access to multi-touch mobile devices. Existing mobile authentication techniques are often cumbersome to use and also vulnerable to shoulder-surfing and smudge attacks. This paper focuses on designing, implementing, and evaluating TouchIn, a two-factor authentication system on multi-touch mobile devices. TouchIn works by letting a user draw on the touchscreen with one or multiple fingers to unlock his mobile device, and the user is authenticated based on the geometric properties of his drawn curves as well as his behavioral and physiological characteristics. TouchIn allows the user to draw on arbitrary regions on the touchscreen without looking at it. This nice sightless feature makes TouchIn very easy to use and also robust to shoulder-surfing and smudge attacks. Comprehensive experiments on Android devices confirm the high security and usability of TouchIn.

negative: Understanding and Using Patterns in Software Development Patterns have shown to be an effective means of capturing and communicating software design experience. However, there is more to patterns than software design patterns: We believe that patterns work for software development on several levels. In this paper we explore what we have come to understand as crucial aspects of the pattern concept, relate patterns to the different models built during software design, discuss pattern forms and how we think that patterns can form larger wholes like pattern handbooks.

negative: Korean pop takes off! Social media strategy of Korean entertainment industry The Korean Wave (K-wave), or Hallyu, referred as “the phenomenon of Korean pop culture, such as TV dramas, films, pop music, fashion, and online games being widely embraced and shared among the people of Japan, China, Hong Kong, Taiwan, and other Asian countries”. After the drama-oriented craze of the first K-wave, Korean pop music (shortly K-pop) took the integral part of the second K-wave. In addition, with the rapid spread of social media like YouTube and Twitter, K-pop has expanded its fandom outside of Asia to the West. The world-wide success of K-pop contributes to improve the `Korea' image and make a positive impact on Korean economy. It is reported that around 1,000 entertainment agencies are active in Korea, while there exist the “big three” record labels and entertainment agencies: SM Entertainment, YG Entertainment and JYP Entertainment. In this case, we address the world-wide phenomenon of K-pop popularity and the role of social media on recent boom of K-pop. Focusing the major agencies above, we present lessons on how to manage social media strategically: align strategic business model with social media; maximize various social media channels; engage customers with on-and offline promotions; and stimulate audience with exclusive contents.

negative: Performance Evaluation of Channel Decoding with Deep Neural Networks With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.

negative: Impact Analysis of Flow Shaping in Ethernet-AVB/TSN and AFDX from Network Calculus and Simulation Perspective Ethernet-AVB/TSN (Audio Video Bridging/Time-Sensitive Networking) and AFDX (Avionics Full DupleX switched Ethernet) are switched Ethernet technologies, which are both candidates for real-time communication in the context of transportation systems. AFDX implements a fixed priority scheduling strategy with two priority levels. Ethernet-AVB/TSN supports a similar fixed priority scheduling with an additional Credit-Based Shaper (CBS) mechanism. Besides, TSN can support time-triggered scheduling strategy. One direct effect of CBS mechanism is to increase the delay of its flows while decreasing the delay of other priority ones. The former effect can be seen as the shaping restriction and the latter effect can be seen as the shaping benefit from CBS. The goal of this paper is to investigate the impact of CBS on different priority flows, especially on the intermediate priority ones, as well as the effect of CBS bandwidth allocation. It is based on a performance comparison of AVB/TSN and AFDX by simulation in an automotive case study. Furthermore, the shaping benefit is modeled based on integral operation from network calculus perspective. Combing with the analysis of shaping restriction and shaping benefit, some configuration suggestions on the setting of CBS bandwidth are given. Results show that the effect of CBS depends on flow loads and CBS configurations. A larger load of high priority flows in AVB tends to a better performance for the intermediate priority flows when compared with AFDX. Shaping benefit can be explained and calculated according to the changing from the permitted maximum burst.

negative: The empathic brain: how, when and why? Recent imaging results suggest that individuals automatically share the emotions of others when exposed to their emotions. We question the assumption of the automaticity and propose a contextual approach, suggesting several modulatory factors that might influence empathic brain responses. Contextual appraisal could occur early in emotional cue evaluation, which then might or might not lead to an empathic brain response, or not until after an empathic brain response is automatically elicited. We propose two major roles for empathy; its epistemological role is to provide information about the future actions of other people, and important environmental properties. Its social role is to serve as the origin of the motivation for cooperative and prosocial behavior, as well as help for effective social communication.

negative: Marine animal classification using UMSLI in HBOI optical test facility Environmental monitoring is a critical aspect of marine renewable energy project success. A new system called Unobtrusive Multistatic Serial LiDAR Imager (UMSLI) has been prepared to capture and classify marine life interaction with electrical generation equipment. We present both hardware and software innovations of the UMSLI system. Underwater marine animal imagery has been captured for the first time using red laser diode serial LiDAR, which has advantages over conventional optical cameras in many areas. Moreover, given the scarcity of existing underwater LiDAR data, a shape matching based classification algorithm is proposed which requires few training data. On top of applying shape descriptors, the algorithm also adopts information theoretical learning based affine shape registration, improving point correspondences found by shape descriptors as well as the final similarity measure. Within Florida Atlantic University’s Harbor Branch Oceanographic Institute optical test facility, experimental LiDAR data are collected through the front end of the UMSLI prototype, on which the classification algorithm is validated.

negative:
“The Internet is a Mask”: High School Students' Suggestions for Preventing Cyberbullying INTRODUCTION
Interactions through technology have an important impact on today's youth. While some of these interactions are positive, there are concerns regarding students engaging in negative interactions like cyberbullying behaviors and the negative impact these behaviors have on others. The purpose of the current study was to explore participant suggestions for both students and adults for preventing cyberbullying incidents.

METHODS
Forty high school students participated in individual, semi-structured interviews. Participant experiences and perceptions were coded using constant comparative methods to illustrate ways in which students and adults may prevent cyberbullying from occurring within their school and community.

RESULTS
Students reported that peers would benefit from increasing online security, as well as becoming more aware of their cyber-surroundings. Regarding adult-provided prevention services, participants often discussed that there is little adults can do to reduce cyberbullying. Reasons included the difficulties in restricting online behaviors or providing effective consequences. However, some students did discuss the use of in-school curricula while suggesting that adults blame people rather than technology as potential ways to prevent cyberbullying.

CONCLUSION
Findings from the current study indicate some potential ways to improve adult efforts to prevent cyberbullying. These strategies include parent/teacher training in technology and cyberbullying, interventions focused more on student behavior than technology restriction, and helping students increase their online safety and awareness.

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

negative: A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.

negative: Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.

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

negative: Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, an experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called Experienced Grey Wolf Optimizer (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various datasets demonstrate an advance of the EGWO over the original GWO and other meta-heuristics such as genetic algorithms and particle swarm optimization.

negative: Development and investigation of efficient artificial bee colony algorithm for numerical function optimization Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and ptimization better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability ods f than other relevant meth

negative: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification ata mining unctional link artificial neural networks ulti-layer perception article swarm optimization mproved particle swarm optimization VM capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO − FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN). SN


query: Collaborative Learning for Deep Neural Networks

positive: Temporal Ensembling for Semi-Supervised Learning In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels.

positive: Gradient descent GAN optimization is locally stable Despite the growing prominence of generative adversarial networks (GANs), optimization in GANs is still a poorly understood topic. In this paper, we analyze the “gradient descent” form of GAN optimization, i.e., the natural setting where we simultaneously take small gradient steps in both generator and discriminator parameters. We show that even though GAN optimization does not correspond to a convex-concave game (even for simple parameterizations), under proper conditions, equilibrium points of this optimization procedure are still locally asymptotically stable for the traditional GAN formulation. On the other hand, we show that the recently proposed Wasserstein GAN can have non-convergent limit cycles near equilibrium. Motivated by this stability analysis, we propose an additional regularization term for gradient descent GAN updates, which is able to guarantee local stability for both the WGAN and the traditional GAN, and also shows practical promise in speeding up convergence and addressing mode collapse.

positive: Born Again Neural Networks Knowledge Distillation (KD) consists of transferring “knowledge” from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student’s compactness, without sacrificing too much performance. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating the effect of the teacher outputs on both predicted and nonpredicted classes.

positive: Learning Internal Representations Probably the most important, problem in machine learning is the preliminary biasing of a learner’s hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt, In this paper a mechanism for automailcall,y learning or biasing the learner’s hypothesis space is introduced. It works by first learning an appropriate [nterna! represenlaiton for a learning en~-ironment and then using that representation to hiss the learner’s hypothesis space for the learning of future tasks drawn from the same environment. Jln internal represent at ion must be learnt by sampling from many s2mtiar tasks, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples m per task required to ensure good generalisation from a representation learner obeys }n = O(a + b/n) where n is the number of tasks being learnt, and a and b are constants. If the tasks are learnt independently (/. e. without a common representation ) then m = 0( a + b). It is argued that for learning environments such as speech and character recognition b >> a and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if n = O(b) (with m = O(a+b/n ) ) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to O(a) (as opposed to {)( a + b) if no represent ation is used). It, is shown that gradient descent can be used to train neural network representations and the results of an experiment are reported in which a neural network representation was learnt for an environment consisting of transJaf ion ail,y in uarlanf Boolean functions. The experiment, provides strong qualitative support for the theoretical results.

positive: The Numerics of GANs In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

negative: Self-Censorship on Facebook We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.

negative: On code reuse from StackOverflow: An exploratory study on Android apps Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from Stack-

negative: On the rate of channel polarization A bound is given on the rate of channel polarization. As a corollary, an earlier bound on the probability of error for polar coding is improved. Specifically, it is shown that, for any binary-input discrete memoryless channel W with symmetric capacity I(W) and any rate R ≪ I(W), the polar-coding block-error probability under successive cancellation decoding satisfies P<inf>e</inf>(N, R) ≤ 2<sup>−Nβ</sup> for any β ≪ 1/2 when the block-length N is large enough.

negative: Natural Language Generation in Interactive Systems An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide introduces graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software, and other resources for pursuing research on natural language generation and interactive systems, including dialogue systems, multimodal interfaces, and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing, and related fields.

negative: Stretching Agile to fit CMMI Level 3 - the story of creating MSF for CMMI Process Improvement at Microsoft Corporation Agile practitioners pride themselves on highly productive, responsive, low ceremony, lightweight, tacit knowledge processes with little waste, adaptive planning and frequent iterative delivery of value. It is often assumed that CMMI compliant processes need to be heavyweight, bureaucratic, slow moving, high ceremony and plan driven. Agile developers often skeptically perceive formal process improvement initiatives as management generated inefficiency that gets in the way of productivity. At Microsoft, we’ve adopted the teachings of W. Edwards Deming and stretched our MSF for Agile Software Development method to fit the requirements for CMMI Level 3. The resultant MSF for CMMI Process Improvement is a highly iterative, adaptive planning method, light on documentation, and heavily automated through tooling. It enables management and organization of software engineering through use of agile metrics such as velocity and cumulative flow but with an added dimension of an understanding of variation – adapted from Deming’s teachings. This is the story of how mixing Deming with Agile produced a lightweight CMMI solution for .Net developers everywhere.

negative: Face Recognition in a Meeting Room In this paper, weinvestigaterecognition of humanfaces in a meetingroom. The major challenges of identifying humanfacesin this environmentincludelow quality of input images,poor illumination,unrestrictedheadposesand continuouslychangingfacial expressionsandocclusion.In order to addresstheseproblemswe proposea novel algorithm, DynamicSpaceWarping (DSW).Thebasic idea of the algorithm is to combinelocal features under certain spatial constraints. We compare DSWwith the eigenface approachondatacollectedfromvariousmeetings.Wehave testedboth front and profile face imagesand imageswith two stagesof occlusion.Theexperimentalresultsindicate thattheDSWapproachoutperformstheeigenfaceapproach in bothcases.

negative: Anomaly Detection in Text: The Value of Domain Knowledge We consider the problem of detecting anomalies from text data. Our hypothesis is that as with classical anomaly detection algorithms, domain-specific features are more important than the linguistic features. We employ the use of first-order logic and demonstrate the effectiveness of useful domain knowledge in two domains. Our results show that the domain-specific features are more predictive and that the relational learning methods exhibit superior performance.

negative: Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems This paper considers real world UK number plates and relates these to ANPR. It considers aspects of the relevant legislation and standards when applying them to real world number plates. The varied manufacturing techniques and varied specifications of component parts are also noted. The varied fixing methodologies and fixing locations are discussed as well as the impact on image capture.

negative: Full 3-D Printed Microwave Termination: A Simple and Low-Cost Solution This paper describes the realization and characterization of microwave 3-D printed loads in rectangular waveguide technology. Several commercial materials were characterized at X-band (8-12 GHz). Their dielectric properties were extracted through the use of a cavity-perturbation method and a transmission/reflection rectangular waveguide method. A lossy carbon-loaded Acrylonitrile Butadiene Styrene (ABS) polymer was selected to realize a matched load between 8 and 12 GHz. Two different types of terminations were realized by fused deposition modeling: a hybrid 3-D printed termination (metallic waveguide + pyramidal polymer absorber + metallic short circuit) and a full 3-D printed termination (self-consistent matched load). Voltage standing wave ratio of less than 1.075 and 1.025 were measured over X-band for the hybrid and full 3-D printed terminations, respectively. Power behavior of the full 3-D printed termination was investigated. A very linear evolution of reflected power as a function of incident power amplitude was observed at 10 GHz up to 11.5 W. These 3-D printed devices appear as a very low cost solution for the realization of microwave matched loads in rectangular waveguide technology.

negative: Captology: A Critical Review This critical review of B.J. Fogg’s book Persuasive Technology regards captology as an eclectic and formative work. It summarises two other reviewers’ work and identifies several new strengths. It scrutinises Fogg’s functional triad computers functioning as tools, media and social actors and some categorical changes are recommended. It investigates further Johnson’s concerns about specific ethical omissions, nominating a new term, compusuasion, for the resultant but unintended, exogenous behaviour/attitude change effects of captological design. The review commences to more carefully define what constitutes persuasion and draws attention to the distinction between persuasion techniques in general and the behavioural changes that result from advocacy and education. The reviewer concludes that a fundamental ethic be that the designer’s intent be exposed at the commencement of the user’s engagement with the program and proffers the idea of persuasion resulting in a new conviction, induced by others, as a helpful definition of

negative:
Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic.
 In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\% certainty.

negative: Finger Sleeve : A Wearable Navigation Device In this paper we present a novel wearable navigation system along with implicit HCI (iHCI) model, where interaction with technology is dissolved into a day-today activity. In this type of HCI model a computer takes the input and tries to output an action that is a proactive anticipation of next action of a user. Usually, in urban areas people use voice assisted navigation systems or navigation guidelines displayed on a mobile phone. Some navigation systems are already installed on car dashboard, which needs explicit attention in order to make driving decisions. A navigation system using haptic perception to guide a user throughout a journey is the key contribution of this paper. It does not ask for explicit user attention and demonstrates the indolent form of technological interaction. This wearable device is an index finger sleeve, which consists of vibrator modules, Bluetooth communication module and Microcontroller Unit (MCU). A working prototype has been built and tested.

negative: Learning to Detect Rooftops in Aerial Images In this paper, we examine the use of machine learning to improve the robustness of systems for image analysis on the task of roof detection. We review the problem of analyzing aerial photographs, and describe an existing vision system that attempts to automate the identiication of buildings in aerial images. After this, we brieey review several well-known learning algorithms that represent a wide variety of inductive biases. We report three experiments designed to illuminate facets of applying machine learning methods to the image analysis task; one experiment focuses on within-image learning, another deals with the cost of diierent errors, and a third addresses between-image learning. Experimental results demonstrate that machine-learned classiiers meet or exceed the accuracy of handcrafted solutions and that useful generalization occurs when training and testing on data derived from diierent images.

negative: Question Answering on Freebase via Relation Extraction and Textual Evidence Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.

negative: SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available1 for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.

negative: Vehicle control system for automatic valet parking with infrastructure sensors This paper presents vehicle control system for automatic valet parking with infrastructure sensors. First, we describe the automatic valet parking service system. In the service, vehicle moves autonomously based on sensing data generated by infrastructure sensors. Second, we implement vehicle control system for automatic valet parking. We design hardware and software components focusing on minimizing the add-on devices and maximizing data integration. Finally, we do a feasibility test for verification of the system.

negative: Comparisons of centerline extraction methods for liver blood vessels in ImageJ and 3D slicer In this paper, we introduce the centerline extraction processes of the liver vessel systems by using the plug-ins in ImageJ and 3D Slicer. The Skeletonize (2D/3D) plug-in in ImageJ is easy to use, and only based on the volumetric data. It performs fast and require no user interactions in terms of seed points selection. The VMTK toolkit in 3D slicer is more powerful. It uses the surface model and can provide more smooth and consistent results. Due to its complexities, it requires more computational resources and user specified seed points. Some possible improvements for these two methods are discussed.

negative: Implantable Myoelectric Sensors (IMESs) for Intramuscular Electromyogram Recording We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.

negative:
Comparative Metagenomics Reveals Host Specific Metavirulomes and Horizontal Gene Transfer Elements in the Chicken Cecum Microbiome BACKGROUND
The complex microbiome of the ceca of chickens plays an important role in nutrient utilization, growth and well-being of these animals. Since we have a very limited understanding of the capabilities of most species present in the cecum, we investigated the role of the microbiome by comparative analyses of both the microbial community structure and functional gene content using random sample pyrosequencing. The overall goal of this study was to characterize the chicken cecal microbiome using a pathogen-free chicken and one that had been challenged with Campylobacter jejuni.

METHODOLOGY/PRINCIPAL FINDINGS
Comparative metagenomic pyrosequencing was used to generate 55,364,266 bases of random sampled pyrosequence data from two chicken cecal samples. SSU rDNA gene tags and environmental gene tags (EGTs) were identified using SEED subsystems-based annotations. The distribution of phylotypes and EGTs detected within each cecal sample were primarily from the Firmicutes, Bacteroidetes and Proteobacteria, consistent with previous SSU rDNA libraries of the chicken cecum. Carbohydrate metabolism and virulence genes are major components of the EGT content of both of these microbiomes. A comparison of the twelve major pathways in the SEED Virulence Subsystem (metavirulome) represented in the chicken cecum, mouse cecum and human fecal microbiomes showed that the metavirulomes differed between these microbiomes and the metavirulomes clustered by host environment. The chicken cecum microbiomes had the broadest range of EGTs within the SEED Conjugative Transposon Subsystem, however the mouse cecum microbiomes showed a greater abundance of EGTs in this subsystem. Gene assemblies (32 contigs) from one microbiome sample were predominately from the Bacteroidetes, and seven of these showed sequence similarity to transposases, whereas the remaining sequences were most similar to those from catabolic gene families.

CONCLUSION/SIGNIFICANCE
This analysis has demonstrated that mobile DNA elements are a major functional component of cecal microbiomes, thus contributing to horizontal gene transfer and functional microbiome evolution. Moreover, the metavirulomes of these microbiomes appear to associate by host environment. These data have implications for defining core and variable microbiome content in a host species. Furthermore, this suggests that the evolution of host specific metavirulomes is a contributing factor in disease resistance to zoonotic pathogens.

negative: A Survey and Analysis of Various Health-Related Knowledge Mining Techniques in Social Media Smart extraction of knowledge from social media has received the recent interest of the Biomedical and Health Informatics community for the simultaneous improvement of healthcare outcomes and lessen the expenses making use of consumergenerated reviews. Social media provides chances for patients and doctors to share their views and experiences without any obtrusion through online communities that might generate information, which is much beyond what is known by the domain experts. Nonetheless, for conventional public health surveillance systems, it is difficult to detect and then monitor the concerns related to health and the changes seen in attitudes of the public towards health-related problems. To solve this problem, several studies have shown the usage of information in social media for the discovery of biomedical and healthrelated information. Several disease-specific knowledge exchanges are now available on Face book and other portals of online social networking. These kind of new sources of information, support, and engagement have gone to become significant for patients who are suffering with the disease, and still the quality and the content of the knowledge contributed in these digital areas are not properly comprehended. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in mat lab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.

negative: Approaching the Limit of Predictability in Human Mobility In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.

negative: Simple Customization of Recursive Neural Networks for Semantic Relation Classification In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.

negative: Effects of communication styles on acceptance of recommendations in intercultural collaboration The objective of this study is to investigate the impact of culture and communication style (explicit versus implicit) on people’s reactions on recommendations in intercultural collaboration. The experimental results from three intercultural collaboration teams were studied: Chinese-American, Chinese-German, and Chinese-Korean. The results indicate that Chinese participants showed more positive evaluations (i.e., higher trust, higher satisfaction, and more future collaboration intention) on the implicit advisor than American and German participants. Compared with Chinese participants, Korean participants accepted explicit recommendations more often and showed more positive evaluations on the explicit advisor. The results also show that when Chinese express recommendations in an explicit way, their recommendations were accepted more often and were more positively evaluated by cross-cultural partners.

negative: R2O, an extensible and semantically based database-to-ontology mapping language We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models.

negative: Low-Dimensional Representation of Spectral Envelope Without Deterioration for Full-Band Speech Analysis/Synthesis System A speech coding for a full-band speech analysis/synthesis system is described. In this work, full-band speech is defined as speech with a sampling frequency above 40 kHz, whose Nyquist frequency covers the audible frequency range. In prior works, speech coding has generally focused on the narrowband speech with a sampling frequency below 16 kHz. On the other hand, statistical parametric speech synthesis currently uses the full-band speech, and low-dimensional representation of speech parameters is being used. The purpose of this study is to achieve speech coding without deterioration for full-band speech. We focus on a high-quality speech analysis/synthesis system and mel-cepstral analysis using frequency warping. In the frequency warping function, we directly use three auditory scales. We carried out a subjective evaluation using the WORLD vocoder and found that the optimum number of dimensions was around 50. The kind of frequency warping did not significantly affect the sound quality in the dimensions.

negative: End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach. Keywords—Machine Learning, Software Radio, Protocol Recognition, Recurrent Neural Networks, LSTM, Protocol Learning, Traffic Classification, Cognitive Radio, Deep Learning

negative: Twitter sentiment analysis using deep learning methods The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.

negative: Deep Neural Network Capacity In recent years, deep neural network exhibits its powerful superiority on information discrimination in many computer vision applications. However, the capacity of deep neural network architecture is still a mystery to the researchers. Intuitively, larger capacity of neural network can always deposit more information to improve the discrimination ability of the model. But, the learnable parameter scale is not feasible to estimate the capacity of deep neural network. Due to the overfitting, directly increasing hidden nodes number and hidden layer number are already demonstrated not necessary to effectively increase the network discrimination ability. In this paper, we propose a novel measurement, named “total valid bits”, to evaluate the capacity of deep neural networks for exploring how to quantitatively understand the deep learning and the insights behind its super performance. Specifically, our scheme to retrieve the total valid bits incorporates the skilled techniques in both training phase and inference phase. In the network training, we design decimal weight regularization and 8-bit forward quantization to obtain the integer-oriented network representations. Moreover, we develop adaptive-bitwidth and non-uniform quantization strategy in the inference phase to find the neural network capacity, total valid bits. By allowing zero bitwidth, our adaptive-bitwidth quantization can execute the model reduction and valid bits finding simultaneously. In our extensive experiments, we first demonstrate that our total valid bits is a good indicator of neural network capacity. We also analyze the impact on network capacity from the network architecture and advanced training

negative: Aspect extraction for opinion mining with a deep convolutional neural network In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

negative: Age and gender estimation using deep residual learning network In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.

negative: Retinal Vessel Segmentation using Deep Neural Networks Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test our method on publiclyavailable DRIVE dataset and our results demonstrate the high effectiveness of the deep learning approach. Our method achieves an average accuracy and AUC of 0.9466 and 0.9749, respectively.

negative: Chemi-net: a graph convolutional network for accurate drug property prediction Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.