Query: SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification

Relevant passages:
MapReduce: Simplified Data Processing on Large Clusters <=SEP=> MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing <=SEP=> We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.

SMOTE: Synthetic Minority Over-sampling Technique <=SEP=> An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

An insight into imbalanced Big Data classification: outcomes and challenges <=SEP=> BigData applications are emerging during the last years, and researchers frommany disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-andconquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware.Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit theMapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current B Alberto Fernández alberto@decsai.ugr.es Sara del Río srio@decsai.ugr.es Nitesh V. Chawla nchawla@nd.edu Francisco Herrera herrera@decsai.ugr.es 1 Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 2 Department of Computer Science and Engineering, 384 Fitzpatrick Hall, University of Notre Dame, Notre Dame, IN 46556, USA 3 Interdisciplinary Center for Network Science and Applications, 384 Nieuwland Hall of Science, University of Notre Dame, Notre Dame, IN 46556, USA research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.

From Databases to Big Data <=SEP=> 

The implementation of a full EMV smartcard for a point-of-sale transaction <=SEP=> This paper examines the changes in the payment card environment as they relate to EMV (named after Europay, MasterCard and Visa). This research shows that if the combined dynamic data authentication (CDA) card variant of the EMV card is deployed in a full EMV environment, given the relevant known vulnerabilities and attacks against the EMV technology, the consequences of unauthorized disclosure of the cardholder data is of significantly reduced value to a criminal.

DeepMind Lab <=SEP=> DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.

Aromatherapy positively affects mood, EEG patterns of alertness and math computations. <=SEP=> EEG activity, alertness, and mood were assessed in 40 adults given 3 minutes of aromatherapy using two aromas, lavender (considered a relaxing odor) or rosemary (considered a stimulating odor). Participants were also given simple math computations before and after the therapy. The lavender group showed increased beta power, suggesting increased drowsiness, they had less depressed mood (POMS) and reported feeling more relaxed and performed the math computations faster and more accurately following aromatherapy. The rosemary group, on the other hand, showed decreased frontal alpha and beta power, suggesting increased alertness. They also had lower state anxiety scores, reported feeling more relaxed and alert and they were only faster, not more accurate, at completing the math computations after the aromatherapy session.

Challenges and chances for smart rail mobility at mmWave and THz bands from the channels viewpoint <=SEP=> In the new era of “smart rail mobility”, infrastructure, trains, and travelers will be interconnected to achieve optimized mobility, higher safety, and lower costs. In order to realize a seamless high-data rate wireless connectivity, up to dozens of GHz bandwidth is required, and this motivates the exploration of the underutilized millimeter wave (mmWave) as well as the largely unexplored Terahertz (THz) bands. In order to realize the smart rail mobility at mmWave and THz bands, it is critical to gain a thorough understanding of the wireless channels. In this paper, according to the state of the art in research on railway wireless channels, we identify the main technical challenges and the corresponding chances concerning the reference scenario modules, accurate and efficient simulation platform, beamforming strategies, and handover design.

News via Voldemort: Parody accounts in topical discussions on Twitter <=SEP=> Parody accounts are prevalent on Twitter, offering irreverent interpretations of public figures, fictional characters, and more. These accounts post comments framed within the context of their fictional universes or stereotypes of their subjects, responding in-character to topical events. This article positions parody accounts as a ritualised social media practice, an extension of fan practices and irreverent internet culture. By providing a typology of parody accounts and analysing the topicality of selected parody accounts’ tweets, the research examines how these accounts contribute to topical discussions. In-character framing of topical comments allows parody accounts to offer original interpretations of breaking news

Design and Sensitivity Analysis of Highly Compact Comparator for Ku-Band Monopulse Radar <=SEP=> This paper gives the design of a highly compact comparator at a Ku-band frequency and presents analysis results of the comparator for the fabrication inaccuracies. First an unconventional magic-t using a nonstandard waveguide is designed at 15.50 GHz. To reduce the volume occupied by the magic-t, its E-arm (or difference port) is kept parallel to the plane of two inputs of the magic-t instead of perpendicular to them as is done in a convention magic-t. The sum and the difference ports of the above folded magic-t are then matched using inductive windows at 15.50 GHz. Keeping the required location of the outputs of the comparator in mind, four of these matched folded magic-ts are suitably interconnected to design a highly compact comparator. The effects of the fabrication errors in the waveguide and matching elements dimensions on the centre frequency, magnitude and phase response of the comparator are also analyzed and presented.

Agglomerative Information Bottleneck <=SEP=> We introduce a novel distributional clustering algorithm that explicitly maximizes the mutual information per cluster between the data and given categories. This algorithm can be considered as a bottom up hard version of the recently introduced “Information Bottleneck Method”. We relate the mutual information between clusters and categories to the Bayesian classification error, which provides another motivation for using the obtained clusters as features. The algorithm is compared with the top-down soft version of the information bottleneck method and a relationship between the hard and soft results is established. We demonstrate the algorithm on the 20 Newsgroups data set. For a subset of two news-groups we achieve compression by 3 orders of magnitudes loosing only 10% of the original mutual information.

DEVELOPMENT OF SEISMIC VULNERABILITY ASSESSMENT METHODOLOGIES OVER THE PAST 30 YEARS <=SEP=> Models capable of estimating losses in future earthquakes are of fundamental importance for emergency planners and for the insurance and reinsurance industries. One of the main ingredients in a loss model is an accurate, transparent and conceptually sound algorithm to assess the seismic vulnerability of the building stock and indeed many tools and methodologies have been proposed over the past 30 years for this purpose. This paper takes a look at some of the most significant contributions in the field of vulnerability assessment and identifies the key advantages and disadvantages of these procedures in order to distinguish the main characteristics of an ideal methodology.

WiFi-SLAM Using Gaussian Process Latent Variable Models <=SEP=> WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

Sentential Representations in Distributional Semantics <=SEP=> This thesis is about the problem of representing sentential meaning in distributional semantics. Distributional semantics obtains the meanings of words through their usage, based on the hypothesis that words occurring in similar contexts will have similar meanings. In this framework, words are modeled as distributions over contexts and are represented as vectors in high dimensional space. Compositional distributional semantics attempts to extend this approach to higher linguistics structures. Some basic composition models proposed in literature to obtain the meaning of phrases or possibly sentences show promising results in modeling simple phrases. The goal of the thesis is to further extend these composition models to obtain sentence meaning representations. The thesis puts more focus on unsupervised methods which make use of the context of phrases and sentences to optimize the parameters of a model. Three different methods are presented. The first model is the PLF model, a practical composition and linguistically motivated model which is based on the lexical function model introduced by Baroni and Zamparelli (2010) and Coecke et al. (2010). The second model is the Chunk-based Smoothed Tree Kernels (CSTKs) model, extending Smoothed Tree Kernels (Mehdad et al., 2010) by utilizing vector representations of chunks. The final model is the C-PHRASE model, a neural network-based approach, which jointly optimizes the vector representations of words and phrases using a context predicting objective. The thesis makes three principal contributions to the field of compositional distributional semantics. The first is to propose a general framework to estimate the parameters and evaluate the basic composition models. This provides a fair way to comparing the models using a set of phrasal datasets. The second is to extend these basic models to the sentence level, using syntactic information to build up the sentence vectors. The third contribution is to evaluate all the proposed models, showing that they perform on par with or outperform competing models presented in the literature. Thesis Supervisor: Marco Baroni Title: Associate Professor

An Automatic Collocation Extraction from Arabic Corpus <=SEP=> Problem statement: The identification of collocations is very importa n part in natural language processing applications that require some degree of semantic interpretation such as, machine translation, information retrieval and text summari z tion. Because of the complexities of Arabic, the collocations undergo some variations such as, morph ological, graphical, syntactic variation that constitutes the difficulties of identifying the col location. Approach: We used the hybrid method for extracting the collocations from Arabic corpus that is based on linguistic information and association measures. Results: This method extracted the bi-gram candidates of Ar abic collocation from corpus and evaluated the association measures by using the n-best evaluation method. We reported the precision values for each association measure in ea ch n-best list. Conclusion: The experimental results showed that the log-likelihood ratio is the best as ociation measure that achieved highest precision.

Burnout, compassion fatigue, compassion satisfaction, and secondary traumatic stress in trauma nurses. <=SEP=> The relationship of burnout (BO), compassion fatigue (CF), compassion satisfaction (CS), and secondary traumatic stress (STS) to personal/environmental characteristics, coping mechanisms, and exposure to traumatic events was explored in 128 trauma nurses. Of this sample, 35.9% had scores consistent with BO, 27.3% reported CF, 7% reported STS, and 78.9% had high CS scores. High BO and high CF scores predicted STS. Common characteristics correlating with BO, CF, and STS were negative coworker relationships, use of medicinals, and higher number of hours worked per shift. High CS correlated with greater strength of supports, higher participation in exercise, use of meditation, and positive coworker relationships. Caring for trauma patients may lead to BO, CF, and STS; identifying predictors of these can inform the development of interventions to mitigate or minimize BO, CF, and STS in trauma nurses.

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection <=SEP=> Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks <=SEP=> Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.

Incremental Task and Motion Planning: A Constraint-Based Approach <=SEP=> We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstractions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-theart, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.

David J. Chalmers The Singularity A Philosophical Analysis <=SEP=> What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to evergreater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the ‘singularity’. The basic argument here was set out by the statistician I.J. Good in his 1965 article ‘Speculations Concerning the First Ultraintelligent Machine’:

Personality Neuroscience and the Biology of Traits <=SEP=> Personality neuroscience involves the use of neuroscience methods to study individual differences in behavior, motivation, emotion, and cognition. Personality psychology has contributed much to identifying the important dimensions of personality, but relatively little to understanding the biological sources of those dimensions. However, the rapidly expanding field of personality neuroscience is increasingly shedding light on this topic. This article provides a survey of progress in the use of neuroscience to study personality traits, organized using a hierarchical model of traits based on the Big Five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect. Evidence is reviewed for hypotheses about the biological systems involved in each trait. The mission of personality psychology is ‘‘to provide an integrative framework for understanding the whole person’’ (McAdams & Pals, 2006; p. 204), and many different methods must be brought to bear to accomplish such an ambitious goal. The field of personality neuroscience rests on the premise that the whole person cannot be understood without understanding the brain. In this article, I discuss the role that neuroscience can play in personality research and review the progress of this rapidly expanding field. (For more in depth review of the field and its influential theories see DeYoung & Gray, 2009; and Zuckerman, 2005.) Personality psychology’s attempt to understand the whole person calls for a broad conception of personality itself, such as the one provided by McAdams and Pals (2006, p. 212): Personality is an individual’s unique variation on the general evolutionary design for human nature, expressed as a developing pattern of dispositional traits, characteristic adaptations, and integrative life stories, complexly and differentially situated in culture. This definition describes three levels at which personality can be analyzed: traits, characteristic adaptations, and life stories. Personality neuroscience has focused primarily on traits, which are relatively stable patterns of behavior, motivation, emotion, and cognition (Pytlik Zillig, Hemenover, & Dienstbier, 2002; Wilt & Revelle, 2009) that are not specific to a particular social milieu or culture. This is not to say that traits are evident to the same extent or with identical manifestations in all cultures, but rather that any trait can be observed in a subset of situations in any culture. In contrast to traits, characteristic adaptations and life stories describe the individual’s specific responses to his or her particular life circumstances. Obviously, the latter two levels of analysis are crucial for understanding any individual, but their complexity renders them less amenable to study by neuroscience, and this article focuses on the biology of traits. Traits can be considered probabilistic descriptions of the frequency and intensity with which individuals exhibit various behavioral, motivational, emotional, and cognitive states Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x a 2010 The Author Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd (Fleeson, 2001; Fleeson & Gallagher, 2009). Individuals who are high in some trait will experience the states associated with that trait more often and more intensely than individuals low in that trait. For example, someone high in Extraversion will be talkative, outgoing, and excited more often than someone low in Extraversion, but even the person low in Extraversion may experience those states occasionally. The aim of personality neuroscience is to understand both the biological systems that are responsible for the states associated with traits and the parameters of those systems that cause them to function differently in different individuals. The systems themselves are presumed to be present in every intact human brain – hence McAdams and Pals’ (2006) reference to ‘‘the general evolutionary design for human nature’’ – but their parameters will vary from person to person. (For example, all people have brain systems that respond to rewards, but in different individuals these systems will respond with different degrees of vigor to a particular reward, and the systems’ average level of response may be associated with some personality trait.) When considering the biological sources of personality, one must distinguish between proximal and distal sources. The proximal sources, just described, consist of stable differences in the functioning of the neural systems that produce the states associated with traits. The distal sources are both genetic and environmental, as indicated by the fact that heritability estimates for personality traits are in the range of 40% to 80%, depending on trait and method (Bouchard & McGue, 2003; Riemann, Angleitner, & Strelau, 1997). (The heritability of a trait indicates the amount of its variation in a population due to genetic, rather than environmental, variation.) It is important to remember that when either genes or situations have lasting effects on traits, they must do so by changing the brain; thus, personality differences are ‘biological’ regardless of their heritability, in the sense that they must be proximally generated by the brain no matter whether they originated in genes or environment. Methods in Personality Neuroscience Scientific investigation of the biological basis of personality has been limited, until relatively recently, by a lack of technology to examine brain structure and function in living human beings. Prior to the development of neuroimaging, the sole means for assessing brain activity was the electroencephalogram (EEG), which measures the brain’s electrical activity at the scalp. Today a number of methods are available for personality neuroscience. Five important categories of neuroscientific methods are (1) neuroimaging (e.g., magnetic resonance imaging [MRI] or positron emission tomography [PET]), which allows assessment of brain structure and function with a relatively high spatial resolution; (2) molecular genetics, which allows assessment of variation in specific genes that are expressed in the brain; (3) EEG, which provides the highest temporal resolution of neural activity of any available method; (4) assays of endogenous psychoactive substances or their byproducts (e.g., hormone levels in saliva or neurotransmitter metabolites in cerebrospinal fluid); and (5) psychopharmocological manipulation (e.g., tryptophan depletion or augmentation to alter levels of the neurotransmitter serotonin). Personality neuroscience employs these methods in conjunction with the methods of personality psychology, attempting to link biological variables to traits. Measurement of traits is typically accomplished by questionnaire, through self-report and ⁄ or ratings by peers or other knowledgeable informants. Questionnaires provide a convenient and reliable method for assessing a broad range of stable individual differences, drawing on raters’ experiences over a far greater span of time than is available in the laboratory. However, 1166 Personality Neuroscience a 2010 The Author Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd traits can also be assessed in other ways, such as through laboratory tasks, behavioral observation, or experience sampling, and personality should not be identified exclusively with the variables provided by personality questionnaires. What we want to explain in personality neuroscience is not how people answer questionnaires, but rather why they exhibit stable patterns of behavior, motivation, emotion, and cognition. Most studies discussed in this review begin with some psychological trait and attempt to discover its biological basis. Another valid and useful approach is to begin with stable individual differences in some biological parameter (such as asymmetry in the level of left versus right cortical hemisphere activity, measured at rest by EEG) and then to attempt to discover what psychological trait or traits are associated with that biological trait. An important caveat for anyone exploring the literature on personality neuroscience or contemplating entering the field as an investigator is that much inconsistency exists in the findings to date. In neuroimaging research, inconsistency is probably due in large part to the use very small samples, due to cost. Unfortunately, small samples often lack statistical power to detect true effects (Yarkoni, 2009). Worse still, low power has the unfortunate additional consequence of increasing the proportion of false positives among significant findings (Green et al., 2008). In genetic studies, though larger samples are typically used, power may still be an issue, and inconsistency may arise, because each trait is influenced by many genes, each accounting for only a very small amount of the variance of the trait (Green et al., 2008). These difficulties highlight the importance of testing reasonably focused hypotheses, rather than simply exploring associations with biological variables in the absence of any theory of the causal mechanisms that might underlie a given trait. The Structure of Personality Traits Personality neuroscience can usefully be guided by existing knowledge about the structure of personality – that is, knowledge about how various traits relate to one another and to the major dimensions of personality. In order to produce a coherent overview of the progress of personality neuroscience, one needs to relate findings to a reasonably comprehensive taxonomy of traits, such as that provided by the Five Factor Model or Big Five, which categorizes the majority of traits within five broad domains, typically labeled Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect (Digman, 199

Interactive Block Games for Assessing Children's Cognitive Skills: Design and Preliminary Evaluation <=SEP=> Background: This paper presents design and results from preliminary evaluation of Tangible Geometric Games (TAG-Games) for cognitive assessment in young children. The TAG-Games technology employs a set of sensor-integrated cube blocks, called SIG-Blocks, and graphical user interfaces for test administration and real-time performance monitoring. TAG-Games were administered to children from 4 to 8 years of age for evaluating preliminary efficacy of this new technology-based approach. Methods: Five different sets of SIG-Blocks comprised of geometric shapes, segmented human faces, segmented animal faces, emoticons, and colors, were used for three types of TAG-Games, including Assembly, Shape Matching, and Sequence Memory. Computational task difficulty measures were defined for each game and used to generate items with varying difficulty. For preliminary evaluation, TAG-Games were tested on 40 children. To explore the clinical utility of the information assessed by TAG-Games, three subtests of the age-appropriate Wechsler tests (i.e., Block Design, Matrix Reasoning, and Picture Concept) were also administered. Results: Internal consistency of TAG-Games was evaluated by the split-half reliability test. Weak to moderate correlations between Assembly and Block Design, Shape Matching and Matrix Reasoning, and Sequence Memory and Picture Concept were found. The computational measure of task complexity for each TAG-Game showed a significant correlation with participants' performance. In addition, age-correlations on TAG-Game scores were found, implying its potential use for assessing children's cognitive skills autonomously.

Comparing Entities in RDF Graphs <=SEP=> The Semantic Web has fuelled the appearance of numerous open-source knowledge bases. Knowledge bases enable new types of information search, going beyond classical query answering and into the realm of exploratory search, and providing answers to new types of user questions. One such question is how two entities are comparable, i.e., what are similarities and differences between the information known about the two entities. Entity comparison is an important task and a widely used functionality available in many information systems. Yet it is usually domain-specific and depends on a fixed set of aspects to compare. In this paper we propose a formal framework for domain-independent entity comparison that provides similarity and difference explanations for input entities. We model explanations as conjunctive queries, we discuss how multiple explanations for an entity pair can be ranked and we provide a polynomial-time algorithm for generating most specific similarity explanations.

Crop disease classification using texture analysis <=SEP=> With the Agriculture Sector being the backbone of not only a large number of industries but also society as a whole, there is a rising need to grow good quality crops which in turn will give a high yield. For this to happen, it is crucial to monitor the crops throughout their growth period. In this paper, Image processing is used to detect and classify sunflower crop diseases based on the image of their leaf. The images are taken through a high resolution digital camera and after preprocessing, are subjected to k-means clustering to get the diseased part of the leaf. These are then run through the various machine learning algorithms and classified based on their color and texture features. A comparison based on accuracy between various machine learning algorithms is done namely K-Nearest Neighbors, Multi-Class Support Vector Machine, Naive Bayes and Multinomial Logistic Regression to achieve maximum accuracy. The implementation has been done using MATLAB.

A meta-analytic review of the effects of acute cortisol administration on human memory <=SEP=> Adrenal glucocorticoids (GC) secreted during stress modulate memory. Animal and human studies investigating the effects of acute GC treatment on memory have reported conflicting (enhancing as well as impairing) results. Several theories have been proposed to integrate these contradictory findings. Among the variables discussed are the timing of the GC treatment (before learning or before retrieval) and the time of day (morning versus afternoon). Here we review meta-analytically the results of 16 studies, which experimentally investigated the acute impact of cortisol treatment on human memory. The results revealed that the timing of GC application in the course of a study is a relevant variable which explains a substantial amount of the significant heterogeneity within the effect sizes. The studies which administered cortisol before retrieval (n = 4) reported a significant decrease (average effect size of d = -.49) in memory performance. Studies which administered cortisol before learning (n =12) found on average no effect (d = .08), but there is heterogeneity within these effect sizes. Further analysis on these experiments indicated that studies, which administered cortisol in the morning found a significant memory impairment (d = -.40), while studies conducted in the afternoon observed a small but significant memory enhancement (d = .22). This meta-analysis supports the idea that the timing of GC administration (before learning or before retrieval) is a major determinant of the effects of GCs on human memory. We discuss methodological limitations of the current analysis and suggest several areas for future research.

Peppermint oil for treatment of irritable bowel syndrome. <=SEP=> 22 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 2 | JANUARY 15, 2016 Peppermint oil for treatment of irritable bowel syndrome Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder that affects 5–15% of people worldwide. Typically, patients with IBS experience abdominal pain or discomfort and constipation or diarrhea with other gastrointestinal symptoms, such as abdominal distention and bloating; these symptoms reduce quality of life and can be difficult to control with currently available prescription agents. For these reasons, patients are often compelled to try nonprescription therapies, including natural products. In studies conducted in Australia and the United Kingdom, about 20–50% of patients with IBS reported using complementary and alternative medicines. Thus, it is important that healthcare professionals are knowledgeable of these therapies, including dietary supplements such as peppermint oil. Historically, the ingestion of peppermint oil has been associated with effects on the gastrointestinal tract. It is a classic essential oil known to have carminative properties (i.e., it is a naturally occurring remedy thought to help decrease bloating and gas by allowing passage of flatus) and may even be beneficial as an antiemetic. Peppermint oil comes from a perennial herb (Mentha × piperita), a plant found across North America and Europe. Mentha × piperita is a sterile hybrid of two herbs, spearmint (Menthaspicata) and water mint (Menthaaquatica). The main constituents of peppermint oil include menthol (35–55%), menthone (20–31%), menthyl acetate (3–10%), isomenthone, 1,8-cineole, limonene, b-myrcene, and carvone. Peppermint oil may be obtained through steam distillation of flowering parts that grow aboveground. It is a volatile oil, with menthol accounting for a majority of its potency. Due to the various constituents of peppermint oil, it has a variety of uses, including topical application as an antiseptic and for aches and pains, inhalation as aromatherapy, and oral formulations for flavoring or for use as digestive aids. Relaxation of intestinal muscle, both in vivo and in vitro, and relaxation of the lower esophageal sphincter have been reported with the use of peppermint oil, which is thought of as an antispasmodic that may confer benefits in conditions such as IBS. Several clinical studies of the use of peppermint oil for the treatment of

Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing <=SEP=> The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud—called an Edge-Cloud model. To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption. PVLDB Reference Format: Philipp M. Grulich and Faisal Nawab. Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing. PVLDB, 11 (12): 2046-2049, 2018. DOI: https://doi.org/10.14778/3229863.3236256

Measuring Motivations of Crowdworkers: The Multidimensional Crowdworker Motivation Scale <=SEP=> Crowd employment is a new form of short term and flexible employment which has emerged during the past decade. For understanding this new form of employment, it is crucial to understand the underlying motivations of the workforce involved in it. This paper presents the Multidimensional Crowdworker Motivation Scale (MCMS), a scale for measuring the motivation of crowdworkers on micro-task platforms. The scale is theoretically grounded in Self-Determination Theory and tailored specifically to the context of crowdsourced micro-labor. The MCMS was validated on data collected in ten countries and three income groups. Furthermore, measurement invariance tests showed that motivations measured with the MCMS are comparable across countries and income groups. This work constitutes a first step towards understanding the motivations of the international crowd workforce.

High-Performance Distributed ML at Scale through Parameter Server Consistency Models <=SEP=> As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Effective use of clusters for ML programs requires considerable expertise in writing distributed code, but existing highlyabstracted frameworks like Hadoop that pose low bar-ed frameworks like Hadoop that pose low barriers to distributed-programming have not, in practice, matched the performance seen in highly specialized and advanced ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML programs into distributed ones, while maintaining high throughput through relaxed “consistency models” that allow asynchronous (and, hence, inconsistent) parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoreticallymotivated but undiscovered opportunities to maximize computational throughput. Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an “eager” PS communication mechanism, and implement it as a new PS system that enables ML programs to reach their solution more quickly.