query: Trust-Aware Review Spam Detection

positive: Trust-Aware Collaborative Filtering for Recommender Systems Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.

positive: Fake Review Detection : Classification and Analysis of Real and Pseudo Reviews In recent years, fake review detection has attracted significant attention from both businesses and the research community. For reviews to reflect genuine user experiences and opinions, detecting fake reviews is an important problem. Supervised learning has been one of the main approaches for solving the problem. However, obtaining labeled fake reviews for training is difficult because it is very hard if not impossible to reliably label fake reviews manually. Existing research has used several types of pseudo fake reviews for training. Perhaps, the most interesting type is the pseudo fake reviews generated using the Amazon Mechanical Turk (AMT) crowdsourcing tool. Using AMT crafted fake reviews, [36] reported an accuracy of 89.6% using only word n-gram features. This high accuracy is quite surprising and very encouraging. However, although fake, the AMT generated reviews are not real fake reviews on a commercial website. The Turkers (AMT authors) are not likely to have the same psychological state of mind while writing such reviews as that of the authors of real fake reviews who have real businesses to promote or to demote. Our experiments attest this hypothesis. Next, it is naturally interesting to compare fake review detection accuracies on pseudo AMT data and real-life data to see whether different states of mind can result in different writings and consequently different classification accuracies. For real review data, we use filtered (fake) and unfiltered (non-fake) reviews from Yelp.com (which are closest to ground truth labels) to perform a comprehensive set of classification experiments also employing only n-gram features. We find that fake review detection on Yelp’s real-life data only gives 67.8% accuracy, but this accuracy still indicates that n-gram features are indeed useful. We then propose a novel and principled method to discover the precise difference between the two types of review data using the information theoretic measure KL-divergence and its asymmetric property. This reveals some very interesting psycholinguistic phenomena about forced and natural fake reviewers. To improve classification on the real Yelp review data, we propose an additional set of behavioral features about reviewers and their reviews for learning, which dramatically improves the classification result on real-life opinion spam data.

positive: Towards More Confident Recommendations : Improving Recommender Systems Using Filtering Approach Based on Rating Variance In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.

positive: Evaluating collaborative filtering recommender systems Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

positive:
Identify Online Store Review Spammers via Social Review Graph Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers’ behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques.
 In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.

negative: A Survey of Text Classification Algorithms The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification

negative: RMiner: a tool set for role mining Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.

negative: Leading us not unto temptation: momentary allurements elicit overriding goal activation. The present research explored the nature of automatic associations formed between short-term motives (temptations) and the overriding goals with which they interfere. Five experimental studies, encompassing several self-regulatory domains, found that temptations tend to activate such higher priority goals, whereas the latter tend to inhibit the temptations. These activation patterns occurred outside of participants' conscious awareness and did not appear to tax their mental resources. Moreover, they varied as a function of subjective goal importance and were more pronounced for successful versus unsuccessful self-regulators in a given domain. Finally, priming by temptation stimuli was found not only to influence the activation of overriding goals but also to affect goal-congruent behavioral choices.

negative: Hedonism , Achievement , and Power : Universal values that characterize the Dark Triad Using a sample of Swedes and Americans (N = 385), we attempted to understand the Dark Triad traits (i.e., Machiavellianism, narcissism, and psychopathy) in terms of universal social values. The Dark Triad traits correlated significantly with all 10 value types, forming a sinusoid pattern corresponding to the value model circumplex. In regression analyses, Machiavellianism and narcissism were positively associated with the values Achievement and Power, while psychopathy was positively associated with the values Hedonism, and Power. In addition, the Dark Triad traits explained significant variance over the Big Five traits in accounting for individual differences in social values. Differences between the Swedish and the US sample in the social value Achievement was mediated by the Dark Triad traits, as well as age. Given the unique complex of values accounted for by the Dark Triad traits compared to the Big Five traits, we argue that the former account for a system of self-enhancing ‘‘dark values’’, often hidden but constantly contributing in evaluations of others. 2015 Elsevier Ltd. All rights reserved.

negative: Parallel Training of Neural Networks for Speech Recognition In this paper we describe parallel implementation of ANN training procedure based on block mode back-propagation learning algorithm. Two different approaches to parallelization were implemented. The first is data parallelization using POSIX threads, it is suitable for multi-core computers. The second is node parallelization using high performance SIMD architecture of GPU with CUDA, suitable for CUDA enabled computers. We compare the speed-up of both approaches by learning typically-sized network on the real-world phoneme-state classification task, showing nearly 10 times reduction when using CUDA version, while the 8-core server with multi-thread version gives only 4 times reduction. In both cases we compared to an already BLAS optimized implementation. The training tool will be released as Open-Source software under project name TNet.

negative: Neurologic foundations of spinal cord fusion (GEMINI). Cephalosomatic anastomosis has been carried out in both monkeys and mice with preservation of brain function. Nonetheless the spinal cord was not reconstructed, leaving the animals unable to move voluntarily. Here we review the details of the GEMINI spinal cord fusion protocol, which aims at restoring electrophysiologic conduction across an acutely transected spinal cord. The existence of the cortico-truncoreticulo-propriospinal pathway, a little-known anatomic entity, is described, and its importance concerning spinal cord fusion emphasized. The use of fusogens and electrical stimulation as adjuvants for nerve fusion is addressed. The possibility of achieving cephalosomatic anastomosis in humans has become reality in principle.

negative: High speed true random number generator based on open loop structures in FPGAs Most hardware ‘‘True’’ Random Number Generators (TRNG) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (FPGA), the classical TRNG structure uses at least two oscillators, build either from PLL or ring oscillators. This creates good TRNG albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as PLL. & 2009 Elsevier Ltd. All rights reserved.

negative: Sources of Customer Satisfaction and Dissatisfaction with Information Technology Help Desks As the use, develophient, and control of information systems continues to become diffused throughout organizations and society, the information technology (IT) help desk function plays an increasingly important role in effective management and utilization of information resources. Variously referred to as information centers, software support centers, software hotlines, and PC help desks, such centers have been established tohelp end users resolve problems and obtain information about new functions in the information systems they use. This study investigates the determinants of customer satisfaction and dissatisfaction with service encounters involving information technologyhelp desks. The IS satisfaction research that has been done to datehas viewed satisfaction as an attitudinal, bipolar evaluative construct (Melone 1990; Heckman 1993). Satisfaction as viewed in this way is a relatively enduring, stable cognitive state. In the marketing literature, however, satisfaction has also been conceptualized as a less enduring post-consumption response. In this study, a conceptualization of the satisfaction construct based on the service encounter and consumer product satisfaction literatures (e.g; Bitner, Boon}s and Tetreault 1990) is adopted as a starting point. After responses to help desk service encounters have been analyzed from this perspective, an attempt is made to integrate these findings with attitudinal satisfaction constructs. The study employs the Critical Incident Technique (CIT), an inductive, qualitative methodology. It consists of a set of specifically defined procedures for collecting observations of human behavior and classifying them in such a way as to make them useful in addressing practical problems (Flanagan 1954). It is a I thod that is comparable to other inductive grouping procedures such as factor analysis, cluster analysis, and multidimensional scaling. Unlike other such procedures, however, CIT uses content analysis of stories rather than quantitative solutions in the data analysis stage of the procedure. The study addressed four research questions: 1. What specific behaviors and events lead to user/customer satisfaction and dissatisfaction with IT help desk service encounters? 2. Are the underlying events and behaviors that lead to satisfactory and dissatisfactory encounters similar? That is, are these events and behaviors opposites or mirror images of each other? 3. Are the underlying events and behaviors in help desk service encounters similar to those found in other contexts? 4. Can an understanding ofuser/customer responses to help desk service encounters shed light on the development and modification ofa tudinal satisfaction constructs such as UIS (Ives, Olson and Baroudi 1983), EUIS (Doll and Torkzadeh 1988), and VMS Satisfaction (Heckman 1993)? Descriptions of approximately500 incidents have been obtained to dateand analyzed. A tentative classification scheme was developed from the preliminary anal>sis. It was modeled after the incident classification scheme developed by Bitner, Booms and Tetreault and uses the same three major categories: core service failure, special customer request, and extraordinary provider behavior. As in the Bitner, Booms and Tetreault analysis, results suggest that a core service failure does not inevitably lead to dissatisfaction. Initial analysis also suggests that while the scheme is applicable in some ways, the knowledge-based nature of the ]T help desk service encounter requires several additional constructs to account for various customer responses.

negative: An evolutionary optimization framework for neural networks and neuromorphic architectures As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

negative: THE DOMAIN AND CONCEPTUAL FOUNDATIONS OF RELATIONSHIP MARKETING

negative: Development and design of a robotic manta ray featuring flexible pectoral fins This paper showed our preliminary work in probing into fish robot with pectoral fin propulsion. The aim of this project is to develop a fish robot mimicking manta rays that swim with flexible pectoral fins in oscillation motion. Based on the analysis of motion feature and skeletal structure of pectoral fin of manta rays, a kind of flexible pectoral fin made of silicone rubber was designed. Experiments on the propulsion of fin prototype were carried out in tank. Experiment results showed that flexible pectoral fin in oscillation motion could generate great thrust and the thrust would increase with fin-beat frequency and amplitude increasing. In the end, a robot fish with flexible pectoral fins was built. The speed of robot fish could reach the maximum of 1.4 times BL (body length) per second. Experiments proved that it was feasible to develop a robot fish featuring flexible pectoral fins in oscillation motion and the method to mimic manta rays was a great idea to improve performance of underwater vehicles.

negative: Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds We study two fundamental issues about video-based human walking estimation, where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model the gait kinematics from different walking styles, and the other is how to represent the gait appearances captured under different views and from individuals of distinct walking styles and body shapes. Our research is conducted in three steps. First, we propose the idea of joint gait-pose manifold (JGPM), which represents gait kinematics by coupling two nonlinear variables, pose (a specific walking stage) and gait (a particular walking style) in a unified latent space. We extend the Gaussian process latent variable model (GPLVM) for JGPM learning, where two heuristic topological priors, a torus and a cylinder, are considered and several JGPMs of different degrees of freedom (DoFs) are introduced for comparative analysis. Second, we develop a validation technique and a series of benchmark tests to evaluate multiple JGPMs and recent GPLVMs in terms of their performance for gait motion modeling. It is shown that the toroidal prior is slightly better than the cylindrical one, and the JGPM of 4 DoFs that balances the toroidal prior with the intrinsic data structure achieves the best performance. Third, a JGPM-based visual gait generative model (JGPM-VGGM) is developed, where JGPM plays a central role to bridge the gap between the gait appearances and the gait kinematics. Our proposed JGPM-VGGM is learned from Carnegie Mellon University MoCap data and tested on the HumanEva-I and HumanEva-II data sets. Our experimental results demonstrate the effectiveness and competitiveness of our algorithms compared with existing algorithms.

negative: Semantic Sentiment Analysis Challenge at ESWC2017 Sentiment Analysis is a widely studied research field in both research and industry, and there are different approaches for addressing sentiment analysis related tasks. Sentiment Analysis engines implement approaches spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems are already evaluated in international research challenges. However, Semantic Sentiment Analysis approaches, which take into account or rely also on large semantic knowledge bases and implement Semantic Web best practices, are not under specific experimental evaluation and comparison by other international challenges. Such approaches may potentially deliver higher performance, since they are also able to analyze the implicit, semantics features associated with natural language concepts. In this paper, we present the fourth edition of the Semantic Sentiment Analysis Challenge, in which systems implementing or relying on semantic features are evaluated in a competition involving large test sets, and on different sentiment tasks. Systems merely based on syntax/word-count or just lexicon-based approaches have been excluded by the evaluation. Then, we present the results of the evaluation for each task and show the winner of the most innovative approach award, that combines several knowledge bases for addressing the sentiment analysis task.

negative: Beidou combined-FFT acquisition algorithm for resource-constrained embedded platform The length of Beidou CB2I code is twice that of the Global Position System (GPS) C/A code, and the resource consumption will be doubled on embedded hardware with a finite resource if it is processed with the traditional algorithm. Hence, this paper proposes an acquisition algorithm based on combined Fast Fourier Transform (FFT), which separates a signal into odd and even sequences and processes them in serial. By doing so, FFT length is halved and memory usage is decreased. The paper then goes on to analyze the space complexity and time complexity of the proposed algorithm and the traditional algorithm on the embedded platform. The results show that the memory usage of the proposed algorithm is reduced by 23.3% and the amount of multiplication by 12% while the acquisition accuracy remains stable as the PTP values of these two algorithms are close. In other words, the proposed algorithm consumes less resource with no acquisition accuracy losses.

negative: Speeding up distributed request-response workflows We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.

negative: Beyond Wise et al . : Neuroleptic-induced “ anhedonia ” in rats : Pimozide blocks reward quality of food O all the neurotransmitters in the brain the one that is best known to the lay public, by far, is dopamine (DA). To the public, and much of the media, DA is the brain’s “pleasure transmitter.” By this popular view, it is a burst of DA that produces the pleasure associated with the receipt or anticipation of natural rewards – a chocolate cake, sex, even social rewards – as well as the euphoria produced by addictive drugs, such as amphetamine, cocaine or heroin. In the popular media it is often suggested that it is pursuit of this DA “rush” that leads to a variety of impulse control disorders, such as over-eating, gambling and addiction. Of all the ideas, in all of neuroscience, this is one of the most widely known, and its origin can be traced to the 1978 paper by Roy Wise, Joan Spindler, Harriet deWit and Gary Gerber, that is the topic of this chapter. This paper is an excellent example of how the interpretation of a seemingly simple experimental finding can profoundly influence the thinking and trajectory of an entire field, and the public imagination, for decades – even if it is wrong. But before getting to that conclusion, we should step back and consider the time leading up to the publication of this extremely influential paper. It was only a little over a decade earlier that DA was first recognized as a neurotransmitter in its own right (rather than just being a precursor of norepinephrine). This came about from a series of important studies by researchers in Sweden who developed a histofluorescence method to identify and map monoamine neurotransmitters in the brain (including DA). They thus effectively created the field of chemical neuroanatomy, as well as vividly mapping the location of dopamine in the brain. They also produced useful methods to selectively lesion monoamine-containing cells with neurotoxins, such as 6-hydroxydopamine (6-OHDA), which would help reveal dopamine’s psychological functions via behavioral consequences. As a result of these studies it became clear that DA-producing neurons located in the midbrain (the substantia nigra and ventral tegmental area) send dense projections to the dorsal and ventral striatum (the caudate-putamen and nucleus accumbens) in the

negative:
Benchmarking distributed data warehouse solutions for storing genomic variant information Database URL
https://github.com/ZSI-Bio/variantsdwh.

negative: Towards Automated Driving: Unmanned Protective Vehicle for Highway Hard Shoulder Road Works Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards road works against moving traffic. The project “Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works” aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation - strongly related to the general challenges in the field of automated driving - are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.

negative: Overconfidence as a cause of diagnostic error in medicine. The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.

negative: Forecasting the weather of Nevada: A deep learning approach This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE) outperforms a standard multilayer feed forward network on this noisy time series prediction task. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.

negative: Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield better returns than the buy-and-hold strategy when disregarding transaction costs both in terms of number of stocks with profitable trades as well as overall returns. Moreover, our findings show that two-way and three-way combinations, i.e., complex trading rules, are important to “beat” the buy-and-hold strategy.

negative: 10 Watts UHF broadband GaN based power amplifier for multi-band applications The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200-500 MHz. A maximum drain efficiency of 71% is achieved.

negative: Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks In order to perform useful tasks, a service robot needs to manipulate objects in its environment. In this paper, we propose a method for experimental evaluation of the suitability of a robotic hand for grasping tasks in service robotics. The method is applied to the Schunk 5-Finger Gripping Hand, which is a mechatronic gripper designed for service robots. During evaluation, it is shown, that it is able to grasp various common household objects and execute the grasps from the well known Cutkosky grasp taxonomy [1]. The result is, that it is a suitable hand for service robot tasks.

negative: Keyphrase Extraction Using Knowledge Graphs Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

negative: The Situated Function - Behaviour - Structure Framework This paper extends the Function-Behaviour-Structure (FBS) framework, which proposed eight fundamental processes involved in designing. This framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.

negative: Finding Deceptive Opinion Spam by Any Stretch of the Imagination Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

negative: UNIK: unsupervised social network spam detection Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this work, we first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by our observations, we first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration. In order to make it highly robust in facing an increased level of spam attacks, we further design an unsupervised spam detection scheme, called UNIK. Instead of detecting spammers directly, UNIK works by deliberately removing non-spammers from the network, leveraging both the social graph and the user-link graph. The underpinning of UNIK is that while spammers constantly change their patterns to evade detection, non-spammers do not have to do so and thus have a relatively non-volatile pattern. UNIK has comparable performance to SD2 when it is applied to a large social network site, and outperforms SD2 significantly when the level of spam attacks increases. Based on detection results of UNIK, we further analyze several identified spam campaigns in this social network site. The result shows that different spammer clusters demonstrate distinct characteristics, implying the volatility of spamming patterns and the ability of UNIK to automatically extract spam signatures.

negative: Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts. Previous authors have suggested that an OSN can augment its takedown algorithms by identifying a “trusted set” of users whose reports are weighted more heavily in the disposition of flagged accounts. Such identification would allow the OSN to improve both speed and accuracy of fake account detection and thus reduce the impact of spam on users. In this work we provide the first public, data-driven assessment of whether the above assumption is true: are some users better at reporting than others? Specifically, is reporting skill both measurable, i.e., possible to distinguish from random guessing; and repeatable, i.e., persistent over repeated sampling? Our main contributions are to develop a statistical framework that describes these properties and to apply this framework to data from LinkedIn, the professional social network. Our data includes member reports of fake profiles as well as the more voluminous, albeit weaker, signal of member responses to connection requests. We find that members demonstrating measurable, repeatable skill in identifying fake profiles do exist but are rare: at most 2.4% of those reporting fakes and at most 1.3% of those rejecting connection requests. We conclude that any reliable “trusted set” of members will be too small to have noticeable impact on spam metrics.

negative: Twitter spam detection based on deep learning Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

negative: "In vivo" spam filtering: a challenge problem for KDD Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue in vivo spam filtering as an accessible domain for investigating them.

negative: Hunting for Spammers: Detecting Evolved Spammers on Twitter Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.

negative: Spam filtering email classification (SFECM) using gain and graph mining algorithm This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.


query: EvoloPy: An Open-source Nature-inspired Optimization Framework in Python

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

positive: The Whale Optimization Algorithm This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html © 2016 Elsevier Ltd. All rights reserved.

positive: Genetic programming - on the programming of computers by means of natural selection Page ii Complex Adaptive Systems John H. Holland, Christopher Langton, and Stewart W. Wilson, advisors Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press edition John H. Holland Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life edited by Francisco J. Varela and Paul Bourgine Genetic Programming: On the Programming of Computers by Means of Natural Selection John R. Koza

positive: Evolution strategies – A comprehensive introduction This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed.

positive: A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II Multi-objectiveevolutionaryalgorithmswhichusenon-dominatedsorting andsharinghave beenmainly criticizedfor their (i) computational complexity (where is thenumberof objectivesand is thepopulationsize), (ii) non-elitismapproach, and(iii) theneedfor specifyingasharingparameter . In this paper , we suggesta non-dominatedsortingbasedmulti-objective evolutionaryalgorithm(wecalledit theNon-dominatedSortingGA-II or NSGA-II) which alleviatesall theabove threedifficulties.Specifically, a fastnon-dominatedsorting approachwith computationalcomplexity is presented. Second,a selectionoperatoris presentedwhich createsa mating pool by combiningthe parentandchild populationsandselectingthe best(with respectto fitnessand spread) solutions.Simulationresultson five difficult testproblemsshow that theproposedNSGA-II is ableto find muchbetterspreadof solutionsin all problemscomparedto PAES—anotherelitist multi-objective EA which paysspecial attentiontowardscreatinga diversePareto-optimalfront. Becauseof NSGA-II’s low computational requirements, elitist approach, andparameter -lesssharingapproach,NSGA-II shouldfind increasingapplicationsin theyearsto come.

negative: Pre- and post-processes for automatic colorization using a fully convolutional network Automatic colorization is a significant task especially for Anime industry. An original trace image to be colorized contains not only outlines but also boundary contour lines of shadows and highlight areas. Unfortunately, these lines tend to decrease the consistency among all images. Thus, this paper provides a method for a cleaning pre-process of anime dataset to improve the prediction quality of a fully convolutional network, and a refinement post-process to enhance the output of the network.

negative: Random Bits Regression: a Strong General Predictor for Big Data * Correspondence: momiao.xiong@gmail.com; Momiao.Xiong@uth.tmc.edu; yin.yao@nih.gov; lijin@fudan.edu.cn Equal contributors Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, USA Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China Full list of author information is available at the end of the article Abstract

negative: How Not to Do a Mindset Intervention: Learning from a Mindset Intervention among Students with Good Grades The present study examined the effectiveness of a Growth Mindset intervention based on Dweck et al.'s (1995) theory in the Hungarian educational context. A cluster randomized controlled trial classroom experiment was carried out within the framework of a train-the-trainer intervention among 55 Hungarian 10th grade students with high Grade Point Average (GPA). The results suggest that students' IQ and personality mindset beliefs were more incremental in the intervention group than in the control group 3 weeks after the intervention. Furthermore, compared to both the baseline measure and the control group, students' amotivation decreased. However, no intrinsic and extrinsic motivation change was found. Students with low grit scores reported lower amotivation following the intervention. However, in the second follow-up measurement-the end of the semester-all positive changes disappeared; and students' GPA did not change compared to the previous semester. These results show that mindset beliefs are temporarily malleable and in given circumstances, they can change back to their pre-intervention state. The potential explanation is discussed in the light of previous mindset intervention studies and recent findings on wise social psychological interventions.

negative: Information Theoretic Learning with Infinitely Divisible Kernels In this paper, we develop a framework for information theore tic l arning based on infinitely divisible matrices. We formulate an entropy-lik e functional on positive definite matrices based on Renyi’s axiomatic definition of en tropy and examine some key properties of this functional that lead to the conce pt of infinite divisibility. The proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel Hilbe rt spaces. As an application example, we derive a supervised metric learning algo rithm using a matrix based analogue to conditional entropy achieving results co mparable with the state of the art.

negative: Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure The introduction of “smart grid” solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from “islands of automation” to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with “openness” requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for “smart grid” solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a “smart grid system”.

negative: Trustworthiness Attributes and Metrics for Engineering Trusted Internet-Based Software Systems Trustworthiness of Internet-based software systems, apps, services and platform is a key success factor for their use and acceptance by organizations and end-users. The notion of trustworthiness, though, is subject to individual interpretation and preference, e.g., organizations require confidence about how their business critical data is handled whereas end-users may be more concerned about usability. As one main contribution, we present an extensive list of software quality attributes that contribute to trustworthiness. Those software quality attributes have been identified by a systematic review of the research literature and by analyzing two real-world use cases. As a second contribution, we sketch an approach for systematically deriving metrics to measure the trustworthiness of software system. Our work thereby contributes to better understanding which software quality attributes should be considered and assured when engineering trustworthy Internet-based software systems.

negative: The foreign-language effect: thinking in a foreign tongue reduces decision biases. Would you make the same decisions in a foreign language as you would in your native tongue? It may be intuitive that people would make the same choices regardless of the language they are using, or that the difficulty of using a foreign language would make decisions less systematic. We discovered, however, that the opposite is true: Using a foreign language reduces decision-making biases. Four experiments show that the framing effect disappears when choices are presented in a foreign tongue. Whereas people were risk averse for gains and risk seeking for losses when choices were presented in their native tongue, they were not influenced by this framing manipulation in a foreign language. Two additional experiments show that using a foreign language reduces loss aversion, increasing the acceptance of both hypothetical and real bets with positive expected value. We propose that these effects arise because a foreign language provides greater cognitive and emotional distance than a native tongue does.

negative: Fixed budget quantized kernel least-mean-square algorithm We present a quantization-based kernel least mean square (QKLMS) algorithm with a fixed memory budget. In order to deal with the growing support inherent in online kernel methods, the proposed method utilizes a growing and pruning combined technique and defines a criterion, significance, based on weighted statistical contribution of a data center. This method doesn’t need any apriori information and its computational complexity is acceptable, linear with the center number. As we show theoretically and experimentally, the introduced algorithm successfully quantifies the least ‘significant’ datum and preserves the most important ones resulting in less system error.

negative: On Extracting Structured Knowledge from Unstructured Business Documents Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-pastemethod, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.

negative: Automated Image Annotation based on YOLOv3 A typical pedestrian protection system requires sophisticated hardware and robust detection algorithms. To solve these problems the existing systems use hybrid sensors where mono and stereo vision merged with active sensors. One of the most assuring pedestrian detection sensors is far infrared range camera. The classical pedestrian detection approach based on Histogram of oriented gradients is not robust enough to be applied in devices which consumers can trust. An application of deep neural network-based approach is able to perform with significantly higher accuracy. However, the deep learning approach requires a high number of labeled data examples. The investigation presented in this paper aimed the acceleration of pedestrian labeling in far-infrared image sequences. In order to accelerate pedestrian labeling in far-infrared camera videos, we have integrated the YOLOv3 object detector into labeling software. The verification of the pre-labeled results was around eleven times faster than manual labeling of every single frame.

negative: STUDENTS ACCEPTANCE OF MOBILE LEARNING FOR HIGHER EDUCATION IN SAUDI ARABIA Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using mLearning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students’ intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.

negative: Integrated microstrip and rectangular waveguide in planar form Usually transitions from microstrip line to rectangular waveguide are made with three-dimensional complex mounting structures. In this paper, a new planar platform is developed in which the microstrip line and rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple taper. Our experiments at 28 GHz show that an effective bandwidth of 12% at 20 dB return loss is obtained with an in-band insertion loss better than 0.3 dB. The new transition allows a complete integration of waveguide components on substrate with MICs and MMICs.

negative: Today the Earwig, Tomorrow Man? Kirsh, D., Today the earwig, tomorrow man?, Artificial Intelligence 47 (1991) 161-184. A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.

negative: A digitally compensated 1.5 GHz CMOS/FBAR frequency reference A temperature-compensated 1.5 GHz film bulk acoustic wave resonator (FBAR)-based frequency reference implemented in a 0.35 ¿m CMOS process is presented. The ultra-small form factor (0.79 mm × 1.72 mm) and low power dissipation (515 ¿A with 2 V supply) of a compensated FBAR oscillator present a promising alternative for the replacement of quartz crystal frequency references. The measured post-compensation frequency drift over a 0-100°C temperature range is <±10 ppm. The measured oscillator phase noise is -133 dBc/ Hz at 100 kHz offset from the 1.5 GHz carrier.

negative: A Lightweight Virtualization Solution for Android Devices Mobile virtualization has emerged fairly recently and is considered a valuable way to mitigate security risks on Android devices. However, major challenges in mobile virtualization include runtime, hardware, resource overhead, and compatibility. In this paper, we propose a lightweight Android virtualization solution named Condroid, which is based on container technology. Condroid utilizes resource isolation based on namespaces feature and resource control based on cgroups feature. By leveraging them, Condroid can host multiple independent Android virtual machines on a single kernel to support mutilple Android containers. Furthermore, our implementation presents both a system service sharing mechanism to reduce memory utilization and a filesystem sharing mechanism to reduce storage usage. The evaluation results on Google Nexus 5 demonstrate that Condroid is feasible in terms of runtime, hardware resource overhead, and compatibility. Therefore, we find that Condroid has a higher performance than other virtualization solutions.

negative: Why an Intelligence Explosion is Probable If a future Artificial Intelligence were to reach the level of human intelligence there is a possibility that it would be able to rapidly redesign itself until its own capabilities far exceeded those of human beings. We analyze the principle factors that might govern the rapidity of this ‘intelligence explosion’ process. We argue that if degree of intelligence is defined using a relatively uncontroversial measure that involves only the relative speed of thought with respect to that of the human mind, and if an intelligence explosion is defined as a thousandfold increase in speed, then there are compelling reasons to believe that none of the barriers to this process look plausible, and therefore an intelligence explosion is highly likely.

negative: Combined Star-Delta Winding Analysis The combined star-delta winding has been attracting the attention of industry because of its potential to improve the efficiency of electrical machines. In order to show the efficiency benefits of this type of winding, many authors have used methods to determine the improvements in winding factors, published in different papers, which have not been validated yet. Some of these methods contradict each other. Furthermore, as a demerit of this winding, a unidirectional rotation has been reported. In this paper, a set of new formulas to calculate the winding factors for combined star-delta windings is proposed. In order to check the validity of the proposed formulas, a 2-D FEM and experimental tests on a salient pole synchronous machine are presented, which allows different combined star-delta winding connections. In contrast to other publications, the measurements show that combined windings can be applied for both directions of rotation without any problems.

negative: Examining Alignment of Frames Using Actor-Network Constructs: The Implementation of an IT Project IT projects often cannot succeed without bridging different stakeholders’ perspectives on system design and function during implementation. These perspectives often start as competing technology frames consisting of different beliefs, interests, technology evaluation routines and artifact characteristics. For successful implementation of IT, these competing frames usually need to be stabilized into a dominant frame which is widely accepted. Prior research has offered few clues on the mechanisms that are at work in aligning competing frames. This paper uses a case study to propose mechanisms for aligning frames—namely, two actor-network theory constructs of “black-boxing” and “obligatory points of passage”—that aid in translating the competing frames into a truce frame. The examined properties of these mechanisms are described and analyzed, thereby extending the literature on technology frames by providing evidence of generic aligning mechanisms.

negative: Swearing, Euphemisms, and Linguistic Relativity Participants read aloud swear words, euphemisms of the swear words, and neutral stimuli while their autonomic activity was measured by electrodermal activity. The key finding was that autonomic responses to swear words were larger than to euphemisms and neutral stimuli. It is argued that the heightened response to swear words reflects a form of verbal conditioning in which the phonological form of the word is directly associated with an affective response. Euphemisms are effective because they replace the trigger (the offending word form) by another word form that expresses a similar idea. That is, word forms exert some control on affect and cognition in turn. We relate these findings to the linguistic relativity hypothesis, and suggest a simple mechanistic account of how language may influence thinking in this context.

negative: Game-Enhanced Second Language Vocabulary Acquisition Strategies: A Systematic Review This paper is a synthesis of 17 qualitative, quantitative, and mixed-method studies published over the last decade. The synthesis suggests that game-enhanced learning provides a set of effective strategies such as language repetitions, contextual clues, interaction with native speakers and peers, and imagery to practice and use second language vocabulary in the authentic context. Some of the strategies such as word lists, dictionaries, and vocabulary exercises are more beneficial when combined with native speakers or peers’ interactions. Due to high interactivity of games, note taking and media strategies provide less support for vocabulary learning. The lack of high quality studies and empirical data makes it difficult to draw conclusions about which video games strategies provide the most benefit. The synthesis of research identifies that generally game-enhanced practices are helpful for second language vocabulary enhancement.

negative: Development of wearable and flexible ultrasonic sensor for skeletal muscle monitoring A wearable and flexible ultrasonic sensor was developed for monitoring of skeletal muscle contraction. The sensor was constructed using a PVDF piezoelectric polymer film without a matching layer or backing material. Due to its lightness (less than 1 gram), thinness (200 μm) and flexibility, this sensor was wearable and enabled non-invasive and continuous muscle monitoring without restricting muscle movement, which is not feasible using a conventional handheld ultrasonic probe. The developed sensor was used to monitor muscle contractions in the index finger by ultrasonic pulse-echo measurements and in the forearm by through-transmission measurements. It was successfully demonstrated that the tissue thickness variations were measured in accordance with the muscle contraction performed.

negative: The attention system of the human brain. The concept of attention as central to human performance extends back to the start of experimental psychology (James 1890), yet even a few years ago, it would not have been possible to outline in even a preliminary form a functional anatomy of the human attentional system. New developments in neuroscience (Hillyard & Picton 1987, Raichle 1983, Wurtz et al 1980) have opened the study of higher cognition to physiological analysis, and have revealed a system of anatomical areas that appear to be basic to the selection of information for focal (conscious) processing. The importance of attention is its unique role in connecting the mental level of description of processes used in cognitive science with the anatomical level common i neuroscience. Sperry (1988, p. 609) describes the central role that mental concepts play in understanding brain function as follows:

negative: Overcoming the Bell-Shaped Dose-Response of Cannabidiol by Using Cannabis Extract Enriched in Cannabidiol Cannabidiol (CBD), a major constituent of Cannabis, has been shown to be a powerful anti‐in‐ flammatory and anti‐anxiety drug, without exerting a psychotropic effect. However, when given either intraperitoneally or orally as a purified product, a bell‐shaped dose‐response was observed, which limits its clinical use. In the present study, we have studied in mice the anti‐inflammatory and anti‐nociceptive activities of standardized plant extracts derived from the Cannabis sativa L., clone 202, which is highly enriched in CBD and hardly contains any psychoactive ingredients. In stark contrast to purified CBD, the clone 202 extract, when given either intraperitoneally or orally, provided a clear correlation between the anti‐inflammatory and anti‐nociceptive responses and the dose, with increasing responses upon increasing doses, which makes this plant medicine ideal for clinical uses. The clone 202 extract reduced zymosan‐induced paw swelling and pain in mice, and prevented TNFα production in vivo. It is likely that other components in the extract synergize with CBD to achieve the desired anti‐inflammatory action that may contribute to overcoming the bell‐shaped dose‐response of purified CBD. We therefore propose that Cannabis clone 202 (Avi‐ dekel) extract is superior over CBD for the treatment of inflammatory conditions.

negative: TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning The growth of robot-assisted minimally invasive surgery has led to sizable datasets of fixed-camera video and kinematic recordings of surgical subtasks. Segmentation of these trajectories into locally-similar contiguous sections can facilitate learning from demonstrations, skill assessment, and salvaging good segments from otherwise inconsistent demonstrations. Manual, or supervised, segmentation can be prone to error and impractical for large datasets. We present Transition State Clustering with Deep Learning (TSC-DL), a new unsupervised algorithm that leverages video and kinematic data for task-level segmentation, and finds regions of the visual feature space that correlate with transition events using features constructed from layers of pre-trained image classification Deep Convolutional Neural Networks (CNNs). We report results on three datasets comparing Deep Learning architectures (AlexNet and VGG), choice of convolutional layer, dimensionality reduction techniques, visual encoding, and the use of Scale Invariant Feature Transforms (SIFT). We find that the deep architectures extract features that result in up-to a 30.4% improvement in Silhouette Score (a measure of cluster tightness) over the traditional “shallow” features from SIFT. We also present cases where TSC-DL discovers human annotator omissions. Supplementary material, data and code is available at: http://berkeleyautomation.github.io/tsc-dl/.

negative:
Effects of concurrent endurance and strength training on running economy and .VO(2) kinetics. PURPOSE
It has been suggested that endurance training influences the running economy (CR) and the oxygen uptake (.VO(2)) kinetics in heavy exercise by accelerating the primary phase and attenuating the .VO(2) slow component. However, the effects of heavy weight training (HWT) in combination with endurance training remain unclear. The purpose of this study was to examine the influence of a concurrent HWT+endurance training on CR and the .VO(2) kinetics in endurance athletes.

METHODS
Fifteen triathletes were assigned to endurance+strength (ES) or endurance-only (E) training for 14 wk. The training program was similar, except ES performed two HWT sessions a week. Before and after the training period, the subjects performed 1) an incremental field running test for determination of .VO(2max) and the velocity associated (V(.VO2max)), the second ventilatory threshold (VT(2)); 2) a 3000-m run at constant velocity, calculated to require 25% of the difference between .VO(2max) and VT(2), to determine CR and the characteristics of the VO(2) kinetics; 3) maximal hopping tests to determine maximal mechanical power and lower-limb stiffness; 4) maximal concentric lower-limb strength measurements.

RESULTS
After the training period, maximal strength were increased (P < 0.01) in ES but remained unchanged in E. Hopping power decreased in E (P < 0.05). After training, economy (P < 0.05) and hopping power (P < 0.001) were greater in ES than in E. .VO(2max), leg hopping stiffness and the .VO(2) kinetics were not significantly affected by training either in ES or E.

CONCLUSION
Additional HWT led to improved maximal strength and running economy with no significant effects on the .VO(2) kinetics pattern in heavy exercise.

negative: Large Scale Distributed Data Science from scratch using Apache Spark 2.0 Apache Spark is an open-source cluster computing framework. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce’s linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala, SQL and R (MapReduce has 2 core calls) , and its core data abstraction, the distributed data frame. In addition, it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. With massive amounts of computational power, deep learning has been shown to produce state-of-the-art results on various tasks in different fields like computer vision, automatic speech recognition, natural language processing and online advertising targeting. Thanks to the open-source frameworks, e.g. Torch, Theano, Caffe, MxNet, Keras and TensorFlow, we can build deep learning model in a much easier way. Among all these framework, TensorFlow is probably the most popular open source deep learning library. TensorFlow 1.0 was released recently, which provide a more stable, flexible and powerful computation tool for numerical computation using data flow graphs. Keras is a highlevel neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This tutorial will provide an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. It is divided into three parts: the first part will cover fundamental Spark concepts, including Spark Core, functional programming ala map-reduce, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more; the second part will focus on hands-on algorithmic design and development with Spark (developing algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as pagerank/shortest path, gradient descent algorithms such as support vectors machines and matrix factorization. Industrial applications and deployments of Spark will also be presented.; the third part will introduce deep learning concepts, how to implement a deep learning model through TensorFlow, Keras and run the model on Spark. Example code will be made available in python (pySpark) notebooks.

negative: Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.

negative: Understanding the Potential of Interpreter-based Optimizations for Python The increasing popularity of scripting languages as general purpose programming environments calls for more efficient execution. Most of these languages, such as Python, Ruby, PHP, and JavaScript are interpreted. Interpretation is a natural implementation given the dynamic nature of these languages and interpreter portability has facilitated wide-spread use. In this work, we analyze the performance of CPython, a commonly used Python interpreter, to identify major sources of overhead. Based on our findings, we investigate the efficiency of a number of optimizations and explore the design options and trade-offs involved.

negative: Python for Microwave and RF Engineers [Application Notes] Python is a powerful programming language for handling engineering and scientific computational tasks efficiently [1]-[5]. Used by companies such as Google and Intel and by organizations including NASA and Los Alamos National Laboratory, it offers an extremely wide selection of tools for tasks such as scientific computing, signal processing, Web site construction, database programming, and graphical user interface (GUI) design. The language is platform independent with most programs running on Linux, Microsoft Windows, or MAC OS virtually unchanged. Python has been distributed as a part of Open Source Initiative, and most versions are General Public License (GPL) compatible. In microwave and radio frequency (RF) engineering, it can be used for numerical programming, automated RF testing, automated monolithic microwave integrated-circuit (MMIC) layout generation, automated netlist generation and simulator sequencing, and other tasks. In this article, we are going to provide examples demonstrating the application areas of Python in microwave and RF engineering. We are also going to give a brief introduction to the language highlighting its salient features.

negative: Integration of an On-line Kaldi Speech Recogniser to the Alex Dialogue Systems Framework This paper describes the integration of an on-line Kaldi speech recogniser into the Alex Dialogue Systems Framework (ADSF). As the Kaldi OnlineLatgenRecogniser is written in C++, we first developed a Python wrapper for the recogniser so that the ADSF, written in Python, could interface with it. Training scripts for acoustic and language modelling were developed and integrated into ADSF, and acoustic and language models were build. Finally, optimal recogniser parameters were determined and evaluated. The dialogue system Alex with the new speech recogniser is evaluated on Public Transport Information (PTI) domain.

negative: The igraph software package for complex network research There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python [7]) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.

negative: Sherpa : Hyperparameter Optimization for Machine Learning Models Sherpa is a free open-source hyperparameter optimization library for machine learning models. It is designed for problems with computationally expensive iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Additionally, the framework makes it easy to implement custom algorithms. Sherpa can be run on either a single machine or a cluster via a grid scheduler with minimal configuration. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning researchers by automating the tedious aspects of model tuning and providing an extensible framework for developing automated hyperparameter-tuning strategies. Its source code and documentation are available at https://github.com/LarsHH/sherpa and https://parameter-sherpa.readthedocs.io/, respectively. A demo can be found at https://youtu.be/L95sasMLgP4. 1 Existing Hyperparameter Optimization Libraries Hyperparameter optimization algorithms for machine learning models have previously been implemented in software packages such as Spearmint [15], HyperOpt [2], Auto-Weka 2.0 [9], and Google Vizier [5] among others. Spearmint is a Python library based on Bayesian optimization using a Gaussian process. Hyperparameter exploration values are specified using the markup language YAML and run on a grid via SGE and MongoDB. Overall, it combines Bayesian optimization with the ability for distributed training. HyperOpt is a hyperparameter optimization framework that uses MongoDB to allow parallel computation. The user manually starts workers which receive tasks from the HyperOpt instance. It offers the use of Random Search and Bayesian optimization based on a Tree of Parzen Estimators. Auto-WEKA 2.0 implements the SMAC [6] algorithm for automatic model selection and hyperparameter optimization within the WEKA machine learning framework. It provides a graphical user interface and supports parallel runs on a single machine. It is meant to be accessible for novice users and specifically targets the problem of choosing a model. Auto-WEKA is related to Auto-Sklearn [4] and Auto-Net [11] which specifically focus on tuning Scikit-Learn models and fully-connected 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Table 1: Comparison to Existing Libraries Spearmint Auto-WEKA HyperOpt Google Vizier Sherpa Early Stopping No No No Yes Yes Dashboard/GUI Yes Yes No Yes Yes Distributed Yes No Yes Yes Yes Open Source Yes Yes Yes No Yes # of Algorithms 2 1 2 3 5 neural networks in Lasagne, respectively. Auto-WEKA, Auto-Sklearn, and Auto-Net focus on an end-to-end automatic approach. This makes it easy for novice users, but restricts the user to the respective machine learning library and the models it implements. In contrast our work aims to give the user more flexibility over library, model and hyper-parameter optimization algorithm selection. Google Vizier is a service provided by Google for its cloud machine learning platform. It incorporates recent innovation in Bayesian optimization such as transfer learning and provides visualizations via a dashboard. Google Vizier provides many key features of a current hyperparameter optimization tool to Google Cloud users and Google engineers, but is not available in an open source version. A similar situation occurs with other cloud based platforms like Microsoft Azure Hyperparameter Tuning 1 and Amazon SageMaker’s Hyperparameter Optimization 2. 2 Need for a new library The field of machine learning has experienced massive growth over recent years. Access to open source machine learning libraries such as Scikit-Learn [14], Keras [3], Tensorflow [1], PyTorch [13], and Caffe [8] allowed research in machine learning to be widely reproduced by the community making it easy for practitioners to apply state of the art methods to real world problems. The field of hyperparameter optimization for machine learning has also seen many innovations recently such as Hyperband [10], Population Based Training [7], Neural Architecture Search [17], and innovation in Bayesian optimization such as [16]. While the basic implementation of some of these algorithms can be trivial, evaluating trials in a distributed fashion and keeping track of results becomes cumbersome which makes it difficult for users to apply these algorithms to real problems. In short, Sherpa aims to curate implementations of these algorithms while providing infrastructure to run these in a distributed way. The aim is for the platform to be scalable from usage on a laptop to a computation grid.