title: ScreenPass: secure password entry on touchscreen devices

abstract: Users routinely access cloud services through third-party apps on smartphones by giving apps login credentials (i.e., a username and password). Unfortunately, users have no assurance that their apps will properly handle this sensitive information. In this paper, we describe the design and implementation of ScreenPass, which significantly improves the security of passwords on touchscreen devices. ScreenPass secures passwords by ensuring that they are entered securely, and uses taint-tracking to monitor where apps send password data. The primary technical challenge addressed by ScreenPass is guaranteeing that trusted code is always aware of when a user is entering a password. ScreenPass provides this guarantee through two techniques. First, ScreenPass includes a trusted software keyboard that encourages users to specify their passwords' domains as they are entered (i.e., to tag their passwords). Second, ScreenPass performs optical character recognition (OCR) on a device's screenbuffer to ensure that passwords are entered only through the trusted software keyboard. We have evaluated ScreenPass through experiments with a prototype implementation, two in-situ user studies, and a small app study. Our prototype detected a wide range of dynamic and static keyboard-spoofing attacks and generated zero false positives. As long as a screen is off, not updated, or not tapped, our prototype consumes zero additional energy; in the worst case, when a highly interactive app rapidly updates the screen, our prototype under a typical configuration introduces only 12% energy overhead. Participants in our user studies tagged their passwords at a high rate and reported that tagging imposed no additional burden. Finally, a study of malicious and non-malicious apps running under ScreenPass revealed several cases of password mishandling.


title: Pleasures of the Mind: What Makes Jokes and Insight Problems Enjoyable

abstract: In this paper, a parallel analysis of the enjoyment derived from humor and insight problem solving is presented, with reference to a “general” Theory of the Pleasures of the Mind (TPM) (Kubovy, 1999) rather than to “local” theories regarding what makes humor and insight problem solving enjoyable. The similarity of these two cognitive activities has already been discussed in previous literature in terms of the cognitive mechanisms which underpin getting a joke or having an insight experience in a problem solving task. The paper explores whether we can learn something new about the similarities and differences between humor and problem solving by means of an investigation of what makes them pleasurable. In the first part of the paper, the framework for this joint analysis is set. Two descriptive studies are then presented in which the participants were asked to report on their experiences relating to solving visuo-spatial insight problems (Study 1) or understanding cartoons (Study 2) in terms of whether they were enjoyable or otherwise. In both studies, the responses were analyzed with reference to a set of categories inspired by the TPM. The results of Study 1 demonstrate that finding the solution to a problem is associated with a positive evaluation and the most frequent explanations for this were reported as being Curiosity, Virtuosity and Violation of expectations. The results of Study 2 suggest that understanding a joke (Joy of Verification) and being surprised by it (Feeling of Surprise) were two essential conditions: when they were not present, the cartoons were perceived as not enjoyable. However, this was not enough to explain the motivations for the choice of the most enjoyable cartoons. Recognizing a Violation of expectations and experiencing a Diminishment in the cleverness or awareness initially attributed to the characters in the cartoon were the aspects which were most frequently indicated by the participants to explain why they enjoyed the joke. These findings are evaluated in the final discussion, together with their limitations and potential future developments.


title: Attentional control of the processing of neural and emotional stimuli.

abstract: Abstract A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple stimuli is evidenced by the mutual suppression of their visually evoked responses and occurs most strongly at the level of the receptive field. The competition among multiple objects can be biased by both bottom-up sensory-driven mechanisms and top-down influences, such as selective attention. Functional brain imaging studies reveal that biasing signals due to selective attention can modulate neural activity in visual cortex not only in the presence but also in the absence of visual stimulation. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals likely derives from a distributed network of areas in frontal and parietal cortex. Competition suggests that once attentional resources are depleted, no further processing is possible. Yet, existing data suggest that emotional stimuli activate brain regions ‘automatically,’ largely immune from attentional control. We tested the alternative possibility, namely, that the neural processing of stimuli with emotional content is not automatic and instead requires some degree of attention. Our results revealed that, contrary to the prevailing view, all brain regions responding differentially to emotional faces, including the amygdala, did so only when sufficient attentional resources were available to process the faces. Thus, similar to the processing of other stimulus categories, the processing of facial expression is under top-down control.


title: Learning Semantic Representations of Users and Products for Document Level Sentiment Classification

abstract: Neural network methods have achieved promising results for sentiment classification of text. However, these models only use semantics of texts, while ignoring users who express the sentiment and products which are evaluated, both of which have great influences on interpreting the sentiment of text. In this paper, we address this issue by incorporating userand productlevel information into a neural network approach for document level sentiment classification. Users and products are modeled using vector space models, the representations of which capture important global clues such as individual preferences of users or overall qualities of products. Such global evidence in turn facilitates embedding learning procedure at document level, yielding better text representations. By combining evidence at user-, productand documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.


Boosted Decision Tree Regression to Predict Yelp Review Stars from Review Text


title: Framework for automatic testing of Set-top boxes

abstract: This paper proposes framework based on Black Box Testing methodology which is used for automatic testing of Set-top boxes (STB). Proposed framework for automatic testing can be used for functional testing, regression testing, performance testing and stress testing of STB. Complete system for automatic testing consists of web tool responsible for requirement and test management along with stand-alone application for execution of automatic test cases in form of scripts written in Python programming language. Paper gives an overview of the whole system for automatic testing along with list of framework functions and their usage for creating automatic test cases.


title: A spatio-temporal, Gaussian process regression, real-estate price predictor

abstract: This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.


title: (C)overt attention and visual speller design in an ERP-based brain-computer interface

abstract: Background ::: In a visual oddball paradigm, attention to an event usually modulates the event-related potential (ERP). An ERP-based brain-computer interface (BCI) exploits this neural mechanism for communication. Hitherto, it was unclear to what extent the accuracy of such a BCI requires eye movements (overt attention) or whether it is also feasible for targets in the visual periphery (covert attention). Also unclear was how the visual design of the BCI can be improved to meet peculiarities of peripheral vision such as low spatial acuity and crowding.


title: Generative Image Modeling Using Spatial LSTMs

abstract: Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multidimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting.


title: What do we gain by adding time perspective to mindfulness? Carpe Diem and mindfulness in a temporal framework

abstract: Abstract Researchers studying mindfulness point to the need for studies and analyses explaining and specifying the nature of this variable as compared to other similar constructs. The aim of this article is to examine the relationship between dispositional mindfulness and the Carpe Diem time perspective (CD) and their predictive value with reference to eudaimonic well-being, namely authenticity and basic needs satisfaction. The participants were 238 Poles (138 female and 100 male, aged 15 to 65 years). We obtained results suggesting that mindfulness and CD are separate constructs that do not correlate with each other. We point out that mindfulness is mainly concerned with the form of awareness, while CD is concerned with its content. The interaction of these two variables has a particularly positive effect on the functioning that enables the satisfaction of basic psychological needs.


title: Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis.

abstract: Based on 7,939 business units in 36 companies, this study used meta-analysis to examine the relationship at the business-unit level between employee satisfaction-engagement and the business-unit outcomes of customer satisfaction, productivity, profit, employee turnover, and accidents. Generalizable relationships large enough to have substantial practical value were found between unit-level employee satisfaction-engagement and these business-unit outcomes. One implication is that changes in management practices that increase employee satisfaction may increase business-unit outcomes, including profit.


title: Improving oracle quality by detecting brittle assertions and unused inputs in tests

abstract: Writing oracles is challenging. As a result, developers often create oracles that check too little, resulting in tests that are unable to detect failures, or check too much, resulting in tests that are brittle and difficult to maintain. In this paper we present a new technique for automatically analyzing test oracles. The technique is based on dynamic tainting and detects both brittle assertions—assertions that depend on values that are derived from uncontrolled inputs—and unused inputs—inputs provided by the test that are not checked by an assertion. We also presented OraclePolish, an implementation of the technique that can analyze tests that are written in Java and use the JUnit testing framework. Using OraclePolish, we conducted an empirical evaluation of more than 4000 real test cases. The results of the evaluation show that OraclePolish is effective; it detected 164 tests that contain brittle assertions and 1618 tests that have unused inputs. In addition, the results also demonstrate that the costs associated with using the technique are reasonable.


title: GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

abstract: In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, the energy consumption is a major concern due to the large scale of such kind of systems. There are a few existing efforts that try to lower the energy consumption of GPU-CPU architectures, but they address either GPU or CPU in an isolated manner and thus cannot achieve maximized energy savings. In this paper, we propose Green GPU, a holistic energy management framework for GPU-CPU heterogeneous architectures. Our solution features a two-tier design. In the first tier, Green GPU dynamically splits and distributes workloads to GPU and CPU based on the workload characteristics, such that both sides can finish approximately at the same time. As a result, the energy wasted on idling and waiting for the slower side to finish is minimized. In the second tier, Green GPU dynamically throttles the frequencies of GPU cores and memory in a coordinated manner, based on their utilizations, for maximized energy savings with only marginal performance degradation. Likewise, the frequency and voltage of the CPU are scaled similarly. We implement Green GPU using the CUDA framework on a real physical test bed with Nvidia GeForce GPUs and AMD Phenom II CPUs. Experiment results show that Green GPU achieves 21.04% average energy savings and outperforms several well-designed baselines.


title: Like-Me ” Simulation as an Effective and Cognitively Plausible Basis for Social Robotics

abstract: We present a successful design approach for social robotics based on a computational cognitive architecture and mental simulation. We discuss an approach to a Theory of Mind known as a “like-me” simulation in which the agent uses its own knowledge and capabilities as a model of another agent to predict that agent’s actions. We present three examples of a “like-me” mental simulation in a social context implemented in the embodied version of the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, ACT-R/E (for ACT-R Embodied). Our examples show the efficacy of a simulation approach in modeling perspective taking (identifying another’s left or right hand), teamwork (simulating a teammate for better team performance), and dominant-submissive social behavior (primate social experiments). We conclude with a discussion of the cognitive plausibility of this approach and our conclusions.


Incremental Topological Sort and Cycle Detection in Õ ( m √ n ) Expected Total Time


title: MICHAC: Defect Prediction via Feature Selection Based on Maximal Information Coefficient with Hierarchical Agglomerative Clustering

abstract: Defect prediction aims to estimate software reliability via learning from historical defect data. A defect prediction method identifies whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which will hurt the performance of defect prediction methods. Existing work employs feature selection to preprocess defect data to filter out useless features. In this paper, we propose a novel feature selection framework, MICHAC, short for defect prediction via Maximal Information Coefficient with Hierarchical Agglomerative Clustering. MICHAC consists of two major stages. First, MICHAC employs maximal information coefficient to rank candidate features to filter out irrelevant ones, second, MICHAC groups features with hierarchical agglomerative clustering and selects one feature from each resulted group to remove redundant features. We evaluate our proposed method on 11 widelystudied NASA projects and four open-source AEEEM projects using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with five existing methods demonstrates that MICHAC is effective in selecting features in defect prediction.


title: Pseudo random number generator and Hash function for embedded microprocessors

abstract: Embedded microprocessors are commonly used for future technologies such as Internet of Things(IoT), RFID and Wireless Sensor Networks(WSN). However, the microprocessors have limited computing power and storages so straight-forward implementation of traditional services on resource constrained devices is not recommenced. To overcome this problem, lightweight implementation techniques should be concerned for practical implementations. Among various requirements, security applications should be conducted on microprocessors for secure and robust service environments. In this paper, we presented a light weight implementation techniques for efficient Pseudo Random Number Generator(PRNG) and Hash function. To reduce memory consumption and accelerate performance, we adopted AES accelerator based implementation. This technique is firstly introduced in INDOCRYPT'12, whose idea exploits peripheral devices for efficient hash computations. With this technique, we presented block cipher based light-weight pseudo random number generator and simple hash function on embedded microprocessors.


title: Improvement methods for stock market prediction using financial news articles

abstract: News articles serve the purpose of spreading company's information to the investors either consciously or unconsciously in their trading strategies on the stock market. Because of the immense growth of the internet in the last decade, the amount of financial articles have experienced a significant growth. It is important to analyze the information as fast as possible so they can support the investors in making the smart trading decisions before the market has had time to adjust itself to the effect of the information. This paper proposes an approach of using time series analysis and improved text mining techniques to predict daily stock market directions. Experiment results show that our system achieved high accuracy (up to 73%) in predicting the stock trends.


title: Thirty years of software problems in the news

abstract: How have the consequences of software problems changed over the past 30 years? To begin to answer this question, we analyzed 386,381 news articles reporting on software problems published between 1980 and 2012, spanning widely circulated newspapers to small trade magazines. Our results show that after an increase in reporting just prior to Y2K, news on software problems has declined in North America, but increased in the rest of the world. Most articles only report minor consequences such as frustration, confusion, anger, or at worst, having to delay some activity for a few hours, usually due to service outages in government, transportation, finance, and information services. However, about once per month, the news reports at least one death, injury, or threatened access to food or shelter due to software problems. Reports of these severe consequences are also increasing, due primarily to stories about transportation and government software.


title: Denial of Service Elusion (DoSE): Keeping Clients Connected for Less

abstract: Denial of Service (DoS) attacks continue to grow in magnitude, duration, and frequency increasing the demand for techniques to protect services from disruption, especially at a low cost. We present Denial of Service Elusion (DoSE) as an inexpensive method for mitigating network layer attacks by utilizing cloud infrastructure and content delivery networks to protect services from disruption. DoSE uses these services to create a relay network between the client and the protected service that evades attack by selectively releasing IP address information. DoSE incorporates client reputation as a function of prior behavior to stop attackers along with a feedback controller to limit costs. We evaluate DoSE by modeling relays, clients, and attackers in an agent-based MATLAB simulator. The results show DoSE can mitigate a single-insider attack on 1,000 legitimate clients in 3.9 minutes while satisfying an average of 88.2% of requests during the attack.


title: Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation

abstract: This paper presents the results of the WMT10 and MetricsMATR10 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 104 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 26 metrics. This year we also investigated increasing the number of human judgments by hiring non-expert annotators through Amazon's Mechanical Turk.


title: Relevance of the Interaction between Alphaherpesvirus UL3.5 and UL48 Proteins for Virion Maturation and Neuroinvasion

abstract: The UL3.5 and UL48 genes, which are conserved in most alphaherpesvirus genomes, are important for maturation of pseudorabies virus (PrV) particles in the cytoplasm of infected cells (W. Fuchs, B. G. Klupp, H. J. Rziha, and T. C. Mettenleiter, J. Virol. 70:3517-3527, 1996; W. Fuchs, H. Granzow, B. G. Klupp, M. Kopp and T. C. Mettenleiter, J. Virol. 76:6729-6742, 2002). In bovine herpesvirus 1 (BoHV-1), the homologous gene products pUL3.5 and pUL48 have been demonstrated to interact physically (N. Lam and G. Letchworth, J. Virol. 74:2876-2884, 2000). Moreover, BoHV-1 pUL3.5 partially complemented a pUL3.5 defect in PrV (W. Fuchs, H. Granzow, and T. C. Mettenleiter, J. Virol. 71:8886-8892, 1997). By using coimmunoprecipitation and yeast two-hybrid studies, we observed a similar interaction between pUL3.5 and pUL48 of PrV, as well as a heterologous interaction between the PrV and BoHV-1 gene products. The relevant domain could be confined to the first 43 amino acids of PrV pUL3.5. Unlike its BoHV-1 homologue, PrV pUL3.5 is processed by proteolytic cleavage, and only an abundant 14-kDa fragment consisting of amino acids 1 to ≥116 could be detected by peptide mass fingerprint analysis of purified wild-type PrV particles, which also contain the pUL48 tegument component. To determine the biological relevance of the protein-protein interaction, pUL3.5-, pUL48-, and double-negative PrV mutants were analyzed in parallel. All deletion mutants were replication competent but exhibited significantly reduced plaque sizes and virus titers in cultured rabbit kidney cells compared to wild-type and rescued viruses, which correlated with a delayed neuroinvasion in intranasally infected mice. Remarkably, the defects of the double-negative mutant were similar to those of pUL48-negative virus. Electron microscopy of cells infected with either deletion mutant revealed the retention of naked nucleocapsids in the cytoplasm and the absence of mature virus particles. In summary, our studies for the first time demonstrate the relevance of the pUL3.5-pUL48 interaction for secondary envelopment of an alphaherpesvirus, give a molecular basis for the observed trans-complementation between the PrV and BHV-1 pUL3.5 homologs, yield conclusive evidence for the incorporation of a proteolytically processed pUL3.5 into PrV virions, and demonstrate the importance of both proteins for neuroinvasion and neurovirulence of PrV.


title: Adolescent stigma towards drug addiction : Effects of age and drug use behaviour

abstract: The objective of this study was to examine adolescent age and experience with drug use on stigmatizing attitudes toward drug addiction. Data were derived from the 2005 cycle of the Ontario Student Drug Use Survey. In total, 4078, 7- to 12-graders completed self-administered questionnaires that included a measure of drug abuse stigma. Results indicated that stigma scores were higher among younger than older adolescents, and the decline across age was robust, occurring among both males and females and those from rural and non-rural areas. The decline, however, was stronger among non-drug users and among those who had no close friends that use drugs. Despite the age-related decline, the level of stigma in general suggested that drug abuse stigma may continue into adulthood. Findings highlight that individual attitudes toward drug use and drug abusers are salient factors for personal drug use. Given that stigma is a barrier to treatment, but reduced stigma may encourage greater adolescent use, this study highlights the need for more in-depth studies of drug stigma.


title: Get Back! You Don’t Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations

abstract: The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and FactCheck.org, are a strategy users can employ to refute false claims made by their peers. While laboratory research suggests such interventions are not effective in persuading people to abandon false ideas, little work considers how such interventions are actually deployed in real-world conversations. Using approximately 1,600 interventions observed on Twitter between 2012 and 2013, we examine the contexts and consequences of fact-checking interventions.W e focus in particular on the social relationship between the individual who issues the fact-check and the individual whose facts are challenged. Our results indicate that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends. Finally, we discuss implications for designing more effective interventions against misinformation.


title: iCare: A Mobile Health Monitoring System for the Elderly

abstract: This paper describes a mobile health monitoring system called iCare for the elderly. We use wireless body sensors and smart phones to monitor the well being of the elderly. It can offer remote monitoring for the elderly anytime anywhere and provide tailored services for each person based on their personal health condition. When detecting an emergency, the smart phone will automatically alert pre-assigned people who could be the old people's family and friends, and call the ambulance of the emergency centre. It also acts as the personal health information system and the medical guidance which offers one communication platform and the medical knowledge database so that the family and friends of the served people can cooperate with doctors to take care of him/her. The system also features some unique functions that cater to the living demands of the elderly, including regular reminder, quick alarm, medical guidance, etc. iCare is not only a real-time health monitoring system for the elderly, but also a living assistant which can make their lives more convenient and comfortable.


title: Eye Contact Detection via Deep Neural Networks.

abstract: With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eye-contact with a device is proposed in [16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.


title: A learning problem that is independent of the set theory ZFC axioms

abstract: We consider the following statistical estimation problem: given a family F of real valued functions over some domain X and an i.i.d. sample drawn from an unknown distribution P over X, find h in F such that the expectation of h w.r.t. P is probably approximately equal to the supremum over expectations on members of F. This Expectation Maximization (EMX) problem captures many well studied learning problems; in fact, it is equivalent to Vapnik's general setting of learning. ::: Surprisingly, we show that the EMX learnability, as well as the learning rates of some basic class F, depend on the cardinality of the continuum and is therefore independent of the set theory ZFC axioms (that are widely accepted as a formalization of the notion of a mathematical proof). ::: We focus on the case where the functions in F are Boolean, which generalizes classification problems. We study the interaction between the statistical sample complexity of F and its combinatorial structure. We introduce a new version of sample compression schemes and show that it characterizes EMX learnability for a wide family of classes. However, we show that for the class of finite subsets of the real line, the existence of such compression schemes is independent of set theory. We conclude that the learnability of that class with respect to the family of probability distributions of countable support is independent of the set theory ZFC axioms. ::: We also explore the existence of a "VC-dimension-like" parameter that captures learnability in this setting. Our results imply that that there exist no "finitary" combinatorial parameter that characterizes EMX learnability in a way similar to the VC-dimension based characterization of binary valued classification problems.


title: ICrafter: A Service Framework for Ubiquitous Computing Environments

abstract: In this paper, we propose ICrafter, a framework for services and their user interfaces in a class of ubiquitous computing environments. The chief objective of ICrafter is to let users flexibly interact with the services in their environment using a variety of modalities and input devices. We extend existing service frameworks in three ways. First, to offload services and user input devices, ICrafter provides infrastructure support for UI selection, generation, and adaptation. Second, ICrafter allows UIs to be associated with service patterns for on-the-fly aggregation of services. Finally, ICrafter facilitates the design of service UIs that are portable but still reflect the context of the local environment. In addition, we also focus on the system properties such as incremental deployability and robustness that are critical for ubiquitous computing environments. We describe the goals and architecture of ICrafter, a prototype implementation that validates its design, and the key lessons learnt from our experiences.


title: A meta-analysis of the contribution of eye movements in processing emotional memories

abstract: Abstract Background and objectives Eye Movement Desensitisation and Reprocessing (EMDR) is now considered evidence based practice in the treatment of trauma symptoms. Yet in a previous meta-analysis, no significant effect was found for the eye movement component. However methodological issues with this study may have resulted in a type II error. The aim of this meta-analysis was to examine current published studies to test whether eye movements significantly affect the processing of distressing memories. Method A systematic review of the literature revealed two groups of studies. The first group comprised 15 clinical trials and compared the effects of EMDR therapy with eye movements to those of EMDR without the eye movements. The second group comprised 11 laboratory trials that investigated the effects of eye movements while thinking of a distressing memory versus the same procedure without the eye movements in a non-therapy context. The total number of participants was 849. Results The effect size for the additive effect of eye movements in EMDR treatment studies was moderate and significant (Cohen's d = 0.41). For the second group of laboratory studies the effect size was large and significant ( d = 0.74). The strongest effect size difference was for vividness measures in the non-therapy studies ( d = 0.91). The data indicated that treatment fidelity acted as a moderator variable on the effect of eye movements in the therapy studies. Conclusions Results were discussed in terms of current theories that suggest the processes involved in EMDR are different from other exposure based therapies.


title: Exosome-delivered EGFR regulates liver microenvironment to promote gastric cancer liver metastasis

abstract: The metastatic organotropism has been one of the cancer's greatest mysteries since the 'seed and soil' hypothesis. Although the role of EGFR in cancer cells is well studied, the effects of secreted EGFR transported by exosomes are less understood. Here we show that EGFR in exosomes secreted from gastric cancer cells can be delivered into the liver and is integrated on the plasma membrane of liver stromal cells. The translocated EGFR is proved to effectively activate hepatocyte growth factor (HGF) by suppressing miR-26a/b expression. Moreover, the upregulated paracrine HGF, which binds the c-MET receptor on the migrated cancer cells, provides fertile 'soil' for the 'seed', facilitating the landing and proliferation of metastatic cancer cells. Thus, we propose that EGFR-containing exosomes derived from cancer cells could favour the development of a liver-like microenvironment promoting liver-specific metastasis.


title: Fashioning Bollywood: The Making and Meaning of Hindi Film Costume

abstract: Introduction Chapter 1: The People and Places of Costume Production Chapter 2: Costume and Character: Wearing and Being Chapter 3: Costume and the Body Chapter 4: Fashion and Spectacle Chapter 5: Dressing the Past Chapter 6: Beyond the Screen Notes Bibliography Filmography Index


title: The diffusion of political memes in social media: keynote abstract

abstract: This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.


title: Temporal collaborative filtering with bayesian probabilistic tensor factorization

abstract: Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an efficient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on several real-world problems including sales prediction and movie recommendation. Empirical results demonstrate the superiority of our temporal model.


title: Health Information Systems (HIS): Concept and Technology

abstract: A health information system (HIS) is the intersection of between healthcare's business process, and information systems to deliver better healthcare services. The nature of healthcare industry, which is highly influenced by economic, social, politic, and technological factors, has changed over time. This paper will address some important concepts of healthcare and related terminologies to provide a holistic view for HIS. Related technological milestones and major events are briefly summarized. The trends and rapid development of health information technologies are also discussed.


title: Neural systems underlying lexical retrieval for sign language

abstract: Positron emission tomography was used to investigate whether signed languages exhibit the same neural organization for lexical retrieval within classical and non-classical language areas as has been described for spoken English. Ten deaf native American sign language (ASL) signers were shown pictures of unique entities (famous persons) and non-unique entities (animals) and were asked to name each stimulus with an overt signed response. Proper name signed responses to famous people were fingerspelled, and common noun responses to animals were both fingerspelled and signed with native ASL signs. In general, retrieving ASL signs activated neural sites similar to those activated by hearing subjects retrieving English words. Naming famous persons activated the left temporal pole (TP), whereas naming animals (whether fingerspelled or signed) activated left inferotemporal (IT) cortex. The retrieval of fingerspelled and native signs generally engaged the same cortical regions, but fingerspelled signs in addition activated a premotor region, perhaps due to the increased motor planning and sequencing demanded by fingerspelling. Native signs activated portions of the left supramarginal gyrus (SMG), an area previously implicated in the retrieval of phonological features of ASL signs. Overall, the findings indicate that similar neuroanatomical areas are involved in lexical retrieval for both signs and words. © 2003 Elsevier Science Ltd. All rights reserved.


title: A taxonomy of temporal data visualization techniques

abstract: A wide range of visualization techniques have been designed to assist users to visually analyze and manipulate temporal data. All the proposed techniques have been designed independently. In this context it is therefore difficult to systematically explore the set of possibilities as well as to thoroughly envision visualization techniques of temporal data. Addressing this problem, we present a taxonomy of visualization techniques of temporal data. Based on a study of thirty-seven visual tools and visualization techniques of temporal data, our taxonomy structures the design space according to the Chi's visualization process steps.


title: Are Leader Stereotypes Masculine? A Meta-Analysis of Three Research Paradigms

abstract: This meta-analysis examined the extent to which stereotypes of leaders are culturally masculine. The primary studies fit into I of 3 paradigms: (a) In Schein's (1973) think manager―think male paradigm, 40 studies with 51 effect sizes compared the similarity of male and leader stereotypes and the similarity of female and leader stereotypes; (b) in Powell and Butterfield's (1979) agency―communion paradigm, 22 studies with 47 effect sizes compared stereotypes of leaders' agency and communion; and (c) in Shinar's (1975) masculinity-femininity paradigm, 7 studies with 101 effect sizes represented stereotypes of leadership-related occupations on a single masculinity-femininity dimension. Analyses implemented appropriate random and mixed effects models. All 3 paradigms demonstrated overall masculinity of leader stereotypes: (a) In the think manager-think male paradigm, intraclass correlation = .25 for the women-leaders similarity and intraclass correlation = .62 for the men-leaders similarity; (b) in the agency-communion paradigm, g = 1.55, indicating greater agency than communion; and (c) in the masculinity-femininity paradigm, g = 0.92, indicating greater masculinity than the androgynous scale midpoint. Subgroup and meta-regression analyses indicated that this masculine construal of leadership has decreased over time and was greater for male than female research participants. In addition, stereotypes portrayed leaders as less masculine in educational organizations than in other domains and in moderate- than in high-status leader roles. This article considers the relation of these findings to Eagly and Karau's (2002) role congruity theory, which proposed contextual influences on the incongruity between stereotypes of women and leaders. The implications for prejudice against women leaders are also considered.


title: On clusterings: Good, bad and spectral

abstract: We motivate and develop a natural bicriteria measure for assessing the quality of a clustering that avoids the drawbacks of existing measures. A simple recursive heuristic is shown to have poly-logarithmic worst-case guarantees under the new measure. The main result of the article is the analysis of a popular spectral algorithm. One variant of spectral clustering turns out to have effective worst-case guarantees; another finds a "good" clustering, if one exists.


title: Fact Proposition Event

abstract: `Peterson is an authority of a philosophical and linguistic industry that began in the 1960s with Vendler's work on nominalization. Natural languages distinguish syntactically and semantically between various sorts of what might be called `gerundive entities' -- events, processes, states of affairs, propositions, facts, ... all referred to by sentence nominals of various kinds. Philosophers have worried for millennia over the ontology of such things or `things', but until twenty years ago they ignored all the useful linguistic evidence. Vendler not only began to straighten out the distinctions, but pursued more specific and more interesting questions such as that of what entities the causality relation relates (events? facts?). And that of the objects of knowledge and belief. But Vendler's work was only a start and Peterson has continued the task from then until now, both philosophically and linguistically. Fact Proposition Event constitutes the state of the art regarding gerundive entities, defended in meticulous detail. Peterson's ontology features just facts, proposition, and events, carefully distinguished from each other. Among his more specific achievements are: a nice treatment of the linguist's distinction between `factive' and nonfactive constructions; a detailed theory of the subjects and objects of causation, which impinges nicely on action theory; an interesting argument that fact, proposition, events are innate ideas in humans; a theory of complex events (with implications for law and philosophy of law); and an overall picture of syntax and semantics of causal sentences and action sentences. Though Peterson does not pursue them here, there are clear and significant implications for the philosophy of science, in particular for our understanding of scientific causation, causal explanation and law likeness.' Professor William Lycan, University of North Carolina, Chapel Hill


title: I feel for my avatar: embodied perception in VEs

abstract: Visual perception is dependent upon one's physical state. The apparent inclination of a hill is overestimated when the observer is carrying a heavy backpack. But, what if the hill is a virtual one and the user is about to navigate the virtual environment through an avatar? In a 2 (user with a backpack vs. user without the backpack) × 2 (avatar with a virtual backpack vs. avatar without a virtual backpack) × 2 (customized avatar vs. assigned avatar) between-subjects experiment (N = 121), participants estimated the hill as being steeper when using a customized avatar rather than an assigned one. When the avatar is encumbered by a heavy virtual backpack, those with a customized avatar perceived the virtual hill as being more difficult to climb. Avatar customization and the physical resources of the avatar (operationalized here in the form of a 'virtual' backpack) were found to be key predictors of embodied perception in virtual environments (VE). This has implications for the design of games and interventions that make use of VEs.


title: 120W Ka Band Power Amplifier Utilizing GaN MMICs and Coaxial Waveguide Spatial Power Combining

abstract: The availability of increasingly higher power commercial microwave monolithic integrated circuit (MMIC) amplifiers enables the construction of solid state amplifiers achieving output powers and performance previously achievable only from traveling wave tube amplifiers (TWTAs). A high efficiency power amplifier incorporating an antipodal finline antenna array within a coaxial waveguide is investigated at Ka Band. The coaxial waveguide combiner structure is used to demonstrate a 120 Watt power amplifier from 27 to 31GHz by combining quantity (16), 10 Watt GaN MMIC devices; achieving typical PAE of 25% for the overall power amplifier assembly.


title: Zipf's law unzipped

abstract: Why does Zipf's law give a good description of data from seemingly completely unrelated phenomena? Here it is argued that the reason is that they can all be described as outcomes of a ubiquitous random group division: the elements can be citizens of a country and the groups family names, or the elements can be all the words making up a novel and the groups the unique words, or the elements could be inhabitants and the groups the cities in a country, and so on. A Random Group Formation (RGF) is presented from which a Bayesian estimate is obtained based on minimal information: it provides the best prediction for the number of groups with $k$ elements, given the total number of elements, groups, and the number of elements in the largest group. For each specification of these three values, the RGF predicts a unique group distribution $N(k)\propto \exp(-bk)/k^{\gamma}$, where the power-law index $\gamma$ is a unique function of the same three values. The universality of the result is made possible by the fact that no system specific assumptions are made about the mechanism responsible for the group division. The direct relation between $\gamma$ and the total number of elements, groups, and the number of elements in the largest group, is calculated. The predictive power of the RGF model is demonstrated by direct comparison with data from a variety of systems. It is shown that $\gamma$ usually takes values in the interval $1\leq\gamma\leq 2$ and that the value for a given phenomena depends in a systematic way on the total size of the data set. The results are put in the context of earlier discussions on Zipf's and Gibrat's laws, $N(k)\propto k^{-2}$ and the connection between growth models and RGF is elucidated.


title: Who invests in renewable electricity production? : Empirical evidence and suggestions for further research

abstract: Transforming energy systems to fulfill the needs of a low-carbon economy requires large investments in renewable electricity production (RES-E). Recent literature underlines the need to take a closer look at the composition of the RES-E investor group in order to understand the motives and investment processes of different types of investors. However, existing energy policies generally consider RES-E investments made on a regional or national level, and target investors who evaluate their RES-E investments according to least-cost high-profit criteria. We present empirical evidence to show that RES-E investments are made by a heterogeneous group of investors, that a variety of investors exist and that their formation varies among the different types of renewable sources. This has direct implications for our understanding of the investment process in RES-E and for the study of motives and driving forces of RES-E investors. We introduce a multi-dimensional framework for analyzing differences between categories of investors, which not only considers to the standard economic dimension which is predominant in the contemporary energy literature, but also considers the entrepreneurship, innovation-adoption and institutional dimensions. The framework emphasizes the influence of four main investor-related factors on the investment process which should be studied in future research: motives, background, resources and personal characteristics.


title: The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment

abstract: In the present study we investigated human-robot and robot-human approach distances. We found that subjects' personality profiles influence personal spatial zones in human-robot interaction experiments. We tested two hypotheses: first, we predicted that approach distances preferred by humans when interacting with a robot would be comparable to those preferred when humans interact socially with each other. Our experiments involving humans interacting with a mobile robot confirm this hypothesis. However, surprisingly, a large minority of subjects in the experiments took up positions which were significantly closer, suggesting that they were not treating the robot as a "social entity". We then tested the hypothesis that common personality factors exist which could be used to predict subjects' likely approach distance preferences. The subjects' personalities were assessed using several traits from the three-factor Eysenck personality model. Further analysis of the data identified four new factors, different from Eysenck's model, tentatively labeled "proactiveness", "social reluctance", "timidity" and "nervousness". When testing for correlations between approach distances and personality data, "proactiveness" correlates with social distance, i.e. subjects that score higher on this factor come less close to the robot. We discuss the potential suitability of personality factors to predict approach distances in human-robot interaction experiments.


title: Towards Making Unlabeled Data Never Hurt

abstract: It is usually expected that, when labeled data are limited, the learning performance can be improved by exploiting unlabeled data. In many cases, however, the performances of current semi-supervised learning approaches may be even worse than purely using the limited labeled data. It is desired to have safe semi-supervised learning approaches which never degenerate learning performance by using unlabeled data. In this paper, we focus on semi-supervised support vector machines (S3VMs) and propose S4VMs, i.e., safe S3VMs. Unlike S3VMs which typically aim at approaching an optimal low-density separator, S4VMs try to exploit the candidate low-density separators simultaneously to reduce the risk of identifying a poor separator with unlabeled data. We describe two implementations of S4VMs, and our comprehensive experiments show that the overall performance of S4VMs are highly competitive to S3VMs, while in contrast to S3VMs which degenerate performance in many cases, S4VMs are never significantly inferior to inductive SVMs.


Typeset by REVTEX 4 for JASA ( A , Y ) Speech production knowledge in automatic speech recognition


title: Medium Access Control (MAC) for Wireless Body Area Network (WBAN): Superframe structure, multiple access technique, taxonomy, and challenges

abstract: Health monitoring using biomedical sensors has witnessed significant attention in recent past due to the evolution of a new research area in sensor network known as Wireless Body Area Networks (WBANs). In WBANs, a number of implantable, wearable, and off-body biomedical sensors are utilized to monitor various vital signs of patient’s body for early detection, and medication of grave diseases. In literature, a number of Medium Access Control (MAC) protocols for WBANs have been suggested for addressing the unique challenges related to reliability, delay, collision and energy in the new research area. The design of MAC protocols is based on multiple access techniques. Understanding the basis of MAC protocol designs for identifying their design objectives in broader perspective, is a quite challenging task. In this context, this paper qualitatively reviews MAC protocols for WBANs. Firstly, 802.15.4 and 802.15.6 based MAC Superframe structures are investigated focusing on design objectives. Secondly, different multiple access techniques such as TDMA, CSMA/CA, Slotted Aloha and Hybrid are explored in terms of design goals. Thirdly, a two-layered taxonomy is presented for MAC protocols. First layer classification is based on multiple access techniques, whereas second layer classification is based on design objectives and characteristics of MAC protocols. Critical and qualitative analysis is carried out for each considered MAC protocol. Comparative study of different MAC protocols is also performed. Finally, some open research challenges in the area are identified with initial research directions.


A New Multi-Target, Multi-Agent Search and Rescue Path Planning Approach


title: Communicative competence and theory of mind in autism: A test of relevance theory

abstract: Abstract Sperber and Wilson's (1986) relevance theory makes explicit the role of the comprehension of intentions in human communication. Autistic people have been hypothesized to suffer from a specific and characteristic impairment in the ability to attribute such mental states (e.g., beliefs, intentions); a lack of “theory of mind”. According to relevance theory, then, autistic people should have specific difficulties with the use of language for communication. Relevance theory allows precise predictions about the levels of communicative competence that should be possible with either no, first-order only, or second-order theory of mind ability. Three experiments are reported which tested predictions following from the analysis of figurative language in terms of relevance and theory of mind, in able autistic and normal young subjects. The results lend support to relevance theory. In addition, the findings suggest that some autistic subjects are eventually able to attribute mental states. Lastly, the results demonstrate close links between social and communicative understanding in autism and normal development.


title: Supporting viewpoint-oriented enterprise architecture

abstract: Increasingly, organisations establish what is called an enterprise architecture. The enterprise architecture combines and relates all architectures describing some aspect of the organization, such as the business process architecture, the information architecture, and the application architecture. It is a blueprint of the organisation, which serves as a starting point for analysis, design and decision making. Viewpoints define abstractions on the set of models representing the enterprise architecture, each aimed at a particular type of stakeholder and addressing a particular set of concerns. The use of viewpoints is widely advocated for managing the inherent complexity in enterprise architecture. Viewpoints can both be used to view certain aspects in isolation, and for relating two or more aspects. However, in order to make such a viewpoint-oriented approach practically feasible, architects require a tool environment, which supports the definition, generation, editing and management of architectural views. Moreover, such an environment should work in concert with existing domain-specific modelling tools. We present the design of such a tool environment for viewpoint-oriented enterprise architecture.


title: Arabic information retrieval: Stemming or lemmatization?

abstract: The Arabic language is expanding in the world. According to UNESCO, the Arabic language is spoken by more than 422 million native speakers around 29 countries and among 1.6 billion Muslims worldwide use it to perform their daily prayers. The presence of the Arabic language on the internet grew around 6.091% in the last fifteen years (2000–2015), it is the highest growth of the ten top online languages. Therefore, the number of Arabic documents increases rapidly. This calls for the necessity to improve Arabic Information Retrieval (IR) techniques. Many researchers agree on the benefits of both stemming and lemmatization in IR, primarily with highly inflective languages, short documents and limited space for storing data. The chief purpose of the current study is assessing the impact of stemming and lemmatization on Arabic IR. In this paper, we illustrate several concepts of Arabic morphology, including stemming and lemmatization algorithms. Then, we highlight the use of these latter and their benefits for different Arabic IR systems. Finally, an experiment is conducted to calculate the occurrence of all Quranic surface word, stem, and lemma forms by searching their similarities in both Classical and Modern Standard Arabic resources. In doing so, recent and efficient analyzers AlKhalil Morpho Sys and MADAMIRA are used.


title: Stress Relaxation in Entangled Polymer Melts

abstract: We present an extensive set of simulation results for the stress relaxation in equilibrium and step-strained bead-spring polymer melts. The data allow us to explore the chain dynamics and the shear relaxation modulus, G(t), into the plateau regime for chains with Z=40 entanglements and into the terminal relaxation regime for Z=10. Using the known (Rouse) mobility of unentangled chains and the melt entanglement length determined via the primitive path analysis of the microscopic topological state of our systems, we have performed parameter-free tests of several different tube models. We find excellent agreement for the Likhtman-McLeish theory using the double reptation approximation for constraint release, if we remove the contribution of high-frequency modes to contour length fluctuations of the primitive chain.


title: Human category learning 2.0

abstract: During the 1990s and early 2000s, cognitive neuroscience investigations of human category learning focused on the primary goal of showing that humans have multiple category-learning systems and on the secondary goals of identifying key qualitative properties of each system and of roughly mapping out the neural networks that mediate each system. Many researchers now accept the strength of the evidence supporting multiple systems, and as a result, during the past few years, work has begun on the second generation of research questions—that is, on questions that begin with the assumption that humans have multiple category-learning systems. This article reviews much of this second generation of research. Topics covered include (1) How do the various systems interact? (2) Are there different neural systems for categorization and category representation? (3) How does automaticity develop in each system? and (4) Exactly how does each system learn?


title: Learning to generate one-sentence biographies from Wikidata

abstract: We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information.


title: A Source Code Similarity System for Plagiarism Detection

abstract: Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Those systems need to recognize a number of lexical and structural source code modifications. For example, by some structural modifications (e.g. modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. Most of the existing source code similarity detection systems can be confused when these structural modifications have been applied to the original source code. To be considered effective, a source code similarity detection system must address these issues. To address them, we designed and developed the source code similarity system for plagiarism detection. To demonstrate that the proposed system has the desired effectiveness, we performed a well-known conformism test. The proposed system showed promising results as compared with the JPlag system in detecting source code similarity when various lexical or structural modifications are applied to plagiarized code. As a confirmation of these results, an independent samples t-test revealed that there was a statistically significant difference between average values of F-measures for the test sets that we used and for the experiments that we have done in the practically usable range of cut-off threshold values of 35–70%.


title: RF SOI Switch FET Design and Modeling Tradeoffs for GSM Applications

abstract: A single-pole double-throw novel switch device in0.18¹m SOI complementary metal-oxide semiconductor(CMOS) process is developed for 0.9 Ghz wireless GSMsystems. The layout of the device is optimized keeping inmind the parameters of interest for the RF switch. A subcircuitmodel, with the standard surface potential (PSP) modelas the intrinsic FET model along with the parasitic elementsis built to predict the Ron and Coff of the switch. Themeasured data agrees well with the model. The eight FETstacked switch achieved an Ron of 2.5 ohms and an Coff of180 fF.


title: Pattern Mining of Multichannel sEMG for Tremor Classification

abstract: Tremor is defined as the involuntary rhythmic or quasi-rhythmic oscillation of a body part, resulting from alternating or simultaneous contractions of antagonistic muscle groups. While tremor may be physiological, those who have disabling pathological tremors find that performing typical activities for daily living to be physically challenging and emotionally draining. Detecting the presence of tremor and its proper identification are crucial in prescribing the appropriate therapy to lessen its deleterious physical, emotional, psychological, and social impact. While diagnosis relies heavily on clinical evaluation, pattern analysis of surface electromyogram (sEMG) signals can be a useful diagnostic aid for an objective identification of tremor types. Using sEMG system attached to several parts of the patient's body while performing several tasks, this research aims to develop a classifier system that automates the process of tremor types recognition. Finding the optimal model and its corresponding parameters is not a straightforward process. The resulting workflow, however, provides valuable information in understanding the interplay and impact of the different features and their parameters to the behavior and performance of the classifier system. The resulting model analysis helps identify the necessary locations for the placement of sEMG electrodes and relevant features that have significant impact in the process of classification. These information can help clinicians in streamlining the process of diagnosis without sacrificing its accuracy.


title: Anytime Point-Based Approximations for Large POMDPs

abstract: The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A well-known technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other state-of-the-art POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.


A Task-based Approach for Ontology Evaluation


title: Improving training needs assessment processes via the Internet: system design and qualitative study

abstract: Purpose – The goal of this research is to demonstrate how web‐based applications improve the effectiveness of a very important human resource (HR) function. It proposes a framework of a web‐based training needs assessment system for HR professionals to effectively assess employee needs for competency‐based training.Design/methodology/approach – A conceptual framework of a survey‐based training needs assessment using the Internet was developed. A use case detailing a walkthrough of a prototype system developed under this proposed framework was created to illustrate how this system works. A two‐phased operation test of the prototype system was then conducted to assess how well human resource (HR) managers would accept and adopt this technology to develop organizational competencies. The first phase involved demonstrating the use case to a group of experienced HR managers and polling their opinion toward the system. The second phase involved one‐on‐one interviews with three select HR managers who were asked ...


title: Using machine learning to parse breast pathology reports

abstract: PURPOSE ::: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. ::: ::: ::: METHODS ::: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. ::: ::: ::: RESULTS ::: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. ::: ::: ::: CONCLUSIONS ::: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.


title: Decision Tree Based Depression Classification from Audio Video and Language Information

abstract: In order to improve the recognition accuracy of the Depression Classification Sub-Challenge (DCC) of the AVEC 2016, in this paper we propose a decision tree for depression classification. The decision tree is constructed according to the distribution of the multimodal prediction of PHQ-8 scores and participants' characteristics (PTSD/Depression Diagnostic, sleep-status, feeling and personality) obtained via the analysis of the transcript files of the participants. The proposed gender specific decision tree provides a way of fusing the upper level language information with the results obtained using low level audio and visual features. Experiments are carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) database, results show that the proposed depression classification schemes obtain very promising results on the development set, with F1 score reaching 0.857 for class depressed and 0.964 for class not depressed. Despite of the over-fitting problem in training the models of predicting the PHQ-8 scores, the classification schemes still obtain satisfying performance on the test set. The F1 score reaches 0.571 for class depressed and 0.877 for class not depressed, with the average 0.724 which is higher than the baseline result 0.700.


title: 6-Element 28/38 GHz dual-band MIMO PIFA for future 5G cellular systems

abstract: This paper presents a 6-element 28/38 GHz dualband multiple-input multiple-output (MIMO) printed inverted F-antenna (PIFA) for the future millimeter-wave (mm-Wave) fifth generation (5G) cellular communication systems. The MIMO antenna system consists of six dual-band PIFA antenna elements in a certain arrangement. This arrangement has been chosen based on an extensive study to select the best one for good matching impedance, better isolation and reasonable radiation pattern and gain.


title: Wavelet transforms and the ECG: a review

abstract: The wavelet transform has emerged over recent years as a powerful time–frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.


title: Relaxed online SVMs for spam filtering

abstract: Spam is a key problem in electronic communication, including large-scale email systems and the growing number of blogs. Content-based filtering is one reliable method of combating this threat in its various forms, but some academic researchers and industrial practitioners disagree on how best to filter spam. The former have advocated the use of Support Vector Machines (SVMs) for content-based filtering, as this machine learning methodology gives state-of-the-art performance for text classification. However, similar performance gains have yet to be demonstrated for online spam filtering. Additionally, practitioners cite the high cost of SVMs as reason to prefer faster (if less statistically robust) Bayesian methods. In this paper, we offer a resolution to this controversy. First, we show that online SVMs indeed give state-of-the-art classification performance on online spam filtering on large benchmark data sets. Second, we show that nearly equivalent performance may be achieved by a Relaxed Online SVM (ROSVM) at greatly reduced computational cost. Our results are experimentally verified on email spam, blog spam, and splog detection tasks.


title: A 4-channel 24-27 GHz UWB phased array transmitter in 0.13 μm CMOS for vehicular radar

abstract: The variable-phase ring oscillator (VPRO) and phase-locked loop (PLL) architecture for integrated phased arrays enables the elimination of mixers, power splitters/combiners and phase shifters and hence allows for compact and power-efficient implementations. This paper extends the architecture to ultra-wideband (UWB) applications through architectural and circuit innovations. The implementation of a wideband VPRO enables the generation of UWB signals through the PLL which are inherently phase-shifted to accomplish beam-steering. The VPRO operates at half of the desired frequency range, and a squarer is interposed between the VPRO and the power amplifier of each channel. This doubles the bandwidth and the inter-channel phase shift and enables the architecture to cover the ±180° phase-shift range required for full beam-steering coverage. In addition, a waveform-adaptive, tunable-narrowband design paradigm is introduced that greatly simplifies the design of UWB RF blocks. A fully-integrated, 4-channel, 24-27 GHz, phased-array transmitter, useful for vehicular radar applications, is implemented in 0.13 μm CMOS to validate these claims.


title: Automatic Detection of Mind Wandering During Reading Using Gaze and Physiology

abstract: Mind wandering (MW) entails an involuntary shift in attention from task-related thoughts to task-unrelated thoughts, and has been shown to have detrimental effects on performance in a number of contexts. This paper proposes an automated multimodal detector of MW using eye gaze and physiology (skin conductance and skin temperature) and aspects of the context (e.g., time on task, task difficulty). Data in the form of eye gaze and physiological signals were collected as 178 participants read four instructional texts from a computer interface. Participants periodically provided self-reports of MW in response to pseudorandom auditory probes during reading. Supervised machine learning models trained on features extracted from participants' gaze fixations, physiological signals, and contextual cues were used to detect pages where participants provided positive responses of MW to the auditory probes. Two methods of combining gaze and physiology features were explored. Feature level fusion entailed building a single model by combining feature vectors from individual modalities. Decision level fusion entailed building individual models for each modality and adjudicating amongst individual decisions. Feature level fusion resulted in an 11% improvement in classification accuracy over the best unimodal model, but there was no comparable improvement for decision level fusion. This was reflected by a small improvement in both precision and recall. An analysis of the features indicated that MW was associated with fewer and longer fixations and saccades, and a higher more deterministic skin temperature. Possible applications of the detector are discussed.


title: Distributed Smart-Home Decision-Making in a Hierarchical Interactive Smart Grid Architecture

abstract: In this paper, we develop a comprehensive real-time interactive framework for the utility and customers in a smart grid while ensuring grid-stability and quality-of-service (QoS). First, we propose a hierarchical architecture for the utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: ( $1$ ) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and ( $2$ ) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer’s (microgrid controller’s) profit. The paper also explicitly discusses how this two-step solution approach can be scaled to be suitable for more complicated smart grid architectures beyond the assumed abstract model.


title: Utility functions in autonomic systems

abstract: Utility functions provide a natural and advantageous framework for achieving self-optimization in distributed autonomic computing systems. We present a distributed architecture, implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of computational resources in a dynamic, heterogeneous environment. Broadly, the architecture is a two-level structure of independent autonomic elements that supports flexibility, modularity, and self-management. Individual autonomic elements manage application resource usage to optimize local service-level utility functions, and a global arbiter allocates resources among application environments based on resource-level utility functions obtained from the managers of the applications. We present empirical data that demonstrate the effectiveness of our utility function scheme in handling realistic, fluctuating Web-based transactional workloads running on a Linux cluster.


Above the Clouds : A View of Cloud Computing


title: Low-Voltage Current Feedback Operational Amplifiers

abstract: A number of current feedback operational amplifier topologies suitable for operation in a low-voltage environment are introduced in this paper. Their realization is based on the corresponding low-voltage second generation current conveyor topologies. Important performance factors such as accuracy, bandwidth, and linearity have been considered, and the obtained simulation results have been compared in order to evaluate the behavior of the proposed topologies.


title: Defending recommender systems: detection of profile injection attacks

abstract: Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system’s recommendation behavior. Prior work has shown when profiles are reverse engineered to maximize influence; even a small number of malicious profiles can significantly bias the system. This paper describes a classification approach to the problem of detecting and responding to profile injection attacks. A number of attributes are identified that distinguish characteristics present in attack profiles in general, as well as an attribute generation approach for detecting profiles based on reverse engineered attack models. Three well-known classification algorithms are then used to demonstrate the combined benefit of these attributes and the impact the selection of classifier has with respect to improving the robustness of the recommender system. Our study demonstrates this technique significantly reduces the impact of the most powerful attack models previously studied, particularly when combined with a support vector machine classifier.


title: An anencephalic monocephalus diprosopus "headed twin": postmortem and CT findings with emphasis on the cranial bones.

abstract: Monocephalus diprosopus is a form of conjoined twinning characterized by a single body, one unusual head and two faces or a spectrum of duplication of the craniofacial structures. Such cases have been mainly described according to postmortem pathologic examination. This presented case is a 26-week-stillborn female fetus, with unusual facial appearance with four eyes, two mouths, two noses, two ears and a defective cranial vault. To our knowledge, a detailed computerized tomography (CT) examination of the aberrant facial and cranial bones of such a case has not been reported to date. In this reported case, we present an anencephalic monocephalus diprosopus "headed twin", and describe the CT findings with emphasis on the cranial bones.


title: Time course and differential responses of the major heat shock protein families in human skeletal muscle following acute nondamaging treadmill exercise

abstract: The exercise-induced expression of heat shock proteins (HSPs) in rodent models is relatively well defined. In contrast, comparable data from human studies are limited and the exercise-induced stress response of human skeletal muscle is far from understood. This study has characterized the time course and magnitude of the HSP response in the skeletal muscles of a healthy active, but untrained, young male population following a running exercise protocol. Eight subjects performed 45 min of treadmill running at a speed corresponding to their lactate threshold (11.7 ± 0.5 km/h; 69.8 ± 4.8% maximum O2 uptake). Muscle biopsies were obtained from the vastus lateralis muscle immediately before and at 24 h, 48 h, 72 h, and 7 days postexercise. Exercise induced a significant (P < 0.05) but variable increase in HSP70, heat shock cognate (HSC) 70, and HSP60 expression with peak increases (typically occurring at 48 h postexercise) to 210, 170, and 139% of preexercise levels, respectively. In contrast, exercise did not ...


title: Design and evaluation of voluntary opening and voluntary closing prosthetic terminal device

abstract: Body-powered prostheses use a cable-operated system to generate forces and move prosthetic joints. However, this control system can only generate forces in one direction, so current body-powered prehensor designs allow the user either to voluntarily open or voluntarily close the tongs. Both voluntary opening (VO) and voluntary closing (VC) modes of operation have advantages for certain tasks, and many end-users desire a terminal device that can switch between the two modes. However, such a terminal device must maintain the same thumb position (i.e., point of Bowden cable attachment) and movement direction in both modes in order to avoid the need to readjust the harness after every mode switch. In this study, we demonstrate a simple design that fulfills these requirements while allowing the user to switch easily between modes. We describe the design concept, describe a rugged split-hook prototype, provide specifications (size, weight, efficiency, etc.), and present a pilot study in which five subjects with intact arms and two subjects with amputation used the VO and VC split-hook prehensor to perform the Southampton Hand Assessment Procedure. Subjects performed an average of 4 to 7 (+/- 0.2) points better when they could choose to switch between modes on a task-by-task basis than when they were constrained to using only VO or VC modes.


Memory Forensics over the IEEE 1394 Interface


Politeness and Speech Acts


title: Echocardiographic quantification of myocardial function using tissue deformation imaging, a guide to image acquisition and analysis using tissue Doppler and speckle tracking

abstract: Recent developments in the field of echocardiography have allowed the cardiologist to objectively quantify regional and global myocardial function. Regional deformation (strain) and deformation rate (strain-rate) can be calculated non-invasively in both the left and right ventricle, providing information on regional (dys-)function in a variety of clinical settings. Although this promising novel technique is increasingly applied in clinical and preclinical research, knowledge about the principles, limitations and technical issues of this technique is mandatory for reliable results and for implementation both in the clinical as well as the scientific field.In this article, we aim to explain the fundamental concepts and potential clinical applicability of strain and strain-rate for both tissue Doppler imaging (TDI) derived and speckle tracking (2D-strain) derived deformation imaging. In addition, a step-by-step approach to image acquisition and post processing is proposed. Finally, clinical examples of deformation imaging in hypertrophic cardiomyopathy (HCM), cardiac resynchronization therapy (CRT) and arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) are presented.


title: A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data

abstract: BACKGROUND ::: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. ::: ::: ::: OBJECTIVE ::: Modify the Elixhauser classification system into a single numeric score for administrative data. ::: ::: ::: METHODS ::: For all hospitalizations at the Ottawa Hospital, Canada, between 1996 and 2008, we determined if International Classification of Disease codes for chronic diagnoses were in any of the 30 Elixhauser comorbidity groups. We then used backward stepwise multivariate logistic regression to determine the independent association of each comorbidity group with death in hospital. Regression coefficients were modified into a scoring system that reflected the strength of each comorbidity group's independent association with hospital death. ::: ::: ::: RESULTS ::: Hospitalizations that were included were 345,795 (derivation: 228,565; validation 117,230). Twenty-one of the 30 groups were independently associated with hospital mortality. The resulting comorbidity score had an equivalent discrimination in the derivation and validation groups (overall c-statistic 0.763, 95% CI: 0.759-0.766). This was similar to models having all Elixhauser groups (0.760, 95% CI: 0.756-0.764) or significant groups only (0.759, 95% CI: 0.754-0.762), but significantly exceeded discrimination when comorbidity was expressed using the Charlson score (0.745, 95% CI: 0.742-0.749). ::: ::: ::: CONCLUSION ::: When analyzing administrative data, the Elixhauser comorbidity system can be condensed to a single numeric score that summarizes disease burden and is adequately discriminative for death in hospital.


title: Feedback-Controlled MEMS Force Sensor for Characterization of Microcantilevers

abstract: This paper outlines the design and characterization of a setup used to measure the stiffness of microcantilevers and other small mechanical devices. Due to the simplicity of fabrication, microcantilevers are used as the basis for a variety of mechanical sensor designs. In a range of applications, knowledge of the stiffness of microcantilevers is essential for the accurate calibration of the sensors in which they are used. Stiffness is most commonly identified through measurement of the microcantilever's resonance frequency, which is applied to an empirically derived model. This paper uses a microelectromechanical system (MEMS)-based force sensor to measure the forces produced by a microcantilever when deformed and a piezoelectric tube-based nanopositioner to displace the microcantilever. A method of calibrating the force sensor is presented that takes advantage of the lumped nature of the mechanical system and the nonlinearity of MEMS electrostatic drives.


title: Covalsa: Covariance estimation from compressive measurements using alternating minimization

abstract: The estimation of covariance matrices from compressive measurements has recently attracted considerable research efforts in various fields of science and engineering. Owing to the small number of observations, the estimation of the covariance matrices is a severely ill-posed problem. This can be overcome by exploiting prior information about the structure of the covariance matrix. This paper presents a class of convex formulations and respective solutions to the highdimensional covariance matrix estimation problem under compressive measurements, imposing either Toeplitz, sparseness, null-pattern, low rank, or low permuted rank structure on the solution, in addition to positive semi-definiteness. To solve the optimization problems, we introduce the CoVariance by Augmented Lagrangian Shrinkage Algorithm (CoVALSA), which is an instance of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). We illustrate the effectiveness of our approach in comparison with state-ofthe-art algorithms.


title: Multiagent-Based Resource Allocation for Energy Minimization in Cloud Computing Systems

abstract: Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the CSP’s problem of efficiently allocating VM resources to physical machines (PMs) with the aim of minimizing the energy consumption. Traditional energy-aware VM allocations either allocate VMs to PMs in a centralized manner or implement VM migrations for energy reduction without considering the migration cost in cloud computing systems. We address these two issues by introducing a decentralized multiagent (MA)-based VM allocation approach. The proposed MA works by first dispatching a cooperative agent to each PM to assist the PM in managing VM resources. Then, an auction-based VM allocation mechanism is designed for these agents to decide the allocations of VMs to PMs. Moreover, to tackle system dynamics and avoid incurring prohibitive VM migration overhead, a local negotiation-based VM consolidation mechanism is devised for the agents to exchange their assigned VMs for energy cost saving. We evaluate the efficiency of the MA approach by using both static and dynamic simulations. The static experimental results demonstrate that the MA can incur acceptable computation time to reduce system energy cost compared with traditional bin packing and genetic algorithm-based centralized approaches. In the dynamic setting, the energy cost of the MA is similar to that of benchmark global-based VM consolidation approaches, but the MA largely reduces the migration cost.


title: The Symbol Grounding Problem

abstract: A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.


title: Chains in Chains - Logic and Challenges of Blockchains in Supply Chains

abstract: Due to the disruptive role of the Bitcoin in the financial sector, both scholars and practitioners are increasingly wondering whether it is possible to replicate the impact of the Blockchain technology in the supply chain context. As a distributed ledger technology characterized by the decentralized consensus, Blockchain is touted by many as the proper platform to collect all the information about supply chains from the producer to the consumer. However, the current technology immaturity and the lack of successful supply chain implementations pave the way for doubt about the disruptive role of this technology in supply chains. To the authors’ knowledge, this work is one of the very first attempts to link the blockchain technology to supply chain and logistics. This paper investigates the state-of-the-art application of blockchain in supply chains, exploring both the literature and the industry initiatives, contributing to the increase of the managerial insight and providing a future research agenda. (Less)


Extracting Causal Claims from Information Systems Papers with Natural Language Processing for Theory Ontology Learning


title: Improved Measures of Integrated Information

abstract: Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 × 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the Φ-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands.


title: Autonomous parking using a sensor based approach

abstract: This paper considers the perpendicular parking problem of car-like vehicles for both forward and reverse maneuvers. A sensor based controller with a weighted control scheme is proposed and is compared with a state of the art path planning approach. The perception problem is threated as well considering a Velodyne VLP-16 as the sensor providing the required exteroceptive information. A methodology to extract the necessary information for both approaches from the sensor data is presented. A fast prototyping environment has been used to develop the parking application, and also used as simulator in order to validate the approach. Preliminary results from simulation and real experimentation show the effectiveness of the proposed approach.


title: Wheelchair.q, a mechanical concept for a stair climbing wheelchair

abstract: This paper present Wheelchair.q, a concept for a stair climbing wheelchair able to move in structured and unstructured environment, to climb over obstacles and to go up and down stairs. The wheelchair passively changes its locomotion, from a rolling on wheels to a stepping on legs one. The different locomotions are triggered only by local and dynamic conditions and not by an external command and so only one motor for each locomotion unit is necessary. A four-bar linkage moves and rotates the chair, in order to avoid wheelchair turning over and to guarantee a comfortable posture to the passenger.


title: Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics

abstract: In analyzing of modern biological data, we are often dealing with ill-posed problems and missing data, mostly due to high dimensionality and multicollinearity of the dataset. In this paper, we have proposed a system based on matrix factorization (MF) and deep recurrent neural networks (DRNNs) for genotype imputation and phenotype sequences prediction. In order to model the long-term dependencies of phenotype data, the new Recurrent Linear Units (ReLU) learning strategy is utilized for the first time. The proposed model is implemented for parallel processing on central processing units (CPUs) and graphic processing units (GPUs). Performance of the proposed model is compared with other training algorithms for learning long-term dependencies as well as the sparse partial least square (SPLS) method on a set of genotype and phenotype data with 604 samples, 1980 single-nucleotide polymorphisms (SNPs), and two traits. The results demonstrate performance of the ReLU training algorithm in learning long-term dependencies in RNNs.