title: Expert-augmented actor-critic for ViZDoom and Montezumas Revenge

abstract: We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in many experiments beating the first world). With an appropriate choice of hyperparameters, our algorithm surpasses the performance of the expert data. In a number of experiments, we have observed an unreported bug in Montezumas Revenge which allowed the agent to score more than 800,000 points.


title: A Transforming Insurance Company and the 4 Types of Health Data Challenges that Arise: A Finnish Case Study

abstract: This paper aims to identify the challenges of health data in the context of an insurance company that is transforming from a reactive company into a proactive one. Using 23 interviews from a case study of an insurance company in Finland, we revealed 4 key areas of challenges that arise during this transition. The identified areas were found to be the following: Access, Ownership, Sharing, and Use. These findings are then discussed in context of the shift towards a proactive paradigm for organizations. The customer experience is suggested to be pivotal for organizations to create value and for managing the 4 identified health data challenges.


title: Evidence from the cytology of Azotobacter chroococcum of a relationship with Rhizobium and the Bacillaceae.

abstract: Summary: The structure of the cell envelopes in Azotobacter chroococcum and the behaviour and mode of formation of the spore-like ‘cysts’ suggest that this organism has a close phylogenetic relationship with the Bacillaceae, and that accordingly the main genera of nitrogen-fixing bacteria, Azotobacter, Rhizobium, and Clostridium, have a relatively recent common origin.


title: Combining Word Embedding and Lexical Database for Semantic Relatedness Measurement

abstract: While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet or Wikitionary, the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform the lexicon-based methods. However, word senses are not disambiguated in the training phase of both Word2Vec and GloVe, two famous word embedding algorithms, and the path length between any two senses of words in lexical databases cannot reflect their true semantic relatedness. In this paper, a novel approach that linearly combines Word2Vec and GloVe with the lexical database WordNet is proposed for measuring semantic relatedness. The experiments show that the simple method outperforms the state-of-the-art model SensEmbed.


title: Endocrine Disorders in Head Neck Region: A Radiographic Perspective in Dental Clinic -

abstract: Endocrine diseases may have manifestations locally or in a generalized manner. When an imbalance in normal bone homeostasis occurs, regulated by various hormones such as parathyroid hormone, growth hormone it results in conditions like hypercalcemia, hypophosphatemia etc. which may affect bone density in a generalized manner. The condition cause: excessive resorption of bone or excessive deposition of bone. Intraoral or extraoral radiographs are thus useful in detecting diseases like hyperparathyroidism, hyperpituitarism, hypoparathyroidism, and hypopituitarism which are subclinical and are detected accidently on routine radiographic examination.


Tools for ontology matching practical considerations


title: Consumption of Fermented Milk Product With Probiotic Modulates Brain Activity

abstract: Background & Aims Changes in gut microbiota have been reported to alter signaling mechanisms, emotional behavior, and visceral nociceptive reflexes in rodents. However, alteration of the intestinal microbiota with antibiotics or probiotics has not been shown to produce these changes in humans. We investigated whether consumption of a fermented milk product with probiotic (FMPP) for 4 weeks by healthy women altered brain intrinsic connectivity or responses to emotional attention tasks. Methods Healthy women with no gastrointestinal or psychiatric symptoms were randomly assigned to groups given FMPP (n = 12), a nonfermented milk product (n = 11, controls), or no intervention (n = 13) twice daily for 4 weeks. The FMPP contained Bifidobacterium animalis subsp Lactis , Streptococcus thermophiles , Lactobacillus bulgaricus , and Lactococcus lactis subsp Lactis . Participants underwent functional magnetic resonance imaging before and after the intervention to measure brain response to an emotional faces attention task and resting brain activity. Multivariate and region of interest analyses were performed. Results FMPP intake was associated with reduced task-related response of a distributed functional network (49% cross-block covariance; P = .004) containing affective, viscerosensory, and somatosensory cortices. Alterations in intrinsic activity of resting brain indicated that ingestion of FMPP was associated with changes in midbrain connectivity, which could explain the observed differences in activity during the task. Conclusions Four-week intake of an FMPP by healthy women affected activity of brain regions that control central processing of emotion and sensation.


title: Learning to detect phishing emails

abstract: Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed websites that harvest information. We present a method for detecting these attacks, which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication. This method is applicable, with slight modification, to detection of phishing websites, or the emails used to direct victims to these sites. We evaluate this method on a set of approximately 860 such phishing emails, and 6950 non-phishing emails, and correctly identify over 96% of the phishing emails while only mis-classifying on the order of 0.1% of the legitimate emails. We conclude with thoughts on the future for such techniques to specifically identify deception, specifically with respect to the evolutionary nature of the attacks and information available.


title: Design of an algorithmic Wallace multiplier using high speed counters

abstract: Wallace tree multipliers provide a power-efficient strategy for high speed multiplication. The use of high speed 7∶3 counters in the Wallace tree reduction can further improve the multiplier speed. This paper presents an algorithmic approach to construct the counter based Wallace tree multipliers. The proposed algorithm can be used to implement the efficient counter based Wallace multiplier of any size suitable for FPGA or ASIC synthesis tools. The designs are synthesized in Synopsys Design Compiler using 90 nm CMOS technology. The detailed comparison of traditional and counter based Wallace multipliers is performed which shows that the counter based Wallace multiplier is up to 22% faster as compared to the traditional Wallace multiplier.


Improving Outcomes for Preschool Inclusion Students


title: What Contributes to the Success of IT Projects? Success Factors, Challenges and Lessons Learned from an Empirical Study of Software Projects in the Norwegian Public Sector

abstract: Context. Each year the public sector invests large amounts of money in the development and modifications of their software systems. These investments are not always successful and many public sector software projects fail to deliver the expected benefits. Goal. This study aims at reducing the waste of resources on failed software projects through better understanding of the success factors and challenges. Method. Thirty-five completed software projects in 11 organizations in the public sector of Norway were analyzed. For each project, representatives from the project owners, project management and the user organization were interviewed. Results. Small and large software projects reported different challenges, especially related to project priority. Taking advantage of agile practices such as flexible scope and frequent delivery increased the success rate of the projects. Projects with time and material contracts and involved clients during execution were more successful than other projects. The respondents experienced that extensive involvement and good competence of the client, high priority of the project, good dialogue between client and provider and appliance of agile practices were main success factors. Main challenges were related to technical issues, project planning and management, transition of the product to the user organization, involvement and competence of the client, and benefit management. Conclusions. Success factors tend to focus on human factors, e.g., involvement, competence and collaboration. Challenges focus on human factors as well as issues of technical nature. Both aspects need to be addressed to enable successful and avoid failed software projects. Competence, client involvement and benefit management are among factors that the public sector should focus on for realizing client benefits.


title: Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans

abstract: The origins of the First Americans remain contentious. Although Native Americans seem to be genetically most closely related to east Asians, there is no consensus with regard to which specific Old World populations they are closest to. Here we sequence the draft genome of an approximately 24,000-year-old individual (MA-1), from Mal'ta in south-central Siberia, to an average depth of 1×. To our knowledge this is the oldest anatomically modern human genome reported to date. The MA-1 mitochondrial genome belongs to haplogroup U, which has also been found at high frequency among Upper Palaeolithic and Mesolithic European hunter-gatherers, and the Y chromosome of MA-1 is basal to modern-day western Eurasians and near the root of most Native American lineages. Similarly, we find autosomal evidence that MA-1 is basal to modern-day western Eurasians and genetically closely related to modern-day Native Americans, with no close affinity to east Asians. This suggests that populations related to contemporary western Eurasians had a more north-easterly distribution 24,000 years ago than commonly thought. Furthermore, we estimate that 14 to 38% of Native American ancestry may originate through gene flow from this ancient population. This is likely to have occurred after the divergence of Native American ancestors from east Asian ancestors, but before the diversification of Native American populations in the New World. Gene flow from the MA-1 lineage into Native American ancestors could explain why several crania from the First Americans have been reported as bearing morphological characteristics that do not resemble those of east Asians. Sequencing of another south-central Siberian, Afontova Gora-2 dating to approximately 17,000 years ago, revealed similar autosomal genetic signatures as MA-1, suggesting that the region was continuously occupied by humans throughout the Last Glacial Maximum. Our findings reveal that western Eurasian genetic signatures in modern-day Native Americans derive not only from post-Columbian admixture, as commonly thought, but also from a mixed ancestry of the First Americans.


Selective grinding and Planas' direct tracks as a source of prevention.


title: Challenging SMT solvers to verify neural networks

abstract: In recent years, Satisfiability Modulo Theory (SMT) solvers are becoming increasingly popular in the Computer Aided Verification and Reasoning community. Used natively or as back-engines, they are accumulating a record of success stories and, as witnessed by the annual SMT competition, their performances and capacity are also increasing steadily. Introduced in previous contributions of ours, a new application domain providing an outstanding challenge for SMT solvers is represented by verification of Multi-Layer Perceptrons (MLPs) a widely-adopted kind of artificial neural network. In this paper we present an extensive evaluation of the current state-of-the-art SMT solvers and assess their potential in the promising domain of MLP verification.


title: Plagiarism Detection: A Tool Survey and Comparison

abstract: We illustrate the state of the art in software plagiarism detection tools by comparing their features and testing them against a wide range of source codes. The source codes were edited according to several types of plagiarism to show the tools accuracy at detecting each type. ::: ::: The decision to focus our research on plagiarism of programming languages is two fold: on one hand, it is a challenging case-study since programming languages impose a structured writing style; on the other hand, we are looking for the integration of such a tool in an Automatic-Grading System (AGS) developed to support teachers in the context of Programming courses. ::: ::: Besides the systematic characterisation of the underlying problem domain, the tools were surveyed with the objective of identifying the most successful approach in order to design the aimed plugin for our AGS.


title: Cognitive radio for medical wireless body area networks

abstract: Cognitive radio (CR) is a paradigm for opportunistic access of licensed (primary) parts of the electromagnetic spectrum by unlicensed (secondary) users. This emerging technology is aimed at improving the efficiency of wireless resource usage. In medical environments, CR has big potential to solve interference problems caused by the scarcity of spectrum allocated to medical applications. Hospital environments such as the operating room (OR) offer challenging scenarios to spectrum managers, in which CR is a viable solution to ensure electromagnetic compatibility (EMC). A recent trend in medical practice is the use of wearable wireless medical sensors. These devices are being introduced in unlicensed bands, where the usual concepts of primary and secondary users do not apply. This paper discusses some of the alternatives for implementing CR in such particular environments. A short survey of CR for hospital environments is also presented, highlighting the differences with the scenarios in which wireless sensors are used.


title: A Model of the Electrophysiological of Thalamocortical Relay Neurons Properties

abstract: 1. A model of the electrophysiological properties of single thalamocortical relay neurons in the rodent and cat dorsal lateral geniculate nucleus was constructed, based in part on the voltage dependence and kinetics of ionic currents detailed with voltage-clamp techniques. The model made the simplifying assumption of a single uniform compartment and incorporated a fast and transient Na+ current, INa; a persistent, depolarization-activated Na+ current, INap; a low-threshold Ca2+ current, I(T); a high-threshold Ca2+ current, IL; a Ca(2+)-activated K+ current, IC; a transient and depolarization-activated K+ current, IA; a slowly inactivating and depolarization-activated K+ current, IK2; a hyperpolarization-activated cation current, Ih; and K+ and Na+ leak currents IKleak and INaleak. 2. The effects of the various ionic currents on the electrophysiological properties of thalamocortical relay neurons were initially investigated through examining the effect of each current individually on passive membrane respo...


title: The effects of brand equity and brand personality on customer value: The case of Uniqlo in Thailand

abstract: Fast Fashion Retailing has been one of the most competitive marketing in the world. Brand and branding strategy has become one of the main strategies for the company in this industry. This study aimed to investigate the consumers' perception on brand equity and brand personality and how these factors influence the customer value. The results indicated that both brand equity and brand personality were significantly influence the customer value.


title: Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

abstract: Active inference is an ambitious theory which treats perception and inference on one side, and action selection on the other side under the heading of a single principle. It also provides biologically plausible explanations for many cognitive phenomena, including consciousness. Action selection in active inference is driven by an objective function which evaluates possible future actions with respect to current, inferred beliefs about the world. Such independence from extrinsic rewards comes with a high level of robustness across e.g. different environments or agent morphologies. In the literature, more functions with these favourable properties have been summarised under the notion of intrinsic motivations. In contrast to active inference however, these formal models of motivation usually come without a commitment to particular inference and action selection mechanisms. Up to now it was not clear whether the inference and action selection machinery of active inference could also be used by alternatives to the originally included intrinsic motivation. Here we reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. The perception-action loop serves as unifying formal concept to relate different components within active inference. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that may also share the biological plausibility of active inference.


title: 2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning

abstract: Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.


title: Diverse Beam Search for Increased Novelty in Abstractive Summarization

abstract: Text summarization condenses a text to a shorter version while retaining the important informations. Abstractive summarization is a recent development that generates new phrases, rather than simply copying or rephrasing sentences within the original text. Recently neural sequence-to-sequence models have achieved good results in the field of abstractive summarization, which opens new possibilities and applications for industrial purposes. However, most practitioners observe that these models still use large parts of the original text in the output summaries, making them often similar to extractive frameworks. To address this drawback, we first introduce a new metric to measure how much of a summary is extracted from the input text. Secondly, we present a novel method, that relies on a diversity factor in computing the neural network loss, to improve the diversity of the summaries generated by any neural abstractive model implementing beam search. Finally, we show that this method not only makes the system less extractive, but also improves the overall rouge score of state-of-the-art methods by at least 2 points.


title: Joint Detection and Identification Feature Learning for Person Search

abstract: Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks---pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.


Evaluation of rain water management practices for sediment load reduction in the (semi) humid Blue Nile basin


title: Brave New World: Uncovering Topical Dynamics in the ACL Anthology Reference Corpus Using Term Life Cycle Information

abstract: One of the main interests in the analysis of large document collections is to discover domains of discourse that are still actively developing, growing in interest and relevance, at a given point in time, and to distinguish them from those topics that are in stagnation or decline. The present paper describes a terminologically inspired approach to this kind of task. The inputs to the method are a corpus spanning several decades of research in computational linguistics and a set of single-word terms that frequently occur in that corpus. The diachronic development of these terms is modelled by means of term life cycle information, namely the parameters relative frequency and productivity. In a second step, k-means clustering is used to identify groups of terms with similar development patterns. The paper describes a mathematical approach to modelling term productivity and discusses what kind of information can be obtained from this measure. The results of the clustering experiment are promising and well motivate future research.


title: Personalized, interactive tag recommendation for flickr

abstract: We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust. Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user's tagging history) can or cannot be improved by global schemes (using everybody's tagging history).


title: A Compact 12-Way Slotted Waveguide Power Combiner for Ka-Band Applications

abstract: A power divider/combiner based on a double sided slotted waveguide geometry suitable for Ka-band applications is proposed. This structure allows up to 50% reduction of the total device length compared to previous designs of this type without compromising manufacturing complexity or combining efficiency. Efficient design guidelines based on an equivalent circuit technique are provided and the performance is demonstrated by means of a 12-way divider/combiner prototype operating in the range 29-31 GHz. Numerical simulations show that back to back insertion loss of 1.19 dB can be achieved, corresponding to a combining efficiency of 87%. The design is validated by means of manufacturing and testing an experimental prototype with measured back-to-back insertion loss of 1.83 dB with a 3 dB bandwidth of 20.8%, corresponding to a combining efficiency of 81%.


title: Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records

abstract: Code Blue is an emergency code used in hospitals to indicate when a patient goes into cardiac arrest and needs resuscitation. When Code Blue is called, an on-call medical team staffed by physicians and nurses is paged and rushes in to try to save the patient's life. It is an intense, chaotic, and resource-intensive process, and despite the considerable effort, survival rates are still less than 20% [4]. Research indicates that patients actually start showing clinical signs of deterioration some time before going into cardiac arrest [1][2[][3], making early prediction, and possibly intervention, feasible. In this paper, we describe our work, in partnership with NorthShore University HealthSystem, that preemptively flags patients who are likely to go into cardiac arrest, using signals extracted from demographic information, hospitalization history, vitals and laboratory measurements in patient-level electronic medical records. We find that early prediction of Code Blue is possible and when compared with state of the art existing method used by hospitals (MEWS - Modified Early Warning Score)[4], our methods perform significantly better. Based on these results, this system is now being considered for deployment in hospital settings.


title: Mapping your innovation strategy.

abstract: By describing the landscape of unmet customer needs and analyzing where new offerings have worked before, you can chart a path that will produce successful innovations time after time.


title: Design wireless multi-sensor fire detection and alarm system based on ARM

abstract: The system uses nRF2401 for short-range wireless communications, GPRS for long-distance wireless communications, ARM9 for center console, Wireless Multi-sensor Fire Detector for node, and BP algorithm is used for judging the probability of fire. Wireless Multi-sensor Fire Detector is formed of the low-power electrochemical carbon monoxide sensor, photoelectric smoke detector and semiconductor temperature sensor. BP algorithm program is embedded in the S3C2440A ARM. The samples of BP algorithm were derived from the fire detection standard room of the State Key Laboratory of Fire Science of China. Center console uses Em GIS(embedded GIS) to show where the fire break out and uses GPRS to transmit SMS to the fire command center. The system is low false alarm rate, low cost, fast response and convenient to install.


title: An Innovative Built Environment Form for Dwellings for the Elderly

abstract: Due to population ageing, countries’ financial capacities to maintain the current level and scope of services and institutional care for the elderly are decreasing, which is why there are increasing demands to rationalize services and residential care for the elderly as much as possible. The demands are feasible, considering that the main idea is to enable the elderly to remain in their homes as long as possible, and to move the services aimed at the elderly to their home environments. This idea can be carried out by appropriately adapting the built environment. The basis for this is offered by modern technologies, which can convert an elderly person’s home into an innovative living environment. This article presents such an of environment offering support to the elderly, and discusses its importance and operation, the efforts and achievements made to date in developing this innovation, and the premises for implementing it in society based on elderly people’s opinions about living in such advanced living environments. The findings show that the main goal of implementation must be assigning meaning to and understanding the concept of innovative built environments among users; in addition, they show that rapid changes in implementing innovative built environments into society are not to be expected. A major step in this regard would already be achieved if the living environments of the elderly were intentionally remodelled following the design-for-all principle, if architectural barriers were removed in them, and if new homes were built without these barriers. This alone would make it possible for the elderly to remain in their homes longer while also providing a basis for converting these living environments into smart homes by using modern assistive technologies in the future.


title: Deep Attention Neural Tensor Network for Visual Question Answering

abstract: Visual question answering (VQA) has drawn great attention in cross-modal learning problems, which enables a machine to answer a natural language question given a reference image. Significant progress has been made by learning rich embedding features from images and questions by bilinear models, while neglects the key role from answers. In this paper, we propose a novel deep attention neural tensor network (DA-NTN) for visual question answering, which can discover the joint correlations over images, questions and answers with tensor-based representations. First, we model one of the pairwise interaction (e.g., image and question) by bilinear features, which is further encoded with the third dimension (e.g., answer) to be a triplet by bilinear tensor product. Second, we decompose the correlation of different triplets by different answer and question types, and further propose a slice-wise attention module on tensor to select the most discriminative reasoning process for inference. Third, we optimize the proposed DA-NTN by learning a label regression with KL-divergence losses. Such a design enables scalable training and fast convergence over a large number of answer set. We integrate the proposed DA-NTN structure into the state-of-the-art VQA models (e.g., MLB and MUTAN). Extensive experiments demonstrate the superior accuracy than the original MLB and MUTAN models, with 1.98%, 1.70% relative increases on VQA-2.0 dataset, respectively.


title: Computational drug repositioning through heterogeneous network clustering

abstract: BackgroundGiven the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.ResultsUsing known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.ConclusionsPrevious computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.


A Method for Predicting Foster Parent Success


title: Analysis of genetic variation in different banana ( Musa species) variety using random amplified polymorphic DNAs (RAPDs)

abstract: The banana (Musa acuminata Colla) is considered as an important crop plant due to its high economic value as good dietary source. Here, we analyze the genetic relationship of four different banana varieties that are cultivated in south India. Random amplified polymorphic DNAs (RAPDs) fingerprinting of these banana varieties (Grand Naine, Red Banana, Nendran and Rasthali) carried out by three primers (OPA-19, OPB-18, OPD-16) led to DNA amplification. 43.47% of the amplification products were monomorphic (common to all the genotypes), whereas 30.43% were unique, but only 26.08% revealed the relationship between these genotypes.


title: Accuracy analysis of the RSSI BLE SensorTag signal for indoor localization purposes

abstract: In this paper we describe possibility of use the RSSI signal (Radio Signal Strength Indication) from Texas Instruments SensorTag CC2650 for indoor positioning purposes. This idea is not a new but in our opinion it is possible to use SensorTags with Bluetooth LE wireless interface for positioning inside buildings in such applications as people findings in hospitals, senior come care, etc. RSSI is mostly selected as the sensor localization method in the indoor circumstances. In this paper, we aim to analyze accuracy, calibrate and map RSSI to distance by doing a series of the experiments. Obtained results are very promising and shows possibility of use this technique for position estimation.


title: Transitioning Native Application into Virtual Machine by Using Hardware Virtualization Extensions

abstract: In presence of known and unknown vulnerabilities in code and flow control of programs, virtual machine alike isolation and sandboxing to confine maliciousness of process, by monitoring and controlling the behaviour of untrusted application, is an effective strategy. A confined malicious application cannot effect system resources and other applications running on same operating system. But present techniques used for sandboxing have some drawbacks ranging from scope to methodology. Some of proposed techniques restrict specific aspect of execution e.g. system calls and file system access. In the same way techniques that truly isolate the application by providing separate execution environment either require modification in kernel or full blown operating system. Moreover these do not provide isolation from top to bottom but only virtualize operating system services. In this paper, we propose a design to confine native Linux process in virtual machine equivalent isolation by using hardware virtualization extensions with nominal initialization and acceptable execution overheads. We implemented our prototype called Process Virtual Machine that transition a native process into virtual machine, provides minimal possible execution environment, intercept and virtualize system calls to execute it on host kernel. Experimental results show effectiveness of our proposed technique.


title: From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN

abstract: The effectiveness of GANs in producing images according to a specific visual domain has shown potential in unsupervised domain adaptation. Source labeled images have been modified to mimic target samples for training classifiers in the target domain, and inverse mappings from the target to the source domain have also been evaluated, without new image generation. In this paper we aim at getting the best of both worlds by introducing a symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. We define a new class consistency loss that aligns the generators in the two directions, imposing to preserve the class identity of an image passing through both domain mappings. A detailed analysis of the reconstructed images, a thorough ablation study and extensive experiments on six different settings confirm the power of our approach.


title: Toward Geometric Deep SLAM

abstract: We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are "SLAM-ready" because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU.


title: Temperament and Personality: Origins and Outcomes

abstract: This article reviews how a temperament approach emphasizing biological and developmental processes can integrate constructs from subdisciplines of psychology to further the study of personality. Basic measurement strategies and findings in the investigation of temperament in infancy and childhood are reviewed. These include linkage of temperament dimensions with basic affective-motivational and attentional systems, including positive affecl/approach, fear, frustration/anger, and effortful control. Contributions of biological models that may support these processes are then reviewed. Research indicating how a temperament approach can lead researchers of social and personality development to investigate important person-environment interactions is also discussed. Lastly, adult research suggesting links between temperament dispositions and the Big Five personality factors is described. Temperament arises from our genetic endowment. It influences and is influenced by the experience of each individual, and one of its outcomes is the adult personality. An important goal of our research has been to specify processes at the levels of biology and social development that may link a child's early endowment to its later expression as an adult. In this article, we suggest that understanding temperament is central to understanding personality. Individual differences in temperament have implications for development in infancy and childhood, and they form the core of personality as it develops. Temperament also provides processoriented models that are often lacking in trait theories of personality, by establishing links between individual differences in behavior and their psychological and biological substrates. Temperament also can be used to relate human individual differences, through evolutionary models, to individual differences in nonhuman animals. The purpose of this article is to review our approach to temperament research, indicating ways in which thinking about temperament can illuminate the understanding of individual differences. Many of the guiding principles of this approach are already familiar to personality researchers, because they were laid out by those who helped to develop modern conceptions of personality. Other principles may be less familiar, because they derive more heavily


Analysis and Opinion mining using SentiwordNet : A Survey


title: Exploitation and exploration in a performance based contextual advertising system

abstract: The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn more about the unknown to improve its knowledge (exploration), since the latter might increase its revenue in the future. The exploitation and exploration (EE) tradeoff has been extensively studied in the reinforcement learning community, however, not been paid much attention in online advertising until recently. In this paper, we develop two novel EE strategies for online advertising. Specifically, our methods can adaptively balance the two aspects of EE by automatically learning the optimal tradeoff and incorporating confidence metrics of historical performance. Within a deliberately designed offline simulation framework we apply our algorithms to an industry leading performance based contextual advertising system and conduct extensive evaluations with real online event log data. The experimental results and detailed analysis reveal several important findings of EE behaviors in online advertising and demonstrate that our algorithms perform superiorly in terms of ad reach and click-through-rate (CTR).


title: The Influences of Negativity and Review Quality on the Helpfulness of Online Reviews

abstract: Building on the interpersonal evaluation theory in social psychology, this study explores the existence of a negativity bias in evaluating the helpfulness of online reviews, i.e., whether users perceive a negative review to be more helpful than a positive review. An analysis of 7659 book reviews from Amazon.co.uk shows that a negativity bias disappears after controlling for moderating factors related to evaluation quality such as readability and length. The finding demonstrates that the negativity bias suggested by the social psychology literature is not readily applicable to consumer-generated online reviews. The study contributes to the theorization of word-of-mouth by exploring the qualitative characteristics of consumer-generated reviews in addition to their valence. The study also makes a theoretical contribution to information systems research by introducing and extending the interpersonal evaluation theory to online review research.


title: New Lane Model and Distance Transform for Lane Detection and Tracking

abstract: Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An efficient lane tracking method is also discussed. The use of this distance transform exploits useful information in lane detection situations, and greatly facilitates the initialization of the particle filter, as well as lane tracking. Finally, the paper validates the algorithm with experimental evidence for lane detection and tracking.


title: A self-determination perspective of strengths use at work: Examining its determinant and performance implications

abstract: We investigate the role of strengths use in the workplace by drawing on self-determination theory (SDT) to propose that strengths use at work can yield performance benefits in terms of task performance and discretionary helping, and that the social context, in the form of leader autonomy support, can promote employees’ strengths use. Further, consistent with an interactional psychology perspective, we contend that the relationship between autonomy support and strengths use will be stronger among individuals with strong independent self-construal. We tested the model using matched data from 194 employees and their supervisors and found evidence for the relevance of strengths use at work, even after accounting for the role of intrinsic motivation. In addition to providing practical implications on developing employee strengths use and how to do so, this study advances theory and research on workplace strength use, SDT, and positive organizational behavior.


title: Anatomy and physiology of genital organs - women.

abstract: Abstract “Anatomy is destiny”: Sigmund Freud viewed human anatomy as a necessary, although not a sufficient, condition for understanding the complexity of human sexual function with a solid biologic basis. The aim of the chapter is to describe women's genital anatomy and physiology, focusing on women's sexual function with a clinically oriented vision. Key points include: embryology, stressing that the “female” is the anatomic “default” program, differentiated into “male” only in the presence of androgens at physiologic levels for the gestational age; sex determination and sex differentiation, describing the interplay between anatomic and endocrine factors; the “clitoral–urethral–vaginal” complex, the most recent anatomy reading of the corpora cavernosa pattern in women; the controversial G spot; the role of the pelvic floor muscles in modulating vaginal receptivity and intercourse feelings, with hyperactivity leading to introital dyspareunia and contributing to provoked vestibulodynia and recurrent postcoital cystitis, whilst lesions during delivery reduce vaginal sensations, genital arousability, and orgasm; innervation, vessels, bones, ligaments; and the physiology of women's sexual response. Attention to physiologic aging focuses on “low-grade inflammation,” genital and systemic, with its impact on women sexual function, especially after the menopause, if the woman does not or cannot use hormone replacement therapy.


title: Another generalization of Wiener’s attack on RSA

abstract: A well-known attack on RSA with low secret-exponent d was given by Wiener in 1990. Wiener showed that using the equation ed - (p - 1)(q - 1)k = 1 and continued fractions, one can efficiently recover the secret-exponent d and factor N = pq from the public key (N, e) as long as d < 1/3 N1/4. In this paper, we present a generalization of Wiener's attack. We show that every public exponent e that satisfies eX - (p - u)(q - v)Y = 1 with 1 ≤ Y < X < 2-1/4 N1/4, |u| < N1/4, v = [-qu/p - u], and all prime factors of p - u or q - v are less than 1050 yields the factorization of N = pq. We show that the number of these exponents is at least N1/2-Ɛ.


title: A smartphone-controlled autonomous robot

abstract: We present a low cost autonomous robot, which is controlled by a standard android smartphone. We describe the complete mechanic and electronic setup. Stereo vision is derived from the smartphone camera using a mirror system. We further describe a dynamical system controller, which allows the robot to navigate around a room towards a target while avoiding obstacles whose position are estimated by the visual system. For heavier computations, a laptop can be used via wireless LAN. Altogether, this design is useful for a variety of mobile robot experiments.


title: Layer-by-layer assembly of poly(L-glutamic acid)/chitosan microcapsules for high loading and sustained release of 5-fluorouracil.

abstract: Hollow polyelectrolyte microcapsules based on poly(l-glutamic acid) (PLGA) and chitosan (CS) with opposite charges were fabricated by layer-by-layer (LbL) assembly technique using melamine formaldehyde (MF) microparticles as sacrificial templates. The LbL assembly of polyelectrolytes and the resultant PLGA/CS microcapsules were characterized. A hydrophilic anticancer drug, 5-fluorouracil (5-FU), was chosen to investigate the loading and release properties of the microcapsules. The PLGA/CS microcapsules show high loading capacity of 5-FU under conditions of high drug concentration and salt adding. The high loading can be ascribed to spontaneous deposition of 5-FU induced by hydrogen bonding between 5-FU and PLGA/CS microcapsules. The PLGA/CS microcapsules show sustained release behavior. The release rate of 5-FU drastically slows down after loading in PLGA/CS microcapsules. The 5-FU release from PLGA/CS microcapsules can be best described using Ritger-Peppas or Baker-Londale models, indicating the diffusion mechanism of 5-FU release from the PLGA/CS microcapsules. In vitro cytotoxicity evaluation by the MTT assay shows good cell viability over the entire concentration range of PLGA/CS microcapsules. Therefore, the novel PLGA/CS microcapsules are expected to find application in drug delivery systems because of the properties of biodegradability, high loading, sustained release and cell compatibility.


title: Diverse Concept-Level Features for Multi-Object Classification

abstract: We consider the problem of image classification with semantic features that are built from a set of base classifier outputs, each reflecting visual concepts. However, existing approaches consider visual concepts independently from each other whereas they are often linked together. When those relations are considered, existing models strongly rely on image low-level features, yielding in irrelevant relations when the low-level representation fails. On the contrary, the approach we propose, uses existing human knowledge, the application context itself and the human categorization mechanism to reflect complex relations between concepts. By nesting this human knowledge and the application context in the concept detection and selection processes, our final semantic feature captures the most useful information for an effective categorization. Thus, it enables to give good representation, even if some important concepts failed to be recognized. Experimental validation is conducted on three publicly available benchmarks of multi-class object classification and leads to results that outperforms comparable approaches.


title: Female Genital Cosmetic Surgery: A Critical Review of Current Knowledge and Contemporary Debates

abstract: Abstract Female genital cosmetic surgery procedures have gained popularity in the West in recent years. Marketing by surgeons promotes the surgeries, but professional organizations have started to question the promotion and practice of these procedures. Despite some surgeon claims of drastic transformations of psychological, emotional, and sexual life associated with the surgery, little reliable evidence of such effects exists. This article achieves two objectives. First, reviewing the published academic work on the topic, it identifies the current state of knowledge around female genital cosmetic procedures, as well as limitations in our knowledge. Second, examining a body of critical scholarship that raises sociological and psychological concerns not typically addressed in medical literature, it summarizes broader issues and debates. Overall, the article demonstrates a paucity of scientific knowledge and highlights a pressing need to consider the broader ramifications of surgical practices. “Today we ha...


title: New advances in treating thrombotic diseases: GPVI as a platelet drug target.

abstract: The recent introduction of highly effective antiplatelet drugs has contributed to the significant improvement in the treatment of acute coronary syndromes. However, limitations remain. Recurrence of ischaemic vascular events results in poor prognosis. Drugs of high antithrombotic efficacy are associated with an increased risk of bleeding, which is important in patients at risk of stroke. An attractive target for the development of new antithrombotics is platelet glycoprotein VI (GPVI) because its blockade seems to combine ideally efficiency and safety. In this review, we summarise current knowledge on the physiological role of GPVI in haemostasis and thrombosis. We also discuss evidence regarding the effectiveness and safety of strategies to inhibit GPVI.


title: Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms

abstract: This study examined specific technology-based behaviors (social comparison and interpersonal feedback-seeking) that may interact with offline individual characteristics to predict concurrent depressive symptoms among adolescents. A total of 619 students (57 % female; mean age 14.6) completed self-report questionnaires at 2 time points. Adolescents reported on levels of depressive symptoms at baseline, and 1 year later on depressive symptoms, frequency of technology use (cell phones, Facebook, and Instagram), excessive reassurance-seeking, and technology-based social comparison and feedback-seeking. Adolescents also completed sociometric nominations of popularity. Consistent with hypotheses, technology-based social comparison and feedback-seeking were associated with depressive symptoms. Popularity and gender served as moderators of this effect, such that the association was particularly strong among females and adolescents low in popularity. Associations were found above and beyond the effects of overall frequency of technology use, offline excessive reassurance-seeking, and prior depressive symptoms. Findings highlight the utility of examining the psychological implications of adolescents’ technology use within the framework of existing interpersonal models of adolescent depression and suggest the importance of more nuanced approaches to the study of adolescents’ media use.


title: A Persona-Based Neural Conversation Model

abstract: We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.


Arbitrage Pricing with Stochastic Volatility


kAFL: Hardware-Assisted Feedback Fuzzing for {OS} Kernels


title: Detection and classification of malicious JavaScript via attack behavior modelling

abstract: Existing malicious JavaScript (JS) detection tools and commercial anti-virus tools mostly use feature-based or signature-based approaches to detect JS malware. These tools are weak in resistance to obfuscation and JS malware variants, not mentioning about providing detailed information of attack behaviors. Such limitations root in the incapability of capturing attack behaviors in these approches. In this paper, we propose to use Deterministic Finite Automaton (DFA) to abstract and summarize common behaviors of malicious JS of the same attack type. We propose an automatic behavior learning framework, named JS*, to learn DFAs from dynamic execution traces of JS malware, where we implement an effective online teacher by combining data dependency analysis, defense rules and trace replay mechanism. We evaluate JS* using real world data of 10000 benign and 276 malicious JS samples to cover 8 most-infectious attack types. The results demonstrate the scalability and effectiveness of our approach in the malware detection and classification, compared with commercial anti-virus tools. We also show how to use our DFAs to detect variants and new attacks.


Bidirectional LSTM for Named Entity Recognition in Twitter Messages


title: Reconfigurable frequency selective surface radome for tunable antenna RCS reduction

abstract: In this paper, a reconfigurable frequency selective surface (FSS) using varactor diodes is presented for low RCS radome application. The FSS is composed of three-layered metallic structures. The varactors are biased with a middle layer bias wire connected to via holes and wire grids on front layer. The back layer is a wideband bandstop structure for out-of-band RCS reduction. The FSS radome without back layer is fabricated and test. Full-wave simulations show the designed FSS radome with back layer has a tuning range from 2.3 GHz to 3.5 GHz with an transmission lower than −10 dB within the X and Ku band.


title: Meta-Analysis of Quantitative Sleep Parameters From Childhood to Old Age in Healthy Individuals: Developing Normative Sleep Values Across the Human Lifespan

abstract: Objectives: The purposes of this study were to identify age-related changes in objectively recorded sleep patterns across the human life span in healthy individuals and to clarify whether sleep latency and percentages of stage 1, stage 2, and rapid eye movement (REM) sleep significantly change with age. Design: Review of literature of articles published between 1960 and 2003 in peer-reviewed journals and meta-analysis. Participants: 65 studies representing 3,577 subjects aged 5 years to 102 years. Measurement: The research reports included in this meta-analysis met the following criteria: (1) included nonclinical participants aged 5 years or older; (2) included measures of sleep characteristics by “all night” polysomnography or actigraphy on sleep latency, sleep efficiency, total sleep time, stage 1 sleep, stage 2 sleep, slow-wave sleep, REM sleep, REM latency, or minutes awake after sleep onset; (3) included numeric presentation of the data; and (4) were published between 1960 and 2003 in peer-reviewed journals. Results: In children and adolescents, total sleep time decreased with age only in studies performed on school days. Percentage of slow-wave sleep was significantly negatively correlated with age. Percentages of stage 2 and REM sleep significantly changed with age. In adults, total sleep time, sleep efficiency, percentage of slow-wave sleep, percentage of REM sleep, and REM latency all significantly decreased with age, while sleep latency, percentage of stage 1 sleep, percentage of stage 2 sleep, and wake after sleep onset significantly increased with age. However, only sleep efficiency continued to significantly decrease after 60 years of age. The magnitudes of the effect sizes noted changed depending on whether or not studied participants were screened for mental disorders, organic diseases, use of drug or alcohol, obstructive sleep apnea syndrome, or other sleep disorders. Conclusions: In adults, it appeared that sleep latency, percentages of stage 1 and stage 2 significantly increased with age while percentage of REM sleep decreased. However, effect sizes for the different sleep parameters were greatly modified by the quality of subject screening, diminishing or even masking age associations with different sleep parameters. The number of studies that examined the evolution of sleep parameters with age are scant among school-aged children, adolescents, and middle-aged adults. There are also very few studies that examined the effect of race on polysomnographic sleep parameters.


title: Parallel Tracking and Verifying

abstract: Being intensively studied, visual object tracking has witnessed great advances in either speed (e.g., with correlation filters) or accuracy (e.g., with deep features). Real-time and high accuracy tracking algorithms, however, remain scarce. In this paper we study the problem from a new perspective and present a novel parallel tracking and verifying (PTAV) framework, by taking advantage of the ubiquity of multi-thread techniques and borrowing ideas from the success of parallel tracking and mapping in visual SLAM. The proposed PTAV framework is typically composed of two components, a (base) tracker T and a verifier V, working in parallel on two separate threads. The tracker T aims to provide a super real-time tracking inference and is expected to perform well most of the time; by contrast, the verifier V validates the tracking results and corrects T when needed. The key innovation is that, V does not work on every frame but only upon the requests from T; on the other end, T may adjust the tracking according to the feedback from V. With such collaboration, PTAV enjoys both the high efficiency provided by T and the strong discriminative power by V. Meanwhile, to adapt V to object appearance changes over time, we maintain a dynamic target template pool for adaptive verification, resulting in further performance improvements. In our extensive experiments on popular benchmarks including OTB2015, TC128, UAV20L and VOT2016, PTAV achieves the best tracking accuracy among all real-time trackers, and in fact even outperforms many deep learning based algorithms. Moreover, as a general framework, PTAV is very flexible with great potentials for future improvement and generalization.


title: Collaborative Filtering with User-Item Co-Autoregressive Models

abstract: Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.


title: Parallel Stochastic Gradient Descent with Sound Combiners

abstract: Stochastic gradient descent (SGD) is a well-known method for regression and classification tasks. However, it is an inherently sequential algorithm — at each step, the processing of the current example depends on the parameters learned from the previous examples. Prior approaches to parallelizing SGD, such as Hogwild! and AllReduce, do not honor these dependences across threads and thus can potentially suffer poor convergence rates and/or poor scalability. This paper proposes SymSGD, a parallel SGD algorithm that retains the sequential semantics of SGD in expectation. Each thread in this approach learns a local model and a probabilistic model combiner that allows the local models to be combined to produce the same result as what a sequential SGD would have produced, in expectation. This SymSGD approach is applicable to any linear learner whose update rule is linear. This paper evaluates SymSGD’s accuracy and performance on 9 datasets on a shared-memory machine shows up-to 13× speedup over our heavily optimized sequential baseline on 16 cores.


title: Sparsity and Incoherence in Compressive Sampling

abstract: We consider the problem of reconstructing a sparse signal x 0 2 R n from a limited number of linear measurements. Given m randomly selected samples of Ux 0 , where U is an orthonormal matrix, we show that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the number of nonzero components in x 0 , and µ is the largest entry in U properly normalized: µ(U) = p n · maxk,j |Uk,j|. The smaller µ, the fewer samples needed. The result holds for “most” sparse signals x 0 supported on a fixed (but arbitrary) set T. Given T, if the sign of x 0 for each nonzero entry on T and the observed values of Ux 0 are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples.


GoGoGo : Improving Deep Neural Network Based Go Playing AI with Residual Networks


title: Novel F-releasing Composite with Improved Mechanical Properties

abstract: In recent years, the authors have been developing novel fluoride-releasing dental composites containing ternary zirconium fluoride chelates. The aim of this study was to improve the physical and mechanical properties of these composites by improving the formulation of the monomers and photoinitiators. The hypothesis was that reduction of hydrophilic monomers and improvement of the photoinitiators could reduce water sorption and significantly increase the mechanical properties of the composite. The degree of conversion of the composites containing different compositions of photoinitiators was studied by Fourier transform near-infrared spectroscopy (FT-NIR). Ten experimental composites containing different compositions of ethoxylated bisphenol-A dimethacrylate (EBPADMA), 1,6-hexanediol dimethacrylate (HDDMA), triethylene glycol dimethacrylate (TEGDMA), and 2,2-bis[4-(2-hydroxy-3-methacryloyloxypropoxy) phenyl]-propane (BisGMA) were tested for flexural strength, viscosity, and water sorption. The experimental composite containing 20% synthesized fluoride-releasing monomer, 30% BisGMA, 30% EBPADMA, and 20% HDDMA showed significantly higher fluoride release and recharge, but physical and mechanical properties similar to those of the control composite containing 40% BisGMA, 40% EBPADMA, and 20% HDDMA.


title: Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence

abstract: In the newsvendor problem a decision maker orders inventory before a one period selling season with stochastic demand. If too much is ordered, stock is left over at the end of the period, whereas if too little is ordered, sales are lost. The expected profit-maximizing order quantity is well known, but little is known about how managers actually make these decisions. We describe two experiments that investigate newsvendor decisions across different profit conditions. Results from these studies demonstrate that choices systematically deviate from those that maximize expected profit. Subjects order too few of high-profit products and too many of low-profit products. These results are not consistent with risk-aversion, risk-seeking preferences, Prospect Theory preferences, waste aversion, stockout aversion, or the consequences of underestimating opportunity costs. Two explanations are consistent with the data. One, subjects behave as if their utility function incorporates a preference to reduce ex-post inventory error, the absolute difference between the chosen quantity and realized demand. Two, subjects suffer from the anchoring and insufficient adjustment bias. Feedback and training did not mitigate inventory order errors. We suggest techniques to improve decision making.


title: NUS-PRO: A New Visual Tracking Challenge

abstract: Numerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.


title: Effectiveness of Brief Mindfulness Techniques in Reducing Symptoms of Anxiety and Stress

abstract: Mindfulness-based skills training has been increasingly incorporated into psychotherapeutic treatment for a variety of presenting complaints, most notably anxiety- and stress-related disorders. While there has been considerable literature documenting efficacy of full mindfulness-based treatment regimen (e.g., Mindfulness-Based Stress Reduction [MBSR]), fewer studies have examined the influence of incorporating distinct elements of these treatments in psychotherapy practice. The present study examined the efficacy of two brief elements of an empirically supported mindfulness-based protocol (MBSR), hatha yoga and body scan, in the reduction of anxiety and stress symptoms. Female undergraduate students (N = 91) completed three weekly 45-min sessions of MBSR-based hatha yoga or body scan, or were included in the waitlisted control condition. Results indicated that there were significant differences between groups (i.e., waitlist control, hatha yoga, and body scan) on indices of anxiety and stress. Women in both the hatha yoga and body scan conditions had significantly greater reductions in anxiety and stress compared to those in the waitlist control condition. Significant differences in postintervention indicators of mindfulness (i.e., present-moment awareness and acceptance) were not observed. Overall, findings are consistent with the argument that brief, discrete elements of the MBSR treatment protocol are effective at reducing symptoms of anxiety and stress, providing further evidence supporting the use of mindfulness-based skills training in psychotherapy.


title: Robust eyelid tracking for fatigue detection

abstract: We develop a non-intrusive system for monitoring fatigue by tracking eyelids with a single web camera. Tracking slow eyelid closures is one of the most reliable ways to monitor fatigue during critical performance tasks. The challenges come from arbitrary head movement, occlusion, reflection of glasses, motion blurs, etc. We model the shape of eyes using a pair of parameterized parabolic curves, and fit the model in each frame to maximize the total likelihood of the eye regions. Our system is able to track face movement and fit eyelids reliably in real time. We test our system with videos captured from both alert and drowsy subjects. The experiment results prove the effectiveness of our system.


title: Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications

abstract: Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from high-dimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this letter has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that, we introduce a nonparametric Bayesian learning method—a sticky hierarchical Dirichlet process hidden Markov model—to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.


title: Type 2 diabetes: principles of pathogenesis and therapy

abstract: Type 2 diabetes mellitus has become an epidemic, and virtually no physician is without patients who have the disease. Whereas insulin insensitivity is an early phenomenon partly related to obesity, pancreas beta-cell function declines gradually over time already before the onset of clinical hyperglycaemia. Several mechanisms have been proposed, including increased non-esterified fatty acids, inflammatory cytokines, adipokines, and mitochondrial dysfunction for insulin resistance, and glucotoxicity, lipotoxicity, and amyloid formation for beta-cell dysfunction. Moreover, the disease has a strong genetic component, but only a handful of genes have been identified so far: genes for calpain 10, potassium inward-rectifier 6.2, peroxisome proliferator-activated receptor gamma, insulin receptor substrate-1, and others. Management includes not only diet and exercise, but also combinations of anti-hyperglycaemic drug treatment with lipid-lowering, antihypertensive, and anti platelet therapy.


title: Deep Sequential Context Networks for Action Prediction

abstract: This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations of partial videos. We reconstruct missing information in the features extracted from partial videos by learning from fully observed action videos. The amount of the information is temporally ordered for the purpose of modeling temporal orderings of action segments. Label information is also used to better separate the learned features of different categories. We develop a new learning formulation that enables efficient model training. Extensive experimental results on UCF101, Sports-1M and BIT datasets demonstrate that our approach remarkably outperforms state-of-the-art methods, and is up to 300x faster than these methods. Results also show that actions differ in their prediction characteristics, some actions can be correctly predicted even though only the beginning 10% portion of videos is observed.


title: A novel mutual authentication scheme for Internet of Things

abstract: This paper presents a novel mutual identity authentication scheme which can be applied securely in Internet of Things. Based on secure hash algorithm(SHA), feature extraction and elliptic curve cryptography(ECC), we propose an asymmetric mutual authentication scheme between the platform and the terminal node, which imposes light computation and communication cost, through security analysis it is also shown that the proposed scheme is secure and feasible for applications in the Internet of Things.


title: A Survey on Augmented Reality Challenges and Tracking

abstract: This survey paper presents a classification of different challenges and tracking techniquesin the field of augmented reality. The challenges in augmented reality arecategorized into performance challenges, alignment challenges, interaction challenges,mobility/portability challenges and visualization challenges. Augmentedreality tracking techniques are mainly divided into sensor-based tracking, visionbasedtracking and hybrid tracking. The sensor-based tracking is further dividedinto optical tracking, magnetic tracking, acoustic tracking, inertial tracking or anycombination of these to form hybrid sensors tracking. Similarly, the vision-basedtracking is divided into marker-based tracking and markerless tracking. Eachtracking technique has its advantages and limitations. Hybrid tracking provides arobust and accurate tracking but it involves financial and tehnical difficulties.


title: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

abstract: Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.


title: Projective clustering by histograms

abstract: Recent research suggests that clustering for high-dimensional data should involve searching for "hidden" subspaces with lower dimensionalities, in which patterns can be observed when data objects are projected onto the subspaces. Discovering such interattribute correlations and location of the corresponding clusters is known as the projective clustering problem. We propose an efficient projective clustering technique by histogram construction (EPCH). The histograms help to generate "signatures", where a signature corresponds to some region in some subspace, and signatures with a large number of data objects are identified as the regions for subspace clusters. Hence, projected clusters and their corresponding subspaces can be uncovered. Compared to the best previous methods to our knowledge, this approach is more flexible in that less prior knowledge on the data set is required, and it is also much more efficient. Our experiments compare behaviors and performances of this approach and other projective clustering algorithms with different data characteristics. The results show that our technique is scalable to very large databases, and it is able to return accurate clustering results.


title: Human pose estimation with global motion cues

abstract: We present a novel method to estimate full-body human pose in video sequence by incorporating global motion cues. It has been demonstrated that temporal constraints can largely enhance the pose estimation. Most current approaches typically employ local motion to propagate pose detections to supplement the pose candidates. However, the local motion estimation is often inaccurate under fast movements of body parts and unhelpful when no strong detections achieved in adjacent frames. In this paper, we propose to propagate the detection in each frame using the global motion estimation. Benefiting from the strong detections, our algorithm first produces reasonable trajectory hypotheses for each body part. Then, we cast pose estimation as an optimization problem defined on these trajectories with spatial links between body parts. In the optimization process, we select body part trajectory rather than body part candidate to infer the human pose. Experimental results demonstrate significant performance improvement in comparison with the state-of-the-art methods.


title: The Karoo Thrush ( Turdus smithi Bonaparte 1850), a southern African endemic

abstract: The Olive Thrush (Turdus olivaceus) species complex is characterised by striking geographical phenotypic variation. Recent consensus has been to recognise three assemblages (1) the olivaceus group restricted to southern Africa, (2) the swynnertoni group of the Zimbabwean and southern Malawi highlands and (3) the abyssinicus group of the montane highlands of eastern and central Africa. We re-examined the status of taxa in the olivaceus group of southern Africa, with specific reference to the central-western taxon T. o. smithi using both morphological and molecular evidence. Sequence divergence between T. o. smithi and other members of the olivaceus group averages 3.5% for the cytochrome-b gene and 4-4.5% for the NADH sub-unit 3 gene of the mitochondrial DNA. There is also both morphological (biometric and plumage) and molecular support for the recognition of T. smithi as a separate species, the common name of which we suggest should be Karoo Thrush.


title: Situation awareness in crowdsensing for disease surveillance in crisis situations

abstract: Crowdsensing can provide real time and detailed information about rapidly evolving crisis situations to facilitate rapid response and effective resource allocation. But while challenges such as heterogeneity of data content and quality, asynchronicity, and volume call for robust data integration and interpretation capabilities, situation awareness in crowdsensing for crisis management remains a largely unexplored area of research. In this paper we extend the mobile4D smartphone-based disaster reporting and alerting system with a situation awareness data interpretation and integration layer and demonstrate its application to the problem of tracking cholera outbreaks. The communication workflow in mobile4D-SA supports interaction between crowdsensed information, system predictions, and multifaceted communication between authorities and affected people on the ground.


title: Adaptive sampling for sensor networks

abstract: A distributed data-stream architecture finds application in sensor networks for monitoring environment and activities. In such a network, large numbers of sensors deliver continuous data to a central server. The rate at which the data is sampled at each sensor affects the communication resource and the computational load at the central server. In this paper, we propose a novel adaptive sampling technique where the sampling rate at each sensor adapts to the streaming-data characteristics. Our approach employs a Kalman-Filter (KF)-based estimation technique wherein the sensor can use the KF estimation error to adaptively adjust its sampling rate within a given range, autonomously. When the desired sampling rate violates the range, a new sampling rate is requested from the server. The server allocates new sampling rates under the constraint of available resources such that KF estimation error over all the active streaming sensors is minimized. Through empirical studies, we demonstrate the flexibility and effectiveness of our model.


title: RIDGE AND LASSO PERFORMANCE IN SPATIAL DATA WITH HETEROGENEITY AND MULTICOLLINEARITY

abstract: Spatial heterogeneity becomes a separate issue on the analysis of spatial data. GWR (Geographically Weighted Regression) is a statistical technique to explore spatial nonstationarity by form the differrent regression models at different point in observation space. Multicollinearity is a condition that the independent variables in model have linear relationship. It would be a problem for estimation parameters process, because that condition produces unstable model. This problem may be found in GWR models, which allow the linear relationship between independent variables at each location called local multicollinearity. GWRR (Geographically Weighted Ridge Regression) and GWL (Geographically Weighted Lasso) which use the concept of ridge and lasso is shrink the regression coefficient in GWR model. GWRR and GWL techniques are consider to be capable of overcoming local multicollinearity to produce more stable models with lower variance. In this study, GWRR and GWL is used to model Gross Regional Domestic Product (GRDP) in Java using kernel exponential weighted function. The results showed that GWL has better performance to predict GRDP with lower RMSE and higher value than GWRR. Keyword : Spatial Heterogeneity, GWR, Local Multicollinearity, Ridge, Lasso


title: Self-assembled propylammonium cations at grain boundaries and the film surface to improve the efficiency and stability of perovskite solar cells

abstract: The major challenge of bringing organic–inorganic hybrid perovskite solar cells towards commercialization is their inherent instability especially with regard to moisture. The introduction of a small amount of hydrophobic cations of large size into a 3D perovskite structure which typically aids in the formation of a quasi-2D perovskite structure has been shown to be a promising method for maintaining high efficiency while introducing a high stability toward moisture. By comparing a family of different cations, the propylammonium (PA) cation is found to be the most effective in mitigating these instability issues. Via femtosecond transient absorption and reflection spectroscopy, the large cations are found to preferentially accumulate at the grain boundaries and film surface, forming heterostructures and preventing moisture penetration. The preferential crystal orientation, crystallization, and grain size in both lateral and vertical directions are enhanced accordingly, in particular when PA is incorporated into the perovskite active layer. Such self-assembled large cations suppress charge recombination and enhance device efficiency. Along with enhancements to efficiency, the addition of the PA cation significantly improves both device and precursor stabilities.


title: CypherDB: A Novel Architecture for Outsourcing Secure Database Processing

abstract: CypherDB addresses the problem of protecting the confidentiality of database stored externally in a cloud and enabling efficient computation over it to thwart any curious-but-honest cloud computing service provider. It works by encrypting the entire outsourced database and executing queries over the encrypted data using our novel CypherDB secure processor architecture. To optimize computational efficiency, our proposed processor architecture provides tightly-coupled datapaths that avoid information leakage during database access and query execution. Our simulation using a well-known database benchmark TPC-H over a commercial grade Database Management System (SQLite) demonstrates that our proposed architecture incurs an average of about 10 percent overhead when compared with the same set of operations without secure database processing.


title: A neurofuzzy approach for the anticipatory control of complex systems

abstract: Anticipatory control refers to system regulation based on information about anticipated future states. A preview of the future is typically obtained via predictive models and decisions about changes of state are made at the present taking into account the output of such models. Significant improvements in soft computing methodologies support the development of anticipatory control that integrates planning and control sequencing functions with feedback control algorithms. We present a neurofuzzy approach for anticipatory control using radial basis neural models and fuzzy rules and demonstrate it through nuclear reactor regulation. The control method does not require knowledge of plant parameters or structure. It is model-independent, and thus may be applied to other nonlinear time-varying dynamic systems. Simulation results show that the neurofuzzy anticipatory control approach improves tracking performance and smoothness and may be quite insensitive to noise. The results suggest that it is a flexible and powerful approach that can easily be extended to other problems, and it is compatible with other techniques. The relevance of the approach to the control of large complex systems is also discussed.


title: Helping Our Own: The HOO 2011 Pilot Shared Task

abstract: The aim of the Helping Our Own (HOO) Shared Task is to promote the development of automated tools and techniques that can assist authors in the writing task, with a specific focus on writing within the natural language processing community. This paper reports on the results of a pilot run of the shared task, in which six teams participated. We describe the nature of the task and the data used, report on the results achieved, and discuss some of the things we learned that will guide future versions of the task.


title: Human-Robot Interactions during the Robot-Assisted Urban Search and Rescue Response at the World Trade Center

abstract: The World Trade Center (WTC) rescue response provided an unfortunate opportunity to study the human-robot interactions (HRI) during a real unstaged rescue for the first time. A post-hoc analysis was performed on the data collected during the response, which resulted in 17 findings on the impact of the environment and conditions on the HRI: the skills displayed and needed by robots and humans, the details of the Urban Search and Rescue (USAR) task, the social informatics in the USAR domain, and what information is communicated at what time. The results of this work impact the field of robotics by providing a case study for HRI in USAR drawn from an unstaged USAR effort. Eleven recommendations are made based on the findings that impact the robotics, computer science, engineering, psychology, and rescue fields. These recommendations call for group organization and user confidence studies, more research into perceptual and assistive interfaces, and formal models of the state of the robot, state of the world, and information as to what has been observed.


title: Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow

abstract: Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as user interactions. We propose a method to map this complex behavior over a parameter range onto a reduced representation based on space-time deformations. In order to represent the complexity of the full space of inputs, we use aligned deformations from optical flow solves, and we leverage the power of generative neural networks to synthesize additional deformations for refinement. We introduce a novel deformation-aware loss function, which enables optimization in the highly non-linear space of multiple deformations. To demonstrate the effectiveness of our approach, we showcase the method with several complex examples in two and four dimensions. Our representation makes it possible to generate implicit surfaces of liquids very efficiently, which allows us to very efficiently display the scene from any angle, and to add secondary effects such as particle systems. We have implemented a mobile application with our full pipeline to demonstrate that real-time interaction is possible with our approach.


IoTFuzzer: Discovering Memory Corruptions in IoT Through App-based Fuzzing


title: Neural Comic Style Transfer: Case Study

abstract: The work by Gatys et al. [1] recently showed a neural style algorithm that can produce an image in the style of another image. Some further works introduced various improvements regarding generalization, quality and efficiency, but each of them was mostly focused on styles such as paintings, abstract images or photo-realistic style. In this paper, we present a comparison of how state-of-the-art style transfer methods cope with transferring various comic styles on different images. We select different combinations of Adaptive Instance Normalization [11] and Universal Style Transfer [16] models and confront them to find their advantages and disadvantages in terms of qualitative and quantitative analysis. Finally, we present the results of a survey conducted on over 100 people that aims at validating the evaluation results in a real-life application of comic style transfer.


title: Monitoring breathing activity and sleep patterns using multimodal non-invasive technologies

abstract: The monitoring of sleeping behavioral patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns and breathing activity: a contact-based pressure mattress and a non-contact 2D image acquisition device. To evaluate our system, we used real data collected in Heracleia Lab's assistive living apartment. Our system uses Machine Learning and Computer Vision techniques to automatically analyze the collected data, recognize sleep patterns and track the breathing behavior. It is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost. Going one step beyond, we developed a mobile application for visualizing the analyzed data and monitor the patient's sleep status remotely.


title: Indirect Effects of Child Reports of Teacher-Student Relationship on Achievement

abstract: The effect of student-reported teacher-student relationship quality (TSRQ) on academic motivation and achievement was investigated among a sample of 690 academically at risk elementary students (52.8% male). Measures of TSRQ, achievement, and motivation were collected annually for 3 consecutive years, beginning when participants were in grade 2 (24.8%) or grade 3 (74.6%). Child-reported conflict was stable across the 3 years, whereas warmth declined. Boys and African American students reported greater conflict than did girls and Caucasian and Hispanic students. Girls and African American students reported higher warmth than boys and non-African American students. Using path analysis, the authors tested the hypothesis that measures of student motivation in Year 2 mediated the effects of conflict and warmth in Year 1 on reading and math achievement in Year 3. Child-perceived conflict predicted cross-year changes in teacher-rated behavioral engagement, which, in turn, predicted cross-year changes in reading and math achievement. Math competence beliefs also mediated the effect of child- perceived warmth on math achievement. Effects controlled for stability of measures across time, the within-wave association between measures, and baseline measures of IQ and economic adversity. Implications of findings for improving the academic achievement of students at-risk for school failure are discussed.


title: An evolutionary and interpretive perspective to knowledge management

abstract: Purpose – The purpose of the paper is to provide a review of knowledge management (KM) literature by adapting and extending McElroy’s KM generations model. Design/methodology/approach – The paper draws from a range of KM research published in the academic and trade literature. An interpretive stance is adopted to provide a holistic understanding and interpretation of organizational KM research and related knowledge management systems (KMS) and models. Findings – To be effective organizations need not only to negotiate their migration from a knowledge sharing (first generation) to a knowledge creation (second generation) culture, but also to create sustained organizational and societal values. The latter form the third generation KM and represent key challenges faced by modern organizations. A true value creation culture is nurtured through a blended approach that factors a number of perspectives to KM, including human networks, social capital, intellectual capital, technology assets, and change processes. Research limitations/implications – The interpretive approach adopted throughout the review is limited to, and focused on, understanding the implementation and organizational implications of KM initiatives and technology. Originality/value – While value creation focuses on the organizational and societal impact of knowledge management, the paper describes how human networks, social capital, intellectual capital, technology assets, and change processes emerge as essential conditions to enable knowledge value creation.


title: Social Image Captioning: Exploring Visual Attention and User Attention

abstract: Image captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user attention, have been neglected in conventional image captioning. Most existing image captioning models cannot be applied directly to social image captioning. In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously.Visual attention is used to compress a large mount of salient visual information, while user attention is applied to adjust the description of the social images with user-contributed tags. Experiments conducted on the Microsoft (MS) COCO dataset demonstrate the superiority of the proposed method of dual attention.


title: A hierarchical lstm model with multiple features for sentiment analysis of sina weibo texts

abstract: Sentiment analysis has long been a hot topic in natural language processing. With the development of social network, sentiment analysis on social media such as Facebook, Twitter and Weibo becomes a new trend in recent years. Many different methods have been proposed for sentiment analysis, including traditional methods (SVM and NB) and deep learning methods (RNN and CNN). In addition, the latter always outperform the former. However, most of existing methods only focus on local text information and ignore the user personality and content characteristics. In this paper, we propose an improved LSTM model with considering the user-based features and content-based features. We first analysis the training dataset to extract artificial features which consists of user-based and content-based. Then we construct a hierarchical LSTM model, named LSTM-MF (a hierarchical LSTM model with multiple features), and introduce the features into the model to generate sentence and document representations. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-the-art methods.