title: An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

abstract: Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated.


title: Goal-Driven Cognition in the Brain: A Computational Framework

abstract: Current theoretical and computational models of dopamine-based reinforcement learning are largely rooted in the classical behaviorist tradition, and envision the organism as a purely reactive recipient of rewards and punishments, with resulting behavior that essentially reflects the sum of this reinforcement history. This framework is missing some fundamental features of the affective nervous system, most importantly, the central role of goals in driving and organizing behavior in a teleological manner. Even when goal-directed behaviors are considered in current frameworks, they are typically conceived of as arising in reaction to the environment, rather than being in place from the start. We hypothesize that goal-driven cognition is primary, and organized into two discrete phases: goal selection and goal engaged, which each have a substantially different effective value function. This dichotomy can potentially explain a wide range of phenomena, playing a central role in many clinical disorders, such as depression, OCD, ADHD, and PTSD, and providing a sensible account of the detailed biology and function of the dopamine system and larger limbic system, including critical ventral and medial prefrontal cortex. Computationally, reasoning backward from active goals to action selection is more tractable than projecting alternative action choices forward to compute possible outcomes. An explicit computational model of these brain areas and their function in this goal-driven framework is described, as are numerous testable predictions from this framework.


title: A language model for statements of software code

abstract: Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement level predictions. In this paper, we have proposed PCC to optimize the token level based language modeling. Specifically, PCC introduced an intermediate representation (IR) for source code, which puts tokens into groups using lexeme and variable relative order. In this way, PCC is able to handle long token sequences, i.e., group sequences, to suggest a complete statement with the precise synthesizer. Further more, PCC employed a fuzzy matching technique which combined genetic and longest common sub-sequence algorithms to make the prediction more accurate. We have implemented a code completion plugin for Eclipse and evaluated it on open-source Java projects. The results have demonstrated the potential of PCC in generating precise long statement level predictions. In 30%-60% of the cases, it can correctly suggest the complete statement with only six candidates, and 40%-90% of the cases with ten candidates.


title: A cluster-based directional routing protocol in VANET

abstract: Vehicular Ad-hoc Network (VANET) is a new application of Mobile Ad-hoc Network (MANET) in the field of Inter-vehicle communication. As the high mobility of vehicles, some traditional MANET routing protocols may not fit the VANET. In this paper, we propose a cluster-based directional routing protocol (CBDRP) for highway scenarios, in which the header of a cluster selects another header according to the moving direction of vehicle to forward packets. Simulation results shows the CBDRP can solve the problem of link stability in VANET, realizing reliable and rapid data transmission.


title: A 12 kW three-phase low THD rectifier with high-frequency isolation and regulated DC output

abstract: A robust 12 kW rectifier with low THD in the line currents, based on an 18-pulse transformer arrangement with reduced kVA capacities followed by a high-frequency isolation stage is presented in this work. Three full-bridge (buck-based) converters are used to allow galvanic isolation and to balance the dc-link currents, without current sensing or current controller. The topology provides a regulated dc output with a very simple and well-known control strategy and natural three-phase power factor correction. The phase-shift PWM technique, with zero-voltage switching is used for the high-frequency dc-dc stage. Analytical results from Fourier analysis of winding currents and the vector diagram of winding voltages are presented. Experimental results from a 12 kW prototype are shown in the paper to verify the efficiency, robustness and simplicity of the command circuitry to the proposed concept.


title: Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning

abstract: In this letter, we propose a map-less visual navigation system for biped humanoid robots, which extracts information from color images to derive motion commands using deep reinforcement learning (DRL). The map-less visual navigation policy is trained using the Deep Deterministic Policy Gradients (DDPG) algorithm, which corresponds to an actor-critic DRL algorithm. The algorithm is implemented using two separate networks, one for the actor and one for the critic, but with similar structures. In addition to convolutional and fully connected layers, Long Short-Term Memory (LSTM) layers are included to address the limited observability present in the problem. As a proof of concept, we consider the case of robotic soccer using humanoid NAO V5 robots, which have reduced computational capabilities, and low-cost Red - Green - Blue (RGB) cameras as main sensors. The use of DRL allowed to obtain a complex and high performant policy from scratch, without any prior knowledge of the domain, or the dynamics involved. The visual navigation policy is trained in a robotic simulator and then successfully transferred to a physical robot, where it is able to run in 20 ms, allowing its use in real-time applications.


title: A 65-nm CMOS Low Dropout Regulator Featuring >60-dB PSRR Over 10-MHz Frequency Range and 100-mA Load Current Range

abstract: One of the most critical attributes of low dropout regulators (LDOs) in increasingly complex systems on chip (SoCs) is high-power supply rejection ratio (PSRR), not only over a wide frequency range but also over a large load current range. This paper presents an LDO, realized in 65-nm CMOS, featuring >60-dB PSRR over a 10-MHz frequency range and a 100-mA large load current range. The high PSRR is achieved by an adaptive feed forward ripple cancellation (FFRC) technique embodying an adaptive load current tracking scheme. By means of embodying an NMOS-based power stage, the LDO also achieves very low dropout voltage of 80 mV and features very small overshoot and undershoot of 2 and 4 mV, respectively.


title: Secure Identity-Based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing

abstract: Cloud computing and social networks are changing the way of healthcare by providing real-time data sharing in a cost-effective manner. However, data security issue is one of the main obstacles to the wide application of mobile healthcare social networks (MHSNs), since health information is considered to be highly sensitive. In this paper, we introduce a secure data sharing and profile matching scheme for the MHSN in cloud computing. The patients can outsource their encrypted health records to cloud storage with an identity-based broadcast encryption technique, and share them with a group of doctors in a secure and efficient manner. We then present an attribute-based conditional data re-encryption construction which permits the doctors who satisfy the pre-defined conditions in the ciphertext to authorize the cloud platform to convert a ciphertext into a new ciphertext of an identity-based encryption scheme for specialist without leaking any sensitive information. Furthermore, we provide a profile matching mechanism in the MHSN based on identity-based encryption with an equality test, which helps patients to find friends in a privacy-preserving way and achieves flexible authorization on the encrypted health records with resisting the keywords guessing attack. Moreover, this mechanism reduces the computation cost on the patient side. The security analysis and experimental evaluation show that our scheme is practical for protecting the data security and privacy in the MHSN.


title: Motivation in online learning: Testing a model of self-determination theory

abstract: As high attrition rates becomes a pressing issue of online learning and a major concern of online educators, it is important to investigate online learner motivation, including its antecedents and outcomes. Drawing on Deci and Ryan's self-determination theory, this study proposed and tested a model for online learner motivation in two online certificate programs (N=262). Results from structural equation modeling provided evidence for the mediating effect of need satisfaction between contextual support and motivation/self-determination; however, motivation/self-determination failed to predict learning outcomes. Additionally, this study supported SDT's main theorizing that intrinsic motivation, extrinsic motivation, and amotivation are distinctive constructs, and found that the direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes. Implications for online learner support were discussed.


Exploring Customer Preferences with Probabilistic Topics Models


title: CD 123 is a membrane biomarker and a therapeutic target in hematologic malignancies

abstract: Recent studies indicate that abnormalities of the alpha-chain of the interleukin-3 receptor (IL-3RA or CD123) are frequently observed in some leukemic disorders and may contribute to the proliferative advantage of leukemic cells. This review analyzes the studies indicating that CD123 is overexpressed in various hematologic malignancies, including a part of acute myeloid and B-lymphoid leukemias, blastic plasmocytoid dendritic neoplasms (BPDCN) and hairy cell leukemia.Given the low/absent CD123 expression on normal hematopoietic stem cells, attempts have been made at preclinical first, and then at clinical level to target this receptor. Since the IL-3R is a membrane receptor there are two relatively simple means to target this molecule, either using its natural ligand or neutralizing monoclonal antibodies. Recent reports using a fusion molecule composed by human IL-3 coupled to a truncated diphteria toxin have shown promising antitumor activity in BPDCN and AML patients.


title: Oxygen consumption during constant-load exercise.

abstract: Previous investigators have reported that oxygen consumption (VO2) continues to rise after the initial 2- to 3-min transient period of exercise when work exceeds approximately 60% of VO2 max. The purpose of this investigation was to examine the possible causes of this slow rise in VO2. Eighteen subjects exercised for 20 min at 65% and at 80% of VO2 max on the bicycle ergometer. VO2, ventilation (VE), and respiratory exchange ratio were monitored by a continuous computer-based system. Blood lactate concentration and rectal temperatures were measured at 2- to 3-min intervals during the exercise. VO2 increased significantly from the 5th to 20th min of exercise in 81% of the tests at both levels of work intensity. The magnitude of the rise was not different for the two work loads. No evidence was found to support the lactacid explanation proposed for this rise. Increased temperature could account for 30% of the rise; the estimated cost of increased VE could account for 30 and 81% of the rise at the two work loads. The sum of these factors could account for 60 and 111% of the rise in VO2 at the 65 and 80% of VO2 max work loads.


title: A Photoplethysmographic Signal Isolated From an Additive Motion Artifact by Frequency Translation

abstract: Acquiring a precise percentage of oxygen saturation (SpO2) from a finger-probe pulse oximeter is dependent on both artifact-free red and infrared photoplethysmoghaphic (PPG) signals. Nonetheless, in real-life situations, these PPG signals are corrupted by a motion artifact (MA) signal that is generated from either finger or hand movement. To resolve this MA interference, the cause of the adulteration of PPG signals by the MA signal is examined. The MA signal is found to behave like an additive noise. Additionally, the frequency responses of the MA and PPG signals show that these signals are in the same frequency band. Hence, instead of direct current, a sinusoidal wave alternating current is proposed to drive an LED source in order to shift the PPG frequency band away from the MA frequency band. Experimentally, a commercial finger-probe pulse oximeter is employed. To determine the performance of the presented scheme, the resulting PPG signals are compared with those from employing the old-fashioned LED-driving method. In addition, the accuracy is verified by computing the SpO2 value. The results reveal that the proposed approach successfully retains the fundamental morphologies of the PPG structures when motion occurs. Moreover, the calculated SpO2 values from the proposed technique provide an average error of approximately 1.4%, whereas the conventional method yields a mean error approximately 4.2%.


title: Progressive Stochastic Learning for Noisy Labels

abstract: Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from "easy" tasks to "complex" tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from "reliable" labels to "noisy" labels. To realize POSTAL mechanism, we design a cluster of "screening losses," which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI1 simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.1UCI is the abbreviation of University of California Irvine.


title: Recurrently Controlled Recurrent Networks

abstract: Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems. This paper proposes a recurrently controlled recurrent network (RCRN) for expressive and powerful sequence encoding. More concretely, the key idea behind our approach is to learn the recurrent gating functions using recurrent networks. Our architecture is split into two components - a controller cell and a listener cell whereby the recurrent controller actively influences the compositionality of the listener cell. We conduct extensive experiments on a myriad of tasks in the NLP domain such as sentiment analysis (SST, IMDb, Amazon reviews, etc.), question classification (TREC), entailment classification (SNLI, SciTail), answer selection (WikiQA, TrecQA) and reading comprehension (NarrativeQA). Across all 26 datasets, our results demonstrate that RCRN not only consistently outperforms BiLSTMs but also stacked BiLSTMs, suggesting that our controller architecture might be a suitable replacement for the widely adopted stacked architecture.


title: CloudETL: scalable dimensional ETL for hive

abstract: Extract-Transform-Load (ETL) programs process data into data warehouses (DWs). Rapidly growing data volumes demand systems that scale out. Recently, much attention has been given to MapReduce for parallel handling of massive data sets in cloud environments. Hive is the most widely used RDBMS-like system for DWs on MapReduce and provides scalable analytics. It is, however, challenging to do proper dimensional ETL processing with Hive; e.g., the concept of slowly changing dimensions (SCDs) is not supported (and due to lacking support for UPDATEs, SCDs are complex to handle manually). Also the powerful Pig platform for data processing on MapReduce does not support such dimensional ETL processing. To remedy this, we present the ETL framework CloudETL which uses Hadoop to parallelize ETL execution and to process data into Hive. The user defines the ETL process by means of high-level constructs and transformations and does not have to worry about technical MapReduce details. CloudETL supports different dimensional concepts such as star schemas and SCDs. We present how CloudETL works and uses different performance optimizations including a purpose-specific data placement policy to co-locate data. Further, we present a performance study and compare with other cloud-enabled systems. The results show that CloudETL scales very well and outperforms the dimensional ETL capabilities of Hive both with respect to performance and programmer productivity. For example, Hive uses 3.9 times as long to load an SCD in an experiment and needs 112 statements while CloudETL only needs 4.


title: A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion

abstract: This paper considers regularized block multiconvex optimization, where the feasible set and objective function are generally nonconvex but convex in each block of variables. It also accepts nonconvex blocks and requires these blocks to be updated by proximal minimization. We review some interesting applications and propose a generalized block coordinate descent method. Under certain conditions, we show that any limit point satisfies the Nash equilibrium conditions. Furthermore, we establish global convergence and estimate the asymptotic convergence rate of the method by assuming a property based on the Kurdyka--Łojasiewicz inequality. The proposed algorithms are tested on nonnegative matrix and tensor factorization, as well as matrix and tensor recovery from incomplete observations. The tests include synthetic data and hyperspectral data, as well as image sets from the CBCL and ORL databases. Compared to the existing state-of-the-art algorithms, the proposed algorithms demonstrate superior performance in ...


title: Automatic Wrappers for Large Scale Web Extraction

abstract: We present a generic framework to make wrapper induction algorithms tolerant to noise in the training data. This enables us to learn wrappers in a completely unsupervised manner from automatically and cheaply obtained noisy training data, e.g., using dictionaries and regular expressions. By removing the site-level supervision that wrapper-based techniques require, we are able to perform information extraction at web-scale, with accuracy unattained with existing unsupervised extraction techniques. Our system is used in production at Yahoo! and powers live applications.


title: Analyzing the log patterns of adult learners in LMS using learning analytics

abstract: In this paper, we describe a process of constructing proxy variables that represent adult learners' time management strategies in an online course. Based upon previous research, three values were selected from a data set. According to the result of empirical validation, an (ir)regularity of the learning interval was proven to be correlative with and predict learning performance. As indicated in previous research, regularity of learning is a strong indicator to explain learners' consistent endeavors. This study demonstrates the possibility of using learning analytics to address a learner's specific competence on the basis of a theoretical background. Implications for the learning analytics field seeking a pedagogical theory-driven approach are discussed.


A Survey of Process Migration Mechanisms


title: Opinion Mining with Deep Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) are connectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, “depth in space” — as an orthogonal notion to “depth in time” — in RNNs has been investigated by stacking multiple layers of RNNs and shown empirically to bring a temporal hierarchy to the architecture. In this work we apply these deep RNNs to the task of opinion expression extraction formulated as a token-level sequence-labeling task. Experimental results show that deep, narrow RNNs outperform traditional shallow, wide RNNs with the same number of parameters. Furthermore, our approach outperforms previous CRF-based baselines, including the state-of-the-art semi-Markov CRF model, and does so without access to the powerful opinion lexicons and syntactic features relied upon by the semi-CRF, as well as without the standard layer-by-layer pre-training typically required of RNN architectures.


title: Psychological and behavioral correlates of family violence in child witnesses and victims.

abstract: Abused and nonabused child witnesses to parental violence temporarily residing in a battered women's shelter were compared to children from a similar economic background on measures of self-esteem, anxiety, depression, and behavior problems, using mothers' and self-reports. Results indicated significantly more distress in the abused-witness children than in the comparison group, with nonabused witness children's scores falling between the two. Age of child and types of violence were mediating factors. Implications of the findings are discussed. Language: en


title: Systematic design of unitary space-time constellations

abstract: We propose a systematic method for creating constellations of unitary space-time signals for multiple-antenna communication links. Unitary space-time signals, which are orthonormal in time across the antennas, have been shown to be well-tailored to a Rayleigh fading channel where neither the transmitter nor the receiver knows the fading coefficients. The signals can achieve low probability of error by exploiting multiple-antenna diversity. Because the fading coefficients are not known, the criterion for creating and evaluating the constellation is nonstandard and differs markedly from the familiar maximum-Euclidean-distance norm. Our construction begins with the first signal in the constellation-an oblong complex-valued matrix whose columns are orthonormal-and systematically produces the remaining signals by successively rotating this signal in a high-dimensional complex space. This construction easily produces large constellations of high-dimensional signals. We demonstrate its efficacy through examples involving one, two, and three transmitter antennas.


title: Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait

abstract: Financial time series are very complex and dynamic as they are characterized by extreme volatility. The major aim of this research is to forecast the Kuwait stock exchange (KSE) closing price movements using data for the period 2001-2003. Two neural network architectures: multi-layer perceptron (MLP) neural networks and generalized regression neural networks are used to predict the KSE closing price movements. The results of this study show that neuro-computational models are useful tools in forecasting stock exchange movements in emerging markets. These results also indicate that the quasi-Newton training algorithm produces less forecasting errors compared to other training algorithms. Due to their robustness and flexibility of modeling algorithms, neuro-computational models are expected to outperform traditional statistical techniques such as regression and ARIMA in forecasting stock exchanges' price movements.


title: Making frameworks work: a project retrospective

abstract: Various issues make framework development harder than regular development. Building product lines and frameworks requires increased coordination and communication between stakeholders and across the organization. The difficulty of building the right abstractions ranges from understanding the domain models, selecting and evaluating the framework architecture, to designing the right interfaces, and adds to the complexity of a framework project.


Knowledge Transfer Social Media as Social Lubricant : How Ambient Awareness Eases


title: Mobile Services Acceptance Model

abstract: Along with the rapid development of information and communication technology, more and more mobile commerce applications are available. However, what is the user's perception and reaction of those new applications? It is critical to study the factors that influence the user adoption of mobile applications. In this research work, we propose an extended technology acceptance model, which is called mobile services acceptance model, with a consideration of trust, context, and personal initiatives and characteristics factors in addition to perceived usefulness and perceived ease of use. Furthermore, in order to demonstrate the applicability of the mobile services acceptance model, one case study with respect to the user adoption of the mobile application---FindMyFriends is presented.


title: A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems

abstract: Renewable energy sources are one key enabler to decrease greenhouse gas emissions and to cope with the anthropogenic climate change. Their intermittent behavior and limited storage capabilities present a new challenge to power system operators to maintain power quality and reliability. Additional technical complexity arises from the large number of small distributed generation units and their allocation within the power system. Market liberalization and changing regulatory framework lead to additional organizational complexity. As a result, the design and operation of the future electric energy system have to be redefined. Sophisticated information and communication architectures, automation concepts, and control approaches are necessary in order to manage the higher complexity of so-called smart grids. This paper provides an overview of the state of the art and recent developments enabling higher intelligence in future smart grids. The integration of renewable sources and storage systems into the power grids is analyzed. Energy management and demand response methods and important automation paradigms and domain standards are also reviewed.


title: Clustering for malware classification

abstract: In this research, we apply clustering techniques to the malware classification problem. We compute clusters using the well-known K-means and Expectation Maximization algorithms, with the underlying scores based on Hidden Markov Models. We compare the results obtained from these two clustering approaches and we carefully consider the interplay between the dimension (i.e., number of models used for clustering), and the number of clusters, with respect to the accuracy of the clustering.


Material Removal Mechanism in Chemical Mechanical Polishing: Theory and Modeling


title: GIS and Transport Modeling—Strengthening the Spatial Perspective

abstract: The movement and transport of people and goods is spatial by its very nature. Thus, geospatial fundamentals of transport systems need to be adequately considered in transport models. Until recently, this was not always the case. Instead, transport research and geography evolved widely independently in domain silos. However, driven by recent conceptual, methodological and technical developments, the need for an integrated approach is obvious. This paper attempts to outline the potential of Geographical Information Systems (GIS) for transport modeling. We identify three fields of transport modeling where the spatial perspective can significantly contribute to a more efficient modeling process and more reliable model results, namely, geospatial data, disaggregated transport models and the role of geo-visualization. For these three fields, available findings from various domains are compiled, before open aspects are formulated as research directions, with exemplary research questions. The overall aim of this paper is to strengthen the spatial perspective in transport modeling and to call for a further integration of GIS in the domain of transport modeling.


title: Hierarchical multi-label classification using local neural networks

abstract: Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.


title: cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

abstract: This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework. cltorch enables training of deep neural networks on GPUs from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch contains sufficient implementation to run models such as AlexNet, VGG, Overfeat, and GoogleNet. It is written using the OpenCL language, a portable compute language, governed by the Khronos Group. cltorch is the top-ranked hardware-agnostic machine learning framework on Chintala's convnet-benchmarks page. ::: This paper presents the technical challenges encountered whilst creating the cltorch backend for Torch, and looks in detail at the challenges related to obtaining a fast hardware-agnostic implementation. ::: The convolutional layers are identified as the key area of focus for accelerating hardware-agnostic frameworks. Possible approaches to accelerating the convolutional implementation are identified including: implementation of the convolutions using the implicitgemm or winograd algorithm, using a GEMM implementation adapted to the geometries associated with the convolutional algorithm, or using a pluggable hardware-specific convolutional implementation.


title: WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

abstract: Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.


title: Welding inverter for optimal arc characteristics is laterally more sensitive to input materials

abstract: Continuous evolution in process and in equipment in arc welding improves compatibility in energy domain. It helps optimize resources such as energy, materials, labor etc, and yield desired productivity and improved quality engineering goals. Modern inverter technology provides multitude of benefits virtually to each entity associated with the process. Efficiency, settling time of output variables and compactness and light weight are major pre-requisites of arc welding equipment. They are all achieved if switching frequency of inverter is kept high. Generally, higher switching frequency is achieved through soft switching. This paper elaborates that input-material-sensitive hard-switched topology is functionally superior to soft-switched topologies and generate superior design for manufacturability issues. It achieves power density ideal for welding power range.


title: Handling Obstacles in Goal-Oriented Requirements Engineering

abstract: Requirements engineering is concerned with the elicitation of high-level goals to be achieved by the envisioned system, the refinement of such goals and their operationalization into specifications of services and constraints and the assignment of responsibilities for the resulting requirements to agents such as humans, devices and software. Requirements engineering processes often result in goals, requirements, and assumptions about agent behavior that are too ideal; some of them are likely not to be satisfied from time to time in the running system due to unexpected agent behavior. The lack of anticipation of exceptional behaviors results in unrealistic, unachievable, and/or incomplete requirements. As a consequence, the software developed from those requirements will not be robust enough and will inevitably result in poor performance or failures, sometimes with critical consequences on the environment. This paper presents formal techniques for reasoning about obstacles to the satisfaction of goals, requirements, and assumptions elaborated in the requirements engineering process. The techniques are based on a temporal logic formalization of goals and domain properties; they are integrated into an existing method for goal-oriented requirements elaboration with the aim of deriving more realistic, complete, and robust requirements specifications. A key principle is to handle exceptions at requirements engineering time and at the goal level, so that more freedom is left for resolving them in a satisfactory way. The various techniques proposed are illustrated and assessed in the context of a real safety-critical system.


title: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

abstract: The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet. The feature-based classifier obtained an Fi score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.


title: Representing others' actions: just like one's own?

abstract: Previous research has shown that observing others' actions can affect individual performance of the same actions. In the present study, we developed a new paradigm to investigate whether and how complementary actions at the disposal of another agent are represented and influence one's own actions. A spatial compatibility task was distributed among two people so that each participant took care of one of two responses. The identical task was performed alone and alongside another participant. There was a spatial compatibility effect in the group setting only. It was similar to the effect obtained when one person took care of both responses. This result suggests that one's own actions and others' actions are represented in a functionally equivalent way.


title: Convolutional Sequence to Sequence Model with Non-Sequential Greedy Decoding for Grapheme to Phoneme Conversion

abstract: The greedy decoding method used in the conventional sequence-to-sequence models is prone to producing a model with a compounding of errors, mainly because it makes inferences in a fixed order, regardless of whether or not the model's previous guesses are correct. We propose a non-sequential greedy decoding method that generalizes the greedy decoding schemes proposed in the past. The proposed method determines not only which token to consider, but also which position in the output sequence to infer at each inference step. Specifically, it allows the model to consider easy parts first, helping the model infer hard parts more easily later by providing more information. We study a grapheme-to-phoneme conversion task with a fully convolutional encoder-decoder model that embeds the proposed decoding method. Experiment results show that our model shows better performance than that of the state-of-the-art model in terms of both phoneme error rate and word error rate.


title: Burnout in Relation to Specific Contributing Factors and Health Outcomes among Nurses: A Systematic Review

abstract: Nurses have been found to experience higher levels of stress-related burnout compared to other health care professionals. Despite studies showing that both job satisfaction and burnout are effects of exposure to stressful working environments, leading to poor health among nurses, little is known about the causal nature and direction of these relationships. The aim of this systematic review is to identify published research that has formally investigated relationships between these variables. Six databases (including CINAHL, COCHRANE, EMBASE, MEDLINE, PROQUEST and PsyINFO) were searched for combinations of keywords, a manual search was conducted and an independent reviewer was asked to cross validate all the electronically identified articles. Of the eighty five articles that were identified from these databases, twenty one articles were excluded based on exclusion criteria; hence, a total of seventy articles were included in the study sample. The majority of identified studies exploring two and three way relationships (n = 63) were conducted in developed countries. Existing research includes predominantly cross-sectional studies (n = 68) with only a few longitudinal studies (n = 2); hence, the evidence base for causality is still very limited. Despite minimal availability of research concerning the small number of studies to investigate the relationships between work-related stress, burnout, job satisfaction and the general health of nurses, this review has identified some contradictory evidence for the role of job satisfaction. This emphasizes the need for further research towards understanding causality.


title: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study

abstract: Traumatic brain injury (TBI) is a serious public health problem. Even injuries classified as mild, the most common, can result in persistent neurobehavioural impairment. Diffuse axonal injury is a common finding after TBI, and is presumed to contribute to outcomes, but may not always be apparent using standard neuroimaging. Diffusion tensor imaging (DTI) is a more recent method of assessing axonal integrity in vivo. The primary objective of the current investigation was to characterize white matter integrity utilizing DTI across the spectrum of chronic TBI of all severities. A secondary objective was to examine the relationship between white matter integrity and cognition. Twenty mild, 17 moderate to severe TBI and 18 controls underwent DTI and neuropsychological testing. Fractional anisotropy, axial diffusivity and radial diffusivity were calculated from the DTI data. Fractional anisotropy was the primary measure of white matter integrity. Region of interest analysis included anterior and posterior corona radiata, cortico-spinal tracts, cingulum fibre bundles, external capsule, forceps minor and major, genu, body and splenium of the corpus callosum, inferior fronto-occipital fasciculus, superior longitudinal fasciculus and sagittal stratum. Cognitive domain scores were calculated from executive, attention and memory testing. Decreased fractional anisotropy was found in all 13 regions of interest for the moderate to severeTBI group, but only in the cortico-spinal tract, sagittal stratum and superior longitudinal fasciculus for the mild TBI group.White Matter Load (a measure of the total number of regions with reduced FA) was negatively correlated with all cognitive domains. Analysis of radial and axial diffusivity values suggested that all severities of TBI can result in a degree of axonal damage, while irreversible myelin damage was only apparent for moderate to severe TBI. The present data emphasize that white matter changes exist on a spectrum, including mild TBI. An index of global white matter neuropathology (White Matter Load) was related to cognitive function, such that greater white matter pathology predicted greater cognitive deficits. Mechanistically, mild TBI white matter changes may be primarily due to axonal damage as opposed to myelin damage. The more severe injuries impact both. DTI provides an objective means for determining the relationship of cognitive deficits to TBI, even in cases where the injury was sustained years prior to the evaluation.


title: A Hierarchical Model of Shape and Appearance for Human Action Classification

abstract: We present a novel model for human action categorization. A video sequence is represented as a collection of spatial and spatial-temporal features by extracting static and dynamic interest points. We propose a hierarchical model that can be characterized as a constellation of bags-of-features and that is able to combine both spatial and spatial-temporal features. Given a novel video sequence, the model is able to categorize human actions in a frame-by-frame basis. We test the model on a publicly available human action dataset [2] and show that our new method performs well on the classification task. We also conducted control experiments to show that the use of the proposed mixture of hierarchical models improves the classification performance over bag of feature models. An additional experiment shows that using both dynamic and static features provides a richer representation of human actions when compared to the use of a single feature type, as demonstrated by our evaluation in the classification task.


title: On Validity of Program Transformations in the Java Memory Model

abstract: We analyse the validity of several common program transformations in multi-threaded Java, as defined by the Memory Model section of the Java Language Specification. The main design goal of the Java Memory Model (JMM) was to allow as many optimisations as possible. However, we find that commonly used optimisations, such as common subexpression elimination, can introduce new behaviours and so are invalid for Java. In this paper, we describe several kinds of transformations and explain the problems with a number of counterexamples. More positively, we also examine some valid transformations, and prove their validity. Our study contributes to the understanding of the JMM, and has the practical impact of revealing some cases where the Sun Hotspot JVM does not comply with the Java Memory Model.


Tweetnet : Finding the Bots in the Flock


title: Configuring Clark-Wilson Integrity Model to Enforce Flexible Protection

abstract: Clark-Wilson model is widely used to protect commercial information against unauthorized modification. However, due to only single-level data protection support and no attention paid on difference of importance level of protected data, the model does not apply to many scenarios. An enhanced Clark-Wilson model is put forward in this paper to provide flexible protection. By using traditional integrity level as basis for modification to objects and regarding procedures as special data items, integrity protection for upgradeable software is provided. The size of access control triple set is reduced greatly by adoption of user-domain-type format. The model is enforced for software protection of a PC-based metrological instrument. Through proper configuration, anti-tampering protection for upgradeable software is achieved with CW-triples reduced to less than 10.


title: iTransformer: Using SSD to Improve Disk Scheduling for High-performance I/O

abstract: The parallel data accesses inherent to large-scale data-intensive scientific computing require that data servers handle very high I/O concurrency. Concurrent requests from different processes or programs to hard disk can cause disk head thrashing between different disk regions, resulting in unacceptably low I/O performance. Current storage systems either rely on the disk scheduler at each data server, or use SSD as storage, to minimize this negative performance effect. However, the ability of the scheduler to alleviate this problem by scheduling requests in memory is limited by concerns such as long disk access times, and potential loss of dirty data with system failure. Meanwhile, SSD is too expensive to be widely used as the major storage device in the HPC environment. We propose iTransformer, a scheme that employs a small SSD to schedule requests for the data on disk. Being less space constrained than with more expensive DRAM, iTransformer can buffer larger amounts of dirty data before writing it back to the disk, or prefetch a larger volume of data in a batch into the SSD. In both cases high disk efficiency can be maintained even for concurrent requests. Furthermore, the scheme allows the scheduling of requests in the background to hide the cost of random disk access behind serving process requests. Finally, as a non-volatile memory, concerns about the quantity of dirty data are obviated. We have implemented iTransformer in the Linux kernel and tested it on a large cluster running PVFS2. Our experiments show that iTransformer can improve the I/O throughput of the cluster by 35% on average for MPI/IO benchmarks of various data access patterns.


title: Defeasible Logic Programming: An Argumentative Approach

abstract: The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query $q$ will succeed when there is an argument ${\mathcal A}$ for $q$ that is warranted, i.e. the argument ${\mathcal A}$ that supports $q$ is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.


title: Internal Hexa-Band Folded Monopole/Dipole/Loop Antenna With Four Resonances for Mobile Device

abstract: A fold monopole/dipole/loop antenna offers unique advantages over a conventional Planar Inverted-F Antenna (PIFA) and monopole antenna for a mobile cellular device. Perhaps the most significant benefit is the fact that up to three resonant modes could be generated in a single continuous loop structure. In this paper, a novel hexa-band internal folded monopole/dipole/loop antenna has been proposed. The proposed antenna has four resonances in which three of them are 0.5λ , 1λ and 1.5λ modes. The distinctive feature of the antenna is an extra 2λ folded dipole mode, which has not been reported so far, is excited and utilized. All these four modes can be employed to cover the Low LTE700/GSM850/900 Band (LB: 698-960 MHz) and the High GSM1850/1900/UMTS2100 Band (HB: 1710-2170 MHz). The proposed loop antenna has been simulated, prototyped and tested. It is shown that the numerically simulated results are in close agreement with the experimental prototype results and proved that the antenna operates as proposed.


title: A Fully Integrated Reconfigurable Switched-Capacitor DC-DC Converter With Four Stacked Output Channels for Voltage Stacking Applications

abstract: This work presents a fully integrated 4-to-1 DC-DC symmetric ladder switched-capacitor converter (SLSCC) for voltage stacking applications. The SLSCC absorbs inter-layer load power mismatch to provide minimum voltage guarantees for the internal rails of a multicore system that implements four-way voltage stacking. A new hybrid feedback control scheme reduces the voltage ripple across stacked voltage layers for high levels of current mismatch, a condition that exacerbates voltage noise in conventional SC converters. Furthermore, the proposed SLSCC dynamically allocates valuable flying capacitor resources according to different load conditions, which improves conversion efficiency and supports more power mismatch between the layers. Implemented in TSMC's 40G process, the SLSCC converts a 3.6 V input voltage down to four stacked output voltage layers, each nominally at 900 mV.


title: An omnidirectional mobile robot: Concept and analysis

abstract: This paper presents a novel omnidirectional wheel mechanism, referred to as MY wheel-II, based on a sliced ball structure. The wheel consists of two balls of equal diameter on a common shaft and both balls are sliced into four spherical crowns. The two sets of spherical crowns are mounted at 45° from each other to produce a combined circular profile. Compared with previous MY wheel mechanism, this improved wheel mechanism not only is more insensitive to fragments and irregularities on the floor but also has a higher payload capacity. A kinematic model of a three-wheeled prototype platform is also derived, and the problem of wheel angular velocity fluctuations caused by the specific mechanical structure is studied. The optimal scale factor (OSF) leading to a minimum of trajectory error is adopted to solve this problem. The factors influencing the OSF are investigated through simulation. In addition, the methods used for determining the OSF are discussed briefly.


title: Playing violent video games and desensitization to violence

abstract: This article examines current research linking exposure to violent video games and desensitization to violence. Data from questionnaire, behavioral, and psychophysiologic research are reviewed to determine if exposure to violent video games is a risk factor for desensitization to violence. Real-world implications of desensitization are discussed. Language: en


title: To Link or Not to Link? A Study on End-to-End Tweet Entity Linking

abstract: Information extraction from microblog posts is an important task, as today microblogs capture an unprecedented amount of information and provide a view into the pulse of the world. As the core component of information extraction, we consider the task of Twitter entity linking in this paper. In the current entity linking literature, mention detection and entity disambiguation are frequently cast as equally important but distinct problems. However, in our task, we find that mention detection is often the performance bottleneck. The reason is that messages on micro-blogs are short, noisy and informal texts with little context, and often contain phrases with ambiguous meanings. To rigorously address the Twitter entity linking problem, we propose a structural SVM algorithm for entity linking that jointly optimizes mention detection and entity disambiguation as a single end-to-end task. By combining structural learning and a variety of firstorder, second-order, and context-sensitive features, our system is able to outperform existing state-of-the art entity linking systems by 15% F1.


title: A Flicker-Free Single-Stage Offline LED Driver With High Power Factor

abstract: A conventional offline single-stage light-emitting diode (LED) driver with a high power factor usually produces a significant twice-line-frequency ripple LED current, where the ripple LED current is presented as flickering to human eye. This paper introduces a ripple cancellation method to remove the twice-line-frequency voltage ripple for an offline single-stage LED driver with a power factor correction. Consequently, a dc LED current can be produced to achieve flicker-free LED driving performance. At the same time, the required storage capacitor for the proposed LED driver can be greatly reduced, and the circuit implementation to achieve ripple cancellation is simple. Thus, the overall cost of the proposed LED driver is low. The proposed LED driver also features high efficiency because of its power structure. A 35-W Flyback experimental prototype and a 10-W Buck–Boost experimental prototype have been built to validate the proposed design and demonstrate its optimal performance.


title: Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users

abstract: The goal in designing an ionospheric time-delay correctionalgorithm for the single-frequency global positioning system userwas to include the main features of the complex behavior of theionosphere, yet require a minimum of coefficients and usercomputational time, while still yielding an rms correction of at least50 percent. The algorithm designed for this purpose, andimplemented in the GPS satellites, requires only eight coefficientssent as part of the satellite message, contains numerousapproximations designed to reduce user computationalrequirements, yet preserves the essential elements required to obtaingroup delay values along multiple satellite viewing directions.


title: C-galactosylflavones from Polygonatum multiflorum

abstract: Abstract 8-C-Galactosylapigenin and 6-C-galactosyl-8-C-arabinosylapigenin were isolated from the leaves of Polygonatum multiflorum (L.) All. Structural assignments for the latter compound were made on the basis of mass, CD and 13C-NMR spectra.


title: Recursive Estimation of Motion, Structure, and Focal Length

abstract: Presents a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both true perspective and orthographic projection. Results on synthetic and real imagery illustrate the performance of the estimator. >


title: Tracking of Enriched Dialog States for Flexible Conversational Information Access

abstract: Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define the dialog state by a set of slot-value pairs. Such representation of dialog states and the slot-filling based DST have been widely employed, but suffer from three drawbacks. (1) The dialog state can contain only a single value for a slot, and (2) can contain only users' affirmative preference over the values for a slot. (3) Current task-based dialog systems mainly focus on the searching task, while the enquiring task is also very common in practice. The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible accessing movie information. The EDST supports the searching task, the enquiring task and their mixed task. We show that the new EDST method not only achieves good results on Iqiyi dataset, but also outperforms other state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and DSTC2.


title: The Attention Skills and Academic Performance of Aggressive/Rejected and Low Aggressive/Popular Children

abstract: Research Findings: Aggressive/rejected children are at risk for continuing conduct and school problems. Some limited research indicates that these children have attention problems. Previous research has linked attention problems with academic performance. The current study investigated group differences in attention skills and the role of these skills in children's academic performance. Kindergarten and 1st-grade children (n = 54, 52% female) were identified as either aggressive/rejected or low aggressive/popular by peer sociometric interviews. Attention was assessed with a novel computer task, the Children's Space Game, as well as parent and teacher report. Teachers reported on children's academic performance. Aggressive/rejected children had lower adult-reported attention skills and academic performance than low aggressive/popular children. Aggressive/rejected children also had lower skills on the computer task. Support was found for an additive model of the influence of children's status and attention ...


Question Answering in the Context of Stories Generated by Computers


title: Action-Based Model of Dissonance: A Review, Integration, and Expansion of Conceptions of Cognitive Conflict

abstract: Abstract An action‐based model of dissonance is presented. This model accepts the original theory's proposal that a sufficient cognitive inconsistency causes the negative affective state of dissonance. It extends the original theory by proposing why cognitive inconsistency prompts dissonance and dissonance reduction. After reviewing past theoretical and empirical developments on cognitive dissonance theory, we describe the action‐based model and present results from behavioral and physiological experiments that have tested predictions derived from this model. In particular, this evidence converges with recent neuroscience evidence in suggesting that the anterior cingulate cortex and left prefrontal cortical region are involved in conflict detection and resolution, respectively. We end by reviewing research on individual differences in dissonance arousal and reduction, and present a new measure designed to assess these individual differences.


title: Simultaneous untangling and smoothing of quadrilateral and hexahedral meshes using an object-oriented framework

abstract: In this work, we present a simultaneous untangling and smoothing technique for quadrilateral and hexahedral meshes. The algorithm iteratively improves a quadrilateral or hexahedral mesh by minimizing an objective function defined in terms of a regularized algebraic distortion measure of the elements. We propose several techniques to improve the robustness and the computational efficiency of the optimization algorithm. In addition, we have adopted an object-oriented paradigm to create a common framework to smooth meshes composed by any type of elements, and using different minimization techniques. Finally, we present several examples to show that the proposed technique obtains valid meshes composed by high-quality quadrilaterals and hexahedra, even when the initial meshes contain a large number of tangled elements.


title: Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations

abstract: CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- N tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. The method also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20-30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N ≥ 10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N ≥ 5 and high rank.


title: Electric arc steam plasma conversion of medicine waste and carbon containing materials

abstract: The electric arc equipment allows to rich the extreme temperatures in any technologies of carbon containing raw processing into gaseous products. In this connection our institutions are carry out the environment-friendly technology and equipment for steam plasma pyrolysis of medical waste. This technology makes available guarantee destroy of thermally-stable bacteria and excludes the formation of dioxins and furans. The conversion of the carbon containing raw into gaseous products may be seen as the next step in plasma steam technology development. The gaseous products, obtained in this process, may be used as alternative fuel or starting material for synthetic liquid fuels production. The modern level of fuels and energy supply price makes it economically attractive. The technology of alternative fuel production may be considered automatically as environment-friendly one as it developed from the equipment for medical waste destruction.


title: The IrPen: A 6-DOF Pen for Interaction with Tablet Computers

abstract: Researchers have proposed various techniques for over-the-surface interaction using pens. However, current pen systems can't properly support these techniques because of their limited tracking abilities. The IrPen, an extension of the IrCube tracker, is a six-degree-of-freedom pen system that properly supports over-the-surface interaction on tablet computers. Besides incorporating the tracker's basic operating principles, the IrPen takes into account tablet-specific requirements. It employs a sensor small enough for tablets, and the pen structure minimizes issues caused by reflections from a tablet surface. The design also addresses issues related to occlusion and ambient lighting.


title: Ground Truth and Performance Evaluation of Lane Border Detection

abstract: Lane-border detection is one of the best-developed modules in vision-based driver assistance systems today. However, there is still a need for further improvement for challenging road and traffic situations, and a need to design tools for quantitative performance evaluation.


title: Visual SLAM with Line and Corner Features

abstract: We propose a new vision-based SLAM (Simultaneous Localization and Mapping) technique using both line and corner features as landmarks in the scene. The proposed SLAM algorithm uses an Extended Kalman Filter based framework to localize and reconstruct 3D line and corner landmarks at the same time and in real time. It provides more accurate localization and map building results than conventional corner feature only-based techniques. Moreover, the reconstructed 3D line landmarks enhance the performance of the robot relocation when robot's pose remains uncertain with corner information only. Experimental results show that the hybrid landmark based SLAM, using lines and corners, produces better performance than corner only one's.


title: Application of 3D body scanning technology to human measurement for clothing Fit

abstract: Complications with garment sizing and poor fit inconvenience many consumers who become dissatisfied with such provision on the high street. It is evident that human measurement and classification of the human body based on size and shape are precedent to accurate clothing fit and therefore fundamental to production and consumption. With advancement in technology, automated 3D body scanners designed to capture the shape and size of a human body in seconds and further produce its true-to-scale 3D body model have been developed. The use of data generated is extensive; and include anthropometric dimensions and morphology for the creation of avatars and mannequins. This technology is currently viewed as the frontier in solving fit problems, for instance by generating accurate data for the development of size charts, enabling a pragmatic approach to the offer of mass customisation and also facilitating virtual model fit trials that enhance online clothing shopping experiences. Consumers have become savvier than ever and as the demand for well-fitted garments is increasing, 3D body scanning technology is being viewed as a significant bridge between craftsmanship and computer-aided design technologies. Currently being explored by academic research and not as yet widely implemented across retail sectors, it is expected to facilitate consumer satisfaction and reduce commercial waste due to ‘ill-fit’ returns. There is therefore a vital need to authenticate procedures and establish systems for practice. This paper seeks to assess the application of one such technology to human measurement for clothing provision and tests procedures for its implementation. The methodology presents a case study approach involving the use of one such stateof-the-art technology in the acquisition of measurement data at a metropolitan university in the UK, and advises on the application of the 3D body scanner in research and sampling activities.


title: #Unconfirmed: Classifying Rumor Stance in Crisis-Related Social Media Messages

abstract: It is well-established that within crisis-related communications, rumors are likely to emerge. False rumors, i.e. misinformation, can be detrimental to crisis communication and response; it is therefore important not only to be able to identify messages that propagate rumors, but also corrections or denials of rumor content. In this work, we explore the task of automatically classifying rumor stances expressed in crisisrelated content posted on social media. Utilizing a dataset of over 4,300 manually coded tweets, we build a supervised machine learning model for this task, achieving an accuracy over 88% across a diverse set of rumors of different types.


title: Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives

abstract: This review presents an in-depth study of the literature on segmentation methods applied in dental imaging. Ten segmentation methods were studied and categorized according to the type of the segmentation method (region-based, threshold-based, cluster-based, boundary-based or watershed-based), type of X-ray images used (intra-oral or extra-oral) and characteristics of the dataset used to evaluate the methods in the state-of-the-art works. We found that the literature has primarily focused on threshold-based segmentation methods (54%). 80% of the reviewed papers have used intra-oral X-ray images in their experiments, demonstrating preference to perform segmentation on images of already isolated parts of the teeth, rather than using extra-oral X-rays, which show tooth structure of the mouth and bones of the face. To fill a scientific gap in the field, a novel data set based on extra-oral X-ray images are proposed here. A statistical comparison of the results found with the 10 image segmentation methods over our proposed data set comprised of 1,500 images is also carried out, providing a more comprehensive source of performance assessment. Discussion on limitations of the methods conceived over the past year as well as future perspectives on exploiting learning-based segmentation methods to improve performance are also provided.


title: Containerized Development and Microservices for Self-Driving Vehicles: Experiences & Best Practices

abstract: In this paper, experiences and best practices from using containerized software microservices for self-driving vehicles are shared. We applied the containerized software paradigm successfully to both the software development and deployment to turn our software architecture in the vehicles following the idea of microservices. Key enabling elements include onboarding of new developers, both researchers and students, traceable development and packaging, convenient and bare-bone deployment, and traceably archiving binary distributions of our quickly evolving software environment. In this paper, we share our experience from working one year with containerized development and deployment for our self-driving vehicles highlighting our reflections and application-specific shortcomings, our approach uses several components from the widely used Docker ecosystem, but the discussion in this paper generalizes these concepts. We conclude that the growingly complex automotive software systems in combination with their computational platforms should be rather understood as data centers on wheels to design both, (a) the software development and deployment processes, and (b) the software architecture in such a way to enable continuous integration, continuous deployment, and continuous experimentation.


title: Joint Embedding of Query and Ad by Leveraging Implicit Feedback

abstract: Sponsored search is at the center of a multibillion dollar market established by search technology. Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities. Lexical features derived from the text of both the query and ads play a significant role, complementing features based on historical click information. The purpose of this paper is to explore the use of word embedding techniques to generate effective text features that can capture not only lexical similarity between query and ads but also the latent user intents. We identify several potential weaknesses of the plain application of conventional word embedding methodologies for ad click prediction. These observations motivated us to propose a set of novel joint word embedding methods by leveraging implicit click feedback. We verify the effectiveness of these new word embedding models by adding features derived from the new models to the click prediction system of a commercial search engine. Our evaluation results clearly demonstrate the effectiveness of the proposed methods. To the best of our knowledge this work is the first successful application of word embedding techniques for the task of click prediction in sponsored search.


title: Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

abstract: Polar codes have gained significant amount of attention during the past few years and have been selected as a coding scheme for the next generation of mobile broadband standard. Among decoding schemes, successive-cancellation list (SCL) decoding provides a reasonable tradeoff between the error-correction performance and hardware implementation complexity when used to decode polar codes, at the cost of limited throughput. The simplified SCL (SSCL) and its extension SSCL-SPC increase the speed of decoding by removing redundant calculations when encountering particular information and frozen bit patterns (rate one and single parity check codes), while keeping the error-correction performance unaltered. In this paper, we improve SSCL and SSCL-SPC by proving that the list size imposes a specific number of path splitting required to decode rate one and single parity check codes. Thus, the number of splitting can be limited while guaranteeing exactly the same error-correction performance as if the paths were forked at each bit estimation. We call the new decoding algorithms Fast-SSCL and Fast-SSCL-SPC. Moreover, we show that the number of path forks in a practical application can be tuned to achieve desirable speed, while keeping the error-correction performance almost unchanged. Hardware architectures implementing both algorithms are then described and implemented: It is shown that our design can achieve $\mathbf {1.86}$ Gb/s throughput, higher than the best state-of-the-art decoders.


title: Review of Micro Thermoelectric Generator

abstract: Used for thermal energy harvesting, thermoelectric generator (TEG) can convert heat into electricity directly. Structurally, the main part of TEG is the thermopile, which consists of thermocouples connected in series electrically and in parallel thermally. Benefiting from massive progress achieved in a microelectromechanical systems technology, micro TEG ( $\mu$ -TEG) with advantages of small volume and high output voltage has obtained attention in recent 20 years. The review gives a comprehensive survey of the development and current status of $\mu$ -TEG. First, the principle of operation is introduced and some key parameters used for characterizing the performance of $\mu$ -TEG are highlighted. Next, $\mu$ -TEGs are classified from the perspectives of structure, material, and fabrication technology. Then, almost all the relevant works are summarized for the convenience of comparison and reference. Summarized information includes the structure, material property, fabrication technology, output performance, and so on. This will provide readers with an overall evaluation of different studies and guide them in choosing the suitable $\mu$ -TEGs for their applications. In addition, the existing and potential applications of $\mu$ -TEG are shown, especially the applications in the Internet of things. Finally, we summarize the challenges encountered in improving the output power of $\mu$ -TEG and predicted that more researchers would focus their efforts on the flexible structure $\mu$ -TEG, and combination of $\mu$ -TEG and other energy harvestings. With the emergence of more low-power devices and the gradual improvement of ZT value of the thermoelectric material, $\mu$ -TEG is promising for applications in various fields. [2017-0610]


title: Deconvolutional networks

abstract: Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images.


title: Multi-label Classification: Dealing with Imbalance by Combining Labels

abstract: Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.


title: Wideband Radar Cross-Section Reduction of a Stacked Patch Array Antenna Using Metasurface

abstract: In this letter, a two-layer metasurface is proposed to achieve radar cross-section (RCS) reduction of a stacked patch antenna at a broadband. The lower layer metasurface is composed of four square patches loaded with four resistors, which is utilized to reduce RCS in the operation band (2.75–3.4 GHz) of the patch antenna. The periodic square loops with four resistors mounted on each side are adopted to construct the upper layer metasurface for absorbing the incoming wave out of band. We first investigate the effectiveness of the proposed metasurface on the RCS reduction of the single stacked patch and then apply this strategy to the $1 \times 4$ stacked patch array. The proposed low RCS stacked patch array antenna is fabricated and measured. The experimental results show that the designed metasurface makes the antenna RCS dramatically reduced in a broadband covering the operation band and out-of-band from 5.5–16 GHz. Moreover, the introduction of metasurface is demonstrated to have little influence on the antenna performance.


title: Blockchain-based Personal Health Data Sharing System Using Cloud Storage

abstract: With the advent of rapid development of wearable technology and mobile computing, huge amount of personal health-related data is being generated and accumulated on continuous basis at every moment. These personal datasets contain valuable information and they belong to and asset of the individual users, hence should be owned and controlled by themselves. Currently most of such datasets are stored and controlled by different service providers and this centralised data storage brings challenges of data security and hinders the data sharing. These personal health data are valuable resources for healthcare research and commercial projects. In this research work, we propose a conceptual design for sharing personal continuous-dynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner. Besides, we also introduce a data quality inspection module based on machine learning techniques to have control over data quality. The primary goal of the proposed system is to enable users to own, control and share their personal health data securely, in a General Data Protection Regulation (GDPR) compliant way to get benefit from their personal datasets. It also provides an efficient way for researchers and commercial data consumers to collect high quality personal health data for research and commercial purposes.


title: Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

abstract: Deep learning models have consistently outperformed traditional machine learning models in various classification tasks, including image classification. As such, they have become increasingly prevalent in many real world applications including those where security is of great concern. Such popularity, however, may attract attackers to exploit the vulnerabilities of the deployed deep learning models and launch attacks against security-sensitive applications. In this paper, we focus on a specific type of data poisoning attack, which we refer to as a \em backdoor injection attack. The main goal of the adversary performing such attack is to generate and inject a backdoor into a deep learning model that can be triggered to recognize certain embedded patterns with a target label of the attacker's choice. Additionally, a backdoor injection attack should occur in a stealthy manner, without undermining the efficacy of the victim model. Specifically, we propose two approaches for generating a backdoor that is hardly perceptible yet effective in poisoning the model. We consider two attack settings, with backdoor injection carried out either before model training or during model updating. We carry out extensive experimental evaluations under various assumptions on the adversary model, and demonstrate that such attacks can be effective and achieve a high attack success rate (above 90%) at a small cost of model accuracy loss with a small injection rate, even under the weakest assumption wherein the adversary has no knowledge either of the original training data or the classifier model.


Cost-analysis : How do monads and comonads differ ?


title: On activation functions for complex-valued neural networks: existence of energy functions

abstract: Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. This paper discusses the properties of activation functions from the standpoint of existence of an energy function for complex-valued neural networks. Two classes of complex functions which are widely used as activation functions in the models of complex-valued neural networks are considered. We investigate the properties of activation functions which assure existence of energy functions and discuss about how to find out complex functions which satisfy the properties.


title: Millimeter wave cellular channel models for system evaluation

abstract: The huge amount of (potentially) available spectrum makes millimeter wave (mmWave) a promising candidate for fifth generation cellular networks. Unfortunately, differences in the propagation environment as a function of frequency make it hard to make comparisons between systems operating at mmWave and microwave frequencies. This paper presents a simple channel model for evaluating system level performance in mmWave cellular networks. The model uses insights from measurement results that show mmWave is sensitive to blockages revealing very different path loss characteristics between line-of-sight (LOS) and non-line-of-sight (NLOS) links. The conventional path loss model with a single log-distance path loss function and a shadowing term is replaced with a stochastic path loss model with a distance-dependent LOS probability and two different path loss functions to account for LOS and NLOS links. The proposed model is used to compare microwave and mmWave networks in simulations. It is observed that mmWave networks can provide comparable coverage probability with a dense deployment, leading to much higher data rates thanks to the large bandwidth available in the mmWave spectrum.


title: Gain Enhancement of Printed Log-Periodic Dipole Array Antenna Using Director Cell

abstract: In this communication, several parasitic dipole elements served as director cell are added to the shorter end of the printed Log-periodic dipole array (PLPDA) antenna for the gain enhancement. This novel antenna is based on the combination of the PLPDA antenna and the quasi-Yagi antenna techniques, so called printed Log-Yagi dipole array antenna (PLYDA). The number and the distribution of the director cell are discussed and optimized. A prototype PLYDA antenna fed by half mode substrate integrated waveguide (HMSIW) is manufactured, as well as a HMSIW PLPDA antenna for comparison. The return loss and far-field radiation patterns were measured and presented in this work. The proposed PLYDA antenna exhibits gain enhancement of 0.2-2 dB in comparison with the equal size PLPDA antenna over the operating frequency range 21-40 GHz. The simulated and measured results are in good agreement, and the gain enhancement using the director cell is verified.


After the Storm: A Guide to Help Children Cope with the Psychological Effects of a Hurricane


title: Design Theory in Information Systems

abstract: The aim of this paper is to explore an important category of information systems knowledge that is termed “design theory”. This knowledge is distinguished as the fifth of five types of theory: (i) theory for analysing and describing, (ii) theory for understanding, (iii) theory for predicting, (iv) theory for explaining and predicting, and (v) theory for design and action. Examples of design theory in information systems are provided, with associated research methods. The limited understanding and recognition of this type of theory in information systems indicates that further debate concerning its nature and role in our discipline is needed.


title: Mining Twitter Feeds for Software User Requirements

abstract: Twitter enables large populations of end-users of software to publicly share their experiences and concerns about software systems in the form of micro-blogs. Such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a three-fold study that is aimed at leveraging Twitter as a main source of software user requirements. The main objective is to enable a responsive, interactive, and adaptive data-driven requirements engineering process. Our analysis is conducted using 4,000 tweets collected from the Twitter feeds of 10 software systems sampled from a broad range of application domains. The results reveal that around 50% of collected tweets contain useful technical information. The results also show that text classifiers such as Support Vector Machines and Naive Bayes can be very effective in capturing and categorizing technically informative tweets. Additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software-relevant tweets.


title: Individual differences in non-verbal number acuity correlate with maths achievement

abstract: Human mathematical competence emerges from two representational systems. Competence in some domains of mathematics, such as calculus, relies on symbolic representations that are unique to humans who have undergone explicit teaching. More basic numerical intuitions are supported by an evolutionarily ancient approximate number system that is shared by adults, infants and non-human animals-these groups can all represent the approximate number of items in visual or auditory arrays without verbally counting, and use this capacity to guide everyday behaviour such as foraging. Despite the widespread nature of the approximate number system both across species and across development, it is not known whether some individuals have a more precise non-verbal 'number sense' than others. Furthermore, the extent to which this system interfaces with the formal, symbolic maths abilities that humans acquire by explicit instruction remains unknown. Here we show that there are large individual differences in the non-verbal approximation abilities of 14-year-old children, and that these individual differences in the present correlate with children's past scores on standardized maths achievement tests, extending all the way back to kindergarten. Moreover, this correlation remains significant when controlling for individual differences in other cognitive and performance factors. Our results show that individual differences in achievement in school mathematics are related to individual differences in the acuity of an evolutionarily ancient, unlearned approximate number sense. Further research will determine whether early differences in number sense acuity affect later maths learning, whether maths education enhances number sense acuity, and the extent to which tertiary factors can affect both.


title: Energy-aware management for cluster-based sensor networks

abstract: Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and has to be managed wisely in order to extend the life of the sensors for the duration of a particular mission. In this paper, we present a novel approach for energy-aware management of sensor networks that maximizes the lifetime of the sensors while achieving acceptable performance for sensed data delivery. The approach is to dynamically set routes and arbitrate medium access in order to minimize energy consumption and maximize sensor life. The approach calls for network clustering and assigns a less-energy-constrained gateway node that acts as a cluster manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. We also describe a time-based medium access control (MAC) protocol and discuss algorithms for assigning time slots for the communicating sensor nodes. Simulation results show an order of magnitude enhancement in the time to network partitioning, 11% enhancement in network lifetime predictability, and 14% enhancement in average energy consumed per packet.


title: Postpartum depression and infant feeding practices in a low income urban settlement in Nairobi-Kenya

abstract: BackgroundPostpartum depression can compromise caregiving activities, including infant feeding practices, resulting in child malnutrition. The purpose of this study was to examine the effects of postpartum depression on infant feeding practices and malnutrition among women in an urban low income settlement in Nairobi-Kenya. We conducted a cross-sectional study based in Kariobangi North Health Centre in Nairobi County. The study sample included 200 mother-infant pairs visiting the Maternal and Child Health clinics for infant immunization at 6–14 weeks postpartum. We assessed postpartum depression using the Edinburgh Postpartum Depression Scale. Infant feeding practices were assessed based on World Health Organization infant and young child feeding guidelines. Nutritional status (weight for age) was ascertained using infants’ growth monitoring card (percentiles and z-score). We conducted logistic regression analyses to determine the relative odds of non-exclusive breast feeding and infant underweight among mothers with postpartum depression.ResultsThe prevalence of PPD was 13.0% (95% CI 8.3–17.7%). Taking into account differences in socioeconomic status of depressed and non-depressed mothers, non-depressed mothers had a 6.14 (95% CI 2.45–13.36) times higher odds of practicing exclusive breastfeeding than mothers who were depressed. Mothers with PPD had a 4.40 (95% CI 1.91–11.93) time higher odds of having an underweight infant than mothers without depression.ConclusionsThis study contributes towards filling the knowledge gap regarding the adverse effects of postpartum depression on infant health in sub-Saharan Africa. We recommend more research on PPD using longitudinal designs to establish temporal ordering of these important public health problems and development of community-based interventions to address post-partum depression.


Knowledge , Attitude and Practices Toward Nutrition and Diet During Pregnancy Among Recently Delivered Women of Syrian Refugees


title: Java performance evaluation through rigorous replay compilation

abstract: A managed runtime environment, such as the Java virtual machine, is non-trivial to benchmark. Java performance is affected in various complex ways by the application and its input, as well as by the virtual machine (JIT optimizer, garbage collector, thread scheduler, etc.). In addition, non-determinism due to timer-based sampling for JIT optimization, thread scheduling, and various system effects further complicate the Java performance benchmarking process. Replay compilation is a recently introduced Java performance analysis methodology that aims at controlling non-determinism to improve experimental repeatability. The key idea of replay compilation is to control the compilation load during experimentation by inducing a pre-recorded compilation plan at replay time. Replay compilation also enables teasing apart performance effects of the application versus the virtual machine. This paper argues that in contrast to current practice which uses a single compilation plan at replay time, multiple compilation plans add statistical rigor to the replay compilation methodology. By doing so, replay compilation better accounts for the variability observed in compilation load across compilation plans. In addition, we propose matched-pair comparison for statistical data analysis. Matched-pair comparison considers the performance measurements per compilation plan before and after an innovation of interest as a pair, which enables limiting the number of compilation plans needed for accurate performance analysis compared to statistical analysis assuming unpaired measurements.


title: Mind-controlled wheelchair using an EEG headset and arduino microcontroller

abstract: Improving the quality of life for the elderly and disabled people and giving them the proper care at the right time is one the most important roles that are to be performed by us being a responsible member of the society. It's not easy for the disabled and elderly people to maneuver a mechanical wheelchair, which many of them normally use for locomotion. Hence there is a need for designing a wheelchair that is intelligent and provides easy maneuverability. In this context, an attempt has been made to propose a thought controlled wheelchair, which uses the captured signals from the brain and eyes and processes it to control the wheelchair. Electroencephalography (EEG) technique deploys an electrode cap that is placed on the user's scalp for the acquisition of the EEG signals which are captured and translated into movement commands by the arduino microcontroller which in turn move the wheelchair.


title: Active tunable inductor using non-foster element

abstract: A novel approach to design of active tunable inductors is presented. The proposed tunable inductor is a connection of a passive inductor and an active circuit to realize a non-Foster element with variable negative inductance. Tunability is achieved by means of a variable capacitor used as the load of negative impedance converter. The concept verification was done for a tunable inductor circuit implemented on lumped elements in printed circuit board technology. Simulated as well as measured data proved the principle and demonstrated the ability to design small-size stable tunable inductors with wide tuning range and high quality factor.


title: Best security practices for android, blackberry, and iOS

abstract: Mobile operating systems have expanded from running exclusively on smartphones to include tablets and music players as well, making them available to a larger audience. These operating systems are responsible for maintaining a multitude of schedules, messages, calls, music, documents, applications, photos, videos, and other personal data with the goal of increasing efficiency. Because of this, developers of mobile operating systems have built in configuration options to these operating systems to better protect the device from security vulnerabilities. However, not all of these operating systems have such settings turned on when the device is pulled out of the box. Furthermore, not all users are aware of how to best protect their information, nor the kind of issues that should be considered when owning and using these devices. This paper suggests best practices for the three major mobile platforms - Android, BlackBerry, and iOS - to mitigate and/or prevent unauthorized access, loss of information, and loss of confidentiality.