title: Measuring psychological and physical distress in cancer patients: structure and application of the Rotterdam Symptom Checklist

abstract: Use of the Rotterdam Symptom Checklist (RSCL) to measure psychological and physical distress as experienced by cancer patients, is discussed in this paper. The stability of the structure of the RSCL was assessed in principal component analyses in three studies: one concerning cancer patients during either chemotherapy or follow-up (n = 86), one done in patients undergoing chemotherapy for advanced ovarian cancer (n = 56), and the third dealing with cancer patients under treatment, disease-free 'patients', and 'normal' controls (n = 611). The psychological dimension proved to be stable across populations. A scale based on this factor was highly reliable (Cronbach's alpha 0.88-0.94). The physical distress is reflected by several dimensions in a homogeneous population (pain, fatigue, gastrointestinal complaints) and undimensionally in a heterogeneous population. Reliability of the physical distress scales is good (0.71-0.88). The current components of the RSCL and the use of individual and disease specific symptoms are discussed.


title: Effects of VDT and paper presentation on consumption and production of information: Psychological and physiological factors

abstract: Two experiments were performed to investigate the influence of VDT (video display terminals) and paper presentation of text on consumption of information (Study 1) measured in the form of convergent production and production of information (Study 2) measured in form of divergent production. The READ test of reading comprehension was used as the convergent task whereas the “Headlines” test was used as the divergent task. Several other factors pertaining to performance were also studied including the PANAS test of positive and negative affect, the STH test of stress, tiredness and hunger, the TRI (Technology Readiness Inventory) and the SE test of stress and energy.The results show that performance in the VDT presentation condition where inferior to that of the Paper presentation condition for both consumption and production of information. Concomitantly, participants in the VDT presentation condition of the consumption of information study reported higher levels of experienced stress and tiredness whereas the participants in the VDT presentation condition of production of information study reported only slightly higher levels of stress.Although the results are discussed in both physiological and psychological terms arguments are made that the incremental effects of VDT text presentation stem mainly from dual-task effects of fulfilling the assignment and working with the computer resulting in a higher cognitive workload.


title: Improving primary care for patients with chronic illness.

abstract: The chronic care model is a guide to higher-quality chronic illness management within primary care. The model predicts that improvement in its 6 interrelated components-self-management support, clinical information systems, delivery system redesign, decision support, health care organization, and community resources-can produce system reform in which informed, activated patients interact with prepared, proactive practice teams. Case studies are provided describing how components of the chronic care model have been implemented in the primary care practices of 4 health care organizations.


title: Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification

abstract: Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Since two different sub-categories is distinguished only by the subtle differences in some specific parts, semantic part localization is crucial for fine-grained image classification. Most previous works improve the accuracy by looking for the semantic parts, but rely heavily upon the use of the object or part annotations of images whose labeling are costly. Recently, some researchers begin to focus on recognizing sub-categories via weakly supervised part detection instead of using the expensive annotations. However, these works ignore the spatial relationship between the object and its parts as well as the interaction of the parts, both of them are helpful to promote part selection. Therefore, this paper proposes a weakly supervised part selection method with spatial constraints for fine-grained image classification, which is free of using any bounding box or part annotations. We first learn a whole-object detector automatically to localize the object through jointly using saliency extraction and co-segmentation. Then two spatial constraints are proposed to select the distinguished parts. The first spatial constraint, called box constraint, defines the relationship between the object and its parts, and aims to ensure that the selected parts are definitely located in the object region, and have the largest overlap with the object region. The second spatial constraint, called parts constraint, defines the relationship of the object's parts, is to reduce the parts' overlap with each other to avoid the information redundancy and ensure the selected parts are the most distinguishing parts from other categories. Combining two spatial constraints promotes parts selection significantly as well as achieves a notable improvement on fine-grained image classification. Experimental results on CUB-200-2011 dataset demonstrate the superiority of our method even compared with those methods using expensive annotations.


title: Adolescent coping style and behaviors: conceptualization and measurement

abstract: The developmental tasks associated with adolescence pose a unique set of stressors and strains. Included in the normative tasks of adolescence are developing an identity, differentiating from the family while still staying connected, and fitting into a peer group. The adolescent's adaptation to these and other, often competing demands is achieved through the process of coping which involves cognitive and behavioral strategies directed at eliminating or reducing demands, redefining demands so as to make them more manageable, increasing resources for dealing with demands, and/or managing the tension which is felt as a result of experiencing demands. In this paper, individual coping theory and family stress theory are reviewed to provide a theoretical foundation for assessing adolescent coping. In addition, the development and testing of an adolescent self-report coping inventory, Adolescent Coping Orientation for Problem Experiences (A-COPE) is presented. Gender differences in coping style are presented and discussed. These coping patterns were validated against criterion indices of adolescents' use of cigarettes, liquor, and marijuana using data from a longitudinal study of 505 families with adolescents. The findings are discussed in terms of coping theory and measurement and in terms of adolescent development and substance use.


title: Force Control of Series Elastic Actuator: Implications for Series Elastic Actuator Design

abstract: The force controller of series elastic actuator (SEA) is an important issue, because it determines the output performance of SEA (the bandwidth and the resonant peak). However, the existing force controllers of SEA attach little importance to the determinants of the performance. This paper develops four different kinds of force controllers and selects the optimal controller by comparing their capabilities. The comparison demonstrates that the parameters of dynamical models are key factors affecting the output performance. Finally, the experiments reveal that the equivalent mass and the damping coefficient of SEA are determinants. The conclusion can provide theoretical implications to SEA design.


Rethinking Connection Security Indicators


title: Apriori-based frequent itemset mining algorithms on MapReduce

abstract: Many parallelization techniques have been proposed to enhance the performance of the Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce functions, MapReduce has emerged and excels in the mining of datasets of terabyte scale or larger in either homogeneous or heterogeneous clusters. Minimizing the scheduling overhead of each map-reduce phase and maximizing the utilization of nodes in each phase are keys to successful MapReduce implementations. In this paper, we propose three algorithms, named SPC, FPC, and DPC, to investigate effective implementations of the Apriori algorithm in the MapReduce framework. DPC features in dynamically combining candidates of various lengths and outperforms both the straight-forward algorithm SPC and the fixed passes combined counting algorithm FPC. Extensive experimental results also show that all the three algorithms scale up linearly with respect to dataset sizes and cluster sizes.


title: The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning

abstract: The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area.


title: Maximum-entropy spectral analysis of radar clutter

abstract: The paper reviews a processor that uses the maximum-entropy spectral estimate to provide a set of Doppler-based features for classifying the different forms of radar clutter as encountered in an air traffic control environment. This enables vectoring aircraft around an area that is made hazardous by the presence of major weather disturbances or migrating flocks of birds. An overview is given of experimental results that demonstrate the practical viability of this Doppler processor.


title: Impact of Organizational Culture on Organizational Performance: An Overview

abstract: This article is on defining and measuring of organizational culture and its impact on the organizational performance, through an analysis of existing empirical studies and models link with the organizational culture and performance. The objective of this article is to demonstrate conceptualization, measurement and examine various concepts on organization culture and performance. After analysis of wide literature, it is found that organizational culture has deep impact on the variety of organizations process, employees and its performance. This also describes the different dimensions of the culture. Research shows that if employee are committed and having the same norms and value as per organizations have, can increase the performance toward achieving the overall organization goals. Balance Scorecard is suggested tool to measure the performance in the performance management system. More research can be done in this area to understand the nature and ability of the culture in manipulating performance of the organization. Managers and leaders are recommended to develop the strong culture in the organization to improve the overall performance of the employees and organization.


title: Application of k Means Clustering algorithm for prediction of Students Academic Performance

abstract: The ability to monitor the progress of students academic performance is a critical issue to the academic community of higher learning. A system for analyzing students results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k mean clustering algorithm for analyzing students result data. The model was combined with the deterministic model to analyze the students results of a private Institution in Nigeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners.


title: Parallelization of Neural Network Training for NLP with Hogwild!

abstract: Abstract Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.


A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches.


title: Fast and Accurate Shift-Reduce Constituent Parsing

abstract: Shift-reduce dependency parsers give comparable accuracies to their chartbased counterparts, yet the best shiftreduce constituent parsers still lag behind the state-of-the-art. One important reason is the existence of unary nodes in phrase structure trees, which leads to different numbers of shift-reduce actions between different outputs for the same input. This turns out to have a large empirical impact on the framework of global training and beam search. We propose a simple yet effective extension to the shift-reduce process, which eliminates size differences between action sequences in beam-search. Our parser gives comparable accuracies to the state-of-the-art chart parsers. With linear run-time complexity, our parser is over an order of magnitude faster than the fastest chart parser.


title: Supply chain ontology: Review, analysis and synthesis

abstract: In an environment where supply chains compete against supply chains, information sharing among supply chain partners using information systems is a competitive tool. Supply chain ontology has been proposed as an important medium for attaining information systems interoperability. Ontology has its origin in philosophy, and the computing community has adopted ontology in its language. This paper presents a study of state of the art research in supply chain ontology and identifies the outstanding research gaps. Six supply chain ontology models were identified from a systematic review of literature. A seven point comparison framework was developed to consider the underlying concepts as well as application of the ontology models. The comparison results were then synthesised into nine gaps to inform future supply chain ontology research. This work is a rigorous and systematic attempt to identify and synthesise the research in supply chain ontology.


title: 3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences

abstract: In this paper, we first review the approaches to recover 3D shape and related movements of a human and then we present an easy and reliable approach to recover a 3D model using just one image or monocular video sequence. A simplification of the perspective camera model is required, due to the absence of stereo view. The human figure is reconstructed in a skeleton form and to improve the visual quality, a pre-defined human model is also fitted to the recovered 3D data.


title: Community Level Diffusion Extraction

abstract: How does online content propagate on social networks? Billions of users generate, consume, and spread tons of information every day. This unprecedented scale of dynamics becomes invaluable to reflect our zeitgeist. However, most present diffusion extraction works have only touched individual user level and cannot obtain comprehensive clues. This paper introduces a new approach, i.e., COmmunity Level Diffusion (COLD), to uncover and explore temporal diffusion. We model topics and communities in a unified latent framework, and extract inter-community influence dynamics. With a well-designed multi-component model structure and a parallel inference implementation on GraphLab, the COLD method is expressive while remaining efficient. The extracted community level patterns enable diffusion exploration from a new perspective. We leverage the compact yet robust representations to develop new prediction and analysis applications. Extensive experiments on large social datasets show significant improvement in prediction accuracy. We can also find communities play very different roles in diffusion processes depending on their interest. Our method guarantees high scalability with increasing data size.


title: Iterative free-energy optimization for recurrent neural networks (INFERNO)

abstract: The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.


title: Hand gesture recognition using input-output hidden Markov models

abstract: A new hand gesture recognition method based on input-output hidden Markov models is presented. This method deals with the dynamic aspects of gestures. Gestures are extracted from a sequence of video images by tracking the skin-color blobs corresponding to the hand into a body-face space centered on the face of the user. Our goal is to recognize two classes of gestures: deictic and symbolic.


title: A Theory of Output-Side Unsupervised Domain Adaptation

abstract: When learning a mapping from an input space to an output space, the assumption that the sample distribution of the training data is the same as that of the test data is often violated. Unsupervised domain shift methods adapt the learned function in order to correct for this shift. Previous work has focused on utilizing unlabeled samples from the target distribution. We consider the complementary problem in which the unlabeled samples are given post mapping, i.e., we are given the outputs of the mapping of unknown samples from the shifted domain. Two other variants are also studied: the two sided version, in which unlabeled samples are give from both the input and the output spaces, and the Domain Transfer problem, which was recently formalized. In all cases, we derive generalization bounds that employ discrepancy terms.


title: Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy

abstract: We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous"alpha"signals, agent's own actions (via market impact), and noise. Unlike traditional agent-based models, our agent aggregates all traders in the market, rather than being a representative agent. Therefore, it can be identified with a bounded-rational component of the market itself, providing a particular implementation of an Invisible Hand market mechanism. In such setting, market dynamics are modeled as a fictitious self-play of such bounded-rational market-agent in its adversarial stochastic environment. As rewards obtained by such self-playing market agent are not observed from market data, we formulate and solve a simple model of such market dynamics based on a neuroscience-inspired Bounded Rational Information Theoretic Inverse Reinforcement Learning (BRIT-IRL). This results in effective asset price dynamics with a non-linear mean reversion - which in our model is generated dynamically, rather than being postulated. We argue that our model can be used in a similar way to the Black-Litterman model. In particular, it represents, in a simple modeling framework, market views of common predictive signals, market impacts and implied optimal dynamic portfolio allocations, and can be used to assess values of private signals. Moreover, it allows one to quantify a"market-implied"optimal investment strategy, along with a measure of market rationality. Our approach is numerically light, and can be implemented using standard off-the-shelf software such as TensorFlow.


title: Neighborhood Discriminant Hashing for Large-Scale Image Retrieval

abstract: With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.


title: Weakly supervised histopathology cancer image segmentation and classification

abstract: Abstract Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.


title: Design and evaluation of a capacitively coupled sensor readout circuit, toward contact-less ECG and EEG

abstract: Electrophysiological signal acquisition such as ECG and EEG play an important part in modern medical monitoring and diagnostics. The measurement of these very low-level, low-frequency signals are normally made from the skin with a directly coupled sensor utilizing a conductive gel to create a low resistance path for the charge. The application of the gel is tedious and time consuming as well as requiring a clinical environment and prevents long period measurements. In this paper, a contact-less, capacitively coupled sensor — without any need for gel — together with an electronic readout circuit using a PCB is presented. A design with a very high input impedance allows for measurements of signals with amplitudes down to a few tens of microvolts and at frequencies between a few hertz to tens of hertz. Measurements show that the circuit could detect an input signal of 25 μV at 10 Hz with an SNR of 9.7 dB.


title: Vipassana meditation: systematic review of current evidence.

abstract: OBJECTIVES ::: Vipassana meditation (VM) is one of the most ancient and diffused types of meditative practices belonging to the pole of mindfulness. Despite the growing interest toward the neurobiological and clinical correlates of many meditative practices, no review has specifically focused on current evidence on neuro-imaging and clinical evidence about VM. ::: ::: ::: METHODS ::: A literature search was undertaken using MEDLINE,((R)) ISI web of knowledge, the Cochrane database, and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to March 2009 were included. ::: ::: ::: RESULTS ::: Seven (7) mainly poor-quality studies were identified. Three (3) neuro-imaging studies suggested that VM practice could be associated with the activation of the prefrontal and the anterior cingulate cortex during meditative periods, and with increased thickness in cortical areas related to attention as well as increased subcortical gray matter in right insula and hippocampus in long-term meditators. Three (3) clinical studies in incarcerated populations suggested that VM could reduce alcohol and substance abuse but not post-traumatic stress disorder symptoms in prisoners. One (1) clinical study in healthy subjects suggested that VM could enhance more mature defenses and copying styles. ::: ::: ::: DISCUSSION ::: Current studies provided preliminary results about neurobiological and clinical changes related to VM practice. Nonetheless, few and mainly low-quality data are available especially for clinical studies and current results have to be considered with caution. Further research is needed to answer critical questions about replications, self-selection, placebo, and long-term effects of VM.


title: Discriminative Representation Combinations for Accurate Face Spoofing Detection

abstract: Abstract Three discriminative representations for face presentation attack detection are introduced in this paper. Firstly we design a descriptor called spatial pyramid coding micro-texture (SPMT) feature to characterize local appearance information. Secondly we utilize the SSD, which is a deep learning framework for detection, to excavate context cues and conduct end-to-end face presentation attack detection. Finally we design a descriptor called template face matched binocular depth (TFBD) feature to characterize stereo structures of real and fake faces. For accurate presentation attack detection, we also design two kinds of representation combinations. Firstly, we propose a decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a simple score fusion strategy to combine face structure cues (TFBD) with local micro-texture features (SPMT). To demonstrate the effectiveness of our design, we evaluate the representation combination of SPMT and SSD on three public datasets, which outperforms all other state-of-the-art methods. In addition, we evaluate the representation combination of SPMT and TFBD on our dataset and excellent performance is also achieved.


Comparative Study of Speaker Recognition Methods : DTW , GMM and SVM


title: Depth-based segmentation — A review

abstract: Computer Vision describes some of its typical assignments as motion analysis, scene reconstruction, image restoration and image segmentation. Enormous tasks could be demonstrated while segmenting an image. The goal might be stated as, “Extraction of object from background” or “Analysing sharp edges in the image”. The goal could be any, but the initial information needed to visualize the difference between different segments of image, should be accurately picked and evaluated. This paper talks about such an initial information i.e. depth value of image pixels and gives an insight of its importance in the field of Image segmentation. Moreover, the paper also describes some of the typical techniques to incur depth information in the process of segmentation, solely or combined with other pixel values like color.


title: An Experimental Comparison of Stereo Algorithms

abstract: While many algorithms for computing stereo correspondence have been proposed, there has been very little work on experimentally evaluating algorithm performance, especially using real (rather than synthetic) imagery. In this paper we propose an experimental comparison of several different stereo algorithms. We use real imagery, and explore two different methodologies, with different strengths and weaknesses. Our first methodology is based upon manual computation of dense ground truth. Here we make use of a two stereo pairs: one of these, from the University of Tsukuba, contains mostly fronto-parallel surfaces; while the other, which we built, is a simple scene with a slanted surface. Our second methodology uses the notion of prediction error, which is the ability of a disparity map to predict an (unseen) third image, taken from a known camera position with respect to the input pair. We present results for both correlation-style stereo algorithms and techniques based on global methods such as energy minimization. Our experiments suggest that the two methodologies give qualitatively consistent results. Source images and additional materials, such as the implementations of various algorithms, are available on the web from http://www.research.microsoft.com/~szeliski/stereo.


title: Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings

abstract: Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian’s locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.


title: Allocating tasks to workers with matching constraints: truthful mechanisms for crowdsourcing markets

abstract: Designing optimal pricing policies and mechanisms for allocating tasks to workers is central to the online crowdsourcing markets. In this paper, we consider the following realistic setting of online crowdsourcing markets -- there is a requester with a limited budget and a heterogeneous set of tasks each requiring certain skills; there is a pool of workers and each worker has certain expertise and interests which define the set of tasks she can and is willing to do. Under the matching constraints given by this bipartite graph between workers and tasks, we design our incentive-compatible mechanism truthuniform which allocates the tasks to the workers, while ensuring budget feasibility and achieves near-optimal utility for the requester. Apart from strong theoretical guarantees, we carry out experiments on a realistic case study of Wikipedia translation project on Mechanical Turk. We note that this is the first paper to address this setting from a mechanism design perspective.


title: QDFS: A quality-aware distributed file storage service based on HDFS

abstract: On the basis of the Hadoop distributed file system (HDFS), this paper presents the design and implementation of QDFS, a distributed file storage service system that employs a backup policy based on recovery volumes and a quality-aware data distribution strategy. The experimental results show that QDFS increases the storage utilization ratio and improves the storage performance by being aware of the quality of service of the DataNodes. The improvements of QDFS compared with HDFS make the Hadoop distributed computing model more suitable to unstable wide area network environments.


title: Stiffness Analysis of a Planar 3-RPS Parallel Manipulator

abstract: This paper studied the stiffness model and characteristics of a planar 3-RPS PM with 3-DOF. The 6 × 6 form stiffness matrix of the planar 3-RPS PM is derived with both active and constrained wrenches considered. To characteristic the stiffness of the planer 3-RPS PM, two decomposition methods including the eigenscrew decomposition and the principle axes decomposition are applied to the stiffness matrix. The stiffness matrix decomposition provides a physical interpretation and allows the identification of the compliant axes of the planar 3-RPS PM.


title: Parser Showdown at the Wall Street Corral: An Empirical Investigation of Error Types in Parser Output

abstract: Constituency parser performance is primarily interpreted through a single metric, F-score on WSJ section 23, that conveys no linguistic information regarding the remaining errors. We classify errors within a set of linguistically meaningful types using tree transformations that repair groups of errors together. We use this analysis to answer a range of questions about parser behaviour, including what linguistic constructions are difficult for state-of-the-art parsers, what types of errors are being resolved by rerankers, and what types are introduced when parsing out-of-domain text.


title: Drug Addiction and the Memory Systems of the Brain

abstract: We review drug addiction from the perspective of the hypothesis that drugs of abuse interact with distinct brain memory systems. We focus on emotional and procedural forms of memory, encompassing Pavlovian and instrumental conditioning, both for action–outcome and for stimulus–response associations. Neural structures encompassed by these systems include the amygdala, hippocampus, nucleus accumbens, and dorsal striatum. Additional influences emanate from the anterior cingulate and prefrontal cortex, which are implicated in the encoding and retrieval of drug-related memories that lead to drug craving and drug use. Finally, we consider the ancillary point that chronic abuse of many drugs may impact directly on neural memory systems via neuroadaptive and neurotoxic effects that lead to cognitive impairments in which memory dysfunction is prominent.


Privacy-Preserving Data Publishing


title: Agent-based computational investing recommender system

abstract: The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.


title: What determines vulnerability to floods; a case study in Georgetown, Guyana:

abstract: This paper describes how a high proportion of Greater Georgetown’s inhabitants are subjected to regular floods and examines also the vulnerability of households to flooding and flood impacts in four of the city’s 49 wards. It demonstrates the importance of incorporating social and economic assets together with physical resources as key “dynamic pressures”(1)in assessments of household and neighbourhood vulnerability to environmental stress. It also identifies households and communities as active agents in the management of vulnerability and examines the potential of such organizations for reducing vulnerability based upon economic poverty.


title: Generating Coherent Patterns of Activity from Chaotic Neural Networks

abstract: Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.


title: Malware detection using bilayer behavior abstraction and improved one-class support vector machines

abstract: Malware detection is one of the most challenging problems in computer security. Recently, methods based on machine learning are very popular in unknown and variant malware detection. In order to achieve a successful learning, extracting discriminant and stable features is the most important prerequisite. In this paper, we propose a bilayer behavior abstraction method based on semantic analysis of dynamic API sequences. Operations on sensitive system resources and complex behaviors are abstracted in an interpretable way at different semantic layers. At the lower layer, raw API calls are combined to abstract low-layer behaviors via data dependency analysis. At the higher layer, low-layer behaviors are further combined to construct more complex high-layer behaviors with good interpretability. The extracted low-layer and high-layer behaviors are finally embedded into a high-dimensional vector space. Hence, the abstracted behaviors can be directly used by many popular machine learning algorithms. Besides, to tackle the problem that benign programs are not adequately sampled or malware and benign programs are severely imbalanced, an improved one-class support vector machine (OC-SVM) named OC-SVM-Neg is proposed which makes use of the available negative samples. Experimental results show that the proposed feature extraction method with OC-SVM-Neg outperforms binary classifiers on the false alarm rate and the generalization ability.


title: Predicting Discourse Connectives for Implicit Discourse Relation Recognition

abstract: Existing works indicate that the absence of explicit discourse connectives makes it difficult to recognize implicit discourse relations. In this paper we attempt to overcome this difficulty for implicit relation recognition by automatically inserting discourse connectives between arguments with the use of a language model. Then we propose two algorithms to leverage the information of these predicted connectives. One is to use these predicted implicit connectives as additional features in a supervised model. The other is to perform implicit relation recognition based only on these predicted connectives. Results on Penn Discourse Treebank 2.0 show that predicted discourse connectives help implicit relation recognition and the first algorithm can achieve an absolute average f-score improvement of 3% over a state of the art baseline system.


title: A universal weak method

abstract: The weak methods occur pervasively in AI systems and may form the basic methods for all intelligent systems. The purpose of this paper is to characterize the weak methods and to explain how and why they arise in intelligent systems. We propose an organization, called a universal weak method, that provides functionality of all the weak methods." A universal weak method is an organizational scheme for knowledge that produces the appropriate search behavior given the available task-domain knowledge. We present a problem solving architecture, called SOAR, in which we realize a universal weak method. We then demonstrate the universal weak method with a variety of weak methods on a set of tasks. This research was sponsored by the Defense Advanced Research Projects Agencv (DOD), ARPA Order No 3597, monitored by the Air Force Avionics Laboratory Under Contract F33615*-78-C-1551 The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. Table of


title: Ambiguity in Categorical Models of Meaning

abstract: Building on existing categorical accounts of natural language semantics, we propose a compositional distributional model of ambiguous meaning. Originally inspired by the high-level category theoretic language of quantum information protocols, the compositional, distributional categorical model provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and an empirical derivation of the meaning of its parts. Grammar is given a type-logical description in a compact closed category while the meaning of words is represented in a finite inner product space model. Since the category of finite-dimensional Hilbert spaces is also compact closed, the type-checking deduction process lifts to a concrete meaning-vector computation via a strong monoidal functor between the two categories. The advantage of reasoning with these structures is that grammatical composition admits an interpretation in terms of flow of meaning between words. Pushing the analogy with quantum mechanics further, we describe ambiguous words as statistical ensembles of unambiguous concepts and extend the semantics of the previous model to a category that supports probabilistic mixing. We introduce two different Frobenius algebras representing different ways of composing the meaning of words, and discuss their properties. We conclude with a range of applications to the case of definitions, including a meaning update rule that reconciles the meaning of an ambiguous word with that of its definition.


title: A pseudo-class-AB telescopic-cascode operational amplifier

abstract: A class-AB architecture for single-stage operational amplifiers is presented. The structure employs a switched-capacitor level shifter to apply the signal to both sink and source output transistors to create the class-AB behavior. Using this structure, the current mirror circuit of traditional class-AB structures can be eliminated. Thus some power can be saved and the operation frequency can be increased. Simulation results of a fast-settling telescopic-cascode op-amp employing this pseudo-class-AB technique confirm the effectiveness of the approach to reduce the power consumption. This approach can be applied to the first stage of a two-stage op-amp as well, to increase the slew rate. The simulated 12-bit 100-Ms/s sample-and-hold amplifier employing the proposed pseudo-class-AB telescopic-cascode op-amp consumes only 16 mW from 3.3 V power supply.


title: Integrating Models of Personality and Emotions into Lifelike Characters

abstract: A growing number of research projects in academia and industry have recently started to develop lifelike agents as a new metaphor for highly personalised human-machine communication. A strong argument in favour of using such characters in the interface is the fact that they make human-computer interaction more enjoyable and allow for communication styles common in human-human dialogue. In this paper we discuss three ongoing projects that use personality and emotions to address different aspects of the affective agent-user interface: (a) Puppet uses affect to teach children how the different emotional states can change or modify a character's behaviour, and how physical and verbal actions in social interactions can induce emotions in others; (b) the Inhabited Market Place uses affect to tailor the roles of actors in a virtual market place; and (c) Presence uses affect to enhance the believability of a virtual character, and produce a more natural conversational manner.


title: Sources of sport confidence, imagery type and performance among competitive athletes: the mediating role of sports confidence.

abstract: Aim. This study explored the mediating role of sport confidence upon (1) sources of sport confidence- performance relationship and (2) imagery- performance relationship. Methods. Participants were 157 competitive athletes who completed state measures of confidence level/ sources, imagery type and performance within one hour after competition. Results. Among the current sample, confirmatory factor analysis revealed appropriate support for the nine-factor SSCQ and the five-factor SIQ. Mediational analysis revealed that sport confidence had a mediating influence upon the achievement source of confidence – performance relationship. In addition, both cognitive and motivational imagery types were found to be important sources of confidence, as sport confidence mediated imagery type- performance relationship. Conclusion. Findings indicated that athletes who construed confidence from their own achievements and report multiple images on a more frequent basis are likely to benefit from enhanced levels of state sport confidence and subsequent performance.


title: Frictionless commerce? a comparison of internet and conventional retailers

abstract: There have been many claims that the Internet represents a new nearly "frictionless market." Our research empirically analyzes the characteristics of the Internet as a channel for two categories of homogeneous products-books and CDs. Using a data set of over 8,500 price observations collected over a period of 15 months, we compare pricing behavior at 41 Internet and conventional retail outlets. ::: ::: We find that prices on the Internet are 9-16% lower than prices in conventional outlets, depending on whether taxes, shipping, and shopping costs are included in the price. Additionally, we find that Internet retailers' price adjustments over time are up to 100 times smaller than conventional retailers' price adjustments-presumably reflecting lower menu costs in Internet channels. We also find that levels of price dispersion depend importantly on the measures employed. When we compare the prices posted by different Internet retailers we find substantial dispersion. Internet retailer prices differ by an average of 33% for books and 25% for CDs. However, when we weight these prices by proxies for market share, we find dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. ::: ::: We conclude that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers.


title: Density-based multiscale data condensation

abstract: A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.


title: An Ethological Approach to Personality Development

abstract: attachment that we have jointly developed is that it is an ethological approach to personality development. We have had a long and happy partnership in pursuing this approach. In this article we wish to give a brief historical account of the initially separate but compatible approaches that eventually merged in the partnership, and how our contributions have intertwined in the course of developing an ethologically oriented theory of attachment and a body of research that has both stemmed from the theory and served to extend and elaborate it.


title: Don't Just Swipe Left, Tell Me Why: Enhancing Gesture-based Feedback with Reason Bins

abstract: Despite several advances in information retrieval systems and user interfaces, the specification of queries over text-based document collections remains a challenging problem. Query specification with keywords is a popular solution. However, given the widespread adoption of gesture-driven interfaces such as multitouch technologies in smartphones and tablets, the lack of a physical keyboard makes query specification with keywords inconvenient. We present BinGO, a novel gestural approach to querying text databases that allows users to refine their queries using a swipe gesture to either "like" or "dislike" candidate documents as well as express the reasons they like or dislike a document by swiping through automatically generated "reason bins". Such reasons refine a user's query with additional keywords. We present an online and efficient bin generation algorithm that presents reason bins at gesture articulation. We motivate and describe BinGo's unique interface design choices. Based on our analysis and user studies, we demonstrate that query specification by swiping through reason bins is easy and expressive.


title: Wikilinks: A Large-scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia

abstract: Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. This paper presents a method for automatically gathering massive amounts of naturally-occurring cross-document reference data. We also present the Wikilinks dataset comprising of 40 million mentions over 3 million entities, gathered using this method. Our method is based on finding hyperlinks to Wikipedia from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, we are able to include many styles of text beyond newswire and many entity types beyond people.


title: Solar photovoltaic charging of lithium-ion batteries

abstract: Solar PV battery charging was tested by using crystalline and amorphous silicon PV modules to recharge lithium-ion battery strings. The iron phosphate type batteries were charged to their maximum capacity with optimum efficiency while avoiding thermal hazards associated with overcharging due to the self-regulating design of the solar charging system.


title: Ku-band sidearm orthomode transducer manufactured by additive layer manufacturing

abstract: This paper investigates the use of additive layer manufacturing (ALM) for waveguide components based on two Ku-band sidearm orthomode transducers (OMT). The advantages and disadvantages of the ALM manufacturing regarding RF waveguide components are discussed and measurement results are compared to those of an equal OMT manufactured by conventional techniques. The paper concludes with an outlook to the capability of advanced manufacturing techniques for RF space applications as well as ongoing development activities.


title: The evolution of virtual machine architecture

abstract: In the early 1960's two major evolutionary steps were taken with regard to computing systems architecture. These were the emergence of I/O processors and the use of multiprogramming to improve resource utilization and overall performance. As a consequence of the first step computing systems became multiprocessor configurations where nonidentical processors could have access to the common main memory of the system. The second step resulted in several computational processes sharing a single processor on a time-multiplexed basis while vying for a common pool of resources.


title: Characteristics of the athletes' brain: Evidence from neurophysiology and neuroimaging

abstract: We review research on athletes' brains based on data obtained using non-invasive neurophysiological and neuroimaging methods; these data pertain to cognitive processing of visual, auditory, and somatosensory (tactile) stimulation as well as to motor processing, including preparation, execution, and imagery. It has been generally accepted that athletes are faster, stronger, able to jump higher, more accurate, more efficient, more consistent, and more automatic in their sports performances than non-athletes. These claims have been substantiated by neuroscientific evidence of the mechanisms underlying the plastic adaptive changes in the neuronal circuits of the brains of athletes. Reinforced neural networks and plastic changes are induced by the acquisition and execution of compound motor skills during extensive daily physical training that requires quick stimulus discrimination, decision making, and specific attention. In addition, it is likely that the manner of neuronal modulation differs among sports. We also discuss several problems that should be addressed in future studies.


title: The Dynamic Nature of Apomixis in the Angiosperms

abstract: Apomixis, the asexual production of seed, is a trait estimated to occur in fewer than 1% of flowering plant species, with an uneven distribution among lineages. In the past decade, targeted research efforts have aimed at clarifying the genetic basis of apomixis, with the goal of engineering or breeding apomictic crops. Recent work suggests a simple genetic basis for apomixis, but it also indicates that natural populations of apomicts are much more complex than is often assumed. For example, in nature, nearly all apomicts that go through a megagametophyte stage (gametophytic apomicts) are polyploid, while their sexual relatives are typically diploid. Although populations have been characterized as obligately sexual or apomictic, it is increasingly clear that many plant populations exhibit some variation in reproductive mode. Many apomicts retain residual sexual function as pollen donors and thus have the potential to spread apomixis via male gametes, thereby increasing the genetic diversity observed within...


title: Singularity analysis of fully-constrained cable-driven parallel robots with seven cables

abstract: Singularity analysis of parallel robots is an important issue. A parallel robot could be out of control or crash in a singular configuration. The unidirectional constraint of cables makes the singularity analysis of cable-driven parallel robots different from that of rigid-link parallel robots even if they have similar kinematical architectures. This paper discusses various categories of singularities of fully-constrained cable-driven parallel robots. Based on their natures, singularities of cable-driven parallel robots are classified into two categories: the Jacobian singularity and the force-closure singularity. A Jacobian singularity occurs when the Jacobian matrix of a cable-driven parallel robot loses its full rank. When the Jacobian matrix of a cable-driven parallel robot has a full rank, the cables' inability to generate tension will lead to force-closure singularities, which can always happen to fully-constrained cable-driven parallel robots. An algorithm of identifying force-closure singularities of fully-constrained cable-driven parallel robots with seven cables is proposed. Understanding of the natures of singularities is important for the design and control of cable-driven parallel robots.


Event minig from Blogsphere using topic words


title: Model Predictive Control design for a convertible Quad Tilt-Wing UAV

abstract: This paper deals with the design of a Model Predictive Control (MPC) approach for the altitude and attitude stabilization and tracking of a Quad Tilt Wing (QTW) type of Unmanned Aerial Vehicles (UAVs). This Vertical Take-Off and Landing (VTOL) aircraft can take-off and landing vertically such as helicopters and is convertible to the fixed-wing configuration for horizontal flight using a tilting mechanism for its rotors/wings. A nonlinear dynamical model, relating to the vertical flight mode of this QTW, is firstly developed using the Newton-Euler formalism, in describing the aerodynamic forces and moments acting on the aircraft. This established model, linearized around an equilibrium operating point, is then used to design a MPC approach for the stabilization and tracking of the QTW attitude and altitude. In order to show the performance superiority of the proposed MPC technique, a comparison with the known Linear Quadratic (LQ) strategy is carried out. All simulation results, obtained for both MPC and LQ approaches, are presented and discussed.


Supply Chain Management : An International Journal Decision theory in sustainable supply chain management : a literature review


title: Hyperspherical Variational Auto-Encoders

abstract: The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or $\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\mathcal{N}$-VAE, in low dimensions on other data types.


title: TrackMAC: An IEEE 802.11ad-compatible beam tracking-based MAC protocol for 5G millimeter-wave local area networks

abstract: This paper presents a novel framework for MAC design for millimeter-wave (mm-wave) mobile wireless networks. Specifically, we consider an infrastructure wireless network in which both the Access Point (AP) and the mobile stations (STAs) communicate in the 60GHz band. In order to overcome the high path loss that is characteristic of mm-wave frequencies, both the transmitter as well as the receivers employ beamforming and use highly directional beams for transmission and reception. In such a scenario, traditional Medium Access Control (MAC) protocols such as CSMA/CA, which rely on the omni-directional nature of transmissions, may no longer be viable for efficient medium access control. It is necessary for the AP to know the directions that the associated STAs are in so that it can steer its transmissions to an intended receiver in that receiver's direction. In light of this, we propose TrackMAC, a directional MAC protocol that (i) has the property that it continuously tracks the direction of every associated station which, in general, is mobile, and (ii) can be implemented squarely within the specifications of the IEEE 802.11ad standard for mm-wave Wireless Local Area Networks (WLAN). The efficacy of the proposed architecture is demonstrated using computer simulations.


title: Learning to Guide Decoding for Image Captioning

abstract: Recently, much advance has been made in image captioning, and an encoder-decoder framework has achieved outstanding performance for this task. In this paper, we propose an extension of the encoder-decoder framework by adding a component called guiding network. The guiding network models the attribute properties of input images, and its output is leveraged to compose the input of the decoder at each time step. The guiding network can be plugged into the current encoder-decoder framework and trained in an end-to-end manner. Hence, the guiding vector can be adaptively learned according to the signal from the decoder, making itself to embed information from both image and language. Additionally, discriminative supervision can be employed to further improve the quality of guidance. The advantages of our proposed approach are verified by experiments carried out on the MS COCO dataset.


title: Multi-threshold CMOS design for low power digital circuits

abstract: Multi-threshold CMOS (MTCMOS) power gating is a design technique in which a power gating transistor is connected between the logic transistors and either power or ground, thus creating a virtual supply rail or virtual ground rail, respectively. Power gating transistor sizing, transition (sleep mode to active mode) current, short circuit current and transition time are design issues for power gating design. The use of power gating design results in the delay overhead in the active mode. If both nMOS and pMOS sleep transistor are used in power gating, delay overhead will increase. This paper proposes the design methodology for reducing the delay of the logic circuits during active mode. This methodology limits the maximum value of transition current to a specified value and eliminates short circuit current. Experiment results show 16.83% reduction in the delay.


EMPLOYING DESIGN AND DEVELOPMENT RESEARCH ( DDR ) APPROACHES IN THE DESIGN AND DEVELOPMENT OF ONLINE ARABIC VOCABULARY LEARNING GAMES PROTOTYPE


title: Low cost virtual reality for medical training

abstract: This demo depicts a low cost virtual reality set-up that may be used for medical training and instruction purposes. Using devices such as the Oculus Rift and Razer Hydra, an immersive experience, including hand interactivity can be given. Software running on a PC integrates these devices and presents an interactive and immersive training environment, where trainees are asked to perform a mixed bag of both, simple and complex tasks. These tasks range from identification of certain organs to performing of an actual incision. Trainees learn by doing, albeit in the virtual world. Components of the system are relatively affordable and simple to use, thereby making such a set-up incredibly easy to deploy.


title: Reputation and social network analysis in multi-agent systems

abstract: The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multi-agent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, among other things, of social relations between agents to overcome this problem.


Antecedents to knowledge sharing behaviour: trust climate as the facilitator andattachment to knowledge as the detractor


title: A Two Sweeping VCO Source for Heterodyne FMCW Radar

abstract: We propose and develop a heterodyne frequency modulated continuous wave (FMCW) radar system. The system operates at 14 GHz with a 500-MHz bandwidth in a 100-μs sweeping period, based on two wideband linear RF voltage-controlled oscillator (VCO) sweeping sources, which are controlled by a direct digital synthesizer (DDS) through a phase-locked scheme, leading to a high linearity of the frequency chirping signal. To achieve low phase noise, the RF signals are synthesized from low-noise baseband signals which are generated through a generation scheme of using phase-locked oscillators operating in the 800-MHz frequency range. Additionally, the proposed architecture can eliminate the phase noise contributions and/or frequency deviation due to the uncorrelation of different VCOs. The results are verified through measurements and derivation.


title: High-Energy Picosecond Pulse Generation by Gain Switching in Asymmetric Waveguide Structure Multiple Quantum Well Lasers

abstract: A multiple quantum well laser diode utilizing an asymmetric waveguide structure with a large equivalent spot size of ∼3 μm is shown to give high energy (∼1 nJ) and short (∼100 ps) isolated optical pulses when injected with <10 A and ∼1-ns current pulses realized with a MOS driver. The active dimensions of the laser diode are 30 μm (stripe width) and 3 mm (cavity length), and it works in a single transversal mode at a wavelength of ∼0.8 μm. Detailed investigation of the laser behavior at elevated temperatures is conducted; it is shown that at high enough injection currents, lasers of the investigated type show low temperature sensitivity. Laser diodes of this type may find use in accurate and miniaturized laser radars utilizing single photon detection in the receiver.


title: Multi‐objective transmission expansion planning with allocation of fixed series compensation under uncertainties

abstract: Summary ::: Integration of wind resources into the power systems has increased the technical and financial concerns in transmission expansion planning. In this paper, a stochastic structure is demonstrated for transmission expansion planning with allocation of fixed series compensation under wind and load uncertainties. Fixed series compensations have the ability to increase the transfer capacity of transmission lines. However, their significance benefit for transmission expansion planning is their higher effectiveness in power dispatching, which results in lower investment costs, compared with planning without fixed series compensations. The proposed planning model simultaneously optimizes targets such as the investment cost, the congestion cost, and the expected energy not supplied in each stage. The presented planning model is a complicated nonlinear optimizing problem. For introducing group of nondominated optimal solutions, multi-objective gray wolf optimizer algorithm based on probabilistic optimal power flow is used. Then, fuzzy approach is used to lead to the best response. For demonstrating the feasibility and susceptibilities of the proposed algorithm, the planning methodology has been illustrated on the IEEE 24-bus and Colombian test systems. The results acquired from the simulations monitor capability of the proposed structure in order to satisfy various planning indices.


title: Charters, Mandates, Roadmaps and Other Artefacts at the Launch of a Project: Characteristics and Similarities

abstract: If the Abernathy-Utterback model about the innovation of technology was applied to the approaches and methodologies of project management, it would be clear that, five decades after the birth of modern project management, the domain would persist in a “fluid phase”, where approaches are still numerous and no dominant design or particular methodology really emerged yet. Comparing the many standards and methodologies one by one would be certainly useful. However, the task is ambitious and can only reasonably be undertaken in bits and pieces. In this article, we are interested in the only artefact that initialize a project: according to the authors and obedience, this one takes different names: project charter, project mandate, project proposal, roadmap, project mission statement, project brief or statement of work (SoW). System engineering also promotes two additional types of documents: ConOps (concept of operations), replaced in 2010 by the OpsCon (operational concept) or OCD (operational concept document). First, we have tried to compare a few documents which reflect the main obedience, according to their temporal locations along a project life cycle, their managerial aims and their typical contents. In a second step, we propose a model of a unifying document that could be satisfying to all. In any case, it is this artefact that the CERN Engineering Department promotes through a system engineering framework called openSE ( www.cern.ch/openSE ) to federate multidisciplinary contributions to the many programmes and projects on going at CERN.


DeScript : A Crowdsourced Corpus for the Acquisition of High-Quality Script Knowledge


title: Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

abstract: Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.


title: Adoption and use of Java generics

abstract: Support for generic programming was added to the Java language in 2004, representing perhaps the most significant change to one of the most widely used programming languages today. Researchers and language designers anticipated this addition would relieve many long-standing problems plaguing developers, but surprisingly, no one has yet measured how generics have been adopted and used in practice. In this paper, we report on the first empirical investigation into how Java generics have been integrated into open source software by automatically mining the history of 40 popular open source Java programs, traversing more than 650 million lines of code in the process. We evaluate five hypotheses and research questions about how Java developers use generics. For example, our results suggest that generics sometimes reduce the number of type casts and that generics are usually adopted by a single champion in a project, rather than all committers. We also offer insights into why some features may be adopted sooner and others features may be held back.


Multi-Shot Human Re-Identification Using Adaptive Fisher Discriminant Analysis


Problem-Based Learning in Teacher Education : Trajectories of Change


title: Question Answering over Linked Data Using First-order Logic

abstract: Question Answering over Linked Data (QALD) aims to evaluate a question answering system over structured data, the key objective of which is to translate questions posed using natural language into structured queries. This technique can help common users to directly access open-structured knowledge on the Web and, accordingly, has attracted much attention. To this end, we propose a novel method using first-order logic. We formulate the knowledge for resolving the ambiguities in the main three steps of QALD (phrase detection, phrase-tosemantic-item mapping and semantic item grouping) as first-order logic clauses in a Markov Logic Network. All clauses can then produce interacted effects in a unified framework and can jointly resolve all ambiguities. Moreover, our method adopts a pattern-learning strategy for semantic item grouping. In this way, our method can cover more text expressions and answer more questions than previous methods using manually designed patterns. The experimental results using open benchmarks demonstrate the effectiveness of the proposed method.


Citation Recommendation via Proximity Full-Text Citation Analysis and Supervised Topical Prior


title: Design and realization of circular patch antenna for S-Band Coastal Radar

abstract: In this paper, a design and realization of a coastal radar antenna that works on S-Band frequency was presented. Type of antenna that had been used in this research is a microstrip patch antenna. One antenna module consist of eight patch that was horizontally arranged. The microstrip antenna had been fabricated using FR-4 epoxy substrate with 4.6 dielectric constant and a thickness of 1.6mm. Measured antenna acquires unidirectional radiation pattern, 16° beamwidth azimuth, 2.93 GHz – 3.093 GHz of operating frequency at Voltage Standing Wave Ratio (VSWR) ≤ 2; 166 MHz of bandwidth and Gain = 11.98 dB. There was a satisfactory agreement between simulation and measurement result. This circular patch antenna will be implemented for S-Band Coastal Radar.


Automatic Topic Labeling in Asynchronous Conversations


title: Operating system support for continuous media applications-RT-Mach extensions

abstract: In this paper, we describe operating system support for continuous media applications based on our experience of using the Real-Time Mach microkernel. We first address the limitations of the current workstation operating systems. Then, we discuss operating system support for continuous media from three different points of view: architectural support, resource management support, and programming support. Several Real-Time Mach examples are also discussed.


title: Dosimetry Inter-Laboratory Comparison between ESTEC, CNA-ALTER/RADLAB, and UCL

abstract: The dosimetry is a key factor to provide confidence on EEE components total dose testing. Although there are accuracy requirements defined in the applicable test methods, it is difficult to determine the degree of correlation between the laboratories, as they use different technical means, procedures and calculations to determine the dose rate and the total dose values declared in the final test reports. To learn about such differences, a measurement inter-comparison activity has been designed and carried out between three well-known laboratories, covering a complete range of conditions, and performing a comparison of the obtained results.


title: A Survey on Financial Applications of Metaheuristics

abstract: Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.


DEEP ENSEMBLE BAYESIAN ACTIVE LEARNING : ADRESSING


title: Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off

abstract: The current state-of-the-art in video classification is based on Bag-of-Words using local visual descriptors. Most commonly these are histogram of oriented gradients (HOG), histogram of optical flow (HOF) and motion boundary histograms (MBH) descriptors. While such approach is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG, HOF and MBH descriptors and release Matlab code; (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method; (3) We investigate the trade-off between accuracy and computational efficiency for computing the feature vocabulary, using and comparing most of the commonly adopted vector quantization techniques: $$k$$k-means, hierarchical $$k$$k-means, Random Forests, Fisher Vectors and VLAD.


title: Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

abstract: Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence-only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data-sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point-process modelling framework (GLMs with subset selection, GLMs fitted with an elastic net penalty, and Maxent). To correct for sampling bias, we applied model-based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial-block cross-validation and a holdout data set. When evaluated with independent, but also sampling-biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic-net models have intermediate performance, with slight advantage for GLMs on cross-validation and Maxent on hold-out evaluation. Model-based bias correction is very useful in data-sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than effort covariates, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias-free presence-absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed. ::: ::: This article is protected by copyright. All rights reserved.


Capability Maturity Model for Software


title: Simsync: A Time Synchronization Simulator for Sensor Networks 1)

abstract: Time synchronization is a critical middleware service of wireless sensor networks. Researchers have already proposed some time synchronization algorithms. However, due to the demands for various synchronization precision, existing time synchronization algorithms often need to be adapted. So it is necessary to evaluate these adapted algorithms before use. Software simulation is a valid way to do it quickly. In this paper, we present a time synchronization simulator, Simsync, for wireless sensor networks. We decompose the packet delay into 6 delay components and model them separately. The frequency of crystal oscillator is modeled as Gaussian. To testify its effectiveness, we simulate the reference broadcast synchronization algorithm(RBS) and the timing-sync synchronization algorithm(TPSN) on Simsync. Simulated results are also presented and analyzed.


title: FPMR: MapReduce Framework on FPGA A Case Study of RankBoost Acceleration

abstract: learning and data mining are gaining increasing attentions of the computing society. FPGA provides a highly parallel, low power, and flexible hardware platform for this domain, while the difficulty of programming FPGA greatly limits its prevalence. MapReduce is a parallel programming framework that could easily utilize inherent parallelism in algorithms. In this paper, we describe FPMR, a MapReduce framework on FPGA, which provides programming abstraction, hardware architecture, and basic building blocks to developers. An on-chip processor scheduler is implemented to maximize the utilization of computation resources and achieve better load balancing. An efficient data access scheme is carefully designed to maximize data reuse and throughput. Meanwhile, the FPMR framework hides the task control, synchronization, and communication away from designers so that more attention can be paid to the application itself. A case study of RankBoost acceleration based on FPMR demonstrates that FPMR efficiently helps with the development productivity; and the speedup is 31.8x versus CPU-based implementation. This performance is comparable to a fully manually designed version, which achieves 33.5x speedup. Two other applications: SVM, PageRank are also discussed to show the generalization of the framework.


title: Inverse dynamics and energy optimal trajectories for a wheeled mobile robot

abstract: Abstract A wheeled mobile robot (WMR) is a typical nonholonomic system in which the wheels have constrained interactions with the ground. To relate these local interactions to some global constraints on the WMR, we start on the contact kinematics and then analyze the robot’s dynamics using the Gibbs–Appell equation. This yields motion equations with minimal dimensions that are free of Lagrange multipliers (which would arise from application of the d’Alembert–Lagrange principle). The inverse dynamics of the WMR can then be solved using two methods. The first method looks for servo constraints to meet the control objective and are consistent with the constraints over the WMR. The second method, applying the elegant differential flatness theory, is based on one-to-one mappings from flat output to the state and the input. We also explain the reason why the WMR’s energy optimal trajectory requires a cost function related to the integral of the Lagrangian. An optimization algorithm based on the Ritz approximation is offered with efficiency illustrated via two examples.


title: Slew rate control of a 600 V 55 mΩ GaN cascode

abstract: This paper presents a 600 V, 55 mS GaN cascode with slew rate control. The time constants of cascodes which determine the switching speed are analyzed, discussed and verified by time domain simulations. Clamped inductive switching measurements of commercial and a custom-built GaN cascodes prove the applicability of the switching speed control mechanisms with additional passive components. The gathered experiences are applied to a custom-built modular cascode, which shows simple switching speed controllability by utilizing a standard gate drive.


title: Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap

abstract: With the increasing demand for more energy efficient buildings, the construction industry is faced with the challenge to ensure that the energy efficiency predicted during the design is realised once a building is in use. There is, however, significant evidence to suggest that buildings are not performing as well as expected and initiatives such as PROBE and CarbonBuzz aim to illustrate the extent of this so called "Performance Gap". This paper discusses the underlying causes of discrepancies between detailed energy modelling predictions and in-use performance of occupied buildings (after the twelve month liability period). Many of the causal factors relate to the use of unrealistic input parameters regarding occupancy behaviour and facilities management in building energy models. In turn, this is associated with the lack of feedback to designers once a building has been constructed and occupied. This paper aims to demonstrate how knowledge acquired from Post-Occupancy Evaluation (POE) can be used to produce more accurate energy performance models. A case study focused specifically on lighting, small power and catering equipment in a high density office building is presented. Results show that by combining monitored data with predictive energy modelling, it was possible to increase the accuracy of the model to within 3% of actual electricity consumption values. Future work will seek to use detailed POE data to develop a set of evidence based benchmarks for energy consumption in office buildings. It is envisioned that these benchmarks will inform designers on the impact of occupancy and management on the actual energy consumption of buildings. Moreover, it should enable the use of more realistic input parameters in energy models, bringing the predicted figures closer to reality.


AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable


title: How physical text layout affects reading from screen

abstract: The primary objective of this paper is to critically evaluate empirical research on some variables relating to the configuration of text on screen to consolidate our current knowledge in these areas. The text layout variables are line length, columns, window size and interlinear spacing, with an emphasis on line length due to the larger number of studies related to this variable. Methodological issues arising from individual studies and from comparisons among studies are identified. A synthesis of results is offered which provides alternative interpretations of some findings and identifies the number of characters per line as the critical variable in looking at line length. Further studies are needed to explore the interactions between characters per line and eye movements, scrolling movements, reading patterns and familiarity with formats.


title: Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera

abstract: 3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.


title: High frequency plant regeneration from leaf derived callus of high Δ9-tetrahydrocannabinol yielding Cannabis sativa L.

abstract: An efficient in vitro propagation protocol for rapidly producing Cannabis sativa plantlets from young leaf tissue was developed. Using gas chromatography-flame ionization detection (GC-FID), high THC yielding elite female clone of a drug-type CANNABIS variety (MX) was screened and its vegetatively propagated clones were used for micropropagation. Calli were induced from leaf explant on Murashige and Skoog medium supplemented with different concentrations (0.5, 1.0, 1.5, and 2.0 µM) of indole- 3-acetic acid (IAA), indole- 3- butyric acid (IBA), naphthalene acetic acid (NAA), and 2,4-dichlorophenoxy-acetic acid (2,4-D) in combination with 1.0 µM of thidiazuron (TDZ) for the production of callus. The optimum callus growth and maintenance was in 0.5 µM NAA plus 1.0 µM TDZ. The two-month-old calli were subcultured to MS media containing different concentrations of cytokinins (BAP, KN, TDZ). The rate of shoot induction and proliferation was highest in 0.5 µM TDZ. Of the various auxins (IAA, IBA, and NAA) tested, regenerated shoots rooted best on half strength MS medium (1/2 - MS) supplemented with 2.5 µM IBA. The rooted plantlets were successfully established in soil and grown to maturity with no gross variations in morphology and cannabinoids content at a survival rate of 95 % in the indoor growroom.


title: Unsupervised Construction of an Indoor Floor Plan Using a Smartphone

abstract: Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.


title: Neural networks for classification: a survey

abstract: Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.


title: Cry me a river: Identifying the behavioral consequences of extremely high-stakes interpersonal deception.

abstract: Deception evolved as a fundamental aspect of human social interaction. Numerous studies have examined behavioral cues to deception, but most have involved inconsequential lies and unmotivated liars in a laboratory context. We conducted the most comprehensive study to date of the behavioral consequences of extremely high-stakes, real-life deception—relative to comparable real-life sincere displays—via 3 communication channels: speech, body language, and emotional facial expressions. Televised footage of a large international sample of individuals (N 78) emotionally pleading to the public for the return of a missing relative was meticulously coded frame-by-frame (30 frames/s for a total of 74,731 frames). About half of the pleaders eventually were convicted of killing the missing person on the basis of overwhelming evidence. Failed attempts to simulate sadness and leakage of happiness revealed deceptive pleaders’ covert emotions. Liars used fewer words but more tentative words than truth-tellers, likely relating to increased cognitive load and psychological distancing. Further, each of these cues explained unique variance in predicting pleader sincerity.


title: Growing up in a Bubble: Using Germ-Free Animals to Assess the Influence of the Gut Microbiota on Brain and Behavior

abstract: There is a growing recognition of the importance of the commensal intestinal microbiota in the development and later function of the central nervous system. Research using germ-free mice (mice raised without any exposure to microorganisms) has provided some of the most persuasive evidence for a role of these bacteria in gut-brain signalling. Key findings show that the microbiota is necessary for normal stress responsivity, anxiety-like behaviors, sociability, and cognition. Furthermore, the microbiota maintains central nervous system homeostasis by regulating immune function and blood brain barrier integrity. Studies have also found that the gut microbiota influences neurotransmitter, synaptic, and neurotrophic signalling systems and neurogenesis. The principle advantage of the germ-free mouse model is in proof-of-principle studies and that a complete microbiota or defined consortiums of bacteria can be introduced at various developmental time points. However, a germ-free upbringing can induce permanent neurodevelopmental deficits that may deem the model unsuitable for specific scientific queries that do not involve early-life microbial deficiency. As such, alternatives and complementary strategies to the germ-free model are warranted and include antibiotic treatment to create microbiota-deficient animals at distinct time points across the lifespan. Increasing our understanding of the impact of the gut microbiota on brain and behavior has the potential to inform novel management strategies for stress-related gastrointestinal and neuropsychiatric disorders.


title: Catching the Temporal Regions-of-Interest for Video Captioning

abstract: As a crucial challenge for video understanding, exploiting the spatial-temporal structure of video has attracted much attention recently, especially on video captioning. Inspired by the insight that people always focus on certain interested regions of video content, we propose a novel approach which will automatically focus on regions-of-interest and catch their temporal structures. In our approach, we utilize a specific attention model to adaptively select regions-of-interest for each video frame. Then a Dual Memory Recurrent Model (DMRM) is introduced to incorporate temporal structure of global features and regions-of-interest features in parallel, which will obtain rough understanding of video content and particular information of regions-of-interest. Since the attention model could not always catch the right interests, we additionally adopt semantic supervision to attend to interested regions more correctly. We evaluate our method for video captioning on two public benchmarks: the Microsoft Video Description Corpus (MSVD) and the Montreal Video Annotation Dataset (M-VAD). The experiments demonstrate that catching temporal regions-of-interest information really enhances the representation of input videos and our approach obtains the state-of-the-art results on popular evaluation metrics like BLEU-4, CIDEr, and METEOR.


title: A novel swarm robot simulation platform for warehousing logistics

abstract: Swarm robot systems are widely applied in the warehouse logistics, which effectively improve logistics efficiency. Task allocation is the core problem for swarm robot systems. Therefore, evaluation of the task allocation strategy has very important practical significance. However, current simulation platforms, such as GAZEBO, require a lot of setup work before testifying the task allocation strategy for various applications. Regarding warehousing logistics, a few special factors need to be considered: man-robot coexistence in working environment, the energy consumption of robots and collision avoidance among robots. Therefore, we propose a novel swarm robot simulation platform, called MultiBots, based on multi-agent pathfinding (MAPF) method and collision avoidance strategy, which can correctly evaluate the effectiveness of task allocation strategy. Moreover, we design the charging process to supplement the energy consumption of robots. The experimental results show that the proposed MultiBots satisfies the requirements of task allocation strategy evaluation in warehouse logistics scenarios. The MultiBots can be applied in testifying the efficiency of task allocation strategy for logistics systems.