title: Student socialization in the age of facebook

abstract: Most research regarding online social networks such as Facebook, MySpace, Linked-In and Friendster has looked at these networks in terms of activity within the online network, such as profile management and friending behavior. In this paper we are instead focusing on offline socializing structures around an online social network (exemplified by Facebook) and how this can facilitate in-person social life for students. Because students lead nomadic lives, they find Facebook a particularly useful tool for initiating and managing social gatherings, and as they adopt mobile technologies that can access online social networks, their ad-hoc social life is further enabled. We conclude that online social networks are a powerful tool for encouraging peripheral friendships, important in particular to students. We emphasize that the use of online social networks must be viewed from a perspective of use that involves both mobile and stationary platforms and that it is important to relate online and offline social practices.


title: Feature Weight Tuning for Recursive Neural Networks

abstract: This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful compositional function for embedding acquisition in recursive neural networks. Experimental results demonstrate the significant improvement over standard neural models.


A CONCEPTUAL FRAMEWORK FOR APPLYING LEAN MANAGEMENT METHODOLOGY IN HIGHER EDUCATION INSTITUTIONS


title: The capillary fringe and its effect on water-table response

abstract: Abstract Shallow water tables are often observed to respond in a highly disproportionate manner to precipitation events. That is, the magnitude of the response is often much greater than would be predicted on the basis of the specific yield of the geologic material and the amount of rainfall. Physical arguments are presented to show that if the capillary fringe (the zone of tension saturation) extends to ground surface, then the addition of a very small amount of water can result in an immediate and large rise in the water table. The arguments are supported by a field experiment in which the addition of 0.3 cm of water caused the water table to rise 30 cm in 0.25 min., thus demonstrating the large and highly transient influence of the capillary fringe on the position of the water table. The evidence in this study, as well as recent work of others, suggests that the capillary-fringe effect could have a major influence on the process of stream-flow generation, contaminant transport to surface waters and contaminant transport in hydrogeologic regimes having shallow water tables. It is further shown that calculations of groundwater recharge and consumptive use, based on water-table response and an assumed specific yield, could be substantially in error.


title: BCI classification based on signal plots and SIFT descriptors

abstract: Brain Computer Interfaces are a challenging technology with amazing prospects but its push into mainstream assistive applications has not arrived yet. In this work a new method to analyze and classify EEG, Electroencefalography, signals, is proposed which is based on the extraction of visually relevant feature descriptors from images of the signal plots. This procedure has the advantage that the features which are used to classify are visually relevant and meaningful to a human observer, particularly to a physician, improving close collaboration and clinical adoption. Moreover, this may allow to tackle this demanding technology from a different perspective and improve the prospects of the BNCI, Brain/Neural Computer Interaction field.


title: Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

abstract: We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification.


title: Bootstrapping domain ontologies from wikipedia: a uniform approach

abstract: Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass of, instance of, part of, and generic related to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.


title: EgoCap: egocentric marker-less motion capture with two fisheye cameras

abstract: Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual-reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a new automatically annotated and augmented dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes.


title: Character strengths in the United Kingdom: the VIA inventory of strengths

abstract: Abstract The development of a classification of strengths, the Values in Action Inventory of Strengths, has done much to advance research into strengths of character. Using an Internet sample of 17,056 UK respondents, we present data on the character strengths of a large UK sample. Women typically scored higher on strengths than did men. However, four of the top five “signature strengths” of the UK men and women overall were the same (open-mindedness, fairness, curiosity, and love of learning). Strengths typically showed small but significant positive associations with age, with the strongest associations with age between curiosity and love of learning (strengths of wisdom and knowledge), fairness (a strength of justice), and forgiveness and self-regulation (strengths of temperance). The discussion addresses potential limitations and suggests pertinent directions for future research.


title: A Survey on ICT Usage and the Perceptions of Social Studies Teachers in Turkey

abstract: Turkey has been undertaking many projects to integrate Information and Communication Technology (ICT) sources into practice in the teaching-learning process in educational institutions. This research study sheds light on the use of ICT tools in primary schools in the social studies subject area, by considering various variables which affect the success of the implementation of the use of these tools. A survey was completed by 326 teachers who teach fourth and fifth grade at primary level. The results showed that although teachers are willing to use ICT resources and are aware of the existing potential, they are facing problems in relation to accessibility to ICT resources and lack of in-service training opportunities.


Linear Off-Policy Actor-Critic


title: Physiology of growth hormone secretion during sleep

abstract: Abstract The temporal relation between the first few hours of sleep and the secretion of growth hormone (GH), which is present in normal persons of both sexes from early childhood until late adulthood, is reviewed. In adults the most reproducible pulse of GH secretion occurs shortly after the onset of sleep in association with the first phase of slow-wave sleep (SWS) (stages III and IV). In men approximately 70% of the GH pulses during sleep coincide with SWS, and the amount of GH secreted during these pulses correlates with the concurrent amount of SWS. Sleep-related secretion of GH appears to be primarily dependent on the release of growth hormone-releasing hormone. Rodent and human studies have shown that growth hormone-releasing hormone injections decrease wakefulness and increase SWS. During the fourth decade of life (ages 30 to 40 years) the total amount of GH secreted over a 24-hour span decreases by two- to threefold. Similarly, the amount of SWS decreases dramatically over the same narrow age range. Because the sleep-onset GH pulse is often the major secretory output in adults, age-related decrements in sleep-related GH secretion likely play a major role in the hyposomatotropism of senescence. (J P EDIATR 1996;128:S32-7)


title: SPPEAR: security & privacy-preserving architecture for participatory-sensing applications

abstract: Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of participatory sensing (PS). The openness of such systems and the richness of user data they entail raise significant concerns for their security, privacy and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a comprehensive solution. That is, a secure and accountable PS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after a PS system that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving PS entities (servers). We address these seemingly contradicting requirements with our SPPEAR architecture. Our full blown implementation and experimental evaluation demonstrate that SPPEAR is efficient, practical, and scalable. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system is a comprehensive solution that significantly extends the state-of-the-art and can catalyze the deployment of PS applications.


title: Instantaneous lateral velocity estimation of a vehicle using Doppler radar

abstract: High-resolution image radars open new opportunities for estimating velocity and direction of movement of extended objects from a single observation. Since radar sensors only measure the radial velocity, a tracking system is normally used to determine the velocity vector of the object. A stable velocity is estimated after several frames at the earliest, resulting in a significant loss of time for reacting to certain situations such as cross-traffic. The following paper presents a robust and model-free approach to determine the velocity vector of an extended target. In contrast to the Kalman filter, it does not require data association in time and space. An instant (~ 50 ms) and bias free estimation of its velocity vector is possible. Our approach can handle noise and systematic variations (e.g., micro-Doppler of wheels) in the signal. It is optimized to deal with measurement errors of the radar sensor not only in the radial velocity, but in the azimuth position as well. The accuracy of this method is increased by the fusion of multiple radar sensors.


title: Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks

abstract: Object detection has been one of the hottest issues in the field of remote sensing image analysis. In this letter, an efficient object detection framework is proposed, which combines the strength of the unsupervised feature learning of deep belief networks (DBNs) and visual saliency. In particular, we propose an efficient coarse object locating method based on a saliency mechanism. The method could avoid an exhaustive search across the image and generate a small number of bounding boxes, which can locate the object quickly and precisely. After that, the trained DBN is used for feature extraction and classification on subimages. The feature learning of the DBN is operated by pretraining each layer of restricted Boltzmann machines (RBMs) using the general layerwise training algorithm. An unsupervised blockwise pretraining strategy is introduced to train the first layer of RBMs, which combines the raw pixels with a saliency map as inputs. This makes an RBM generate local and edge filters. The precise edge position information and pixel value information are more efficient to build a good model of images. Comparative experiments are conducted on the data set acquired by QuickBird with a 60-cm resolution. The results demonstrate the accuracy and efficiency of our method.


title: Multi-lane detection in urban driving environments using conditional random fields

abstract: Over the past few decades, the need has arisen for multi-lane detection algorithms for use in vehicle safety-related applications. In this paper we propose a new multi-lane detection algorithm that works well in urban situations. This algorithm detects four lane marks, including driving lane marks and adjacent lane marks. Conventional research assumes that lanes are parallel. In contrast, our approach does not require this assumption, thus enabling the algorithm to manage various non-parallel lane situations, such as are found at intersections, in splitting lanes, and in merging lanes. To detect multi-lane marks successfully in the absence of parallelism, we adopt Conditional Random Fields (CRFs), which are strong models for solving multiple association tasks. We show that CRFs are very effective tools for multi-lane detection because they find an optimal association of multiple lane marks in complex and challenging urban road situations. Through simulations, and by using video sequences with 752-480 resolution and Caltech Lane Datasets with runtime rates of 30 fps, we verify that our algorithm successfully detects non-parallel lanes as well as parallel lanes appearing in urban streets.


title: Depth-of-focus of the human eye in the near retinal periphery

abstract: Abstract Although the depth-of-focus in the foveal region has been well investigated, knowledge regarding the effect of retinal eccentricity on blur detection and sensitivity is limited. In the present study, the depth-of-focus at the fovea and in the near retinal periphery (0°–8°) was assessed psychophysically in 7 human subjects using a 5 mm artificial pupil with accommodation paralyzed. The group mean total depth-of-focus progressively increased linearly from 0.89 D at the fovea to 3.51 D at a retinal eccentricity of 8° at the rate of 0.29 D/degree, with response variability (S.E.M.) remaining relatively constant (±0.17 D). We speculate that the reduced detection and sensitivity to blur in the near periphery may be attributed to retinal topography, sharpness overconstancy, optical aberrations, and visual attention in peripheral vision.


title: Shaping program repair space with existing patches and similar code

abstract: Automated program repair (APR) has great potential to reduce bug-fixing effort and many approaches have been proposed in recent years. APRs are often treated as a search problem where the search space consists of all the possible patches and the goal is to identify the correct patch in the space. Many techniques take a data-driven approach and analyze data sources such as existing patches and similar source code to help identify the correct patch. However, while existing patches and similar code provide complementary information, existing techniques analyze only a single source and cannot be easily extended to analyze both. In this paper, we propose a novel automatic program repair approach that utilizes both existing patches and similar code. Our approach mines an abstract search space from existing patches and obtains a concrete search space by differencing with similar code snippets. Then we search within the intersection of the two search spaces. We have implemented our approach as a tool called SimFix, and evaluated it on the Defects4J benchmark. Our tool successfully fixed 34 bugs. To our best knowledge, this is the largest number of bugs fixed by a single technology on the Defects4J benchmark. Furthermore, as far as we know, 13 bugs fixed by our approach have never been fixed by the current approaches.


title: Effects of the Transcendental Meditation Technique on Trait Anxiety: A Meta-Analysis of Randomized Controlled Trials

abstract: Abstract Objective: This meta-analysis of randomized controlled trials (RCTs) on the Transcendental Meditation® (TM) technique updates previous meta-analyses and assesses the effects of initial anxiety level, age, duration of practice, regularity of practice, research quality, author affiliation, and type of control group on effect sizes. Design: This systematic review of the literature used the Comprehensive Meta-Analysis (CMA) program for core analyses of effect sizes, bias analysis, meta-regression, and moderator variable analysis. Comprehensive literature searches included databases devoted to meditation research. Results: More than 600 TM research papers were identified; 14 of these addressed trait anxiety and reported on 16 studies among 1295 participants with diverse demographic characteristics. No adverse effects were reported. The standardized difference in mean, d, for the TM technique compared with controls receiving an active alternative treatment (10 studies) was d=−0.50 (95% CI, −.70 to −0.3...


title: On Curating Multimodal Sensory Data for Health and Wellness Platforms

abstract: In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user's lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user's sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user's lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.


Definitions of women ‘ s sexual d ysf u nct i on recons id ered : advocating expansion and revision


title: Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance

abstract: Abiotic stresses, such as drought, salinity, extreme temperatures, chemical toxicity and oxidative stress are serious threats to agriculture and the natural status of the environment. Increased salinization of arable land is expected to have devastating global effects, resulting in 30% land loss within the next 25 years, and up to 50% by the year 2050. Therefore, breeding for drought and salinity stress tolerance in crop plants (for food supply) and in forest trees (a central component of the global ecosystem) should be given high research priority in plant biotechnology programs. Molecular control mechanisms for abiotic stress tolerance are based on the activation and regulation of specific stress-related genes. These genes are involved in the whole sequence of stress responses, such as signaling, transcriptional control, protection of membranes and proteins, and free-radical and toxic-compound scavenging. Recently, research into the molecular mechanisms of stress responses has started to bear fruit and, in parallel, genetic modification of stress tolerance has also shown promising results that may ultimately apply to agriculturally and ecologically important plants. The present review summarizes the recent advances in elucidating stress-response mechanisms and their biotechnological applications. Emphasis is placed on transgenic plants that have been engineered based on different stress-response mechanisms. The review examines the following aspects: regulatory controls, metabolite engineering, ion transport, antioxidants and detoxification, late embryogenesis abundant (LEA) and heat-shock proteins.


title: Part of Speech Tagging Java Method Names

abstract: Numerous software engineering tools for evolution and comprehension, including code search, comment generation, and analyzing bug reports, make use of part-of-speech (POS) information. However, many POS taggers are developed for, and trained on, natural language. In this paper, we investigate the accuracy of 9 POS taggers on over 200 source code identifiers taken from method names in open source Java programs. The set of taggers includes traditional POS taggers for English as well as some tuned to source code identifiers. Our results indicate that taggers tailored for source code are significantly more effective.


title: Evolving Deep Convolutional Neural Networks for Image Classification

abstract: Evolutionary paradigms have been successfully applied to neural network designs for two decades. Unfortunately, these methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the potentially optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minimum that is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the state-of-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the state-of-the-art designs in terms of classification error rate and the number of parameters (weights).


title: Automated Linguistic Analysis of Deceptive and Truthful Synchronous Computer-Mediated Communication

abstract: The present study investigates changes in both the sender's and the target's linguistic style across truthful and deceptive dyadic communication in a synchronous text-based setting. A computer-based analysis of 242 transcripts revealed that senders produced more words overall, decreased their use of self-oriented pronouns but increased other-oriented pronouns, and used more sense-based descriptions (e.g., seeing, touching) when lying than when telling the truth. In addition, motivated senders avoided causal terms during deception, while unmotivated senders relied more heavily on simple negations. Receivers used more words when being deceived, but they also asked more questions and used shorter sentences when being lied to than when being told the truth, especially when the sender was unmotivated. These findings are discussed in terms of their implications for linguistic style matching and interpersonal deception theory.


title: Integrating Heterogeneous Healthcare Datasets and Visual Analytics for Disease Bio-surveillance and Dynamics

abstract: n this paper, we present an overview of the big data chal- lenges in disease bio-surveillance and then discuss the use of visual analytics for integrating data and turning it into knowl- edge. We will explore two integration scenarios: (1) combining text and multimedia sources to improve situational awareness and (2) enhancing disease spread model data with real-time bio-surveillance data. Together, the proposed integration methodologies can improve awareness about when, where and how emerging diseases can affect wide geographic regions.


Proposing a streaming Big Data analytics (SBDA) platform for condition based maintenance (CBM) and monitoring transportation systems


title: Neural networks for stock price prediction

abstract: Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.


title: Adaptive Stochastic Gradient Descent Optimisation for Image Registration

abstract: We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz (J. Math. Sci. 120(1):964---973, 2004). Our main methodological contribution is the derivation of an image-driven mechanism to select proper values for the most important free parameters of the method. The selection mechanism employs general characteristics of the cost functions that commonly occur in intensity-based image registration. Also, the theoretical convergence conditions of the optimisation method are taken into account. The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive Robbins-Monro (RM) algorithm. Both ASGD and RM employ a stochastic subsampling technique to accelerate the optimisation process. Registration experiments were performed on 3D CT and MR data of the head, lungs, and prostate, using various similarity measures and transformation models. The results indicate that ASGD is robust to these variations in the registration framework and is less sensitive to the settings of the user-defined parameters than RM. The main disadvantage of RM is the need for a predetermined step size function. The ASGD method provides a solution for that issue.


title: A simple parallel algorithm for the maximal independent set problem

abstract: Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intelligence. One of the main contributions of this paper is the development of powerful and general techniques for converting Monte Carlo algorithms into deterministic algorithms. These techniques are used to convert the Monte Carlo algorithm for the MIS problem into a simple deterministic algorithm with the same parallel running time.


title: Hybrid Model For Word Prediction Using Naive Bayes and Latent Information

abstract: Historically, the Natural Language Processing area has been given too much attention by many researchers. One of the main motivation beyond this interest is related to the word prediction problem, which states that given a set words in a sentence, one can recommend the next word. In literature, this problem is solved by methods based on syntactic or semantic analysis. Solely, each of these analysis cannot achieve practical results for end-user applications. For instance, the Latent Semantic Analysis can handle semantic features of text, but cannot suggest words considering syntactical rules. On the other hand, there are models that treat both methods together and achieve state-of-the-art results, e.g. Deep Learning. These models can demand high computational effort, which can make the model infeasible for certain types of applications. With the advance of the technology and mathematical models, it is possible to develop faster systems with more accuracy. This work proposes a hybrid word suggestion model, based on Naive Bayes and Latent Semantic Analysis, considering neighbouring words around unfilled gaps. Results show that this model could achieve 44.2% of accuracy in the MSR Sentence Completion Challenge.


title: Tutorial on agent-based modeling and simulation

abstract: Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.


title: Design guidelines for a wearable robotic extra-finger

abstract: Wearable robotics is strongly oriented to humans. New applications for wearable robots are encouraged by the lightness and portability of new devices and the progress in human-robot cooperation strategies. In this paper, we propose the different design guidelines to realize a robotic extra-finger for human grasping enhancement. Such guidelines were followed for the realization of three prototypes obtained using rapid prototyping techniques, i.e., a 3D printer and an open hardware development platform. Both fully actuated and under-actuated solutions have been explored. In the proposed wearable design, the robotic extra-finger can be worn as a bracelet in its rest position. The availability of a supplementary finger in the human hand allows to enlarge its workspace, improving grasping and manipulation capabilities. This preliminary work is a first step towards the development of robotic extra-limbs able to increase human workspace and dexterity.


title: The Success of Natural Products in Drug Discovery

abstract: Drug discovery leading to robust and viable lead candidates’ remains a challenging scientific task, which is the transition from a screening hit to a drug candidate, requires expertise and experience. Natural products and their derivatives have been recognized for many years as a source of therapeutic agents and of structural diversity. However, in addition to their chemical structure diversity and their biodiversity, the development of new technologies has revolutionized the screening of natural products in discovering new drugs. Applying these technologies compensates for the inherent limitations of natural products and offers a unique opportunity to re-establish natural products as a major source for drug discovery. The present article attempts to describe the utilization of compounds derived from natural resources as drug candidates, with a focus on the success of these resources in the process of finding and discovering new and effective drug compounds, an approach commonly referred to as “natural product drug discovery”.


title: Facial expression recognition and emotion classification system for sentiment analysis

abstract: A huge amount of data is available to the web users with evolution of web technology. The available resources in web are used by the users and also they involve in giving feedbacks and thus generate additional information. It is very essential to explore, analyze and organize their opinions and feedbacks in an efficient way for better decision making. So sentiment analysis (SA) is used to find the users opinion about a particular topic, product or problem in a more efficient way. The main aim of SA is to solve the problems in relation to opinions about products, movies, politics, review sites etc. Sentiment analysis can be done with different modalities by taking inputs from text, image, audio and video. This paper proposes a system that will automatically recognise the facial expression from the image and classify emotions for final decision. The system uses a simplified method called ‘Viola Jones Face Detection’ algorithm for face localization. Then facial features are extracted using three methods ‘Zernike moments’, ‘LBP’ and ‘DCT transform’. The different feature vectors are combined together using a subset feature selection algorithm to improve the performance of recognition and classification process. Finally the combined features are trained and classified using SVM, Random Forest and KNN classifier. Experiments are conducted on JAFFE database and finally performance of the system is evaluated.


title: Vehicle detection using simplified fast R-CNN

abstract: This paper proposes a simplified fast region-based convolutional neural network (R-CNN) for vehicle detection. Fast R-CNN is a well-known method for object recognition using deep convolution networks. The original fast R-CNN consists of two separated parts: regional proposal and object recognition. The object recognition part in Fast R-CNN is redundant for our system which can be removed to speed up the training process. In the experiments, we test our method by using SHRP 2 NDS database [10] offered by Virginia Tech Transportation Institute (VTTI) to show the detection accuracy.


Social Media in Government


title: A bat-neural network multi-agent system (BNNMAS) for stock price prediction

abstract: Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.


title: Transferring instances for model-based reinforcement learning

abstract: Reinforcement learningagents typically require a significant amount of data before performing well on complex tasks. Transfer learningmethods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel , a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrel can significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel 's effectiveness.


title: GENETIC VARIABILITY OF NORTHERN WHITE-BREASTED HEDGEHOG (Erinaceus roumanicus) FROM SERBIA

abstract: Northern white-breasted hedgehog ( Erinaceus roumanicus ) is a small mammal distributed in eastern Europe and Ponto-Mediterranean region. Since genetic variability underpins a populations long-term potential for survival and adaptation, the aim of this study was to estimate genetic diversity in E. roumanicus population from Serbia. Partial fragment of mtDNA control region was amplified and sequenced for 74 road killed individuals. In total, 12 haplotypes were detected with haplotype diversity Hd=0.827±0.001, nucleotide diversity π=0.0036 and average number of nucleotide differences k=1.490. Spatial clustering of individuals based on the mtDNA variability indicated presence of three groups named approximately after sampling localities as eastern (E), south-central (SC) and southwestern-western-central (SWC) group, with haplotype and nucleotide diversity respectively as follow: Hd=0.733±0.024, π=0.0034; Hd=0.897±0.003, π=0.0045; Hd=0.725±0.002, π=0.0030. AMOVA further supported genetic differentiation (F ST =0.148, p=0.002), even though the most of the variability is due to the within group variation. Eastern group showed higher genetic differentiation (E-SWC F ST =0.221; E-SC F ST =0.192), while F ST value between SC and SWC was 0.106. No shared haplotypes are found between E and two other groups, in contrast to 5 shared of 7 detected in both SC and SWC groups. Mismatch distribution was unimodal, with insignificant SSD values supporting a pattern of demographic expansion, while none of the neutrality tests (Fu’s Fs and Tajima’s D) was significant, both yielded negative values, except for the group E. The results of this study indicate a moderate genetic diversity and signal of population expansion in E. roumanicus from Serbia.


Three-dimensional imaging of solvent-cleared organs using 3 DISCO


title: A high performance 2-dof over-actuated parallel mechanism for ankle rehabilitation

abstract: This paper presents the mechanical design of an ankle rehabilitation robotic device based on a 2-dof, redundantly actuated parallel mechanism. The parallel mechanism introduced in this paper has the advantage of mechanical and kinematic simplicity when compared to existing platforms while at the same time it is fully capable of carrying out all the exercises required by ankle rehabilitation protocols. The proposed device makes use of actuation redundancy to eliminate singularity and greatly improve the workspace dexterity. In addition, the requirements for high torque capacity and back-drivability are satisfied with the employment of a custom made cable driven linear electric actuator that combines the high force capacity with excellent back-drivability. The analysis undergoes the optimal design towards the maximization of manipulator workspace, dexterity, torque output and compactness of the device. Finally, the performance of the custom linear actuator and the prototype of the rehabilitation device are shown.


title: A discriminative deep model for pedestrian detection with occlusion handling

abstract: Part-based models have demonstrated their merit in object detection. However, there is a key issue to be solved on how to integrate the inaccurate scores of part detectors when there are occlusions or large deformations. To handle the imperfectness of part detectors, this paper presents a probabilistic pedestrian detection framework. In this framework, a deformable part-based model is used to obtain the scores of part detectors and the visibilities of parts are modeled as hidden variables. Unlike previous occlusion handling approaches that assume independence among visibility probabilities of parts or manually define rules for the visibility relationship, a discriminative deep model is used in this paper for learning the visibility relationship among overlapping parts at multiple layers. Experimental results on three public datasets (Caltech, ETH and Daimler) and a new CUHK occlusion dataset1 specially designed for the evaluation of occlusion handling approaches show the effectiveness of the proposed approach.


title: Transformational Leadership: Relations to the Five-Factor Model and Team Performance in Typical and Maximum Contexts.

abstract: This study examined the 5-factor model of personality, transformational leadership, and team performance under conditions similar to typical and maximum performance contexts. Data were collected from 39 combat teams from an Asian military sample (N = 276). Results found that neuroticism and agreeabl


title: On Time-Series Topological Data Analysis: New Data and Opportunities

abstract: This work introduces a new dataset and framework for the exploration of topological data analysis (TDA) techniques applied to time-series data. We examine the end-toend TDA processing pipeline for persistent homology applied to time-delay embeddings of time series – embeddings that capture the underlying system dynamics from which time series data is acquired. In particular, we consider stability with respect to time series length, the approximation accuracy of sparse filtration methods, and the discriminating ability of persistence diagrams as a feature for learning. We explore these properties across a wide range of time-series datasets spanning multiple domains for single source multi-segment signals as well as multi-source single segment signals. Our analysis and dataset captures the entire TDA processing pipeline and includes time-delay embeddings, persistence diagrams, topological distance measures, as well as kernels for similarity learning and classification tasks for a broad set of time-series data sources. We outline the TDA framework and rationale behind the dataset and provide insights into the role of TDA for time-series analysis as well as opportunities for new work.


title: A Random Forest Guided Tour

abstract: The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is versatile enough to be applied to large-scale problems, is easily adapted to various ad-hoc learning tasks, and returns measures of variable importance. The present article reviews the most recent theoretical and methodological developments for random forests. Emphasis is placed on the mathematical forces driving the algorithm, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures. This review is intended to provide non-experts easy access to the main ideas.


title: A Compact Coupled Inductor for Interleaved Multiphase DC–DC Converters

abstract: A compact coupled inductor structure is proposed for interleaved multiphase synchronous buck converters used to power computer processors and memories that require high current and fast current slew rate. The new coupled-inductor structure reduces the winding resistor power loss and makes it possible to utilize ferrite magnetic material with low core loss. In this letter, several proposed converter implementations to achieve inverse inductor coupling are illustrated, and inductance and coupling coefficient variations are studied through a simplified reluctance model and Maxwell magnetic simulation. The inductor structure not only can be extended to multiple phases but also can be simplified to a single phase. The operation of the inductor is experimentally verified in a two-phase synchronous buck converter at switching frequency of 1 MHz.


title: Advanced Learning Algorithms for Cross-Language Patent Retrieval and Classification

abstract: We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese-English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents.


title: Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning

abstract: Machine learning systems, though being successful in many real-world applications, are known to remain prone to errors and attacks. A major attack, called data pollution, injects maliciously crafted training data samples into the training set, causing the system to learn an incorrect model and subsequently misclassify testing samples. A natural solution to a data pollution attack is to remove the polluted data from the training set and relearn a clean model. Unfortunately, the training set of a real-world machine learning system can contain millions of samples; it is thus hopeless for an administrator to manually inspect all of them to weed out the polluted ones. This paper presents an approach called causal unlearning and a corresponding system called KARMA to efficiently repair a polluted learning system. KARMA dramatically reduces the manual effort of administrators by automatically detecting the set of polluted training data samples with high precision and recall. Evaluation on three learning systems show that KARMA greatly reduces manual effort for repair, and has high precision and recall.


SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers


title: Discovering Recurring Patterns in Time Series

abstract: Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. We classify partial periodic patterns into two types: (i) regular patterns−patterns exhibiting periodic behavior throughout a series with some exceptions and (ii) recurring patterns−patterns exhibiting periodic behavior only for particular time intervals within a series. Past studies on partial periodic search have been primarily focused on finding regular patterns. One cannot ignore the knowledge pertaining to recurring patterns. This is because they provide useful information pertaining to seasonal or temporal associations between events. Finding recurring patterns is a non-trivial task because of two main reasons. (i) Each recurring pattern is associated with temporal information pertaining to its durations of periodic appearances in a series. Obtaining this information is challenging because the information can vary within and across patterns. (ii) Finding all recurring patterns is a computationally expensive process since they do not satisfy the anti-monotonic property. In this paper, we propose recurring pattern model by addressing the above issues. We also propose Recurring Pattern growth algorithm along with an efficient pruning technique to discover these patterns. Experimental results show that recurring patterns can be useful and that our algorithm is efficient.


title: Developmental dyslexia and specific language impairment: same or different

abstract: Developmental dyslexia and specific language impairment (SLI) were for many years treated as distinct disorders but are now often regarded as different manifestations of the same underlying problem, differing only in severity or developmental stage. The merging of these categories has been motivated by the reconceptualization of dyslexia as a language disorder in which phonological processing is deficient. The authors argue that this focus underestimates the independent influence of semantic and syntactic deficits, which are widespread in SLI and which affect reading comprehension and impair attainment of fluent reading in adolescence. The authors suggest that 2 dimensions of impairment are needed to conceptualize the relationship between these disorders and to capture phenotypic features that are important for identifying neurobiologically and etiologically coherent subgroups. Specific language impairment (SLI) and developmental dyslexia (also known as specific reading disability; SRD) are common developmental disorders that have a serious impact on a child’s educational and psychosocial outcome. SLI affects around 3%– 10% of children (Tomblin et al., 1997) and is diagnosed when oral language lags behind other areas of development for no apparent reason (L. B. Leonard, 1998). Similar prevalence levels are reported for developmental dyslexia, which is identified if a child has poor literacy skills despite adequate intelligence and opportunity to learn (Snowling, 2000). In both SLI and dyslexia, the diagnostic criteria specify that the child has to have adequate hearing and no major handicapping condition that might interfere with learning. For many years, research on these two disorders followed separate paths, with the study of SLI being largely carried out by speech and language pathologists, whereas dyslexia was the focus of educators and psychologists. In the 1960s dyslexia was frequently conceptualized as a visual perceptual disorder. However, over the past 30 years, there has been a shift of emphasis, with growing recognition that most children with dyslexia have major difficulties in phonological processing, that is, categorizing speech sounds and relating these to orthography (Snowling, 1987; Vellutino, 1987).


title: A 55dB SNR with 240Hz frame scan rate mutual capacitor 30×24 touch-screen panel read-out IC using code-division multiple sensing technique

abstract: Capacitive touch-screen technology introduces new concepts to user interfaces, such as multi-touch, pinch zoom-in/out gestures, thus expanding the smartphone market. However, capacitive touch-screen technology still suffers from performance degradation like a low frame scan rate and poor accuracy, etc. One of the key performance factors is the immunity to external noise, which intrudes randomly into the touch-screen system. HUM, display noise, and SMPS are such noise sources. The main electrical power source produces HUM, one of the most important sources of noise, which has a 50 or 60Hz component. Display noise is emitted when an LCD or OLED is driven by the internal timing controller, which generates the driving signal in the tens of kHz range. The touch performance of On-Cell or In-Cell touch displays is seriously affected by this kind of noise, because the distance between the display pixel layer and the capacitive touchscreen panel is getting smaller. SMPS is another noise source that ranges up to 300kHz. The charger for a smart-phone, the USB port in a computer, a tri-phosphor fluorescent light bulb are all examples of sources of SMPS. There have been many attempts to remove such noise. Amplitude modulation with frequency hopping is proposed in [1]. However, when the noise environment changes, this method needs recalibration, resulting in non-constant touch response time. Another method tries to filter the noise from the display [2], but it does not remove other noise sources like HUM or SMPS.


title: Resource Elasticity for Large-Scale Machine Learning

abstract: Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks. State-of-the-art compilers in this context are very sensitive to memory constraints of the master process and MR cluster configuration. Different memory configurations can lead to significant performance differences. Interestingly, resource negotiation frameworks like YARN allow us to explicitly request preferred resources including memory. This capability enables automatic resource elasticity, which is not just important for performance but also removes the need for a static cluster configuration, which is always a compromise in multi-tenancy environments. In this paper, we introduce a simple and robust approach to automatic resource elasticity for large-scale ML. This includes (1) a resource optimizer to find near-optimal memory configurations for a given ML program, and (2) dynamic plan migration to adapt memory configurations during runtime. These techniques adapt resources according to data, program, and cluster characteristics. Our experiments demonstrate significant improvements up to 21x without unnecessary over-provisioning and low optimization overhead.


title: The Bitcoin P2P Network

abstract: The Bitcoin virtual currency is built on the top of a decentralized peer-to-peer (P2P) network used to propagate system information such as transactions or blockchain updates. In this paper, we have performed a data collection process identifying more than 872000 different Bitcoin nodes. This data allows us to present information on the size of the Bitcoin P2P network, the node geographic distribution, the network stability in terms of interrupted availability of nodes, as well as some data regarding the propagation time of the transmitted information. Furthermore, although not every Bitcoin user can be identified as a P2P network node, measurements of the P2P network can be considered as a lower bound for Bitcoin usage, and they provide interesting results on the adoption of such virtual currency.


title: Cyber forensics framework for big data analytics in IoT environment using machine learning

abstract: Forensic analyst skills are at stake for processing of growing data from IoT based environment platforms. Tangible sources often have the size limits, but that’s not the case for communication traffic source. Hence, increasing the thirst for an efficient benchmarking for big data analysis. Available solutions to date have used an anomaly-based approach or have proposed approaches based on the deviation from a regular pattern. To tackle the seized bytes, authors have proposed an approach for big data forensics, with efficient sensitivity and precision. In the presented work, a generalized forensic framework has been proposed that use Google’s programming model, MapReduce as the backbone for traffic translation, extraction, and analysis of dynamic traffic features. For the proposed technique, authors have used open source tools like Hadoop, Hive, and Mahout and R. Apart from being open source, these tools support scalability and parallel processing. Also, comparative analysis of globally accepted machine learning models of P2P malware analysis in mocked real-time is presented. Dataset from CAIDA was taken and executed in parallel to validate the proposed model. Finally, the forensic performance metrics of the model shows the results with the sensitivity of 99%.


title: Design and simulation of the antenna for RF energy harvesting systems

abstract: In this paper, a multi-band fractal antenna is designed and simulated for the using in RF energy harvesting systems. FR4 glass epoxy material is used for antenna design which have the dielectric constant 4.4, with the thickness 1.6 mm and coaxial feed connection is made from the center triangle of the patch. The designed fractal antenna is simulated using Ansys Electronics Desktop 2017, HFSS (High Frequency Electromagnetic Field Simulation). The antenna resonates at 5 different frequencies which are 0.9 GHz, 2.17 GHz, 3.61 GHz, 5.76 GHz and 6.70 GHz respectively. The bandwidths of the antenna at these frequencies are 161.1 MHz, 47.8 MHz, 491.2 MHz, 12.92 MHz and 37.36 MHz respectively. As a result, designed fractal antenna can be used in RF energy harvesting applications with combining rectifier and matching circuits. So antenna can be used to feed electronic components such as wireless sensor networks.


title: Phase shift and duty cycle control of interleaved boost three port converter with multiplier rectifier

abstract: A full bridge three port converter is designed which includes a multiplier rectifier stage. For effective regulation of the power flow among the three ports, both phase shift as well as duty cycle are controlled. Input power flow and output power flow can be regulated independently using duty cycle and phase shift control. Isolation, bidirectional and soft switching properties are also included in the design. The presence of energy storage element makes the converter more reliable and efficient. The converter has its application in hybrid renewable energy systems which demands uninterrupted power flow to the load.


title: A Study of Analogy in Computer Science Tutorial Dialogues

abstract: Analogy plays an important role in learning, but its role in teaching Computer Science has hardly been explored. ::: ::: We annotated and analyzed analogy in a corpus of tutoring dialogues on Computer Science data ::: ::: structures. Via linear regression analysis, we established that the presence of analogy and of specific dialogue ::: ::: acts within analogy episodes correlate with learning. We have integrated our findings in our ChiQat-Tutor ::: ::: system, and are currently evaluating the effect of analogy within the system.


title: Occupancy patterns of large mammals in the Far North of Ontario under imperfect detection and spatial autocorrelation

abstract: Aim ::: ::: An understanding of the factors that influence species distributions in heterogeneous landscapes is important when making decisions regarding conservation. Moreover, occupancy probabilities based on detection data can reveal important species–habitat relationships. Accounting for the spatial autocorrelation of detection data increases the statistical validity of occupancy models, but is not often considered. Using novel occupancy modelling that explicitly incorporates detectability and spatial autocorrelation, we assessed the influence of habitat on occupancy patterns of woodland caribou (Rangifer tarandus caribou), moose (Alces alces) and wolves (Canis lupus) across a broad biogeographical extent where fire is the dominant agent of disturbance. ::: ::: ::: ::: Location ::: ::: Northern Ontario, Canada. ::: ::: ::: ::: Methods ::: ::: We aerially surveyed 3851 sampling units, each covering 100 km2, for woodland caribou, moose and wolves in February–March in 2009, 2010 and 2011, and visited 1663 units more than once to estimate detectability. We used restricted spatial regression to model occupancy probabilities of each species with respect to habitat factors in two ecozones, accounting for both imperfect detection and lack of independence of sampling units. ::: ::: ::: ::: Results ::: ::: Covariates influencing species detection varied among ecozones and species. Caribou occupancy was positively related to bogs and negatively related to disturbed areas, while moose occupancy showed opposite responses to these covariates. Wolf occupancy was related to high prey occupancy. Explicitly accounting for spatial autocorrelation in detection data reduced the chance of type I error in occupancy estimates compared with non-spatial models. ::: ::: ::: ::: Main conclusions ::: ::: Habitat relationships and occupancy patterns support the hypothesis that caribou remain spatially segregated from moose to reduce predation risk. The broad scale of analysis indicated changes in species–habitat relationships, suggesting that limiting factors vary across biogeographical gradients. The spatial pattern in caribou occupancy allowed us to identify important areas used by caribou across the region, including the ecotone between fire-driven boreal forests and peatland complexes. The evidence for significant relationships between caribou and land cover, predators and alternate prey underscores the need for careful planning of development and infrastructure in the area.


title: CyberCode: designing augmented reality environments with visual tags

abstract: The CyberCode is a visual tagging system based on a 2D-barcode technology and provides several features not provided by other tagging systems. CyberCode tags can be recognized by the low-cost CMOS or CCD cameras found in more and more mobile devices, and it can also be used to determine the 3D position of the tagged object as well as its ID number. This paper describes examples of augmented reality applications based on CyberCode, and discusses some key characteristics of tagging technologies that must be taken into account when designing augmented reality environments.


title: Norms as a basis for governing sociotechnical systems

abstract: We understand a sociotechnical system as a microsociety in which autonomous parties interact with and about technical objects. We define governance as the administration of such a system by its participants. We develop an approach for governance based on a computational representation of norms. Our approach has the benefit of capturing stakeholder needs precisely while yielding adaptive resource allocation in the face of changes both in stakeholder needs and the environment. We are currently extending this approach to address the problem of secure collaboration and to contribute to the emerging science of cybersecurity.


title: A Mobile GPRS-Sensors Array for Air Pollution Monitoring

abstract: An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution-Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies. The Pollution-Server is interfaced to Google Maps to display real-time pollutants levels and locations in large metropolitan areas. The system was successfully tested in the city of Sharjah, UAE. The system reports real-time pollutants level and their location on a 24-h/7-day basis.


title: Design Techniques for a 66 Gb/s 46 mW 3-Tap Decision Feedback Equalizer in 65 nm CMOS

abstract: This paper analyzes and describes design techniques enabling energy-efficient multi-tap decision feedback equalizers operated at or near the speed limits of the technology. We propose a closed-loop architecture utilizing three techniques to achieve this goal, namely a merged latch and summer, reduced latch gain, and a dynamic latch design. A 65 nm CMOS 3-tap implementation of the proposed architecture operates at up to 66 Gb/s while drawing only 46 mW of power from a 1.2 V supply, achieving 0.7 pJ/bit energy efficiency.


Multi-Channel Speech Recognition : LSTMs All the Way Through


title: A Novel Bottom-Up Saliency Detection Method for Video With Dynamic Background

abstract: After years of extensive studies, the salient motion detection problem has gained plausible performance improvement that was primarily propelled by the rapid development of self-adaptive top-down modeling techniques. Nevertheless, almost all the conventional solutions are still not robust enough to handle video sequences captured by hand-hold cameras. This is mainly due to the absence of the position alignment information that is indispensable for top-down background modeling. In contrast, the bottom-up video saliency detection methods, though achieving excellent salient motion detection in either stationary or nonstationary videos, still have rather poor detection performance in scenarios with massive dynamic background. In this letter, we explore a bottom-up saliency framework by introducing a novel spatial-temporal regional filter method to handle the dynamic background problem. Our key rationale is to assign large saliency value to those regions with stable spatial-temporal coherency while eliminating irregular, repeating dynamic background. As far as we know, this is the first work to address the dynamic background problem from the perspective of the bottom-up video saliency. We conduct massive quantitative evaluations over public available benchmarks to validate the effectiveness and robustness of our method.


title: A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network

abstract: This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.


title: Peek-a-Boo: I see your smart home activities, even encrypted!

abstract: A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying particular types of IoT devices, their actions, states, and ongoing user activities in a cascading style by only observing passively the wireless traffic from smart home devices. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the network traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. In contrast to earlier straightforward approaches, our multi-stage privacy attack can perform activity detection and identification automatically without extensive background knowledge or specifications of the analyzed protocols. This allows an adversary to efficiently aggregate extensive behavior profiles of targeted users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed network traffic to hide the real activities of the devices. We also demonstrate that the provided solution provides better protection than existing solutions.


title: A cued-recall and emotion classification graphical password authentication scheme

abstract: This paper presents an alternative visual authentication scheme with two secure layers for desktops or laptops. The first layer is a recognition-based scheme that addresses human factors for protection against bots by recognizing a Captcha and images with specific patterns. The second layer uses a clicked based Cued-Recall graphical password scheme for authentication, it also exploits emotions perceived by humans and use them as decision factor. The proposed authentication system is effective against brute-force, online guessing and relay attacks. We believe that the perception of security is enhaced using human emotions as main decision factor. The proposed scheme usability was tested using the Computer System Usability Questionnaires, results showed that it is highly usable and could improve the security level on ATM machines.


title: Max-min d-cluster formation in wireless ad hoc networks

abstract: An ad hoc network may be logically represented as a set of clusters. The clusterheads form a d-hop dominating set. Each node is at most d hops from a clusterhead. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Previous heuristics restricted themselves to 1-hop clusters. We show that the minimum d-hop dominating set problem is NP-complete. Then we present a heuristic to form d-clusters in a wireless ad hoc network. Nodes are assumed to have a non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. When the heuristic terminates, a node either becomes a clusterhead, or is at most d wireless hops away from its clusterhead. The value of d is a parameter of the heuristic. The heuristic can be run either at regular intervals, or whenever the network configuration changes. One of the features of the heuristic is that it tends to re-elect existing clusterheads even when the network configuration changes. This helps to reduce the communication overheads during transition from old clusterheads to new clusterheads. Also, there is a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic is fair and stable. Simulation experiments demonstrate that the proposed heuristic is better than the two earlier heuristics, namely the LCA and degree-based solutions.


title: Keyword extraction based on tf/idf for Chinese news document

abstract: Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.


title: Bone regeneration adjacent to titanium dental implants using guided tissue regeneration: a report of two cases.

abstract: The biologic principle of guided tissue regeneration was applied to regenerate alveolar bone in conjunction with the placement of titanium dental implants. In one case, complete osseointegration of an implant was achieved by the placement of a Teflon membrane over an implant that had been inserted into an alveolus immediately following tooth extraction. In a second case, the same biologic principle was used to increase the volume (height and width) of a resorbed, edentulous alveolar ridge to provide adequate bone dimensions for implant installation. In both cases, the membranes appear to have prevented the repopulation of the wound area by cells other than those derived from surrounding bone tissue. These two different applications of the principle of guided tissue regeneration open new avenues for reconstructive osseous surgery.


title: Clear cell acanthoma.

abstract: Clear cell acanthoma (CCA), is an asymptomatic benign lesion of unknown origin and aetiology, which typically presents as a red to brown, dome-shaped papule on the leg. We discuss the case of a patient with an irregular plaque of hypopigmented papules whose diagnosis of CCA was only made by biopsy, and review the characteristic presentation, uncommon variants, locations and associated conditions of this lesion. The diagnosis is based on histopathology and/or immunohistochemistry, but the variable clinical presentation may make diagnosis difficult. The differential diagnosis includes skin tumours and inflammatory and pigmentary dermatoses. The lesions do not regress spontaneously, and excisional removal is the preferred treatment.


title: An Embedded Lightweight Folded Printed Quadrifilar Helix Antenna: UAV telemetry and remote control systems.

abstract: An embedded folded, printed, quadrifilar helix antenna (FPQHA) with a wide-angle coverage for unmanned aerial vehicles (UAVs) telemetry and remote control systems is presented in this article. The novelty of this design is that the FPQHA needs to be designed carefully due to UAV tail dimensions and weight constraints while maintaining a high performance to be integrated in the inner part of the UAV tail fuselage to reduce aerodynamic drag. The radiating terminal, formed by a folded, printed, four-helix, radiating section and a compact feeding network, is designed to provide left-handed, circular polarization (LHCP). The complete design offers a very homogeneous pattern in azimuth with a very good axial-ratio (AR) level over a wide range of elevation angles. The use of low-loss and lightweight materials is also an advantage of this design. The wide radiation pattern favors its use for multielement communication systems. Finally, the antenna performance results are obtained mounted inside a UAV tail platform.


title: Managing stress, sleep and technologies: an exploratory study of Australian university students

abstract: Student life is stressful and many students do not get enough sleep to perform well in class and in assignments. Technology use has been found to contribute to sleep deficits, but it offers a means to investigate sleep patterns and student workload. Hence the aim of this research is to explore how students at Australian universities manage stress, sleep and technologies throughout the semester. We combined rich qualitative insights from contextual interviews and cultural probes with quantitative insights from a student survey. The findings showed that (1) students consciously prioritise academic success over a healthy sleeping pattern; (2) students are sleeping one hour less than they think they should; (3) technologies both add to sleep deficits as well as help to wind down; and (4) students manage sleep and stress through a combination of planning ahead and pushing through stressful times with little sleep. We discuss the implications of these findings and highlight design opportunities for new technologies to support the strategies of planning and pushing through to encourage healthy sleep patterns and foster wellbeing.


title: NLTK: The Natural Language Toolkit

abstract: The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.


title: Locating the nodes: cooperative localization in wireless sensor networks

abstract: Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.


title: The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields

abstract: Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. Thissingle motion assumptionis violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This paper presents a framework based onrobust estimationthat addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how therobust estimation frameworkcan be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions. The approach has been applied to three standard techniques for recovering optical flow: area-based regression, correlation, and regularization with motion discontinuities. This paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewise-smooth flow fields, and provides examples with natural and synthetic image sequences.


title: An intelligent system approach to higher-dimensional classification of volume data

abstract: In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner. The user works in the volume data space by directly painting on sample slices of the volume and the painted voxels are used in an iterative training process. The trained system can then classify the entire volume. Both classification and rendering can be hardware accelerated, providing immediate visual feedback as painting progresses. Such an intelligent system approach enables the user to perform classification in a much higher dimensional space without explicitly specifying the mapping for every dimension used. Furthermore, the trained system for one data set may be reused to classify other data sets with similar characteristics.


title: Relevance Feature Discovery for Text Mining

abstract: It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.


title: An Aggregation Approach to Short-Term Traffic Flow Prediction

abstract: In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naive, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.


title: Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform

abstract: Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), neuromorphic vision toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. The results are good, but the reason is questionable.


title: Topology of a Bidirectional Converter for Energy Interaction between Electric Vehicles and the Grid

abstract: In vehicle-to-grid (V2G) systems, electric vehicles interact with the grid as distributed energy storage systems that offer many potential benefits. As an energy interface between a vehicle and the grid, the bidirectional converter plays a crucial role in their interaction. Its reliability, safety, cost, efficiency, weight, size, harmonics, and other factors are of essential importance for V2G realization, especially for on-board operations. Beyond the common existing topologies for bidirectional chargers, this paper introduces a novel high-power-factor bidirectional single-stage full-bridge (BSS-FBC) topology, which offers advantages in power density, size, weight, cost, efficiency, power quality, dynamic characteristic, reliability, and complexity. Its operational principles and control strategies are presented. Harmonic analysis on the basis of double-Fourier integral is performed with detailed comparison of line current harmonic characteristics between the BSS-FBC topology and unipolar/bipolar controlled single-phase pulse width modulation (PWM) converters. A dynamic model of the topology is derived, its dynamic behavior analyzed, and its compensator design method developed. Simulation and experimental results are employed to verify the design and analysis. Design considerations for the key parameters are discussed. A 3.3 kW prototype is developed for this topology and validated in its vehicle applications. The results demonstrate clearly the benefits and advantages of the new topology.


title: Open source tools for large-scale neuroscience

abstract: New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience.


Overview - Fog Computing and Internet-of-Things (IOT)


title: High frequency low cost DC-AC inverter design with fuel cell source for home applications

abstract: This paper presents a new design of high frequency DC/AC inverter for home applications using fuel cells or photovoltaic array sources. A battery bank parallel to the DC link is provided to take care of the slow dynamic response of the source. The design is based on a push-pull DC/DC converter followed by a full-bridge PWM inverter topology. The nominal power rating is 10 kW. Actual design parameters, procedure and experimental results of a 1.5 kW prototype are provided. The objective of this paper is to explore the possibility of making renewable sources of energy utility interactive by means of low cost power electronic interface.


title: SoK: SSL and HTTPS: Revisiting Past Challenges and Evaluating Certificate Trust Model Enhancements

abstract: Internet users today depend daily on HTTPS for secure communication with sites they intend to visit. Over the years, many attacks on HTTPS and the certificate trust model it uses have been hypothesized, executed, and/or evolved. Meanwhile the number of browser-trusted (and thus, de facto, user-trusted) certificate authorities has proliferated, while the due diligence in baseline certificate issuance has declined. We survey and categorize prominent security issues with HTTPS and provide a systematic treatment of the history and on-going challenges, intending to provide context for future directions. We also provide a comparative evaluation of current proposals for enhancing the certificate infrastructure used in practice.


title: An overview of the K semantic framework

abstract: K is an executable semantic framework in which programming languages, calculi, as well as type systems or formal analysis tools can be defined making use of configurations, computations and rules. Configurations organize the system/program state in units called cells, which are labeled and can be nested. Computations carry “computational meaning” as special nested list structures sequentializing computational tasks, such as fragments of program; in particular, computations extend the original language or calculus syntax. K (rewrite) rules generalize conventional rewrite rules by making it explicit which parts of the term they read-only, write-only, or do not care about. This distinction makes K a suitable framework for defining truly concurrent languages or calculi even in the presence of sharing. Since computations can be handled like any other terms in a rewriting environment, that is, they can be matched, moved from one place to another in the original term, modified, or even deleted, K is particularly suitable for defining control-intensive language features such as abrupt termination, exceptions or call/cc. This paper gives an overview of the K framework: what it is, how it can be used, and where it has been used so far. It also proposes and discusses the K definition of Challenge, a programming language that aims at challenging and exposing the limitations of the various existing semantic frameworks.


title: Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

abstract: An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.


title: Design of low power hamming code encoding, decoding and correcting circuits using reversible logic

abstract: An ideal communication relies on error detection and correction techniques for faultless data transmission. Hamming code is widely known among those techniques for single bit error detection and correction capacity. Low power circuit design yields many favorable conditions like increased performance, system capacity, minimized cost etc. Reversible logic is an excellent approach to optimize heat dissipation and information loss. As hamming code is designed using irreversible logic gates, there is undesired power dissipation. So, to improvise this downside, this paper elucidates the design of low power hamming code using reversible logic gates which detects and corrects the error if any. The mathematical analysis of quantum cost calculation, garbage outputs, delay and power is presented in this paper. Finally, simulation results are attained by using cadence virtuoso. The power dissipation for the proposed design is 7.2μW when the power of individual components is added theoretically. The overall power of the circuit is 5.8μW.


title: Attribute Dominance: What Pops Out?

abstract: When we look at an image, some properties or attributes of the image stand out more than others. When describing an image, people are likely to describe these dominant attributes first. Attribute dominance is a result of a complex interplay between the various properties present or absent in the image. Which attributes in an image are more dominant than others reveals rich information about the content of the image. In this paper we tap into this information by modeling attribute dominance. We show that this helps improve the performance of vision systems on a variety of human-centric applications such as zero-shot learning, image search and generating textual descriptions of images.


title: The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

abstract: MotivationBiomedical ontologists to date have concentrated on ontological descriptions of biomedical entities such as gene products and their attributes, phenotypes and so on. Recently, effort has diversified to descriptions of the laboratory investigations by which these entities were produced. However, much biological insight is gained from the analysis of the data produced from these investigations, and there is a lack of adequate descriptions of the wide range of software that are central to bioinformatics. We need to describe how data are analyzed for discovery, audit trails, provenance and reproducibility.ResultsThe Software Ontology (SWO) is a description of software used to store, manage and analyze data. Input to the SWO has come from beyond the life sciences, but its main focus is the life sciences. We used agile techniques to gather input for the SWO and keep engagement with our users. The result is an ontology that meets the needs of a broad range of users by describing software, its information processing tasks, data inputs and outputs, data formats versions and so on. Recently, the SWO has incorporated EDAM, a vocabulary for describing data and related concepts in bioinformatics. The SWO is currently being used to describe software used in multiple biomedical applications.ConclusionThe SWO is another element of the biomedical ontology landscape that is necessary for the description of biomedical entities and how they were discovered. An ontology of software used to analyze data produced by investigations in the life sciences can be made in such a way that it covers the important features requested and prioritized by its users. The SWO thus fits into the landscape of biomedical ontologies and is produced using techniques designed to keep it in line with user’s needs.AvailabilityThe Software Ontology is available under an Apache 2.0 license at http://theswo.sourceforge.net/; the Software Ontology blog can be read at http://softwareontology.wordpress.com.


A revised UTAUT model to investigate E -health acceptance of health professionals in Africa


title: Learning concepts through conversations in spoken dialogue systems

abstract: Spoken dialogue systems must be able to recover gracefully from unexpected user inputs. In many cases, these unexpected utterances may be within the scope of the system, but include previously unseen phrases that the system cannot interpret. In this work, we augment a spoken dialogue system with the ability to learn about new concepts by conversing with the user in natural language. We present a novel model that detects phrases corresponding to such concepts, using information from a neural slotfiller as well as syntactic cues. The system then prompts the user for a definition of the detected phrases, and uses these definitions to re-parse the original utterance. We demonstrate significant gains by learning from the user, compared to a baseline system.