title: Mobile Cardiac Health-care Monitoring and Notification with Real Time Tachycardia and Bradycardia Arrhythmia Detection

abstract: The increasing trend of heart disease has turned the attention of researchers toward the use of portable connected technologies. The necessity of continuous special care for cardiovascular patients is an inevitable fact. Methods: In this research, a new wireless electrocardiographic (ECG) signalmonitoring system based on smartphone is presented. This system has two main sections. The first section consists of a sensor which receives ECG signals via an amplifier, then filters and digitizes the signal, and prepares it to be transmitted. The signals are stored, processed, and then displayed in a mobile application. The application alarms in dangerous situations and sends the location of the cardiac patient to family or health-care staff. Results: The results obtained from the analysis of the electrocardiogram signals on 20 different people have been compared with the traditional ECG in hospital by a cardiologist. The signal is instantly transmitted by 200 sample per second to mobile phone. The raw data are processed, the anomaly is detected, and the signal is drawn on the interface in about 70 s. Therefore, the delay is not noticeable by the patient. With respect to rate of data transmission to hospital, different internet connections such as 2G, 3G, 4G, WiFi, WiMax, or Long-Term Evolution (LTE) could be used. Data transmission ranges from 9.6 kbps to 20 Mbps. Therefore, the physician could receive data with no delay. Conclusions: A performance accuracy of 91.62% is obtained from the wireless ECG system. It conforms to the hospital â€™ s diagnostic standard system while providing a portable monitoring anywhere at anytime.


The effectiveness of psychoanalysis and psychoanalytic psychotherapy : A literature review of recent international and Australian research


title: Construction industry offsite production: A virtual reality interactive training environment prototype

abstract: The 'traditional' construction industry has constantly been challenged to improve its inherent problematic practices. Offsite production (OSP), under the umbrella of modern methods of construction (MMC), has been acknowledged as a means to help improve construction industry performance as well as meet new market demands through the provision of improved, adaptable, and sustainable buildings. However, the deployment of OSP systems, if not managed properly, may adversely affect the end result and be counterproductive. It is therefore imperative that the construction industry stakeholders learn and appreciate the specifics, merits, as well as the risks associated with OSP systems in order to achieve the desired outcomes and consequently improve industry performance. On-the-job-training (OJT) is usually sought to facilitate 'experiential' learning, which is argued to be particularly effective where a great deal of independence is granted to the task performer. However, OJT has been criticised for being expensive, limited, and sometimes devoid of the actual training context. In order to address the problems encountered with OJT, several virtual reality (VR) solutions have been proposed. This paper introduces one such VR solution prototype, in order to provide a risk-free environment for learning without the 'do-or-die' consequences often faced on real construction projects. The proffered solution provides a unique VR environment for practicing new working conditions associated with OSP practices. While the 'scenes' of the VR environment take place on a construction site, the environment predominantly targets professionals, such as project managers, construction managers, architects, designers, suppliers and manufacturers, to allow multidisciplinary learning to occur, and hence overcome 'knowledge silos' or 'knowledge compartmentation'. The VR environment enables unforeseen problems often caused by professionals' decisions, faulty work, and health and safety issues to occur; where the implications of which can be evaluated in respect of time, cost and resources. The VR environment proposed does not aim to resolve problems associated with OSP per se, rather aims to allow 'things to go wrong' and consequently allows users not only to 'experience' the resulting implications but also to reflect on those implications as part of the learning process. This paper discusses and presents the prototype for the first development phase of the VR interactive training environment. While the prototype was tested and validated with domain experts from industry, the research community, and academia from different EU countries, the data used in developing the prototype was constrained to one project in the UK which may limit the generalisability of results.


title: Re-Stream : Real-time and energy-efficient resource scheduling in big data stream computing environments

abstract: To achieve high energy efficiency and low response time in big data stream computing environments, it is required to model an energy-efficient resource scheduling and optimization framework. In this paper, we propose a real-time and energy-efficient resource scheduling and optimization framework, termed the Re-Stream. Firstly, the Re-Stream profiles a mathematical relationship among energy consumption, response time, and resource utilization, and obtains the conditions to meet high energy efficiency and low response time. Secondly, a data stream graph is modeled by using the distributed stream computing theories, which identifies the critical path within the data stream graph. Such a methodology aids in calculating the energy consumption of a resource allocation scheme for a data stream graph at a given data stream speed. Thirdly, the Re-Stream allocates tasks by utilizing an energy-efficient heuristic and a critical path scheduling mechanism subject to the architectural requirements. This is done to optimize the scheduling mechanism online by reallocating the critical vertices on the critical path of a data stream graph to minimize the response time and system fluctuations. Moreover, the Re-Stream consolidates the non-critical vertices on the non-critical path so as to improve energy efficiency. We evaluate the Re-Stream to measure energy efficiency and response time for big data stream computing environments. The experimental results demonstrate that the Re-Stream has the ability to improve energy efficiency of a big data stream computing system, and to reduce average response time. The Re-Stream provides an elegant trade-off between increased energy efficiency and decreased response time effectively within big data stream computing environments.


title: CloudSpeller: query spelling correction by using a unified hidden markov model with web-scale resources

abstract: Query spelling correction is an important component of modern search engines that can help users to express an information need more accurately and thus improve search quality. In this work we proposed and implemented an end-to-end speller correction system, namely CloudSpeller. The CloudSpeller system uses a Hidden Markov Model to effectively model major types of spelling errors in a unified framework, in which we integrate a large-scale lexicon constructed using Wikipedia, an error model trained from high confidence correction pairs, and the Microsoft Web N-gram service. Our system achieves excellent performance on two search query spelling correction datasets, reaching 0.960 and 0.937 F1 scores on the TREC dataset and the MSN dataset respectively.


title: Cyberbullying: The New Face of Workplace Bullying?

abstract: While the subject of cyberbullying of children and adolescents has begun to be addressed, there has been less attention or research on cyberbullying in the workplace. Whilst male-dominated workplaces such as manufacturing settings have been found to have an increased risk of workplace bullying, the prevalence of cyberbullying in this sector is not known. This exploratory study investigated the prevalence and methods of face-to-face bullying and cyberbullying of males at work. One hundred and three surveys (a modified version of the NAQ-R1), were returned from randomly selected members of the Australian Manufacturing Worker’s Union (AMWU). The results showed that 34% of the respondents were bullied face-to-face, and 10.7% were cyberbullied. All victims of cyberbullying also experienced face-to-face bullying. The implications for organisations of their “duty of care” in regards to this new form of bullying are indicated.


title: The amygdala and ventromedial prefrontal cortex in morality and psychopathy

abstract: Recent work has implicated the amygdala and ventromedial prefrontal cortex in morality and, when dysfunctional, psychopathy. This model proposes that the amygdala, through stimulus-reinforcement learning, enables the association of actions that harm others with the aversive reinforcement of the victims' distress. Consequent information on reinforcement expectancy, fed forward to the ventromedial prefrontal cortex, can guide the healthy individual away from moral transgressions. In psychopathy, dysfunction in these structures means that care-based moral reasoning is compromised and the risk that antisocial behavior is used instrumentally to achieve goals is increased.


title: Reconstructing Indian population history

abstract: India has been underrepresented in genome-wide surveys of human variation. We analyse 25 diverse groups in India to provide strong evidence for two ancient populations, genetically divergent, that are ancestral to most Indians today. One, the 'Ancestral North Indians' (ANI), is genetically close to Middle Easterners, Central Asians, and Europeans, whereas the other, the 'Ancestral South Indians' (ASI), is as distinct from ANI and East Asians as they are from each other. By introducing methods that can estimate ancestry without accurate ancestral populations, we show that ANI ancestry ranges from 39-71% in most Indian groups, and is higher in traditionally upper caste and Indo-European speakers. Groups with only ASI ancestry may no longer exist in mainland India. However, the indigenous Andaman Islanders are unique in being ASI-related groups without ANI ancestry. Allele frequency differences between groups in India are larger than in Europe, reflecting strong founder effects whose signatures have been maintained for thousands of years owing to endogamy. We therefore predict that there will be an excess of recessive diseases in India, which should be possible to screen and map genetically.


title: Training Data Selection for Support Vector Machines

abstract: In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained quadratic programming problem, which requires large memory and enormous amounts of training time for large-scale problems. In contrast, the SVM decision function is fully determined by a small subset of the training data, called support vectors. Therefore, it is desirable to remove from the training set the data that is irrelevant to the final decision function. In this paper we propose two new methods that select a subset of data for SVM training. Using real-world datasets, we compare the effectiveness of the proposed data selection strategies in terms of their ability to reduce the training set size while maintaining the generalization performance of the resulting SVM classifiers. Our experimental results show that a significant amount of training data can be removed by our proposed methods without degrading the performance of the resulting SVM classifiers.


title: Unsupervised Cross-Dataset Transfer Learning for Person Re-identification

abstract: Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task dictionary learning method which is able to learn a dataset-shared but targetdata-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.


title: Active transfer learning and selective instance transfer with active learning for motor imagery based BCI

abstract: Non-invasive EEG signal based brain computer interface (BCI) for motor imagery task - classification requires large number of subject specific training samples for each user session that reduces the user feasibility of BCI. A generalized classifier using few subject specific sample will ease the real world implementation of motor imagery based BCI. At first, this paper applies an improved active transfer learning (ATL) on motor imagery based BCI. Then, it proposes a noble method of transferring selective instances (selected by few new subject specific data) from other subjects to new subject combining with selecting most informative subject specific data determined by active learning. Experimental results on BCI competition IV 2B dataset show that improved ATL works well on six out of nine subjects and proposed SIITAL method overcomes ATL limitation for other subjects. This means, it can achieve similar or better accuracy with a lower quantity of subject specific training data. Thus, it reduces the calibration effort.


title: Accuracy of Topographic Maps Derived from ERS-1 Interferometric Radar

abstract: An interferometric radar technique for topographic mapping of surfaces promises a high-resolution approach to the generation of digital elevation models. The authors present analyses of data collected by the synthetic aperture radar instrument on-board the ERS-1 satellite on successive orbits. Use of a single satellite in a nearly repeating orbit is attractive for reducing cost and spaceborne hardware complexity; also it permits inference of changes in the surface from the correlation properties of the radar echoes. The data have been reduced to correlation maps and digital elevation models. The correlation maps show that temporal correlation decreases significantly with time, but not necessarily at a constant well-defined rate, likely depending on environmental factors. When correlation among passes remains high, however, it is possible to form digital elevation models. Analyses of noise expected in ERS-1 interferometric data collected over Alaska and the southwestern United States indicate that maps with relative errors less than 5 m rms are possible in some regions. However, orbit uncertainties imply that tie points are required in order to reduce absolute height errors to a similar magnitude. The authors find that about 6 tie points per 40/spl times/40 km scene with 5 m rms or better height accuracy are needed to keep systematic map height errors below 5 m rms. The performance of the ERS-1 radar system for topographic applications, though useful for a variety of regional and local discipline studies, may be improved with respect to temporal decorrelation errors and absolute height acuity by modifying the orbit repeat period and incorporating precise orbit determination techniques. The resulting implementation will meet many, but not all, objectives of a global mapping mission. >


title: A generalized backdoor criterion

abstract: We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.


title: Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network

abstract: In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing. We also propose a form of DPC that can be performed in a distributed manner with local channel state information, allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.


title: Parametric analysis and design guidelines of RF-to-DC Dickson charge pumps for RFID energy harvesting

abstract: This paper presents the effects of design parameters on output efficiency and input impedance of RF-to-DC Dick-son charge pumps by varying input parameters in simulation. Diode parasitics and input impedance mismatch between the charge pump and antenna are found to significantly decrease the effectiveness of charge pumps, while stage capacitance size appears to have little effect on efficiency. Off-the-shelf diodes are also compared through simulation to find which diodes perform best at each ISM frequency band and various power levels. The investigation is summarized by guidelines to assist designers in developing efficient RF-to-DC Dickson charge pumps as well as concluding with a design methodology.


title: Improving K-means through better initialization and normalization

abstract: K-means is still a popular clustering algorithm and active research area. The research is majorly focused at improving efficiency and effectiveness of the method. This paper proposes combined approach of a ranked initialization and normalization of data values with k-means. Three variations of a score based initialization approach is proposed. Experiments are performed on normalized data to prove the superiority of the proposed algorithm.


title: Training highly multiclass classifiers

abstract: Classification problems with thousands or more classes often have a large range of class-confusabilities, and we show that the more-confusable classes add more noise to the empirical loss that is minimized during training. We propose an online solution that reduces the effect of highly confusable classes in training the classifier parameters, and focuses the training on pairs of classes that are easier to differentiate at any given time in the training. We also show that the adagrad method, recently proposed for automatically decreasing step sizes for convex stochastic gradient descent, can also be profitably applied to the nonconvex joint training of supervised dimensionality reduction and linear classifiers as done in Wsabie. Experiments on ImageNet benchmark data sets and proprietary image recognition problems with 15,000 to 97,000 classes show substantial gains in classification accuracy compared to one-vs-all linear SVMs and Wsabie.


title: Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other

abstract: This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications. © 2012 Wiley Periodicals, Inc.


title: Mobile phones as tool to increase communication and location awareness of users

abstract: Mobile phones have become a ubiquitous technology and for many people a daily companion. The prime function is to enable communication and information access anywhere and anytime, but the fact that the phone is accompanying the user makes it an interesting platform for building persuasive applications and for use as ambient display. It offers means for collecting information and sensing behaviour as well as for the presentation of recommendations (in specific situations as well as in an ambient way). We analyze the technical capabilities of mobile phones that can support the creation of persuasive technologies. The focus of the work is to explore the domain of ambient displays and persuasive technology with regard to communication. We present designs of how the screen saver on a phone can raise users' awareness of their personal communication behaviour. Additionally, we show how this concept is applicable to further application areas such as increasing the awareness of the user on how his time is spent or creating stronger bonds in peer groups.


title: FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems

abstract: Recommender systems have many successful applications in e-commerce and social media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most popular recommendation approaches; the original user-product rating matrix R with millions of rows and columns is decomposed into a user matrix Q and an item matrix P, such that the product QT P approximates R. Each column q (p) of Q (P) holds the latent factors of the corresponding user (item), and qT p is a prediction of the rating to item p by user q. Recommender systems based on MF suggest to a user in q the items with the top-k scores in qT P. For this problem, we propose a Fast and EXact Inner PROduct retrieval (FEXIPRO) framework, based on sequential scan, which includes three elements. First, FEXIPRO applies an SVD transformation to P, after which the first several dimensions capture a large percentage of the inner products. This enables us to prune item vectors by only computing their partial inner products with q. Second, we construct an integer approximation version of P, which can be used to compute fast upper bounds for the inner products that can prune item vectors. Finally, we apply a lossless transformation to P, such that the resulting matrix has only positive values, allowing for the inner products to be monotonically increasing with dimensionality. Experiments on real data demonstrate that our framework outperforms alternative approaches typically by an order of magnitude.


title: Animating Still Landscape Photographs Through Cloud Motion Creation

abstract: Animating landscape photographs can create an engaging viewing experience and has long been an active area of multimedia research. Images can be used to create an illusion of movement, thereby providing more vivid and interesting visuals . Among numerous objects in visual scenes, clouds appear as a common element in landscape photos but are challenging to manipulate because clouds are ill-posed structures without concrete forms and present motion with very high degrees of freedom and arbitrary shape deformation. This paper addresses the generation of dynamic imagery from a still photo through automatic motion creation. First, a cloud appearance model is acquired by representing each pixel in the sky region as a combination of two computable image properties , i.e., Cloudiness estimates cloud density and Cloud Structure gives the corresponding cloud shape and texture. Then, based on the obtained Cloudiness and Cloud Structure, a content-aware wind field is generated to synthesize the motion of cloud flow. Experimental results demonstrate that our approach can work well on various types of cloud images and produce visually convincing dynamic imagery of landscape photos. Furthermore , we showcased several extended multimedia applications to validate that the proposed framework is generic and widely extensible to other vapor-like objects like mist and smoke. Please see http://mclab.citi.sinica.edu.tw/demo/dynamicimagery.htm for demos.


title: Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

abstract: In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.


title: HapWRAP: Soft Growing Wearable Haptic Device

abstract: Soft robotics and pneumatic actuation present opportunities for lightweight wearable haptic devices that provide distributed touch feedback to the skin. Ideally, such devices would be easily donned and doffed, since permanent coverage of a large area of the skin is undesirable. Here we present the design and evaluation of a concept device called HapWRAP: a growing haptic device constructed from flexible low density polyethylene. Controlled air flow through tubes and pouches allows HapWRAP to grow out of a compact housing unit and provide a combination of directional and force feedback to a user. When activated, HapWRAP grows up and around the forearm; its loops form a temporary sleeve. After growth, pneumatic actuators inflate and deflate to stimulate mechanoreceptors in the skin at distinguishable locations. This paper describes the design and manufacturing of HapWRAP, reports its performance metrics, and tests its suitability as a haptic feedback device. Participants were able to interpret force and direction cues from HapWRAP with 92.5% accuracy. These findings suggest that HapWRAP can be successfully used for applications where both force and direction cues are necessary.


title: A micro spherical rolling and flying robot

abstract: This paper presents the design, fabrication, modeling, and demonstration of a micro spherical rolling and flying robot, with a total mass and payload of 35 g and 10 g, respectively. The micro aerial terrestrial robot (ATR) has the ability to fly through the air or roll on the ground, for applications that include search and rescue, mapping, and surveillance. Its unique size makes is easily portable and enables the robot to enter and maneuver around in tight spaces such as air ducts. The design centers around a micro-quadcopter encased in a lightweight spherical exoskeleton that can rotate about the quadcopter. The spherical exoskeleton offers agile ground locomotion while maintaining characteristics of a basic aerial robot in flying mode. Details of the system modeling, design and fabrication are discussed, including the robot's turning capabilities over ground and the lightweight spring-steel exoskeleton. The prototype ATR is experimentally validated in aerial and terrestrial mode, and results show that the ATR traveling over the same distance in rolling mode is 260 percent more efficient than a traditional flying-only robot and in flying mode the system is only 39 percent less efficient. Experimental results also demonstrate transition between modes of locomotion and curved, rolling trajectories.


title: A unified design method utilizing decomposability and composability for secure systems

abstract: In this paper, we perform a study of unifying two design concepts of decomposition and composition. The meaning of a unified approach is defined in a sense that, in order to solve a design problem to meet a certain requirement such as security or complexity or performance, an integrated thinking process utilizing both decomposability and composability is employed. This work is done at an architectural and design level. The main idea of our study is rooted from the study of the structural properties of finite state machines. We introduce the notion of (n, m)-design in some detail and illustrate the main idea using an example. Finally, we provide a list of potential applications.


title: Deconstructing International Organization Immunity

abstract: In the United States, public international organizations derive privileges and immunities from a variety of sources of law. In particular, the International Organizations Immunities Act of 1945 (IOIA) grants certain international organizations “the same immunity from suit and every form of judicial process as is enjoyed by foreign governments.” When the IOIA was enacted, those organizations had few legal protections but foreign sovereigns generally had “absolute immunity” from civil suits in U.S. courts. Over time, the international community, including the United States, clarified exceptions to foreign sovereign immunity, allowing some suits against foreign states. This rule of “restrictive immunity” was codified in the United States by the Foreign Sovereign Immunities Act of 1976 (FSIA). However, some courts, like the D.C. Circuit, have continued to grant absolute immunity to international organizations covered by the IOIA — exceeding the level of protection enjoyed today by foreign governments — while others, such as the Third Circuit, have not.This Note discusses what privileges and immunities public international organizations have in the United States based on the IOIA, the FSIA, and recent Supreme Court jurisprudence. It concludes that the text and the legislative history of those statutes do not demonstrate congressional intent to incorporate developments in the law of foreign sovereign immunity into the privileges and immunities of international organizations. Moreover, the Supreme Court’s recent decisions in Dole Food and Samantar help clarify which entities U.S. courts should consider covered by the FSIA, and international organizations do not appear to be included under that statute. International organizations may also benefit from other sources of law, including international agreements, other congressional or administrative action, and common-law doctrines. However, there is probably no customary international law yet that defines the boundaries of privileges and immunities of international organizations.


title: Supervised and Unsupervised methods to detect Insider Threat from Enterprise Social and Online Activity Data

abstract: Insider threat is a significant security risk for organizations, and detection of insider threat is of paramount concern to organizations. In this paper, we attempt to discover insider threat by analyzing enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we take two approaches to detect insider threat: (i) an unsupervised approach where we identify statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and (ii) a supervised approach where we use labels indicating when employees quit the company as a proxy for insider threat activity to design a classifier. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77 for the unsupervised approach, and a classification accuracy of 73.4% for the supervised approach. These results indicate that our proposed approaches are fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.


title: Off-Policy Deep Reinforcement Learning without Exploration

abstract: Reinforcement learning traditionally considers the task of balancing exploration and exploitation. This work examines batch reinforcement learning--the task of maximally exploiting a given batch of off-policy data, without further data collection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data.


title: Model based control of series elastic actuators

abstract: This contribution presents a comprehensible model based approach of a control structure to control the output force of series elastic actuators. The controller is based on a linear model of the actuator, which can be reduced to a PT 2 element if a disturbance compensator is included. This allows for a straightforward design of a state space controller and an intuitive choice of the controller parameters. An actuator design is presented and the required system parameters are identified. The performance of the control structure is proven by experiment.


title: ClaimEval: integrated and flexible framework for claim evaluation using credibility of sources

abstract: The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., "Chicken meat is healthy"). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on real-world datasets, we demonstrate ClaimEval's capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.


title: Would static analysis tools help developers with code reviews?

abstract: Code reviews have been conducted since decades in software projects, with the aim of improving code quality from many different points of view. During code reviews, developers are supported by checklists, coding standards and, possibly, by various kinds of static analysis tools. This paper investigates whether warnings highlighted by static analysis tools are taken care of during code reviews and, whether there are kinds of warnings that tend to be removed more than others. Results of a study conducted by mining the Gerrit repository of six Java open source projects indicate that the density of warnings only slightly vary after each review. The overall percentage of warnings removed during reviews is slightly higher than what previous studies found for the overall project evolution history. However, when looking (quantitatively and qualitatively) at specific categories of warnings, we found that during code reviews developers focus on certain kinds of problems. For such categories of warnings the removal percentage tend to be very high, often above 50% and sometimes up to 100%. Examples of those are warnings in the imports, regular expressions, and type resolution categories. In conclusion, while a broad warning detection might produce way too many false positives, enforcing the removal of certain warnings prior to the patch submission could reduce the amount of effort provided during the code review process.


title: Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

abstract: Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed fully dense UNet (FD-UNet) for removing artifacts from two-dimensional PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.


title: Synaptic patterning and the timescales of cortical dynamics

abstract: Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.


title: Position error compensation in quadrature analog magnetic encoders through an iterative optimization algorithm

abstract: This paper analyses the position error compensation in quadrature analog magnetic encoders through the iterative linear search optimization algorithm of "steepest descent". In current literature, the sine/cosine signals from the encoder are compensated considering only the gain mismatch, the DC offset components and the non-orthogonality. Nevertheless, the magnetic field distortion due to the non-homogeneity of the magnet and the distortion of the signals due to the mechanical misalignment between the rotation axis of the motor shaft, the magnet center and the chip sensor are generally neglected. In this work, all of these factors are taken into account. The results show that a higher order approximation for modeling the harmonic distortion contained in the encoder signals leads to a slight biasing in the convergence of the compensating parameters. However, the unconstrained and multivariate optimization algorithm of steepest descent fairly minimizes the error from the objective function F(x n ) allowing achieve a compensation efficiency of up to 66%, thus increasing the overall accuracy of this kind of magnetic encoders and improving the performance of any application where they provide rotary position feedback.


title: FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

abstract: Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture. By utilizing a novel set of optimizations that enable efficient mapping of binarized neural networks to hardware, we implement fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. On a ZC706 embedded FPGA platform drawing less than 25 W total system power, we demonstrate up to 12.3 million image classifications per second with 0.31 μs latency on the MNIST dataset with 95.8% accuracy, and 21906 image classifications per second with 283 μs latency on the CIFAR-10 and SVHN datasets with respectively 80.1% and 94.9% accuracy. To the best of our knowledge, ours are the fastest classification rates reported to date on these benchmarks.


title: Highly efficient electrocatalysts derived from carbon black supported non-precious metal macrocycle catalysts for oxygen reduction reaction

abstract: Abstract For the sake of accelerating the commercial application of fuel cells, non-noble metal catalysts with high activity and high stability have been widely developed to replace platinum-based catalysts. Here, we report a simple but cost-effective synthetic strategy using iron tetra-amino phthalocyanine (FePC-NH2) and modified carbon black (HCB) to obtain a novel oxygen reduction electrocatalyst (named as FePC-NH2/HCB-800) with Fe2O3 wrapped in nitrogen-doped carbon (N-carbon) as active site. The HCB as template can effectively promotes the formation of Fe2O3 active site in the catalysts. Compared to commercial Pt/C catalyst, the FePC-NH2/HCB-800 catalyst exhibits high electrocatalytic activity for oxygen reduction reaction (ORR) with onset potential of 0.98 V and half-wave potential with 0.84 V vs. reversible hydrogen electrode (RHE). Meanwhile, the catalyst also shows excellent circulation stability. We believe that this work provides a platform for ORR and is conducive to the commercialization of fuel cells and metal-air batteries.


title: Development of an extensible SSVEP-BCI software platform and application to wheelchair control

abstract: Visual-evoked potential (VEP)-based BCIs have been shown to provide the highest information transfer rates and reliability among BCI approaches. However, to date, no flexible software platform exists that allows investigators and end-users to easily evaluate and optimize VEP stimulus parameters such as size, position, flashing rate, color, etc., with seamless integration to an application environment. This paper provides an overview of the development of such a customizable VEP-BCI software platform called Visual Evoked Stimulation and SELection Software (VESSELS), and its implementation for control of a motorized wheelchair.


title: Ultrafast Graphene Oxide Humidity Sensors

abstract: Sensors allow an electronic device to become a gateway between the digital and physical worlds, and sensor materials with unprecedented performance can create new applications and new avenues for user interaction. Graphene oxide can be exploited in humidity and temperature sensors with a number of convenient features such as flexibility, transparency and suitability for large-scale manufacturing. Here we show that the two-dimensional nature of graphene oxide and its superpermeability to water combine to enable humidity sensors with unprecedented response speed (∼30 ms response and recovery times). This opens the door to various applications, such as touchless user interfaces, which we demonstrate with a ‘whistling’ recognition analysis.


title: Enhanced Blowfish Algorithm for Image Encryption and Decryption with Supplementary Key

abstract: Security is a major concern while sending and receiving sensitive data over web. This paper is aimed to design and develop a method to address this problem. The proposed method is based on Blowfish algorithm with enhanced features. It has been enhanced with a supplementary key approach to strengthen the security of image or any sensitive data which are communicated electronically. The proposed algorithm is developed and tested with different data sets. The performance of the proposed methods is measured in terms of time, space complexity and also security. The results are recorded and show better performance.


title: Miniaturized Balanced Antenna with Integrated Balun for Practical LTE Applications

abstract: A design of dual-band balanced antenna structure operating in the 700 and 2600MHz LTE bands is studied and investigated. The overall dimensions of the radiator are 50 × 18 × 7 mm^3 allowing it to be easily concealed within mobile handsets. A broad-band balun is designed and integrated with the antenna handset in order to provide the feeding network and perform the measurements of the antenna radiation performance. Prototypes of proposed antenna with and without balun are fabricated and verified. The simulated and practical results with and without the handheld effects in terms of reflection coefficient, power gain and radiation pattern, are studied and shown reasonable agreement.


title: Learning Linear Regression Models over Factorized Joins

abstract: We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end-to-end solution to learning over joins. We propose a new paradigm for computing batch gradient descent that exploits the factorized computation and representation of the training datasets, a rewriting of the regression objective function that decouples the computation of cofactors of model parameters from their convergence, and the commutativity of cofactor computation with relational union and projection. We introduce three flavors of this approach: F/FDB computes the cofactors in one pass over the materialized factorized join; Favoids this materialization and intermixes cofactor and join computation; F/SQL expresses this mixture as one SQL query. Our approach has the complexity of join factorization, which can be exponentially lower than of standard joins. Experiments with commercial, public, and synthetic datasets show that it outperforms MADlib, Python StatsModels, and R, by up to three orders of magnitude.


title: Usable web-based calendaring for blind users

abstract: While a calendar is generally thought of as a visual representation of a day, week, month or year; in its simplest form, a calendar consists of scheduled events with associated dates, times, locations and descriptions. In this paper we describe the design and evaluation of a web-based calendar prototype for blind individuals who use screen readers to access their computers and the Internet. With our new web-based calendar prototype, the successful completion across common calendar tasks improved from a mean of 43 per cent across other web-based email calendars to a mean of 96 per cent in our prototype calendar. This empirical study illustrates the types of simple design changes that can dramatically improve the level of access and usability of dynamic, web-based calendar interfaces for blind users.


title: Identifying Varietals in the Discourse of American and Korean Scientists: A Contrastive Corpus Analysis Using the Gramulator

abstract: In this study, we identify systematic discourse characteristics in the academic writings of Korean and American scientists. Specifically, we employ Contrastive Corpus Analysis (CCA) using the computational tool, The Gramulator to extract statistically improbable “n gram” features that occur across texts. The results indicate that Korean scientists use non standard varietal forms of English (i.e., forms that are correct but relatively uncommon with their American counterparts). We conclude that the Korean scientists’ use of these varietals may be a key factor in interpreting and evaluating their work as non protypical in terms of discourse style. Our findings bring to light language characteristics and a methodology that may be helpful to language learners as well as materials developers.


title: CFD Analysis of Winglets at Low Subsonic Flow

abstract:  Abstract— A winglet is a device attached at the wingtip, used to improve aircraft efficiency by lowering the induced drag caused by wingtip vortices. It is a vertical or angled extension at the tips of each wing. Winglets work by increasing the effective aspect ratio of a wing without adding greatly to the structural stress and hence necessary weight of the wing structure. This paper describes a CFD 3-dimensional winglets analysis that was performed on a rectangular wing of NACA653218 cross sectional airfoil. The wing is of 660 mm span and 121 mm chord and was analyzed for two shape configurations, semicircle and elliptical. The objectives of the analysis were to compare the aerodynamic characteristics of the two winglet configurations and to investigate the performance of the two winglets shape simulated at selected cant angle of 0, 45 and 60 degrees. The computational simulation was carried out by FLUENT 6.2 solver using Finite Volume Approach. The simulation was done at low subsonic flow and at various angles of attack using Spalart-Allmaras couple implicit solver. A comparison of aerodynamics characteristics of lift coefficient CL, drag coefficient CD and lift to drag ratio, L/D was made and it was found that the addition of the elliptical and semi circular winglet gave a larger lift curve slope and higher Lift-to-Drag Ratio in comparison to the baseline wing alone. Elliptical winglet with 45 degree cant angle was the best overall design giving about 8 percent increase in lift curve slope and the best Lift-to-Drag Ratio.


Going for Broke: The Fate of Farm Workers in Arid South Africa


title: Large Area Solution Processed Poly (Dimethylsiloxane)-Based Thin Film Sensor Patch for Wearable Electrocardiogram Detection

abstract: This letter develops a thin film electrocardiography (ECG) sensor patch using poly (dimethylsiloxane) as the base material, which can be fabricated with low cost and large area compatible coating processes. This design enables easy integration with the readout circuit board to form a compact ECG patch system for wearable uses. The ultra-thin and soft PDMS film make it conform well to the curvilinear skin surface for low electrode-to-skin impedance and strong adhesion. By using dispensed silver paste on the copper tape film, reliable and highly conductive connection is built between the skin surface electrode and the readout circuit. A two-electrode configuration ECG patch system was made and worn on a volunteer for a whole day. The test results show that the measured signal quality with this ECG sensor patch will not be affected after normal activities.


title: Real-Time Re-Textured Geometry Modeling Using Microsoft HoloLens

abstract: We implemented live-textured geometry model creation with immediate coverage feedback visualizations in AR on the Microsoft HoloLens. A user walking and looking around a physical space can create a textured model of the space, ready for remote exploration and AR collaboration. Out of the box, a HoloLens builds a triangle mesh of the environment while scanning and being tracked in a new environment. The mesh contains vertices, triangles, and normals, but not color. We take the video stream from the color camera and use it to color a UV texture to be mapped to the mesh. Due to the limited graphics memory of the HoloLens, we use a fixed-size texture. Since the mesh generation dynamically changes in real time, we use an adaptive mapping scheme that evenly distributes every triangle of the dynamic mesh onto the fixed-size texture and adapts to new geometry without compromising existing color data. Occlusion is also considered. The user can walk around their environment and continuously fill in the texture while growing the mesh in real-time. We describe our texture generation algorithm and illustrate benefits and limitations of our system with example modeling sessions. Having first-person immediate AR feedback on the quality of modeled physical infrastructure, both in terms of mesh resolution and texture quality, helps the creation of high-quality colored meshes with this standalone wireless device and a fixed memory footprint in real-time.


title: Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares

abstract: In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size. In previous studies, researchers conducted random sampling which is according to the input data but independent on the response variable, however the response variable may also be informative for sampling. In this paper we propose an adaptive sampling called the gradient-based sampling which is dependent on both the input data and the output for fast solving of least-square (LS) problems. We draw the data points by random sampling from the full data according to their gradient values. This sampling is computationally saving, since the running time of computing the sampling probabilities is reduced to O(nd) where n is the full sample size and d is the dimension of the input. Theoretically, we establish an error bound analysis of the general importance sampling with respect to LS solution from full data. The result establishes an improved performance of the use of our gradient- based sampling. Synthetic and real data sets are used to empirically argue that the gradient-based sampling has an obvious advantage over existing sampling methods from two aspects of statistical efficiency and computational saving.


Toward Higher Learning


title: Energy disaggregation for real-time building flexibility detection

abstract: Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.


title: DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation

abstract: This paper investigates the piracy problem of deep learning models. Designing and training a well-performing model is generally expensive. However, when releasing them, attackers may reverse engineer the models and pirate their design. This paper, therefore, proposes deep learning obfuscation, aiming at obstructing attackers from pirating a deep learning model. In particular, we focus on obfuscating convolutional neural networks (CNN), a widely employed type of deep learning architectures for image recognition. Our approach obfuscates a CNN model eventually by simulating its feature extractor with a shallow and sequential convolutional block. To this end, we employ a recursive simulation method and a joint training method to train the simulation network. The joint training method leverages both the intermediate knowledge generated by a feature extractor and data labels to train a simulation network. In this way, we can obtain an obfuscated model without accuracy loss. We have verified the feasibility of our approach with three prevalent CNNs, i.e., GoogLeNet, ResNet, and DenseNet. Although these networks are very deep with tens or hundreds of layers, we can simulate them in a shallow network including only five or seven convolutional layers. The obfuscated models are even more efficient than the original models. Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers. Moreover, it can also thwart attackers from pirating the model with transfer learning or incremental learning techniques because the shallow simulation network bears poor learning ability. To our best knowledge, this paper serves as a first attempt to obfuscate deep learning models, which may shed light on more future studies.


title: Adversarial Distillation of Bayesian Neural Network Posteriors

abstract: Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches. However, SGLD and its extensions require storage of many copies of the model parameters, a potentially prohibitive cost, especially for large neural networks. We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN). At test-time, samples are generated by the GAN. We show that this distillation framework incurs no loss in performance on recent BNN applications including anomaly detection, active learning, and defense against adversarial attacks. By construction, our framework not only distills the Bayesian predictive distribution, but the posterior itself. This allows one to compute quantities such as the approximate model variance, which is useful in downstream tasks. To our knowledge, these are the first results applying MCMC-based BNNs to the aforementioned downstream applications.


title: Automatic target classifier for a Ground Surveillance Radar using linear discriminant analysis and Logistic regression

abstract: This paper presents the design of an automatic target classifier for a Ground Surveillance Radar namely NUST Radar∗ (NR-V3). The classifier is developed to distinguish between pedestrians, vehicles and no target (noise) classes. Feature vectors are extracted from the FFT spectrum of radar audio signal. Logistic regression and linear discriminant analysis based classifiers are used for classification of feature vectors. The classifiers are trained and tested using radar data collected with NR-V3. Overall classification accuracy of 95.6% and 92% is achieved for Logistic regression and linear discriminant analysis classifiers respectively.


title: Compressive sensing: Performance comparison of sparse recovery algorithms

abstract: Spectrum sensing is an important process in cognitive radio. A number of sensing techniques that have been proposed suffer from high processing time, hardware cost and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this goal. A number of sparse recovery algorithms have been proposed. This paper surveys the sparse recovery algorithms, classify them into categories, and compares their performances. For the comparison, we used several metrics such as recovery error, recovery time, covariance, and phase transition diagram. The results show that techniques under Greedy category are faster, techniques of Convex and Relaxation category perform better in term of recovery error, and Bayesian based techniques are observed to have an advantageous balance of small recovery error and a short recovery time.


title: Internet and Mobile Phone Addiction among High School Students: A Cross Sectional Study from Iran

abstract: Addiction to internet and mobile phone may be affecting all aspect of student’s life. Knowledge about prevalence and related factors of internet and mobile phone addiction is necessary for planning for prevention and treatment. This study was conducted to evaluate the prevalence of internet and mobile phone addiction among Iranian students. Methods: This cross sectional study conducted from Jun to April 2015 in Rasht Iran. With using stratified sampling method, 581 high school students from two region of Rasht in North of Iran were recruited as the subjects for this study. Data were collected with using demographics questionnaire, Cell phone Overuse Scale (COS), and the Internet Addiction Test (IAT). Analysis was performed using Statistical Package for Social Sciences (SPSS) 17 21 version. Results: Of the 581 students, who participate in present study, 53.5% were female and the rest were male. The mean age of students was 16.28±1.01 years. The mean score of IAT was 42.03±18.22. Of the 581 students, 312 (53.7%), 218 (37.5%) and 51 (8.8%) showed normal, mild and moderate level of internet addiction. The mean score of COS was 55.10±19.86.Of the 581 students, 27(6/4%), 451(6/77) and 103 (7/17) showed low, moderate and high level of mobile phone addiction. Conclusion: according to finding of present study, rate of mobile phone and internet addiction were high among Iranian students. Health care authorities should pay more attention to these problems. Keywords: Internet addiction, mobile phone addiction, students, developing country, Cell phone Overuse Scale, the Internet Addiction Test


CORPORATE SOCIAL RESPONSIBILITY-ROLE OF GOVERNMENT


title: From genes to behavior in developmental dyslexia

abstract: All four genes thus far linked to developmental dyslexia participate in brain development, and abnormalities in brain development are increasingly reported in dyslexia. Comparable abnormalities induced in young rodent brains cause auditory and cognitive deficits, underscoring the potential relevance of these brain changes to dyslexia. Our perspective on dyslexia is that some of the brain changes cause phonological processing abnormalities as well as auditory processing abnormalities; the latter, we speculate, resolve in a proportion of individuals during development, but contribute early on to the phonological disorder in dyslexia. Thus, we propose a tentative pathway between a genetic effect, developmental brain changes, and perceptual and cognitive deficits associated with dyslexia.


title: Efficient mini-batch training for stochastic optimization

abstract: Stochastic gradient descent (SGD) is a popular technique for large-scale optimization problems in machine learning. In order to parallelize SGD, minibatch training needs to be employed to reduce the communication cost. However, an increase in minibatch size typically decreases the rate of convergence. This paper introduces a technique based on approximate optimization of a conservatively regularized objective function within each minibatch. We prove that the convergence rate does not decrease with increasing minibatch size. Experiments demonstrate that with suitable implementations of approximate optimization, the resulting algorithm can outperform standard SGD in many scenarios.


title: Fathom: reference workloads for modern deep learning methods

abstract: Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling.


title: Naturalness Preserved Image Enhancement Using a Priori Multi-Layer Lightness Statistics

abstract: The enhancement of non-uniformly illuminated images often suffers from over-enhancement and produces unnatural results. This paper presents a naturalness preserved enhancement method for non-uniformly illuminated images, using a priori multi-layer lightness statistics acquired from high-quality images. This paper makes three important contributions: designing a novel multi-layer image enhancement model; deriving the multi-layer lightness statistics of high-quality outdoor images, which are incorporated into the multi-layer enhancement model; and showing that the overall quality rating of enhanced images is consistent with a combination of contrast enhancement and naturalness preservation. Two separate human observer evaluation studies were conducted on naturalness preservation and overall image quality. The results showed the proposed method outperformed four compared state-of-the-art enhancement methods.


title: Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing

abstract: Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture. During the last few years, nonnegative matrix factorization (NMF), as a suitable candidate for the linear spectral mixture model, has been applied to unmix hyperspectral data. Unfortunately, the local minima caused by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well-defined problem. Therefore, in this paper, two inherent characteristics of hyperspectral data, piecewise smoothness (both temporal and spatial) of spectral data and sparseness of abundance fraction of every material, are introduced to NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities in spectral data. At the same time, two NMF algorithms, nonsmooth NMF and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. A gradient-based optimization algorithm is presented, and the monotonic convergence of the algorithm is proved. Three important facts are exploited in our method: First, both the spectra and abundances are nonnegative; second, the variation of the material spectra and abundance images is piecewise smooth in wavelength and spatial spaces, respectively; third, the abundance distribution of each material is almost sparse in the scene. Experiments using synthetic and real data demonstrate that the proposed algorithm provides an effective unsupervised technique for hyperspectral unmixing.


title: Regular self-examination or clinical examination for early detection of breast cancer

abstract: BACKGROUND ::: Breast self-examination and clinical breast examination have been promoted for many years as general screening methods to diagnose breast cancer at an earlier stage in order to decrease morbidity and or mortality. The possible benefits and harms remain unclear. ::: ::: ::: OBJECTIVES ::: To determine whether screening for breast cancer by regular self-examination or clinical breast examination reduces breast cancer mortality and morbidity. ::: ::: ::: SEARCH STRATEGY ::: The Cochrane Library and Medline were searched for randomised trials; date of last search October 2002. The specialised register maintained by the Cochrane Breast Cancer Group was searched. ::: ::: ::: SELECTION CRITERIA ::: Randomised clinical trials, including cluster randomised trials. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Decisions on which trials to include were taken independently by the reviewers based on the methods of trial. Disagreements were resolved by discussion. Intention to treat analyses were conducted using a fixed effect model with 95% confidence intervals. ::: ::: ::: MAIN RESULTS ::: Two large population-based studies (388,535 women) from Russia and Shanghai that compared breast self-examination with no intervention were included. There was no statistically significant difference in breast cancer mortality, relative risk 1.05 (95% confidence interval (CI) 0.90 to 1.24) (587 deaths in total). In Russia, more cancers were found in the breast self-examination group than in the control group (relative risk 1.24, 95% CI 1.09 to 1.41), while this was not the case in Shanghai (relative risk 0.97, 95% CI 0.88 to 1.06). Almost twice as many biopsies (3406) with benign results were performed in the screening group compared to the control group (1856), relative risk 1.89, 95% CI 1.79 to 2.00. ::: ::: ::: REVIEWER'S CONCLUSIONS ::: Data from two large trials do not suggest a beneficial effect of screening by breast self-examination whereas there is evidence for harms. There were no randomised trials of clinical breast examination. At present, breast self-examination cannot be recommended.


title: The antibiotic resistance “mobilome”: searching for the link between environment and clinic

abstract: Antibiotic resistance is an ancient problem, owing to the co-evolution of antibiotic-producing and target organisms in the soil and other environments over millennia. The environmental ‘resistome’ is the collection of all genes that directly or indirectly contribute to antibiotic resistance. Many of these resistance determinants originate in antibiotic-producing organisms (where they serve to mediate self-immunity), while others become resistance determinants only when mobilized and over-expressed in non-native hosts (like plasmid-based β-lactamases). The modern environmental resistome is under selective pressure from human activities such as agriculture, which may influence the composition of the local resistome and lead to gene transfer events. Beyond the environment, we are challenged in the clinic by the rise in both frequency and diversity of antibiotic resistant pathogens. We assume that clinical resistance originated in the environment, but few examples of direct gene exchange between the environmental resistome and the clinical resistome have been documented. Strong evidence exists to suggest that clinical aminoglycoside and vancomycin resistance enzymes, the extended-spectrum β-lactamase CTX-M and the quinolone resistance gene Qnr have direct links to the environmental resistome. In this review, we highlight recent advances in our understanding of horizontal gene transfer of antibiotic resistance genes from the environment to the clinic. Improvements in sequencing technologies coupled with functional metagenomic studies have revealed previously underappreciated diversity in the environmental resistome, and also established novel genetic links to the clinic. Understanding mechanisms of gene exchange becomes vital in controlling the future dissemination of antibiotic resistance.


title: Collaborative effort towards common ground in situated human-robot dialogue

abstract: In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world.


title: Correlation of Built-In Potential and I–V Crossover in Thin-Film Solar Cells

abstract: Thin-film solar cells often show a crossover between the illuminated and dark I–V characteristics. Several device specific reasons for crossover exist and have been discussed extensively. In this paper, we show that a low contact-to-contact built-in potential can produce a voltage-dependent photocurrent that leads to I–V crossover at a voltage that is almost exactly the device built-in potential. This mechanism can produce crossover in the absence of carrier trapping or recombination. It can be a contributing factor to crossover, but when an anomalously low contact-to-contact built-in potential exists, it can be the dominant factor. Using numerical simulations, we examine a variety of model solar cell structures with low contact-to-contact built-in potential and show a strong correlation of the crossover and built-in potential voltages. These simulations also suggest that a plot of the illuminated minus dark current may help identify when a low $V_{bi}$ is limiting device performance.


title: Service-oriented architecture and business process choreography in an order management scenario: rationale, concepts, lessons learned

abstract: Effective and affordable business-to-business process integration is a key success factor in the telecommunications industry. A large telecommunication wholesaler, supplying its services to more than 150 different service retailers, enhanced the process integration capabilities of its core order management system through widespread use of SOA, business process choreography and Web services concepts. This core order management system processes 120 different complex order types.On this project, challenging requirements such as complexity of business process models and multi-channel accessibility turned out to be true proof points for the applied SOA concepts, tools, and runtime environments. To implement an automated and secured business-to-business Web services channel and to introduce a process choreography layer into a large existing application were two of the key requirements that had to be addressed. The solution complies with the Web Services Interoperability Basic Profile 1.0 and makes use of executable business process models defined in the Business Process Execution Language (BPEL).This paper discusses the rationale behind the decision for SOA, process choreography, and Web services, and gives an overview of the BPEL-centric process choreography architecture. Furthermore, it features lessons learned and best practices identified during design, implementation, and rollout of the solution.


title: The Role of the iPad in the Hands of the Learner

abstract: The iPad is a well-known handheld interactive multimedia tool that has been quite popular lately among teachers and students. Previous research indicates that handheld digital technology is, in general, capable of supporting learning and educational literacy; however, iPad-focused studies are still scarce. In this study, we access and review the instructional benefits of using the iPad in educational spaces, such as classrooms and laboratories, by reviewing a vast body of empirical and theoretical findings reported by multidisciplinary literature on digital technology and children, digital technology use in classrooms, and the impact of interactive technology on learning, instruction, and educational literature. We found that while some studies suggest that the iPad motivates and brings positive impact among students towards their studies, other studies suggest that the long-term impact of the iPad on learning could be even negative. Therefore, we highlight the common misconceptions and conflicts about the use of this new technology and discuss its advantages and challenges. We also sketch some of the future trends relevant to incorporating the iPad into our learning setups.


title: GSM Based Home Automation System Using App-Inventor for Android Mobile Phone

abstract: Nowadays, the remote Home Automation turns out to be more and more significant and appealing. It improves the value of our lives by automating various electrical appliances or instruments. This paper describes GSM (Global System Messaging) based secured device control system using App Inventor for Android mobile phones. App Inventor is a latest visual programming platform for developing mobile applications for Android-based smart phones. The Android Mobile Phone Platform becomes more and more popular among software developers, because of its powerful capabilities and open architecture. It is a fantastic platform for the real world interface control, as it offers an ample of resources and already incorporates a lot of sensors. No need to write programming codes to develop apps in the App Inventor, instead it provides visual design interface as the way the apps looks and use blocks of interlocking components to control the app’s behaviour. The App Inventor aims to make programming enjoyable and accessible to novices.


title: Spectral clustering algorithms for ultrasound image segmentation.

abstract: Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.


title: Pruning decision trees and lists

abstract: Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects—patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work—in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.


title: FeatureInsight: Visual support for error-driven feature ideation in text classification

abstract: Machine learning requires an effective combination of data, features, and algorithms. While many tools exist for working with machine learning data and algorithms, support for thinking of new features, or feature ideation, remains poor. In this paper, we investigate two general approaches to support feature ideation: visual summaries and sets of errors. We present FeatureInsight, an interactive visual analytics tool for building new dictionary features (semantically related groups of words) for text classification problems. FeatureInsight supports an error-driven feature ideation process and provides interactive visual summaries of sets of misclassified documents. We conducted a controlled experiment evaluating both visual summaries and sets of errors in FeatureInsight. Our results show that visual summaries significantly improve feature ideation, especially in combination with sets of errors. Users preferred visual summaries over viewing raw data, and only preferred examining sets when visual summaries were provided. We discuss extensions of both approaches to data types other than text, and point to areas for future research.


title: Data Mining Framework for Direct Marketing: A Case Study of Bank Marketing

abstract: Direct marketing has generated an increasing interest among academics and practitioners over the past few years. This is due to competitive market environment, advancement in technology and changing behavior of customers which are difficult to predict. Despite of numerous studies that have provided important insights into the direct marketing, the understanding of this topic of growing interest and importance still remains deficient. Therefore, the objective of this paper is to provide a comprehensive framework to guide research efforts focusing on direct marketing strategy and aid practitioners in their quest to achieve direct marketing success using data mining methods. The framework builds on the literature from direct marketing concepts and data mining methods that provides a systematic approach to users who have little knowledge in data mining in order to carry out effective marketing campaigns. A case study on bank marketing campaigns was used for evaluating the feasibility of the framework.


title: Unbalanced Three-Phase Distribution Grid Topology Estimation and Bus Phase Identification

abstract: There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.


Prediction and Uncertainty Quantification of Daily Airport Flight Delays.


title: Unbiased diffeomorphic atlas construction for computational anatomy

abstract: Construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of 2 years of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations.


Named Entity Recognition for Code Mixing in Indian Languages using Hybrid Approach


title: Robust Face Detection Using the Hausdorff Distance

abstract: The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement.


A FLEXIBLE MODEL FOR PROVIDING TRANSACTIONAL BEHAVIOR TO SERVICE COORDINATION IN AN ORTHOGONAL WAY


title: Sparse Algorithm for Robust LSSVM in Primal Space

abstract: As enjoying the closed form solution, least squares support vector machine (LSSVM) has been widely used for classification and regression problems having the comparable performance with other types of SVMs. However, LSSVM has two drawbacks: sensitive to outliers and lacking sparseness. Robust LSSVM (R-LSSVM) overcomes the first partly via nonconvex truncated loss function, but the current algorithms for R-LSSVM with the dense solution are faced with the second drawback and are inefficient for training large-scale problems. In this paper, we interpret the robustness of R-LSSVM from a re-weighted viewpoint and give a primal R-LSSVM by the representer theorem. The new model may have sparse solution if the corresponding kernel matrix has low rank. Then approximating the kernel matrix by a low-rank matrix and smoothing the loss function by entropy penalty function, we propose a convergent sparse R-LSSVM (SR-LSSVM) algorithm to achieve the sparse solution of primal R-LSSVM, which overcomes two drawbacks of LSSVM simultaneously. The proposed algorithm has lower complexity than the existing algorithms and is very efficient for training large-scale problems. Many experimental results illustrate that SR-LSSVM can achieve better or comparable performance with less training time than related algorithms, especially for training large scale problems.


title: Capacitor-Less Design of Power-Rail ESD Clamp Circuit With Adjustable Holding Voltage for On-Chip ESD Protection

abstract: The RC-based power-rail ESD clamp circuit with the n-channel metal-oxide-semiconductor (NMOS) transistor drawn in the layout style of big field-effect transistor (BigFET) has been utilized to effectively enhance the ESD robustness of CMOS ICs. In this work, a new ESD-transient detection circuit without using the capacitor has been proposed and verified in a 65 nm 1.2 V CMOS process. The layout area of the new ESD-transient detection circuit can be greatly reduced by more than 54%, as compared to the traditional RC-based ESD-transient detection circuit realized with capacitor. From the experimental results, the new proposed ESD-transient detection circuit with adjustable holding voltage can achieve long enough turn-on duration under the ESD stress condition, as well as better immunity against mistrigger and transient-induced latch-on event under the fast power-on and transient noise conditions.


title: OWL: Yet to arrive on the Web of Data?

abstract: Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL’s second version, there is still no “right” standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).


Coming to Terms: Scaffolding


title: Burst-mode and ScanSAR interferometry

abstract: ScanSAR interferometry is an attractive option for efficient topographic mapping of large areas and for monitoring of large-scale motions. Only ScanSAR interferometry made it possible to map almost the entire landmass of the Earth in the 11-day Shuttle Radar Topography Mission. Also the operational satellites RADARSAT and ENVISAT offer ScanSAR imaging modes and thus allow for repeat-pass ScanSAR interferometry. This paper gives a complete description of ScanSAR and burst-mode interferometric signal properties and compares different processing algorithms. The problems addressed are azimuth scanning pattern synchronization, spectral shift filtering in the presence of high squint, Doppler centroid estimation, different phase-preserving ScanSAR processing algorithms, ScanSAR interferogram formation, coregistration, and beam alignment. Interferograms and digital elevation models from RADARSAT ScanSAR narrow modes are presented. The novel "pack-and-go" algorithm for efficient burst-mode range processing and a new time-variant fast interpolator for interferometric coregistration are introduced.


An Intrusion Detection System , ( IDS ) with Machine Learning ( ML ) Model Combining Hybrid Classifiers


title: Blurriness-Guided Unsharp Masking

abstract: In this paper, a highly-adaptive unsharp masking (UM) method is proposed and called the blurriness-guided UM, or BUM, in short. The proposed BUM exploits the estimated local blurriness as the guidance information to perform pixel-wise enhancement. The consideration of local blurriness is motivated by the fact that enhancing a highly-sharp or a highly-blurred image region is undesirable, since this could easily yield unpleasant image artifacts due to over-enhancement or noise enhancement, respectively. Our proposed BUM algorithm has two powerful adaptations as follows. First, the enhancement strength is adjusted for each pixel on the input image according to the degree of local blurriness measured at the local region of this pixel's location. All such measurements collectively form the blurriness map, from which the scaling matrix can be obtained using our proposed mapping process. Second, we also consider the type of layer-decomposition filter exploited for generating the base layer and the detail layer, since this consideration would effectively help to prevent over-enhancement artifacts. In this paper, the layer-decomposition filter is considered from the viewpoint of edge-preserving type versus non-edge-preserving type. Extensive simulations experimented on various test images have clearly demonstrated that our proposed BUM is able to consistently yield superior enhanced images with better perceptual quality to that of using a fixed enhancement strength or other state-of-the-art adaptive UM methods.


title: Scene classification using local and global features with collaborative representation fusion

abstract: A scene classification based on collaborative representation fusion is proposed.The complementary nature of local and global spatial features is investigated.Weighted fusion is designed based on residuals from two types of features.Proposed LGF overcomes difficulties residing in feature or decision level fusion. This paper presents an effective scene classification approach based on collaborative representation fusion of local and global spatial features. First, a visual word codebook is constructed by partitioning an image into dense regions, followed by the typical k-means clustering. A locality-constrained linear coding is employed on dense regions via the visual codebook, and a spatial pyramid matching strategy is then used to combine local features of the entire image. For global feature extraction, the method called multiscale completed local binary patterns (MS-CLBP) is applied to both the original gray scale image and its Gabor feature images. Finally, kernel collaborative representation-based classification (KCRC) is employed on the extracted local and global features, and class label of the testing image is assigned according to the minimal approximation residual after fusion. The proposed method is evaluated by using four commonly-used datasets including two remote sensing images datasets, an indoor and outdoor scenes dataset, and a sports action dataset. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods.


title: ANALISIS KESIAPAN POS KESEHATAN DESA DALAM PENGEMBANGAN DESA SIAGA DI KABUPATEN KEPULAUAN MENTAWAI PROVINSI SUMATERA BARAT TAHUN 2008

abstract: Backgroud: There were 14 units of rural health center in the district of Mentawai archipelago that planned to be built in 2007, but it was failed in the implementation. In 2008, there were 12 units rural health center planned to be built, following the determining of Desa Siaga development policy. Therefore, the readiness has to be known first. Methods: The research was conducted to study the readiness of rural health center in the development of Desa Siaga. Qualitative research method was used, by indepth interview to the person in charge program in the health and family planning board in the district, Puskesmas and staff of rural health center, that their the village is proclaimed as Desa Siaga, and through the study of rural health center in the development of Desa Siaga readiness documents. Result : There is variation of readiness in rural health center / Poskesdes which is located in Mentawai. The variation range from ready, quite ready, until not ready. But there are 5 variables which are ready: Rural health center physical infrastructure, instrument and logistic, budgeting, planning and monitoring. Eventhough not 100% fit to the standard of Health Department. For the rural health center physic/construction model, there were built with special design which suitable for the isolated, retarded and archipelago area. Conclusion: Rural health center model developed by Health Department can not be applied in the Mentawai archipelago and need modification. The Health Departement is suggested to analyze the model of rural health center in Mentawai, and the health and family planning board in the district of Mentawai archipelago are suggested to accelerate the realization of the physical infrastructure building with its equipments. Keywords: rural health center, development, Desa Siaga


A Robust Voiced/Unvoiced Phoneme Classification from Whispered Speech Using the ‘Color’ of Whispered Phonemes and Deep Neural Network


title: Discriminative Feature Learning for Video Semantic Segmentation

abstract: In this paper, we propose a novel deep learning based method for video semantic segmentation. Specially, we utilize 3D convolution neural network (3D CNN) to learn discriminative hierarchical features from spatial-temporal volumes for accurate pixel labelling. The learned features are capable of capturing both appearance and motion information. To align the pixel labels along real object boundaries, as well as maintain local consistency, we further perform graph-cut on a graph constructed on coherent 3d regions, or super-voxels, extracted from input video. Experiments demonstrate that due to the discriminative capability of learned features, our approach can obtain competitive labelling accuracy compared to the state-of-art in absence of sophisticated inference models, even with few training samples.