title: Poisson-driven seamless completion of triangular meshes

abstract: The production of high-quality 3D mesh models has seen important technological advancements in recent years and is increasingly becoming a crucial asset for several application domains. However, the intrinsic nature of the problem (acquisition constraints and real objects complexity) makes hole filling and mesh completion still critical tasks for the effectual finalization of the models. In this work, we propose a new solution for filling holes and gaps and for reconstructing missing parts of mesh models that is capable to guarantee the full preservation of the input mesh. On the implicit function obtained from a Poisson Reconstruction computed on a set of oriented points derived from the input mesh, we develop an interpolation method which allows a fast and continuous surface-oracle computation which is exploited to efficiently guide a restricted Delaunay triangulation. This can be used to confine the tessellation inside the holed zones, while protecting the input mesh and the boundary curves so that simple tailoring routines can merge the obtained surface-reconstruction driven patches with the input mesh without stitching artifacts. The proposed technique is versatile and evidences its effectiveness in dealing with multiple complex holes, gaps and surface blending, and it compares favorably with respect to different, and usually more specialized, hole filling and global mesh repair techniques. As a parallel benefit, we also show how the same proposed solution can be exploited as an effective and high-quality meshing or remeshing tool.


title: MOOCs Inside Universities An Analysis of MOOC Discourse as Represented in HE Magazines

abstract: Digital news media discourse on MOOCs has been pervasive in educational publications over recent years, ::: ::: and has often focused on debates over the disruptive potential of MOOCs at one extreme, and their survival ::: ::: at the other. Whether such articles reflect the concerns of academics and other internal university ::: ::: stakeholders is difficult to ascertain. This paper aims to determine the main concerns of internal university ::: ::: stakeholders in terms of their MOOC development and implementation work, and whether these concerns ::: ::: are reflected in the mainstream educational media. The study combines data from 2 previous studies (a ::: ::: content analysis of MOOC literature, and a grounded theory case study of internal university stakeholders) ::: ::: to establish key themes of concern for those working on MOOCs in Higher Education. An analysis of these ::: ::: themes in 3 educational media publications is then conducted for the year 2014. The findings indicate a ::: ::: clear focus in education media and among university stakeholders on new teaching practices and working ::: ::: dynamics in Higher Education as a result of involvement in MOOC development work. We argue that for ::: ::: many working on MOOCs in Higher Education, the debate about the future of MOOCs is over, and that ::: ::: more practical concerns of appropriate implementation and effective working practices are of greater ::: ::: importance.


title: An Anomaly Detection Method for Medicare Fraud Detection

abstract: With the improvement of medical insurance system, the coverage of medicare increases a lot. However, while the expenditure of this system is continuously rising, medicare fraud is causing huge losses for this system. Traditional medicare fraud detection greatly depends on the experience of domain experts, which is not accurate enough and costs much time and labor.In this study, we propose a medicare fraud detection framework based on the technology of anomaly detection. Our method consists of two parts. First part is a spatial density based algorithm, called improved local outlier factor (imLOF), which is more applicable than simple local outlier factor in medical insurance data. Second part is robust regression to depict the linear dependence between variables. Some experiments are conducted on real world data to measure the efficiency of our method.


title: Software Language Identification with Natural Language Classifiers

abstract: Software language identification techniques are applicable to many situations from universal IDE support to legacy code analysis. Most widely used heuristics are based on software artefact metadata such as file extensions or on grammar-based text analysis such as keyword search. In this paper we propose to use statistical language models from the natural language processing field such as n-grams, skip-grams, multinominal naive Bayes and normalised compression distance. Our preliminary experiments show that some of these models used as classifiers can achieve high precision and recall and can be used to properly identify language families, languages and even deal with embedded code fragments.


title: A novel zero-current-detector for DCM operation in synchronous converter

abstract: Discontinuous-conduction mode (DCM) operation is usually employed in DC-DC converters for small inductor on printed circuit board (PCB) and high efficiency at light load. However, it is normally difficult for synchronous converter to realize the DCM operation, especially in high frequency applications, which requires a high speed and high precision comparator to detect the zero crossing point at cost of extra power losses. In this paper, a novel zero current detector (ZCD) circuit with an adaptive delay control loop for high frequency synchronous buck converter is presented. Compared to the conventional ZCD, proposed technique is proven to offer 8.5% efficiency enhancement when performed in a buck converter at the switching frequency of 4MHz and showed less sensitivity to the transistor mismatch of the sensor circuit.


title: Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification

abstract: The diagnosis of blood-related diseases involves the identification and characterization of a patient’s blood sample. As such, automated methods for detecting and classifying the types of blood cells have important medical applications in this field. Although deep convolutional neural network (CNN) and the traditional machine learning methods have shown good results in the classification of blood cell images, they are unable to fully exploit the long-term dependence relationship between certain key features of images and image labels. To resolve this problem, we have introduced the recurrent neural networks (RNNs). Specifically, we combined the CNN and RNN in order to propose the CNN–RNN framework that can deepen the understanding of image content and learn the structured features of images and to begin end-to-end training of big data in medical image analysis. In particular, we apply the transfer learning method to transfer the weight parameters that were pre-trained on the ImageNet dataset to the CNN section and adopted a custom loss function to allow our network to train and converge faster and with more accurate weight parameters. Experimental results show that compared with the other CNN models such as ResNet and Inception V3, our proposed network model is more accurate and efficient in classifying blood cell images.


The Critical Success Factors for Effective ICT Governance in Malaysian Public Sector: A Delphi Study


title: Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis

abstract: Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.


title: Design, fabrication and control of a multi-material-multi-actuator soft robot inspired by burrowing worms

abstract: We present the design, fabrication and feedback control of an earthworm-inspired multi-material multi-actuator soft robot capable of locomoting inside pipes. The bodies of natural earthworms are composed of repeated deformable structural units, called metameres, that generate the peristaltic body motions required for limbless underground burrowing and above-ground crawling. In an earthworm, each individual metamere is actuated by circular and longitudinal muscles that are activated synchronously by the animal's nervous system. Here, adopting the basic functional principles of metameric worms, we propose a new pneumatically-driven soft robotic system that mimics the motions and replicates the functionality of a single burrowing earthworm's segment. The suitability of the proposed approach is demonstrated experimentally through three basic locomotion tests: horizontal motion, vertical motion and oblique motion inside a varying-slope transparent pipe.


title: How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions

abstract: Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks, recent increasingly popular projection-based CLE models are almost exclusively evaluated on a single task only: bilingual lexicon induction (BLI). Even BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we make the first step towards a comprehensive evaluation of cross-lingual word embeddings. We thoroughly evaluate both supervised and unsupervised CLE models on a large number of language pairs in the BLI task and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI can result in deteriorated downstream performance. We indicate the most robust supervised and unsupervised CLE models and emphasize the need to reassess existing baselines, which still display competitive performance across the board. We hope that our work will catalyze further work on CLE evaluation and model analysis.


title: Cross Modal Distillation for Supervision Transfer

abstract: In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers.


title: Multiscale sequence modeling with a learned dictionary

abstract: We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multi-scale model makes predictions over multiple, potentially overlapping multi-symbol tokens. A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that the model is trained with. When applied to language modelling, our model has the flexibility of character-level models while maintaining many of the performance benefits of word-level models. Our experiments show that this model performs better than a regular LSTM on language modeling tasks, especially for smaller models.


title: Questions vs. Queries in Informational Search Tasks

abstract: Search systems traditionally require searchers to formulate information needs as keywords rather than in a more natural form, such as questions. Recent studies have found that Web search engines are observing an increase in the fraction of queries phrased as natural language. As part of building better search engines, it is important to understand the nature and prevalence of these intentions, and the impact of this increase on search engine performance. In this work, we show that while 10.3% of queries issued to a search engine have direct question intent, only 3.2% of them are formulated as natural language questions. We investigate whether search engines perform better when search intent is stated as queries or questions, and we find that they perform equally well to both


Stability considerations for thickened tailings due to freezing and thawing


title: A Comparative Review Of Camera Calibrating Methods with Accuracy Evaluation

abstract: Camera calibrating is a crucial problem for further metric scene measurement. Many techniques and some studies concerning calibration have been presented in the last few years. However, it is still difficult to go into details of a determined calibrating technique and compare its accuracy with respect to other methods. Principally, this problem emerges from the lack of a standardized notation and the existence of various methods of accuracy evaluation to choose from. This article presents a detailed review of some of the most used calibrating techniques in which the principal idea has been to present them all with the same notation. Furthermore, the techniques surveyed have been tested and their accuracy evaluated. Comparative results are shown and discussed in the article. Moreover, code and results are available in internet.


title: Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

abstract: Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. ::: ::: Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ::: ::: Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F -measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. ::: ::: Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.


title: Opportunities of Fan-out Wafer Level Packaging (FOWLP) for RF applications

abstract: Fan-out Wafer Level Packaging (FOWLP) is one of the latest packaging trends in microelectronics. For FOWLP known good bare dies are embedded into mold compound forming a reconfigured wafer. A thin film redistribution layer is applied on the reconfigured wafer routes the die pads to the space around the die on the mold compound (fan-out). After solder ball placement and package singulation by dicing a SMD compatible package is completed. FOWLP has a high potential in significant package miniaturization concerning package volume but also in thickness. Main advantages of FOWLP are the substrate-less package, lower thermal resistance, higher performance due to shorter interconnects together with direct IC connection by thin film metallization instead of wire bonds or flip chip bumps and lower parasitic effects. Especially the inductance of the FOWLP is much lower compared to FC-BGA packages. In addition the redistribution layer can also provide embedded passives (R, L, C) as well as antenna structures using a multi-layer structure. It can be used for multi-chip packages for System in Package (SiP) and heterogeneous integration. Hence, technology is well suited for RF applications.


title: A low cost Bluetooth powered wearable digital stethoscope for cardiac murmur

abstract: A low-cost and effective wearable digital stethoscope for healthcare monitoring through wireless transmission module is proposed in this paper. The design integrates heart and lung sound signal acquisition, amplification, wireless transmission and signal processing using National Instruments Labview. The device can be used to record patients' cardiopulmonary sound. It has further applications for real time monitoring over long distance. The accuracy, robust and cost-effectiveness of this design make it a suitable method for real time visualized signal monitoring over long distance, data storage, replay function and auto disease preliminary assessment.


title: The GSM/UMTS Phone Number Catcher

abstract: For the individual privacy protection, the GSM system transmits the IMSI or TMSI number over the air instead of the phone number which will leak the identity of the users. The MSC, HLR/VLR will translate the IMSI/TMSI to the phone number in the core network. This paper proposed an idea about catching the GSM/UMTS Phone Number over the air by man-in-the-middle attack. The GSM/UMTS phone number catcher proposed in this paper was implemented on a pseudo base station with a mobile terminal which can transmit any frames we wanted. While the legal terminals are trying to access, the phone number catcher will relay all its frame to the operator's network and try to break the shared authentication key Ki. Then the system will get the phone number by pretending the legal terminal to call a designated phone. We will analyze the GSM protocol which is relevant to the phone number catcher and later present the performance of such a system by real tests and demonstrate its feasibility.


title: Angry Crowds: Detecting Violent Events in Videos

abstract: Approaches inspired by Newtonian mechanics have been successfully applied for detecting abnormal behaviors in crowd scenarios, being the most notable example the Social Force Model (SFM). This class of approaches describes the movements and local interactions among individuals in crowds by means of repulsive and attractive forces. Despite their promising performance, recent socio-psychology studies have shown that current SFM-based methods may not be capable of explaining behaviors in complex crowd scenarios. An alternative approach consists in describing the cognitive processes that gives rise to the behavioral patterns observed in crowd using heuristics. Inspired by these studies, we propose a new hybrid framework to detect violent events in crowd videos. More specifically, (i) we define a set of simple behavioral heuristics to describe people behaviors in crowd, and (ii) we implement these heuristics into physical equations, being able to model and classify such behaviors in the videos. The resulting heuristic maps are used to extract video features to distinguish violence from normal events. Our violence detection results set the new state of the art on several standard benchmarks and demonstrate the superiority of our method compared to standard motion descriptors, previous physics-inspired models used for crowd analysis and pre-trained ConvNet for crowd behavior analysis.


title: Adolescent and parent views of family meals

abstract: Abstract Objective To examine and compare the family mealtime environment from the perspectives of both adolescents and parents. Design Adolescents completed a school-based survey and parents participated in a telephone interview as part of Project EAT (Eating Among Teens). Subjects/setting Participants were 902 adolescent females (n=424) and males (n=478) and one of their guardians/parents. Statistical analyses performed Frequencies, χ 2 analyses, and Spearman correlations were used to assess relationships. Results Parents were more likely than adolescents to report eating five or more family meals per week, the importance of eating together, and scheduling difficulties ( P P P P Conclusions Family meals are perceived positively by both adolescents and parents. Family meals may be a useful mechanism for enhancing family togetherness, and for role modeling behaviors that parents would like their children to emulate. Dietetics professionals can capitalize on positive attitudes toward family meals to help promote their frequency. Helping families learn to cook healthful, quick meals may reduce dependency on less healthful meal options, reduce the frequency of eating outside of the home, and promote greater nutritional intake.


title: Ada and grace: toward realistic and engaging virtual museum guides

abstract: To increase the interest and engagement of middle school students in science and technology, the InterFaces project has created virtual museum guides that are in use at the Museum of Science, Boston. The characters use natural language interaction and have near photoreal appearance to increase and presents reports from museum staff on visitor reaction.


title: New Design Formulas for Impedance-Transforming 3-dB Marchand Baluns

abstract: New design formulas for impedance-transforming 3-dB Marchand baluns are proposed. They are about the even- and odd-mode impedances of the coupled transmission-line sections of the Marchand baluns and determined by coupling coefficients together with termination impedances. The particular property proposed in this paper is to choose the coupling coefficient arbitrarily, resulting in infinite sets of design formulas available. This is quite different from the conventional design approach in which only one coupling coefficient is possible. For the perfect isolation of the Marchand balun, an isolation circuit (IC) is needed, being composed of two 90° transmission-line sections and resistance(s). Sufficient area to build such a long IC is, however, inherently not available. For this, ways to reduce the IC size are also suggested. To validate them, a microstrip Marchand balun terminated in 130 and 70 Ω is designed at a design center frequency of 1.5 GHz and tested. The measured results are in good agreement with prediction, showing that power divisions are 3.57 and 3.262 dB, return losses at all ports are better than 21 dB, and the isolation is better than 20 dB around the design center frequency. The measured phase difference between two balanced signals is 180°±2° in about 50% bandwidth.


title: Chemometric analysis reveals links in the formation of fragrant bio-molecules during agarwood (Aquilaria malaccensis) and fungal interactions

abstract: Chemometric analysis reveals links in the formation of fragrant bio-molecules during agarwood ( Aquilaria malaccensis ) and fungal interactions


title: Computational approaches to fMRI analysis

abstract: A revolution is underway in cognitive neuroscience, where tools and techniques from computer science and the tech industry are helping to extract more meaningful cognitive signals from noisy and increasingly large fMRI datasets. In this paper, the authors review the cutting edge of such computational analyses and discuss future opportunities and challenges.


title: A learning approach to optimizing exploration–exploitation tradeoff in relevance feedback

abstract: Relevance feedback is an effective technique for improving search accuracy in interactive information retrieval. In this paper, we study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user's perspective). Optimizing such an exploration---exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We formally frame this tradeoff as a problem of optimizing the diversification of search results since relevance judgments on more diversified results have been shown to be more useful for relevance feedback. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results on three representative retrieval test collections show that the proposed learning approach can effectively optimize the exploration---exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration.


title: Multiband 10-Antenna Array for Sub-6 GHz MIMO Applications in 5-G Smartphones

abstract: A multi-band 10-antenna array working at the sub-6-GHz spectrum (LTE bands 42/43 and LTE band 46) for massive multiple-input multiple-output (MIMO) applications in future 5G smartphones is proposed. To realize $10\times 10$ MIMO applications in three LTE bands, 10 T-shaped coupled-fed slot antenna elements that can excite dual resonant modes are integrated into a system circuit board. Spatial and polarization diversity techniques are implemented on these elements so that the improved isolation and mitigated coupling effects can be achieved. The proposed antenna array was manufactured and experimentally measured. Desirable antenna efficiencies of higher than 42% and 62% were measured in the low band and high band, respectively. Vital results, such as the envelope correlation coefficient, channel capacity, and mean effective gain ratio, have also been computed and analyzed. The calculated ergodic channel capacities of the $10\times 10$ MIMO system working in the LTE bands 42/43 and LTE band 46 reached up to 48 and 51.4 b/s/Hz, respectively.


title: Categories of digital investigation analysis techniques based on the computer history model

abstract: Several digital forensic frameworks have been proposed, yet no conclusions have been reached about which are more appropriate. This is partly because each framework may work well for different types of investigations, but it hasn't been shown if any are sufficient for all types of investigations. To address this problem, this work uses a model based on the history of a computer to define categories and classes of analysis techniques. The model is more lower-level than existing frameworks and the categories and classes of analysis techniques that are defined support the existing higher-level frameworks. Therefore, they can be used to more clearly compare the frameworks. Proofs can be given to show the completeness of the analysis techniques and therefore the completeness of the frameworks can also be addressed.


title: Computing Many-to-Many Shortest Paths Using Highway Hierarchies

abstract: We present a fast algorithm for computing all shortest paths between source nodes s ∈ S and target nodes t ∈ T. This problem is important as an initial step for many operations research problems (e.g., the vehicle routing problem), which require the distances between S and T as input. Our approach is based on highway hierarchies, which are also used for the currently fastest speedup techniques for shortest path queries in road networks. We show how to use highway hierarchies so that for example, a 10 000 × 10 000 distance table in the European road network can be computed in about one minute. These results are based on a simple basic idea, several refinements, and careful engineering of the approach. We also explain how the approach can be parallelized and how the computation can be restricted to computing only the k closest connections.


title: Therapeutic angiogenesis of adipose-derived stem cells for ischemic diseases

abstract: Ischemic diseases, the leading cause of disability and death, are caused by the stenosis or obstruction of arterioles/capillaries that is not compensated for by vessel dilatation or collateral circulation. Angiogenesis is a complex process leading to new blood vessel formation and is triggered by ischemic conditions. Adequate angiogenesis, as a compensatory mechanism in response to ischemia, may increase oxygen and nutrient supplies to tissues and protect their function. Therapeutic angiogenesis has been the most promising therapy for treating ischemic diseases. In recent years, stem cell transplantation has been recognized as a new technique with therapeutic angiogenic effects on ischemic diseases. Adipose-derived stem cells, characterized by their ease of acquisition, high yields, proliferative growth, and low immunogenicity, are an ideal cell source. In this review, the characterization of adipose-derived stem cells and the role of angiogenesis in ischemic attack are summarized. The angiogenic effects of adipose-derived stem cells are discussed from the perspectives of in-vitro, in-vivo, and clinical trial studies for the treatment of ischemic diseases, including ischemic cardiac, cerebral, and peripheral vascular diseases and wound healing. The microvesicles/exosomes released from adipose-derived stem cells are also presented as a novel therapeutic prospect for treating ischemic diseases.


title: Assessment of the Suitability of Fog Computing in the Context of Internet of Things

abstract: This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.


title: Age-related changes in grey and white matter structure throughout adulthood

abstract: Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.


title: Twitter Sentiment around the Earnings Announcement Events

abstract: We investigate the relationship between social media, Twitter in particular, and stock market. We provide an in-depth analysis of the Twitter volume and sentiment about the 30 companies in the Dow Jones Industrial Average index, over a period of three years. We focus on Earnings Announcements and show that there is a considerable difference with respect to when the announcements are made: before the market opens or after the market closes. The two different timings of the Earnings Announcements were already investigated in the financial literature, but not yet in the social media. We analyze the differences in terms of the Twitter volumes, cumulative abnormal returns, trade returns, and earnings surprises. We report mixed results. On the one hand, we show that the Twitter sentiment (the collective opinion of the users) on the day of the announcement very well reflects the stock moves on the same day. We demonstrate this by applying the event study methodology, where the polarity of the Earnings Announcements is computed from the Twitter sentiment. Cumulative abnormal returns are high (2–4%) and statistically significant. On the other hand, we find only weak predictive power of the Twitter sentiment one day in advance. It turns out that it is important how to account for the announcements made after the market closes. These after-hours announcements draw high Twitter activity immediately, but volume and price changes in trading are observed only on the next day. On the day before the announcements, the Twitter volume is low, and the sentiment has very weak predictive power. A useful lesson learned is the importance of the proper alignment between the announcements, trading and Twitter data.


title: Credit card fraud detection with a neural-network

abstract: Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud and NRI (non-received issue) fraud. The network detected significantly more fraud accounts (an order of magnitude more) with significantly fewer false positives (reduced by a factor of 20) over rule-based fraud detection procedures. We discuss the performance of the network on this data set in terms of detection accuracy and earliness of fraud detection. The system has been installed on an IBM 3090 at Mellon Bank and is currently in use for fraud detection on that bank's credit card portfolio. >


title: Reinforcement Learning of Heuristic EV Fleet Charging in a Day-Ahead Electricity Market

abstract: This paper addresses the problem of defining a day-ahead consumption plan for charging a fleet of electric vehicles (EVs), and following this plan during operation. A challenge herein is the beforehand unknown charging flexibility of EVs, which depends on numerous details about each EV (e.g., plug-in times, power limitations, battery size, power curve, etc.). To cope with this challenge, EV charging is controlled during opertion by a heuristic scheme, and the resulting charging behavior of the EV fleet is learned by using batch mode reinforcement learning. Based on this learned behavior, a cost-effective day-ahead consumption plan can be defined. In simulation experiments, our approach is benchmarked against a multistage stochastic programming solution, which uses an exact model of each EVs charging flexibility. Results show that our approach is able to find a day-ahead consumption plan with comparable quality to the benchmark solution, without requiring an exact day-ahead model of each EVs charging flexibility.


title: Improving data collection and integration of electronic healthcare records in Tanzania

abstract: This study aims at developing a framework for collecting electronic data in health research institutions and integrating heterogeneous data using web service technology. The article reports on modeling of a generic tool for collecting eHealth data with relational database in a low bandwidth environment. It also explores critical factors that are associated with the process of electronic data collection, and inter-organization data integration and sharing. After all, the proposed system is capable of synchronizing data from the point of care to the central database.


title: Boogie: A modular reusable verifier for object-oriented programs

abstract: A program verifier is a complex system that uses compiler technology, program semantics, property inference, verification-condition generation, automatic decision procedures, and a user interface. This paper describes the architecture of a state-of-the-art program verifier for object-oriented programs.


title: Fast inverse compositional image alignment with missing data and re-weighting

abstract: This paper proposes a novel method of performing inverse compositional image alignment which elegantly deals with missing data and re-weighting, and does not require the Jacobians and Hessian to be re-computed at every iteration. We show how missing data and re-weighting can be handled through preconditioning. We propose a few preconditioning techniques and analyse how each technique models the effects of missing data and re-weighting for inverse composition. We show through extensive experiments on different applications that our method improves the convergence rate of the conventional re-weighted inverse compositional method while remaining robust to outliers. We also show that the the update parameters are usually underestimated and how this can be used to further speed up convergence of image alignment methods.


title: Effects of salinity on the characteristics of biomass and membrane fouling in membrane bioreactors

abstract: This study investigated the effects of high salinity on the performance and membrane fouling of membrane bioreactor (MBR) with saline wastewater. Synthetic wastewaters containing 5-20 g/L salts (NaCl) were treated in identical lab-scale (7 L) MBRs monitoring removals of dissolved organic carbon (DOC) and ammonia. Increase in salt concentrations did not significantly change the removal efficiency of DOC in the MBRs. However, the ammonia removals decreased from 87% to 46% with increasing salt concentrations. PCR-DGGE analysis indicated changes in the microbial communities' composition due to high salinity; and the changes in microbial composition in turn have affected the performance of the MBRs. Membrane fouling was accelerated by the increased pore blocking resistance at higher salt concentrations. Analysis results of physicochemical and biological characteristics of biomass (EPS, floc size, zeta potential) verified the impacts of high salinity on the increased membrane fouling.


title: Design of multi-dimensional transfer functions using dimensional reduction

abstract: Direct volume rendering techniques allow visualization of volume data without extracting intermediate geometry. The mapping from voxel attributes to optical properties is performed by transfer functions which, consequently, play a crucial role in building informative images from the data. One-dimensional transfer functions, which are based only on a scalar value per voxel, often do not provide proper visualizations. On the other hand, multidimensional transfer functions can perform more sophisticated data classification, based on vectorial voxel signatures. The transfer function design is a non-trivial and unintuitive task, especially in the multi-dimensional case. In this paper we propose a multi-dimensional transfer function design technique that uses self-organizing maps to perform dimensional reduction. Our approach gives uniform treatment to volume data containing voxel signatures of arbitrary dimension, and allows the use of any type of voxel attribute as part of the voxel signatures.


title: Alcohol Use and Comorbid Anxiety, Traumatic Stress, and Hopelessness Among Hispanics

abstract: Little is known about the comorbidity of alcohol use, anxiety, hopelessness, and trauma among Mexican Americans, especially those living in impoverished and often isolated communities or neighborhoods (colonias in Spanish) along the U.S.-Mexico border that may be particularly vulnerable due to stressful living conditions. The current study utilized a community participatory model to investigate the relationships of alcohol use, acculturation, anxiety, hopelessness, and trauma in 100 Mexican origin colonia residents. Significant comorbidity was expected and that anxiety, hopelessness, and post-traumatic symptoms were hypothesized to be associated with the severity of the alcohol use disorders of participants. Participants who met DSM-IV criteria for alcohol dependence reported significantly more symptoms of anxiety and posttraumatic stress, and symptoms of anxiety were significantly associated with alcohol use disorders. This study provides evidence of the need for further investigation of stress, trauma, anxiety, hopelessness, and alcohol abuse in Mexican American residents and to inform future prevention and treatment efforts to improve both the physical and mental health of this population.


title: Thumb motor performance varies with thumb and wrist posture during single-handed mobile phone use

abstract: Design features of mobile computing technology such as device size and key location may affect thumb motor performance during single-handed use. Since single-handed use requires the thumb posture to vary with key location, we hypothesize that motor performance is associated with thumb and wrist joint postures. A repeated measures laboratory experiment of 10 right-handed participants measured thumb and wrist joint postures during reciprocal tapping tasks between two keys for different key pairs among 12 emulated keys. Fitts' effective index of performance and joint postures at contact with each key were averaged across trials for each key. Thumb motor performance varied for different keys, with poorest performances being associated with excessive thumb flexion such as when tapping on keys closest to the base of the thumb in the bottom right corner of the phone. Motor performance was greatest when the thumb was in a typical resting posture, neither significantly flexed nor fully extended with slight CMC joint abduction and supination, such as when tapping on keys located in the top right and middle left areas on the phone. Grip was also significantly affected by key location, with the most extreme differences being between the top left and bottom right corners of the phone. These results suggest that keypad designs aimed at promoting performance for single-handed use should avoid placing frequently used functions and keys close to the base of the thumb and instead should consider key locations that require a thumb posture away from its limits in flexion/extension, as these postures promote motor performance.


title: Influence of personality on travel-related consumer-generated media creation

abstract: While a growing number of travelers engages in consumer-generated media (CGM) use and creation, the gap between the number of users and the number of actual content creators remains large. It is important to find out what drives this minority of creators and what makes them different from those who only use CGM. Personality has been found to be a particularly influential trait that predicts behavior. The influence of personality on travel CGM creation was investigated and the results indicate that travelers' personality traits significantly influence perceived barriers to content creation, motivations to engage in CGM creation, and specific creation behaviors. Contributions and implications are discussed from both a theoretical and a practical perspective.


Towards Automatic Detection of Narrative Structure


title: Development of a stereo vision system for industrial robots

abstract: Machine vision systems become to play an important role in robot based factory automation, because they enable robots to recognize the environment around the workspace like human visual systems and make the manufacturing process flexible. So a large number of 2-dimensional (2D) vision systems have been successfully applied for industrial robots, but there are increasing needs for 3-dimensional (3D) vision systems providing accurate 3D position of work-pieces. For the 3D vision system, the stereo vision based position measurement systems have been applied in various fields. However the stereo vision systems which have been applied were specific for their particular tasks only. This paper introduces a general-purpose stereo vision system applicable to wide range of tasks with industrial robots.


title: MAXIMUM POWER POINT TRACKING OF PV ARRAYS UNDER PARTIAL SHADING CONDITION USING SEPIC CONVERTER

abstract: There exists a variety of maximum power point tracking (MPPT) techniques, each having its own merits and demerits. Under partialshading conditions, the conventional tracking techniques fail to guarantee successful tracking of the global maximum power, ie the conventional MPPT methods such as perturb and observe and incremental conductance may converge on local maximum power point resulting in significant reduction of power generated. This paper discusses about an improved technique for tracking global maximum power point of photovoltaic arrays that has better performance under partial shading conditions. The first stage in this method is to find out global maximum power point among the local maxima. Once the global maximum power point is found then by adjusting the duty ratio, the voltage corresponding to maximum power can be found out. The control is then transferred to perturb and observe algorithm stage. This technique could be applied for both stand alone and grid connected PV system. A comparison study between a SEPIC converter and a buck boost converter with the above mentioned algorithm has also been carried out in order to verify the performance of both the converters. The above mentioned converters have been designed for 150W at a switching frequency of 10 KHz. Modified algorithm has been simulated using MATLAB/Simulink and results are obtained. Partial shading condition was modelled in MATLAB/Simscape and analysed the solar array characteristics under various shading conditions. From the simulation results it was found that the SEPIC converter is much more efficient and is highly suitable for photo voltaic applications.


title: Dual Sparse Constrained Cascade Regression for Robust Face Alignment

abstract: Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem continues to be challenging due to the large variability in expression, illumination, pose, and the existence of occlusions in real-world face images. In this paper, we present a dual sparse constrained cascade regression model for robust face alignment. Instead of using the least-squares method during the training process of regressors, sparse constraint is introduced to select robust features and compress the size of the model. Moreover, sparse shape constraint is incorporated between each cascade regression, and the explicit shape constraints are able to suppress the ambiguity in local features. To improve the model’s adaptation to large pose variation, face pose is estimated by five fiducial landmarks located by deep convolutional neuron network, which is used to adaptively design the cascade regression model. To the best of our best knowledge, this is the first attempt to fuse explicit shape constraint (sparse shape constraint) and implicit context information (sparse feature selection) for robust face alignment in the framework of cascade regression. Extensive experiments on nine challenging wild data sets demonstrate the advantages of the proposed method over the state-of-the-art methods.


title: The S(c) spectrum machine to visualize the motion in galaxies

abstract: The behavior of the orbits in a galaxy model composed of an harmonic core and a strong bar potential is studied. Numerical calculations show that a large number of orbits display chaotic motion. These orbits are low angular momentun orbits. The percentage of chaotic orbits increases as the angular velocity of the system increases or the strength of the harmonic term decreases. A new dynamical parameter, the S(c) spectrum, is introduced and used to detect the island motion and the evolution of the sticky regions. Comparison to previously obtained results reveals the leading role of the new spectrum. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)


title: Distribution-matching embedding for visual domain adaptation

abstract: Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized. In other words, we seek to extract the information that is invariant across the source and target data. In particular, we study two different distances to compare the source and target distributions: the Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our approach on the tasks of visual object recognition, text categorization, and WiFi localization.


title: Scene-free multi-class weather classification on single images

abstract: Multi-class weather classification is a fundamental and significant technique which has many potential applications, such as video surveillance and intelligent transportation. However, it is a challenging task due to the diversity of weather and lack of discriminate feature. Most existing weather classification methods only consider two-class weather conditions such as sunny-rainy or sunny-cloudy weather. Moreover, they predominantly focus on a fixed scene such as popular tourism and traffic scenario. In this paper, we propose a novel method for scene-free multi-class weather classification from single images based on multiple category-specific dictionary learning and multiple kernel learning. To improve the discrimination of image representation and enhance the performance of multiple weather classification, our approach extracts multiple weather features and learns dictionaries based on these features. To select a good subset of features, we utilize multiple kernel learning algorithm to learn an optimal linear combination of feature kernels. In addition, to evaluate the proposed approach, we collect an outdoor image set that contains 20K images, called MWI (Multi-class Weather Image) set. Experimental results show the effectiveness of the proposed method.


title: Learning, Memory, and Synesthesia

abstract: People with color-grapheme synesthesia experience color when viewing written letters or numerals, usually with a particular color evoked by each grapheme. Here, we report on data from 11 color-grapheme synesthetes who had startlingly similar color-grapheme pairings traceable to childhood toys containing colored letters. These are the first and only data to show learned synesthesia of this kind in more than a single individual. Whereas some researchers have focused on genetic and perceptual aspects of synesthesia, our results indicate that a complete explanation of synesthesia must also incorporate a central role for learning and memory. We argue that these two positions can be reconciled by thinking of synesthesia as the automatic retrieval of highly specific mnemonic associations, in which perceptual contents are brought to mind in a manner akin to mental imagery or the perceptual-reinstatement effects found in memory studies.


title: ORGANIZATIONAL USE OF BIG DATA AND COMPETITIVE ADVANTAGE – EXPLORATION OF ANTECEDENTS

abstract: The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.


title: Finding your voice: A singing lesson from functional imaging

abstract: Vocal singing (singing with lyrics) shares features common to music and language but it is not clear to what extent they use the same brain systems, particularly at the higher cortical level, and how this varies with expertise. Twenty-six participants of varying singing ability performed two functional imaging tasks. The first examined covert generative language using orthographic lexical retrieval while the second required covert vocal singing of a well-known song. The neural networks subserving covert vocal singing and language were found to be proximally located, and their extent of cortical overlap varied with singing expertise. Nonexpert singers showed greater engagement of their language network during vocal singing, likely accounting for their less tuneful performance. In contrast, expert singers showed a more unilateral pattern of activation associated with reduced engagement of the right frontal lobe. The findings indicate that singing expertise promotes independence from the language network with decoupling producing more tuneful performance. This means that the age-old singing practice of ‘finding your singing voice’ may be neurologically mediated by changing how strongly singing is coupled to the language system. Hum Brain Mapp, 2011. © 2010 Wiley Periodicals, Inc.


title: Advanced simulations of automotive EMC measurement setups using stochastic cable bundle models

abstract: This paper deals with modeling and simulation of cable bundles for automotive EMC tests. A new stochastic cable bundle model is presented. The parameters of the model are grouped into parameters for geometry, simulation, and disorder. Using a particularly developed cable bundle testbench, the model is validated against measurements and the parameter dependencies are investigated. The parameter study shows that the model is governed by one universal constant which describes the stochastics of the cable bundle and a characteristic density which is directly related to the cable bundle geometry. Finally, the model is used in an exemplary EMC setup whose device under test is a partially populated printed circuit board of an automotive electronic control unit. The simulation agrees very well with the according real life measurement setup.


title: Digital jewelry: wearable technology for everyday life

abstract: Pervasive technology devices that intend to be worn must not only meet our functional requirements but also our social, emotional, and aesthetic needs. Current pervasive devices such as the PDA or cell phone are more portable than wearable, yet still they elicit strong consumer demand for intuitive interfaces and well-designed forms. Looking to the future of wearable pervasive devices, we can imagine an even greater demand for meaningful forms for objects nestled so close to our bodies. They will need to reflect our tastes and moods, and allow us to express our personalities, cultural beliefs, and values. Digital Jewelry explores a new wearable technology form that is based in jewelry design, not in technology. Through prototypes and meaningful scenarios, digital jewelry offers new ideas to consider in the design of wearable devices.


title: Bony Anatomic Landmarks to Avoid Injury to the Marginal Mandibular Nerve

abstract: BACKGROUND ::: Marginal mandibular nerve injuries are more likely to be symptomatic than other facial nerve injuries following facelift procedures. The marginal mandibular nerve courses over the facial artery in the region of the mandible. The nerve is most susceptible to injury in this location because it lies superficial to the anterior facial artery. ::: ::: ::: OBJECTIVES ::: The authors describe the location of the marginal mandibular nerve based on superficial anatomic landmarks as it crosses the facial artery above the mandibular border, in order to help surgeons avoid injury to this nerve during facelift procedures. ::: ::: ::: METHODS ::: Eighteen cadaveric facial halves were dissected with the aid of loupe magnification. The distance from the facial artery to the palpable masseteric tuberosity at the angle of the mandible was measured. The distance from the masseteric tuberosity to the mental midline was also measured to determine a ratio of the facial nerve from the masseteric tuberosity to the mental midline. ::: ::: ::: RESULTS ::: The facial artery was found to be an average of 3.05 ± 0.13 cm anterior to the masseteric tuberosity along the mandible. The marginal mandibular nerve crossed the facial artery along the mandibular border approximately 3 cm anterior to the masseteric tuberosity. The distance from the masseteric tuberosity to the mental midline averaged 11.3 ± 0.54 cm. Therefore, the marginal mandibular nerve courses superficial to the facial artery at approximately one-fourth of the distance from the masseteric tuberosity to the mental midline. ::: ::: ::: CONCLUSIONS ::: Knowledge of the masseteric tuberosity and mental midline landmarks of the facial artery can provide a reliable and safe approach to surgery of the lower face.


title: Multidimensional chromatography in food analysis.

abstract: In this work, the main developments and applications of multidimensional chromatographic techniques in food analysis are reviewed. Different aspects related to the existing couplings involving chromatographic techniques are examined. These couplings include multidimensional GC, multidimensional LC, multidimensional SFC as well as all their possible combinations. Main advantages and drawbacks of each coupling are critically discussed and their key applications in food analysis described.


title: Boosting with structural sparsity

abstract: We derive generalizations of AdaBoost and related gradient-based coordinate descent methods that incorporate sparsity-promoting penalties for the norm of the predictor that is being learned. The end result is a family of coordinate descent algorithms that integrate forward feature induction and back-pruning through regularization and give an automatic stopping criterion for feature induction. We study penalties based on the l1, l2, and l∞ norms of the predictor and introduce mixed-norm penalties that build upon the initial penalties. The mixed-norm regularizers facilitate structural sparsity in parameter space, which is a useful property in multiclass prediction and other related tasks. We report empirical results that demonstrate the power of our approach in building accurate and structurally sparse models.


title: On the Number of Linear Regions of Deep Neural Networks

abstract: We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation exponentially often in terms of the network's depth. This paper investigates the complexity of such compositional maps and contributes new theoretical results regarding the advantage of depth for neural networks with piecewise linear activation functions. In particular, our analysis is not specific to a single family of models, and as an example, we employ it for rectifier and maxout networks. We improve complexity bounds from pre-existing work and investigate the behavior of units in higher layers.


title: 2013 Special Issue: Autonomous reinforcement learning with experience replay

abstract: This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time.


title: Fansmitter: Acoustic Data Exfiltration from (Speakerless) Air-Gapped Computers

abstract: Because computers may contain or interact with sensitive information, they are often air-gapped and in this way kept isolated and disconnected from the Internet. In recent years the ability of malware to communicate over an air-gap by transmitting sonic and ultrasonic signals from a computer speaker to a nearby receiver has been shown. In order to eliminate such acoustic channels, current best practice recommends the elimination of speakers (internal or external) in secure computers, thereby creating a so-called 'audio-gap'. In this paper, we present Fansmitter, a malware that can acoustically exfiltrate data from air-gapped computers, even when audio hardware and speakers are not present. Our method utilizes the noise emitted from the CPU and chassis fans which are present in virtually every computer today. We show that a software can regulate the internal fans' speed in order to control the acoustic waveform emitted from a computer. Binary data can be modulated and transmitted over these audio signals to a remote microphone (e.g., on a nearby mobile phone). We present Fansmitter's design considerations, including acoustic signature analysis, data modulation, and data transmission. We also evaluate the acoustic channel, present our results, and discuss countermeasures. Using our method we successfully transmitted data from air-gapped computer without audio hardware, to a smartphone receiver in the same room. We demonstrated the effective transmission of encryption keys and passwords from a distance of zero to eight meters, with bit rate of up to 900 bits/hour. We show that our method can also be used to leak data from different types of IT equipment, embedded systems, and IoT devices that have no audio hardware, but contain fans of various types and sizes.


title: Pipelined Java Virtual Machine Interpreters

abstract: The performance of a Java Virtual Machine (JVM) interpreter running on a very long instruction word (VLIW) processor can be improved by means of pipelining. While one bytecode is in its execute stage, the next bytecode is in its decode stage, and the next bytecode is in its fetch stage. The paper describes how we implemented threading and pipelining by rewriting the source code of the interpreter and several modifications in the compiler. Experiments for evaluating the effectiveness of pipelining are described. Pipelining improves the execution speed of a threaded interpreter by 19.4% in terms of instruction count and 14.4% in terms of cycle count. Most of the simple bytecodes, like additions and multiplications, execute in four cycles. This number corresponds to the branch latency of our target VLIW processor. Thus most of the code of the interpreter is executed in branch delay slots.


title: Big data management processing with Hadoop MapReduce and spark technology: A comparison

abstract: Hadoop MapReduce is processed for analysis large volume of data through multiple nodes in parallel. However MapReduce has two function Map and Reduce, large data is stored through HDFS. Lack of facility involve in MapReduce so Spark is designed to run for real time stream data and for fast queries. Spark jobs perform work on Resilient Distributed Datasets and directed acyclic graph execution engine. In this paper, we extend Hadoop MapReduce working and Spark architecture with supporting kind of operation to perform. We also show the differences between Hadoop MapReduce and Spark through Map and Reduce phase individually.


title: Exploring motor vehicle theft in Australia

abstract: Motor vehicle theft costs the Australian community approximately one billion dollars every year. This paper looks at current trends associated with motor vehicle theft, and identifies various situational and technological theft prevention strategies.


title: Reducing the sensation of electrical stimulation with dry electrodes by using an array of constant current sources

abstract: Hydrogel electrodes are commonly used for functional and other electrical stimulation applications since the hydrogel layer has been shown to considerably reduce the perception of stimulation compared to dry electrodes. However, these hydrogel electrodes must be changed regularly as they dry out or become contaminated with skin cells and sweat products, thus losing their adhesiveness and resistive properties. Dry electrodes are longer lasting but are more uncomfortable due to unequal current distribution (current hogging). We hypothesise that if current through a dry electrode is equally shared amongst an array of small sub-electrodes, current hogging and thus the sensitivity perceived due to stimulation will be reduced. We constructed an 8 × 8 array of millimetre sized dry electrodes that could either be activated as individual current sources, or together as one large source. A study was performed with 13 participants to investigate the differences in sensation between the two modes of operation. The results showed that 12 out of 13 participants found the new (distributed-constant-current) approach allowed higher stimulation for the same sensation. The differences in sensation between single and multiple sources became larger with higher intensity levels.


title: Anytime Heuristic Search

abstract: We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.


title: Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients1

abstract: OBJECTIVES ::: to identify the number of electro-medical pieces of equipment in a coronary care unit, characterize their types, and analyze implications for the safety of patients from the perspective of alarm fatigue. ::: ::: ::: METHOD ::: this quantitative, observational, descriptive, non-participatory study was conducted in a coronary care unit of a cardiology hospital with 170 beds. ::: ::: ::: RESULTS ::: a total of 426 alarms were recorded in 40 hours of observation: 227 were triggered by multi-parametric monitors and 199 were triggered by other equipment (infusion pumps, dialysis pumps, mechanical ventilators, and intra-aortic balloons); that is an average of 10.6 alarms per hour. ::: ::: ::: CONCLUSION ::: the results reinforce the importance of properly configuring physiological variables, the volume and parameters of alarms of multi-parametric monitors within the routine of intensive care units. The alarms of equipment intended to protect patients have increased noise within the unit, the level of distraction and interruptions in the workflow, leading to a false sense of security.


title: swDNN: A Library for Accelerating Deep Learning Applications on Sunway TaihuLight

abstract: To explore the potential of training complex deep neural networks (DNNs) on other commercial chips rather than GPUs, we report our work on swDNN, which is a highly-efficient library for accelerating deep learning applications on the newly announced world-leading supercomputer, Sunway TaihuLight. Targeting SW26010 processor, we derive a performance model that guides us in the process of identifying the most suitable approach for mapping the convolutional neural networks (CNNs) onto the 260 cores within the chip. By performing a systematic optimization that explores major factors, such as organization of convolution loops, blocking techniques, register data communication schemes, as well as reordering strategies for the two pipelines of instructions, we manage to achieve a double-precision performance over 1.6 Tflops for the convolution kernel, achieving 54% of the theoretical peak. Compared with Tesla K40m with cuDNNv5, swDNN results in 1.91-9.75x performance speedup in an evaluation with over 100 parameter configurations.


title: Video accessibility enhancement for hearing-impaired users

abstract: There are more than 66 million people suffering from hearing impairment and this disability brings them difficulty in video content understanding due to the loss of audio information. If the scripts are available, captioning technology can help them in a certain degree by synchronously illustrating the scripts during the playing of videos. However, we show that the existing captioning techniques are far from satisfactory in assisting the hearing-impaired audience to enjoy videos. In this article, we introduce a scheme to enhance video accessibility using a Dynamic Captioning approach, which explores a rich set of technologies including face detection and recognition, visual saliency analysis, text-speech alignment, etc. Different from the existing methods that are categorized as static captioning, dynamic captioning puts scripts at suitable positions to help the hearing-impaired audience better recognize the speaking characters. In addition, it progressively highlights the scripts word-by-word via aligning them with the speech signal and illustrates the variation of voice volume. In this way, the special audience can better track the scripts and perceive the moods that are conveyed by the variation of volume. We implemented the technology on 20 video clips and conducted an in-depth study with 60 real hearing-impaired users. The results demonstrated the effectiveness and usefulness of the video accessibility enhancement scheme.


title: Compact Noise Models for MOSFETs

abstract: A physical understanding of both intrinsic and extrinsic noise mechanisms in a MOSFET is developed. Intrinsic noise mechanisms fundamental to device operation include channel thermal noise, induced gate noise, and induced substrate noise. While the effect of channel thermal noise is observable at zero drain-to-source voltage, the induced gate and substrate noise do not manifest themselves under these conditions. However, the attendant fluctuations in the channel charge are observable by the passage of electric current through the device. Extrinsic noise mechanisms manifested due to structural evolution of the MOSFET include the distributed gate resistance noise, distributed substrate resistance noise, bulk charge effects, substrate current supershot noise, gate current noise, excess channel noise, and 1/f noise. Where available, compact noise models covering these noise mechanisms are explained. Also, where possible, methods of suppression of these mechanisms are highlighted. A survey of current public domain MOS models is presented, and a lack of comprehensive coverage of noise models is noted. Open areas of MOSFET noise research in the sub-hundred-nanometer regime are also highlighted. With suitable adaptation, noise concepts elucidated in the context of MOS transistors have a much wider applicability to the operation of HEMTs, JFETs, MESFETs, and other field-effect devices


title: Action observation and acquired motor skills: An fMRI study with expert dancers

abstract: When we observe someone performing an action, do our brains simulate making that action? Acquired motor skills offer a unique way to test this question, since people differ widely in the actions they have learned to perform. We used functional magnetic resonance imaging to study differences in brain activity between watching an action that one has learned to do and an action that one has not, in order to assess whether the brain processes of action observation are modulated by the expertise and motor repertoire of the observer. Experts in classical ballet, experts in capoeira and inexpertcontrolsubjectsviewedvideosofballetorcapoeiraactions. Comparingthebrainactivitywhendancerswatchedtheirowndance style versus the other style therefore reveals the influence of motor expertise on action observation. We found greater bilateral activations in premotor cortex and intraparietal sulcus, right superior parietal lobe and left posterior superior temporal sulcus when expert dancers viewed movements that they had been trained to perform compared to movements they had not. Our results show that this ‘mirror system’ integrates observed actions of others with an individual’s personal motor repertoire, and suggest that the human brain understands actions by motor simulation.


title: Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature

abstract: Background: Several thousand mobile phone apps are available to download to mobile phones for health and fitness. Mobile phones may provide a unique means of administering health interventions to populations. Objective: The purpose of this systematic review was to systematically search and describe the literature on mobile apps used in health behavior interventions, describe the behavioral features and focus of health apps, and to evaluate the potential of apps to disseminate health behavior interventions. Methods: We conducted a review of the literature in September 2014 using key search terms in several relevant scientific journal databases. Only English articles pertaining to health interventions using mobile phone apps were included in the final sample. Results: The 24 studies identified for this review were primarily feasibility and pilot studies of mobile apps with small sample sizes. All studies were informed by behavioral theories or strategies, with self-monitoring as the most common construct. Acceptability of mobile phone apps was high among mobile phone users. Conclusions: The lack of large sample studies using mobile phone apps may signal a need for additional studies on the potential use of mobile apps to assist individuals in changing their health behaviors. Of these studies, there is early evidence that apps are well received by users. Based on available research, mobile apps may be considered a feasible and acceptable means of administering health interventions, but a greater number of studies and more rigorous research and evaluations are needed to determine efficacy and establish evidence for best practices. [JMIR Mhealth Uhealth 2015;3(1):e20]


title: Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computation

abstract: This paper introduces a new Evolutionary Computation (EC) framework named Open BEAGLE, that we have been developing and improving since 1999. Coded in C++, this framework offers solid object oriented foundations based on design patterns. It contains a basic generic EC framework on which other specialized frameworks can easily be constructed. Release 1.0 of Open BEAGLE implements two specialized frameworks: a simple genetic algorithms framework, and a complete genetic programming framework. Its power and ease of use is demonstrated through an example of the latter for the classic symbolic regression problem.


Social-Ecological Systems A General Framework for Analyzing Sustainability


title: A Proposed Conceptual Framework for Rewards and Motivation among Administrators of Higher Educational Provider in Malaysia

abstract: This study will examine the relationship between rewards and employee’s motivation in University Sultan Zainal Abidin (UniSZA), Kuala Terengganu, Malaysia. It designs to understand how intrinsic rewards as compare with extrinsic rewards are perceived as sources of motivation by administrators of the UniSZA. The study proposes a conceptual framework by linking the relationship between rewards and motivation. Two independent variables consisting of extrinsic and intrinsic rewards will be employed with motivation as the dependent variable. The findings of this research are expected to provide references to registrar department of UniSZA in developing an appropriate motivation approach for their administrators.


title: TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning.

abstract: Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness-related attributes, such as body mass index or diseases tendency, as well as understanding of global dependencies between wellness attributes and users' behavior is of crucial importance to various applications in personal and public wellness domains. Meanwhile, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse, and this study represents one of the first attempts in this direction. Specifically, to infer personal wellness attributes, we proposed multi-source individual user profile learning framework named "TweetFit". "TweetFit" can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. Our experimental results show that the integration of the data from sensors and multiple social media sources can substantially boost the wellness profiling performance.


Memo No . 90 June 29 , 2018 Theory IIIb : Generalization in Deep Networks


title: ARMin - Exoskeleton for Arm Therapy in Stroke Patients

abstract: Task-oriented repetitive movement can improve movement performance in patients with neurological lesions. The application of robotics can serve to assist, enhance, evaluate and document rehabilitation of movements. ARMin is a robot for arm therapy applicable to the arm training in clinics. It has an exoskeleton structure and is equipped with position and force sensors. Our latest version ARMin II has six degrees of freedom. The mechanical structure, the actuators, and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. The device can work in three therapy modes: passive mobilization, game therapy, and task-oriented training. This paper presents the technical components of the new version ARMin II, the therapy modes, the control strategy for a new example of a game therapy, and clinical results of a pilot study with 11 chronic stroke patients and of single case studies conducted with three chronic stroke patients.


title: A 0.31-pJ/bit 20-Gb/s DFE With 1 Discrete Tap and 2 IIR Filters Feedback in 40-nm-LP CMOS

abstract: This brief presents a low-power 20-Gb/s decision feedback equalizer (DFE) with one discrete tap and two infinite impulse response (IIR) filters feedback. The advantage of the IIR-DFE lies in both great energy and area efficiency for large channel attenuations. To further enhance the power efficiency of the IIR-DFE, the charge-steering logic (CSL) is utilized in this brief. Besides, the quarter-rate topology is adopted to alleviate the race condition of the CSL-based DFE. Fabricated in a 40-nm-LP CMOS process, the DFE core circuits only consumes 6.2 mW from the 1-V supply. Moreover, the core entirely occupies an area of 5700 $\mu {\mathrm{ m}}^{2}$ . Measured with PRBS7, the bit error rates are all less than $10^{-12}$ for channel loss from −7.98 to −18.3 dB. Finally, the power efficiency of this IIR-DFE is 0.31 pJ/bit.


Logic Programming for Software-Defined Networks


Investigating User Privacy in Android Ad Libraries


title: Study on sliding-mode based MPPT for PV array and supplying power to AC grid

abstract: This paper aims to study the recent work regarding the maximum power point tracker (MPPT) based on a sliding-mode manner for a PV array. Such a MPPT accompanies a perturbation and observation algorithm as well as a boost converter. Moreover, it needs a straight sliding-line and some adjustments on its location until a PV characteristic curve and this sliding line cross each other at the maximum power point (MPP). The switching pattern of the boost converter is influenced by this straight line so that the PV operating point is compelled to move along an instant PV curve and onto the sliding line. Thereby the PV array will generate the maximum power. An easier explanation for convergence towards the sliding line is provided. Through the boost converter and the single-phase inverter, power delivery from a PV array to an AC grid under the above MPPT will be also considered.


DIGITAL HYDRAULIC POWER MANAGEMENT SYSTEM – TOWARDS LOSSLESS HYDRAULICS


title: Empirical evidence for resource-rational anchoring and adjustment

abstract: People’s estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people’s rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people’s knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.


title: An 11.5 Gb/s 1/4th Baud-Rate CTLE and Two-Tap DFE With Boosted High Frequency Gain in 110-nm CMOS

abstract: As the data rate has been increased over 10 Gb/s with copper interconnect, the intersymbol interference (ISI) caused from the channel loss should be compensated. While a decision feedback equalizer (DFE), which is widely used in the receiver can compensate the ISI, its ability to enhance the signal-to-noise ratio (SNR) is limited especially for high frequency data patterns (alternating data patterns). Even a DFE with a large number of taps, which has powerful compensation capacity for ISI, can improve only limited amount of SNR. To improve SNR, this brief presents a 1/4th baud-rate continuous-time linear equalizer (CTLE) and a two-tap DFE. The 1/4th baud-rate CTLE recovers data to be adequate for the DFE, and remaining ISI is removed by the DFE. To boost the high frequency gain of the DFE, exclusive or merged adders are introduced, so the proposed equalizer is more tolerant against channel noise. It compensates 21.7-dB channel loss and operates with 11.5-Gb/s data rate as it consumes 25.35 mW from 1.3 V supply in a 110-nm CMOS technology.


Achievements and Challenges in Software Resource Estimation


title: Survey on Fraud Detection Techniques Using Data Mining

abstract: Now a days, fraud is a million dollar business and every year it is increasing more and more. In a crime survey in 2009, tells that close to 30% of the company’s worldwide reported falling victim to fraud in the past year. Fraud involves individual or in a form of groups who intentionally act secretly to rob another of something of worth, for their own profit. Fraud is as against the humanity and it is unlimited variety of different forms. So in this paper, we introduce different methods and techniques to tackle the fraud and how to prevent the fraud into the organization, businesses, and hospital and insurance companies. Fraud prevention is a continuing battle. Many website owners must continually fight against many kinds of crime ranging from stealing identity theft and credit cards and many other types of fraud related to that. Internet Fraud is also very popular, where fraudster can steal the credit card number and buy thing from the different websites.


title: Iron Loss Calculation in a Three-Phase-Laminated-Core Variable Inductor Based on Reluctance Network Analysis

abstract: Variable inductors, which consist of only a magnetic core and primary dc and secondary ac windings, can control effective inductance of the secondary ac winding by primary dc excitation. Therefore, the variable inductors can be applied as a var compensator in electric power systems. In a previous paper, we have proposed a novel three-phase-laminated-core variable inductor. It has only one laminated core in which the three-phase secondary ac windings are installed to reduce size and weight in comparison with a conventional single-phase variable inductor. Good controllability and sinusoidal output current of the proposed variable inductor were demonstrated. This paper presents a method for calculating iron loss of the three-phase-laminated-core variable inductor based on reluctance network analysis (RNA). The validity of the proposed method is proved by comparing with measured values.


title: Benefit of large field-of-view cameras for visual odometry

abstract: The transition of visual-odometry technology from research demonstrators to commercial applications naturally raises the question: “what is the optimal camera for vision-based motion estimation?” This question is crucial as the choice of camera has a tremendous impact on the robustness and accuracy of the employed visual odometry algorithm. While many properties of a camera (e.g. resolution, frame-rate, global-shutter/rolling-shutter) could be considered, in this work we focus on evaluating the impact of the camera field-of-view (FoV) and optics (i.e., fisheye or catadioptric) on the quality of the motion estimate. Since the motion-estimation performance depends highly on the geometry of the scene and the motion of the camera, we analyze two common operational environments in mobile robotics: an urban environment and an indoor scene. To confirm the theoretical observations, we implement a state-of-the-art VO pipeline that works with large FoV fisheye and catadioptric cameras. We evaluate the proposed VO pipeline in both synthetic and real experiments. The experiments point out that it is advantageous to use a large FoV camera (e.g., fisheye or catadioptric) for indoor scenes and a smaller FoV for urban canyon environments.


title: Materializing Resistant Identities Among the Medieval Peasantry An Examination of Dress Accessories from English Rural Settlement Sites

abstract: This article examines metal dress accessories from a range of late medieval English rural settlement sites. It is argued at the outset that medieval archaeology has been very slow to consider the c...


title: Transductive Visual-Semantic Embedding for Zero-shot Learning

abstract: Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both semantic representations and visual features are not effectively incorporated into the learned projection function. Moreover, these methods may suffer from the inherent projection shift problem, due to the disjointness between seen and unseen classes. To overcome these drawbacks, we propose a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL. In specific, TVSE first learns a latent embedding space to incorporate the manifold structures in both labeled source instances and unlabeled target instances under the transductive setting. In the learned space, each instance is viewed as a mixture of seen class scores. TVSE then effectively constructs the relational mapping between seen and unseen classes using the available semantic representations, and applies it to map the seen class scores of the target instances to their predictions of unseen classes. Extensive experiments on four benchmark datasets demonstrate that the proposed TVSE achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.


title: Unsupervised Ensemble Regression

abstract: Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the least or most accurate experts? In this work we propose a framework to study these questions, based on the assumption that the $m$ experts have uncorrelated deviations from the optimal predictor. Assuming the first two moments of the response are known, we develop methods to detect the best and worst regressors, and derive U-PCR, a novel principal components approach for unsupervised ensemble regression. We provide theoretical support for U-PCR and illustrate its improved accuracy over the ensemble mean and median on a variety of regression problems.


title: Microbiome and mucosal inflammation as extra-articular triggers for rheumatoid arthritis and autoimmunity

abstract: PURPOSE OF REVIEW ::: Despite the progress toward understanding the molecular pathogenesis of rheumatoid arthritis (RA), its cause remains elusive. Genes are important but rather insufficient to explain the majority of RA cases. This review describes the novel data supporting the microbiome and its interactions with the human host as potential en('in')vironmental factors in RA pathogenesis. ::: ::: ::: RECENT FINDINGS ::: Animal models of inflammatory arthritis have shown that the presence of bacteria in mucosal surfaces is sufficient to alter local and systemic host immune responses and elicit joint inflammation. Human RA studies have focused on three mucosal sites: the gut, the gingiva, and the respiratory tree. The oral microbiome, and specifically Porphyromonas gingivalis, has long been implicated. Novel sequencing technologies have allowed investigations into the role of the gut microbiome in the development of autoimmune arthritis. Most recently, the pulmonary parenchyma has also been described as yet another possible mucosal site of initiation of autoimmunity in RA. ::: ::: ::: SUMMARY ::: Emerging data implicate the microbiome in RA pathogenesis. Mucosal sites exposed to a high load of bacterial antigens--such as the periodontium, lung, and gut--may represent the initial site of autoimmune generation. If validated, these findings could lead to the discovery of potential biomarkers and therapeutic approaches in the preclinical and clinical phases of RA.


title: Stabilizing lithium metal using ionic liquids for long-lived batteries

abstract: Suppressing dendrite formation at lithium metal anodes during cycling is critical for the ::: implementation of future lithium metal-based battery technology. Here we report that it can ::: be achieved via the facile process of immersing the electrodes in ionic liquid electrolytes for a ::: period of time before battery assembly. This creates a durable and lithium ion-permeable ::: solid–electrolyte interphase that allows safe charge–discharge cycling of commercially ::: applicable Li|electrolyte|LiFePO4 batteries for 1,000 cycles with Coulombic efficiencies ::: 499.5%. The tailored solid–electrolyte interphase is prepared using a variety of electrolytes ::: based on the N-propyl-N-methylpyrrolidinium bis(fluorosulfonyl)imide room temperature ::: ionic liquid containing lithium salts. The formation is both time- and lithium salt-dependant, ::: showing dynamic morphology changes, which when optimized prevent dendrite formation ::: and consumption of electrolyte during cycling. This work illustrates that a simple, effective ::: and industrially applicable lithium metal pretreatment process results in a commercially ::: viable cycle life for a lithium metal battery.