title: DeepReID: Deep Filter Pairing Neural Network for Person Re-identification

abstract: Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection. In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. The learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13, 164 images of 1, 360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.


title: Blockchain - A Financial Technology for Future Sustainable Development

abstract: After the global financial crisis 2008, the world has been putting more effort in tightening banking and financial activities with stricter regulations. However, the effectiveness of this policy has remained controversial as many people believe that policy makers should promote freedom and transparency by empowering the public to directly interfere and change the system for public interest. This article attempts to synthesize and analyze available information with a focus on the role of blockchain, a financial tool that can potentially play an important role in the sustainable development of the global economy. The new technology is expected to bring massive benefits to consumers, to current banking system and to the whole society in general.


title: GSIS: A Secure and Privacy-Preserving Protocol for Vehicular Communications

abstract: In this paper, we first identify some unique design requirements in the aspects of security and privacy preservation for communications between different communication devices in vehicular ad hoc networks. We then propose a secure and privacy-preserving protocol based on group signature and identity (ID)-based signature techniques. We demonstrate that the proposed protocol cannot only guarantee the requirements of security and privacy but can also provide the desired traceability of each vehicle in the case where the ID of the message sender has to be revealed by the authority for any dispute event. Extensive simulation is conducted to verify the efficiency, effectiveness, and applicability of the proposed protocol in various application scenarios under different road systems.


The Bayesian Bootstrap


title: Methods for 18F-labeling of RGD peptides: comparison of aminooxy [18F]fluorobenzaldehyde condensation with ‘click labeling’ using 2-[18F]fluoroethylazide, and S-alkylation with [18F]fluoropropanethiol

abstract: Three strategies for chemoselective labeling of RGD peptides with 18F have been compared. Aminooxy [18F]fluorobenzaldehyde conjugation provided 40 ± 12% decay-corrected radiochemical yield using a fully automated method. An one-pot protocol for ‘click labeling’ of the RGD scaffold with 2-[18F]fluoroethylazide afforded 47 ± 8% decay-corrected radiochemical yield. Attempted conjugation with 3-[18F]fluoropropanethiol led to extensive decomposition and was therefore found unsuitable for labeling of the RGD peptide investigated. The results suggest that ‘click labeling’ of RGD peptides provides an attractive alternative to aminooxy aldehyde condensation, however, 2-[18F]-fluoroethylazide may be too small to allow separation of large 18F-labeled RGD peptides from their precursors.


title: A novel sensor for wrist based optical heart rate monitor

abstract: The growing demands of continuous healthcare and hence physiological monitoring necessitates a system with high reliability and accuracy. Wearable used for continuous cardiological parameter estimation from wrist use reflective photoplethysmography technique that has certain limitations which are imperative. One such constraint is skin pigmentation of the subject. In the present work a sensor module design is proposed addressing to the anomalies due to optical properties of skin. A novel 590 nm (yellow-orange) wavelength based optical system is tailored suitably to maximize the signal quality acquired. The proposed setup is validated on a conglomeration of subjects in terms of age, gender and skin tone. A generous agreement between coherent measures for signal quality shows that the proposed wavelength holds an advantage over its shorter counterpart when subjected to varied skin pigmentation levels. A maximum improvement factor of 71 is observed in case of perfusion index, 31 for pulsatile strength and 3 for SNR. The details of sensor design, experimental setup, validation protocol, observations and inferences drawn from the study are presented.


Towards conversational interfaces to web applications


title: process improvement: a maturity-based analysis

abstract: Original article can be found at: http://www.sciencedirect.com/science/journal/01641212 Copyright Elsevier Inc. [Full text of this article is not available in the UHRA]


title: On the injective chromatic number of graphs

abstract: We define the concepts of an injective colouring and the injective chromatic number of a graph and give some upper and lower bounds in general, plus some exact values. We explore in particular the injective chromatic number of the hypercube and put it in the context of previous work on similar concepts, especially the theory of error-correcting codes. Finally, we give necessary, and sufficient conditions for the injective chromatic number te be equal to the degree for a regular graph.


title: Distributed Internet-Based Load Altering Attacks Against Smart Power Grids

abstract: With the increase in use of information technology in advanced demand side management and given the growth in power consumption in the computation and communications sectors, a new class of cyberintrusion plans is emerging that aims to alter the load through the Internet and by means of automatic and distributed software intruding agents. These attacks work by compromising direct load control command signals, demand side management price signals, or cloud computation load distribution algorithms to affect the load at the most crucial locations in the grid in order to cause circuit overflow or other malfunctions and damage the power system equipment. To gain insights into these less-examined yet important intrusion strategies, in this paper, we identify a variety of practical loads that can be volnurable to Internet-based load altering attacks. In addition, we overview a collection of defense mechanisms that can help in blocking these attacks or minimizing the damage caused by them. Our simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that our proposed cost-efficent load protection strategy can significantly reduce the cost of load protection while it guarantees that no Internet-based load altering attack may overload the power distribution system.


title: Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

abstract: Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/).


title: Network Slices toward 5G Communications: Slicing the LTE Network

abstract: The upcoming 5G ecosystem is envisioned to build business-driven network slices to accommodate the different needs of divergent service types, applications, and services in support of vertical industries. In this article, we describe the network slicing concept by unveiling a novel network slicing architecture for integrated 5G communications. Further, we demonstrate its realization for the case of evolved LTE using state-of-the-art technologies. Finally, we elaborate on the LTE-specific requirements toward 5G, and point out existing challenges and open issues.


title: ADANA: Active Name Disambiguation

abstract: Name ambiguity has long been viewed as a challenging problem in many applications, such as scientific literature management, people search, and social network analysis. When we search a person name in these systems, many documents (e.g., papers, web pages) containing that person's name may be returned. It is hard to determine which documents are about the person we care about. Although much research has been conducted, the problem remains largely unsolved, especially with the rapid growth of the people information available on the Web. In this paper, we try to study this problem from a new perspective and propose an ADANA method for disambiguating person names via active user interactions. In ADANA, we first introduce a pairwise factor graph (PFG) model for person name disambiguation. The model is flexible and can be easily extended by incorporating various features. Based on the PFG model, we propose an active name disambiguation algorithm, aiming to improve the disambiguation performance by maximizing the utility of the user's correction. Experimental results on three different genres of data sets show that with only a few user corrections, the error rate of name disambiguation can be reduced to 3.1%. A real system has been developed based on the proposed method and is available online.


title: DHCC: Divisive hierarchical clustering of categorical data

abstract: Clustering categorical data poses two challenges defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this paper, we propose a novel divisive hierarchical clustering algorithm for categorical data, named DHCC. We view the task of clustering categorical data from an optimization perspective, and propose effective procedures to initialize and refine the splitting of clusters. The initialization of the splitting is based on multiple correspondence analysis (MCA). We also devise a strategy for deciding when to terminate the splitting process. The proposed algorithm has five merits. First, due to its hierarchical nature, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. Second, it is parameter-free, fully automatic and, in particular, requires no assumption regarding the number of clusters. Third, it is independent of the order in which the data is processed. Fourth, it is scalable to large data sets. And finally, our algorithm is capable of seamlessly discovering clusters embedded in subspaces, thanks to its use of a novel data representation and Chi-square dissimilarity measures. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.


title: A Longitudinal Examination of 10-Year Change in Vocational and Educational Activities for Adults With Autism Spectrum Disorders

abstract: The transition from adolescence to adulthood has been shown to be a time of amplified risk for individuals with autism spectrum disorders (ASD). It is unknown, however, whether problems in educational attainment and employment in the years after high school exit represent momentary perturbations in development or a turning point with long-lasting effects throughout adulthood. The present study addressed this question by examining 10-year trajectories of vocational and educational activities for adults with ASD, as well as the personal characteristics and environmental resources that predicted these activities. Participants were 161 adults with ASD (ages 18–52 years at the start of the study; M 30.9) who were part of a larger longitudinal study. Data were collected at 6 time points over a 10-year period. Results indicated significant declines in the level of independence and engagement in vocational/educational activities over the study period, particularly for women. Greater independence in vocational activities was found for those with more independence in activities of daily living. After controlling for personal characteristics, receipt of more services was marginally related to greater improvement in vocational independence.


title: Inference Suboptimality in Variational Autoencoders

abstract: Amortized inference has led to efficient approximate inference for large datasets. The quality of posterior inference is largely determined by two factors: a) the ability of the variational distribution to model the true posterior and b) the capacity of the recognition network to generalize inference over all datapoints. We analyze approximate inference in variational autoencoders in terms of these factors. We find that suboptimal inference is often due to amortizing inference rather than the limited complexity of the approximating distribution. We show that this is due partly to the generator learning to accommodate the choice of approximation. Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation.


title: Receiver-operating characteristics in recognition memory: Evidence for a dual-process model.

abstract: Evidence is presented that recognition judgments are based on an assessment of familiarity, as is described by signal detection theory, but that a separate recollection process also contributes to performance. In 3 receiver-operating characteristics (ROC) experiments, the process dissociation procedure was used to examine the contribution of these processes to recognition memory. In Experiments 1 and 2, reducing the length of the study list increased the intercept (d') but decreased the slope of the ROC and increased the probability of recollection but left familiarity relatively unaffected. In Experiment 3, increasing study time increased the intercept but left the slope of the ROC unaffected and increased both recollection and familiarity. In all 3 experiments, judgments based on familiarity produced a symmetrical ROC (slope = 1), but recollection introduced a skew such that the slope of the ROC decreased.


title: Crowd-powered parameter analysis for visual design exploration

abstract: Parameter tweaking is one of the fundamental tasks in the editing of visual digital contents, such as correcting photo color or executing blendshape facial expression control. A problem with parameter tweaking is that it often requires much time and effort to explore a high-dimensional parameter space. We present a new technique to analyze such high-dimensional parameter space to obtain a distribution of human preference. Our method uses crowdsourcing to gather pairwise comparisons between various parameter sets. As a result of analysis, the user obtains a goodness function that computes the goodness value of a given parameter set. This goodness function enables two interfaces for exploration: Smart Suggestion, which provides suggestions of preferable parameter sets, and VisOpt Slider, which interactively visualizes the distribution of goodness values on sliders and gently optimizes slider values while the user is editing. We created four applications with different design parameter spaces. As a result, the system could facilitate the user's design exploration.


title: To buy or not to buy online: adopters and non-adopters of online shopping in Singapore

abstract: The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers' buying behaviour in the electronic marketplace. Implications of the results are discussed.


title: Evaluation of Slotted CSMA/CA of IEEE 802.15.4

abstract: IEEE 802.15.4 standard is specifically designed for low Rate Wireless Personal Area Network (LR-WPAN) with low data rate and low power capabilities. Due to very low power consumption with duty cycle even less than 0.1, the standard is being widely applied in Wireless Sensor Networks applications. It operates in Beacon and Non Beacon enabled modes. During Beacon enabled mode, it has Contention Access Period (CAP) and optional Contention Free Period. We have analyzed its performance during CAP where slotted CSMA/CA algorithm is used. The performance analysis includes channel access busy, transmission failure chances along with reliability and throughput against all three frequency bands with load variation.


title: An Ontology of Preference-Based Multiobjective Metaheuristics

abstract: User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multi-objective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A review of the existing work is provided, based on which an ontology is built and instantiated with state-of-the-art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different use-cases, including querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.


title: Performing a check-in: emerging practices, norms and 'conflicts' in location-sharing using foursquare

abstract: Location-sharing services have a long history in research, but have only recently become available for consumers. Most popular commercial location-sharing services differ from previous research efforts in important ways: they use manual 'check-ins' to pair user location with semantically named venues rather than tracking; venues are visible to all users; location is shared with a potentially very large audience; and they employ incentives. By analysis of 20 in-depth interviews with foursquare users and 47 survey responses, we gained insight into emerging social practices surrounding location-sharing. We see a shift from privacy issues and data deluge, to more performative considerations in sharing one's location. We discuss performance aspects enabled by check-ins to public venues, and show emergent, but sometimes conflicting norms (not) to check-in.


title: Evaluating the benefits of relaxed BEOL pitch for deeply scaled ICs

abstract: CMOS scaling improves transistor density and lower the cost per transistor at a cost to other factors. Tall, narrowly spaced wires have made lateral coupling capacitance dominant for the deeply scaled CMOS back-end-of-line (BEOL) metals, which impacts worst-case timing and noise. Narrow wires further impact electromigration, yield and reliability. Since the densest routing does not certainly provide the best overall design quality, we systematically evaluate the impacts of relaxing the BEOL (r-BEOL) pitch. To implement sample designs, we used a commercial 28 nm CMOS technology and standard physical synthesis tools. Evaluation of these sample 28 nm designs show that relaxing the pitch brings improved signal integrity, design-for-manufacturability (DFM), and timing closure, at modest cost to area and no cost to performance. This suggests that r-BEOL might be a more optimal approach for low and medium volume products where the design and masks cost can dominate the cost of the wafers.


title: Control of a novel omni-directional platform

abstract: A unique design for an omni-directional platform to be used in conjunction with a robotic arm is presented. The platform differs from other omni-wheel layouts in that its drive axes do not intersect its geometric centre. The platform can be controlled in a local method using a three degree-of-freedom (DOF) joystick interfaced directly with the platform through a microcontroller. A global control method is introduced using encoder feedback to determine velocity and a wireless pose detection system to determine the position and orientation of the platform relative to a defined coordinate system.


title: Reconstruction-based Unsupervised Feature Selection: An Embedded Approach

abstract: Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. Since real-world data is usually unlabeled, unsupervised feature selection has received increasing attention in recent years. Without label information, unsupervised feature selection needs alternative criteria to define feature relevance. Recently, data reconstruction error emerged as a new criterion for unsupervised feature selection, which defines feature relevance as the capability of features to approximate original data via a reconstruction function. Most existing algorithms in this family assume predefined, linear reconstruction functions. However, the reconstruction function should be data dependent and may not always be linear especially when the original data is high-dimensional. In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection framework REFS, which embeds the reconstruction function learning process into feature selection. Experiments on various types of real-world datasets demonstrate the effectiveness of the proposed framework REFS.


title: Information Constraints on Auto-Encoding Variational Bayes

abstract: Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on Auto-Encoding Variational Bayes and whose search space is constrained via kernel-based measures of independence. In particular, our method employs the $d$-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations. We also present a full-fledged application to single-cell RNA sequencing (scRNA-seq). In this setting the biological signal is mixed in complex ways with sequencing errors and sampling effects. We show that our method out-performs the state-of-the-art in this domain.


Kinematics Simulation and Analysis of 2-DOF Parallel Manipulator with Highly Redundant Actuation


FPGA implementation of elliptic curve cryptography engine for personal communication systems


title: Limits on the majority vote accuracy in classifier fusion

abstract: We derive upper and lower limits on the majority vote accuracy with respect to individual accuracy p, the number of classiers in the pool (L), and the pairwise dependence between classiers, measured by Yule's Q statistic. Independence between individual classi- ers is typically viewed as an asset in classier fusion. We show that the majority vote with dependent classiers can potentially oer a dramatic improvement both over independent clas- siers and over the individual accuracy p. A functional relationship between the limits and the pairwise dependence Q is derived. Two patterns of the joint distribution for classier outputs (correct/incorrect) are identied to derive the limits: the pattern of success and the pattern of failure. The results support the intuition that negative pairwise dependence is benecial although not straightforwardly related to the accuracy. The pattern of success showed that for the highest improvement over p, all pairs of classiers in the pool should have the same negative dependence.


title: Math Anxiety, Working Memory, and Math Achievement in Early Elementary School

abstract: Although math anxiety is associated with poor mathematical knowledge and low course grades (Ashcraft & Krause, 2007), research establishing a connection between math anxiety and math achievement has generally been conducted with young adults, ignoring the emergence of math anxiety in young children. In the current study, we explored whether math anxiety relates to young children's math achievement. One hundred and fifty-four first- and second-grade children (69 boys, 85 girls) were given a measure of math achievement and working memory (WM). Several days later, children's math anxiety was assessed using a newly developed scale. Paralleling work with adults (Beilock, 2008), we found a negative relation between math anxiety and math achievement for children who were higher but not lower in WM. High-WM individuals tend to rely on WM-intensive solution strategies, and these strategies are likely disrupted when WM capacity is co-opted by math anxiety. We argue that early identification and treatment of math an...


title: Extracting representative API patterns of malware families using multiple sequence alignments

abstract: Nowadays malware developers use various techniques to avoid detection of antivirus software. For variants of malware, existing signature based detection method could be avoidable because those have some differences in static information like code or strings. Therefore, to detect and classify malware variants, a behavior based detection is required. This paper proposes a technique to extract a representative API pattern from API call sequences of a malware family using multiple sequence alignment (MSA) algorithm to measure similarities among malware variants. To extract API call sequences of malware, a sandbox tool was used. After that, the Clustal algorithm, a popular MSA algorithm used in the Bioinformatics field, was applied to malware API call sequences, and the representative API pattern was extracted from the results of MSA. Experiments to test the extracted API patterns that are used to classify malware variants were carried out, and we measured classification accuracy of the representative API pattern of each family. The experimental results show that our proposed method can be effective to classify malware families.


title: A Unit Selection Methodology for Music Generation Using Deep Neural Networks

abstract: Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. We do this by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forced-choiced ranking task. We compare our model to a note-level generative baseline that consists of a stacked LSTM trained to predict forward by one note.


title: A holistic approach to visualizing business models for the internet of things

abstract: The Internet of Things (IoT) promises huge potential economic benefits. However, current IoT applications are in their infancy and the full potential of possible business opportunities is yet to be discovered. To help realize these economic benefits, workable business models are required that show where opportunities exist. In this article we describe the Business DNA Model - a representation of a business model in terms of Design, Needs, and Aspirations, which greatly simplifies presentation, analysis, and design of business models. This model can be used by IoT stakeholders to generate and analyse stories, models, and projects for strategic management, business strategy, and innovation. We present one scenario - smart logistics - to illustrate how the Business DNA Model might be applied.


title: A High Efficiency High Power Density Harmonic-Tuned Ka Band Stacked-FET GaAs Power Amplifier

abstract: A stacked-FET power amplifier (PA) with harmonic- tuned output matching network is demonstrated using a 0.15-µm Gallium Arsenide (GaAs) technology. The fabricated PA exhibits 28.5 dBm output power, 12 dB gain and 38.4% power added efficiency (PAE). To the best of our knowledge, this is for the first time stacked-FET technique is used in combining with harmonic-tuned output network to achieve high PAE and high power density simultaneously in a GaAs PA.


title: The problems of computer-assisted animation

abstract: In the last few years several systems have been written for aiding in the conventional two-dimensional animation process. The main purpose of these systems has been to let the computer produce missing drawings based on extreme drawings produced by animators. While there has been some success and a great deal of optimism, the promise of higher output and quality using a computer has not been realized. The transition from simple drawings optimized for use on the computer to the complicated and detailed drawings of quality conventional animation has been much harder than expected. The principle difficulty is that the animators drawings are really two dimensional projections of three dimensional characters as visualized in the animators head, hence information is lost, ie. one leg obscures another. The problem of making a program infer the original object from its projections is akin to extremely difficult artificial intelligence problems. Efforts to overcome this by drawing skeletons or increasing the number of overlays require more manual intervention thereby offsetting the gains of using the computer. One way to analyze an approach is to determine the average time required of an artist or operator at all stages of animation for every frame. A second problem not generally recognized is that a production animation system requires the management of hundreds of thousands of drawings, hence data base management techniques not normally found in experimental animation systems.


title: Wideband Dual-Polarized Linear Array of Tightly Coupled Elements

abstract: Previous studies on tightly coupled dipole arrays (TCDAs) are mainly focused on 2-D phased arrays, where very wide bandwidth can be achieved as compared to 1-D TCDAs. Moreover, for the dual-polarized case, the interelement coupling is not sufficient in the polarization along the transverse direction of linear array, which leads to a much narrower bandwidth. In this communication, a wideband dual-polarized tightly coupled dipole linear array (TCDLA) is proposed. A novel vertical parasitic superstrate layer is employed in the dual-polarized linear array to compensate the impedance variation in beam scanning cases. As compared to traditional wide-angle impedance matching layer composed of layered dielectric sheets, the vertical parasitic superstrate layer greatly reduces the weight of the array. Simulation results show that the TCDLA achieves 5:1 bandwidth and 4:1 bandwidth in the vertical polarization and horizontal polarization, respectively, for VSWR $1 \times 26$ linear array is fabricated and measured. Good agreement is achieved between measured and simulation results.


title: Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs

abstract: Phishing is the third cyber-security threat globally and the first cyber-security threat in China. There were 61.69 million phishing victims in China alone from June 2011 to June 2012, with the total annual monetary loss more than 4.64 billion US dollars. These phishing attacks were highly concentrated in targeting at a few major Websites. Many phishing Webpages had a very short life span. In this paper, we assume the Websites to protect against phishing attacks are known, and study the effectiveness of machine learning based phishing detection using only lexical and domain features, which are available even when the phishing Webpages are inaccessible. We propose several novel highly effective features, and use the real phishing attack data against Taobao and Tencent, two main phishing targets in China, in studying the effectiveness of each feature, and each group of features. We then select an optimal set of features in our phishing detector, which has achieved a detection rate better than 98%, with a false positive rate of 0.64% or less. The detector is still effective when the distribution of phishing URLs changes.


title: Identifying some important success factors in adopting agile software development practices

abstract: Agile software development (ASD) is an emerging approach in software engineering, initially advocated by a group of 17 software professionals who practice a set of ''lightweight'' methods, and share a common set of values of software development. In this paper, we advance the state-of-the-art of the research in this area by conducting a survey-based ex-post-facto study for identifying factors from the perspective of the ASD practitioners that will influence the success of projects that adopt ASD practices. In this paper, we describe a hypothetical success factors framework we developed to address our research question, the hypotheses we conjectured, the research methodology, the data analysis techniques we used to validate the hypotheses, and the results we obtained from data analysis. The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past. The study indicates that nine of the 14 hypothesized factors have statistically significant relationship with ''Success''. The important success factors that were found are: customer satisfaction, customer collaboration, customer commitment, decision time, corporate culture, control, personal characteristics, societal culture, and training and learning.


title: Real time implementation of hybrid personal tracking system for anomaly detection

abstract: The major threats witnessed by mankind in recent years, demands very accurate and trustworthy personal tracking system (PTS). Using the advancement in the current technology, it becomes a promising solution to meet the above requirement. In this research, an attempt is made to integrate both GPS and GSM technologies to detect and track the position of the target (mankind). Initially, the position of the target is tracked by the authorized caretaker using GPS technology. Whenever the GPS system is unable to track the target, an alternative GSM technology is activated and the information about the concerned individual is obtained. The proposed hybrid tracking system is implemented in real-time using a customized embedded device.


title: Drugs which influence serotonin transporter and serotonergic receptors: Pharmacological and clinical properties in the treatment of depression

abstract: Abstract Depression is nowadays a major contributor to global burden of disease. The most commonly prescribed drugs influence monoaminergic pathways, mainly concentrating on serotonin. Unfortunately, there are several drawbacks associated with these drugs, namely late onset of action, risk of suicide and adverse effects: mainly nausea, vomiting and sexual dysfunction. Therefore there is still need for new drugs with possibly high efficacy and fewer side effects. In this paper selected compounds which inhibit serotonin reuptake by acting on the serotonin transporter (SERT) and various serotoninergic receptors are presented. We also discuss the ways in which their mechanism of action can be modified to improve pharmacological profile. Here, we focus on describing drugs’ potency, efficacy and adverse effects. Additional applications, apart from depression, are also discussed.


title: Modified abdominoplasty for patients with the Prune Belly syndrome.

abstract: OBJECTIVE ::: To present the results of a new technique for abdominoplasty in patients with the Prune Belly syndrome (PBS). ::: ::: ::: METHODS ::: Since 1985, 46 children with PBS underwent surgical treatment that included urinary tract reconstruction (UTR), orchidopexy, and abdominoplasty. In 41 patients, we performed the abdominoplasty as follows: (1) fusiform longitudinal resection of the mid-abdominal skin and subcutaneous tissue, with preservation of the musculo-aponeurotic fascia (MAF) and umbilicus, (2) ellipsoid unilateral longitudinal incision of the MAF in the most weakened side of the abdomen, producing 2 flaps, with the umbilicus being kept intact in the widest flap, (3) after UTR and bilateral orchiopexy, suture fixation of the widest MAF layer to the inner side of the contralateral abdominal wall, creating an inner MAF layer, (4) lateral suture fixation of the other flap over the inner layer, creating an outer MAF layer with a buttonhole exposing the umbilicus, that is sutured to the outer layer, and (5) approximation of the skin edges with incorporation of the umbilicus in the suture. ::: ::: ::: RESULTS ::: Skin coaptation was excellent in all patients, and no trimming was necessary in incision extremities. There was no dehiscence or skin necrosis and all patients presented immediate improvement of the abdominal tonus and appearance. Further improvement with growth was observed in all except 4 patients, 2 requiring secondary abdominoplasties. ::: ::: ::: CONCLUSION ::: We conclude that this technique is applicable in all forms of weakened abdomen typical of PBS, even in asymmetrical cases, requiring only 1 MAF incision, with good cosmetic and functional results.


Multipiple Face Detection and Tracking using Adaboost and Camshift Algorithm


title: Diffusion Generated Motion using Signed Distance Functions

abstract: We describe a new class of algorithms for generating a variety of geometric interfacial motions by alternating two steps: Construction of the signed distance function (i.e. redistancing) to the interface, and convolution with a suitable kernel. These algorithms can be seen as variants of Merriman, Bence, and Osher's threshold dynamics [25]. The new algorithms proposed here preserve the computational efficiency of the original threshold dynamics algorithm. However, unlike threshold dynamics, the new algorithms also allow attaining high accuracy on uniform grids, without adaptive refinement.


title: Optimally using the Bluetooth subband codec

abstract: The Bluetooth Special Interest Group (SIG) has standardized the subband coding (SBC) audio codec to connect headphones via wireless Bluetooth links with the A2DP profile. SBC compresses audio at high fidelity while having an ultra-low algorithm delay and very low complexity. SBC is configurable to a very large extend. Six parameters can be changed including sampling frequency, bit rate and algorithmic delay. In total, more than 60000 different configuration can be selected. In this publication, we objectively measure the audio and speech quality, if SBC runs under those configurations to find the most suitable parameter under given rate and delay constrains. Finally, we present objective tests comparing the audio codecs SBC, CELT, APT-X and ULD.


title: Saturation throughput analysis of IEEE 802.11ad wireless LAN in the contention based access period(CBAP)

abstract: The IEEE 802.11ad millimeter wave (mmWave) wireless LAN (WLAN) standard specifies the use of a hybrid MAC protocol consisting of CSMA/CA during the contention based access periods (CBAPs) and TDMA during the service periods (SPs). To mitigate the effects of higher attenuation in the mmWave band, the standard advocates the use of directional antennas. To provide channel access during CBAP, the coverage area around the AP can be divided into several quasi omni (QO) levels. In this paper, our focus is on uplink channel access during CBAP in which every multi-gigabit station (DMG STA) under a QO level contend for channel access using CSMA/CA protocol. We develop an accurate analytical model to compute the saturation throughput of each QO level and that of the network in the CBAP, by closely following the 802.11ad MAC specifications. We demonstrate the effectiveness of directional communication in 802.11ad WLANs.


title: Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

abstract: Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.


title: Pupil Location Method Based on Feature Searching and C-V Extension Model

abstract: Pupil images are usually disturbed by pupil deformation, eyebrows, eyelid and eyelash occlusions, uneven illumination and light spots, which results into inaccurate pupil location. For pupil images with interference, this paper proposes a pupil location method based on the feature searching and a C-V extension model with the constraint of standard deviation-mean and convexity. Firstly, a novel contrast stretching function is used in image preprocessing; Secondly, an objective function of feature searching is constructed to locate the pupil center and estimate the pupil area, positioning pupil preliminarily; Finally, through the C-V extension model with the constraint of standard deviation-mean and convexity, the initial contour curve extends outward and locates the pupil, positioning pupil accurately. Experimental results of processing collected images show that the method proposed in this paper can locate the pupil accurately and quickly.


title: Validation study of urban air dispersion model of Viana do Castelo

abstract: The City of Viana do Castelo in Portugal has developed an air quality program, which includes prediction of pollutant concentration for NO2, CO, PM10, O3 and C6H6. A range of numerical models were used to produce the concentration maps: the ADMS-Urban model for the pollutants dispersion; the Hills model to calculate air flow and turbulence over complex terrain, including the effects of variable surface roughness; the CORINAIR v.5 to estimate traffic emissions. This paper presents the methodology developed to validate the modelled results. Predicted concentrations were compared against measured concentrations of a chosen pollutant: Carbon Monoxide, CO. Five comparison statistics were calculated for three test points in order to find out the quality of the modelled results. Additionally, a hourly profile of predicted versus measured concentrations was developed.


title: A 3-D printed miniaturized log-periodic dipole antenna

abstract: This paper presents a miniaturized, light-weight, log-periodic dipole antenna fabricated with 3-D printing techniques. The antenna substrate is built of Acrylonitrile Butadiene Styrene material using the fused deposition modeling process and then metalized through direct print additive manufacturing using Dupont CB-028 silver ink. The antenna weighs 1.8 g and has a measured gain > 4 dBi and front-to-back ratio > 20 dB over the 2.25-3.75 GHz range. Compared to the standard log-periodic dipole antenna, the size is reduced by 13% with minimal gain degradation.


title: Business intelligence system adoption theories in SMES: A literature review

abstract: Today, business intelligence system (BIS) is universally considered as an essential tool to aid business decision making process. There is consensus that BIS has significant effects on predicting current and prospective views of business operations. These effects will only be realized if, and when, BIS are widely spread and used. An imperative study of BIS adoption theories is crucial in order to understand the determinants of BIS adoption. Previous studies on the BIS adoption theories in the global Small Medium Enterprise (SME) are limited. To fill the gap, this study is aimed to study the BIS adoption theories as reported by prior researchers and propose the BIS adoption model for SME in Malaysia. Through systematic literature review (SLR), this paper discusses two prominent models being adopted in SMEs. These two prominent models; diffusion of innovation (DOI) theory, and the technology, organization, and environment (TOE) framework are discussed in details in this paper. This paper also makes recommendations for future research.


title: Broadband Rat-Race Couplers With Coupled-Line Section and Impedance Transformers

abstract: Design of broadband rat-race couplers with the use of a shorted coupled-line section and impedance transformers has been comprehensively investigated. A broadband rat-race coupler utilizing a shorted coupled-line section and two shorted quarter-wave long transmission lines has been modified allowing for achieving ideal isolations between two pairs of respective ports. Also it has been shown that the bandwidth of such couplers can be increased when multisection impedance transformers are applied, and that the Darlington synthesis procedure can be used for calculation of transmission-line impedances. A tabular data for rat-race couplers with up to four-section impedance transformers is given. The theoretical analysis has been verified by measurements of the manufactured broadband rat-race coupler operating within 0.8 to 2.8 GHz frequency range.


Load balancing of irregular parallel applications on heterogeneous computing environments


title: Association of Digital Media Use With Subsequent Symptoms of Attention-Deficit/Hyperactivity Disorder Among Adolescents

abstract: Importance Modern digital platforms are easily accessible and intensely stimulating; it is unknown whether frequent use of digital media may be associated with symptoms of attention-deficit/hyperactivity disorder (ADHD). Objective To determine whether the frequency of using digital media among 15- and 16-year-olds without significant ADHD symptoms is associated with subsequent occurrence of ADHD symptoms during a 24-month follow-up. Design, Setting, and Participants Longitudinal cohort of students in 10 Los Angeles County, California, high schools recruited through convenience sampling. Baseline and 6-, 12-, 18-, and 24-month follow-up surveys were administered from September 2014 (10th grade) to December 2016 (12th grade). Of 4100 eligible students, 3051 10th-graders (74%) were surveyed at the baseline assessment. Exposures Self-reported use of 14 different modern digital media activities at a high-frequency rate over the preceding week was defined as many times a day (yes/no) and was summed in a cumulative index (range, 0-14). Main Outcomes and Measures Self-rated frequency of 18 ADHD symptoms (never/rare, sometimes, often, very often) in the 6 months preceding the survey. The total numbers of 9 inattentive symptoms (range, 0-9) and 9 hyperactive-impulsive symptoms (range, 0-9) that students rated as experiencing often or very often were calculated. Students who had reported experiencing often or very often 6 or more symptoms in either category were classified as being ADHD symptom-positive. Results Among the 2587 adolescents (63% eligible students; 54.4% girls; mean [SD] age 15.5 years [0.5 years]) who did not have significant symptoms of ADHD at baseline, the median follow-up was 22.6 months (interquartile range [IQR], 21.8-23.0, months). The mean (SD) number of baseline digital media activities used at a high-frequency rate was 3.62 (3.30); 1398 students (54.1%) indicated high frequency of checking social media (95% CI, 52.1%-56.0%), which was the most common media activity. High-frequency engagement in each additional digital media activity at baseline was associated with a significantly higher odds of having symptoms of ADHD across follow-ups (OR, 1.11; 95% CI, 1.06-1.16). This association persisted after covariate adjustment (OR, 1.10; 95% CI, 1.05-1.15). The 495 students who reported no high-frequency media use at baseline had a 4.6% mean rate of having ADHD symptoms across follow-ups vs 9.5% among the 114 who reported 7 high-frequency activities (difference; 4.9%; 95% CI, 2.5%-7.3%) and vs 10.5% among the 51 students who reported 14 high-frequency activities (difference, 5.9%; 95% CI, 2.6%-9.2%). Conclusions and Relevance Among adolescents followed up over 2 years, there was a statistically significant but modest association between higher frequency of digital media use and subsequent symptoms of ADHD. Further research is needed to determine whether this association is causal.


title: Modelling of healthcare IoT using the queueing theory

abstract: In the field of health, networked medical devices are closely intertwined in the structure of the Internet of things. A healthcare IoT infrastructure with a brief description of each component is presented. These components are a device with a reader, Cloud, healthcare provider and communication channel. As the title implies the paper presents simple cases with a few models of healthcare IoT system based on the queueing theory. The models describe streams of the requests and attacks on vulnerabilities and procedure of recovery by restart and eliminating of ones.


title: Optimizing schedules of retrieval practice for durable and efficient learning: How much is enough?

abstract: The literature on testing effects is vast but supports surprisingly few prescriptive conclusions for how to schedule practice to achieve both durable and efficient learning. Key limitations are that few studies have examined the effects of initial learning criterion or the effects of relearning, and no prior research has examined the combined effects of these 2 factors. Across 3 experiments, 533 students learned conceptual material via retrieval practice with restudy. Items were practiced until they were correctly recalled from 1 to 4 times during an initial learning session and were then practiced again to 1 correct recall in 1–5 subsequent relearning sessions (across experiments, more than 100,000 short-answer recall responses were collected and hand-scored). Durability was measured by cued recall and rate of relearning 1–4 months after practice, and efficiency was measured by total practice trials across sessions. A consistent qualitative pattern emerged: The effects of initial learning criterion and relearning were subadditive, such that the effects of initial learning criterion were strong prior to relearning but then diminished as relearning increased. Relearning had pronounced effects on long-term retention with a relatively minimal cost in terms of additional practice trials. On the basis of the overall patterns of durability and efficiency, our prescriptive conclusion for students is to practice recalling concepts to an initial criterion of 3 correct recalls and then to relearn them 3 times at widely spaced intervals.


title: Influence of Channel and Gate Engineering on the Analog and RF Performance of DG MOSFETs

abstract: The design of analog and RF circuits in CMOS technology has become increasingly more difficult as device modeling faces new challenges in the deep-submicrometer regime and emerging circuit applications. In this paper, we investigate the influence of both channel and gate engineering on the analog and RF performances of double-gate (DG) MOSFETs for system-on-chip applications. The gate engineering technique used here is the dual-metal gate technology, and the channel engineering technique is the conventional halo doping process. For analog applications, importance is given to the subthreshold regime as CMOS circuits operated in this regime are very much attractive for ultralow-power high-gain performances. Gate- and channel-engineered devices show an increase of gain by 45% and 35%, respectively, compared with the single-metal DG MOSFET. The gate-engineered device shows an improvement of 21.6% and 20% in the case of fT and fMAX values, whereas the channel-engineered device exhibits a reduction of fT by 2.7% with nearly equal fMAX.


title: Identifying Speakers and Addressees in Dialogues Extracted from Literary Fiction

abstract: This paper describes an approach to identifying speakers and addressees in dialogues extracted from literary fiction, along with a dataset annotated for speaker and addressee. The overall purpose o ...


title: Alcohol and the fetus in the west of Scotland.

abstract: Forty children with the fetal alcohol syndrome were identified in the west of Scotland. All were growth retarded and had abnormal facial features, and all those who were tested were found to have neurological or developmental abnormalities. Two children died of associated physical defects. Most of the mothers were socially deprived, and all had drunk heavily while pregnant. Three women had subsequently died. These findings provide clear evidence that in the west of Scotland maternal alcohol abuse during pregnancy is a significant cause of morbidity and mortality in children.


title: Cloud4NFV: A platform for Virtual Network Functions

abstract: The principle of Network Functions Virtualization (NFV) aims to transform network architectures by implementing Network Functions (NFs) in software that can run on commodity hardware. There are several challenges inherent to NFV, among which is the need for an orchestration and management framework. This paper presents the Cloud4NFV platform, which follows the major NFV standard guidelines. The platform is presented in detail and special attention is given to data modelling aspects. Further, insights on the current implementation of the platform are given, showing that part of its foundations lay on cloud infrastructure management and Software Defined Networking (SDN) platforms. Finally, it is presented a proof-of-concept (PoC) that illustrates how the platform can be used to deliver a novel service to end customers, focusing on Customer Premises Equipment (CPE) related functions.


title: Wrench-Closure Workspace Generation for Cable Driven Parallel Manipulators using a Hybrid Analytical-Numerical Approach

abstract: In this paper, a technique to generate the wrench-closure workspace for general case completely restrained cable driven parallel mechanisms is proposed. Existing methods can be classified as either numerically or analytically based approaches. Numerical techniques exhaustively sample the task space, which can be inaccurate due to discretisation and is computationally expensive. In comparison, analytical formulations have higher accuracy, but often provides only qualitative workspace information. The proposed hybrid approach combines the high accuracy of the analytical approach and the algorithmic versatility of the numerical approach. Additionally, this is achieved with significantly lower computational costs compared to numerical methods. It is shown that the wrench-closure workspace can be reduced to a set of univariate polynomial inequalities with respect to a single variable of the end-effector motion. In this form, the workspace can then be efficiently determined and quantitatively evaluated. The proposed technique is described for a 3-DOF and a 6-DOF cable driven parallel manipulator. A detailed example in workspace determination using the proposed approach and comparison against the conventional numerical approach are presented.


Multilingual Training of Crosslingual Word Embeddings


title: Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

abstract: The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the results of femtosecond X-ray free electron laser (XFEL) experiments. An increasing number of experimental observations have shown that although nuclear motion can be negligible, given a short enough incident pulse duration, electronic motion cannot be ignored. The current and widely accepted models assume that although electrons undergo dynamics driven by interaction with the pulse, their motion could largely be considered 'random'. This would then allow the supposedly incoherent contribution from the electronic motion to be treated as a continuous background signal and thus ignored. The original aim of our experiment was to precisely measure the change in intensity of individual Bragg peaks, due to X-ray induced electronic damage in a model system, crystalline C60. Contrary to this expectation, we observed that at the highest X-ray intensities, the electron dynamics in C60 were in fact highly correlated, and over sufficiently long distances that the positions of the Bragg reflections are significantly altered. This paper describes in detail the methods and protocols used for these experiments, which were conducted both at the Linac Coherent Light Source (LCLS) and the Australian Synchrotron (AS) as well as the crystallographic approaches used to analyse the data.


Philippine batflies of the family Nycteribiidae (Diptera: Pupipara)


title: Photosensitivity From Men's Colognes

abstract: To the Editor.— We have had the opportunity to care for several patients with photosensitive contact dermatitis to men's after-shave products. Our identification of the fragrance ingredient that caused the photosensitivity prompts us to report preliminary findings in two of these cases. Methods.— Patch testing was done according to the recommendations of the North American Contact Dermatitis Group (NACDG). 1 Scoring of results was done according to NACDG criteria. Photopatch testing was done according to the following method. Suspected photosensitizers were applied in duplicate as routine closed patch tests. After 24 hours one set of patches was removed and exposed to ultraviolet-A radiation (maximumoutput at 365 nm, ≈ 6 mW/sq cm) for ten minutes at a distance of 16 cm. The second set of patches was uncovered 48 hours after application, and the results were scored according to NACDG criteria two and seven days after application. Report of Cases


title: How Does Knowledge Come By?

abstract: Although the amount of knowledge that the humans possess has been gradually increasing, we still do not know the procedure and conditions that lead to the creation of new knowledge. An understanding of the modus operandi for the creation of knowledge may help in accelerating the existing pace of building knowledge. Our state of ignorance regarding various aspects of the process of knowledge building is highlighted by the existing literature in the domain. The reason behind it has been our inability to acquire the underlying data of this complex process. However, current time shows great promise of improvements in the knowledge building domain due to the availability of several online knowledge building portals. In this report, we emphasise that these portals act as prototypes for universal knowledge building process. The analysis of big data availed from these portals may equip the knowledge building researchers with the much needed meta-knowledge.


title: Problem-Solving Strategies and the Writing Process

abstract: IN THE MIDST of the composition renaissance, an odd fact stands out: our basic methods of teaching writing are the same ones English academics were using in the seventeenth century.' We still undertake to teach people to write primarily by dissecting and describing a completed piece of writing. The student is (a) exposed to the formal descriptive categories of rhetoric (modes of argumentdefinition, cause and effect, etc.-and modes of discourse-description, persuasion, etc.), (b) offered good examples (usually professional ones) and bad examples (usually his/her own) and (c) encouraged to absorb the features of a socially approved style, with emphasis on grammar and usage. We help our students analyze the product, but we leave the process of writing up to inspiration.2 Within the classroom, "writing" appears to be a set of rules and models for the correct arrangement of preexistent ideas. In contrast, outside of school, in private life and professions, writing is a highly goal-oriented, intellectual performance. It is both a strategic action and a thinking problem. But because writing as an act of thinking is messy and mysterious compared to the concrete reality of the product, we tend to leave composing up to the vagaries of chance and god-given talent to relegate it to independent warm-up exercises designated as "pre-writing."' The inner, intellectual process of composing, the complex and


title: Face recognition: A summary of 1995-1997

abstract: The development of face recognition over the past years allows an organization into three types of r ecognition algorithms, namely frontal, profile, and v iew-tolerant recognition, depending on the kind o f imagery and the according recognition algorithms. While frontal recognition certainly is the c lassical approach, view-tolerant algorithms usually p erform recognition in a more sophisticated fashion by taking into consideration some of the underlying phy sics, geometry, and statistics. Profile schemes as s tand-alone systems have a rather marginal significance for identification. However, they are very p ractical either f or f ast coarse pre-searches of large face databases to reduce the c omputational l oad for a subsequent sophisticated algorithm, or as part of a hybrid recognition scheme. Such hyb rid approaches have a special status among face recognition systems as they combine different recognition approaches in an either serial or parallel order to ov ercome the shortcomings of the individual components.


title: Temporal perception deficits in schizophrenia: integration is the problem, not deployment of attentions

abstract: Patients with schizophrenia are known to have impairments in sensory processing. In order to understand the specific temporal perception deficits of schizophrenia, we investigated and determined to what extent impairments in temporal integration can be dissociated from attention deployment using Attentional Blink (AB). Our findings showed that there was no evident deficit in the deployment of attention in patients with schizophrenia. However, patients showed an increased temporal integration deficit within a hundred-millisecond timescale. The degree of such integration dysfunction was correlated with the clinical manifestations of schizophrenia. There was no difference between individuals with/without schizotypal personality disorder in temporal integration. Differently from previous studies using the AB, we did not find a significant impairment in deployment of attention in schizophrenia. Instead, we used both theoretical and empirical approaches to show that previous findings (using the suppression ratio to correct for the baseline difference) produced a systematic exaggeration of the attention deficits. Instead, we modulated the perceptual difficulty of the task to bring the baseline levels of target detection between the groups into closer alignment. We found that the integration dysfunction rather than deployment of attention is clinically relevant, and thus should be an additional focus of research in schizophrenia.


title: A 300 nW, 15 ppm/$^{\circ}$C, 20 ppm/V CMOS Voltage Reference Circuit Consisting of Subthreshold MOSFETs

abstract: A low-power CMOS voltage reference was developed using a 0.35 mum standard CMOS process technology. The device consists of MOSFET circuits operated in the subthreshold region and uses no resistors. It generates two voltages having opposite temperature coefficients and adds them to produce an output voltage with a near-zero temperature coefficient. The resulting voltage is equal to the extrapolated threshold voltage of a MOSFET at absolute zero temperature, which was about 745 mV for the MOSFETs we used. The temperature coefficient of the voltage was 7 ppm/degC at best and 15 ppm/degC on average, in a range from - 20 to 80degC. The line sensitivity was 20 ppm/V in a supply voltage range of 1.4-3 V, and the power supply rejection ratio (PSRR) was -45 dB at 100 Hz. The power dissipation was 0.3 muW at 80degC. The chip area was 0.05 mm2 . Our device would be suitable for use in subthreshold-operated, power-aware LSIs.


title: Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

abstract: When human annotators are given a choice about what to label in an image, they apply their own subjective judgments on what to ignore and what to mention. We refer to these noisy "human-centric" annotations as exhibiting human reporting bias. Examples of such annotations include image tags and keywords found on photo sharing sites, or in datasets containing image captions. In this paper, we use these noisy annotations for learning visually correct image classifiers. Such annotations do not use consistent vocabulary, and miss a significant amount of the information present in an image; however, we demonstrate that the noise in these annotations exhibits structure and can be modeled. We propose an algorithm to decouple the human reporting bias from the correct visually grounded labels. Our results are highly interpretable for reporting "what's in the image" versus "what's worth saying." We demonstrate the algorithm's efficacy along a variety of metrics and datasets, including MS COCO and Yahoo Flickr 100M. We show significant improvements over traditional algorithms for both image classification and image captioning, doubling the performance of existing methods in some cases.


title: An extensive review of research in swarm robotics

abstract: Swarm robotics is a new approach to the coordination of multi-robot systems which consist of large numbers of relatively simple robots which takes its inspiration from social insects. The most remarkable characteristic of swarm robots are the ability to work cooperatively to achieve a common goal. In this paper, classification of existing researches, problems and algorithms aroused in the study of swarm robotics are presented. The existing studies are classified into major areas and relevant sub-categories in the major areas.


title: Studies on structure of calcium oxalate monohydrate renal papillary calculi. Mechanism of formation.

abstract: A scanning electron microscopy study of the ultrastructure of 18 calcium oxalate monohydrate papillary calculi was performed with the purpose of establishing the main steps in calculus formation. It is concluded that these calculi originate in a "core" located near the central part of the calculus. Significant quantities of organic matter as well as calcium phosphates can be found in the "core" and at the surface of adhesion to the papilla and, in some cases, fibers and calcified tubules can also be found in the contact zone. In no case did this material affect the crystalline structure of the calculi, indicating that its formation follows the calculus genesis. The study of the compact columnar zone revealed that its formation starts in a practically continuous surface formed by organic matter and crystals that surround the core. This layer favors the growth of oriented calcium oxalate monohydrate crystals upon it. Based on these observations, a feasible mechanism of papillary calcium oxalate monohydrate calculus formation is proposed.


title: Toward Large-Scale Face Recognition Using Social Network Context

abstract: Personal photographs are being captured in digital form at an accelerating rate, and our computational tools for searching, browsing, and sharing these photos are struggling to keep pace. One promising approach is automatic face recognition, which would allow photos to be organized by the identities of the individuals they contain. However, achieving accurate recognition at the scale of the Web requires discriminating among hundreds of millions of individuals and would seem to be a daunting task. This paper argues that social network context may be the key for large-scale face recogni- tion to succeed. Many personal photographs are shared on the Web through online social network sites, and we can leverage the resources and structure of such social networks to improve face recognition rates on the images shared. Drawing upon real photo collections from volunteers who are members of a popular online social network, we asses the availability of resources to improve face recognition and discuss techniques for applying these resources.


SRILM -- An extensible language modeling toolkit


title: A Footwear-Based Methodology for Fall Detection

abstract: Automatic fall detection is an active research area since several years. Basically, this is motivated by the impact that falls have, in terms of mortality, morbidity, and social costs, which make them comparable to road traffic injuries. The early detection of a fall can be critical to reduce the mortality rate and to limit the associated health consequences. Technological solutions designed to automatically detect and notify a fall may be classified into wearable and non-wearable. Among the former ones, the use of specific devices to be worn by the subject is a very common assumption, but it fails to address user’s acceptability issues. In fact, the position of the sensor or its visibility may be perceived as a stigma associated with the primary function of fall detection. To address such an issue, this paper presents a methodology for fall detection that relies on a pair of smart shoes, equipped with force sensors and a tri-axial accelerometer, able to detect a fall and notify it to a supervising system. The instrumented footwear enables the analysis of the subject’s motion and foot orientation, recognizing abnormal configurations. The developed algorithm is not computationally intensive, and therefore, can be easily executed on board the wearable device. Laboratory tests provided satisfactory performances in falls detection and correct classification: on 544 falls and 136 activities of daily living, performed by 17 healthy subjects, a 97.1% accuracy has been achieved. Further experiments involving two elderly users demonstrate the effectiveness of the proposed method in a real-life scenario.


title: On Uncertainty, Ambiguity, and Complexity in Project Management

abstract: This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.A key concept is theadequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure--that is, systems for planning, coordination and incentives, and monitoring.


title: [Depression and burden on primary caregivers of elderly persons with physical dependence of the UMF 171].

abstract: BACKGROUND ::: According to the National Health and Nutrition Survey of 2012, more than a quarter of older adults (26.9%) have some degree of disability, requiring a primary caregiver to perform basic activities of daily living. The aim is to determine the prevalence of depression and burden on primary caregivers of elderly persons with physical dependence. ::: ::: ::: METHODS ::: A descriptive cross-sectional study with non-probability sampling that included the primary caregivers of elderly patients with physical dependence. Barthel scale was applied as a tool to measure the level of physical dependence in elderly patients; while the primary caregivers were applied to the Beck Depression and Zarit scale for assessing the level of caregiver burden. A sample of 76 primary caregivers was calculated and descriptive statistical analysis was performed. ::: ::: ::: RESULTS ::: Of the 76 primary caregivers, 55.3% were without depression, 32.9% had mild depression, and 11.8% with moderate depression. According to the Zarit scale, 40.8% had no burden, 44.7% had burden light, and 14.5% intense burden. ::: ::: ::: CONCLUSIONS ::: The role of primary caregiver is a stressful task which can interfere with their family health; so our role is to provide care not only to the geriatric dependent patients, but also to their caregiver.


title: Automatic Prediction of Hit Songs

abstract: We explore the automatic analysis of music to identify likely hit songs. We extract both acoustic and lyric information from each song and separate hits from non-hits using standard classifiers, specifically Support Vector Machines and boosting classifiers. Our features are based on global sounds learnt in an unsupervised fashion from acoustic data or global topics learnt from a lyrics database. Experiments on a corpus of 1700 songs demonstrate performance that is much better than random. The lyricbased features are slightly more useful than the acoustic features in correctly identifying hit songs. Concatenating the two features does not produce significant improvements. Analysis of the lyric-based features shows that the absence of certain semantic information indicates that a song is more likely to be a hit.


title: Deep Learning Based Chatbot Models

abstract: A conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence. While chatbots can be used for various tasks, in general they have to understand users' utterances and provide responses that are relevant to the problem at hand. ::: In my work, I conduct an in-depth survey of recent literature, examining over 70 publications related to chatbots published in the last 3 years. Then, I proceed to make the argument that the very nature of the general conversation domain demands approaches that are different from current state-of-of-the-art architectures. Based on several examples from the literature I show why current chatbot models fail to take into account enough priors when generating responses and how this affects the quality of the conversation. In the case of chatbots, these priors can be outside sources of information that the conversation is conditioned on like the persona or mood of the conversers. In addition to presenting the reasons behind this problem, I propose several ideas on how it could be remedied. ::: The next section focuses on adapting the very recent Transformer model to the chatbot domain, which is currently state-of-the-art in neural machine translation. I first present experiments with the vanilla model, using conversations extracted from the Cornell Movie-Dialog Corpus. Secondly, I augment the model with some of my ideas regarding the issues of encoder-decoder architectures. More specifically, I feed additional features into the model like mood or persona together with the raw conversation data. Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features affect the quality of the generated responses.


title: A framework for defining logics

abstract: The Edinburgh Logical Framework (LF) provides a means to define (or present) logics. It is based on a general treatment of syntax, rules, and proofs by means of a typed λ-calculus with dependent types. Syntax is treated in a style similar to, but more general than, Martin-Lo¨f's system of arities. The treatment of rules and proofs focuses on his notion of a judgment . Logics are represented in LF via a new principle, the judgments as types principle, whereby each judgment is identified with the type of its proofs. This allows for a smooth treatment of discharge and variable occurence conditions and leads to a uniform treatment of rules and proofs whereby rules are viewed as proofs of higher-order judgments and proof checking is reduced to type checking. The practical benefit of our treatment of formal systems is that logic-independent tools, such as proof editors and proof checkers, can be constructed.


title: Adaptive Provisioning of Stream Processing Systems in the Cloud

abstract: With the advent of data-intensive applications that generate large volumes of real-time data, distributed stream processing systems (DSPS) become increasingly important in domains such as social networking and web analytics. In practice, DSPSs must handle highly variable workloads caused by unpredictable changes in stream rates. Cloud computing offers an elastic infrastructure that DSPSs can use to obtain resources on-demand, but an open problem is to decide on the correct resource allocation when deploying DSPSs in the cloud. This paper proposes an adaptive approach for provisioning virtual machines (VMs) for the use of a DSPS in the cloud. We initially perform a set of benchmarks across performance metrics such as network latency and jitter to explore the feasibility of cloud-based DSPS deployments. Based on these results, we propose an algorithm for VM provisioning for DSPSs that reacts to changes in the stream workload. Through a prototype implementation on Amazon EC2, we show that our approach can achieve low-latency stream processing when VMs are not overloaded, while adjusting resources dynamically with workload changes.


title: Developing E-Commerce Strategies Based on Axiomatic Design

abstract: Electronic commerce is dramatically changing the traditional way of doing business and furthermore, the growth of the Internet is creating new business opportunities. Today many products, processes and organizations are complex systems that have to be designed in order to meet specific customer requirements. Axiomatic design is a scientifically based design theory that guides designers through the process of first mapping customer needs into functional requirements, then mapping these requirements into design parameters, and then finally figuring out processes to provide those design parameters. Once the created strategies from high-level goals down to the specific area of interest are mapped with axiomatic design, it is easy to follow an optimization of the implementation process. Axiomatic design can also be defined as a theory that provides designers with decision making criteria for the entire design process. In this paper, axiomatic design will be used to provide a framework for the ecommerce applications in electronic retailing. The high level goals and strategies will form the roots of the decomposition tree for the e-commerce strategy, and the process will continue to zig-zag back and forth between the goal domain and the strategy domain until the design is completely decomposed. The structured design and decomposition method assures that the decisions made in the strategy development are made in proper sequence. The model developed in this working paper can be used in the strategy formulation of an internet business, as well as the organizational analysis of an e-commerce strategy in order to identify goals without corresponding strategies or strategies that have no corresponding goals. The axiomatic design approach and new relations derived during these investigations may provide a new look at the development of enterprise e-commerce strategies.


title: A stage-bypass SOI-CMOS switch for multi-mode multi-band applications

abstract: A double-pole double-throw SOI CMOS switch is presented, which can be exploited to bypass a power stage in a radio transmitter with the aim of improving efficiency in applications requiring transmit power control. The switch is designed through transistors stacking. It is able to manage up to a 35 dBm input power with less than 0.35 dB insertion loss from 500 MHz through 3 GHz. A series-shunt topology allows a better than 40 dB isolation to be obtained in high-power mode. Wide bandwidth and high linearity make the switch suitable for multi-standard front end.


title: Children learning computer science concepts via Alice game-programming

abstract: Programming environments that incorporate drag-and-drop methods and many pre-defined objects and operations are being widely used in K-12 settings. But can middle school students learn complex computer science concepts by using these programming environments when computer science is not the focus of the course? In this paper, we describe a semester-long game-programming course where 325 middle school students used Alice. We report on our analysis of 231 final games where we measured the frequency of successful execution of programming constructs. Our results show that many games exhibit successful uses of high level computer science concepts such as student-created abstractions, concurrent execution, and event handlers.We discuss the implications of these results for designing effective game programming courses for young students.


title: Deep learning with long short-term memory networks for financial market predictions

abstract: Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memory-free classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). The outperformance relative to the general market is very clear from 1992 to 2009, but as of 2010, excess returns seem to have been arbitraged away with LSTM profitability fluctuating around zero after transaction costs. We further unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading – they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that yields 0.23 percent prior to transaction costs. Further regression analysis unveils low exposure of the LSTM returns to common sources of systematic risk – also compared to the three benchmark models.


title: Deep classification in large-scale text hierarchies

abstract: Most classification algorithms are best at categorizing the Web documents into a few categories, such as the top two levels in the Open Directory Project. Such a classification method does not give very detailed topic-related class information for the user because the first two levels are often too coarse. However, classification on a large-scale hierarchy is known to be intractable for many target categories with cross-link relationships among them. In this paper, we propose a novel deep-classification approach to categorize Web documents into categories in a large-scale taxonomy. The approach consists of two stages: a search stage and a classification stage. In the first stage, a category-search algorithm is used to acquire the category candidates for a given document. Based on the category candidates, we prune the large-scale hierarchy to focus our classification effort on a small subset of the original hierarchy. As a result, the classification model is trained on the small subset before being applied to assign the category for a new document. Since the category candidates are sufficiently close to each other in the hierarchy, a statistical-language-model based classifier using n-gram features is exploited. Furthermore, the structure of the taxonomy can be utilized in this stage to improve the performance of classification. We demonstrate the performance of our proposed algorithms on the Open Directory Project with over 130,000 categories. Experimental results show that our proposed approach can reach 51.8% on the measure of Mi-F1 at the 5th level, which is 77.7% improvement over top-down based SVM classification algorithms.


title: A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing

abstract: The anticipated uptake of Cloud computing, built on well-established research in Web Services, networks, utility computing, distributed computing and virtualisation, will bring many advantages in cost, flexibility and availability for service users. These benefits are expected to further drive the demand for Cloud services, increasing both the Cloud's customer base and the scale of Cloud installations. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including fault tolerance, high availability and scalability. Central to these issues is the establishment of effective load balancing techniques. It is clear the scale and complexity of these systems makes centralized assignment of jobs to specific servers infeasible; requiring an effective distributed solution. This paper investigates three possible distributed solutions proposed for load balancing; approaches inspired by Honeybee Foraging Behaviour, Biased Random Sampling and Active Clustering.


Inducing Conceptual Embedding Spaces from Wikipedia


title: Lipid ion channels and the role of proteins

abstract: Synthetic lipid membranes in the absence of proteins can display quantized conduction events for ions that are virtually indistinguishable from those of protein channel. By indistinguishable we mean that one cannot decide based on the current trace alone whether conductance events originate from a membrane, which does or does not contain channel proteins. Additional evidence is required to distinguish between the two cases, and it is not always certain that such evidence can be provided. The phenomenological similarities are striking and span a wide range of phenomena: The typical conductances are of equal order and both lifetime distributions and current histograms are similar. One finds conduction bursts, flickering, and multistep-conductance. Lipid channels can be gated by voltage, and can be blocked by drugs. They respond to changes in lateral membrane tension and temperature. Thus, they behave like voltage-gated, temperature-gated and mechano-sensitive protein channels, or like receptors. Lipid channels are remarkably under-appreciated. However, the similarity between lipid and protein channels poses an eminent problem for the interpretation of protein channel data. For instance, the Hodgkin-Huxley theory for nerve pulse conduction requires a selective mechanism for the conduction of sodium and potassium ions. To this end, the lipid membrane must act both as a capacitor and as an insulator. Non-selective ion conductance by mechanisms other than the gated protein-channels challenges the proposed mechanism for pulse propagation. ... Some important questions arise: Are lipid and protein channels similar due a common mechanism, or are these similarities fortuitous? Is it possible that both phenomena are different aspects of the same phenomenon? Are lipid and protein channels different at all? ... (abbreviated)


Anomaly Detection in Data Mining: A Review


title: High-$Q$ Fully Reconfigurable Tunable Bandpass Filters

abstract: In this paper, the authors present a design technique that enables inter-resonator and external coupling control for high-quality-factor (Q) tunable bandpass filters. The design incorporates low-Q varactors as part of the inter-resonator and external coupling mechanisms without degrading the overall high Q of the original filter. Detailed design methodology and equations are presented to illustrate the concepts. A first-time demonstration of these concepts is presented for a widely tunable high-Q evanescent-mode cavity bandpass filter. The cavities are integrated in a low-loss substrate with commercially available piezoelectric actuators and solid-state varactors for frequency and bandwidth tuning. This technique allows for reduced bandwidth variation over large tuning ranges. As one example, a constant 25-MHz absolute-bandwidth filter in the 0.8-1.43-GHz tuning range with loss that is as low as 1.6 dB is presented as an example. The filter third-order intercept point is between 32.8 and 35.9 dBm over this tuning range. To further show the impact of the technique on high- Q filters, a filter Q that is as high as 750 is demonstrated in the range of 3-5.6 GHz, while using low-Q varactors (Q < 30 at 5 GHz for a 0.4-pF capacitance) to achieve more than 50% reduction in bandwidth variation over the tuning range.


title: The future prospects of microbial cellulose in biomedical applications

abstract: Microbial cellulose has proven to be a remarkably versatile biomaterial and can be used in wide variety of applied scientific endeavors, such as paper products, electronics, acoustics, and biomedical devices. In fact, biomedical devices recently have gained a significant amount of attention because of an increased interest in tissue-engineered products for both wound care and the regeneration of damaged or diseased organs. Due to its unique nanostructure and properties, microbial cellulose is a natural candidate for numerous medical and tissue-engineered applications. For example, a microbial cellulose membrane has been successfully used as a wound-healing device for severely damaged skin and as a small-diameter blood vessel replacement. The nonwoven ribbons of microbial cellulose microfibrils closely resemble the structure of native extracellullar matrices, suggesting that it could function as a scaffold for the production of many tissue-engineered constructs. In addition, microbial cellulose membranes, ...


title: Automatic detection of rumor on Sina Weibo

abstract: The problem of gauging information credibility on social networks has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift the premise and study the problem of information credibility on Sina Weibo, China's leading micro-blogging service provider. With eight times more users than Twitter, Sina Weibo is more of a Facebook-Twitter hybrid than a pure Twitter clone, and exhibits several important characteristics that distinguish it from Twitter. We collect an extensive set of microblogs which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Sina Weibo. Unlike previous studies on Twitter where the labeling of rumors is done manually by the participants of the experiments, the official nature of this service ensures the high quality of the dataset. We then examine an extensive set of features that can be extracted from the microblogs, and train a classifier to automatically detect the rumors from a mixed set of true information and false information. The experiments show that some of the new features we propose are indeed effective in the classification, and even the features considered in previous studies have different implications with Sina Weibo than with Twitter. To the best of our knowledge, this is the first study on rumor analysis and detection on Sina Weibo.


title: Beta band modulations underlie action representations for movement planning

abstract: Abstract To be able to interact with our environment, we need to transform incoming sensory information into goal-directed motor outputs. Whereas our ability to plan an appropriate movement based on sensory information appears effortless and simple, the underlying brain dynamics are still largely unknown. Here we used magnetoencephalography (MEG) to investigate this issue by recording brain activity during the planning of non-visually guided reaching and grasping actions, performed with either the left or right hand. Adopting a combination of univariate and multivariate analyses, we revealed specific patterns of beta power modulations underlying varying levels of neural representations during movement planning. (1) Effector-specific modulations were evident as a decrease in power in the beta band. Within both hemispheres, this decrease was stronger while planning a movement with the contralateral hand. (2) The comparison of planned grasping and reaching led to a relative increase in power in the beta band. These power changes were localized within temporal, premotor and posterior parietal cortices. Action-related modulations overlapped with effector-related beta power changes within widespread frontal and parietal regions, suggesting the possible integration of these two types of neural representations. (3) Multivariate analyses of action-specific power changes revealed that part of this broadband beta modulation also contributed to the encoding of an effector-independent neural representation of a planned action within fronto-parietal and temporal regions. Our results suggest that beta band power modulations play a central role in movement planning, within both the dorsal and ventral stream, by coding and integrating different levels of neural representations, ranging from the simple representation of the to-be-moved effector up to an abstract, effector-independent representation of the upcoming action.