title: Sentiment Analysis for Social Media Images

abstract: In this proposal, we study the problem of understandinghuman sentiments from large scale collection ofInternet images based on both image features and contextualsocial network information (such as friend comments anduser description). Despite the great strides in analyzing usersentiment based on text information, the analysis of sentimentbehind the image content has largely been ignored. Thus, we extend the significant advances in text-based sentimentprediction tasks to the higherlevel challenge of predicting theunderlying sentiments behind the images. We show that neithervisual features nor the textual features are by themselvessufficient for accurate sentiment labeling. Thus, we provide away of using both of them, and formulate sentiment predictionproblem in two scenarios: supervised and unsupervised. Wedevelop an optimization algorithm for finding a local-optimasolution under the proposed framework. With experiments ontwo large-scale datasets, we show that the proposed methodimproves significantly over existing state-of-the-art methods. In the future, we are going to incorporating more informationon the social network and explore sentiment on signed social network.


title: On the Convergence of Block Coordinate Descent Type Methods

abstract: In this paper we study smooth convex programming problems where the decision variables vector is split into several blocks of variables. We analyze the block coordinate gradient projection method in which each iteration consists of performing a gradient projection step with respect to a certain block taken in a cyclic order. Global sublinear rate of convergence of this method is established and it is shown that it can be accelerated when the problem is unconstrained. In the unconstrained setting we also prove a sublinear rate of convergence result for the so-called alternating minimization method when the number of blocks is two. When the objective function is also assumed to be strongly convex, linear rate of convergence is established.


title: Segmenting consumers using smart meter data

abstract: Existing electricity market segmentation analysis techniques only make use of limited consumption statistics (usually averages and variances). In this paper we use power demand distributions (PDDs) obtained from fine-grain smart meter data to perform market segmentation based on distributional clustering. We apply this approach to mining 8 months of readings from about 1000 US Google employees.


title: A solution to the single-question crowd wisdom problem

abstract: Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard 'most popular' or 'most confident' principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.


title: Hericium erinaceus, an amazing medicinal mushroom

abstract: Medicinal mushrooms have become a compelling topic because the bioactive compounds they contain promise a plethora of therapeutic properties. Hericium erinaceus commonly known as “Houtou” or “Shishigashira” in China and “Yamabushitake” in Japan, has commonly been prescribed in traditional Chinese medicine (TCM), because its consumption has been shown to be beneficial to human health. The species is found throughout the northern hemisphere in Europe, Asia, and North America. Hericium erinaceus has been firmly established as an important medicinal mushroom and its numerous bioactive compounds have been developed into food supplements and alternative medicines. However, the correspondence of the active components that cause the observed effects is often not clear. The mushroom as well as the fermented mycelia have been reported to produce several classes of bioactive molecules, including polysaccharides, proteins, lectins, phenols, and terpenoids. Most interestingly, two classes of terpenoid compounds, hericenones and erinacines, from fruiting bodies and cultured mycelia, respectively, have been found to stimulate nerve growth factor (NGF) synthesis. In this review we examine the scientific literature to explore and highlight the scientific facts concerning medicinal properties of H. erinaceus. We provide up-to-date information on this mushroom, including its taxonomy and a summary of bioactive compounds that appear related to the therapeutic potential of H. erinaceus.


title: A 77-GHz Cooperative Radar System Based on Multi-Channel FMCW Stations for Local Positioning Applications

abstract: In this paper, a radar system for local positioning applications is presented. The system consists of frequency modulated continuous-wave (FMCW) stations operating in W-band that are loosely coupled using time-delayed ramp start signals. A centralized signal processing approach allows to relax the required synchronization accuracy between the stations and further leads to a cancellation of phase noise and phase distortions caused by imperfect FMCW ramps. All stations are equipped with an antenna array and multiple receivers. Thus, a signal processing approach is developed in this work that combines the positive effects from the centralized processing with the information from the antenna array in a digital-beamforming based approach to improve the multipath robustness of the overall system. The developed theory is confirmed in measurements carried out with a prototype system consisting of two stations. Various measurements in multipath environments confirm the improved robustness leading to a worst-case root-mean-square position error of 15 mm under strong multipath conditions which were simulated inside an anechoic chamber.


title: Collaboration Using Multiple PDAs Connected to a PC

abstract: The Pebbles project is creating applications to connect multiple Personal Digital Assistants (PDAs) to a main computer such as a PC. We are using 3Com PalmPilots because they are starting to be ubiquitous. We created the “Remote Commander” application to allow users to take turns sending input from their PalmPilots to the PC as if they were using the PC’s mouse and keyboard. “PebblesDraw” is a shared whiteboard application we built that allows all of the users to send input simultaneously while sharing the same PC display. We are investigating the use of these applications in various contexts, such as co-located meetings.


title: Superpixel-based saliency detection

abstract: In this paper, we propose an effective superpixel-based saliency model. First, the original image is simplified by performing superpixel segmentation and adaptive color quantization. On the basis of superpixel representation, inter-superpixel similarity measures are then calculated based on difference of histograms and spatial distance between each pair of superpixels. For each superpixel, its global contrast measure and spatial sparsity measure are evaluated, and refined with the integration of inter-superpixel similarity measures to finally generate the superpixel-level saliency map. Experimental results on a dataset containing 1,000 test images with ground truths demonstrate that the proposed saliency model outperforms state-of-the-art saliency models.


title: Combination of Structural and Factual Descriptors for Document Stream Segmentation

abstract: This paper extends a previous work being done by [4]. Having no information about the document separation in the flow, the system operates progressively by examining successive pairs of pages looking for continuity or rupture descriptors. Four document levels have been introduced to better extract those descriptors and reduce the ambiguity in their extraction: records, technical documents, fundamental documents and cases. At each level, structural and factual descriptors are first extracted and then compared between pairs of pages or documents. To reinforce the descriptor interest and focus the system on equivalent descriptors in the pairs, the descriptors are accompanied by their context. The extraction of the context is facilitated by the determination of the physical and logical structure in the pages. Contextual rules based on these descriptors are employed for the determination of either a continuity, a rupture or an uncertainty between the pairs. To overcome the problem of information emptiness in the current page, a logbook is used to gather the descriptors in all the previous pages of the record and a buffer allows to delay the comparison. These latter points were added to the previous work that widely reinforce the current system increasing its precision of more than 6%.


title: Probiotics restore bowel flora and improve liver enzymes in human alcohol-induced liver injury: a pilot study.

abstract: The effects of chronic alcohol consumption on the bowel flora and the potential therapeutic role of probiotics in alcohol-induced liver injury have not previously been evaluated. In this study, 66 adult Russian males admitted to a psychiatric hospital with a diagnosis of alcoholic psychosis were enrolled in a prospective, randomized, clinical trial to study the effects of alcohol and probiotics on the bowel flora and alcohol-induced liver injury. Patients were randomized to receive 5 days of Bifidobacterium bifidum and Lactobacillus plantarum 8PA3 versus standard therapy alone (abstinence plus vitamins). Stool cultures and liver enzymes were performed at baseline and again after therapy. Results were compared between groups and with 24 healthy, matched controls who did not consume alcohol. Compared to healthy controls, alcoholic patients had significantly reduced numbers of bifidobacteria (6.3 vs. 7.5 log colony-forming unit [CFU]/g), lactobacilli (3.15 vs. 4.59 log CFU/g), and enterococci (4.43 vs. 5.5 log CFU/g). The mean baseline alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transpeptidase (GGT) activities were significantly elevated in the alcoholic group compared to the healthy control group (AST: 104.1 vs. 29.15 U/L; ALT: 50.49 vs. 22.96 U/L; GGT 161.5 vs. 51.88 U/L), indicating that these patients did have mild alcohol-induced liver injury. After 5 days of probiotic therapy, alcoholic patients had significantly increased numbers of both bifidobacteria (7.9 vs. 6.81 log CFU/g) and lactobacilli (4.2 vs. 3.2 log CFU/g) compared to the standard therapy arm. Despite similar values at study initiation, patients treated with probiotics had significantly lower AST and ALT activity at the end of treatment than those treated with standard therapy alone (AST: 54.67 vs. 76.43 U/L; ALT 36.69 vs. 51.26 U/L). In a subgroup of 26 subjects with well-characterized mild alcoholic hepatitis (defined as AST and ALT greater than 30 U/L with AST-to-ALT ratio greater than one), probiotic therapy was associated with a significant end of treatment reduction in ALT, AST, GGT, lactate dehydrogenase, and total bilirubin. In this subgroup, there was a significant end of treatment mean ALT reduction in the probiotic arm versus the standard therapy arm. In conclusion, patients with alcohol-induced liver injury have altered bowel flora compared to healthy controls. Short-term oral supplementation with B. bifidum and L. plantarum 8PA3 was associated with restoration of the bowel flora and greater improvement in alcohol-induced liver injury than standard therapy alone.


title: Audiovisual speech synthesis

abstract: Comprehensive overview of the various techniques for audiovisual speech synthesis.Innovative categorization of the techniques based on multiple aspects.Important future directives for the field of audiovisual speech synthesis.Bundles a lot of information that was scattered in the scientific literature. We live in a world where there are countless interactions with computer systems in every-day situations. In the most ideal case, this interaction feels as familiar and as natural as the communication we experience with other humans. To this end, an ideal means of communication between a user and a computer system consists of audiovisual speech signals. Audiovisual text-to-speech technology allows the computer system to utter any spoken message towards its users. Over the last decades, a wide range of techniques for performing audiovisual speech synthesis has been developed. This paper gives a comprehensive overview on these approaches using a categorization of the systems based on multiple important aspects that determine the properties of the synthesized speech signals. The paper makes a clear distinction between the techniques that are used to model the virtual speaker and the techniques that are used to generate the appropriate speech gestures. In addition, the paper discusses the evaluation of audiovisual speech synthesizers, it elaborates on the hardware requirements for performing visual speech synthesis and it describes some important future directions that should stimulate the use of audiovisual speech synthesis technology in real-life applications.


title: Toward a theory of organizational creativity

abstract: In this article we develop a theoretical framework for understanding creativity in complex social settings. We define organizational creativity as the creation of a valuable, useful new product, service, idea, procedure, or process by individuals working together in a complex social system. The starting point for our theoretical development is provided by the interactionist model of creative behavior developed by Woodman and Schoenfeldt (1989). This model and supporting literature on creative behavior and organizational innovation are used to develop an interactional framework for organizational creativity. The theoretical framework is summarized by three propositions that can effectively guide the development of testable hypotheses.


title: Real-parameter optimization with differential evolution

abstract: This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization. The test bed includes 25 scalable functions, many of which are both non-separable and highly multi-modal. Results include DE's performance on the 10 and 30-dimensional versions of each function


title: The successful management of a small logistics company

abstract: In this paper, a case study conducted on a small third‐party logistics (3PL) company in Hong Kong is presented. This company is interesting in that it has been designated as the “king” of Hong Kong's 3PL (in‐bound) logistics companies. The company has been successful in its overall business performance and in satisfying customers. This company's strategic alliances with both clients and customers have helped to improve the utilization of its resources, such as warehouse space and transportation fleets. Also, the company is in the process of expanding its operations across greater China, with the objective of becoming a full‐pledged 3PL company. The analysis of this case focuses on the critical success factors (strategies and technologies) that have allowed a small company started only in 1996 to become so successful in its operations. Also, a framework has been provided for the company to develop its logistics operations as a full‐pledged 3PL company.


title: AutoConnect: computational design of 3D-printable connectors

abstract: We present AutoConnect, an automatic method that creates customized, 3D-printable connectors attaching two physical objects together. Users simply position and orient virtual models of the two objects that they want to connect and indicate some auxiliary information such as weight and dimensions. Then, AutoConnect creates several alternative designs that users can choose from for 3D printing. The design of the connector is created by combining two holders, one for each object. We categorize the holders into two types. The first type holds standard objects such as pipes and planes. We utilize a database of parameterized mechanical holders and optimize the holder shape based on the grip strength and material consumption. The second type holds free-form objects. These are procedurally generated shell-gripper designs created based on geometric analysis of the object. We illustrate the use of our method by demonstrating many examples of connectors and practical use cases.


title: Draining our Glass: An Energy and Heat Characterization of Google Glass

abstract: The Google Glass is a mobile device designed to be worn as eyeglasses. This form factor enables new use cases, such as hands-free video chat and web search. However, its shape also hampers its potential: (1) battery size, and therefore lifetime, is limited by a need for the device to be lightweight, and (2) high-power processing leads to significant heat, which should be limited due to the compact form factor and proximity to the user's skin. We use an Explorer Edition of Glass (XE12) to study the power and thermal characteristics of optical head-mounted display devices. We share insights and implications to limit power draw to increase the safety and utility of head-mounted devices.


title: Applied Problem Solving in Children with ADHD: The Mediating Roles of Working Memory and Mathematical Calculation

abstract: The difficulties children with ADHD experience solving applied math problems are well documented; however, the independent and/or interactive contributions of cognitive processes underlying these difficulties are not fully understood and warrant scrutiny. The current study examines two primary cognitive processes integral to children’s ability to solve applied math problems: working memory (WM) and math calculation skills (i.e., the ability to utilize specific facts, skills, or processes related to basic math operations stored in long-term memory). Thirty-six boys with ADHD-combined presentation and 33 typically developing (TD) boys aged 8–12 years old were administered multiple counterbalanced tasks to assess upper (central executive [CE]) and lower level (phonological [PH STM] and visuospatial [VS STM] short-term memory) WM processes, and standardized measures of mathematical abilities. Bias-corrected, bootstrapped mediation analyses revealed that CE ability fully mediated between-group differences in applied problem solving whereas math calculation ability partially mediated the relation. Neither PH STM nor VS STM was a significant mediator. When modeled together via serial mediation analysis, CE in tandem with math calculation ability fully mediated the relation, explained 79% of the variance, and provided a more parsimonious explication of applied mathematical problem solving differences among children with ADHD. Results suggest that interventions designed to address applied math difficulties in children with ADHD will likely benefit from targeting basic knowledge of math facts and skills while simultaneously promoting the active interplay of these skills with CE processes.


title: Multi-model semantic interaction for text analytics

abstract: Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection.


title: Identifying Authorship by Byte-Level N-Grams: The Source Code Author Profile (SCAP) Method

abstract: Source code author identification deals with identifying the most likely author of a computer program, given a set of predefined author candidates. There are several scenarios where digital evidence of this kind plays a role in investigation and adjudication, such as code authorship disputes, intellectual property infringement, tracing the source of code left in the system after a cyber attack, and so forth. As in any identification task, the disputed program is compared to undisputed, known programming samples by the predefined author candidates. We present a new approach, called the SCAP (Source Code Author Profiles) approach, based on byte-level n-gram profiles representing the source code author’s style. The SCAP method extends a method originally applied to natural language text authorship attribution; we show that an n-gram approach also suits the characteristics of source code analysis. The methodological extension includes a simplified profile and a less complicated, but more effective, similarity measure. Experiments on data sets of different programming-language (Java or C++) and commented/commentless code demonstrate the effectiveness of these extensions. The SCAP approach is programming-language independent. Moreover, the SCAP approach deals surprisingly well with cases where only a limited amount of very short programs per programmer is available for training. Finally, it is also demonstrated that SCAP effectiveness persists even in the absence of comments in the source code, a condition usually met in cyber-crime cases. 1. The Forensic Significance of Source Code Nowadays, in a wide variety of legal cases it is important to identify the author of a usually limited piece of programming code. Such situations include cyber attacks in the form of viruses, Trojan horses, logic bombs, fraud, and credit card cloning, code authorship disputes, and intellectual property infringement. Identifying the authorship of malicious or stolen source code in a reliable way has become a primary goal for digital investigators (Spafford and Weeber 1993). Please see Appendix 1 for a legal analysis of the forensic significance of source code.


title: New Trichoscopy Findings in Trichotillomania: Flame Hairs, V-sign, Hook Hairs, Hair Powder, Tulip Hairs

abstract: Differential diagnosis of trichotillomania is often difficult in clinical practice. Trichoscopy (hair and scalp dermoscopy) effectively supports differential diagnosis of various hair and scalp diseases. The aim of this study was to assess the usefulness of trichoscopy in diagnosing trichotillomania. The study included 370 patients (44 with trichotillomania, 314 with alopecia areata and 12 with tinea capitis). Statistical analysis revealed that the main and most characteristic trichoscopic findings of trichotillomania are: irregularly broken hairs (44/44; 100% of patients), v-sign (24/44; 57%), flame hairs (11/44; 25%), hair powder (7/44; 16%) and coiled hairs (17/44; 39%). Flame hairs, v-sign, tulip hairs, and hair powder were newly identified in this study. In conclusion, we describe here specific trichoscopy features, which may be applied in quick, non-invasive, in-office differential diagnosis of trichotillomania.


title: Security and privacy in online social networks: A survey

abstract: Social networking becomes increasingly important due to the recent surge in online interaction. Social network analysis can be used to study the functioning of computer networks, information flow patterns in communities, and emergent behavior of physical and biological systems. In this paper, the mathematical formulation and computational models for security and privacy of social network data are discussed. Several possible ways for an attacker to attack are presented so that the mathematical formulation can take them into account. The metrics for measuring the amount of security and privacy in an online social network (OSN) are discussed so that we have an idea of how good a model is. Based on these current techniques and attack strategies, future directions of research are discussed.


title: MODIFIED BROADBAND SCHIFFMAN PHASE SHIFTER USING DENTATE MICROSTRIP AND PATTERNED GROUND PLANE

abstract: In this letter, a modifled broadband 90 - phase shifter is proposed. By using a dentate microstrip and a patterned ground plane, an extremely tight coupling can be obtained, and consequently a constant phase shift over a wide bandwidth can be achieved. To verify the proposed idea, a topology is implemented, the measured results of a phase difierence of 90 § 5 - in 79.5% bandwidth, better than 10dB return loss across the whole operating band, are also given. The measurement results agree well with the full-wave electromagnetic simulated responses.


title: SEED: Public Energy and Environment Dataset for Optimizing HVAC Operation in Subway Stations

abstract: For sustainability and energy saving, the problem to optimize the control of heating, ventilating, and air-conditioning (HVAC) systems has attracted great attentions, but analyzing the signatures of thermal environments and HVAC systems and the evaluation of the optimization policies has encountered inefficiency and inconvenient problems due to the lack of public dataset. In this paper, we present the Subway station Energy and Environment Dataset (SEED), which was collected from a line of Beijing subway stations, providing minute-resolution data regarding the environment dynamics (temperature, humidity, CO2, etc.) working states and energy consumptions of the HVAC systems (ventilators, refrigerators, pumps), and hour-resolution data of passenger flows. We describe the sensor deployments and the HVAC systems for data collection and for environment control, and also present initial investigation for the energy disaggregation of HVAC system, the signatures of the thermal load, cooling supply, and the passenger flow using the dataset.


title: Measuring the effects of business intelligence systems: The relationship between business process and organizational performance

abstract: Business intelligence (BI) systems provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. They leverage the large data infrastructure investments (e.g. ERP systems) made by firms, and have the potential to realise the substantial value locked up in a firm's data resources. While substantial business investment in BI systems is continuing to accelerate, there is a complete absence of a specific and rigorous method to measure the realised business value, if any. By exploiting the lessons learned from prior attempts to measure business value of IT-intensive systems, we develop a new measure that is based on an understanding of the characteristics of BI systems in a process-oriented framework. We then employ the measure in an examination of the relationship between the business process performance and organizational performance, finding significant differences in the strength of the relationship between industry sectors. This study reinforces the need to consider the specific context of use when designing performance measurement for IT-intensive systems, and highlights the need for further research examining contextual moderators to the realisation of such performance benefits.


title: Tag and Resource-Aware Collaborative Filtering Algorithms for Resource Recommendation

abstract: Recommender systems suggest resources to users based on collaborative filtering techniques, typically by exploiting correlations between individual user ratings of the resources they are interested in. Tags are a new form of metadata increasingly used in social bookmarking sites by users to annotate bookmarked resources. Our goal is to harness the implicit knowledge contained in these tags to improve the quality of recommendations to users. We use both tag and resource-interest knowledge in our user-based collaborative filtering algorithms to profile users and compute similarity between them. Sparsity is a challenge which occurs in a Social Recommendation System when the number of tags and resources to profile a user are inadequate to provide good quality recommendations. To address this problem, we designed a Tripartite Nearest Neighbor Algorithm (TRNNA) which combines three views of the data: the tags (TNNA), the resources (RNNA) and the collection of tags for a resource (Resource Vector of Tags or RVTA). TRNNA computes distance between users based on Cosine Similarity, which in turn is used to provide a high quality recommendation of resources. Our empirical evaluation, based on a user study in which research papers were recommended to participants and relevance of recommendation was evaluated, indicates that TRNNA and RNNA provide better recommendation than TNNA and RVTA.


title: Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work

abstract: Recently we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority-attack from taking place.


title: Algorithmic Authority: The Case of Bitcoin

abstract: In this paper, we propose a new concept for understanding the role of algorithms in daily life: algorithmic authority. Algorithmic authority is the legitimate power of algorithms to direct human action and to impact which information is considered true. We use this concept to examine the culture of users of Bit coin, a crypto-currency and payment platform. Through Bit coin, we explore what it means to trust in algorithms. Our study utilizes interview and survey data. We found that Bit coin users prefer algorithmic authority to the authority of conventional institutions, which they see as untrustworthy. However, we argue that Bit coin users do not have blind faith in algorithms, rather, they acknowledge the need for mediating algorithmic authority with human judgment. We examine the tension between members of the Bit coin community who would prefer to integrate Bit coin with existing institutions and those who would prefer to resist integration.


title: reCAPTCHA: Human-Based Character Recognition via Web Security Measures

abstract: CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.


title: Multi-patch convolution neural network for iris liveness detection

abstract: Attacking iris systems with fake iris patterns has become the largest security risk of iris recognition systems. Therefore iris liveness detection which discriminate genuine or fake iris images is of significant importance to iris recognition systems. However, the state-of-the-art algorithms mainly rely on hand-crafted texture features which can only identify fake iris images with single pattern. This paper proposes a Multi-patch Convolution Neural Network (MCNN) that is capable of handling different types of fake iris images. MCNN directly learns the mapping function between raw pixels of the input iris patch and the labels. The outputs of each patch are fed into a decision layer which determines the final decision. Our proposed algorithm automatically learns the features to detect hybrid pattern of fake iris images rather than handcraft. The decision layer helps to improve the robustness and accuracy for iris liveness detection. Experimental results demonstrate an extremely higher accuracy of iris liveness detection than other state-of-the-art algorithms. The proposed MCNN remarkably achieve the best results with nearly 100% accuracy on ND-Contact and CAISA-Iris-Fake datasets.


title: Visual exploration of machine learning results using data cube analysis

abstract: As complex machine learning systems become more widely adopted, it becomes increasingly challenging for users to understand models or interpret the results generated from the models. We present our ongoing work on developing interactive and visual approaches for exploring and understanding machine learning results using data cube analysis. We propose MLCube, a data cube inspired framework that enables users to define instance subsets using feature conditions and computes aggregate statistics and evaluation metrics over the subsets. We also design MLCube Explorer, an interactive visualization tool for comparing models' performances over the subsets. Users can interactively specify operations, such as drilling down to specific instance subsets, to perform more in-depth exploration. Through a usage scenario, we demonstrate how MLCube Explorer works with a public advertisement click log data set, to help a user build new advertisement click prediction models that advance over an existing model.


title: Distortion analysis of bootstrap switch using volterra series

abstract: Linear behaviour of bootstrap switches is of critical importance in low-voltage analogue circuits and understanding the major factors affecting the linearity helps design a better switch. This study presents a theoretical approach for evaluating the distortion of bootstrap switches in the frequency domain based on the Volterra series. Five major factors affecting the linearity of the bootstrap switch are examined. In order to obtain a general design guideline the analysis is done in two parts. First, the distortion because of the non-linear I-V characteristic of the main transistor of the switch is considered. In the second part, the distortion because of sampling errors, such as clock feed-through and charge injection, are added to the analysis. The theoretical results are verified by circuit simulations in a 0.18 mum CMOS process, using HSpice.


title: On Erd\H{o}s-Gallai and Havel-Hakimi algorithms

abstract: Havel in 1955, Erd\H{o}s and Gallai in 1960, Hakimi in 1962, Ruskey, Cohen, Eades and Scott in 1994, Barnes and Savage in 1997, Kohnert in 2004, Tripathi, Venugopalan and West in 2010 proposed a method to decide, whether a sequence of nonnegative integers can be the degree sequence of a simple graph. The running time of their algorithms is $\Omega(n^2)$ in worst case. In this paper we propose a new algorithm called EGL (Erd\H{o}s-Gallai Linear algorithm), whose worst running time is $\Theta(n).$ As an application of this quick algorithm we computed the number of the different degree sequences of simple graphs for $24, ...,29$ vertices.


title: A unified framework for frequency control and congestion management

abstract: The existing frequency control framework in power systems is challenged by lower inertia and more volatile power injections. We propose a new framework for frequency control and congestion management. We formulate an optimization problem that rebalances power, restores the nominal frequency, restores inter-area flows and maintains line flows below their limits in a way that minimizes the control cost. The cost can be squared deviations from the reference generations, minimizing the disruption from the last optimal dispatch. Our control thus maintains system security without interfering with the market operation. By deriving a primal-dual algorithm to solve this optimization, we design a completely decentralized primary frequency control without the need for explicit communication among the participating agents, and a distributed unified control which integrates primary and secondary frequency control and congestion management. Simulations show that the unified control not only achieves all the desired control goals in system equilibrium, but also improves the transient compared to traditional control schemes.


title: AC power meter design for home electrical appliances

abstract: Monitoring power consumption of portable appliances is a step towards energy saving plans for any electrical equipment. This paper proposes a customizable power meter design employing voltage and current transformers, and Arduino. This meter is to monitor RMS voltage and current, real and apparent power, and power factor in real time. The voltage, the current, and the real power are calibrated with respect to a reference power meter. The resolutions are derived from analytical approach and the accuracies are evaluated from experiments in a variable of appliances and current transformers.


title: Analysis and Classification of Multi-Criteria Recommender Systems

abstract: Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.


title: Software division and square root using Goldschmidt’s algorithms

abstract: Goldschmidt's Algorithms for division and square root are often characterized as being useful for hardware implementation, and lacking self-correction. A reexam- ination of these algorithms show that there are good software counterparts that retain the speed advantage of Goldschmidt's Algorithm over the Newton-Raphson iteration. A final step is needed, however, to get the last bit rounded correctly.


POLY: Mining Relational Paraphrases from Multilingual Sentences


title: Modeling Agent’s Preferences Based On Prospect Theory

abstract: It is well known that human preferences in decisions underrisk do not always complies with expected utility theory(EUT). In fact, there are several effects that are inconsistent with basic tenets of EUT. Alternative theories have been proposed and perhaps the most well studied is Prospect Theory(PT). Recent work showed experimental results that support the idea that financial professionals may behave according toPT and violate EUT. Meanwhile, some argue that economy needs agent-based modeling, because it may be a better way to help guide financial policies than mathematical models.If financial professional behave according to PT in markets,then agent-based modeling needs PT based agents. Our ideais creating trading agents based on PT to simulate a market.However, the creation of an artificial agent based on PT as originally proposed is very hard and limited to two outcome prospects. We propose an agent model based on an extension of PT called Smooth Prospect Theory (SPT). We used this model to create agents to populate an artificial market withSPT and EUT agents. It was used to predict real market behavior for short periods. SPT agents provided more accurate predictions in crisis periods than EUT agents.


title: A Multiband Antenna for Satellite Communications on the Move

abstract: The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included


title: Student performance prediction using classification data mining techniques

abstract: Students opting engineering as their disciple is increasing rapidly. But due to various factors and inappropriate primary education in India dropout rates are high. Students are unable to excel in core engineering subjects which are complex and mathematical, hence mostly get drop / keep term (kt) in that subject. With the help of data mining techniques we can predict the performance of students in terms of grades and dropout for a subject. This paper compares various techniques such as na\"ive Bayes, LibSVM, J48, random forest, and JRip and try to choose one of them as per our needs and their accuracy. Based on the rules obtained from this technique(s), we derive the key factors influencing student performance.


title: How Progesterone Impairs Memory for Biologically Salient Stimuli in Healthy Young Women

abstract: Progesterone, or rather its neuroactive metabolite allopregnanolone, modulates amygdala activity and thereby influences anxiety. Cognition and, in particular, memory are also altered by allopregnanolone. In the present study, we investigated whether allopregnanolone modulates memory for biologically salient stimuli by influencing amygdala activity, which in turn may affect neural processes in other brain regions. A single progesterone dose was administered orally to healthy young women in a double-blind, placebo-controlled, crossover design, and participants were asked to memorize and recognize faces while undergoing functional magnetic resonance imaging. Progesterone decreased recognition accuracy without affecting reaction times. The imaging results show that the amygdala, hippocampus, and fusiform gyrus supported memory formation. Importantly, progesterone decreased responses to faces in the amygdala and fusiform gyrus during memory encoding, whereas it increased hippocampal responses. The progesterone-induced decrease in neural activity in the amygdala and fusiform gyrus predicted the decrease in memory performance across subjects. However, progesterone did not modulate the differential activation between subsequently remembered and subsequently forgotten faces in these areas. A similar pattern of results was observed in the fusiform gyrus and prefrontal cortex during memory retrieval. These results suggest that allopregnanolone impairs memory by reducing the recruitment of those brain regions that support memory formation and retrieval. Given the important role of the amygdala in the modulation of memory, these results suggest that allopregnanolone alters memory by influencing amygdala activity, which in turn may affect memory processes in other brain regions.


title: A study of crowdsourced segment-level surgical skill assessment using pairwise rankings

abstract: Purpose ::: Currently available methods for surgical skills assessment are either subjective or only provide global evaluations for the overall task. Such global evaluations do not inform trainees about where in the task they need to perform better. In this study, we investigated the reliability and validity of a framework to generate objective skill assessments for segments within a task, and compared assessments from our framework using crowdsourced segment ratings from surgically untrained individuals and expert surgeons against manually assigned global rating scores.


title: Exploring the public sector adoption of HRIS

abstract: Purpose – Human resources information systems (HRIS) are becoming increasingly important in helping modern organizations manage their human assets effectively. Yet, HRIS adoption remains an under‐researched phenomenon. The purpose of this paper to isolate the factors that influence the organizational adoption of HRIS in public sector organizations.Design/methodology/approach – Adopting the technology‐organization‐environment model as an analytical framework, the paper draws on qualitative evidence from 16 interviews across 11 Australian public sector organizations.Findings – The authors find that champions in public sector organizations should demonstrate HRIS benefits before their adoption can succeed. With standardization trends adopted by HRIS vendors, complete organizational fit between adopted HRIS and business processes may be elusive for adopters suggesting that post‐adoption vendor support must be negotiated if costly customizations are to be minimized. In addition to various organizational factor...


title: Feature Weighting in k-Means Clustering

abstract: Data sets with multiple, heterogeneous feature spaces occur frequently. We present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm. Our main ideas are (i) to represent each data object as a tuple of multiple feature vectors, (ii) to assign a suitable (and possibly different) distortion measure to each feature space, (iii) to combine distortions on different feature spaces, in a convex fashion, by assigning (possibly) different relative weights to each, (iv) for a fixed weighting, to cluster using the proposed convex k-means algorithm, and (v) to determine the optimal feature weighting to be the one that yields the clustering that simultaneously minimizes the average within-cluster dispersion and maximizes the average between-cluster dispersion along all the feature spaces. Using precision/recall evaluations and known ground truth classifications, we empirically demonstrate the effectiveness of feature weighting in clustering on several different application domains.


Low-Dimensional Representation of Spectral Envelope Without Deterioration for Full-Band Speech Analysis/Synthesis System


Smart Supervisory Control for Optimized Power Management System of hybrid Micro-Grid


title: Cooperative Denoising for Distantly Supervised Relation Extraction

abstract: Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novel COopeRative Denoising framework, which ::: consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi -directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.


title: A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

abstract: Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.


Narrative Schema Stability in News Text


title: Determining the minimum-area encasing rectangle for an arbitrary closed curve

abstract: This paper describes a method for finding the rectangle of minimum area in which a given arbitrary plane curve can be contained. The method is of interest in certain packing and optimum layout problems. It consists of first determining the minimal-perimeter convex polygon that encloses the given curve and then selecting the rectangle of minimum area capable of containing this polygon. Three theorems are introduced to show that one side of the minimum-area rectangle must be collinear with an edge of the enclosed polygon and that the minimum-area encasing rectangle for the convex polygon is also the minimum-area rectangle for the curve.


The Omnimate: A Guidewire-Free Agv For Highly Flexible Automation


Regularized Least-Squares Classification


BIOLOGICALLY INSPIRED QUADRUPED ROBOT BIOSBOT : MODELING , SIMULATION AND EXPERIMENT


title: SEC-TOE Framework: Exploring Security Determinants in Big Data Solutions Adoption

abstract: As in any new technology adoption in organizations, big data solutions (BDS) also presents some security threat and challenges, especially due to the characteristics of big data itself the volume, velocity and variety of data. Even though many security considerations associated to the adoption of BDS have been publicized, it remains unclear whether these publicized facts have any actual impact on the adoption of the solutions. Hence, it is the intent of this research-in-progress to examine the security determinants by focusing on the influence that various technological factors in security, organizational security view and security related environmental factors have on BDS adoption. One technology adoption framework, the TOE (technological-organizational-environmental) framework is adopted as the main conceptual research framework. This research will be conducted using a Sequential Explanatory Mixed Method approach. Quantitative method will be used for the first part of the research, specifically using an online questionnaire survey. The result of this first quantitative process will then be further explored and complemented with a case study. Results generated from both quantitative and qualitative phases will then be triangulated and a cross-study synthesis will be conducted to form the final result and discussion.


title: The Effect of Bikram Yoga on Arterial Stiffness in Young and Older Adults

abstract: Abstract Background and Objective: Bikram yoga is the most popular form of hot yoga, despite the limited information available on its cardiovascular benefits. This study sought to determine the effect of Bikram yoga on arterial stiffness and insulin resistance in young and older adults. Methods: Twenty-four young (mean age±standard deviation, 30±1 years) and 18 middle-aged and older (mean age, 53±2 years) adults completed an 8-week Bikram yoga intervention. Bikram yoga classes were performed for 90 minutes per session, three times per week, in a room heated to 40.5°C with 40%–-60% relative humidity. Results: Body mass, body fat percentage, blood pressure, and fasting blood glucose and triglyceride concentrations did not significantly change as a result of the intervention in either the young or the older group. Trunk flexibility, as measured by the sit-and-reach test, increased in both groups (p<0.01). Total (p<0.05) and low-density lipoprotein cholesterol (p<0.05) levels, plasma insulin concentrations (p...


title: Correlative data based sparse denoising auto-encoder for feature learning

abstract: Feature learning based on deep learning is now widely used in various fields. When dealing with data missing or data noise, auto-encoder-based methods are proposed to learn more robust features. However, the existing methods based on deep learning which aim to deal with missing data or noise depend on the kind of signals itself. In order to enhance the fault-tolerant mechanism and improve the anti-noise ability and learn more robust features, this paper proposes a correlative data based Sparse Denoising Auto-Encoder which uses the cooperative mechanism among signals. We correlate the electromyography and the electromyogram signals to evaluate the validity of this model and compare the results with the Sparse Denoising Auto-Encoder. The experimental results show that our model has better performance in feature learning than the traditional models, which means that our model has better fault-tolerant mechanism and stronger anti-noise ability.


title: Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

abstract: Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.


title: Parallel stiffness in a bounding quadruped with flexible spine

abstract: Legged locomotion involves periodic negative and positive work, which usually results in high power consumption. Improvement of the energy efficiency is possible by using energy storage elements to reversibly store the negative work performed during a walking or running cycle. While series elastics with high impedance (high gear ratio) actuators are widely used, we investigate the application of parallel stiffness with highly backdriveable actuators. We specifically show that the use of parallel springs in a bounding quadruped with a flexible spine can lower power consumption by over 50%.


title: Coexisting Success Probability and Throughput of Multi-RAT Wireless Networks With Unlicensed Band Access

abstract: In this letter, the coexisting success probability and throughput of a wireless network consisting of multiple subnetworks of different radio access technologies (RATs) are investigated. The coexisting success probability that is defined as the average of all success probabilities of all subnetworks is found in closed-form and it will be shown to have the concavity over the number of channels in the unlicensed band. The optimal deployment densities of all different RATs access points (APs) that maximize the coexisting success probability are shown to exist and can be found under the derived constraint on network parameters. The coexisting throughput is defined as the per-channel sum of all spectrum efficiencies of all subnetworks and numerical results show that it is significantly higher than the throughput of the unlicensed band only accessed by WiFi APs.


title: Psychiatric disorder comorbidity and association with eating disorders in bariatric surgery patients: A cross-sectional study using structured interview-based diagnosis.

abstract: Objective: This study examined the prevalence of DSM-IV Axis I psychiatric disorders in severely obese bariatric surgery candidates and explored whether eating disorders were associated with psychiatric comorbidity. Method: The Structured Clinical Interview for DSM-IV Axis I Disorders was administered to a study group of 174 consecutively evaluated bariatric surgery candidates. All evaluations were completed between September 2002 and November 2004. Results: Overall, 36.8% of the participants met criteria for at least one lifetime psychiatric disorder, with 24.1 % meeting criteria for a current disorder. The most commonly observed lifetime psychiatric diagnoses were affective disorders (22.4%), anxiety disorders (15.5%), and eating disorders (13.8%). Participants with eating disorders were significantly more likely than those without eating disorders to meet criteria for psychiatric disorders overall (66.7% vs. 26.7%) and specifically for anxiety disorders (45.8% vs. 10.7%). Conclusions: Psychiatric disorders are not uncommon among severely obese patients who present for bariatric surgery. The observed prevalence rates based on structured diagnostic interviews are lower than previously reported based on questionnaire, clinical, and chart review methods but are similar to those reported for nationally representative samples. Among bariatric surgery candidates, the presence of eating disorders is associated with higher rates of other psychiatric disorders. The findings highlight the importance of systematic diagnostic assessment using a structured diagnostic interview for determining the full spectrum of Axis I disorders.


title: Design and Implementation of a Substation Automation Unit

abstract: The increment of distributed energy resources such as small photovoltaic plants or microcogeneration systems, together with the increase of electric vehicles connected to the grid, is bringing new challenges to distribution system operators (DSOs). In order to support DSOs to face such new challenges, this paper proposes a new solution based on a framework for the implementation of substation automation units. This framework supports the development of smart distribution systems solutions. The monitoring and control functionalities of the proposed solution are decentralized and, therefore, closer to the associated field devices compared to traditional solutions. Thus, the proposed approach allows us to increase performance and to reduce the system complexity by avoiding the concentration of data in one single point. An automation unit placed in the electrical substation is proposed and characterized for automation in distribution networks. Test results in laboratory and real-world environments will show some results of the proposed solution.


title: A tourism destination recommender system using users’ sentiment and temporal dynamics

abstract: With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.


title: Factors Affecting Adoption of Electronic Banking System in Ethiopian Banking Industry

abstract: The aim of this paper is to identify factors that affect adoption of E-banking in the Ethiopian banking industry. The study was conducted based on the data gathered from four banks in Ethiopia; three private banks (Dashen bank, Zemen bank and Wegagen bank) and one state owned bank (commercial bank of Ethiopia). A mixed research approach was used to answer the research questions that emerge through the review of existing literature and the experiences of the researcher in respect of the E-banking system in Ethiopia. The study statistically analyzes data obtained from the survey questionnaire. A research framework developed based on technology-organizationenvironment model (TOE) developed by Tornatzky and Fleischer. The result of the study indicated that, the major barriers Ethiopian banking industry faces in the adoption of Electronic banking are: security risk, lack of trust, lack of legal and regulatory frame work, Lack of ICT infrastructure and absence of competition between local and foreign banks. The study suggests a series of measures which could be taken by the banking industry and by government to address various challenges identified. These measures include: Establishing a clear set of legal framework on the use of technology in banking industry, supporting banking industry by investing on ICT infrastructure and banks needs to be focused on technological innovation competition rather than traditional bases of retail bank competition.


title: Chemistry and Biological Activities of Flavonoids: An Overview

abstract: There has been increasing interest in the research on flavonoids from plant sources because of their versatile health benefits reported in various epidemiological studies. Since flavonoids are directly associated with human dietary ingredients and health, there is need to evaluate structure and function relationship. The bioavailability, metabolism, and biological activity of flavonoids depend upon the configuration, total number of hydroxyl groups, and substitution of functional groups about their nuclear structure. Fruits and vegetables are the main dietary sources of flavonoids for humans, along with tea and wine. Most recent researches have focused on the health aspects of flavonoids for humans. Many flavonoids are shown to have antioxidative activity, free radical scavenging capacity, coronary heart disease prevention, hepatoprotective, anti-inflammatory, and anticancer activities, while some flavonoids exhibit potential antiviral activities. In plant systems, flavonoids help in combating oxidative stress and act as growth regulators. For pharmaceutical purposes cost-effective bulk production of different types of flavonoids has been made possible with the help of microbial biotechnology. This review highlights the structural features of flavonoids, their beneficial roles in human health, and significance in plants as well as their microbial production.


title: Enriching Archaeological Parks with Contextual Sounds and Mobile Technology

abstract: The importance of cultural heritage in forging a sense of identity is becoming increasingly evident. Information and communication technologies have a great potential to promote a greater awareness and appreciation of cultural heritage. This article presents some findings on how mobile technology can be used to foster a better understanding of an archaeological site by reconstructing the ancient environment and life. Children aged 11--13 years old are the target of our research. To motivate and engage them, a pervasive educational game has been developed and implemented in Explore!, a system aimed at supporting children exploring sites of cultural interest. Special attention has been devoted to the design of a soundscape that may improve players’ navigation in degraded physical environments and enrich their overall experience. A field study indicated that children judged their experience both useful and entertaining: not only did they enjoy playing the game, but they also learned historical notions and facts related to ancient Roman life. Contextual sounds were found to have a facilitating effect on space navigation, reducing the need for map reading and improving spatial orientation. This work provides insights into the design of educational games for use with cultural heritage and a model to enrich historical sites through the creation of soundscapes which can help visitors to navigate a site and feel its historical atmosphere.


title: Personalized Group Recommender Systems for Location- and Event-Based Social Networks

abstract: Location-Based Social Networks (LBSNs) such as Foursquare, Google+ Local, and so on, and Event-Based Social Networks (EBSNs) such as Meetup, Plancast, and so on, have become popular platforms for users to plan, organize, and attend social events with friends and acquaintances. These LBSNs and EBSNs provide rich content such as online and offline user interactions, location/event descriptions that can be leveraged for personalized group recommendations. In this article, we propose novel Collaborative Filtering-based Bayesian models to capture the location or event semantics and group dynamics such as user interactions, user group membership, user influence, and the like for personalized group recommendations. Empirical experiments on two large real-world datasets (Gowalla LBSN dataset and Meetup EBSN dataset) show that our models outperform the state-of-the-art group recommender systems. We discuss the group characteristics of our datasets and show that modeling of group dynamics learns better group preferences than aggregating individual user preferences. Moreover, our model provides human interpretable results that can be used to understand group participation behavior and location/event popularity.


title: A cost optimized battery management system with active cell balancing for lithium ion battery stacks

abstract: A method for active cell balancing of lithium ion battery stacks is presented. Balancing the charge of cells in a multi-cell lithium ion battery stack is often employed to guard against damage and improve the lifetime of the battery. Battery stacks which are in production today largely use a passive cell balancing method, which dissipates charge through a resistor, as heat. The method described uses a flyback converter to pass charge from cell to cell with low losses and only a small cost increase from a typical passive system.


title: Combining Musical and Cultural Features for Intelligent Style Detection

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title: An ultra wideband 1 to 4 wilkinson power divider with high Q band-notched using symmetric defected ground structure

abstract: Reducing the interference between ultra wideband (UWB) and other existing signals is an important issue in UWB systems and devices design. In this study, we propose a 2-stage 1 to 4 power divider (PD) which enables the suppression of 802.11a standard's frequency (5.2GHz) by means of a symmetric spiral defected ground structure (DGS) in the first stage. To avoid extra loose of UWB signal information, the notch shouldn't be very wide. So, the quality factor (Q) of involved resonator is an important criterion. The Q can be controlled by line width. The analysis proves that a 50–100–50 ohm transformation in PD structure provides the required line widths for a high Q factor. This configuration also eliminates the matching loop needed in the first PD stage and consequently results a smaller and simpler design. A three-section 1 to 2 100–50Ω PD forms the second cascading stage. The constructed PD with a small size of 6.2×6.5cm2 operates well in the designated frequency band with good matching and isolation. The measured Q factor is 14.857 which provides more than 30 dB suppression.


title: Automatic Hookworm Detection in Wireless Capsule Endoscopy Images

abstract: Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique to examine inflammatory bowel diseases and disorders. As one of the most common human helminths, hookworm is a kind of small tubular structure with grayish white or pinkish semi-transparent body, which is with a number of 600 million people infection around the world. Automatic hookworm detection is a challenging task due to poor quality of images, presence of extraneous matters, complex structure of gastrointestinal, and diverse appearances in terms of color and texture. This is the first few works to comprehensively explore the automatic hookworm detection for WCE images. To capture the properties of hookworms, the multi scale dual matched filter is first applied to detect the location of tubular structure. Piecewise parallel region detection method is then proposed to identify the potential regions having hookworm bodies. To discriminate the unique visual features for different components of gastrointestinal, the histogram of average intensity is proposed to represent their properties. In order to deal with the problem of imbalance data, Rusboost is deployed to classify WCE images. Experiments on a diverse and large scale dataset with 440 K WCE images demonstrate that the proposed approach achieves a promising performance and outperforms the state-of-the-art methods. Moreover, the high sensitivity in detecting hookworms indicates the potential of our approach for future clinical application.


title: A Discriminative Latent Variable Model for Online Clustering

abstract: This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, L3M outperforms several existing online as well as batch supervised clustering techniques.


title: Time series contextual anomaly detection for detecting market manipulation in stock market

abstract: Anomaly detection in time series is one of the fundamental issues in data mining that addresses various problems in different domains such as intrusion detection in computer networks, irregularity detection in healthcare sensory data and fraud detection in insurance or securities. Although, there has been extensive work on anomaly detection, majority of the techniques look for individual objects that are different from normal objects but do not take the temporal aspect of data into consideration. We are particularly interested in contextual outlier detection methods for time series that are applicable to fraud detection in securities. This has significant impacts on national and international securities markets. In this paper, we propose a prediction-based Contextual Anomaly Detection (CAD) method for complex time series that are not described through deterministic models. The proposed method improves the recall from 7% to 33% compared to kNN and Random Walk without compromising the precision.


title: What Can Help Pedestrian Detection?

abstract: Aggregating extra features has been considered as an effective approach to boost traditional pedestrian detection methods. However, there is still a lack of studies on whether and how CNN-based pedestrian detectors can benefit from these extra features. The first contribution of this paper is exploring this issue by aggregating extra features into CNN-based pedestrian detection framework. Through extensive experiments, we evaluate the effects of different kinds of extra features quantitatively. Moreover, we propose a novel network architecture, namely HyperLearner, to jointly learn pedestrian detection as well as the given extra feature. By multi-task training, HyperLearner is able to utilize the information of given features and improve detection performance without extra inputs in inference. The experimental results on multiple pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.


title: A simple electroless plating solution for 3D printed microwave components

abstract: Using a modified version of the Tollens' Test, acrylate-based polymer prints made using a consumer-grade Digital Light Projection Stereolithographic (DLP-SLA) 3D printer are successfully silver plated, without the need for complex surface preparation techniques. A single-piece prototype waveguide design is used for testing the plating process, and a discussion is provided on minimizing printing process variables such as polymerization shrinkage and undesirable geometric tolerance variance. Measurement results of plated WR-10 1-inch waveguide sections show reflection coefficients of less than −21dB and an insertion loss of less than 0.53dB, which are comparable to similar studies using specialized plating and split-block designs. Furthermore, this approach shows great potential in providing an affordable passive microwave component rapid prototyping solution for research environments.


title: Approximately detecting duplicates for streaming data using stable bloom filters

abstract: Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environments given a limited space. Based on a well-known bitmap sketch, we introduce a data structure, Stable Bloom Filter, and a novel and simple algorithm. The basic idea is as follows: since there is no way to store the whole history of the stream, SBF continuously evicts the stale information so that SBF has room for those more recent elements. After finding some properties of SBF analytically, we show that a tight upper bound of false positive rates is guaranteed. In our empirical study, we compare SBF to alternative methods. The results show that our method is superior in terms of both accuracy and time effciency when a fixed small space and an acceptable false positive rate are given.


title: Interacting with Recommenders—Overview and Research Directions

abstract: Automated recommendations have become a ubiquitous part of today’s online user experience. These systems point us to additional items to purchase in online shops, they make suggestions to us on movies to watch, or recommend us people to connect with on social websites. In many of today’s applications, however, the only way for users to interact with the system is to inspect the recommended items. Often, no mechanisms are implemented for users to give the system feedback on the recommendations or to explicitly specify preferences, which can limit the potential overall value of the system for its users. Academic research in recommender systems is largely focused on algorithmic approaches for item selection and ranking. Nonetheless, over the years a variety of proposals were made on how to design more interactive recommenders. This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems. We cover existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process. Throughout the work, we furthermore discuss examples of real-world systems and outline possible directions for future works.


title: Zygomatic implants--protocol for immediate occlusal loading: a preliminary report.

abstract: PURPOSE ::: To investigate the modified protocol for immediate occlusal loading of the zygomatic implants and to report the preliminary results of this modified protocol. ::: ::: ::: MATERIALS AND METHODS ::: Four male patients and 1 female patient with edentulous maxillae were consecutively treated with the zygomatic implants under general anesthesia. All 5 patients were examined by computed tomography and investigated by the SimPlant software (Materialise NV, Leuven, Belgium). Based on the virtual surgical plans, mucosa-supported surgical guides were manufactured by rapid prototyping technique before implant operation. Instead of making a Le Fort I Osteotomy incision or a crestal incision, buccal vestibular incision was used to expose the surgical site for the zygomatic implant osteotomy and placement. Three patients had their remaining upper teeth removed on the same day as implant placement. One patient had undergone simultaneous placement of upper and lower implants followed by immediate loading. The immediate loading protocol was a 2-stage method using a customized provisional fixed prosthesis. ::: ::: ::: RESULTS ::: Ten zygomatic implants and 20 normal implants were installed in these 5 patients. These 5 patients were reviewed regularly for 6 to 10 months after immediate loading. The zygomatic implants were considered to be successful when they were asymptomatic with no clinical mobility and no sign of infection. All the zygomatic implants and normal implants were investigated individually after removing the provisional prosthesis and were found to be clinically stable and asymptomatic. ::: ::: ::: CONCLUSION ::: According to our observation, immediate occlusal loading of the zygomatic implants has a very good potential for success, as much as immediate occlusal loading of normal dental implants. The surgical placement of the zygomatic implant is simplified and facilitated by making use of the computer-assisted planning and the rapid-prototyping surgical guides.


title: Reliability Analysis of DSRC Wireless Communication for Vehicle Safety Applications

abstract: The 802.11-p based dedicated short range communication (DSRC) is being seriously considered as a promising wireless technology for enhancing transportation safety and highway efficiency. However, to-date, there is very little research done in characterizing the reliability of DSRC communication based on real-world experimental data, and its effect on the reliability of vehicle safety applications. Our experimental set-up includes a fleet of three vehicles equipped with DSRC communication system, GPS receiver and a number of vehicle safety applications based on vehicle-to-vehicle communication. This paper analyzes the link-level behavior of DSRC vehicle-to-vehicle communication in a wide variety of traffic environments based on real-world experimental data. In addition, we also characterize the application level reliability of DSRC for vehicle safety communication (VSC) system. Based on our experiments, we show that the reliability of DSRC vehicle-to-vehicle communication is adequate since packet drops do not occur in bursts most of the time. We also show that the application level reliability of VSC applications based on DSRC communication is quite satisfactory. Finally, we develop an analytical model to relate application level reliability with communication reliability and VSC system parameter, laying out a clear way to improve reliability of VSC applications under harsh traffic environments. To the best of our knowledge, this paper is the first to characterize the application-level reliability of DSRC communication for VSC applications based on real-world experimental data. Our findings develop a deep insight into significant characteristics of DSRC communication for highly mobile vehicle-to-vehicle wireless network, which will contribute to better design and evaluation of communication protocols for VSC applications in the future


title: Cerebral Blood Flow Correlates of Apathy in Alzheimer Disease

abstract: BACKGROUND ::: Apathy is a pervasive noncognitive neuropsychiatric disturbance in Alzheimer disease, which causes significant caregiver distress. The neuroanatomical substrate of apathy is not well understood. ::: ::: ::: OBJECTIVE ::: To study the relationship between regional cerebral blood flow and the presence and severity of the personality disturbance, apathy, in individuals with Alzheimer disease. ::: ::: ::: DESIGN ::: Analysis of the relationship between regional cerebral blood flow as measured by single photon emission computed tomography and severity of apathy as measured by the Neuropsychiatric Inventory using an analysis of variance design. We examined regional cerebral perfusion alterations as measured by xenon 133Xecalibrated technetium Tc 99m hexamethyl-propyleneamine-oxime single photon emission computed tomography in relation to the presence and severity of apathy. ::: ::: ::: SETTING ::: The neurology clinics of the University of California, Los Angeles, UCLA School of Medicine, and Harbor-UCLA Medical Center. ::: ::: ::: PARTICIPANTS ::: Thirty-one community-dwelling patients fulfilling National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association diagnostic criteria for probable Alzheimer disease who had a single photon computed tomographic scan performed within 3 months of administration of the Neuropsychiatric Inventory. ::: ::: ::: RESULTS ::: The presence of apathy was associated with more severe prefrontal and anterior temporal dysfunction. These regional cerebral perfusion relationships with apathy were independent of cognitive decline except in the dorsolateral prefrontal cortex. ::: ::: ::: CONCLUSIONS ::: These results demonstrate the association of apathetic syndromes with prefrontal and anterior temporal regional brain dysfunction and are consistent with similar findings previously reported in other disorders.


title: Functional neuroimaging of belief, disbelief, and uncertainty.

abstract: Objective ::: The difference between believing and disbelieving a proposition is one of the most potent regulators of human behavior and emotion. When one accepts a statement as true, it becomes the basis for further thought and action; rejected as false, it remains a string of words. The purpose of this study was to differentiate belief, disbelief, and uncertainty at the level of the brain. ::: ::: Methods ::: We used functional magnetic resonance imaging (fMRI) to study the brains of 14 adults while they judged written statements to be “true” (belief), “false” (disbelief), or “undecidable” (uncertainty). To characterize belief, disbelief, and uncertainty in a content-independent manner, we included statements from a wide range of categories: autobiographical, mathematical, geographical, religious, ethical, semantic, and factual. ::: ::: Results ::: The states of belief, disbelief, and uncertainty differentially activated distinct regions of the prefrontal and parietal cortices, as well as the basal ganglia. ::: ::: Interpretation ::: Belief and disbelief differ from uncertainty in that both provide information that can subsequently inform behavior and emotion. The mechanism underlying this difference appears to involve the anterior cingulate cortex and the caudate. Although many areas of higher cognition are likely involved in assessing the truth-value of linguistic propositions, the final acceptance of a statement as “true” or its rejection as “false” appears to rely on more primitive, hedonic processing in the medial prefrontal cortex and the anterior insula. Truth may be beauty, and beauty truth, in more than a metaphorical sense, and false propositions may actually disgust us. Ann Neurol 2007


title: Adaptive methods for job recommendation based on user clustering

abstract: Job recommender systems are designed to suggest a ranked list of jobs that could be associated with employee's interest. Most of existing systems use only one approach to make recommendation for all employees, while a specific method normally is good enough for a group of employees. Therefore, this study proposes an adaptive solution to make job recommendation for different groups of user. The proposed methods are based on employee clustering. Firstly, we group employees into different clusters. Then, we select a suitable method for each user cluster based on empirical evaluation. The proposed methods include CB-Plus, CF-jFilter and HyR-jFilter have applied for different three clusters. Empirical results show that our proposed methods is outperformed than traditional methods.


title: A Large-scale Dataset and Benchmark for Similar Trademark Retrieval

abstract: Trademark retrieval (TR) has become an important yet challenging problem due to an ever increasing trend in trademark applications and infringement incidents. There have been many promising attempts for the TR problem, which, however, fell impracticable since they were evaluated with limited and mostly trivial datasets. In this paper, we provide a large-scale dataset with benchmark queries with which different TR approaches can be evaluated systematically. Moreover, we provide a baseline on this benchmark using the widely-used methods applied to TR in the literature. Furthermore, we identify and correct two important issues in TR approaches that were not addressed before: reversal of contrast, and presence of irrelevant text in trademarks severely affect the TR methods. Lastly, we applied deep learning, namely, several popular Convolutional Neural Network models, to the TR problem. To the best of the authors, this is the first attempt to do so.


title: Evaluation of objective measures for speech enhancement

abstract: In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two SNRs by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the correlations of five common objective measures with these three subjective measures. Improvements to the PESQ measure are reported along with new composite objective measures.


title: Multi agent simulation of unorganized traffic

abstract: Traffic simulation is one of the most complex simulation projects that can be undertaken. The main issues are: modeling of autonomous behavior of drivers, modeling of their interaction, and ability to simulate the traffic and procure reliable realistic results. Organized traffic with drivers heeding to well defined traffic rules is less dynamic and erratic, than modeling unorganized traffic, wherein the drivers either do not heed to well defined traffic rules, or there are no traffic rules in place. This paper shows the viability of applying multi-agent simulation for unorganized traffic. In particular, we model the behavior of drivers, as being cautious, normal, and aggressive, and show results about average speed of vehicles in traffic, number of overtakes, and number of accidents occurring with different proportions of aggressive and cautious drivers. A multi-agent simulator with graphics interface has been implemented to visualize and evaluate the traffic flow.


title: 16-element Vivaldi antenna array fed by a single ridge substrate integrated waveguide with over 75% bandwidth

abstract: A 16-element Vivaldi antenna array with a single ridge SIW feed network has been developed using a two-layer substrate. The single ridge substrate integrated waveguide (RSIW) structure is used to sustain the inherent wide bandwidth performance of the Vivaldi array. The array itself is fed by a novel compact and wide band RSIW eight way divider that is excited by a wideband GCPW to RSIW transition. Vivaldi elements are arranged in an elegant way to avoid wide spacing between the array elements to avoid grating lobes, which is typical for waveguide fed antenna arrays with large apertures. The 16-element uniform linear array was fabricated and its measured 10-dB bandwidth exceeded 75% with a 12-16 dB and 75% efficiency gain and efficiency respectively, over 6 to 11 GHz. Measured results are consistent with simulated ones.


title: 3D foot scanner based on 360 degree rotating-type laser triangulation sensor

abstract: 3D foot scanner is a non-contact type device that measures the shape of a foot using laser triangulation sensors. In general, a laser triangulation sensor is equipped with a single camera and a line-laser. Using only one laser triangulation sensor is insufficient to acquire accurate surface information of the full foot. In addition, the single camera in the laser triangulation sensor could cause occlusions when the projected laser line is blocked by protruding surface areas of the foot. To overcome these problems, modern 3D foot scanners use multiple laser triangulation sensors that consist of dual cameras. Most of the general 3D foot scanners use a total of eight cameras and four line-lasers making the device costly. In this paper, we propose an architecture of a 360' rotation-based 3D foot scanner that consists of a single rotating-type 3D sensor and a linear-type 3D sensor. The rotating-type 3D sensor consists of two line-lasers and a single camera and acquires 360' surface information of the upper part of the foot. The linear-type 3D sensor consists of a single line-laser and a camera and acquires sole surface information. Since the proposed 3D foot scanner has only two cameras and three line-lasers, it can be used as a cheap alternative to many existing models.


title: Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

abstract: In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through analysis and experiments, we show that gPoE of Gaussian processes (GP) have these qualities, while no other existing combination schemes satisfy all of them at the same time. The resulting GP-gPoE is highly scalable as individual GP experts can be independently learned in parallel; very expressive as the way experts are combined depends on the input rather than fixed; the combined prediction is still a valid probabilistic model with natural interpretation; and finally robust to unreliable predictions from individual experts.


title: Labor Costs and Foreign Direct Investment: A Panel VAR Approach

abstract: This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the dynamic association between labor costs and FDI. Further, results suggest that labor market policies should focus on productivity-enhancing tools in addition to price hindering tools.


title: Generalised Pre-Coding Aided Spatial Modulation

abstract: A new Multiple Input Multiple Output (MIMO) transmission scheme termed as Generalised Pre-coding aided Spatial Modulation (GPSM) is proposed, where the key idea is that a particular subset of receive antennas is activated and the activation pattern itself conveys useful information. This is in contrast to the previously proposed Spatial Modulation (SM) schemes, which operated by activating a specific subset of transmit antennas. We provide both analytical and numerical results characterizing the GPSM scheme proposed for both conventional as well as for large-dimensional MIMO configurations subject to both realistic imperfect Channel State Information at the Transmitter (CSIT) and to the low-rank approximation invoked for large-dimensional MIMO configurations. We also design a so-called reinforcement matrix for attaining substantial performance improvements for our proposed GPSM scheme. Our investigations show that the GPSM scheme constitutes a flexible alternative to the state-of-the-art MIMO transmission schemes, especially because it is capable of achieving a high throughput. Moreover, the benefits of mapping information to the spatial domain rather than relying on conventional modulation has substantial benefits in the medium to high Signal to Noise Ratio (SNR) region. Quantitatively, GPSM is capable of supporting the same throughput as the conventional full-multiplexing gain based MIMO arrangements at an SNR gain of about 1dB at the same receiver complexity. Furthermore, the reinforcement matrix aided GPSM scheme attains a further 3-4dB performance improvement as compared to the conventional GPSM scheme.


title: High bandwidth underwater optical communication.

abstract: We report error-free underwater optical transmission measurements at 1 Gbit/s (10(9) bits/s) over a 2 m path in a laboratory water pipe with up to 36 dB of extinction. The source at 532 nm was derived from a 1064 nm continuous-wave laser diode that was intensity modulated, amplified, and frequency doubled in periodically poled lithium niobate. Measurements were made over a range of extinction by the addition of a Mg(OH)(2) and Al(OH)(3) suspension to the water path, and we were not able to observe any evidence of temporal pulse broadening. Results of Monte Carlo simulations over ocean water paths of several tens of meters indicate that optical communication data rates >1 Gbit/s can be supported and are compatible with high-capacity data transfer applications that require no physical contact.


title: Merge-Weighted Dynamic Time Warping for Speech Recognition

abstract: Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, languageindependent (LI) with lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a convenient option to solve the problem. The dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several limitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping (MWDTW) algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model (HMM) significantly, and is also six times faster than DTW overall.


title: Model-based diagnosis of an automotive engine using several types of fault models

abstract: Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.