title: Neural pathways of embarrassment and their modulation by social anxiety

abstract: While being in the center of attention and exposed to other's evaluations humans are prone to experience embarrassment. To characterize the neural underpinnings of such aversive moments, we induced genuine experiences of embarrassment during person–group interactions in a functional neuroimaging study. Using a mock-up scenario with three confederates, we examined how the presence of an audience affected physiological and neural responses and the reported emotional experiences of failures and achievements. The results indicated that publicity induced activations in mentalizing areas and failures led to activations in arousal processing systems. Mentalizing activity as well as attention towards the audience were increased in socially anxious participants. The converging integration of information from mentalizing areas and arousal processing systems within the ventral anterior insula and amygdala forms the neural pathways of embarrassment. Targeting these neural markers of embarrassment in the (para-)limbic system provides new perspectives for developing treatment strategies for social anxiety disorders.


title: Correlating S&P 500 stocks with Twitter data

abstract: Twitter is a widely used online social media. One important characteristic of Twitter is its real-time nature. In this paper, we investigate whether the daily number of tweets that mention Standard & Poor 500 (S&P 500) stocks is correlated with S&P 500 stock indicators (stock price and traded volume) at three different levels, from the stock market to industry sector and individual company stocks. We further apply a linear regression with exogenous input model to predict stock market indicators, using Twitter data as exogenous input. Our preliminary results demonstrate that daily number of tweets is correlated with certain stock market indicators at each level. Furthermore, it appears that Twitter is helpful to predict stock market. Specifically, at the stock market level, we find that whether S&P 500 closing price will go up or down can be predicted more accurately when including Twitter data in the model.


title: Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators

abstract: Abstract An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for successful assessment. Today, the increasing number of progressively complex systems has led to an urgent need to produce tools that support the expert-led process of system-security assessment. In this research, we use weighted averages (WAs) and ordered weighted averages (OWAs) with evolutionary algorithms (EAs) to create aggregation operators that model parts of the assessment process. We show how individual overall ratings for security components can be produced from ratings of their characteristics, and how these individual overall ratings can be aggregated to produce overall rankings of potential attacks on a system. As well as the identification of salient attacks and weak points in a prospective system, the proposed method also highlights which factors and security components contribute most to a component's difficulty and attack ranking respectively. A real world scenario is used in which experts were asked to rank a set of technical attacks, and to answer a series of questions about the security components that are the subject of the attacks. The work shows how finding good aggregation operators, and identifying important components and factors of a cyber-security problem can be automated. The resulting operators have the potential for use as decision aids for systems designers and cyber-security experts, increasing the amount of assessment that can be achieved with the limited resources available.


title: Smartparking: A secure and intelligent parking system using NOTICE

abstract: Parking is costly and limited in almost every major city in the world. Innovative parking systems for meeting near-term parking demand are needed. This paper proposes a novel, secure, and intelligent parking system (SmartParking) based on secured wireless network and sensor communication. From the point of users' view, SmartParking is a secure and intelligent parking service. The parking reservation is safe and privacy preserved. The parking navigation is convenient and efficient. The whole parking process will be a non-stop service. From the point of management's view, SmartParking is an intelligent parking system. The parking process can be modeled as birth-death stochastic process and the prediction of revenues can be made. Based on the prediction, new business promotion can be made, for example, on-sale prices and new parking fees. In SmartParking, new promotions can be published through wireless network. We address hardware/software architecture, implementations, and analytical models and results. The evaluation of this proposed system proves its efficiency.


title: Object segmentation by long term analysis of point trajectories

abstract: Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon as objects move. In this paper, we present a method that uses long term point trajectories based on dense optical flow. Defining pair-wise distances between these trajectories allows to cluster them, which results in temporally consistent segmentations of moving objects in a video shot. In contrast to multi-body factorization, points and even whole objects may appear or disappear during the shot. We provide a benchmark dataset and an evaluation method for this so far uncovered setting.


title: Automatic Detection and Notification of Potholes and Humps on Roads to Aid Drivers

abstract: One of the major problems in developing countries is maintenance of roads. Well maintained roads contribute a major portion to the country's economy. Identification of pavement distress such as potholes and humps not only helps drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses previous pothole detection methods that have been developed and proposes a cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Ultrasonic sensors are used to identify the potholes and humps and also to measure their depth and height, respectively. The proposed system captures the geographical location coordinates of the potholes and humps using a global positioning system receiver. The sensed-data includes pothole depth, height of hump, and geographic location, which is stored in the database (cloud). This serves as a valuable source of information to the government authorities and vehicle drivers. An android application is used to alert drivers so that precautionary measures can be taken to evade accidents. Alerts are given in the form of a flash messages with an audio beep.


title: Aquatherapy for Neurodegenerative Disorders

abstract: Aquatherapy is used for rehabilitation and exercise; water provides a challenging, yet safe exercise environment for many special populations. We have reviewed the use of aquatherapy programs in four neurodegenerative disorders: Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, and Huntington's disease. Results support the use of aquatherapy in Parkinson's disease and multiple sclerosis, however further evidence is required to make specific recommendations in all of the aforementioned disorders.


title: Facial volume restoration of the aging face with poly-l-lactic acid.

abstract: The purpose of this article is to discuss current techniques used with poly-l-lactic acid to safely and effectively address changes observed in the aging face. Several important points deserve mention. First, this unique agent is not a filler but a stimulator of the host's own collagen, which then acts to volumize tissue in a gradual, progressive, and predictable manner. The technical differences between the use of biostimulatory agents and replacement fillers are simple and straightforward, but are critically important to the safe and successful use of these products and will be reviewed in detail. Second, in addition to gains in technical insights that have improved our understanding of how to use the product to best advantage, where to use the product to best advantage in facial filling has also improved with ever-evolving insights into the changes observed in the aging face. Finally, it is important to recognize that a patient's final outcome, and the amount of product and work it will take to get there, is a reflection of the quality of tissues with which they start. This is, of course, an issue of patient selection and not product selection.


title: Text mining in healthcare. Applications and opportunities.

abstract: Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.


title: Temporal Difference Networks for Video Action Recognition

abstract: Deep convolutional neural networks have been great success for image based recognition tasks. However, it is still unclear how to model the temporal evolution of videos effectively by deep networks. While recent deep models for videos show improvement by incorporating optical flow or aggregating high level appearance across frames, they focus on modeling either the long term temporal relations or short term motion. We propose Temporal Difference Networks (TDN) that model both long term relations and short term motion from videos. We leverage a simple but effective motion representation: difference of CNN features in our network and jointly modeling the motion at multiple scales in a single CNN. It achieves state-of-the-art performance on three different video classification benchmarks, showing the effectiveness of our approach to learn temporal relations in videos.


title: Trend following algorithms in automated derivatives market trading

abstract: Trend following (TF) is trading philosophy by which buying/selling decisions are made solely according to the observed market trend. For many years, many manifestations of TF such as a software program called Turtle Trader, for example, emerged in the industry. Surprisingly little has been studied in academic research about its algorithms and applications. Unlike financial forecasting, TF does not predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has been popular among speculators. However it remains as a trading method where human judgment is applied in setting the rules (aka the strategy) manually. Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we evaluated and compared a collection of TF algorithms that can be programmed in a computer system for automated trading. In particular, a new version of TF called trend recalling model is presented. It works by partially matching the current market trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this method has an edge over the other trend following methods in profitability. The results show that TF however is still limited by market fluctuation (volatility), and the ability to identify trend signal.


title: Non-psychotropic plant cannabinoids : new therapeutic opportunities from an ancient herb

abstract: Delta(9)-tetrahydrocannabinol binds cannabinoid (CB(1) and CB(2)) receptors, which are activated by endogenous compounds (endocannabinoids) and are involved in a wide range of physiopathological processes (e.g. modulation of neurotransmitter release, regulation of pain perception, and of cardiovascular, gastrointestinal and liver functions). The well-known psychotropic effects of Delta(9)-tetrahydrocannabinol, which are mediated by activation of brain CB(1) receptors, have greatly limited its clinical use. However, the plant Cannabis contains many cannabinoids with weak or no psychoactivity that, therapeutically, might be more promising than Delta(9)-tetrahydrocannabinol. Here, we provide an overview of the recent pharmacological advances, novel mechanisms of action, and potential therapeutic applications of such non-psychotropic plant-derived cannabinoids. Special emphasis is given to cannabidiol, the possible applications of which have recently emerged in inflammation, diabetes, cancer, affective and neurodegenerative diseases, and to Delta(9)-tetrahydrocannabivarin, a novel CB(1) antagonist which exerts potentially useful actions in the treatment of epilepsy and obesity.


title: Reasoning About Knowledge

abstract: Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms. Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes.


title: Vivaldi antenna taper design based on impedance matching

abstract: A method for the design of slot-line tapers for Vivaldi antennas is presented. A stepped quarter-wave Chebychev transformer is first designed in slotline, and then a smooth curve is fit to the steps. The validity of the method is verified through measurements of experimental designs for the 1 to 3 GHz range, with overall dimensions of 30.5 cm times 22 cm times 1.27 mm.


title: The prediction of curve progression in untreated idiopathic scoliosis during growth.

abstract: We reviewed the cases of 727 patients with idiopathic scoliosis in whom the initial curve measured from 5 to 29 degrees. The patients were followed either to the end of skeletal growth or until the curve progressed. One hundred and sixty-nine patients (23.2 per cent) showed progression of the curve. The incidence of curve progression was found to be related to the pattern and magnitude of the curve, the patient's age at presentation, the Risser sign, and the patient's menarchal status. We found no correlation between progression of the curve and the patient's sex, Harrington factor, rotational prominence, family history, or radiographic measurements. A progression factor was calculated using the three strongest correlations available at initial examination: the magnitude of the curve, the Risser sign, and the patient's chronological age. A graph and nomogram are presented that can serve as a guide for advising patients' families and for planning continuing care.


title: Context Ontology Implementation for Smart Home

abstract: Context awareness is one of the important fields in ubiquitous computing. Smart Home, a specific instance of ubiquitous computing, provides every family with opportunities to enjoy the power of hi-tech home living. Discovering that relationship among user, activity and context data in home environment is semantic, therefore, we apply ontology to model these relationships and then reason them as the semantic information. In this paper, we present the realization of smart home's context-aware system based on ontology. We discuss the current challenges in realizing the ontology context base. These challenges can be listed as collecting context information from heterogeneous sources, such as devices, agents, sensors into ontology, ontology management, ontology querying, and the issue related to environment database explosion.


title: Machine learning models and bankruptcy prediction

abstract: Machine learning models show improved bankruptcy prediction accuracy over traditional models.Various models were tested using different accuracy metrics.Boosting, bagging, and random forest models provide better results. There has been intensive research from academics and practitioners regarding models for predicting bankruptcy and default events, for credit risk management. Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks). In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. We use data from 1985 to 2013 on North American firms, integrating information from the Salomon Center database and Compustat, analysing more than 10,000 firm-year observations. The key insight of the study is a substantial improvement in prediction accuracy using machine learning techniques especially when, in addition to the original Altmans Z-score variables, we include six complementary financial indicators. Based on Carton and Hofer (2006), we use new variables, such as the operating margin, change in return-on-equity, change in price-to-book, and growth measures related to assets, sales, and number of employees, as predictive variables. Machine learning models show, on average, approximately 10% more accuracy in relation to traditional models. Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. Our research adds to the discussion of the continuing debate about superiority of computational methods over statistical techniques such as in Tsai, Hsu, and Yen (2014) and Yeh, Chi, and Lin (2014). In particular, for machine learning mechanisms, we do not find SVM to lead to higher accuracy rates than other models. This result contradicts outcomes from Danenas and Garsva (2015) and Cleofas-Sanchez, Garcia, Marques, and Senchez (2016), but corroborates, for instance, Wang, Ma, and Yang (2014), Liang, Lu, Tsai, and Shih (2016), and Cano etal. (2017). Our study supports the applicability of the expert systems by practitioners as in Heo and Yang (2014), Kim, Kang, and Kim (2015) and Xiao, Xiao, and Wang (2016).


title: Identify Crime Detection Using Data Mining Techniques

abstract: Credit-card-based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In the second kind of purchase, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information.The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the "usual" spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. The existing nondata mining detection system of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real time, this paper proposes a new multilayered detection system complemented withtwo additional layers: communal detection (CD) and spike detection (SD).CD finds realsocial relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes.


title: Abstinence Makes the State Grow Stronger: The Politics of Sex Education in Croatia*

abstract: As the title of my paper suggests the subject matter deals with the Republic of Croatia and its policy on sex education. Similar to many former socialist countries, the religious authorities in Croatia have taken an influential role in the affairs of the state. My aim in this paper is to demonstrate how the interjection of a church-backed sex education policy into the public sphere fuels the imagery of heterosexual nationalism among Croatian citizens. A movement that produces and reinforces notions of ethnoheterosexualism. I use the term “ethno-heterosexual” to emphasize the particularities of the relationship between heterosexuality and Croatian nationalism. Borrowing from Joane Nagel’s concept of ethnosexuality, in which she claims that “ethnic boundaries are also sexual boundaries,” I extend the concept to foreground how the interplay between ethnicity and religion galvanize to produce a hyper-heterosexualized Croatian national body. Drawing on Michel Foucault’s theory of discourse, I interrogate how state and church apparatus channel power through ethnically charged heterosexual discourses to discipline citizen’s bodies. This perspective carves out a lens through which we can see how church-backed values map onto the social body, grounding ethno-heterosexuality as a form of social control that shores up Croatia’s institutional power.


title: Explicitly representing expected cost: an alternative to ROC representation

abstract: ABSTRACT This paper proposes an alternative to ROC representation, in which the expected cost of a classi er is represented explicitly. This expected cost representation maintains many of the advantages of ROC representation, but is easier to understand. It allows the experimenter to immediately see the range of costs and class frequencies where a particular classi er is the best and quantitatively how much better it is than other classi ers. This paper demonstrates there is a point/line duality between the two representations. A point in ROC space representing a classi er becomes a line segment spanning the full range of costs and class frequencies. This duality produces equivalent operations in the two spaces, allowing most techniques used in ROC analysis to be readily reproduced in the cost space.


title: A genetic algorithm based distance-aware routing protocol for wireless sensor networks

abstract: A new cluster head selection algorithm in wireless sensor networks, coined as GADA-LEACH is proposed to reduce energy dissipation and improve the network lifetime.It makes use of Genetic Algorithm (GA) for optimized CH selection and concept of relay node for Distance Aware (DA) routing.Fitness function incorporates residual energy of nodes, distance between CH and nodes, and distance of BS from all CH's for better CH selection. A Wireless Sensor Network (WSN) is an aggregation of sensor nodes which are remotely deployed in large numbers, operate autonomously in an unattended environment and have limited energy resource. In most of the hierarchical routing protocols, the cluster head (CH) selection is on the basis of random probability equation. There is a scope to reduce the energy dissipation by improving CH selection procedure. The proposed scheme, coined as GADA-LEACH, makes use of evolutionary genetic algorithm for improving CH selection in legacy LEACH routing protocol in sensor networks. The concept of relay node is introduced which acts as an intermediary between CH and base station (BS) to ease the communication between the CH and BS. The simulation results obtained supports that our proposed algorithm is efficient in terms of network lifetime. Display Omitted


title: Lactic acid bacteria as probiotics.

abstract: A number of Lactobacillus species, Bifidobacterium sp, Saccharomyces boulardii, and some other microbes have been proposed as and are used as probiotic strains, i.e. live microorganisms as food supplement in order to benefit health. The health claims range from rather vague as regulation of bowel activity and increasing of well-being to more specific, such as exerting antagonistic effect on the gastroenteric pathogens Clostridium difficile, Campylobacter jejuni, Helicobacter pylori and rotavirus, neutralising food mutagens produced in colon, shifting the immune response towards a Th2 response, and thereby alleviating allergic reactions, and lowering serum cholesterol (Tannock, 2002). Unfortunately, most publications are case reports, uncontrolled studies in humans, or reports of animal or in vitro studies. Whether or not the probiotic strains employed shall be of human origin is a matter of debate but this is not a matter of concern, as long as the strains can be shown to survive the transport in the human gastrointestinal (GI) tract and to colonise the human large intestine. This includes survival in the stressful environment of the stomach - acidic pH and bile - with induction of new genes encoding a number of stress proteins. Since the availability of antioxidants decreases rostrally in the GI tract production of antioxidants by colonic bacteria provides a beneficial effect in scavenging free radicals. LAB strains commonly produce antimicrobial substance(s) with activity against the homologous strain, but LAB strains also often produce microbicidal substances with effect against gastric and intestinal pathogens and other microbes, or compete for cell surface and mucin binding sites. This could be the mechanism behind reports that some probiotic strains inhibit or decrease translocation of bacteria from the gut to the liver. A protective effect against cancer development can be ascribed to binding of mutagens by intestinal bacteria, reduction of the enzymes beta-glucuronidase and beta-glucosidase, and deconjugation of bile acids, or merely by enhancing the immune system of the host. The latter has attracted considerable interest, and LAB have been tested in several clinical trials in allergic diseases. Characteristics ascribed to a probiotic strain are in general strain specific, and individual strains have to be tested for each property. Survival of strains during production, packing and storage of a viable cell mass has to be tested and declared.


title: Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

abstract: Traditional Collaborative Filtering algorithms for recommendation are designed for stationary data. Likewise, conventional evaluation methodologies are only applicable in offline experiments, where data and models are static. However, in real world systems, user feedback is continuously being generated, at unpredictable rates. One way to deal with this data stream is to perform online model updates as new data points become available. This requires algorithms able to process data at least as fast as it is generated. One other issue is how to evaluate algorithms in such a streaming data environment. In this paper we introduce a simple but fast incremental Matrix Factorization algorithm for positive-only feedback. We also contribute with a prequential evaluation protocol for recommender systems, suitable for streaming data environments. Using this evaluation methodology, we compare our algorithm with other state-of-the-art proposals. Our experiments reveal that despite its simplicity, our algorithm has competitive accuracy, while being significantly faster.


title: Infrared camera calibration for dense depth map construction

abstract: In this paper, we introduce a novel and cost effective approach to calibrate the geometric properties of a far-infrared (IR) sensor. We further demonstrate that fully automatic sensor-to-sensor calibration is feasible in a setup involving a laser range scanner, IR cameras as well as conventional cameras. The calibration result then serves as a basis for upsampling range measurements to the resolution of the IR or visible-light camera images. Since our approach allows to rely on IR information instead of visible-light information for upsampling, bad light conditions or even no visible light at all are no limitation. From a practical point of view, we only require one calibration board of relatively small size which facilitates application in outdoor environments and further allows seamless integration of the IR camera in an existing multi-sensor platform. Our experimental results demonstrate that IR images are particularly useful to obtain reasonable depth information for living objects, when visible-light cameras are either blind or require impractical exposure times. In fact, our approach provides a convenient solution to IR camera calibration and integration, an issue which is particularly important in scenarios where sensors are not permanently mounted on vehicles and consequently require on-site adjustment and calibration.


Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach


title: Automated classification of malaria parasite species on thick blood film using support vector machine

abstract: Malaria is a serious global health problem. It requires fast and effective diagnosis for detecting and classifying the type of infection. Proper treatment should be administered in a timely fashion to prevent an outbreak. Microscopic examination of thick blood films is one of the current standards for malaria diagnosis. However, inspecting a thick blood film is time-consuming and requires experienced technicians. Hence, for developing countries where most cases of malaria occur but microscopy expertise may not be available, a computerized system to aid such diagnosis is desirable. In this paper, an automated classification system operating on digitized images of thick blood film has been developed to classify between Plasmodium falciparum and Plasmodium vivax malaria parasite species. The system is fully automated. It is fast and can be handled by non-experts. We calculate five statistical features — mean, standard deviation, kurtosis, skewness and entropy — from four color channels (green, intensity, saturation, and value) of these images. The features are then projected onto a subspace representing image characteristics from both species. The projected features are used by the support vector machine for classification. It is found that the algorithm has acceptable training error and can classify test images with good accuracy.


title: Oral signs of acute leukemia for early detection

abstract: Purpose ::: Systemic disease can manifest oral signs at an early phase, which may be crucial for the diagnosis and timing of treatment. This report describes two patients who presented with gingival enlargement as an early sign of acute leukemia.


title: ComVis: A Coordinated Multiple Views System for Prototyping New Visualization Technology

abstract: There is a large number of interactive visualization tools, however no universal tool exists that covers all relevant aspects for all possible application domains. We have developed a tool, ComVis, which was intended to be used as are search prototype for new visualization techniques. We have identified some interesting aspects from developers' and users' points of view during tool development. In this paper we describe lessons learned during the process, and share our findings with visualization research community. In the end, some examples prove the usefulness of the developed tool. One particular example, the concept of families of function graphs and application to analysis of fuel injection concludes the paper.


A lesson on authentication protocol design


title: Performance analysis of network-assisted two-hop D2D communications

abstract: Network-assisted single-hop device-to-device (D2D) communication can increase the spectral and energy efficiency of cellular networks by taking advantage of the proximity, reuse, and hop gains when radio resources are properly managed between the cellular and D2D layers. In this paper we argue that D2D technology can be used to further increase the spectral and energy efficiency if the key D2D radio resource management algorithms are suitably extended to support network assisted multi-hop D2D communications. Specifically, we propose a novel, distributed utility maximizing D2D power control (PC) scheme that is able to balance spectral and energy efficiency while taking into account mode selection and resource allocation constraints that are important in the integrated cellular-D2D environment. Our analysis and numerical results indicate that multi-hop D2D communications combined with the proposed PC scheme can be useful not only for harvesting the potential gains previously identified in the literature, but also for extending the coverage of cellular networks.


title: Experimental kinematics for wheeled skid-steer mobile robots

abstract: This work aims at improving real-time motion control and dead-reckoning of wheeled skid-steer vehicles by considering the effects of slippage, but without introducing the complexity of dynamics computations in the loop. This traction scheme is found both in many off-the-shelf mobile robots due to its mechanical simplicity and in outdoor applications due to its maneuverability. In previous works, we reported a method to experimentally obtain an optimized kinematic model for skid-steer tracked vehicles based on the boundedness of the instantaneous centers of rotation (ICRs) of treads on the motion plane. This paper provides further insight on this method, which is now proposed for wheeled skid-steer vehicles. It has been successfully applied to a popular research robotic platform, pioneer P3-AT, with different kinds of tires and terrain types.


title: A stochastic model for beaconless IEEE 802.15.4 MAC operation

abstract: IEEE 802.15.4 is a popular choice for MAC/PHY protocols in low power and low data rate wireless sensor networks. In this paper, we develop a stochastic model for the beaconless operation of IEEE 802.15.4 MAC protocol. Given the number of nodes competing for channel access and their packet generation rates, the model predicts the packet loss probability and the packet latency. We compared the model predictions with NS2 simulation results and found an excellent match between the two for a wide range of the packet generation rates and the number of competing nodes in the network.


title: Recommending relevant projects via user behaviour: an exploratory study on github

abstract: Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd-based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many fields have been using user behavior data to discover user preferences and interests to achieve various purposes. In the field of software engineering however, there has been few studies in mining large amount of user behavior data. Our goal is to design an approach based on user behavior data, to recommend relevant open source projects to developers, which can be helpful in activities like searching for the right open source solutions to quickly build prototypes. In this paper, we explore the possibilities of such a method by conducting a set of experiments on selected data sets from Github. We find it a promising direction in mining projects' relevance from user behavior data. Our study also obtain some important issues that is worth considering in this method.


title: DCnet: A new data center network architecture

abstract: The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.


title: Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

abstract: A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.


title: A wideband base station antenna with stable radiation pattern

abstract: This paper presents the configuration and experimental results of a novel wideband dual-polarized base station antenna with superior performance. The proposed antenna consists of four electric folded dipoles arranged in an octagon shape that are excited simultaneously for each polarization. Experimental results show that this element has a wide bandwidth of 46.4% from 1.69 GHz to 2.71 GHz with ≥ 15 dB return loss. Across this wide band, the variations of the half-power-beamwidths (HPBWs) of the two polarizations are all within 66.5° ± 5.5°, the port-to-port isolation is > 28 dB, and the cross-polarization discrimination (XPD) is > 25 dB.


title: Recognizing emotions expressed by body pose: a biologically inspired neural model.

abstract: Research into the visual perception of human emotion has traditionally focused on the facial expression of emotions. Recently researchers have turned to the more challenging field of emotional body language, i.e. emotion expression through body pose and motion. In this work, we approach recognition of basic emotional categories from a computational perspective. In keeping with recent computational models of the visual cortex, we construct a biologically plausible hierarchy of neural detectors, which can discriminate seven basic emotional states from static views of associated body poses. The model is evaluated against human test subjects on a recent set of stimuli manufactured for research on emotional body language.


title: An Energy-Efficient Sleep Scheduling With QoS Consideration in 3GPP LTE-Advanced Networks for Internet of Things

abstract: With the design of data communications in mind, 3GPP LTE-advanced (LTE-A) is probably the most promising technology for Internet of Things (IoT). For IoT applications, continuous low-rate streaming data may be reported from devices over a long period of time, imposing stringent requirements on power saving. To manage power consumption, 3GPP LTE-A has defined the discontinuous reception/transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. Existing literature has paid much attention to evaluate the performance of DRX/DTX; however, how to tune DRX/DTX parameters to optimize energy cost is still left open. This paper addresses the DRX/DTX optimization, by asking how to maximize the sleep periods of devices while guarantee their quality-of-service (QoS), especially on the aspects of traffic bit-rate, packet delay, and packet loss rate in IoT applications. Efficient schemes to optimize DRX/DTX parameters and schedule devices' packets with the base station are proposed. The key idea of our schemes is to balance the impacts between QoS parameters and DRX/DTX configurations. Simulation results show that our schemes can guarantee traffic bit-rate, packet delay, and packet loss rate while save energy of user equipments .


title: Gaussian mixture models for affordance learning using Bayesian Networks

abstract: Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.


title: DIGTOBI: a recommendation system for Digg articles using probabilistic modeling

abstract: Digg is a social news website that lets people submit articles to share their favorite web pages (e.g. blog postings or news articles) and vote the articles posted by others. Digg service currently lists the articles in the front page by popularity without considering each user's preference to the topics in the articles. Helping users to find the most interesting Digg articles tailored to each user's own interests will be very useful, but it is not an easy task to classify the articles according to their topics in order to recommend the articles differently to each user. In this paper, we propose DIGTOBI, a personalized recommendation system for Digg articles using a novel probabilistic modeling. Our model considers the relevant articles with low Digg scores important as well. We show that our model can handle both warm-start and cold-start scenarios seamlessly through a single model. We next propose an EM algorithm to learn the parameters of our probabilistic model. Our performance study with Digg data confirms the effectiveness of DIGTOBI compared to the traditional recommendations algorithms.


title: Wiener's Polynomial Chaos for the Analysis and Control of Nonlinear Dynamical Systems with Probabilistic Uncertainties [Historical Perspectives]

abstract: One purpose of the "Historical Perspectives" column is to look back at work done by pioneers in control and related fields that has been neglected for many years but was later revived in the control literature. This column discusses the topic of Norbert Wiener's most cited paper, which proposed polynomial chaos expansions (PCEs) as a method for probabilistic uncertainty quantification in nonlinear dynamical systems. PCEs were almost completely ignored until the turn of the new millennium, when they rather suddenly attracted a huge amount of interest in the noncontrol literature. Although the control engineering community has studied uncertain systems for decades, all but a handful of researchers in the systems and control community have ignored PCEs. The purpose of this column is to present a concise introduction to PCEs, provide an overview of the theory and applications of PCE methods in the control literature, and to consider the question of why PCEs have only recently appeared in the control literature.


Denoising of EMG Signals Based on Wavelet Transform


title: You get what you give: theory and evidence of reciprocity in the sharing economy

abstract: We develop an analytical framework of peer interaction in the sharing economy that incorporates reciprocity, the tendency to increase (decrease) effort in response to others’ increased (decreased) effort. In our model, buyers (sellers) can induce sellers (buyers) to exert more effort by behaving well themselves. We demonstrate that this joint increased effort can improve the utility of both parties and influence the market equilibrium. We also show that bilateral reputation systems, which allow both buyers and sellers to review each other, are more responsive to reciprocity than unilateral reputation systems. By rewarding reciprocal behavior, bilateral reputation systems generate trust among strangers and informally regulate their behavior. We test the predictions of our model using data from Airbnb, a popular peer-to-peer accommodation platform. We show that Airbnb hosts that are more reciprocal receive higher ratings and that higher rated hosts can increase their prices. Therefore, reciprocity affects equilibrium prices on Airbnb through its impact on ratings, as predicted by our analytical framework.


metaTED: a Corpus of Metadiscourse for Spoken Language.


title: How many memory systems are there

abstract: Memory is made up of a number of interrelated systems, organized structures of operating components consisting of neural substrates and their behavioral and cognitive correlates. A ternary clas- sificatory scheme of memory is proposed in which procedural, semantic, and episodic memory constitute a "monohierarchical" arrangement: Episodic memory is a specialized subsystem of semantic memory, and semantic memory is a specialized subsystem of procedural memory. The three memory systems differ from one another in a number of ways, including the kind of consciousness that characterizes their operations. The ternary scheme overlaps with di- chotomies and trichotomies of memory proposed by others. Evidence for multiple systems is derived from many sources. Illustrative data are provided by ex- periments in which direct priming effects are found to be both functionally and stochastically independent of recognition memory. Solving puzzles in science has much in common with solving puzzles for amusement, but the two differ in important respects. Consider, for instance, the jigsaw puzzle that scientific activity frequently imitates. The everyday version of the puzzle is determinate: It consists of a target picture and jigsaw pieces that, when properly assembled, are guaranteed to match the picture. Scientific puzzles are indeter- minate: The number of pieces required to complete a picture is unpredictable; a particular piece may fit many pictures or none; it may fit only one picture, but the picture itself may be unknown; or the hypothetical picture may be imagined, but its com- ponent pieces may remain undiscovered. This article is about a current puzzle in the science of memory. It entails an imaginary picture and a search for pieces that fit it. The picture, or the hypothesis, depicts memory as consisting of a number of systems, each system serving somewhat different purposes and operating according to some- what different principles. Together they form the marvelous capacity that we call by the single name of memory, the capacity that permits organisms to benefit from their past experiences. Such a picture is at variance with conventional wisdom that holds memory to be essentially a single system, the idea that "memory is memory." The article consists of three main sections. In the first, 1 present some pretheoretical reasons for hypothesizing the existence of multiple memory systems and briefly discuss the concept of memory system. In the second, I describe a ternary classifi- catory scheme of memory--consisting of procedural, semantic, and episodic memory--and briefly com- pare this scheme with those proposed by others. In the third, I discuss the nature and logic of evidence for multiple systems and describe some experiments that have yielded data revealing independent effects of one and the same act of learning, effects seemingly at variance with the idea of a single system. I answer the question posed in the title of the article in the short concluding section.


title: A New Human Identification Method: Sclera Recognition

abstract: The blood vessel structure of the sclera is unique to each person, and it can be remotely obtained nonintrusively in the visible wavelengths. Therefore, it is well suited for human identification (ID). In this paper, we propose a new concept for human ID: sclera recognition. This is a challenging research problem because images of sclera vessel patterns are often defocused and/or saturated and, most importantly, the vessel structure in the sclera is multilayered and has complex nonlinear deformations. This paper has several contributions. First, we proposed the new approach for human ID: sclera recognition. Second, we developed a new method for sclera segmentation which works for both color and grayscale images. Third, we designed a Gabor wavelet-based sclera pattern enhancement method to emphasize and binarize the sclera vessel patterns. Finally, we proposed a line-descriptor-based feature extraction, registration, and matching method that is illumination, scale, orientation, and deformation invariant and can mitigate the multilayered deformation effects and tolerate segmentation error. The experimental results show that sclera recognition is a promising new biometrics for positive human ID.


title: Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

abstract: Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a sufficiently large set of samples with 3D annotations for learning to succeed.


title: Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules

abstract: Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.


title: Using artificial neural networks to predict first-year traditional students second year retention rates

abstract: This research investigates the use of Artificial Neural Networks (ANNs) to predict first year student retention rates. Based on a significant body of previous research, this work expands on previous attempts to predict student outcomes using machine-learning techniques. Using a large data set provided by Columbus State University's Information Technology department, ANNs were used to analyze incoming first-year traditional freshmen students' data over a period from 2005--2011. Using several different network designs, the students' data was analyzed, and a basic predictive network was devised. While the overall accuracy was high when including the first and second semesters worth of data, once the data set was reduced to a single semester, the overall accuracy dropped significantly. Using different network designs, more complex learning algorithms, and better training strategies, the prediction accuracy rate for a student's return to the second year approached 75% overall. Since the rate is still low, there is room for improvements, and several techniques that might increase the reliability of these networks are discussed.


title: Click-through-based Deep Visual-Semantic Embedding for Image Search

abstract: The problem of image search is mostly considered from the perspectives of feature-based vector model and image ranker learning. A fundamental issue that underlies the success of these approaches is the similarity learning between query and image. The need of image surrounding texts in feature-based vector model, however, makes the similarity sensitive to the quality of text descriptions. On the other, the image ranker learning can suffer from robustness problem, originating from the fact that human labeled query-image pairs do not always predict user search intention precisely. We demonstrate in this paper that the above two issues can be well mitigated by jointly exploring visual-semantic embedding and the use of click-through data. Specifically, we propose a novel click-through-based deep visual-semantic embedding (C-DVSE) model for learning query and image similarity. The proposed model consists of two components: a deep convolutional neural networks followed by an image embedding layer for learning visual embedding, and a deep neural networks for generating query semantic embedding. The objective of our model is to maximize the correlation between semantic (query) and visual (clicked image) embedding. When the visual-semantic embedding is learnt, query-image similarity can be directly computed by cosine similarity on this embedding space. On a large-scale click-based image dataset with 11.7 million queries and one million images, our model is shown to be powerful for keyword-based image search with superior performance over several state-of-the-art methods.


title: A tale of two cannabinoids: The therapeutic rationale for combining tetrahydrocannabinol and cannabidiol

abstract: This study examines the current knowledge of physiological and clinical effects of tetrahydrocannabinol (THC) and cannabidiol (CBD) and presents a rationale for their combination in pharmaceutical preparations. Cannabinoid and vanilloid receptor effects as well as non-receptor mechanisms are explored, such as the capability of THC and CBD to act as anti-inflammatory substances independent of cyclo-oxygenase (COX) inhibition. CBD is demonstrated to antagonise some undesirable effects of THC including intoxication, sedation and tachycardia, while contributing analgesic, anti-emetic, and anti-carcinogenic properties in its own right. In modern clinical trials, this has permitted the administration of higher doses of THC, providing evidence for clinical efficacy and safety for cannabis based extracts in treatment of spasticity, central pain and lower urinary tract symptoms in multiple sclerosis, as well as sleep disturbances, peripheral neuropathic pain, brachial plexus avulsion symptoms, rheumatoid arthritis and intractable cancer pain. Prospects for future application of whole cannabis extracts in neuroprotection, drug dependency, and neoplastic disorders are further examined. The hypothesis that the combination of THC and CBD increases clinical efficacy while reducing adverse events is supported.


title: Research on non-invasive glucose concentration measurement by NIR transmission

abstract: Diabetes is a widely spreading disease which is known as one of the life threatening disease in the world. It occurs not only among adults and elderly, but also among infants and children. Blood glucose measurements are indispensable to diabetes patients to determine their insulin dose intake. Invasive blood glucose measurement ways which are high in accuracy are common but they are uncomfortable and have higher risk of infections especially for elders, pregnant and children. As a change, non-invasive blood glucose measurement techniques are introduced to provide a reliable and pain free method for monitoring glucose level without puncturing the skin. In this paper, a non-invasive glucose monitoring setup was developed using near infrared by detecting the transmission laser power. The detecting system included the semiconductor laser diode as light source, the S302C light power probe which detected the incident light and, the PM100USB transmit data to the computer. The specific infrared spectrum (1310 nm) was used as the incident beam. A proportional relationship between the laser power and the glucose concentration was proved by comparing the resulting laser power for a few of glucose aqueous solution samples with glucose concentration estimated value at the same circumstances.


title: Multi-criteria IoT Resource Discovery: A Comparative Analysis

abstract: The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Pareto-optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.


title: Throughput enhanced wireless in local loop (TWiLL): the architecture, protocols, and pricing schemes

abstract: Traditionally, voice communication over the local loop has been provided by wired systems. In particular, twisted pair has been the standard means of connection for homes and offices for several years. However in the recent past there has been an increased interest in the use of radio access technologies in local loops. Such systems which are now popular for their ease and low cost of installation and maintenance are called Wireless in Local Loop (WLL) systems. Subscribers' demands for greater capacity has grown over the years especially with the advent of the Internet. Wired local loops have responded to these increasing demands through the use of digital technologies such as ISDN and xDSL. Demands for enhanced data rates are being faced by WLL system operators too, thus entailing efforts towards more efficient bandwidth use. Multi-hop communication has already been studied extensively in Ad hoc network environments and has begun making forays into cellular systems as well. Multi-hop communication has been proven as one of the best ways to enhance throughput in a wireless network. Through this effort we study the issues involved in multi-hop communication in a wireless local loop system and propose a novel WLL architecture called Throughput enhanced Wireless in Local Loop (TWiLL). Through a realistic simulation model we show the tremendous performance improvement achieved by TWiLL over WLL. Traditional pricing schemes employed in single hop wireless networks cannot be applied in TWiLL -- a multi-hop environment. We also propose three novel cost reimbursement based pricing schemes which could be applied in such a multi-hop environment.


title: Dielectric ring resonator band pass filter for 2.4 GHz WLAN frequencies

abstract: A Microwave band pass filter employing dielectric ceramic ring resonator is designed, fabricated and tested. Band pass filter characteristics are studied by measuring the return loss and transmission characteristics of the microstrip line fed dielectric ring resonator. S-parameters, electric field and magnetic field distributions are computed using FEM method and compared with measured results. The measured values agree with predicted values obtained using FEM. Centered at 2404 MHz the fabricated dielectric ring resonator filter shows a measured 3-dB band width of 20.6 MHz.


title: Software-specific part-of-speech tagging: an experimental study on stack overflow

abstract: Part-of-speech (POS) tagging performance degrades on out-of-domain data due to the lack of domain knowledge. Software engineering knowledge, embodied in textual documentations, bug reports and online forum discussions, is expressed in natural language, but is full of domain terms, software entities and software-specific informal languages. Such software texts call for software-specific POS tagging. In the software engineering community, there have been several attempts leveraging POS tagging technique to help solve software engineering tasks. However, little work is done for POS tagging on software natural language texts. In this paper, we build a software-specific POS tagger, called S-POS, for processing the textual discussions on Stack Overflow. We target at Stack Overflow because it has become an important developer-generated knowledge repository for software engineering. We define a POS tagset that is suitable for describing software engineering knowledge, select corpus, develop a custom tokenizer, annotate data, design features for supervised model training, and demonstrate that the tagging accuracy of S-POS outperforms that of the Stanford POS Tagger when tagging software texts. Our work presents a feasible roadmap to build software-specific POS tagger for the socio-professional contents on Stack Overflow, and reveals challenges and opportunities for advanced software-specific information extraction.


title: Analysis and reduction of quadrature errors in the material point method ( MPM )

abstract: SUMMARY The Material Point Method (MPM) has demonstrated itself as a computationally effective particle method for solving solid mechanics problems involving large deformations and/or fragmentation of structures which are sometimes problematic for finite element methods. However, like most methods which employ mixed Lagrangian (particle) and Eulerian strategies, analysis of the method is not straightforward. The lack of an analysis framework for MPM, as is found in finite element methods, makes it challenging to explain anomalies found in its employment and makes it difficult to propose methodology improvements with predictable outcomes. In this paper we present an analysis of the quadrature errors found in the computation of (material) internal force in MPM and use this analysis to direct proposed improvements. In particular, we demonstrate that lack of regularity in the grid functions used for representing the solution of the equations of motion can hamper spatial convergence of the method. We propose the use of a quadratic B-spline basis for representing solutions on the grid, and we demonstrate computationally and explain theoretically why such a small change can have significant impact on the reduction of the internal force quadrature error (and corresponding “grid crossing error”) often experienced when using the material point method. Copyright c 2008 John Wiley & Sons, Ltd.


title: A Wideband Bandpass Filter With Reconfigurable Bandwidth Based on Cross-Shaped Resonator

abstract: A wideband bandpass filter (BPF) with reconfigurable bandwidth (BW) is proposed based on a parallel-coupled line structure and a cross-shaped resonator with open stubs. The p-i-n diodes are used as the tuning elements, which can implement three reconfigurable BW states. The prototype of the designed filter reports an absolute BW tuning range of 1.22 GHz, while the fractional BW is varied from 34.8% to 56.5% when centered at 5.7 GHz. The simulation and measured results are in good agreement. Comparing with previous works, the proposed reconfigurable BPF features wider BW tuning range with maximum number of tuning states.


title: Implementing Cognitive Theory of Multimedia Learning in Existing Academic Programs

abstract: With introduction of multimedia learning techniques in teaching and learning process, a great number of traditionally prepared course materials are transformed and transferred to the multimedia environment. Usually, this transformation is based on cognitive theory of multimedia learning and several principles that are devised from it. Although these principles are primarily meant to be used in the multimedia teaching environment, some of them can be used to enhance traditional classroom lectures, especially in engineering teaching courses. In this paper, an approach for introducing multimedia teaching principles to the traditional teaching methods have been described. As presented in the gained results, proposed approach proved to promise better results, especially in engineering learning courses.


title: Network characteristics of video streaming traffic

abstract: Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly.


title: Protein structure alignment by incremental combinatorial extension ( CE ) of the optimal path

abstract: A new algorithm is reported which builds an alignment between two protein structures. The algorithm involves a combinatorial extension (CE) of an alignment path defined by aligned fragment pairs (AFPs) rather than the more conventional techniques using dynamic programming and Monte Carlo optimization. AFPs, as the name suggests, are pairs of fragments, one from each protein, which confer structure similarity. AFPs are based on local geometry, rather than global features such as orientation of secondary structures and overall topology. Combinations of AFPs that represent possible continuous alignment paths are selectively extended or discarded thereby leading to a single optimal alignment. The algorithm is fast and accurate in finding an optimal structure alignment and hence suitable for database scanning and detailed analysis of large protein families. The method has been tested and compared with results from Dali and VAST using a representative sample of similar structures. Several new structural similarities not detected by these other methods are reported. Specific one-on-one alignments and searches against all structures as found in the Protein Data Bank (PDB) can be performed via the Web at http://cl.sdsc.edu/ce.html.


title: Rigid and elastic registration for coronary artery IVUS images

abstract: BACKGROUND: Intravascular ultrasound (IVUS) has been widely used in diagnosis and interventional treatment of cardiac vessel diseases. The coronary artery IVUS images are usually polluted by motion artifacts caused by cardiac motion, pulsatile blood and catheter twist during continuous pullback acquisition. OBJECTIVE: Strategies for rigid and elastic registration of coronary artery IVUS studies are developed to suppress the longitudinal motion and misalignment between successive frames. METHODS: Rigid registration is performed by searching for the optimal matching for each frame in other cycles based on the cyclic variation of gray-scale features. The image sequence is gated to properly identify the frames in each cardiac phase. Then, elastic registration between frames is achieved through an optimization algorithm based on thin plate spline (TPS) to correct the misalignment of successive slices. RESULTS: Experimental results with in vivo image data shows that the rigid registration performs better than the offline ECG gating. The elastic mapping relation between lumen contours in successive frames is smooth and continuous. CONCLUSION: The serrated vessel wall borders in longitudinal cuts are smoothed after rigid registration while image segmentation and feature extraction are required. The point-to-point correspondence between lumen contours detected from two matched frames is obtained with elastic registration.


title: Examining the Relations Among Narcissism, Impulsivity, and Self‐Defeating Behaviors

abstract: A recent meta-analysis (S. Vazire & D. C. Funder, 2006) suggested that narcissism and impulsivity are related and that impulsivity partially accounts for the relation between narcissism and self-defeating behaviors (SDB). This research examines these hypotheses in two studies and tests a competing hypothesis that Extraversion and Agreeableness account for this relation. In Study 1, we examined the relations among narcissism, impulsivity, and aggression. Both narcissism and impulsivity predicted aggression, but impulsivity did not mediate the narcissism-aggression relation. In Study 2, narcissism was related to a measure of SDB and manifested divergent relations with a range of impulsivity traits from three measures. None of the impulsivity models accounted for the narcissism-SDB relation, although there were unique mediating paths for traits related to sensation and fun seeking. The domains of Extraversion and low Agreeableness successfully mediated the entire narcissism-SDB relation. We address the discrepancy between the current and meta-analytic findings. Language: en


title: Shelly Gupta et al. / Indian Journal of Computer Science and Engineering (IJCSE) DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS

abstract: Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the classification problems. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis. In this paper we present an overview of the current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis. Breast cancer has become the leading cause of death in women in developed countries. The most effective way to reduce breast cancer deaths is detect it earlier. Early diagnosis requires an accurate and reliable diagnosis procedure that allows physicians to distinguish benign breast tumors from malignant ones without going for surgical biopsy. The objective of these predictions is to assign patients to either a "benign" group that is non- cancerous or a "malignant" group that is cancerous. The prognosis problem is the long-term outlook for the disease for patients whose cancer has been surgically removed. In this problem a patient is classified as a 'recur' if the disease is observed at some subsequent time to tumor excision and a patient for whom cancer has not recurred and may never recur. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. Thus, breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. Cancer research is generally clinical and/or biological in nature, data driven statistical research has become a common complement. Predicting the outcome of a disease is one of the most interesting and challenging tasks where to develop data mining applications. As the use of computers powered with automated tools, large volumes of medical data are being collected and made available to the medical research groups. As a result, Knowledge Discovery in Databases (KDD), which includes data mining techniques, has become a popular research tool for medical researchers to identify and exploit patterns and relationships among large number of variables, and made them able to predict the outcome of a disease using the historical cases stored within datasets. The objective of this study is to summarise various review and technical articles on diagnosis and prognosis of breast cancer. It gives an overview of the current research being carried out on various breast cancer datasets using the data mining techniques to enhance the breast cancer diagnosis and prognosis.


title: Swinging in the brain: shared neural substrates for behaviors related to sequencing and music

abstract: Music consists of precisely patterned sequences of both movement and sound that engage the mind in a multitude of experiences. We move in response to music and we move in order to make music. Because of the intimate coupling between perception and action, music provides a panoramic window through which we can examine the neural organization of complex behaviors that are at the core of human nature. Although the cognitive neuroscience of music is still in its infancy, a considerable behavioral and neuroimaging literature has amassed that pertains to neural mechanisms that underlie musical experience. Here we review neuroimaging studies of explicit sequence learning and temporal production—findings that ultimately lay the groundwork for understanding how more complex musical sequences are represented and produced by the brain. These studies are also brought into an existing framework concerning the interaction of attention and time-keeping mechanisms in perceiving complex patterns of information that are distributed in time, such as those that occur in music.


title: mSwitch: a highly-scalable, modular software switch

abstract: In recent years software network switches have regained eminence as a result of a number of growing trends, including the prominence of software-defined networks, as well as their use as back-ends to virtualization technologies, to name a few. Consequently, a number of high performance switches have been recently proposed in the literature, though none of these simultaneously provide (1) high packet rates, (2) high throughput, (3) low CPU usage, (4) high port density and (5) a flexible data plane. This is not by chance: these features conflict, and while achieving one or a few of them is (now) a solved problem, addressing the combination requires significant new design effort. In this paper we fill the gap by presenting mSwitch. To prove the flexibility and performance of our approach, we use mSwitch to build four distinct modules: a learning bridge consisting of 45 lines of code that outperforms FreeBSD's bridge by up to 8 times; an accelerated Open vSwitch module requiring small changes to the code and boosting performance by 2.6--3 times; a protocol demultiplexer for userspace protocol stacks; and a filtering module that can direct packets to virtualized middleboxes.


title: Evaluating Two-Stream CNN for Video Classification

abstract: Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g.,"skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.


title: Semi-automatic image annotation

abstract: A novel approach to semi-automatically and progressively annotating images with keywords is presented. The progressive annotation process is embedded in the course of integrated keyword-based and content-based image retrieval and user feedback. When the user submits a keyword query and then provides relevance feedback, the search keywords are automatically added to the images that receive positive feedback and can then facilitate keyword-based image retrieval in the future. The coverage and quality of image annotation in such a database system is improved progressively as the cycle of search and feedback increases. The strategy of semi-automatic image annotation is better than manual annotation in terms of efficiency and better than automatic annotation in terms of accuracy. A performance study is presented which shows that high annotation coverage can be achieved with this approach, and a preliminary user study is described showing that users view annotations as important and will likely use them in image retrieval. The user study also suggested user interface enhancements needed to support relevance feedback. We believe that similar approaches could also be applied to annotating and managing other forms of multimedia objects.


title: A pragmatic theory of generic language

abstract: Generalizations about categories are central to human understanding, and generic language (e.g. "Dogs bark.") provides a simple and ubiquitous way to communicate these generalizations. Yet the meaning of generic language is philosophically puzzling and has resisted precise formalization. We explore the idea that the core meaning of a generic sentence is simple but underspecified, and that general principles of pragmatic reasoning are responsible for establishing the precise meaning in context. Building on recent probabilistic models of language understanding, we provide a formal model for the evaluation and comprehension of generic sentences. This model explains the puzzling flexibility in usage of generics in terms of diverse prior beliefs about properties. We elicit these priors experimentally and show that the resulting model predictions explain almost all of the variance in human judgments for both common and novel generics. We probe the theory in more detail, and find that generic language depends in a fundamental way on subjective beliefs, not mere frequency. This theory provides the mathematical bridge between the words we use and the concepts they describe.


Revisiting quality for international research education: towards an engagement model


title: A Comparison of Graphical Models and Structural Equation Models for the Analysis of Longitudinal Survey Data

abstract: Graphical chain modelling (GCM) and structural equation modelling (SEM) are two approaches to modelling longitudinal data. Both approaches have their origins in path analysis and provide pictorial representations of the association between variables, which are usually ordered temporally. Both methods also aim to identify the direct and indirect effects of one variable on another.While the SEMapproach specifies a singlemodel for the complete system of variables being studied, the GCM approach permits a model for the complete system to be built up by fitting a sequence of submodels. In this chapter we briefly discuss the similarities and differences of the GCM and SEM approaches to modelling univariate recursive systems and their application to complex survey data. We identify the strengths and limitations of each approach. By using an example of the relationship between changes in labour force status following entry into parenthood, and changes in gender role attitude, we illustrate their use with survey data. A sample of 632 women, childless and aged between 16 and 39 in 1991, taken from the British Household Panel Survey (BHPS), is used. This survey is a particularly useful resource for this type of analysis since it is nationally representative, continuous since 1991 and collects a wide range of socio-demographic and attitudinal information (Taylor et al., 2007). Information on employment and parenthood status is obtained annually and attitude statements concerning gender roles are collected biennially in 1991, 1993, 1995 and 1997.


title: Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

abstract: We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.


title: A graph-based multi-level linguistic representation for document understanding

abstract: We proposed a graph-based representation that considers multiple linguistic levels.We introduced MinText, a technique useful for extracting features from the graph.We presented a study case for analyzing the performance of the methods proposed. Document understanding goal requires discovery of meaningful patterns in text, which in turn requires analyzing documents and extracting information useful for a purpose. The documents to be analyzed are expected to be represented in some way. It is true that different representations of the same piece of text might have different information extraction outcomes. Therefore, it is very important to propose a reliable text representation schema that may incorporate as many features as possible, and at the same time provides use of efficient document understanding algorithms. In this paper, we propose a graph-based representation of textual documents that employs different levels of formal representation of natural language. This schema takes into account different linguistic levels, such as lexical, morphological, syntactical and semantics. The representation schema proposed is accompanied with a proposal for a technique which allows to extract useful text patterns based on the idea of minimum paths in the graph. The efficiency of the representation schema proposed has been tested in one case of study (Question-Answering for machine Reading Evaluation - QA4MRE), and the results of experiments carried in it, are described. The results obtained show that the proposed graph-based multi-level linguistic representation schema may be successfully used in the broader framework of document understanding.


title: Robust Surface Reconstruction

abstract: We propose a method to reconstruct surfaces from oriented point clouds corrupted by errors arising from range imaging sensors. The core of this technique is the formulation of the problem as a convex minimization that reconstructs the indicator function of the surface's interior and substitutes the usual least-squares fidelity terms by Huber penalties to be robust to outliers, recover sharp corners, and avoid the shrinking bias of least-squares models. To achieve both flexibility and accuracy, we couple an implicit parametrization that reconstructs surfaces of unknown topology with adaptive discretizations that avoid the high memory and computational cost of volumetric representations. The hierarchical structure of the discretizations speeds minimization through multiresolution, while the proposed splitting algorithm minimizes nondifferentiable functionals and is easy to parallelize. In experiments, our model improves reconstruction from synthetic and real data, while the choice of discretization affects ...


title: Continuous Feedings of Fortified Human Milk Lead to Nutrient Losses of Fat, Calcium and Phosphorous

abstract: Substantial losses of nutrients may occur during tube (gavage) feeding of fortified human milk. Our objective was to compare the losses of key macronutrients and minerals based on method of fortification and gavage feeding method. We used clinically available gavage feeding systems and measured pre- and post-feeding (end-point) nutrient content of calcium (Ca), phosphorus (Phos), protein, and fat. Comparisons were made between continuous, gravity bolus, and 30-minute infusion pump feeding systems, as well as human milk fortified with donor human milk-based and bovine milk-based human milk fortifier using an in vitro model. Feeding method was significantly associated with fat and Ca losses, with increased losses in continuous feeds. Fat losses in continuous feeds were substantial, with 40 ± 3 % of initial fat lost during the feeding process. After correction for feeding method, human milk fortified with donor milk-based fortifier was associated with significantly less loss of Ca (8 ± 4% vs. 28 ± 4%, p< 0.001), Phos (3 ± 4% vs. 24 ± 4%, p < 0.001), and fat (17 ± 2% vs. 25 ± 2%, p = 0.001) than human milk fortified with a bovine milk-based fortifier (Mean ± SEM).


title: Survey of scheduling research involving due date determination decisions

abstract: Abstract We attempt to present in this paper a critical review of a particular segment of scheduling research in which the due to date assignment decision is of primary interest. The literature is classified into the static and dynamic job shop situations. The static job shop is analyzed from two different perspectives: the due date is constrained to be greater than or equal to makespan, and the optimal due date and optimal sequence are to be determined when the method of assigning due dates is specified. The literature on dynamic job shops is also reviewed under two broad categories. First, we discuss all the literature concerned with comparative and investigative studies to identify the most desirable due date assignment method. Second, we discuss the literature dealing with determination of optimal due dates. We note that computer simulation and analytical methods are two common approaches for the second type of problems. We observe that while the static single-machine problem with constant or common due dates has been well researched, very little or no work has been done on the dynamic multi-machine problem with sophisticated due date assignment methods. Finally, we identify and suggest some worthwhile areas for future research.


title: Short Communication: Churn models for prepaid customers in the cellular telecommunication industry using large data marts

abstract: In this article, we test the usefulness of the popular data mining models to predict churn of the clients of the Polish cellular telecommunication company. When comparing to previous studies on this topic, our research is novel in the following areas: (1) we deal with prepaid clients (previous studies dealt with postpaid clients) who are far more likely to churn, are less stable and much less is known about them (no application, demographical or personal data), (2) we have 1381 potential variables derived from the clients' usage (previous studies dealt with data with at least tens of variables) and (3) we test the stability of models across time for all the percentiles of the lift curve - our test sample is collected six months after the estimation of the model. The main finding from our research is that linear models, especially logistic regression, are a very good choice when modelling churn of the prepaid clients. Decision trees are unstable in high percentiles of the lift curve, and we do not recommend their usage.


title: Fast Newton-type Methods for Total Variation Regularization

abstract: Numerous applications in statistics, signal processing, and machine learning regularize using Total Variation (TV) penalties. We study anisotropic (l1-based) TV and also a related l2-norm variant. We consider for both variants associated (1D) proximity operators, which lead to challenging optimization problems. We solve these problems by developing Newton-type methods that outperform the state-of-the-art algorithms. More importantly, our ID-TV algorithms serve as building blocks for solving the harder task of computing 2- (and higher)-dimensional TV proximity. We illustrate the computational benefits of our methods by applying them to several applications: (i) image de-noising; (ii) image deconvolution (by plugging in our TV solvers into publicly available software); and (iii) four variants of fused-lasso. The results show large speedups—and to support our claims, we provide software accompanying this paper.


title: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

abstract: Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at http://github.com/lh3/bwa. Contact: hengli@broadinstitute.org


title: A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods

abstract: Currently, face recognition system is growing sustainably on a larger scope. A few years ago, face recognition was used as a personal identification with a limited scope, now this technology has grown in the field of security, in terms of preventing fraudsters, criminals, and terrorists. In addition, face recognition is also used in detecting how tired a driver is, reducing the occurrence of road accidents, as well as in marketing, advertising, health, and others. Many method are developed to give the best accuracy in face recognition. Deep learning approach become trend in this field because of stunning results, and fast computation. However, the problem about accuracy, complexity, and scalability become a challenges in face recognition. This paper focus on recognizing the importance of this technology, how to achieve high accuracy with low complexity. Deep learning and non-deep learning methods are discussed and compared to analyze their advantages and disadvantages. From critical analysis using experiment with YALE dataset, non-deep learning algorithm can reach up to 90.6% for low-high complexity and 94.67% in deep learning method for low-high complexity. Genetic algorithm combining with CNN and SVM was an optimization method for overcome accuracy and complexity problems.


title: A cooperative Internet of Things (IoT) for rural healthcare monitoring and control

abstract: Internet of Things (IoT) concept enables the possibility of information discovery about a tagged object or a tagged person by browsing an internet addresses or database entry that corresponds to a particular active RFID with sensing capability. It is a media for information retrieval from physical world to a digital world. With cooperative wireless communication, the wireless node entities can increase their effective quality of service (QoS) via cooperation. In developing countries the death rates due to lack of timely available medical treatments are quite high as compared to other developed countries. The majority of these deaths are preventable through quality care. This paper proposes a cooperative IoT approach for the better health monitoring and control of rural and poor human being's health parameters like blood pressure (BP), hemoglobin (HB), blood sugar, abnormal cellular growth in any part of the body, etc.


title: SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis

abstract: This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D convolutional network which is achieved by encoding the camera parameters together with the images in a 3D voxel representation. We evaluate SurfaceNet on the large-scale DTU benchmark.


title: Neural Decision Trees

abstract: In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one layer is used. This way, each MLP can be seen as a node of the tree. We then show that with the weight sharing asumption among those units, we end up with a Hashing Neural Network (HNN) which is a multilayer perceptron with sigmoid activation function for the last layer as opposed to the standard softmax. The output units then jointly represent the probability to be in a particular region. The proposed framework allows for global optimization as opposed to greedy in DT and differentiability w.r.t. all parameters and the input, allowing easy integration in any learnable pipeline, for example after CNNs for computer vision tasks. We also demonstrate the modeling power of HNN allowing to learn union of disjoint regions for final clustering or classification making it more general and powerful than standard softmax MLP requiring linear separability thus reducing the need on the inner layer to perform complex data transformations. We finally show experiments for supervised, semi-suppervised and unsupervised tasks and compare results with standard DTs and MLPs.