title: Holistically-Nested Edge Detection

abstract: We develop a new edge detection algorithm that addresses two critical issues in this long-standing vision problem: (1) holistic image training, and (2) multi-scale feature learning. Our proposed method, holistically-nested edge detection (HED), turns pixel-wise edge classification into image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are crucially important in order to approach the human ability to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of 0.782) and the NYU Depth dataset (ODS F-score of 0.746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than recent CNN-based edge detection algorithms.


title: Communication-efficient distributed monitoring of thresholded counts

abstract: Monitoring is an issue of primary concern in current and next generation networked systems. For ex, the objective of sensor networks is to monitor their surroundings for a variety of different applications like atmospheric conditions, wildlife behavior, and troop movements among others. Similarly, monitoring in data networks is critical not only for accounting and management, but also for detecting anomalies and attacks. Such monitoring applications are inherently continuous and distributed, and must be designed to minimize the communication overhead that they introduce. In this context we introduce and study a fundamental class of problems called "thresholded counts" where we must return the aggregate frequency count of an event that is continuously monitored by distributed nodes with a user-specified accuracy whenever the actual count exceeds a given threshold value.In this paper we propose to address the problem of thresholded counts by setting local thresholds at each monitoring node and initiating communication only when the locally observed data exceeds these local thresholds. We explore algorithms in two categories: static and adaptive thresholds. In the static case, we consider thresholds based on a linear combination of two alternate strategies, and show that there exists an optimal blend of the two strategies that results in minimum communication overhead. We further show that this optimal blend can be found using a steepest descent search. In the adaptive case, we propose algorithms that adjust the local thresholds based on the observed distributions of updated information. We use extensive simulations not only to verify the accuracy of our algorithms and validate our theoretical results, but also to evaluate the performance of our algorithms. We find that both approaches yield significant savings over the naive approach of centralized processing.


title: TaskRec: A Task Recommendation Framework in Crowdsourcing Systems

abstract: Crowdsourcing is evolving as a distributed problem-solving and business production model in recent years. In crowdsourcing paradigm, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. In crowdsourcing systems, task recommendation can help workers to find their right tasks faster as well as help requesters to receive good quality output quicker. However, previously proposed classification based task recommendation approach, which is the only one in the literature, does not consider the dynamic scenarios of new workers and new tasks in the crowdsourcing system. In this paper, we propose a Task Recommendation (TaskRec) framework based on a unified probabilistic matrix factorization, aiming to recommend tasks to workers in dynamic scenarios. Unlike traditional recommendation systems, workers do not provide their ratings on tasks in crowdsourcing systems, thus we infer user ratings from their interacting behaviors. This conversion helps task recommendation in crowdsourcing systems. Complexity analysis shows that our framework is efficient and is scalable to large datasets. Finally, we conduct experiments on real-world datasets for performance evaluation. Experimental results show that TaskRec outperforms the state-of-the-art approach.


title: Efficacy and safety of duloxetine in the treatment of older adult patients with generalized anxiety disorder: a randomized, double-blind, placebo-controlled trial

abstract: Objective ::: ::: This was a flexible-dosed study to evaluate the efficacy and safety of duloxetine 30–120 mg once daily in the treatment of generalized anxiety disorder (GAD) in older adult patients. ::: ::: ::: ::: Methods ::: ::: Patients with GAD, who were at least 65 years of age, were randomly assigned to double-blind treatment with either duloxetine (N = 151) or placebo (N = 140). The primary efficacy measure was the Hamilton Anxiety Rating Scale (HAM-A) total score, and the primary endpoint was at week 10. Global functioning was assessed by the Sheehan Disability Scale (SDS). Safety and tolerability was assessed by the occurrence of treatment-emergent adverse events, serious adverse events, laboratory analyses, and vital signs. Analyses were conducted on an intent-to-treat basis. ::: ::: ::: ::: Results ::: ::: The overall baseline mean HAM-A total score was 24, and SDS global score was 14. Completion rates were 75% for placebo and 76% for duloxetine. At week 10, duloxetine was superior to placebo on mean changes from baseline in HAM-A total scores (−15.9 vs. −11.7, p < 0.001) and in SDS global scores (−8.6 vs. −5.4, p < 0.001). Treatment-emergent adverse events occurred in ≥5% of duloxetine-treated patients and twice the rate than with placebo including constipation (9% vs. 4%, p = 0.06), dry mouth (7% vs. 1%, p = 0.02), and somnolence (6% vs. 2%, p = 0.14). ::: ::: ::: ::: Conclusion ::: ::: Duloxetine treatment was efficacious in the improvement of anxiety and functioning in older adult patients with GAD, and the safety profile was consistent with previous GAD studies. © 2014 The Authors. International Journal of Geriatric Psychiatry published by John Wiley & Sons, Ltd.


title: When hard realtime matters: Software for complex mechatronic systems

abstract: A still growing number of software concepts and frameworks have been proposed to meet the challenges in the development of more and more complex robotic systems, like humanoids or networked robotics. The issue of hard realtime, however, has not been the main focus of such concepts, but is essential for building and controlling mechatronic systems. Here we discuss the specific demands of complex mechatronic systems and present a software concept, the ''agile Robot Development'' (aRD) concept, which we developed at our institute to pragmatically address these demands. We show that the performance of current computing and communication hardware allows for a flexible component-based concept with distributed execution, even in hard realtime with rates in the kHz range.


Occupational therapy students’ self-efficacy, experience of supervision, and perception of meaningfulness of Level II fieldwork


The origins of morality in early childhood relationships


title: Digital Forensics and Cyber Crime

abstract: This book constitutes the thoroughly refereed post-conference proceedings of the 5th International ICST Conference on Digital Forensics and Cyber Crime, ICDF2C 2013, held in September 2013 in Moscow, Russia. The 16 revised full papers presented together with 2 extended abstracts and 1 poster paper were carefully reviewed and selected from 38 submissions. The papers cover diverse topics in the field of digital forensics and cybercrime, ranging from regulation of social networks to file carving, as well as technical issues, information warfare, cyber terrorism, critical infrastructure protection, standards, certification, accreditation, automation and digital forensics in the cloud.


title: Novel air blowing control for balancing a unicycle robot

abstract: This paper presents the implementation of a novel control method of using air for balancing the roll angle of a unicycle robot. The unicycle robot is designed and built. The roll angle of the unicycle robot is controlled by blowing air while the pitch angle is also controlled by DC motors. Successful balancing performances are demonstrated.


title: Modeling and simulation of PV module and zeta converter

abstract: Renewable Energy Sources (RES) plays a vital role today, owing to its excess availability and they are gaining importance because of the merits like pollution free, eco friendly, no maintenance, sustainability etc. Solar rays consist of photons, which are considered to be stream of energetic light particles. The solar cell or Photovoltaic cell (PV) converts photons present in solar rays to current. Intensity of sunlight striking the surface, efficiency and size of PV are the factors responsible for current generation from PV cell. However, change in irradiance, temperature or shadow effect will affect the overall PV performance. This will lead to decrease in output power from the solar cell. While modeling the PV in MATLAB, single diode model is given more importance over double diode model, because of the advantages like moderate complexity and acceptable accuracy. The PV is modeled in MATLAB considering the design equations of photon current and reverse saturation current. The design of power electronic converter is very important. To ensure reliability, safety, and to provide maximum efficiency to the PV system, selection and design of power electronic converters should be correct as well as optimal. The power converter is interfaced between PV panel and load. The Reference voltage is fixed based on the open circuit voltage available at the output of the converter. The benefits of zeta converter over other converters include non pulsating output current, lower settling time, adaptability, etc. In order to satisfy load voltage ripple requirement, and because of the non pulsating output current, zeta converter permits the use of small output capacitor. The objective of this paper is to maintain the constant output voltage, irrespective of change in irradiance. Change in irradiance, causes the change of output voltage from PV panel, which causes the duty cycle (D) to vary and duty cycle depends on both output voltage from PV module and reference voltage. Change in duty cycle makes the zeta converter to operate either in buck or boost mode.


title: Pollution, bad-mouthing, and local marketing: The underground of location-based social networks

abstract: Abstract Location Based Social Networks (LBSNs) are new Web 2.0 systems that are attracting new users in exponential rates. LBSNs like Foursquare and Yelp allow users to share their geographic location with friends through smartphones equipped with GPS, search for interesting places as well as posting tips about existing locations. By allowing users to comment on locations, LBSNs increasingly have to deal with new forms of spammers, which aim at advertising unsolicited messages on tips about locations. Spammers may jeopardize the trust of users on the system, thus, compromising its success in promoting location-based social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we investigated the task of identifying different types of tip spam on a popular Brazilian LBSN system, namely Apontador. Based on a labeled collection of tips provided by Apontador as well as crawled information about users and locations, we identified three types of irregular tips, namely local marketing, pollution and, bad-mouthing. We leveraged our characterization study towards a classification approach able to differentiate these tips with high accuracy.


title: The benefits of playing video games.

abstract: Video games are a ubiquitous part of almost all children's and adolescents' lives, with 97% playing for at least one hour per day in the United States. The vast majority of research by psychologists on the effects of "gaming" has been on its negative impact: the potential harm related to violence, addiction, and depression. We recognize the value of that research; however, we argue that a more balanced perspective is needed, one that considers not only the possible negative effects but also the benefits of playing these games. Considering these potential benefits is important, in part, because the nature of these games has changed dramatically in the last decade, becoming increasingly complex, diverse, realistic, and social in nature. A small but significant body of research has begun to emerge, mostly in the last five years, documenting these benefits. In this article, we summarize the research on the positive effects of playing video games, focusing on four main domains: cognitive, motivational, emotional, and social. By integrating insights from developmental, positive, and social psychology, as well as media psychology, we propose some candidate mechanisms by which playing video games may foster real-world psychosocial benefits. Our aim is to provide strong enough evidence and a theoretical rationale to inspire new programs of research on the largely unexplored mental health benefits of gaming. Finally, we end with a call to intervention researchers and practitioners to test the positive uses of video games, and we suggest several promising directions for doing so. (PsycINFO Database Record (c) 2013 APA, all rights reserved). Language: en


title: A Social Spider Algorithm for Global Optimization

abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.


title: Generating Multi-sentence Natural Language Descriptions of Indoor Scenes.

abstract: This paper proposes a novel framework for generating lingual descriptions of indoor scenes. This is an important problem, as an effective solution to this problem can enable many exciting real-world applications, such as human robot interaction, image/video synopsis, and automatic caption generation. Whereas substantial efforts have been made to tackle this problem, previous approaches focusing primarily on generating a single sentence for each image, which is not sufficient for describing complex scenes. We attempt to go beyond this, by generating coherent descriptions with multiple sentences. Particularly, we are interested in generating multi-sentence descriptions of cluttered indoor scenes. Complex, multi-sentence output requires us to deal with challenging problems such as consistent co-referrals to visual entities across sentences. Furthermore, the sequence of sentences needs to be as natural as possible, mimicking how humans describe the scene. This is especially important for example in the context of social robotics to enable realistic communications. Towards this goal, we develop a framework with three major components: (1) a holistic visual parser based on [3] that couples the inference of objects, attributes, and relations to produce a semantic representation of a 3D scene (Fig. 1); (2) a generative grammar automatically learned from training text; and (3) a text generation algorithm that takes into account subtle dependencies across sentences, such as logical order, diversity, saliency of objects, and co-reference resolution.


title: On the use of fingernail images as transient biometric identifiers

abstract: The significant advantages that biometric recognition technologies offer are in danger of being left aside in everyday life due to concerns over the misuse of such data. The biometric data employed so far focuses on the permanence of the characteristics involved. A concept known as `the right to be forgotten' is gaining momentum in international law and this should further hamper the adoption of permanent biometric recognition technologies. However, a multitude of common applications are short-term and, therefore, non-permanent biometric characteristics would suffice for them. In this paper we discuss `transient biometrics,' i.e. recognition via biometric characteristics that will change in the short term and show that images of the fingernail plate can be used as a transient biometric with a useful life-span of less than 6 months. A direct approach is proposed that requires no training and a relevant evaluation dataset is made publicly available.


title: Speech Recognition using Neural Networks

abstract: This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness. Neural networks avoid many of these assumptions, while they can also learn complex functions, generalize effectively, tolerate noise, and support parallelism. While neural networks can readily be applied to acoustic modeling, it is not yet clear how they can be used for temporal modeling. Therefore, we explore a class of systems called NN-HMM hybrids, in which neural networks perform acoustic modeling, and HMMs perform temporal modeling. We argue that a NN-HMM hybrid has several theoretical advantages over a pure HMM system, including better acoustic modeling accuracy, better context sensitivity, more natural discrimination, and a more economical use of parameters. These advantages are confirmed experimentally by a NN-HMM hybrid that we developed, based on context-independent phoneme models, that achieved 90.5% word accuracy on the Resource Management database, in contrast to only 86.0% accuracy achieved by a pure HMM under similar conditions. ::: In the course of developing this system, we explored two different ways to use neural networks for acoustic modeling: prediction and classification. We found that predictive networks yield poor results because of a lack of discrimination, but classification networks gave excellent results. We verified that, in accordance with theory, the output activations of a classification network form highly accurate estimates of the posterior probabilities P(class/input), and we showed how these can easily be converted to likelihoods P(input/class) for standard HMM recognition algorithms. Finally, this thesis reports how we optimized the accuracy of our system with many natural techniques, such as expanding the input window size, normalizing the inputs, increasing the number of hidden units, converting the network's output activations to log likelihoods, optimizing the learning rate schedule by automatic search, backpropagating error from word level outputs, and using gender dependent networks.


title: GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking

abstract: Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.


title: QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees

abstract: Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. Unfortunately, the computational cost of these ranking models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QuickScorer, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache-aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to-Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.


title: Probabilistic Community and Role Model for Social Networks

abstract: Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction. However, the results are still far from satisfactory. One of the biggest challenges is how to capture all the information of a social network in a unified manner, such as links, communities, user attributes, roles and behaviors. In this paper, we propose a unified probabilistic framework, the Community Role Model (CRM), to model a social network. CRM incorporates all the information of nodes and edges that form a social network. We propose methods based on Gibbs sampling and an EM algorithm to estimate the model's parameters and fit our model to real social networks. Real data experiments show that CRM can be used not only to represent a social network, but also to handle various application problems with better performance than a baseline model, without any modification to the model, showing its great advantages.


title: Pilot Study of Essential Drug Quality in Two Major Cities in India

abstract: Background: India is an increasingly influential player in the global pharmaceutical market. Key parts of the drug regulatory system are controlled by the states, each of which applies its own standards for enforcement, not always consistent with others. A pilot study was conducted in two major cities in India, Delhi and Chennai, to explore the question/hypothesis/extent of substandard and counterfeit drugs available in the market and to discuss how the Indian state and federal governments could improve drug regulation and more importantly regulatory enforcement to combat these drugs.Methodology/Principal Findings: Random samples of antimalarial, antibiotic, and antimycobacterial drugs were collected from pharmacies in urban and peri-urban areas of Delhi and Chennai, India. Semi-quantitative thin-layer chromatography and disintegration testing were used to measure the concentration of active ingredients against internationally acceptable standards. 12% of all samples tested from Delhi failed either one or both tests, and were substandard. 5% of all samples tested from Chennai failed either one or both tests, and were substandard. Spatial heterogeneity between pharmacies was observed, with some having more or less substandard drugs (30% and 0% respectively), as was product heterogeneity, with some drugs being more or less frequently substandard (12% and 7% respectively).Conclusions/Significance: In a study using basic field-deployable techniques of lesser sensitivity rather than the most advanced laboratory-based techniques, the prevalence of substandard drugs in Delhi and Chennai is confirmed to be roughly in accordance with the Indian government's current estimates. However, important spatial and product heterogeneity exists, which suggests that India's substandard drug problem is not ubiquitous, but driven by a subset of manufacturers and pharmacies which thrive in an inadequately regulated environment. It is likely that the drug regulatory system in India needs to be improved for domestic consumption, and because India is an increasingly important exporter of drugs for both developed and developing countries. Some poor countries with high burdens of disease have weak drug regulatory systems and import many HIV/AIDS, tuberculosis and malaria drugs from India.


Management accounting software and accounting practices: empirical study on SME enterprises


title: Document Clustering Using K-Means, Heuristic K-Means and Fuzzy C-Means

abstract: Document clustering refers to unsupervised classification (categorization) of documents into groups (clusters) in such a way that the documents in a cluster are similar, whereas documents in different clusters are dissimilar. The documents may be web pages, blog posts, news articles, or other text files. This paper presents our experimental work on applying K-means, heuristic K-means and fuzzy C-means algorithms for clustering text documents. We have experimented with different representations (tf, tf.idf & Boolean) and different feature selection schemes (with or without stop word removal & with or without stemming). We ran our implementations on some standard datasets and computed various performance measures for these algorithms. The results indicate that tf.idf representation, and use of stemming obtains better clustering. Moreover, fuzzy clustering produces better results than both K-means and heuristic K-means on almost all datasets, and is a more stable method.


title: Customer satisfaction measurement in hotel industry: Content analysis study

abstract: One of the biggest contemporary challenges of management in service industries is providing and maintaining customer satisfaction. Service quality and customer satisfaction have increasingly been identified as key factors in the battle for competitive differentiation and customer retention. Lam and Zhang (1999) claim that overwhelming customer demand for quality products and service has in recent years become increasingly evident to professionals in the tourism and hotel industry. Among all customer demands, service quality has been increasingly recognized as a critical factor in the success of any business (Gronroos, 1990 ; Parasuraman et al., 1988). The purpose of this study is to examine hotel guest comment cards (GCCs) and customer satisfaction management schemes in Opatija’ s hotels. This study adopts the applied content analysis approach which Gilbert and Horsnell (1998) used in their study, utilizing comparison and recording of findings based on a set of best practice criteria. The results of this study provide hotel professionals with an assessment of current methods of measuring and managing customer satisfaction in Opatija’ s hotels.


title: Analyzing Business Issues—With Excel: The Case of Superior Log Cabins, Inc.

abstract: ABSTRACT: This paper describes an instructional module developed for the principles of management accounting course to strengthen students' analytical problem-solving skills using spreadsheets. The module and all its components are presented first, followed by one of the four mini-cases used in a recent semester along with implementation guidance. The Analyzing Business Issues—with Excel (ABI-WE) mini-cases present a short business scenario, data related to the business issue, and a set of requirements to be completed using specific Excel functions from a directory of 40 covered in a semester. The mini-case itself is in Excel, so students can readily copy data into a solution worksheet. The primary focus is on analysis of the business issue using managerial accounting data, and the secondary learning objective is to develop an advanced level of expertise in selected spreadsheet functions; such as pivot tables, regression, if/then statements, V-lookup, conditional formatting, goal seek, and net present value.


title: Short-Term Variations and Long-Term Dynamics in Commodity Prices

abstract: In this article, we develop a two-factor model of commodity prices that allows meanreversion in short-term prices and uncertainty in the equilibrium level to which prices revert. Although these two factors are not directly observable, they may be estimated from spot and futures prices. Intuitively, movements in prices for long-maturity futures contracts provide information about the equilibrium price level, and differences between the prices for the short-and long-term contracts provide information about short-term variations in prices. We show that, although this model does not explicitly consider changes in convenience yields over time, this short-term/long-term model is equivalent to the stochastic convenience yield model developed in Gibson and Schwartz 1990. We estimate the parameters of the model using prices for oil futures contracts and apply the model to some hypothetical oil-linked assets to demonstrate its use and some of its advantages over the Gibson-Schwartz model.


title: Driving Hotzenplotz: A Hybrid Interface for Vehicle Control Aiming to Maximize Pleasure in Highway Driving

abstract: A prerequisite to foster proliferation of automated driving is common system acceptance. However, different users groups (novice, enthusiasts) decline automation, which could be, in turn, problematic for a successful market launch. We see a feasible solution in the combination of the advantages of manual (autonomy) and automated (increased safety) driving. Hence, we've developed the Hotzenplotz interface, combining possibility-driven design with psychological user needs. A simulator study (N=30) was carried-out to assess user experience with subjective criteria (Need Scale, PANAS/-X, HEMA, AttrakDiff) and quantitative measures (driving behavior, HR/HRV) in different conditions. Our results confirm that pure AD is significantly less able to satisfy user needs compared to manual driving and make people feeling bored/out of control. In contrast, the Hotzenplotz interface has proven to reduce the negative effects of AD. Our implication is that drivers should be provided with different control options to secure acceptance and avoid deskilling.


title: Convolutional neural networks applied to high-frequency market microstructure forecasting

abstract: Highly sophisticated artificial neural networks have achieved unprecedented performance across a variety of complex real-world problems over the past years, driven by the ability to detect significant patterns autonomously. Modern electronic stock markets produce large volumes of data, which are very suitable for use with these algorithms. This research explores new scientific ground by designing and evaluating a convolutional neural network in predicting future financial outcomes. A visually inspired transformation process translates high-frequency market microstructure data from the London Stock Exchange into four market-event based input channels, which are used to train six deep networks. Primary results indicate that con-volutional networks behave reasonably well on this task and extract interesting microstructure patterns, which are in line with previous theoretical findings. Furthermore, it demonstrates a new approach using modern deep-learning techniques for exploiting and analysing market microstructure behaviour.


title: MakerShoe: towards a wearable e-textile construction kit to support creativity, playful making, and self-expression

abstract: Electronic textile (e-textile) toolkits have been successful in broadening participation in STEAM-related activities, in expanding perceptions of computing, and in engaging users in creative, expressive, and meaningful digital-physical design. While a range of well-designed e-textile toolkits exist (e.g., LilyPad), they cater primarily to adults and older children and have a high barrier of entry for some users. We are investigating new approaches to support younger children (K-4) in the creative design, play, and customization of e-textiles and wearables without requiring the creation of code. This demo paper presents one such example of ongoing work: MakerShoe, an e-textile platform for designing shoe-based interactive wearable experiences. We discuss our two participatory design sessions as well as our initial prototype, which uses single-function magnetically attachable electronic modules to support circuit creation and the design of responsive, interactive behaviors.


title: Impacto: Simulating Physical Impact by Combining Tactile Stimulation with Electrical Muscle Stimulation

abstract: We present impacto, a device designed to render the haptic sensation of hitting or being hit in virtual reality. The key idea that allows the small and light impacto device to simulate a strong hit is that it decomposes the stimulus: it renders the tactile aspect of being hit by tapping the skin using a solenoid; it adds impact to the hit by thrusting the user's arm backwards using electrical muscle stimulation. The device is self-contained, wireless, and small enough for wearable use, thus leaves the user unencumbered and able to walk around freely in a virtual environment. The device is of generic shape, allowing it to also be worn on legs, so as to enhance the experience of kicking, or merged into props, such as a baseball bat. We demonstrate how to assemble multiple impacto units into a simple haptic suit. Participants of our study rated impact simulated using impacto's combination of solenoid hit and electrical muscle stimulation as more realistic than either technique in isolation.


Research Proposal Title: Fast Planar Fracture Detection From Sinusoids In Borehole Im- agery


title: The Carotid Revascularization Endarterectomy Versus Stenting Trial (CREST): Stenting Versus Carotid Endarterectomy for Carotid Disease

abstract: Background and Purpose—Carotid artery stenosis causes up to 10% of all ischemic strokes. Carotid endarterectomy (CEA) was introduced as a treatment to prevent stroke in the early 1950s. Carotid stenting (CAS) was introduced as a treatment to prevent stroke in 1994. Methods—The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) is a randomized trial with blinded end point adjudication. Symptomatic and asymptomatic patients were randomized to CAS or CEA. The primary end point was the composite of any stroke, myocardial infarction, or death during the periprocedural period and ipsilateral stroke thereafter, up to 4 years. Results—There was no significant difference in the rates of the primary end point between CAS and CEA (7.2% versus 6.8%; hazard ratio, 1.11; 95% CI, 0.81 to 1.51; P=0.51). Symptomatic status and sex did not modify the treatment effect, but an interaction with age and treatment was detected (P=0.02). Outcomes were slightly better after CAS for patients aged <70 years and ...


title: BBM: bayesian browsing model from petabyte-scale data

abstract: Given a quarter of petabyte click log data, how can we estimate the relevance of each URL for a given query? In this paper, we propose the Bayesian Browsing Model (BBM), a new modeling technique with following advantages: (a) it does exact inference; (b) it is single-pass and parallelizable; (c) it is effective. We present two sets of experiments to test model effectiveness and efficiency. On the first set of over 50 million search instances of 1.1 million distinct queries, BBM out-performs the state-of-the-art competitor by 29.2% in log-likelihood while being 57 times faster. On the second click-log set, spanning a quarter of petabyte data, we showcase the scalability of BBM: we implemented it on a commercial MapReduce cluster, and it took only 3 hours to compute the relevance for 1.15 billion distinct query-URL pairs.


title: Evaluating capacitive touch input on clothes

abstract: Wearable computing and smart clothing have attracted a lot of attention in the last years. For a variety of applications, it can be seen as potential future direction of mobile user interfaces. In this paper, we concentrate on usability and applicability issues concerned with capacitive touch input on clothing. To be able to perform user studies, we built a generic platform for attaching, e.g., capacitive sensors of different types. On top of that, several prototypes of wearable accessories and clothing and implemented various application scenarios. We report on two studies we undertook with these implementations with a user group randomly sampled at a shopping mall. We provide a significant set of guidelines and lessons learned that emerged from our experiences and those studies. Thus, developers of similar projects have to put major efforts into minimizing the delay between button activation and feedback and to make location and identification of controls and their function as simple and quick as possible. Issues that have to be treated in all designs include the requirement of one-handed interaction and that, even for minimal functionality, to find a general solution with regard to layout and button-to-function mapping is hardly possible. Additionally, in order to generate a satisfactory user experience good usability must be combined with aesthetical factors.


title: Fine-grained access control of personal data

abstract: The immensity and variety of personal information (e.g., profile, photo, and microblog) on social sites require access control policies tailored to individuals' privacy needs. Today such policies are still mainly specified manually by ordinary users, which is usually coarse-grained, tedious, and error-prone. This paper presents the design, implementation, and evaluation of an automated access control policy specification tool, XACCESS, that helps non-expert users effectively specify who should have access to which part of their data. A series of key features distinguish XACCESS from prior work: 1) it adopts a role-based access control model (instead of the conventional rule-based paradigm) to capture the implicit privacy/interest preference of social site users; 2) it employs a novel hybrid mining method to extract a set of semantically interpretable, functional "social roles", from both static network structures and dynamic historical activities; 3) based on the identified social roles, confidentiality setting of personal data, and (optional and possibly inconsistent) predefined user-permission assignments, it recommends a set of high-quality privacy settings; 4) it allows user feedback in every phase of the process to further improve the quality of the suggested privacy policies. A comprehensive experimental evaluation is conducted over real social network and user study data to validate the efficacy of XACCESS.


Chapter 5 Musical Virtuosity and Creativity


title: Generating event logs for high-level process models

abstract: Business Process Model and Notation (BPMN) is a de-facto standard for practitioners working in the Business Process Management (BPM) field. The BPMN standard [1] offers high-level modeling constructs, such as subprocesses, events, data and message flows, lanes, and is widely used to model processes in various domains. Recently several BPMN-based process mining techniques [2, 3, 4] were introduced. These techniques allow representing processes, discovered from the event logs of process-aware information systems, in a convenient way, using the BPMN standard. To test these mining approaches an appropriate tool for the generation of event logs from BPMN models is needed. In this work we suggest such a tool. We propose a formal token-based executable BPMN semantics, which takes into account BPMN 2.0 with its expressive constructs. The developed tool is based on these semantics and allows simulation of hierarchical process models (including models with cancellations), models with data flows and pools, and models interacting through message flows. To manage the control flow, script-based gateways and choice preferences are implemented as well. The proposed simulation technique was implemented on top of existing plug-ins for ProM (Process Mining Framework) [5], and was verified on models created by practitioners from various domains.


title: Interference Mitigation in D2D Communication Underlaying LTE-A Network

abstract: The mobile data traffic has risen exponentially in recent days due to the emergence of data intensive applications, such as online gaming and video sharing. It is driving the telecommunication industry as well as the research community to come up with new paradigms that will support such high data rate requirements within the existing wireless access network, in an efficient and effective manner. To respond to this challenge, device-to-device (D2D) communication in cellular networks is viewed as a promising solution, which is expected to operate, either within the coverage area of the existing eNB and under the same cellular spectrum (in-band) or separate spectrum (out-band). D2D provides the opportunity for users located in close proximity of each other to communicate directly, without traversing data traffic through the eNB. It results in several transmission gains, such as improved throughput, energy gain, hop gain, and reuse gain. However, integration of D2D communication in cellular systems at the same time introduces new technical challenges that need to be addressed. Containment of the interference among D2D nodes and cellular users is one of the major problems. D2D transmission radiates in all directions, generating undesirable interference to primary cellular users and other D2D users sharing the same radio resources resulting in severe performance degradation. Efficient interference mitigation schemes are a principal requirement in order to optimize the system performance. This paper presents a comprehensive review of the existing interference mitigation schemes present in the open literature. Based on the subjective and objective analysis of the work available to date, it is also envisaged that adopting a multi-antenna beamforming mechanism with power control, such that the transmit power is maximized toward the direction of the intended D2D receiver node and limited in all other directions will minimize the interference in the network. This could maximize the sum throughput and hence, guarantees the reliability of both the D2D and cellular connections.


title: Probabilistic Skimlets Fusion for Summarizing Multiple Consumer Landmark Videos

abstract: It is difficult to develop a computational model that can accurately predict the quality of the video summary. This paper proposes a novel algorithm to summarize one-shot landmark videos. The algorithm can optimally combine multiple unedited consumer video skims into an aesthetically pleasing summary. In particular, to effectively select the representative key frames from multiple videos, an active learning algorithm is derived by taking advantage of the locality of the frames within each video. Toward a smooth video summary, we define skimlet, a video clip with adjustable length, starting frame, and positioned by each skim. Thereby, a probabilistic framework is developed to transfer the visual cues from a collection of aesthetically pleasing photos into the video summary. The length and the starting frame of each skimlet are calculated to maximally smoothen the video summary. At the same time, the unstable frames are removed from each skimlet. Experiments on multiple videos taken from different sceneries demonstrated the aesthetics, the smoothness, and the stability of the generated summary.


title: A Server-Assigned Spatial Crowdsourcing Framework

abstract: With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.


Attention Modeling for Targeted Sentiment


Watch Implicit Memory Retention Without Remembering


title: Interpreting 2D gesture annotations in 3D augmented reality

abstract: A 2D gesture annotation provides a simple way to annotate the physical world in augmented reality for a range of applications such as remote collaboration. When rendered from novel viewpoints, these annotations have previously only worked with statically positioned cameras or planar scenes. However, if the camera moves and is observing an arbitrary environment, 2D gesture annotations can easily lose their meaning when shown from novel viewpoints due to perspective effects. In this paper, we present a new approach towards solving this problem by using a gesture enhanced annotation interpretation. By first classifying which type of gesture the user drew, we show that it is possible to render the 2D annotations in 3D in a way that conforms more to the original intention of the user than with traditional methods. We first determined a generic vocabulary of important 2D gestures for an augmented reality enhanced remote collaboration scenario by running an Amazon Mechanical Turk study with 88 participants. Next, we designed a novel real-time method to automatically handle the two most common 2D gesture annotations — arrows and circles — and give a detailed analysis of the ambiguities that must be handled in each case. Arrow gestures are interpreted by identifying their anchor points and using scene surface normals for better perspective rendering. For circle gestures, we designed a novel energy function to help infer the object of interest using both 2D image cues and 3D geometric cues. Results indicate that our method outperforms previous approaches by better conveying the meaning of the original drawing from different viewpoints.


title: Butler lies: awareness, deception and design

abstract: Instant messaging (IM) is a common and popular way for co-workers, friends, and family to stay in touch, but its"always-on properties can sometimes lead people to feel overexposed or too readily available to others for conversation. This, in turn, may lead people to deceive others about their actual status or availability. In this paper, we introduce the notion of the "butler lie to describe lies that allow for polite initiation and termination of conversations. We present results from a field study of 50 IM users, in which participants rated each of their messages at the time of sending to indicate whether or not it was deceptive. About one tenth of all IM messages were rated as lies and, of these, about one fifth were butler lies. These results suggest that butler lies are an important social practice in IM, and that existing approaches to interpersonal awareness, which focus on accurate assessment of availability, may need to take deception and other social practices into account.


title: Quantifying Creativity in Art Networks

abstract: Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the "time machine experiment".


title: Decentralized Control of Multi-AGV Systems in Autonomous Warehousing Applications

abstract: In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle’s private zone , i.e., the preallocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups—a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.


title: PLASMA: Portable Programming for SIMD Heterogeneous Accelerators

abstract: Data-parallel accelerators have emerged as highperformance alternatives to general-purpose processors for many applications. The Cell BE, GPUs from NVIDIA and ATI, and the like can outperform conventional superscalar architectures, but only for applications that can take advantage of these accelerators’ SIMD architectures, large number of cores, and local memories. Coupled with the SIMD extensions on general-purpose processors, these heterogeneous computing architectures provide a powerful platform to accelerate data-parallel programs. Unfortunately, each accelerator provides its own programming model, and programmers are often forced to confront issues of distributed memory, multithreading, load-balancing and computation scheduling. This necessitates a framework which can exploit different types of parallelism across heterogeneous functional units and supports multiple types of high-level programming languages including stream programming or traditional shared or distributed memory programming framework or prototyping languages such as MATLAB. Towards this goal, in this paper, we present PLASMA, a programming framework that enables the writing of portable SIMD programs. The main component of PLASMA is an intermediate representation (IR), which provides succinct and clean abstractions to enable programs to be compiled to different accelerators. With the assistance of a runtime, these programs can then be automatically multithreaded, run on multiple heterogeneous accelerators transparently and are oblivious of distributed memory. We demonstrate a prototype compiler and runtime that targets PLASMA programs to scalar processors, processors with SIMD extensions and GPUs.


title: SAG: Smart Aggregation Technique for Continuous-Monitoring in Wireless Sensor Networks

abstract: Diversified opportunities (possible numerous applications) and challenges (resource constrained nodes) are contributing factors behind the extensive research in sensor networks. Continuous monitoring of the natural phenomenon is one of the major streams of the possible application in this arena. Sensing and monitoring of natural phenomenon involve dealing with correlated data in both spatial and temporal dimension. Aggregation technique plays a significant role by reducing the amount of data transmission in wireless sensor networks (WSN)as correlation is dominating in the real world. This reduction on the amount of data is vital due to the energy scarcity of the WSN. This paper presents a simple aggregation technique named smart aggregation (SAG) for the continuous-monitoring applications in WSNs. SAG maintains a tolerable deviation i.e. limited error in the aggregated data while compressing both spatially and temporarily. We present simulated results that rationalize the development of the protocol. The comparison is made with k-hop aggregation and CM-EDR algorithm in terms of energy efficiency showing the potential improvement gained by applying our aggregation scheme.


title: AtomTracker: A Comprehensive Approach to Atomic Region Inference and Violation Detection

abstract: A particularly insidious type of concurrency bug is atomicity violations. While there has been substantial work on automatic detection of atomicity violations, each existing technique has focused on a certain type of atomic region. To address this limitation, this paper presents Atom Tracker, a comprehensive approach to atomic region inference and violation detection. Atom Tracker is the first scheme to (1) automatically infer generic atomic regions (not limited by issues such as the number of variables accessed, the number of instructions included, or the type of code construct the region is embedded in) and (2) automatically detect violations of them at runtime with negligible execution overhead. Atom Tracker provides novel algorithms to infer generic atomic regions and to detect atomicity violations of them. Moreover, we present a hardware implementation of the violation detection algorithm that leverages cache coherence state transitions in a multiprocessor. In our evaluation, we take eight atomicity violation bugs from real-world codes like Apache, MySql, and Mozilla, and show that Atom Tracker detects them all. In addition, Atom Tracker automatically infers all of the atomic regions in a set of micro benchmarks accurately. Finally, we also show that the hardware implementation induces a negligible execution time overhead of 0.2–4.0% and, therefore, enables Atom Tracker to find atomicity violations on-the-fly in production runs.


title: A survey of recent advances in SAT-based formal verification

abstract: Dramatic improvements in SAT solver technology over the last decade and the growing need for more efficient and scalable verification solutions have fueled research in verification methods based on SAT solvers. This paper presents a survey of the latest developments in SAT-based formal verification, including incomplete methods such as bounded model checking and complete methods for model checking. We focus on how the surveyed techniques formulate the verification problem as a SAT problem and how they exploit crucial aspects of a SAT solver, such as application-specific heuristics and conflict-driven learning. Finally, we summarize the noteworthy achievements in this area so far and note the major challenges in making this technology more pervasive in industrial design verification flows.


title: Bartholin's, vulval and perineal abscesses.

abstract: Perineal infections are a common presenting complaint in women attending acutely to a gynaecology department. Specifically, Bartholin's abscesses can occur in approximately 2% of women. Conservative versus surgical approaches to manage these infections aims to reduce the need for hospital admission. We summarise the literature on the diagnosis and management of Bartholin's and other types of perineal infections and abscesses specific to gynaecology.


title: Shared-network scheme of SMV and GOOSE in smart substation

abstract: The network structure of the smart substation in common use was introduced, and the technical problems of the shared-network of sampled measured value (SMV) and generic object oriented substation event (GOOSE) were analyzed, such as the processing ability of network device and the intelligent device, the data real-time property and the network reliability, the effects to the substation in the condition of network fault, etc. On this basis, the feasibility of the shared-network of SMV and GOOSE was discussed, the implement scheme was presented, and eventually the solution of the shared-network of SMV and GOOSE was put forward, which based on the applications of the message priority control, restricting the switch number, virtual local area network (VLAN) and GARP multicast registration protocol (GMRP) classification flow control, flow rate limiting, etc. In the test-bed, the cases of shared-network and separate-network of SMV and GOOSE were compared and analyzed, and the result was valuable for reference.


title: Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms

abstract: Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.


title: Document Image Binarization using LSTM: A Sequence Learning Approach

abstract: We propose to address the problem of Document Image Binarization (DIB) using Long Short-Term Memory (LSTM) which is specialized in processing very long sequences. Thus, the image is considered as a 2D sequence of pixels and in accordance to this a 2D LSTM is employed for the classification of each pixel as text or background. The proposed approach processes the information using local context and then propagates the information globally in order to achieve better visual coherence. The method is robust against most of the document artifacts. We show that with a very simple network without any feature extraction and with limited amount of data the proposed approach works reasonably well for the DIBCO 2013 dataset. Furthermore a synthetic dataset is considered to measure the performance of the proposed approach with both binarization and OCR groundtruth. The proposed approach significantly outperforms standard binarization approaches both for F-Measure and OCR accuracy with the availability of enough training samples.


title: Graph-based named entity linking with wikipedia

abstract: Named entity linking (NEL) grounds entity mentions to their corresponding Wikipedia article. State-of-the-art supervised NEL systems use features over the rich Wikipedia document and link-graph structure. ::: ::: Graph-based measures have been effective over WordNet for word sense disambiguation (WSD). We draw parallels between NEL and WSD, motivating our unsupervised NEL approach that exploits the Wikipedia article and category link graphs. Our system achieves 85.5% accuracy on the TAC 2010 shared task -- competitive with the best supervised and unsupervised systems.


title: Cytokinin regulates root meristem activity via modulation of the polar auxin transport

abstract: Plant development is governed by signaling molecules called phytohormones. Typically, in certain developmental processes more than 1 hormone is implicated and, thus, coordination of their overlapping activities is crucial for correct plant development. However, molecular mechanisms underlying the hormonal crosstalk are only poorly understood. Multiple hormones including cytokinin and auxin have been implicated in the regulation of root development. Here we dissect the roles of cytokinin in modulating growth of the primary root. We show that cytokinin effect on root elongation occurs through ethylene signaling whereas cytokinin effect on the root meristem size involves ethylene-independent modulation of transport-dependent asymmetric auxin distribution. Exogenous or endogenous modification of cytokinin levels and cytokinin signaling lead to specific changes in transcription of several auxin efflux carrier genes from the PIN family having a direct impact on auxin efflux from cultured cells and on auxin distribution in the root apex. We propose a novel model for cytokinin action in regulating root growth: Cytokinin influences cell-to-cell auxin transport by modification of expression of several auxin transport components and thus modulates auxin distribution important for regulation of activity and size of the root meristem.


The effects of water stress on evapotranspiration, water use efficiency, yield and quality parameters in watermelon irrigated by drip irrigation.


title: Automatic License Plate Recognition Using Local Binary Pattern and Histogram Matching

abstract: This paper proposes new real time license plate recognition (LPR) system that is capable of motion tracking and recognition of license plate. The best frame taken from the video has been chosen which is found to be about 4 m apart from camera position. For further processing, lower half section of vehicle image has been cropped of sized (450 × 140) while tracking. Local Binary Pattern (LBP) and histogram matching technique are used to detect license plate. Due to the robustness of LBP features, this method can adaptively deal with various changes such as rotation, scaling, and illumination in the license plate. Segmentation of the plate region into disjoint characters has been done with bounding box technique with some modifications. Recognition has been done by calculating histogram features. Minimum distance classifier has been used for features matching. The system is tested on more than 300 images and it gives 96.14% detection and 89.35% of recognition accuracy. This system is designed to recognize license plate of small, medium as well as large vehicles. It is also capable to detect single line and two line license plates format.


title: Linearization Approach for 5G Millimeter-Wave N-Array Beamforming Device

abstract: Implementation principles are proposed, and a comparison of linearization solutions is presented for beamforming antenna arrays driven by multiple power amplifiers. The implementation of the feedback portion in a digital predistortion (DPD) system for an array of power amplifiers (PA), which are configured to operate either as one array or as multiple sub-arrays, is shown to be critically important in determining the overall performance of the beamformed transmitter. The proposed feedback solution, based on combining the outputs of multiple PAs in a passive structure embedded in the transmitter’s printed-circuit board (PCB), is shown to be effective in the realization of the DPD system of the beamformed transmitter.


title: A structure for children-oriented Human Computer Interaction

abstract: To improve the existing techniques of Human Computer Interaction (HCI) for children, some principles for HCI design are proposed based on children's psychological and physiological characteristics. Then a HCI structure WisPad is presented for children according to the principles. WisPad incorporates tangible user interface, gesture interaction, Avatar-based HCI, high-level semantics interactive technology and realizes the natural interaction between children and computers.


title: Overextension of conjunctive concepts: Evidence for a unitary model of concept typicality and class inclusion

abstract: Four experiments investigated how people judge both the typicality and membership of items in conjunctive concepts such as school furniture or sports which are games. Judgments of membership in conjunctions were overextended, and there was asymmetry between the constituent concepts in their influence on relative conjunctive concept membership. The results are discussed in the light of recent theoretical disputes about the modeling of concept representations and the process of forming conjunctions (Cohen & Murphy, 1984; Osherson & Smith, 1981, 1982; Smith & Osherson, 1984). A theory is proposed in which constituent intensions are combined to form a composite prototype for the conjunction. Membership in both single and conjunctive concepts is then determined in the same unitary fashion, by placing a membership criterion on the perceived similarity of possible exemplars to the prototype. An important issue in the study of natural language concepts is the way in which common semantic concepts combine to form conjunctions. For a wide range of concepts, two very reliable phenomena have been established: One, members of concept categories vary in their representativeness, and two, for many concepts the boundary around the class of concept members is unclear or "fuzzy." However, to date few empirical studies have considered what happens when two of these fuzzy concepts are placed in conjunction. Do conjunctions show similar typicality and fuzziness phenomena? If so, how can they be related to typicality in the constituent concepts? Do fuzzy concepts follow the same logical rules for conjunction as well-defined concepts? The first aim of this article is to provide empirical evidence that may begin to answer these questions. The current interest in concept conjunctions comes largely from their relevance to the basic question of concept definitions: that is, how a concept picks out an extensional set of concept members. A second aim of this research is therefore to use the study of conceptual combination to provide important constraints on models of conceptualization. In particular, it will be argued that evidence on conjunctions can have theoretical implications for distinguishing the following two opposing accounts of concept definitions.


title: Sonic Interaction Design for Paper Wearables

abstract: The paper reports a workshop on sonic interaction design conceived and led by the authors in the context of a living lab born from the collaboration between a music conservatory and an IT university department. The main subject was the application of non-verbal sound in the process of product design, focusing on the augmentation of clothes and wearable accessories. The workshop resulted in exercises exploring the interactive role of the sound within three different scenarios: (a) abstract/relational, (b) strictly functional, (c) aesthetic/performative. Each exercise was carried on by a group of four to five participants working as a team. The presentation of the exercises comes along considerations regarding the participatory approach, that matched design techniques and tools with practices and theoretical foundations of electroacoustic music. Results are briefly discussed and improvements and further steps are accounted for.


title: SVD-based collaborative filtering with privacy

abstract: Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques recommend products to customers using similar users' preference data. The performance of CF systems degrades with increasing number of customers and products. To reduce the dimensionality of filtering databases and to improve the performance, Singular Value Decomposition (SVD) is applied for CF. Although filtering systems are widely used by E-commerce sites, they fail to protect users' privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from customers is not an easy task. CF systems using these data might produce inaccurate recommendations. In this paper, we discuss SVD-based CF with privacy. To protect users' privacy while still providing recommendations with decent accuracy, we propose a randomized perturbation-based scheme.


New Multibase Non-Adjacent Form Scalar Multiplication and its Application to Elliptic Curve Cryptosystems


title: Information leakage from optical emanations

abstract: A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required; the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack.


title: A Normative Agent System to Prevent Cyberbullying

abstract: Automated approaches to prevent the negative effects of cyber bullying mainly focus on affective agents that provide support for victims. The current paper takes a complementary approach, which attempts to minimise the amount of occurrences of cyber bullying in the first place. The approach consists of a system of normative agents, which are physically present in a virtual society. The agents, which reason based on a BDI-model, use a number of techniques to detect various norm violations, including insulting and following. By using rewards and punishments, they try to reinforce the desired behaviour of the users. The system has been implemented and tested within a virtual environment for children between 6 and 12 years old, called Club Time Machine. In a real world experiment, the behaviour of the users of the virtual environment has been logged and analysed by means of a logic-based checking tool. The results show that the normative agents have the potential to reduce the amount of norm violations on the long term.


title: Ear recognition using pyramid histogram of orientation gradients

abstract: Ear recognition Is still a standing problem In biometrics and has become an open research area in recent years. In this paper, we explore a new local feature extraction technique pyramid histogram of oriented gradients (PHOG) to represent ear images. However, the PHOG descriptor of the ear image is significantly large. To reduce the dimension of the PHOG descriptor, linear discriminant analysis (LDA) has been used to remove noise and avoid over fitting. Finally, the discriminant features are classified using nearest neighbor classifier. The PHOG has inherent properties to efficiently handle the problems of change in pose and partial occlusion of the ear images. The results of the proposed method are evaluated using two public ear databases, namely IIT Delhi ear database and University of Notre Dame ear database (Collections E). Our method with reduced features using LDA offers promising results and largely improves the recognition accuracy over existing methods.


title: Parapagus dicephalus dibrachus dipus: A case of conjoined twins.

abstract: In this report, we describe the case of a woman with a prenatal diagnosis of parapagus dicephalus dibrachus dipus conjoined twins at 14-15 weeks of gestation via two-dimensional ultrasonography. The parents elected to terminate the pregnancy and the patient delivered a 15-cm 130-g male fetus. There were two heads and necks which appeared grossly normal. The thoracic and abdominal cavities were shared. The fetus had four normal limbs. On internal examination, there were two separate structurally normal hearts. There were two larynges and four lungs. Two esophagi fused to enter a single stomach. The diaphragm was common and separated a single abdominal cavity. Distally, the alimentary system including the liver and gallbladder was single.


title: Engineering design thinking, teaching, and learning

abstract: This paper is based on the premises that the purpose of engineering education is to graduate engineers who can design, and that design thinking is complex. The paper begins by briefly reviewing the history and role of design in the engineering curriculum. Several dimensions of design thinking are then detailed, explaining why design is hard to learn and harder still to teach, and outlining the research available on how well design thinking skills are learned. The currently most-favored pedagogical model for teaching design, project-based learning (PBL), is explored next, along with available assessment data on its success. Two contexts for PBL are emphasized: first-year cornerstone courses and globally dispersed PBL courses. Finally, the paper lists some of the open research questions that must be answered to identify the best pedagogical practices of improving design learning, after which it closes by making recommendations for research aimed at enhancing design learning.


title: Recurrent excitation in neocortical circuits.

abstract: The majority of synapses in the mammalian cortex originate from cortical neurons. Indeed, the largest input to cortical cells comes from neighboring excitatory cells. However, most models of cortical development and processing do not reflect the anatomy and physiology of feedback excitation and are restricted to serial feedforward excitation. This report describes how populations of neurons in cat visual cortex can use excitatory feedback, characterized as an effective "network conductance", to amplify their feedforward input signals and demonstrates how neuronal discharge can be kept proportional to stimulus strength despite strong, recurrent connections that threaten to cause runaway excitation. These principles are incorporated into models of cortical direction and orientation selectivity that emphasize the basic design principles of cortical architectures.


title: A Review on Multi-Label Learning Algorithms

abstract: Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.


title: Hue-Preserving Color Blending

abstract: We propose a new perception-guided compositing operator for color blending. The operator maintains the same rules for achromatic compositing as standard operators (such as the over operator), but it modifies the computation of the chromatic channels. Chromatic compositing aims at preserving the hue of the input colors; color continuity is achieved by reducing the saturation of colors that are to change their hue value. The main benefit of hue preservation is that color can be used for proper visual labeling, even under the constraint of transparency rendering or image overlays. Therefore, the visualization of nominal data is improved. Hue-preserving blending can be used in any existing compositing algorithm, and it is particularly useful for volume rendering. The usefulness of hue-preserving blending and its visual characteristics are shown for several examples of volume visualization.


title: Fingerprint-Based Recognition

abstract: Biometric recognition, or biometrics, refers to the authentication of an individual based on her or his biometric traits. Among the various biometric traits (e.g., face, iris, fingerprint, voice), fingerprint-based authentication has the longest history, and it has been successfully adopted in both forensic and civilian applications. Advances in fingerprint capture technology have resulted in new large-scale civilian applications (e.g., US–VISIT program); however, these systems still encounter difficulties due to various noise factors present in operating environments. The purpose of this article is to give an overview of fingerprint-based recognition and discuss research opportunities for making these systems perform more effectively.


An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records


title: Automatic Reverse Engineering of Data Structures from Binary Execution

abstract: With only the binary executable of a program, it is useful to discover the program's data structures and infer their syntactic and semantic definitions. Such knowledge is highly valuable in a variety of security and forensic applications. Although there exist efforts in program data structure inference, the existing solutions are not suitable for our targeted application scenarios. In this paper, we propose a reverse engineering technique to automatically reveal program data structures from binaries. Our technique, called REWARDS, is based on dynamic analysis. More specifically, each memory location accessed by the program is tagged with a timestamped type attribute. Following the program's runtime data flow, this attribute is propagated to other memory locations and registers that share the same type. During the propagation, a variable's type gets resolved if it is involved in a type-revealing execution point or type sink. More importantly, besides the forward type propagation, REWARDS involves a backward type resolution procedure where the types of some previously accessed variables get recursively resolved starting from a type sink. This procedure is constrained by the timestamps of relevant memory locations to disambiguate variables re-using the same memory location. In addition, REWARDS is able to reconstruct in-memory data structure layout based on the type information derived. We demonstrate that REWARDS provides unique benefits to two applications: memory image forensics and binary fuzzing for vulnerability discovery.


title: Automatic Test Case Generation using Sequence Diagram

abstract: Software Testing plays an important role in Software development because it can minimize the development cost. We Propose a Technique for Test Sequence Generation using UML Model Sequence Diagram.UML models give a lot of information that should not be ignored in testing. In This paper main features extract from Sequence Diagram after that we can write the Java Source code for that Features According to ModelJunit Library. ModelJUnit is an extended library of Junit Library. By using that Source code we can Generate Test Case Automatic and Test Coverage. This paper describes a systematic Test Case Generation Technique performed on model based testing (MBT) approaches By Using Sequence Diagram.


abstract: © 2012 Cassisi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining


title: Factors affecting marketing channel choice decisions of smallholder Citrus growers

abstract: Purpose ::: ::: ::: ::: ::: Despite the fact that citrus industry is growing in Pakistan where production is dominated by small-scale citrus growers, no research has been done to evaluate and analyse the marketing channel choice decision-making process of these citrus growers. The purpose of this paper is to review the existing factors affecting decision making of small-size citrus growers in citrus supply chain in Pakistan and also measures and analyses the factors that affect the marketing channel decisions of these small size growers. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: A multivariate decision analysis technique, conjoint analysis, was used to analyse and evaluate the major factors affecting marketing channel choice decisions of small-size citrus growers. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: It was concluded that among nine very important factors, six factors played a major role in the marketing channel choice decision making. Only three factors namely, “price”, “time of complete fruit picking” and “number of fruit pickings” were considered highly important factors and reflected that citrus growers not only want to secure his payments but also to save their orchards for next season(s). ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The research would provide valuable information about citrus grower’s marketing decision-making process and thus would contribute to improving the efficiency and effectiveness of citrus industry in Pakistan. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This analysis would suggest them to make rational decisions and help choosing them the most appropriate combination of different levels of different factors which would increase their profits. This study identifies the most relevant factors and their levels which citrus growers and pre-harvest contractors preferred in citrus marketing channel choice decision making, thus, server as a guideline for the buyer’s to deal with them accordingly. This would improve the efficiency and reduce the transaction cost along the citrus supply chain in Pakistan.


title: On the Wrong Track: Process and Content in Moral Psychology

abstract: According to Joshua Greene's influential dual process model of moral judgment, different modes of processing are associated with distinct moral outputs: automatic processing with deontological judgment, and controlled processing with utilitarian judgment. This article aims to clarify and assess Greene's model. I argue that the proposed tie between process and content is based on a misinterpretation of the evidence, and that the supposed evidence for controlled processing in utilitarian judgment is actually likely to reflect, not ‘utilitarian reasoning’, but a form of moral deliberation which, ironically, is actually in serious tension with a utilitarian outlook. This alternative account is further supported by the results of a neuroimaging study showing that intuitive and counterintuitive judgments have similar neural correlates whether or not their content is utilitarian or deontological.


title: ROAD DETECTION FROM HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS

abstract: Abstract This article treats the possibility of using artificial neural networks for road detection from high-resolution satellite images on a part of RGB Ikonos and Quick-Bird images from Kish Island and Bushehr Harbor, respectively. Attempts are also made to verify the impacts of different input parameters on network's ability to find out optimum input vector for the problem. A variety of network structures with different iteration times are used to determine the best network structure and termination condition in training stage. It was found that when the input parameters are made up of spectral information and distances of pixels to road mean vector in a 3 × 3 window, the network's ability in both road and background detection can be improved in comparison with simple networks that simply use spectral information of a single pixel in their input vector.


title: Finer-Net: Cascaded Human Parsing with Hierarchical Granularity

abstract: Human parsing is a challenging and important task in various applications, such as dress collocation, clothing recommendation and action analysis. However, the existing methods are easily affected by pose variation and occlusion with requiring massive intensive annotations for fine-grained human segmentation. In this paper, we design a cascaded segmentation network with three stages to solve the above problems. Given a human image, we firstly predict the human joints as pose features. Secondly, these features along with the input image are fed into the first stage to obtain a primitive segmentation map to separate the human and the background. The primitive segmentation is then fed into the second stage with the original image to give a rough segmentation of human body. This procedure is repeated in the third stage to acquire a refined segmentation. Experimental results demonstrate the proposed method achieve superior performance than state-of-the-arts and show great generalization ability.


title: Anonymity and roles associated with aggressive posts in an online forum

abstract: Cyberbullying is a growing concern in online communications. Cyberbullying has negative impacts such as distress or suicide of a victim. One common type of cyberbullying attack utilizes aggressive forum posts to insult or threaten a victim. Automated tools to classify cyberbullying may aid in avoiding or reducing the negative impacts of cyberbullying. One approach to produce an automated tool is to identify features of forum posts which may be indicators of cyberbullying. One feature of a forum post is the role of the author of the forum post, such as a bully, victim, or defender. Another feature is whether the forum post insults or threatens an individual (e.g., contains insults directed at a victim). Attackers may use aggressive forum posts to attack someone and defenders may use aggressive forum posts to retaliate against attackers. Another feature is whether the communication is anonymous (e.g., sending forum posts with no identifier) since cyberbullies utilize anonymity to reduce the ability of the victim to defend themselves and to shield the cyberbully from social consequences. In this paper, forum posts were labeled in an online forum for these features. Text matching techniques had some success in identifying aggressiveness forum posts including both attacks and defends. Anonymity of forum posts (i.e., forum posts with no identifier) was identified as a criterion to distinguish attackers (more anonymous relative to non-aggressive communications) from defenders (less anonymous relative to non-aggressive communications).


title: Place Recognition Using Regional Point Descriptors for 3D Mapping

abstract: In order to operate in unstructured outdoor environments, globally consistent 3D maps are often required. In the absence of a absolute position sensor such as GPS or modifications to the environment, the ability to recognize previously observed locations is necessary to identify loop closures. Regional point or keypoint descriptors are a way to encode the structure within a small local region as a fixedsized vector, though individually do not include enough context to fully identify a previously seen place. Multiple queries to a database of descriptor vectors can quickly identify similar features, and places can be recognized from a consistent set of descriptor matches.We investigate the problem of designing informative keypoint descriptors for 3D laser maps. Several models are considered and evaluated, with a particular focus on the optimal descriptor scale and keypoint sampling density. The approach is evaluated on 3D laser point cloud data collected from a vehicle driving in unstructured off-road environments. Consistent 3D maps constructed from this data without assistance from any other sensor (such as wheel encoders, GPS, or IMU) demonstrate the effectiveness of our approach.


title: 2D ear classification based on unsupervised clustering

abstract: Ear classification refers to the process by which an input ear image is assigned to one of several pre-defined classes based on a set of features extracted from the image. In the context of large-scale ear identification, where the input probe image has to be compared against a large set of gallery images in order to locate a matching identity, classification can be used to restrict the matching process to only those images in the gallery that belong to the same class as the probe. In this work, we utilize an unsupervised clustering scheme to partition ear images into multiple classes (i.e., clusters), with each class being denoted by a prototype or a centroid. A given ear image is assigned class labels (i.e., cluster indices) that correspond to the clusters whose centroids are closest to it. We compare the classification performance of three different texture descriptors, viz. Histograms of Oriented Gradients, uniform Local Binary Patterns and Local Phase Quantization. Extensive experiments using three different ear datasets suggest that the Local Phase Quantization texture descriptor scheme along with PCA for dimensionality reduction results in a 96.89% hit rate (i.e., 3.11% pre-selection error rate) with a penetration rate of 32.08%. Further, we demonstrate that the hit rate improves to 99.01% with a penetration rate of 47.10% when a multi-cluster search strategy is employed.


title: Microsystem technologies for medical applications.

abstract: Medical technologies are evolving at a very rapid pace. Portable communication devices and other handheld electronics are influencing our expectations of future medical tools. The advanced medical technologies of our future will not necessarily be large expensive systems. They are just as likely to be small and disposable. This paper reviews how microsystems are already impacting health care as commercial products or in clinical development. Example systems for point-of-care testing (POCT), patient monitoring tools, systemic drug delivery, local drug delivery, and surgical tools are described. These technologies are moving care from hospitals to outpatient settings, physicians' offices, community health centers, nursing homes, and the patients' homes. Microsystems that are rapidly adopted fulfill significant medical needs and are compatible with existing clinical practice.


title: TPAD: Hardware Trojan Prevention and Detection for Trusted Integrated Circuits

abstract: There are increasing concerns about possible malicious modifications of integrated circuits (ICs) used in critical applications. Such attacks are often referred to as hardware Trojans. While many techniques focus on hardware Trojan detection during IC testing, it is still possible for attacks to go undetected. Using a combination of new design techniques and new memory technologies, we present a new approach that detects a wide variety of hardware Trojans during IC testing and also during system operation in the field. Our approach can also prevent a wide variety of attacks during synthesis, place-and-route, and fabrication of ICs. It can be applied to any digital system, and can be tuned for both traditional and split-manufacturing methods. We demonstrate its applicability for both application-specified integrated circuits and field-programmable gate arrays. Using fabricated test chips with Trojan emulation capabilities and also using simulations, we demonstrate: 1) the area and power costs of our approach can range between 7.4%–165% and 7%–60%, respectively, depending on the design and the attacks targeted; 2) the speed impact can be minimal (close to 0%); 3) our approach can detect 99.998% of Trojans (emulated using test chips) that do not require detailed knowledge of the design being attacked; 4) our approach can prevent 99.98% of specific attacks (simulated) that utilize detailed knowledge of the design being attacked (e.g., through reverse engineering); and 5) our approach never produces any false positives, i.e., it does not report attacks when the IC operates correctly.


title: Consistent depth maps recovery from a trinocular video sequence

abstract: In this paper, we propose a novel dense depth recovery method for a trinocular video sequence. Specifically, we contribute a novel trinocular stereo matching model, which can effectively utilize the advantages of trinocular stereo images, and incorporate the visibility term with segmentation prior for robust depth estimate. In order to make the recovered depth maps more accurate and temporally consistent, we propose to first classify the pixels to static and dynamic ones, and then perform spatio-temporal depth optimization for them in different ways. Especially, we propose two motion models for handling dynamic pixels. The traditional bundle optimization model and our spatio-temporal optimization model are softly combined in a probabilistic way, so that the depths of both static and dynamic pixels can be effectively refined. Our automatic depth recovery approach is evaluated using a variety of challenging trinocular video sequences.


title: E-commerce in Indonesia: Purchasing Decision of Shopping Online

abstract: The purpose of this study is to identify relationships between trust, convenience and purchase decision for shopping online in Indonesia. We do set variable trust in eight sub-variable, conveniences in five sub-variable and purchase decision in four sub-variable. We use regression analysis to describe how trust and convenience affect the customer to purchase services and goods by online. We collect the data by online questionnaire from 118 respondents. The finding of this research study show that trust and convenience is significantly positive effect to purchase decision for customer in Indonesia. The findings may be useful for online store owner in treating consumer by trust and convenience improvement.


Symmetry and efficiency in complex FIR filters


title: A Dual-Polarization Slotted Waveguide Array Antenna With Polarization-Tracking Capability and Reduced Sidelobe Level

abstract: This communication presents an innovative dual-polarization antenna working at Ku band ( $\text{14} \div \text{14.5}\ {\text{GHz}}$ ) based on slotted waveguide array (SWGA) technology. The proposed solution allows to simultaneously generate two orthogonal circular polarizations that are combined to generate two orthogonal linear polarizations with polarization-tracking capabilities and very low cross-polarization level thanks to the symmetry of the structure. The antenna is composed of two interleaved slotted ridge waveguide arrays connected to a polarizer, realized with an array of triangular-base ridged cavities. Slots, located at the waveguide centerline, are excited by means of posts avoiding offsets and leading to a regular grid arrangement. Radiating elements are placed in a triangular lattice to minimize the azimuth sidelobe level and provide high EIRP levels.


Enriching Text Summarization using Fuzzy Logic


title: Organizational memory information systems: a transactive memory approach

abstract: Effective management of organizational memory (OM) is critical to collaboration and knowledge sharing in organizations. We present a framework for managing organizational memory based on transactive memory, a mechanism of collective memory in small groups. While being effective in small groups, there are difficulties hindering the extension of transactive memory to larger groups. We claim that information technology can be used to help overcome these difficulties. We present a formal architecture for directories of meta-memories required in extended transactive memory systems and propose the use of meta-knowledge to substitute for the lack of tacit group knowledge that exists in small groups.


Flow Benefits of Autonomous Intelligent Cruise Control in Mixed Manual and Automated Traffic