title: Iris Recognition in Less Constrained Environment

abstract: Biometric based on the physical and behavioural characteristics are widely adopted and are used to recognize person uniquely in a natural and intuitive way. Biometric traits are unique to the individual. Among biometric traits such as face, signature, thumb etc, Iris biometric is the most reliable authentication method. Most of the commercial iris biometric systems operate in a constrained environment. Iris images captured in the constrained environment have sufficient information to discriminate individual from another. Such iris recognition system shows good recognition rate but Performance of the system degrades in noisy environment. This paper presents the study of iris recognition in less constrained environment. Also the challenges are discussed.


title: Twitter as a Learning Community in Higher Education

abstract: Introduction Didactic methods incorporating the new technologies are evolving slowly, which is why teachers should also update their skills to include the use of Information and Communication Technologies (ICT). Still, these resources are starting to contribute to the promotion of new forms of learning, forming part of a new emerging paradigm involving the construction and production of knowledge in cyberspace. Access to such knowledge makes vast quantities of information available to people, instantaneously or with a certain time delay, as well as facilitating contact with and knowledge of countless resources and communication platforms in any place at any time (Johnson, Smith, Willis, Levine, & Haywood, 2011; Kop, 2012). The Internet is a valuable resource in higher education, especially as a means of bridging long distances, thereby promoting the feedback process, a key component of learning. Moreover, it promotes the motivation and regulation of students in the training process. Certain authors go even further (Fried, 2008; Hembrooke & Gay, 2003), indicating that ICTs have a negative impact on learning, as they disperse attention and promote the diversification of the type of tasks carried out on an autonomous basis. Although others researchers as Hsu and Ching (2012) indicated that the proliferation of Web 2.0 applications--especially the social media--among people and the ever greater use of mobile devices also contributes to broaden the possibilities of ubiquitous, collaborative learning. Still, the Internet as a source of learning continues to expand and gain ground, as it generates and facilitates communication and the acquisition of a great number and diversity of resources, and provides different ways to apply fundamental principles of learning in higher education (Gernsbacher, 2015). It is also worth noting that it gives rise to knowledge, though most of its content is not directly associated with regulated learning and the physical space of classrooms. On the Internet we can all be consumers, creators and disseminators at the same time. This is one of the premises of the EMIREC model (Cloutier, 1975): any emitter is a receiver and any receiver is also an emitter. Kaplan and Haenlein (2010) define social media as the applications that are supported on the Internet and are based on the ideological and technological foundations of Web 2.0 and allow creating and interacting with contents generated by users by open and free means. In this sense, Khan (2013) outlined that they provide opportunities to users to develop relationships, communication, and collaboration (sharing contents). These social media integrate a large variety of emergent tools and technologies as wikis, blogs, and microblogging, content communities (YouTube, Pinterest, Slideshare), social nets (Twitter, Facebook, etc.), social tagging and folksonomies (Delicious, Addthis, Diigo), online games, collaborative plataforms, and other web 2.0 platforms where users create, exchange, comment, and value their own contents. Today no one questions the great educational potential of Web 2.0 tools, but their integration into learning contexts still has to take off, and one cannot as yet speak of widespread use. Diaz-Gandasegui (2011) holds that social media are one of the main sources of leisure among the younger generations. This helps them to acquire the technical skills required for the use of the new technologies, although it does not ensure a widespread familiarisation or acquisition of skills within this group. It is also worth noting that, owing to the way in which they invade people's lives, social media can be seen either as a potential threat, or alternatively as a great opportunity that can be extended to different facets (Stoughton, Thompson, & Meade, 2015). However, the use of social media as a didactic tool is still at an incipient stage, despite the accessibility made possible by their application, as they are easy to use and available as an open resource on the Internet. …


title: Financial cost of social exclusion: follow up study of antisocial children into adulthood

abstract: Abstract Objectives: To compare the cumulative costs of public services used through to adulthood by individuals with three levels of antisocial behaviour in childhood. Design: Costs applied to data of 10 year old children from the inner London longitudinal study selectively followed up to adulthood. Setting: Inner London borough. Participants: 142 individuals divided into three groups in childhood: no problems, conduct problems, and conduct disorder. Main outcome measures: Costs in 1998 prices for public services (excluding private, voluntary agency, indirect, and personal costs) used over and above basic universal provision. Results: By age 28, costs for individuals with conduct disorder were 10.0 times higher than for those with no problems (95% confidence interval of bootstrap ratio 3.6 to 20.9) and 3.5 times higher than for those with conduct problems (1.7 to 6.2). Mean individual total costs were £70 019 for the conduct disorder group (bootstrap mean difference from no problem group £62 898; £22 692 to £117 896) and £24 324 (£16 707; £6594 to £28 149) for the conduct problem group, compared with £7423 for the no problem group. In all groups crime incurred the greatest cost, followed by extra educational provision, foster and residential care, and state benefits; health costs were smaller. Parental social class had a relatively small effect on antisocial behaviour, and although substantial independent contributions came from being male, having a low reading age, and attending more than two primary schools, conduct disorder still predicted the greatest cost. Conclusions: Antisocial behaviour in childhood is a major predictor of how much an individual will cost society. The cost is large and falls on many agencies, yet few agencies contribute to prevention, which could be cost effective. What is already known on this topic Children who show substantial antisocial behaviour have poor social functioning as adults and are at high risk of social exclusion Costs are available for particular items of public service such as receiving remedial education or appearing in court What this study adds Costs of antisocial behaviour incurred by individuals from childhood to adulthood were 10 times greater for those who were seriously antisocial in childhood than for those who were not The costs fell on a wide range of agencies Reduction of antisocial behaviour in childhood could result in large cost savings


title: Total vaginectomy and urethral lengthening at time of neourethral prelamination in transgender men

abstract: For transgender men (TGM), gender-affirmation surgery (GAS) is often the final stage of their gender transition. GAS involves creating a neophallus, typically using tissue remote from the genital region, such as radial forearm free-flap phalloplasty. Essential to this process is vaginectomy. Complexity of vaginal fascial attachments, atrophy due to testosterone use, and need to preserve integrity of the vaginal epithelium for tissue rearrangement add to the intricacy of the procedure during GAS. We designed the technique presented here to minimize complications and contribute to overall success of the phalloplasty procedure. After obtaining approval from the Institutional Review Board, our transgender (TG) database at the University of Miami Hospital was reviewed to identify cases with vaginectomy and urethral elongation performed at the time of radial forearm free-flap phalloplasty prelamination. Surgical technique for posterior vaginectomy and anterior vaginal wall-flap harvest with subsequent urethral lengthening is detailed. Six patients underwent total vaginectomy and urethral elongation at the time of radial forearm free-flap phalloplasty prelamination. Mean estimated blood loss (EBL) was 290 ± 199.4 ml for the vaginectomy and urethral elongation, and no one required transfusion. There were no intraoperative complications (cystotomy, ureteral obstruction, enterotomy, proctotomy, or neurological injury). One patient had a urologic complication (urethral stricture) in the neobulbar urethra. Total vaginectomy and urethral lengthening procedures at the time of GAS are relatively safe procedures, and using the described technique provides excellent tissue for urethral prelamination and a low complication rate in both the short and long term.


title: The State-of-the-Art in IC Reverse Engineering

abstract: This paper gives an overview of the place of reverse engineering (RE) in the semiconductor industry, and the techniques used to obtain information from semiconductor products. ::: ::: The continuous drive of Moore's law to increase the integration level of silicon chips has presented major challenges to the reverse engineer, obsolescing simple teardowns, and demanding the adoption of new and more sophisticated technology to analyse chips. Hardware encryption embedded in chips adds a whole other level of difficulty to IC analysis. ::: ::: This paper covers product teardowns, and discusses the techniques used for system-level analysis, both hardware and software; circuit extraction, taking the chip down to the transistor level, and working back up through the interconnects to create schematics; and process analysis, looking at how a chip is made, and what it is made of. Examples are also given of each type of RE. The paper concludes with a case study of the analysis of an IC with embedded encryption hardware.


title: Automated product taxonomy mapping in an e-commerce environment

abstract: We propose an algorithm for automatic product taxonomy mapping in e-commerce.The algorithm uses word sense disambiguation techniques to handle heterogeneity.Our algorithm copes with composite categories in product category names.We compute path-similarities using lexical relatedness and structural information.Using real-world data, we show that we improve existing state-of-the-art methods. Over the last few years, we have experienced a steady growth in e-commerce. This growth introduces many problems for services that want to aggregate product information and offerings. One of the problems that aggregation services face is the matching of product categories from different Web shops. This paper proposes an algorithm to perform this task automatically, making it possible to aggregate product information from multiple Web sites, in order to deploy it for search, comparison, or recommender systems applications. The algorithm uses word sense disambiguation techniques to address varying denominations between different taxonomies. Path similarity is assessed between source and candidate target categories, based on lexical relatedness and structural information. The main focus of the proposed solution is to improve the disambiguation procedure in comparison to an existing state-of-the-art approach, while coping with product taxonomy-specific characteristics, like composite categories, and re-examining lexical similarity and similarity aggregation in this context. The performance evaluation based on data from three real-world Web shops demonstrates that the proposed algorithm improves the benchmarked approach by 62% on average F 1 -measure.


Bregman Divergences and Triangle Inequality


title: STRIP: A Defence Against Trojan Attacks on Deep Neural Networks

abstract: A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker's chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STR ong I ntentional P erturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model---malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input---a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks.


title: Goffman in the Gallery: Interactive Art and Visitor Shyness

abstract: In an effort to facilitate public engagement, contemporary art galleries and museums house interactive exhibits incorporating digital media. Despite removing traditional barriers of cultural capital, however, these ::: exhibitions now presume a level of technological and performative competence, which can feel equally intimidating to visitors. Reporting on an UK-based ethnographic study and using dramaturgical theory, we show how interactive exhibitions can evoke situational shyness in visitors, through the combination of a demand for active, performative engagement and the deliberate restriction of instructional and explanatory information. In this ambiguous setting, visitors search for a social script to guide their action, the absence or opaqueness of which creates self-conscious inhibition. Actors adapt to this resourcefully by looking toward others to provide a replacement script; these may be companion visitors, strangers, or imaginary audiences. Some visitors, meanwhile, demonstrate resistance by refusing to engage with ::: the interactive art agenda altogether, preferring to assume a role of detached spectatorship.


title: Detailing Motivation in a Gamification Process

abstract: Gamification has been increasingly implemented in systems to motivate users. To assist designers in the gamification process there are several frameworks which are generic structures that serve as a basis to the gamification. Some frameworks address motivation superficially while others focus exclusively on motivation and are not broad enough for the entire process. This work presents 5W2H+M emphasized, an extension of the 5W2H framework with its motivational aspects, based on Self-Determination Theory.


title: Time-band limiting and the bispectral problem

abstract: Film forwarding mechanism comprising a reciprocally slidable plate carrying pawl to engage and forward film. The plate is normally held at a first position, is released subsequent to camera shutter action, moved to and held at an intermediate position until released therefrom by action of a rotary drive mechanism, moved to a second position, then moved by the drive mechanism from the second position to the first position, the pawl forwarding film during plate movement from the second to the first position.


title: Decade bandwidth high-efficiency GaN VHF/UHF power amplifier

abstract: This work discusses the design of a GaN power amplifier demonstrating high efficiency over more than a decade bandwidth using coaxial baluns and transformer matching networks to achieve over a 50MHz-500 MHz bandwidth. The power amplifier demonstrates a power added efficiency of 83%-64% over full bandwidth with 15 dB compressed gain at peak PAE.


title: A Survey Investigating Usage of Virtual Personal Assistants

abstract: Despite significant improvements in automatic speech recognition and spoken language understanding - human interaction with Virtual Personal Assistants (VPAs) through speech remains irregular and sporadic. According to recent studies, currently the usage of VPAs is constrained to basic tasks such as checking facts, playing music, and obtaining weather updates.In this paper, we present results of a survey (N = 118) that analyses usage of VPAs by frequent and infrequent users. We investigate how usage experience, performance expectations, and privacy concerns differ between these two groups. The results indicate that, compared with infrequent users, frequent users of VPAs are more satisfied with their assistants, more eager to use them in a variety of settings, yet equally concerned about their privacy.


title: Personalized recommendation on dynamic content using predictive bilinear models

abstract: In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.


Dynamic-CSR : A Format for Dynamic Sparse-Matrix Updates


An Ontology Based Formalism for the Arabic Language Using Verbs and their Derivatives


title: A smart city initiative: a case study of Porto Alegre 156

abstract: Many cities around the world are investing in "smart" ways to manage the problems associated with urbanization and agglomeration of people. The challenge is how to implement initiatives to attend citizen's necessities aiming a better quality of life. The purpose of this paper is to describe the "156 Speaks Porto Alegre", channel driven to citizen municipal demands, and investigate its integration and relationship with the eight dimensions of the Smart City Initiatives Framework: technology, management and organization, policy context, governance, people and communities, economy, built infrastructure, and natural environment. The main findings show that coordination between agencies and departments are needed to achieve citizen better quality of life. They also demonstrate that the use of the right technology is really important and a smart city initiative must have mobility and easy interaction with citizen. Finally, interdepartmental collaboration and cooperation are considered central issues to achieve initiatives' objectives.


title: FORMULATION AND NUMERICAL SOLUTION OF ROBOT MANIPULATORS IN POINT-TO-POINT MOTION WITH MAXIMUM LOAD CARRYING CAPACITY

abstract: In this paper, a formulation is developed for obtaining the optimal trajectory of robot manipulators to maximize the load carrying capacity for a given point-to-point task. The presented method is based on open loop optimal control. The indirect approach is employed to derive optimality conditions based on Pontryagin's Minimum Principle. The obtained necessary conditions for optimality lead to a two-point boundary-value problem solved via a multiple shooting method with the BVP4C command in MATLAB r . Since the carrying payload is one of the system parameters, a computational algorithm is developed, which provides the capability of calculating the maximum payload for a point-to-point task. The main advantage of this method is obtaining various optimal trajectories with di erent maximum payloads and path characteristics by changing the penalty matrices values. To demonstrate the e ciency of the proposed method and algorithm in obtaining the maximum payload trajectory, simulation is performed on a two-link manipulator.


title: Sketches by Paul the robot

abstract: In this paper we describe Paul, a robotic installation that produces observational sketches of people. The sketches produced have been considered of interest by fine art professionals in recent art fairs and exhibitions, as well as by the public at large. We identify factors that may account for the perceived qualities of the produced sketches. A technical overview of the system is also presented.


title: Dense estimation of fluid flows

abstract: In this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from computer vision, originally designed for quasi-rigid motions with stable salient features, are not well adapted in this context. We thus investigate a dedicated minimization-based motion estimator. The cost function to be minimized includes a novel data term relying on an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements. This term is associated with an original second-order div-curl regularization which prevents the washing out of the salient vorticity and divergence structures. The performance of the resulting fluid flow estimator is demonstrated on meteorological satellite images. In addition, we show how the sequences of dense motion fields we estimate can be reliably used to reconstruct trajectories and to extract the regions of high vorticity and divergence.


title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics

abstract: While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of these data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/.


title: Confiding in and Listening to Virtual Agents: The Effect of Personality

abstract: We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a real-world recruiting event. We present findings from completed interviews with 316 actual job applicants. Notably, users are more willing to confide in and listen to a virtual interviewer with a serious, assertive personality. Moreover, users' personality traits, inferred from their chat text, influence their perception of a virtual interviewer, and their willingness to confide in and listen to a virtual interviewer. Finally, we discuss the implications of our work on building hyper- personalized, intelligent agents based on user traits.


title: A Systematic Approach to Generate Inputs to Test UML Design Models

abstract: Practical model validation techniques are needed for model driven development (MDD) techniques to succeed. This paper presents an approach to generating inputs to test UML design models that are produced in the detailed design phase of an MDD project. A symbolic execution based approach is used to derive test input constraints from the paths of a Variable Assignment Graph, which integrates information from UML class and sequence diagrams. The constraints are solved using Alloy, a configuration constraint solver, to obtain the test inputs. The results of a pilot study carried out to explore the fault detection capability of the test inputs are reported.


title: HappyJIT: a tracing JIT compiler for PHP

abstract: Current websites are a combination of server-generated dynamic content with client-side interactive programs. Dynamically - typed languages have gained a lot of ground in both of these domains. The growth of Web 2.0 has introduced a myriad of websites which contain personalized content, which is specific to the user. PHP or Python programs generate the actual HTML page after querying a database and processing the results, which are then presented by the browser. It is becoming more and more vital to accelerate the execution of these programs, as this is a significant part of the total time needed to present the page to the user. This paper presents a novel interpreter for the PHP language written in RPython, which the PyPy translator then translates into C. The translator integrates into the interpreter a tracing just-in-time compiler which optimizes the hottest loops in the interpreted programs. We also describe a data model that supports all the data types in the PHP language, such as references and iterators. We evaluate the performance of this interpreter, showing that speedups up to a factor of 8 are observed using this approach.


title: Improvisation as a tool to develop creativity mini-workshop divergent thinking

abstract: This workshop is meant for all lecturers and researchers of the conference, to experience the benefits of improvisation for themselves or for their students. In educating the inventors of our future, it is important to address creativity as a condition for innovation in Engineering courses of study. Part of the creative potential lies in the ability of divergent thinking. This looking for not yet existing patterns is also one of the characteristics of improvisation. Breaking the routine, staying positive, putting focus on others, staying in the present moment and daring to fail are the basic rules for improvisation, with having fun as a bonus. As Albert Einstein said, no problem can be solved from the same level of consciousness that created it. A disruption in the existing thinking and acting patterns has to be initiated. Giving participants no time to prepare or think things through, in other words giving them the opportunity and obligation to improvise is a mean to cause such a disruption. This way, participants get in the right mood, step out of their comfort zone and focus on the things that are happening in the here-and-now, in essence the right conditions for enhancing divergent thinking. The workshop in improvisation skills lasts 90 minutes and has the following structure: 00.00-00.20 hours: physical and concentration warming up 00.20-00.40 hours: exercises in pairs 00.40-01.10 hours: games 01.10-01.20 hours: cooling down 01.20-01.30 hours: evaluation In all parts of the workshop, people are challenged to solve problems with no more than their imagination. This way, they learn to take initiative, listen carefully, react spontaneously and enjoy making mistakes. The essence of improvisation touches the heart of creativity: acting in the here-and-now, accepting mistakes and learning to look for novel associations. The workshop can serve 30 participants maximum. The workshop leader is a lecturer professional skills who recently started a Ph.D.-research on innovative behavior in higher Engineering education. She teaches improvisation for almost six years now in an Engineering course of study in the Netherlands. She is also the co-author of a book (in press) about improvisation and entrepreneurship. After the workshop, participants will leave in a good mood, while thinking of ways how to stimulate creativity. A hand-out with more information will be provided.


title: Kinetics of electrolytic hydrogen and deuterium evolution

abstract: Calculations of the isotopic ratio of exchange currents (i$\_0$) for cathodic hydrogen and deuterium evolution have been made for limiting cases in a previous publication and the dependence of the ratio upon reaction mechanism discussed. Experimental values of (i$\_0$)$\_H$/(i$\_0$)$_D$ have now been obtained for nickel, copper, silver, gold, platinized platinum and palladium under conditions of high solution purity and cleanliness of electrode surface. Control experiments on the kinetics of hydrogen evolution at each of the above metals have also been carried out. The isotopic ratios of exchange currents are discussed in terms of reaction mechanisms. Previous calculations are improved by taking into account surface coverage by adsorbed hydrogen or deuterium. A series of new limiting values for the isotopic ratios of exchange currents is presented and assignments of rate-determining mechanism in cathodic hydrogen and deuterium evolution are suggested for several metals.


title: Outdoor Mapping and Navigation using Stereo Vision

abstract: We consider the problem of autonomous navigation in an unstructured outdoor environment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass, shadows, deadfall, and other obstacles predominate. Not surprisingly, the biggest challenge is acquiring and using a reliable map of the new area. Although work in outdoor navigation has preferentially used laser rangefinders [14,2,6], we use stereo vision as the main sensor. Vision sensors allow us to use more distant objects as landmarks for navigation, and to learn and use color and texture models of the environment, in looking further ahead than is possible with range sensors alone.


title: Being in the zone: Flow state and the underlying neural dynamics in video game playing

abstract: Flow is a hyper-engaged state of consciousness most commonly described in athletics, popularly termed being in the zone. Quantitative research into flow has been hampered by the disruptive nature of gathering subjective reports. Here we show that a passive probe (suppression of Auditory Evoked Potential in EEG) that allowed our participants to remain engaged in a first-person shooting game while we continually tracked the depth of their immersion corresponded with the participants' subjective experiences, and with their objective performance levels. Comparing this time-varying record of flow against the overall EEG record, we identified neural correlates of flow in the anterior cingulate cortex and the temporal pole. These areas displayed increased beta band activity, mutual connectivity, and feedback connectivity with primary motor cortex. These results corroborate the notion that the flow state is an objective and quantifiable state of consciousness, which we identify and characterize across subjective, behavioral and neural measures.


title: Splish: A Visual Programming Environment for Arduino to Accelerate Physical Computing Experiences

abstract: This paper presents an overview of visual programming environment named Splish which enables an icon-based visual programming to develop a program which runs on a microcontroller board family called Arduino which is a popular platform for physical computing. A user program can be developed visually on a PC side, and the compiled code will be transferred to the microcontroller board so that runtime environment of Splish can execute the complied code by interpreting the machine instructions of a simple virtual stack machine. This functional distribution allows the interactive debugging when the microcontroller board is connected to a PC. Because physical computing attracts wide variety of people including non-specialists and students, the visual programming and the interactive debugging capabilities of Splish will accelerate their physical computing experiences. Splish is developed with JavaFX to achieve platform independence.


title: Cloud-based monitoring and analysis of yield efficiency in precision farming

abstract: Yield mapping visualizes yield rate per geological distribution. It is frequently used as a baseline metric to measure yield efficiency in precision farming. A major challenge in mapping yield for specialty crops is how to collect accurate yield data without incurring substantial overhead to a farming operation. We design a yield efficiency analysis system that uses a cloud-based computing platform to acquire and analyze yield data. By reusing labor data collected by a cloud-based labor monitoring system that we developed earlier, our system calculates yield data from labor data, and computes yield map in real time and without the overhead for data acquisition. A distinctive feature of our approach is the introduction of a customizable yield distribution function that quantifies the probability of geographic distribution of fruits weighted at a Labor Monitoring Device. Practitioners may define yield distribution functions based on operational characteristics of an orchard, enabling our system adaptive for a variety of orchards with different harvesting operations and canopy architecture. Using a multi-tenancy software architecture, our system can support multiple orchards concurrently with improved scalability and data privacy. Our system has been deployed and tested on Amazon Web Services (AWS).


title: Encoded bitmap indexing for data warehouses

abstract: Complex query types, huge data volumes, and very high read/update ratios make the indexing techniques designed and tuned for traditional database systems unsuitable for data warehouses (DW). We propose an encoded bitmap indexing for DWs which improves the performance of known bitmap indexing in the case of large cardinality domains. A performance analysis and theorems which identify properties of good encodings for better performance are presented. We compare encoded bitmap indexing with related techniques, such as bit slicing, projection-, dynamic-, and range-based indexing.


title: The Gut Microbiota and Autism Spectrum Disorders

abstract: Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.


title: Blockchain technology innovations

abstract: Digital world has produced efficiencies, new innovative products, and close customer relationships globally by the effective use of mobile, IoT (Internet of Things), social media, analytics and cloud technology to generate models for better decisions. Blockchain is recently introduced and revolutionizing the digital world bringing a new perspective to security, resiliency and efficiency of systems. While initially popularized by Bitcoin, Blockchain is much more than a foundation for crypto currency. It offers a secure way to exchange any kind of good, service, or transaction. Industrial growth increasingly depends on trusted partnerships; but increasing regulation, cybercrime and fraud are inhibiting expansion. To address these challenges, Blockchain will enable more agile value chains, faster product innovations, closer customer relationships, and quicker integration with the IoT and cloud technology. Further Blockchain provides a lower cost of trade with a trusted contract monitored without intervention from third parties who may not add direct value. It facilitates smart contracts, engagements, and agreements with inherent, robust cyber security features. This paper is an effort to break the ground for presenting and demonstrating the use of Blockchain technology in multiple industrial applications. A healthcare industry application, Healthchain, is formalized and developed on the foundation of Blockchain using IBM Blockchain initiative. The concepts are transferable to a wide range of industries as finance, government and manufacturing where security, scalability and efficiency must meet.


title: Design and realization of a single-phase inverter with numerical control based on an Atmega32

abstract: This article aims to develop the control circuit of a single-phase inverter following our previous studies on the MPPT control and photovoltaic systems, this inverter produces a pure sine wave with an output voltage that has the same amplitude and frequency as Network voltage. A microcontroller, based on advanced technology to generate a sinusoid with fewer harmonics, less cost and a simpler design. The technique used is the sinusoidal pulse width modulation generated by the ATmega32 microcontroller. The designed inverter is tested on various AC loads and is primarily focused on applications such as an AC lamp. Proposed model of the inverter can improve the waveform of the inverter and control the dead time. The finished design is simulated in Proteus to ensure output results that is virtually verified.


title: Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

abstract: Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a “rain component” and a “nonrain component” by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.


title: Flexible control of small wind turbines with grid failure detection operating in stand-alone and grid-connected mode

abstract: This paper presents the development and test of a flexible control strategy for an 11-kW wind turbine with a back-to-back power converter capable of working in both stand-alone and grid-connection mode. The stand-alone control is featured with a complex output voltage controller capable of handling nonlinear load and excess or deficit of generated power. Grid-connection mode with current control is also enabled for the case of isolated local grid involving other dispersed power generators such as other wind turbines or diesel generators. A novel automatic mode switch method based on a phase-locked loop controller is developed in order to detect the grid failure or recovery and switch the operation mode accordingly. A flexible digital signal processor (DSP) system that allows user-friendly code development and online tuning is used to implement and test the different control strategies. The back-to-back power conversion configuration is chosen where the generator converter uses a built-in standard flux vector control to control the speed of the turbine shaft while the grid-side converter uses a standard pulse-width modulation active rectifier control strategy implemented in a DSP controller. The design of the longitudinal conversion loss filter and of the involved PI-controllers are described in detail. Test results show the proposed methods works properly.


title: Decision tree based mobile crowdsourcing for agriculture advisory system

abstract: In agriculture, the major cause of loss of yield and quality of harvest is due to the outbreak of pest and diseases. It is difficult for fewer experts to visit a large number of farms to analyze the pest and diseases and to provide alerts to other farmers in the same region. Therefore, remote classification of pest and diseases is essential to advise on appropriate corrective actions to the farmers. In this paper, we propose a mobile phone based crowdsourcing approach to detect the emergence of plant diseases in a region by aggregating information from multiple farmers. The proposed crowdsourcing approach is based on the binary decision tree that helps to minimize the number of questions to be asked to the farmers and results in an accurate and reliable decisions on plant diseases. The results are presented by using the soybean disease large dataset from the UCI machine learning repository for the classification of 15 prevalent diseases in the soybean crop.


title: Detecting (and creating !) a HVM rootkit (aka BluePill-like)

abstract: Since the first systems and networks developed, virus and worms matched them to follow these advances. So after a few technical evolutions, rootkits could moved easily from userland to kernelland, attaining the holy grail: to gain full power on computers. Those last years also saw the emergence of the virtualization techniques, allowing the deployment of software virtualization solutions and at the same time to reinforce computer security. Giving means to a processor to manipulate virtualization have not only significantly increased software virtualization performance, but also have provide new techniques to virus writers. These effects had as impact to create a tremendous polemic about this new kind of rootkits--HVM (hardware-based virtual machine)--and especially the most (in)famous of them: Bluepill. Some people claim them to be invisible and consequently undetectable thus making antivirus software or HIDS definitively useless, while for others HVM rootkits are nothing but fanciful. However, the recent release of the source code of the first HVM rootkit, Bluepill, allowed to form a clear picture of those different claims. HVM can indeed change the state of a whole operating system by toggling it into a virtual machine and thus taking the full control on the host and on the operating system itself. In this paper, we haven striven to demystify that new kind of rootkit. Ona first hand we are providing clear and reliable technical data about the conception of such rootkit to explain what is possible and what is not. On a second hand, we provide an efficient, operational detection technique that make possible to systematically detect Bluepill-like rootkits (aka HVM-rootkits).


title: Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study

abstract: Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform best in lung cancer diagnosis. Methods: Non-small cell lung carcinoma and small cell lung carcinoma biopsy specimens were consecutively obtained and stained. The specimen slides were diagnosed by two experienced pathologists (over 20 years). Several deep learning models were trained to discriminate cancer and non-cancer biopsies. Result: Deep learning models give reasonable AUC from 0.8810 to 0.9119. Conclusion: The deep learning analysis could help to speed up the detection process for the whole-slide image (WSI) and keep the comparable detection rate with human observer.


title: Transductive Multi-label Zero-shot Learning

abstract: Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.


title: Finalizing PTSD in DSM-5: getting here from there and where to go next.

abstract: The process that resulted in the diagnostic criteria for posttraumatic stress disorder (PTSD) in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association; ) was empirically based and rigorous. There was a high threshold for any changes in any DSM-IV diagnostic criterion. The process is described in this article. The rationale is presented that led to the creation of the new chapter, "Trauma- and Stressor-Related Disorders," within the DSM-5 metastructure. Specific issues discussed about the DSM-5 PTSD criteria themselves include a broad versus narrow PTSD construct, the decisions regarding Criterion A, the evidence supporting other PTSD symptom clusters and specifiers, the addition of the dissociative and preschool subtypes, research on the new criteria from both Internet surveys and the DSM-5 field trials, the addition of PTSD subtypes, the noninclusion of complex PTSD, and comparisons between DSM-5 versus the World Health Association's forthcoming International Classification of Diseases (ICD-11) criteria for PTSD. The PTSD construct continues to evolve. In DSM-5, it has moved beyond a narrow fear-based anxiety disorder to include dysphoric/anhedonic and externalizing PTSD phenotypes. The dissociative subtype may open the way to a fresh approach to complex PTSD. The preschool subtype incorporates important developmental factors affecting the expression of PTSD in young children. Finally, the very different approaches taken by DSM-5 and ICD-11 should have a profound effect on future research and practice. Language: en


title: Creating facial motions of Cybernetic Human HRP-4C

abstract: HRP-4C is our novel biped humanoid robot whose body shape and face appearance are quite similar to those of female humans. It can make not only various whole body motions including biped locomotion but also various facial motions using eleven axes joints inside the head. This paper focuses on the face of HRP-4C, the mechanical structure of it and the software system for creating its facial motions. To make creation process of the facial motions easy and efficient is one of the important technologies for the practical use of the robot. Our system allows controlling the face of actual robot from the graphical user interface running on a client PC with the high responsiveness. This feature enables a creator to efficiently edit detailed facial motions with powerful functions including overdub loop edit, key pose sequence edit with the lip-sync mode, and automatic blinking. By using this system, we were able to rapidly make facial motions for the demonstrations of HRP-4C.


title: Detection of Alzheimer's disease using advanced local binary pattern from hippocampus and whole brain of MR images

abstract: Alzheimer's disease as one type of dementia can cause problems to human memory, thinking and behavior. The brain damage can be detected using brain volume and whole brain form. The correlation between brain shrinkage and reduction of brain volume can affect to deformation texture. In this research, the enhancement texture approach was proposed, called advanced local binary pattern (ALBP) method. ALBP is introduced as a 2D and 3D feature extraction descriptor. In the ALBP, sign and magnitude value were introduced as an enhancement to the previous LBP method. Due to a great number of features are produced by ALBP, the principal component analysis (PCA) and factor analysis are used as feature selection method. Furthermore, SVM classifier is applied for multiclass classification including Alzheimer's, mild cognitive impairment, and normal condition of whole brain and hippocampus. The experimental results from two scenarios (ALBP sign magnitude (2D) and ALBP sign magnitude using three orthogonal planes (3D) methods) show better accuracy and performance compare to previous method. Our proposed method achieved the average value of accuracy between 80% - 100% for both the whole brain and hippocampus data. In addition, uniform rotation invariant ALBP sign magnitude using three orthogonal planes as a 3D descriptor also outperforms other approaches with an average accuracy of 96.28% for multiclass classifications for whole brain image.


title: MDID: A multiply distorted image database for image quality assessment

abstract: Abstract In this paper, we present a new database, the multiply distorted image database (MDID), to evaluate image quality assessment (IQA) metrics on multiply distorted images. The database contains 20 reference images and 1600 distorted images. The latter images are obtained by contamination of the former with multiple distortions of random types and levels, so multiple types of distortions appear in each distorted image. Pair comparison sorting (PCS) is used as a new subjective rating method to evaluate image quality. This method allows subjects to make equal decisions on images whose difference in quality cannot be easily evaluated visually. A total of 192 subjects participated in the subjective rating, in which mean opinion scores and standard deviations were obtained. In IQA research, subjective scores and algorithm predictions are generally related by a nonlinear regression. We further propose a method to initialize the parameters of the nonlinear regression. The experiments of IQA metrics conducted on MDID validate that this database is advisable and challenging.


title: Does Parental Involvement Matter for Student Achievement and Mental Health in High School?

abstract: Parental involvement in education remains important for facilitating positive youth development. This study conceptualized parental involvement as a multidimensional construct—including school-based involvement, home-based involvement, and academic socialization—and examined the effects of different types of parental involvement in 10th grade on student achievement and depression in 11th grade (approximately ages 15–17 years). In addition, this study tested whether parental involvement influenced adolescent outcomes by increasing their academic engagement in school. A total of 1,056 adolescents participated in the study (51% males; 53% European American, 40% African American, and 7% other). Parental involvement was found to improve academic and emotional functioning among adolescents. In addition, parental involvement predicted adolescent academic success and mental health both directly and indirectly through behavioral and emotional engagement.


title: Speeding Up Distributed Machine Learning Using Codes

abstract: Codes are widely used in many engineering applications to offer robustness against noise . In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms—straggler nodes, system failures, or communication bottlenecks—but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on how coded solutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling . For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is $n$ , and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of $\log n$ . For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction $\alpha $ of the data matrix can be cached at each worker, and $n$ is the number of workers, coded shuffling reduces the communication cost by a factor of $\left({\alpha + \frac {1}{n}}\right)\gamma (n)$ compared with uncoded shuffling, where $\gamma (n)$ is the ratio of the cost of unicasting $n$ messages to $n$ users to multicasting a common message (of the same size) to $n$ users. For instance, $\gamma (n) \simeq n$ if multicasting a message to $n$ users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.


title: A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint

abstract: Occlusion and lack of visibility in dense crowded scenes make it very difficult to track individual people correctly and consistently. This problem is particularly hard to tackle in single camera systems. We present a multi-view approach to tracking people in crowded scenes where people may be partially or completely occluding each other. Our approach is to use multiple views in synergy so that information from all views is combined to detect objects. To achieve this we present a novel planar homography constraint to resolve occlusions and robustly determine locations on the ground plane corresponding to the feet of the people. To find tracks we obtain feet regions over a window of frames and stack them creating a space time volume. Feet regions belonging to the same person form contiguous spatio-temporal regions that, are clustered using a graph cuts segmentation approach. Each cluster is the track of a person and a slice in time of this cluster gives the tracked location. Experimental results are shown in scenes of dense crowds where severe occlusions are quite common. The algorithm is able to accurately track people in all views maintaining correct correspondences across views. Our algorithm is ideally suited for conditions when occlusions between people would seriously hamper tracking performance or if there simply are not enough features to distinguish between different people.


title: Fake face detection based on radiometric distortions

abstract: Securing face recognition systems against replay attacks has been recognized as a real challenge. In this work, the problem of fake face detection is addressed by modelling radiometric distortions involved in the recapturing process. The originality of our approach is that the fake face detection process occurs after the face identification process. Having access to enrolment data of each client, it becomes possible to estimate the exposure transformation between a test sample and its enrolment counterpart. A compact parametric representation is proposed to model those radiometric transforms and is used as features for classification. We evaluate the proposed method on Replay-Attack, CASIA and MSU public databases and prove that our method is competitive with state of the art countermeasures.


title: A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications

abstract: The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.


title: A cluster centers initialization method for clustering categorical data

abstract: The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers. Currently, most methods of initialization cluster centers are mainly for numerical data. Due to lack of geometry for the categorical data, these methods used in cluster centers initialization for numerical data are not applicable to categorical data. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes algorithm. The method integrates the distance and the density together to select initial cluster centers and overcomes shortcomings of the existing initialization methods for categorical data. Experimental results illustrate the proposed initialization method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data objects.


title: Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation

abstract: We present a parser for Abstract Meaning Representation (AMR). We treat English-to-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.


title: Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Cognitive-emotional interactions Language as context for the

abstract: In the blink of an eye, people can easily see emotion in another person's face. This fact leads many to assume that emotion perception is given and proceeds independently of conceptual processes such as language. In this paper we suggest otherwise and offer the hypothesis that language functions as a context in emotion perception. We review a variety of evidence consistent with the language-as-context view and then discuss how a linguistically relative approach to emotion perception allows for intriguing and generative questions about the extent to which language shapes the sensory processing involved in seeing emotion in another person's face.


title: Context-Based Few-Shot Word Representation Learning

abstract: Word representation learning methods have mostly been designed for and evaluated on frequent words. However, in real-world settings, deep neural architectures are often expected to accept a large vocabulary of possible input words. In this paper, we investigate context-based techniques for few-shot learning of representations for infrequent words. We first adapt word2vec to account for expanded contexts and subsequently introduce an additional smoothing procedure. Experiments on similarity benchmarks show significant improvements for rare words.


title: Short-Read Mapping by a Systolic Custom FPGA Computation

abstract: The mapping of reads, i.e. short DNA base pair strings, to large genome databases has become a critical operation for genetic analysis and diagnosis. Although this mapping operation is a simple string search tolerant of some character mismatches, it is yet extremely challenging due to the tremendous size of the searched genome databases. It is the heavy use of search heuristics such as BLAST, Maq and Bowtie, which makes the economic deployment of read mappers possible. While these heuristics achieve feasible computation times, they also sacrifice the accuracy of the mapping results, which is itself a high value for reliable diagnostics. The traditional software implementations are unable to exploit the tremendous parallelism, which is available in the mapping of thousands and millions of reads. Merely a handful of concurrent control flows, and thus searches, can be performed efficiently on contemporary multicores. Even GPU assistance only enables a few dozens of parallel searches. This paper proposes a systolic custom computation on FPGA, which implements the read mapping on a massively parallel architecture. It implements a true search and guarantees to find all read mappings under a configurable threshold of base pair mismatches. The highly regular design from compact string matchers enables the implementation of thousands of parallel search engines on a single FPGA device. The presented map per platform combines highest computational performance with an excellent result accuracy. Its performance is more than twice as high as that of a recently published comparable FPGA map per. Already when implemented on a contemporary mid-size FPGA, it meets the search speed of software heuristics, which only detect little more than half of the valid read mappings. The map per easily scales to large FPGA devices, which can, thus, implement accurate high-performance volume mappers. Accurate mapping is made available in application domains that could only afford fuzzy heuristics by now.


title: Image Classification Using Convolutional Neural Networks

abstract: Deep Learning has emerged as a new area in machine learning and is applied to a number of signal and image applications.The main purpose of the work presented in this paper, is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on various standard datasets, like remote sensing data of aerial images (UC Merced Land Use Dataset) and scene images from SUN database. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The graphical representation of the experimental results is given on the basis of MSE against the number of training epochs. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets.


title: Antitumor Activity of Gemcitabine and Oxaliplatin Is Augmented by Thymoquinone in Pancreatic Cancer

abstract: Previous studies have shown biological activity of thymoquinone, an active compound extracted from Nigella sativa , in pancreatic cancer cells; however, preclinical animal studies are lacking. Here, we report, for the first time, the chemosensitizing effect of thymoquinone to conventional chemotherapeutic agents both in vitro and in vivo using an orthotopic model of pancreatic cancer. In vitro studies revealed that preexposure of cells with thymoquinone (25 μmol/L) for 48 h followed by gemcitabine or oxaliplatin resulted in 60% to 80% growth inhibition compared with 15% to 25% when gemcitabine or oxaliplatin was used alone. Moreover, we found that thymoquinone could potentiate the killing of pancreatic cancer cells induced by chemotherapeutic agents by down-regulation of nuclear factor-κB (NF-κB), Bcl-2 family, and NF-κB-dependent antiapoptotic genes (X-linked inhibitors of apoptosis, survivin, and cyclooxygenase-2). As shown previously by our laboratory, NF-κB gets activated on exposure of pancreatic cancer cells to conventional chemotherapeutic agents; interestingly, thymoquinone was able to down-regulate NF-κB in vitro , resulting in chemosensitization. In addition to in vitro results, here we show for the first time, that thymoquinone in combination with gemcitabine and/or oxaliplatin is much more effective as an antitumor agent compared with either agent alone. Most importantly, our data also showed that a specific target, such as NF-κB, was inactivated in animal tumors pretreated with thymoquinone followed by gemcitabine and/or oxaliplatin. These results provide strong in vivo molecular evidence in support of our hypothesis that thymoquinone could abrogate gemcitabine- or oxaliplatin-induced activation of NF-κB, resulting in the chemosensitization of pancreatic tumors to conventional therapeutics. [Cancer Res 2009;69(13):5575–83]


title: System test case prioritization of new and regression test cases

abstract: Test case prioritization techniques have been shown to be beneficial for improving regression-testing activities. With prioritization, the rate of fault detection is improved, thus allowing testers to detect faults earlier in the system-testing phase. Most of the prioritization techniques to date have been code coverage-based. These techniques may treat all faults equally. We build upon prior test case prioritization research with two main goals: (1) to improve user-perceived software quality in a cost effective way by considering potential defect severity and (2) to improve the rate of detection of severe faults during system-level testing of new code and regression testing of existing code. We present a value-driven approach to system-level test case prioritization called the prioritization of requirements for test (PORT). PORT prioritizes system test cases based upon four factors: requirements volatility, customer priority, implementation complexity, and fault proneness of the requirements. We conducted a PORT case study on four projects developed by students in advanced graduate software testing class. Our results show that PORT prioritization at the system level improves the rate of detection of severe faults. Additionally, customer priority was shown to be one of the most important prioritization factors contributing to the improved rate of fault detection.


title: Subdivision surfaces in character animation

abstract: The creation of believable and endearing characters in computer graphics presents a number of technical challenges, including the modeling, animation and rendering of complex shapes such as heads, hands, and clothing. Traditionally, these shapes have been modeled with NURBS surfaces despite the severe topological restrictions that NURBS impose. In order to move beyond these restrictions, we have recently introduced subdivision surfaces into our production environment. Subdivision surfaces are not new, but their use in high-end CG production has been limited. Here we describe a series of developments that were required in order for subdivision surfaces to meet the demands of high-end production. First, we devised a practical technique for constructing provably smooth variable-radius fillets and blends. Second, we developed methods for using subdivision surfaces in clothing simulation including a new algorithm for efficient collision detection. Third, we developed a method for constructing smooth scalar fields on subdivision surfaces, thereby enabling the use of a wider class of programmable shaders. These developments, which were used extensively in our recently completed short film Geri’s game, have become a highly valued feature of our production environment. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; I.3.3 [Computer Graphics]: Picture/Image Generation.


title: Recommendations for Implementing Tango Classes for Persons with Parkinson Disease

abstract: Several studies have recently been published regarding the physical and emotional benefits of Argentine tango dance for individuals with Parkinson disease (PD). These papers focused on the effects of tango interventions rather than methods used to implement and deliver the interventions. The focus of the present paper is on methods for implementing PD-specific tango programs, to facilitate safe and appropriate implementation of community-based partnered dance programs for this population. We report successful methods for an Argentine Tango-based class, but these recommendations could be applicable to other partnered dances.


title: Do We Teach Useful Statistics for Performance Evaluation?

abstract: Basic topics from probability and statistics -- such as probability distributions, parameter estimation, confidence intervals and statistical hypothesis testing -- are often included in computing curricula and used as tools for experimental performance evaluation. Unfortunately, data collected through experiments may not meet the requirements of many statistical analysis methods, such as independent sampling or normal distribution. As a result, the analysis methods may be more tricky to apply and the analysis results may be more tricky to interpret than one might expect. Here, we look at some of the issues on methods and experiments that would be considered basic in performance evaluation education.


title: A flexible exoskeleton for hip assistance

abstract: This paper proposes a new concept called “a flexible exoskeleton” and presents the design of a wearable walking assistance device to provide physical gait assistance for the elderly. To overcome some limitations of previous wearable walking assistance devices, 3 novel mechanisms are proposed: 1) flexible thigh frames that withstand the designated load direction, 2) an adjustable flexible hip brace surrounding the wearer's body, and 3) slim and backdrivable actuators that minimize the maximum height and resistance. The proposed walking assistance device has reduced metabolic energy consumption by 13.2±4% compared with a free condition.


title: Age differences in experiential and deliberative processes in unambiguous and ambiguous decision making.

abstract: Older adults experience declines in deliberative decisional capacities, while their affective or experiential abilities tend to remain intact (Peters & Bruine de Bruin, 2012). The current study used this framework to investigate age differences in description-based and experience-based decision-making tasks. Description-based tasks emphasize deliberative processing by allowing decision makers to analyze explicit descriptions of choice-reward information. Experience-based tasks emphasize affective or experiential processing because they lack the explicit choice-reward information, forcing decision makers to rely on feelings and information derived from past experiences. This study used the Columbia Card Task (CCT) as a description-based task where probability information is provided and the Iowa Gambling Task (IGT) as an experience-based task, where it is not. As predicted, compared to younger adults (N = 65), older adults (N = 65) performed more poorly on the CCT but performed similarly on the IGT. Deliberative capacities (i.e., executive control and numeracy abilities) explained the relationship between age and performance on the CCT, suggesting that age-related differences in description-based decision-making tasks are related to declines in deliberative capacities. However, deliberative capacities were not associated with performance on the IGT for either older or younger adults. Nevertheless, on the IGT, older adults reported more use of affect-based strategies versus deliberative strategies, whereas younger adults reported similar use of these strategies. This finding offers partial support for the idea that decision-making tasks that rely on deliberate processing are more likely to demonstrate age effects than those that are more experiential.


title: Anatomy- and physics-based facial animation for craniofacial surgery simulations

abstract: A modelling approach for the realistic simulation of facial expressions of emotion in craniofacial surgery planning is presented. The method is different from conventional, non-physical techniques for character animation in computer graphics. A consistent physiological mechanism for facial expressions was assumed, which was the effect of contracting muscles on soft tissues. For the numerical solution of the linear elastic boundary values, the finite element method on tetrahedral grids was used. The approach was validated on a geometrical model of a human head derived from tomographic data. Using this model, individual facial expressions of emotion were estimated by the superpositioning of precomputed single muscle actions.


title: NoSQL databases: Critical analysis and comparison

abstract: The current research explores and differentiates between various forms in which NoSQL databases exist. It examines the need of NoSQL and how they have become an important alternative to relational databases. NoSQL databases can be categorized into four major classifications which are: key value stores, graph databases, wide column stores, and document stores. These categories are compared on the basis of functional features and non-functional features. The non-functional features include performance, scalability, flexibility, structure and complexity. The functional features include de-normalization, joins, atomicity, aggregation and keys. Then for further analysis, one database is selected from each of these categories that is, MongoDB (document stores), Cassandra (wide column stores), Redis (key value stores), and Neo4j (graph databases). Selected databases are compared on their data model, CAP theorem, distributive properties and other factors. By performing the comparison on non-functional features, it has been found that a document store can be used if high performance, flexibility and scalability are required and if we have represented the data in JSON format. Column store can be used for semi structured data which requires high performance and scalability. Redis is anin-memory store and performs exceptionally fast in the case of single shard operation. Graph databases can be used when it comes to highly interconnected data and continuously evolving data models. The comparison between MongoDB, Cassandra, Redis and Neo4j concluded that all of them follow horizontal scaling and are schema free. Except Neo4j, others don't have complete ACID properties. Write and delete operations are fast for databases MongoDB, Redis and Cassandra, whereas read operation is comparatively slow in Cassandra. In case of Neo4j, REST performance is similar to MongoDB, whereas embedded is comparatively slow. We also discuss how these databases work in a distributed environment.


title: A 5–6 GHz 1-V CMOS Direct-Conversion Receiver With an Integrated Quadrature Coupler

abstract: This paper describes a novel monolithic low voltage (1-V) CMOS RF front-end architecture with an integrated quadrature coupler (QC) and two subharmonic mixers for direct-down conversion. The LC-folded-cascode technique is adopted to achieve low-voltage operation while the subharmonic mixers in conjunction with the QC are used to eliminate LO self-mixing. In addition, the inherent bandpass characteristic of the LC tanks helps suppression of LO leakage at RF port. The circuit was fabricated in a standard 0.18-mum CMOS process for 5-6 GHz applications. At 5.4 GHz, the RF front-end exhibits a voltage gain of 26.2 dB and a noise figure of 5.2 dB while dissipating 45.5 mW from a 1.0-V supply. The achieved input-referred DC-offset due to LO self-mixing is below -110.7 dBm.


title: 5.6 Adaptive clocking system for improved power efficiency in a 28nm x86-64 microprocessor

abstract: In high-performance microprocessor cores, the on-die supply voltage seen by the transistors is non-ideal and exhibits significant fluctuations. These supply fluctuations are caused by sudden changes in the current consumed by the microprocessor in response to variations in workloads. This non-ideal supply can cause performance degradation or functional failures. Therefore, a significant amount of margin (10-15%) needs to be added to the ideal voltage (if there were no AC voltage variations) to ensure that the processor always executes correctly at the committed voltage-frequency points. This excess voltage wastes power proportional to the square of the voltage increase.


title: Cluster By : A New SQL Extension for Spatial Data Aggregation

abstract: The development of areas such as remote and airborne sensing, location based services, and geosensor networks enables the collection of large volumes of spatial data. These datasets necessitate the wide application of spatial databases. Queries on these geo-referenced data often require the aggregation of isolated data points to form spatial clusters and obtain properties of the clusters. However, current SQL standard does not provide an effective way to form and query spatial clusters. In this paper, we aim at introducing Cluster By into spatial databases to allow a broad range of interesting queries to be posted on spatial clusters. We also provide a language construct to specify spatial clustering algorithms. The extension is demonstrated with several motivating examples.


title: Design of UWB Monopole Antenna with Dual Notched Bands Using One Modified Electromagnetic-Bandgap Structure

abstract: A modified electromagnetic-bandgap (M-EBG) structure and its application to planar monopole ultra-wideband (UWB) antenna are presented. The proposed M-EBG which comprises two strip patch and an edge-located via can perform dual notched bands. By properly designing and placing strip patch near the feedline, the proposed M-EBG not only possesses a simple structure and compact size but also exhibits good band rejection. Moreover, it is easy to tune the dual notched bands by altering the dimensions of the M-EBG. A demonstration antenna with dual band-notched characteristics is designed and fabricated to validate the proposed method. The results show that the proposed antenna can satisfy the requirements of VSWR < 2 over UWB 3.1–10.6 GHz, except for the rejected bands of the world interoperability for microwave access (WiMAX) and the wireless local area network (WLAN) at 3.5 GHz and 5.5 GHz, respectively.


title: Robust tracking of position and velocity with Kalman snakes

abstract: A new Kalman-filter based active contour model is proposed for tracking of nonrigid objects in combined spatio-velocity space. The model employs measurements of gradient-based image potential and of optical-flow along the contour as system measurements. In order to improve robustness to image clutter and to occlusions an optical-flow based detection mechanism is proposed. The method detects and rejects spurious measurements which are not consistent with previous estimation of image motion.


title: MobilityFirst: a robust and trustworthy mobility-centric architecture for the future internet

abstract: This paper presents an overview of the MobilityFirst network architecture, currently under development as part of the US National Science Foundation's Future Internet Architecture (FIA) program. The proposed architecture is intended to directly address the challenges of wireless access and mobility at scale, while also providing new services needed for emerging mobile Internet application scenarios. After briefly outlining the original design goals of the project, we provide a discussion of the main architectural concepts behind the network design, identifying key features such as separation of names from addresses, public-key based globally unique identifiers (GUIDs) for named objects, global name resolution service (GNRS) for dynamic binding of names to addresses, storage-aware routing and late binding, content- and context-aware services, optional in-network compute layer, and so on. This is followed by a brief description of the MobilityFirst protocol stack as a whole, along with an explanation of how the protocol works at end-user devices and inside network routers. Example of specific advanced services supported by the protocol stack, including multi-homing, mobility with disconnection, and content retrieval/caching are given for illustration. Further design details of two key protocol components, the GNRS name resolution service and the GSTAR routing protocol, are also described along with sample results from evaluation. In conclusion, a brief description of an ongoing multi-site experimental proof-of-concept deployment of the MobilityFirst protocol stack on the GENI testbed is provided.


title: Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space

abstract: Recently, different adaptations of Implicit Shape Models (ISM) for 3D shape classification have been presented. In this paper we propose a new method with a continuous voting space and keypoint extraction by uniform sampling. We evaluate different sets of typical parameters involved in the ISM algorithm and compare the proposed algorithm on a large public dataset with state of the art approaches.


title: Instagram Post Data Analysis

abstract: Because of the spread of the Internet, social platforms become big data pools. From there we can learn about the trends, culture and hot topics. This project focuses on analyzing the data from Instagram. It shows the relationship of Instagram filter data with location and number of likes to give users filter suggestion on achieving more likes based on their location. It also analyzes the popular hashtags in different locations to show visual culture differences between different cities.


title: Optimization Task Scheduling Algorithm in Cloud Computing

abstract: Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This research studies the existing approaches of task scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate tasks to resources properly and decrease runtime. In this paper we proposed a new method for task scheduling by learning automata (LA). This method where has named RAOLA is trained by historical information of task execution on the cloud, then divide task to many classes and evaluate them. Next, manage virtual machine for capture physical resources at any period based on rate of task classes, such that improve efficiency of cloud network.


title: Intelligent Agents on the Internet: Fact, Fiction, and Forecast

abstract: In the future, intelligent software agents will help us navigate the information superhighway by serving as backseat drivers or taxi drivers. Better yet, they have the potential to act as sophisticated concierges who make it unnecessary for us to approach the highway at all. We describe a selective sample of software agents currently under development, to give an idea of the agents that will emerge in the next few years. We organize our discussion by the agents' sophistication and the degree to which they make the information superhighway disappear. Following the information superhighway metaphor, an intelligent agent may be a backseat driver who makes suggestions at every rum, a taxi driver who takes you to your destination, or even a concierge whose knowledge and skills make it unnecessary for you to approach the superhighway at all. The Internet Softbot is discussed as a case study. >


title: Semi-Supervised Learning Using Greedy Max-Cut

abstract: Graph-based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems, particularly in agnostic settings when no parametric information or other prior knowledge is available about the data distribution. Given the constructed graph represented by a weight matrix, transductive inference is used to propagate known labels to predict the values of all unlabeled vertices. Designing a robust label diffusion algorithm for such graphs is a widely studied problem and various methods have recently been suggested. Many of these can be formalized as regularized function estimation through the minimization of a quadratic cost. However, most existing label diffusion methods minimize a univariate cost with the classification function as the only variable of interest. Since the observed labels seed the diffusion process, such univariate frameworks are extremely sensitive to the initial label choice and any label noise. To alleviate the dependency on the initial observed labels, this article proposes a bivariate formulation for graph-based SSL, where both the binary label information and a continuous classification function are arguments of the optimization. This bivariate formulation is shown to be equivalent to a linearly constrained Max-Cut problem. Finally an efficient solution via greedy gradient Max-Cut (GGMC) is derived which gradually assigns unlabeled vertices to each class with minimum connectivity. Once convergence guarantees are established, this greedy Max-Cut based SSL is applied on both artificial and standard benchmark data sets where it obtains superior classification accuracy compared to existing state-of-the-art SSL methods. Moreover, GGMC shows robustness with respect to the graph construction method and maintains high accuracy over extensive experiments with various edge linking and weighting schemes.


title: Deep Convolutional Neural Network for 6-DOF Image Localization

abstract: We present an accurate and robust method for six degree of freedom image localization. There are two key-points of our method, 1. automatic immense photo synthesis and labeling from point cloud model and, 2. pose estimation with deep convolutional neural networks regression. Our model can directly regresses 6-DOF camera poses from images, accurately describing where and how it was captured. We achieved an accuracy within 1 meters and 1 degree on our out-door dataset, which covers about 2 acres on our school campus.


title: Finding near-duplicate web pages: a large-scale evaluation of algorithms

abstract: Broder et al.'s [3] shingling algorithm and Charikar's [4] random projection based approach are considered "state-of-the-art" algorithms for finding near-duplicate web pages. Both algorithms were either developed at or used by popular web search engines. We compare the two algorithms on a very large scale, namely on a set of 1.6B distinct web pages. The results show that neither of the algorithms works well for finding near-duplicate pairs on the same site, while both achieve high precision for near-duplicate pairs on different sites. Since Charikar's algorithm finds more near-duplicate pairs on different sites, it achieves a better precision overall, namely 0.50 versus 0.38 for Broder et al.'s algorithm. We present a combined algorithm which achieves precision 0.79 with 79% of the recall of the other algorithms.


title: Object-oriented programming for linear algebra

abstract: Many numerical analysts and Lisp/Smalltalk programmers share the assumption that languages like Fortran are more appropriate for traditional, quantitative scientific programming than object-oriented languages. To show how straightforward application of object-oriented design to standard algorithms in numerical linear algebra improves clarity and expressiveness, without sacrificing speed or accuracy, I describe parts of Cactus, a system for numerical linear algebra and constrained optimization, implemented in Common Lisp and CLOS.


title: Single-Phase Phase Angle Measurements in Electric Power Systems

abstract: Computation of phase angles using the discrete Fourier transform (DFT) algorithm has been proven to be sufficiently accurate in a variety of power system applications when the input signal is three-phase voltage and the voltage frequency is near the nominal value. However, the performance of the DFT algorithm in time of the singe-phase voltage input with a large frequency excursion so far has not been well explored, much less documented. This paper briefly discusses the importance of the synchronized phase angle measurement in power systems, and brings up the necessity of research on single-phase measurement systems. The performance of the DFT algorithm in power system phase angle measurements is thoroughly analyzed. Corresponding rectifying methods, including quasi-positive-sequence DFT and offset compensation, are proposed and examined by relevant numerical simulations and necessary laboratory tests. The results of the simulations and tests show a perfect match between the derived equations and the phase angle data, and thus verify the effectiveness of the proposed algorithm, which is believed to be able to greatly improve the performance of DFT in the aspect of phase angle calculation when severe power system upsets with remarkable frequency excursions are present.


title: ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

abstract: The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available.


title: Strategic Enterprise Management for Innovative Companies: The Last Decade of the Balanced Scorecard

abstract: Setting corporate vision, strategy, and associated unit-based strategic goals are crucial for any company to create and to sustain the competitive advantage. This concept has been frequently discussed in theory and practice. The case is no different from the innovation perspective of enterprises. Therefore, this article is aimed at exploring the integration of balanced scorecard into innovative strategic management, providing a framework of interrelated issues based on the existing literature and application areas in this research field. Along with the brief review of innovation based strategic management, which includes 13 peer reviewed journal articles; 8 book chapters, 41 peer-reviewed scientific journal articles, including a content analysis, was conducted to show the last ten years of the balanced scorecard approach from the innovation perspective. The literature review revealed that: (1) the number of related publications on the topic is showing similarity over the last few years; (2) the explored research field has been mainly grounded on balanced scorecard research, but has also been enriched by innovation perspective; and (3) there are wide range of industry applications based on innovation conducted for the balanced scorecard approach. The literature review provides a summary of the most important issues that emerged in this field which can shed lights on the new opportunities and challenges for further research.


title: Design and Evaluation of Visual Interpenetration Cues in Virtual Grasping

abstract: We present design and impact studies of visual feedback for virtual grasping. The studies suggest new or updated guidelines for feedback. Recent grasping techniques incorporate visual cues to help resolve undesirable visual or performance artifacts encountered after real fingers enter a virtual object. Prior guidelines about such visuals are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We address this with a two-stage study. In the first stage, users adjusted parameters of various feedback types, including some novel aspects, to identify promising settings and to give insight into preferences regarding the parameters. In the next stage, the tuned feedback techniques were evaluated in terms of objective performance (finger penetration, release time, and precision) and subjective rankings (visual quality, perceived behavior impact, and overall preference). Additionally, subjects commented on the techniques while reviewing them in a final session. Performancewise, the most promising techniques directly reveal penetrating hand configuration in some way. Subjectively, subjects appreciated visual cues about interpenetration or grasp force, and color changes are most promising. The results enable selection of the best cues based on understanding the relevant tradeoffs and reasonable parameter values. The results also provide a needed basis for more focused studies of specific visual cues and for choosing conditions in comparisons to other feedback modes, such as haptic, audio, or multimodal. Considering results, we propose that 3D interaction guidelines must be updated to capture the importance of interpenetration cues, possible performance benefits of direct representations, and tradeoffs involved in cue selection.


title: HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition

abstract: In this paper, we present a large scale off-line handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinesecharacters written by 1,000 different subjects. The writers’ information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose.


title: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

abstract: Nature 542, 115–118 (2017); doi:10.1038/nature21056 In the Acknowledgements section of this Letter, the sentence: “This study was supported by the Baxter Foundation, California Institute for Regenerative Medicine (CIRM) grants TT3-05501 and RB5-07469 and US National Institutes of Health (NIH) grantsAG044815, AG009521, NS089533, AR063963 and AG020961 (H.M.B.)” should have read: “This study was supported by funding from the Baxter Foundation to H.M.B.” Furthermore, the last line of the Acknowledgements section should have read: “In addition, this work was supported by a National Institutes of Health (NIH) National Center for Advancing Translational Science Clinical and Translational Science Award (UL1 TR001085). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.” The original Letter has been corrected online.


DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language


title: Computational Analysis of Persuasiveness in Social Multimedia: A Novel Dataset and Multimodal Prediction Approach

abstract: Our lives are heavily influenced by persuasive communication, and it is essential in almost any types of social interactions from business negotiation to conversation with our friends and family. With the rapid growth of social multimedia websites, it is becoming ever more important and useful to understand persuasiveness in the context of social multimedia content online. In this paper, we introduce our newly created multimedia corpus of 1,000 movie review videos obtained from a social multimedia website called ExpoTV.com, which will be made freely available to the research community. Our research results presented here revolve around the following 3 main research hypotheses. Firstly, we show that computational descriptors derived from verbal and nonverbal behavior can be predictive of persuasiveness. We further show that combining descriptors from multiple communication modalities (audio, text and visual) improve the prediction performance compared to using those from single modality alone. Secondly, we investigate if having prior knowledge of a speaker expressing a positive or negative opinion helps better predict the speaker's persuasiveness. Lastly, we show that it is possible to make comparable prediction of persuasiveness by only looking at thin slices (shorter time windows) of a speaker's behavior.