title: SHESHADRI ET AL.: EXEMPLAR DRIVEN CHARACTER RECOGNITION IN THE WILD 1 Exemplar Driven Character Recognition in the Wild

abstract: Character recognition in natural scenes continues to represent a formidable challenge in computer vision. Traditional optical character recognition (OCR) methods fail to perform well on characters from scene text owing to a variety of difficulties in background clutter, binarisation, and arbitrary skew. Further, English characters group into only 62 classes whereas many of the world’s languages have several hundred classes. In particular, most Indic script languages such as Kannada exhibit large intra class diversity, while the only difference between two classes may be in a minor contour above or below the character. These considerations motivate an exemplar approach to classification; one which does not seek intra class commonality among extreme examples which are essentially sub classes of their own. Exemplar SVM’s have been recently introduced in the object recognition context. The essence of the exemplar approach is that rather than seeking to establish commonality within classes, a separate classifier is learnt for each exemplar in the dataset. To make individual classification simple, linear SVM’s are used and each classifier is hence an exemplar specific weight vector. Each exemplar in the dataset is resized to standard dimensions, and thence HOG features are densely extracted to create a rigid template xE . A set of negative samples NE are created by the same process from classes not corresponding to the exemplar. Each classifier (wE ,bE ) maximizes the separation between xE and every window in NE . This is equivalent to optimizing the convex objective[4]:


title: Introduction to response to intervention: What, why, and how valid is it?

abstract: On December 3, 2004, President Bush signed into law the Individuals with Disabilities Education Improvement Act (IDEA, 2004). The revised law is different from the previous version in at least one important respect. Whereas practitioners were previously encouraged to use IQ–achievement discrepancy to identify children with learning disabilities (LD), they now may use “Response to Intervention,” or RTI, a new, alternative method. It is also a means of providing early intervention to all children at risk for school failure. IDEA 2004 permits districts to use as much as 15% of their special education monies to fund early intervention activities. All this has implications for the number and type of children identified, the kinds of educational services provided, and who delivers them. RTI may be especially important to Reading Research Quarterly readers because roughly 80% of those with an LD label have been described as reading disabled (Lyon, 1995). With RTI, there may be a larger role for reading specialists, which in turn might affect preand inservice professional development activities conducted by universities and school districts. Yet much still needs to be understood to ensure that RTI implementation will promote effective early intervention and represents a valid means of LD identification. In this article, we explain important features of RTI, why it has been promoted as a substitute for IQ–achievement discrepancy, and what remains to be understood before it may be seen as a valid means of LD identification. What is RTI?


title: Articulatory and spectrum features integration using generalized distillation framework

abstract: It has been shown that by combining the acoustic and articulatory information significant performance improvements in automatic speech recognition (ASR) task can be achieved. In practice, however, articulatory information is not available during recognition and the general approach is to estimate it from the acoustic signal. In this paper, we propose a different approach based on the generalized distillation framework, where acoustic-articulatory inversion is not necessary. We trained two DNN models: one called “teacher” learns from both acoustic and articulatory features and the other one called “student” is trained on acoustic features only, but its training process is guided by the “teacher” model and can reach a better performance that can't be obtained by regular training even without articulatory feature inputs during test time. The paper is organized as follows: Section 1 gives the introduction and briefly discusses some related works. Section 2 describes the distillation training process, Section 3 describes ASR system used in this paper. Section 4 presents the experiments and the paper is concluded by Section 5.


title: A blessing, not a curse: Experimental evidence for beneficial effects of visual aesthetics on performance

abstract: The present experiment investigated the effect of visual aesthetics on performance. A total of 257 volunteers completed a series of search tasks on a website providing health-related information. Four versions of the website were created by manipulating visual aesthetics (high vs. low) and usability (good vs. poor) in a 2 × 2 between-subjects design. Task completion times and error rates were used as performance measures. A main effect of usability on both error rates and completion time was observed. Additionally, a significant interaction of visual aesthetics and usability revealed that high aesthetics enhanced performance under conditions of poor usability. Thus, in contrast to the notion that visual aesthetics may worsen performance, visual aesthetics even compensated for poor usability by speeding up task completion. The practical and theoretical implications of this finding are discussed.


title: Self-oscillating resonant converter for led applications at 500 Khz

abstract: In this paper, an improved design approach is proposed for the self-oscillating command circuit as a driver for resonant converters on LED applications. The improvements, built upon the classic design methodology, contemplate the influence of the MOSFET input capacitance, and make it suitable for high frequency application, where such parasitic elements lead to increased discrepancies. When operated at higher frequencies, resonant converters like the half-bridge series resonant inverter, imply reduced size and weight for the reactive elements, which combined to the simplicity and low cost of the self-oscillating command circuit, translates into a suitable high-power density application. The self-oscillating resonant converter design is based on the describing function method and the extended Nyquist stability criterion, granting the system a proper design prediction around a self-sustained frequency. Simulation and experimental results are shown, comparing the classic design methodology with the improved design approach up to 500 kHz, displaying the feasibility of the design.


title: Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.


title: DHA status of vegetarians.

abstract: BACKGROUND ::: Docosahexaenoic acid (22:6n-3; DHA) is absent from vegan diets and present in limited amounts in vegetarian diets. ::: ::: ::: OBJECTIVE ::: To review DHA status in vegetarians and vegans. ::: ::: ::: DESIGN ::: To identify published studies and review their findings. ::: ::: ::: RESULTS ::: Dietary analyses show that vegan diets are devoid of DHA and vegetarian diets that included dairy food and eggs only provide about 0.02 g DHA/d. Vegetarian and especially vegan diets supply more linoleic acid (18:2n-6) than omnivore diets. The intake of alpha-linolenic acid (18:3n-3) also tends to be similar or greater but depends on culinary oils used. The proportions of DHA in plasma, blood cells, breast milk, and tissues are substantially lower in vegans and vegetarians compared with omnivores. The lower proportions of DHA are accompanied by correspondingly higher proportions of the long-chain derivatives of linoleic acid, indicating that the capacity to synthesize long-chain polyunsaturated fatty acids is not limited. Short-term dietary supplementation with alpha-linolenic acid increases the proportion of eicosapentaenoic acid (20:5n-3) but does not increase the proportion of DHA in blood lipids. Small amounts of preformed DHA (as low as 200mg) result in a large increase in the proportion of DHA in blood lipids in vegetarians and vegans. There is no evidence of adverse effects on health or cognitive function with lower DHA intake in vegetarians. ::: ::: ::: CONCLUSIONS ::: Preformed DHA in the diet of omnivores explains the relatively higher proportion of this fatty acid in blood and tissue lipids compared with vegetarians. The pathophysiological significance of this difference remains to be determined.


title: On the Gap between Outcomes of Voting Rules

abstract: Various voting rules (or social choice procedures) have been proposed to select a winner from the preferences of an entire population: Plurality, veto, Borda, Minimax, Copeland, etc. Although in theory, these rules may yield drastically different outcomes, for real-world datasets, behavioral social choice analyses have found that the rules are often in perfect agreement with each other! This work attempts to give a mathematical explanation of this phenomenon. We quantify the gap between the outcomes of two voting rules by the pairwise margin between their winners. We show that for many common voting rules, the gap between them can be almost as large as 1 when the votes are unrestricted. As a counter, we study the behavior of voting rules when the vote distribution is a uniform mixture of a small number of multinomial logit distributions. This scenario corresponds to a population consisting of a small number of groups, each voting according to a latent preference ranking. We show that for any such voting profile on g groups, at least 1/2g fraction of the population prefers the winner of a Borda election to any other candidate.


title: Virginity sparing surgery for imperforate hymen: report of two cases and review of literature.

abstract: Imperforate Hymen (IH), an obstructive congenital anomaly of the female genital tract, is seen in 1 in 2000 female births. Treatment of IH is hymenotomy or hymenectomy. Different types of incisions are mentioned in the literature. We reported two cases of IH with different clinical presentations and described a simple virginity preserving and socially acceptable procedure to protect the virginity of the patient. In cultures and religions where the destruction of the hymen is a social problem in unmarried girls, virginity sparing surgery should be chosen in gynecological practice. Here we described a simple procedure without need for prophylactic antibiotic treatment and foley catheter application to form an intact annular hymen in two cases.


title: Multirate Multimodal Video Captioning

abstract: Automatically describing videos with natural language is a crucial challenge of video understanding. Compared to images, videos have specific spatial-temporal structure and various modality information. In this paper, we propose a Multirate Multimodal Approach for video captioning. Considering that the speed of motion in videos varies constantly, we utilize a Multirate GRU to capture temporal structure of videos. It encodes video frames with different intervals and has a strong ability to deal with motion speed variance. As videos contain different modality cues, we design a particular multimodal fusion method. By incorporating visual, motion, and topic information together, we construct a well-designed video representation. Then the video representation is fed into a RNN-based language model for generating natural language descriptions. We evaluate our approach for video captioning on "Microsoft Research - Video to Text" (MSR-VTT), a large-scale video benchmark for video understanding. And our approach gets great performance on the 2nd MSR Video to Language Challenge.


title: Color Constancy: A Method for Recovering Surface Spectral Reflectance

abstract: Human and machine visual sensing is enhanced when surface properties of objects in scenes, including color, can be reliably estimated despite changes in the ambient lighting conditions. We describe a computational method for estimating surface spectral reflectance when the spectral power distribution of the ambient light is not known.


title: Head-shoulder detection using joint HOG features for people counting and video surveillance in library

abstract: Pedestrian detection is an important problem in video surveillance. While pedestrians often have diverse postures and mutual occlusion which make the detection quite difficult, their head-shoulder portions are relatively stable. Thus we choose to use head-shoulder outline features of a pedestrian for detecting. First, we apply a hierarchical classification method using Haar features and HOG features to head-shoulder location detection. Second, we define a combined feature named Joint HOG based on the symmetry of head-shoulder portion. Third, we filter out most negative samples by using the Haar classifier. Finally, we execute an elaborate HOG verification and thus obtain the head-shoulder target box expected. Experimental results show that our method achieved a real-time processing accuracy rate of nearly 90%, arguing that it is applicable to people counting and video surveillance in library.


title: A high efficiency DC-DC converter based on bidirectional half-bridge

abstract: Bidirectional DC-DC converter (BDC), is being widely used in all kinds of fields as both charger and discharger which can greatly lessen the volume and weight of the whole applied system. This paper analyzes mainstream topological structures of BDC and then proposes a bidirectional half-bridge converter with Micro Control Unit (MCU) in control of three working modes: buck charging mode, boost discharging mode and buck-boost mode. Afterwards, experiments are made to validate the availability of the proposed prototype. Besides, the constancy of the output voltage is tested via simulation, and prototype tests are also accomplished to verify the constancy of both discharging voltage and charging current and the automatic switching of the system. As a result, the system attains high conversion efficiency that runs up to 94%.


title: Part-to-part morphing for planar curves

abstract: This paper presents a shape-morphing technique that interpolates a pair of 2D polygons or curves. Firstly, a user-guided feature point correspondence is introduced to associate similar parts between the source and target shapes, which allows user to control the correspondence results effectively and flexibly. Secondly, to fully capture the global and local motions of the shapes, we define a simple structure called a part figure to represent these movements in terms of the shape parts. The part figure representation is general and applicable to various part associations between the shapes. By interpolating the part figures of the shapes, the smooth transition of the global and local movements from the source to the target shape is generated, which results in in-between shape parts with least distortion. Then, the coherent intermediate shapes of the morphing sequence are formed from these intermediate shape parts. Experimental results show that the method can transform the source shape into the target shape as expected and generate natural and visually pleasing effects.


title: The digital native – myth and reality

abstract: Purpose – The purpose of this paper is to develop and promote a realistic understanding of young people and digital technology with a view to supporting information professionals in playing useful and meaningful roles in supporting current generations of young people. In particular the paper aims to offer a critical perspective on popular and political understandings of young people and digital technologies – characterised by notions of “digital natives”, the “net generation” and other commonsense portrayals of expert young technology users. The paper seeks to consider the accuracy of such descriptions in reflecting young people's actual uses of digital technology and digital information.Design/methodology/approach – The paper provides a comprehensive review of the recent published literatures on young people and digital technology in information sciences, education studies and media/communication studies.Findings – The findings show that young people's engagements with digital technologies are varied and...


title: A comparative study of the wine fermentation performance of Saccharomyces paradoxus under different nitrogen concentrations and glucose/fructose ratios.

abstract: AIMS ::: The main goal of the present study is to determine the effects of different nitrogen concentrations and glucose/fructose ratios on the fermentation performance of Saccharomyces paradoxus, a nonconventional species used for winemaking. ::: ::: ::: METHODS AND RESULTS ::: Ethanol yield, residual sugar concentration, as well as glycerol and acetic acid production were determined for diverse wine fermentations conducted by S. paradoxus. Experiments were also carried out with a commercial Saccharomyces cerevisiae wine strain used as control. The values obtained were compared to test significant differences by means of a factorial anova and the Scheffé test. Our results show that S. paradoxus strain was able to complete the fermentation even in the nonoptimal conditions of low nitrogen content and high fructose concentration. In addition, the S. paradoxus strain showed significant higher glycerol synthesis and lower acetic acid production than S. cerevisiae in media enriched with nitrogen, as well as a lower, but not significant, ethanol yield. ::: ::: ::: CONCLUSIONS ::: The response of S. paradoxus was different with respect to the commercial S. cerevisiae strain, especially to glycerol and acetic acid synthesis. ::: ::: ::: SIGNIFICANCE AND IMPACT OF THE STUDY ::: The present study has an important implication for the implementation of S. paradoxus strains as new wine yeast starters exhibiting interesting enological properties.


title: An efficient genetic algorithm for the traveling salesman problem with precedence constraints

abstract: Abstract The traveling salesman problem with precedence constraints (TSPPC) is one of the most difficult combinatorial optimization problems. In this paper, an efficient genetic algorithm (GA) to solve the TSPPC is presented. The key concept of the proposed GA is a topological sort (TS), which is defined as an ordering of vertices in a directed graph. Also, a new crossover operation is developed for the proposed GA. The results of numerical experiments show that the proposed GA produces an optimal solution and shows superior performance compared to the traditional algorithms.


title: Design of output dv/dt filter for motor drives

abstract: Modern PWM inverter output voltage has high dv/dt, which causes problems such as voltage doubling that can lead to insulation failure, ground currents that results in electromagnetic interference concerns. The IGBT switching device used in such inverter are becoming faster, exacerbating these problems. This paper proposes a new procedure for designing the LC clamp filter. The filter increases the rise time of the output voltage of inverter, resulting in smaller dv/dt. In addition suitable selection of resonance frequency gives LCL filter configuration with improved attenuation. By adding this filter at output terminal of inverter which uses long cable, voltage doubling effect is reduced at the motor terminal. The design procedure is carried out in terms of the power converter based per unit scheme. This generalizes the design procedure to a wide range of power level and to study optimum designs. The effectiveness of the design is verified by computer simulation and experimental measurements.


title: An examination of the relationship among structure, trust, and conflict management styles in virtual teams

abstract: The emergence of new technologies has made it increasingly easy for distributed collaboration in both educational and noneducational settings. Although the effectiveness in traditional settings of the dynamics of small group work has been widely researched, there is limited research that offers evidence on how teams can work effectively in a virtual environment. The purpose of this study is to examine the relationship among team structure, trust, and conflict management style, in addition to their impact on teamwork effectiveness in a virtual environment. An experimental design was used to assess the effects of structure on team performance. Forty-four groups, divided into hierarchical and nonhierarchical groups, worked on an online simulation project in an online MBA course. The results suggest that team structure is strongly associated with team performance, whereas trust and a collaboration conflict management style contribute to teamwork satisfaction.


title: The Wind Farm Layout Optimization Problem

abstract: An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This chapter describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research.


title: Open Source Software and the "Private-Collective" Innovation Model: Issues for Organization Science

abstract: Currently, two models of innovation are prevalent in organization science. The "private investment" model assumes returns to the innovator result from private goods and efficient regimes of intellectual property protection. The "collective action" model assumes that under conditions of market failure, innovators collaborate in order to produce a public good. The phenomenon of open source software development shows that users program to solve their own as well as shared technical problems, and freely reveal their innovations without appropriating private returns from selling the software. In this paper, we propose that open source software development is an exemplar of a compound "private-collective" model of innovation that contains elements of both the private investment and the collective action models and can offer society the "best of both worlds" under many conditions. We describe a new set of research questions this model raises for scholars in organization science. We offer some details regarding the types of data available for open source projects in order to ease access for researchers who are unfamiliar with these, and also offer some advice on conducting empirical studies on open source software development processes.


title: Augmented reality sandbox: a platform for educative experiences

abstract: In this paper, we describe the implementation of an Augmented Reality Sandbox as a platform for educative experiences. Usually, the traditional Augmented Reality experiences, where the interface is a screen of a mobile devices, here the interface is real sand. It can form real landscapes where a virtual layer of information is displayed. We constructed the Sandbox from open source software and with accessible hardware. In this paper we discuss the special features of this installation, we evaluate the bottlenecks toward its implementation in real classrooms and the contents that can be teach together with the expected benefits.


title: Nazca: Detecting Malware Distribution in Large-Scale Networks

abstract: Malware remains one of the most significant secu- rity threats on the Internet. Antivirus solutions and blacklists, the main weapons of defense against these attacks, have only been (partially) successful. One reason is that cyber-criminals take active steps to bypass defenses, for example, by distribut- ing constantly changing (obfuscated) variants of their malware programs, and by quickly churning through domains and IP addresses that are used for distributing exploit code and botnet commands. We analyze one of the core tasks that malware authors have to achieve to be successful: They must distribute and install malware programs onto as many victim machines as possible. A main vec- tor to accomplish this is through drive-by download attacks where victims are lured onto web pages that launch exploits against the users' web browsers and their components. Once an exploit is successful, the injected shellcode automatically downloads and launches the malware program. While a significant amount of previous work has focused on detecting the drive-by exploit step and the subsequent network traffic produced by malware programs, little attention has been paid to the intermediate step where the malware binary is downloaded. In this paper, we study how clients in real-world networks download and install malware, and present Nazca, a system that detects infections in large scale networks. Nazca does not operate on individual connections, nor looks at properties of the downloaded programs or the reputation of the servers hosting them. Instead, it looks at the telltale signs of the malicious network infrastructures that orchestrate these malware installa- tion that become apparent when looking at the collective traffic produced and becomes apparent when looking at the collective traffic produced by many users in a large network. Being content agnostic, Nazca does not suffer from coverage gaps in reputation databases (blacklists), and is not susceptible to code obfuscation. We have run Nazca on seven days of traffic from a large Internet Service Provider, where it has detected previously-unseen malware with very low false positive rates


title: Dynamic load balancing on single- and multi-GPU systems

abstract: The computational power provided by many-core graphics processing units (GPUs) has been exploited in many applications. The programming techniques currently employed on these GPUs are not sufficient to address problems exhibiting irregular, and unbalanced workload. The problem is exacerbated when trying to effectively exploit multiple GPUs concurrently, which are commonly available in many modern systems. In this paper, we propose a task-based dynamic load-balancing solution for single-and multi-GPU systems. The solution allows load balancing at a finer granularity than what is supported in current GPU programming APIs, such as NVIDIA's CUDA. We evaluate our approach using both micro-benchmarks and a molecular dynamics application that exhibits significant load imbalance. Experimental results with a single-GPU configuration show that our fine-grained task solution can utilize the hardware more efficiently than the CUDA scheduler for unbalanced workload. On multi-GPU systems, our solution achieves near-linear speedup, load balance, and significant performance improvement over techniques based on standard CUDA APIs.


title: Sexual and reproductive health and rights of adolescent girls: Evidence from low- and middle-income countries

abstract: This paper reviews the evidence on sexual and reproductive health and rights (SRHR) of adolescent girls in low-income and middle-income countries (LMIC) in light of the policy and programme commitments made at the International Conference on Population and Development (ICPD), analyses progress since 1994, and maps challenges in and opportunities for protecting their health and human rights. Findings indicate that many countries have yet to make significant progress in delaying marriage and childbearing, reducing unintended childbearing, narrowing gender disparities that put girls at risk of poor SRH outcomes, expanding health awareness or enabling access to SRH services. While governments have reaffirmed many commitments, policy development and programme implementation fall far short of realising these commitments. Future success requires increased political will and engagement of young people in the formulation and implementation of policies and programmes, along with increased investments to deliver at scale comprehensive sexuality education, health services that are approachable and not judgemental, safe spaces programmes, especially for vulnerable girls, and programmes that engage families and communities. Stronger policy-making and programming also require expanding the evidence on adolescent health and rights in LMICs for both younger and older adolescents, boys and girls, and relating to a range of key health matters affecting adolescents.


title: Internet Censorship in Iran : A First Look

abstract: The Iranian government operates one of the largest and most sophisticated Internet censorship regimes in the world, but the mechanisms it employs have received little research attention, primarily due to lack of access to network connections within the country and personal risks to Iranian citizens who take part. In this paper, we examine the status of Internet censorship in Iran based on network measurements conducted from a major Iranian ISP during the lead up to the June 2013 presidential election. We measure the scope of the censorship by probing Alexa’s top 500 websites in 18 different categories. We investigate the technical mechanisms used for HTTP Host‐based blocking, keyword filtering, DNS hijacking, and protocol-based throttling. Finally, we map the network topology of the censorship infrastructure and find evidence that it relies heavily on centralized equipment, a property that might be fruitfully exploited by next generation approaches to censorship circumvention.


title: Image understanding for iris biometrics : A survey

abstract: This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.


Image Encryption using AES Algorithm based on FPGA


Heat Balance Method for Spines, Longitudinal and Radial Fins with Contact Conductance and End Cooling


title: Sounding the Bell for Improving Internet (of Things) Security

abstract: The fragility of the Internet of Things (iot) ecosystem poses serious threats to Internet security, and the proliferation of iot devices only exacerbates this situation by providing vulnerable end-points to be exploited and used as attack sources. While industry and academia are working hard on designing innovative solutions to detect, mitigate and thwart massive botnet-based ddos attacks, the space of solutions appears disjoint and fragmented. The lack of cooperation between the iot device manufacturers, network operators, content providers, end users, and other players precipitates in point solutions which offer at best a veneer of security. In this paper we alert the community to the security challenges posed by the fragile iot ecosystem, discuss the space of solutions, and present the need for a distributed, concerted effort, e.g., among end users, ISPs, and CDNs, to improve Internet security. We do not claim to solve the problem, but offer design guidelines and discuss the key implementation challenges to inform the debates on iot security.


title: Social Motivations To Use Gamification: An Empirical Study Of Gamifying Exercise

abstract: This paper investigates how social factors predict attitude toward gamification and intention to continue using gamified services, as well as intention to recommend gamified services. The paper employs structural equation modelling for analyses of data (n=107) gathered through a survey that was conducted among users of one of the world’s largest gamification applications for physical exercise. The results indicate that social factors are strong predictors for attitudes towards gamification, and, further, continued use intentions and intentions to recommend the related service.


title: Acquiring knowledge of affective events from blogs using label propagation

abstract: Many common events in our daily life affect us in positive and negative ways. For example, going on vacation is typically an enjoyable event, while being rushed to the hospital is an undesirable event. In narrative stories and personal conversations, recognizing that some events have a strong affective polarity is essential to understand the discourse and the emotional states of the affected people. However, current NLP systems mainly depend on sentiment analysis tools, which fail to recognize many events that are implicitly affective based on human knowledge about the event itself and cultural norms. Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. Our research creates an event context graph from a large collection of blog posts and uses a sentiment classifier and semi-supervised label propagation algorithm to discover affective events. We explore several graph configurations that propagate affective polarity across edges using local context, discourse proximity, and event-event co-occurrence. We then harvest highly affective events from the graph and evaluate the agreement of the polarities with human judgements.


MINIATURE FIVE-FINGERED ROBOT HAND DRIVEN BY SHAPE MEMORY ALLOY ACTUATORS


title: Path Generation Using ${\mbi \eta}^4$-Splines for a Truck and Trailer Vehicle

abstract: Generation of high-quality drive paths is a significant issue for automated wheeled vehicles. To achieve this aim for a truck and trailer vehicle, the paper proposes the use of a parameterized curve primitive, the η4-spline. Using this spline, generation and shaping of smooth feasible paths is made possible as well as the transfer between arbitrary dynamic configurations of the articulated vehicle. The η4-spline is a ninth-order polynomial curve that can interpolate given Cartesian points with associated arbitrary unit tangent vector, curvature, and first and second derivatives of curvature. It depends on a set of eight (eta) parameters that can be freely chosen to modify the path shape without changing the interpolations conditions at the path endpoints. Completeness, minimality, and symmetry of the η4-spline are established. An example on a parking maneuver of the articulated vehicle is presented and the pertinent optimal path planning is also discussed.


title: Head Gesture Recognition for Hands-free Control of an Intelligent Wheelchair

abstract: Purpose – This paper presents a novel hands‐free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures.Design/methodology/approach – A robust head gesture‐based interface (HGI), is designed for head gesture recognition of the RoboChair user. The recognised gestures are used to generate motion control commands to the low‐level DSP motion controller so that it can control the motion of the RoboChair according to the user's intention. Adaboost face detection algorithm and Camshift object tracking algorithm are combined in our system to achieve accurate face detection, tracking and gesture recognition in real time. It is intended to be used as a human‐friendly interface for elderly and disabled people to operate our intelligent wheelchair using their head gestures rather than their hands.Findings – This is an extremely useful system for the users who have restricted limb movements caused by some diseases such as Parkinson's disease and quadriplegics.Practical implicatio...


title: Information security management needs more holistic approach: A literature review

abstract: This paper is aimed at synthesizing the existing literature to suggest that why a more holistic approach of information security management is needed in management context.The paper entertains article on the related context for last ten years.A rigorous method for literature search is used with predetermined inclusion and exclusion criteria.At first more than 300 articles were downloaded for further processing and finally 39 articles were deemed to be relevant to the context under study.The paper suggests that management role should be considered in information security management. Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers' attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management's roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area.


title: Deformable Classifiers

abstract: Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transformations. In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output, separately for each class. The classifier outputs for each class, after transformation, are compared to yield the final decision. As a by-product of the classification this yields a transformation of the input object to a reference pose, which can be used for downstream tasks such as the computation of object support. We apply a two-step training mechanism for our framework, which alternates between optimizing over the latent transformation variables and the classifier parameters to minimize the loss function. We show that multilayer perceptrons, also known as deep networks, are well suited for this approach and achieve state of the art results on the rotated MNIST and the Google Earth dataset, and produce competitive results on MNIST and CIFAR-10 when training on smaller subsets of training data.


title: The Basal Ganglia Is Necessary for Learning Spectral, but Not Temporal, Features of Birdsong

abstract: SUMMARY Executing a motor skill requires the brain to control which muscles to activate at what times. How these aspects of control—motor implementation and timing—are acquired, and whether the learning processes underlying them differ, is not well understood. To address this, we used a reinforcement learning paradigm to independently manipulate both spectral and temporal features of birdsong, a complex learned motor sequence, while recording and perturbing activity in underlying circuits. Our results uncovered a striking dissociation in how neural circuits underlie learning in the two domains. The basal ganglia was required for modifying spectral, but not temporal, structure. This functional dissociation extended to the descending motor pathway, where recordings from a premotor cortex analog nucleus reflected changes to temporal, but not spectral, structure. Our results reveal a strategy in which the nervous system employs different and largely independent circuits to learn distinct aspects of a motor skill.


title: i, poet: automatic poetry composition through recurrent neural networks with iterative polishing schema

abstract: Part of the long lasting cultural heritage of humanity is the art of classical poems, which are created by fitting words into certain formats and representations. Automatic poetry composition by computers is considered as a challenging problem which requires high Artificial Intelligence assistance. This study attracts more and more attention in the research community. In this paper, we formulate the poetry composition task as a natural language generation problem using recurrent neural networks. Given user specified writing intents, the system generates a poem via sequential language modeling. Unlike the traditional one-pass generation for previous neural network models, poetry composition needs polishing to satisfy certain requirements. Hence, we propose a new generative model with a polishing schema, and output a refined poem composition. In this way, the poem is generated incrementally and iteratively by refining each line. We run experiments based on large datasets of 61,960 classic poems in Chinese. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.


title: Surgery versus conservative treatment in patients with type A distal radius fractures, a randomized controlled trial

abstract: Fractures of the distal radius are common and account for an estimated 17% of all fractures diagnosed. Two-thirds of these fractures are displaced and require reduction. Although distal radius fractures, especially extra-articular fractures, are considered to be relatively harmless, inadequate treatment may result in impaired function of the wrist. Initial treatment according to Dutch guidelines consists of closed reduction and plaster immobilisation. If fracture redisplacement occurs, surgical treatment is recommended. Recently, the use of volar locking plates has become more popular. The aim of this study is to compare the functional outcome following surgical reduction and fixation with a volar locking plate with the functional outcome following closed reduction and plaster immobilisation in patients with displaced extra-articular distal radius fractures. This single blinded randomised controlled trial will randomise between open reduction and internal fixation with a volar locking plate (intervention group) and closed reduction followed by plaster immobilisation (control group). The study population will consist of all consecutive adult patients who are diagnosed with a displaced extra-articular distal radius fracture, which has been adequately reduced at the Emergency Department. The primary outcome (functional outcome) will be assessed by means of the Disability Arm Shoulder Hand Score (DASH). Secondary outcomes comprise the Patient-Rated Wrist Evaluation score (PRWE), quality of life, pain, range of motion, radiological parameters, complications and cross-overs. Since the treatment allocated involves a surgical procedure, randomisation status will not be blinded. However, the researcher assessing the outcome at one year will be unaware of the treatment allocation. In total, 90 patients will be included and this trial will require an estimated time of two years to complete and will be conducted in the Academic Medical Centre Amsterdam and its partners of the regional trauma care network. Ideally, patients would be randomised before any kind of treatment has been commenced. However, we deem it not patient-friendly to approach possible participants before adequate reduction has been obtained. This study is registered at the Netherlands Trial Register (NTR3113) and was granted permission by the Medical Ethical Review Committee of the Academic Medical Centre on 01-10-2012.


title: Life satisfaction and problematic Internet use: Evidence for gender specific effects

abstract: The present study investigates, using a large sample (N=4852 participants; 51.71% males), how problematic Internet use (PIU) relates to general life satisfaction and distinct facets of everyday life such as job, leisure, and health. Data on Internet usage was gathered using a short form of the Young Internet Addiction Test. Life satisfaction was measured with standardized items taken from the socioeconomic panel (Germany). Highly significant associations were observed between PIU and the facets of life satisfaction, health and leisure. Of note, these associations between the mentioned facets of life satisfaction and PIU were significantly higher for females compared to males, although the reported total level of PIU was significantly lower for females. This suggests the presence of different thresholds for males and females with respect to negative effects on well-being due to PIU. The current study underlines the importance of including gender as a critical variable when investigating the association between life satisfaction and PIU.


title: Personality and IT security: An application of the five-factor model

abstract: Despite numerous advances in IT security, many computer users are still vulnerable to security-related risks because they do not comply with organizational policies and procedures. In a network setting, individual risk can extend to all networked users. Endpoint security refers to the set of organizational policies, procedures, and practices directed at securing the endpoint of the network connections – the individual end user. As such, the challenges facing IT managers in providing effective endpoint security are unique in that they often rely heavily on end user participation. But vulnerability can be minimized through modification of desktop security programs and increased vigilance on the part of the system administrator or CSO. The cost-prohibitive nature of these measures generally dictates targeting high-risk users on an individual basis. It is therefore important to differentiate between individuals who are most likely to pose a security risk and those who will likely follow most organizational policies and procedures.


A NOVEL CMOS DYNAMIC LATCH COMPARATOR FOR LOW POWER AND HIGH SPEED


title: Experimental and Quasi-Experimental Designs for Research

abstract: A survey drawn from social-science research which deals with correlational, ex post facto, true experimental, and quasi-experimental designs and makes methodological recommendations. Bibliogs.


title: On the Geometry of Covariance Matrices

abstract: We introduce and compare certain distance measures between covariance matrices. These originate in information theory, quantum mechanics and optimal transport. More specifically, we show that the Bures/Hellinger distance between covariance matrices coincides with the Wasserstein-2 distance between the corresponding Gaussian distributions. We also note that this Bures/Hellinger/Wasserstein distance can be expressed as the solution to a linear matrix inequality (LMI). A consequence of this fact is that the computational cost in covariance approximation problems scales nicely with the size of the matrices involved. We discuss the relevance of this metric in spectral-line detection and spectral morphing.


title: Learning to Generate Reviews and Discovering Sentiment

abstract: We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.


title: A highly-efficient 138–170 GHz SiGe HBT frequency doubler for power-constrained applications

abstract: This paper presents a 138–170 GHz active frequency doubler implemented in a 0.13 µm SiGe BiCMOS technology with a peak output power of 5.6 dBm and peak power-added efficiency of 7.6%. The doubler achieves a peak conversion gain of 4.9 dB and consumes only 36 mW of DC power at peak drive through the use of a push-push frequency doubling stage optimized for low drive power, along with a low-power output buffer. To the best of our knowledge, this doubler achieves the highest output power, efficiency, and fundamental frequency suppression of all D-band and G-band SiGe HBT frequency doublers to date.


title: Searching and Browsing Linked Data with SWSE: the Semantic Web Search Engine

abstract: In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data - loosely also known as Linked Data - which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web - in terms of scale, unreliability, inconsistency and noise - are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.


title: Dense Dielectric Patch Array Antenna With Improved Radiation Characteristics Using EBG Ground Structure and Dielectric Superstrate for Future 5G Cellular Networks

abstract: In this paper, a new dense dielectric (DD) patch array antenna prototype operating at 28 GHz for future fifth generation (5G) cellular networks is presented. This array antenna is proposed and designed with a standard printed circuit board process to be suitable for integration with radio frequency/microwave circuitry. The proposed structure employs four circular-shaped DD patch radiator antenna elements fed by a 1-to-4 Wilkinson power divider. To improve the array radiation characteristics, a ground structure based on a compact uniplanar electromagnetic bandgap unit cell has been used. The DD patch shows better radiation and total efficiencies compared with the metallic patch radiator. For further gain improvement, a dielectric layer of a superstrate is applied above the array antenna. The measured impedance bandwidth of the proposed array antenna ranges from 27 to beyond 32 GHz for a reflection coefficient (S11) of less than -10 dB. The proposed design exhibits stable radiation patterns over the whole frequency band of interest, with a total realized gain more than 16 dBi. Due to the remarkable performance of the proposed array, it can be considered as a good candidate for 5G communication applications.


title: Cost-Efficient Strategies for Restraining Rumor Spreading in Mobile Social Networks

abstract: With the popularity of mobile devices, mobile social networks (MSNs) have become an important platform for information dissemination. However, the spread of rumors in MSNs present a massive social threat. Currently, there are two kinds of methods to address this: blocking rumors at influential users and spreading truth to clarify rumors. However, most existing works either overlook the cost of various methods or only consider different methods individually. This paper proposes a heterogeneous-network-based epidemic model that incorporates the two kinds of methods to describe rumor spreading in MSNs. Moreover, two cost-efficient strategies are designed to restrain rumors. The first strategy is the real-time optimization strategy that minimizes the rumor-restraining cost by optimally combining various rumor-restraining methods such that a rumor can be extinct within an expected time period. The second strategy is the pulse spreading truth and continuous blocking rumor strategy that restrains rumor spreading through spreading truth periodically. The two strategies can restrain rumors in a continuous or periodical manner and guarantee cost efficiency. The experiments toward the Digg2009 data set demonstrate the effectiveness of the proposed model and the efficiency of the two strategies.


title: A Multilayer and Time-varying Structural Analysis of the Brazilian Air Transportation Network

abstract: This paper provides a multilayer and time-varying structural analysis of one air transportation network, having the Brazilian air transportation network as a case study. Using a single mathematical object called MultiAspect Graph (MAG) for this analysis, the multilayer perspective enables the unveiling of the particular strategies of each airline to both establish and adapt in a moment of crisis its specific flight network. Similarly, the time-varying perspective allows multi-scale analysis considering different time periods, and thus assessing the impact of the economic crisis on how the different airlines establish their routes as well as the flights that use these routes. Altogether, besides the multilayer and time-varying structural analysis of the Brazilian air transportation network, this paper also acts as a proof-of-concept for the MAG potential for the modeling and analysis of high-order networks.


title: A hybrid recommendation approach for a tourism system

abstract: Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.


title: A Survey of Vehicular Ad hoc Networks Routing Protocols

abstract: In recent years, the aspect of vehicular ad hoc network (VANET) is becoming an interesting research area; VANET is a mobile ad hoc network considered as a special case of mobile ad hoc network (MANET). Similar to MANET, VANET is characterized as autonomous and self-configured wireless network. However, VANET has very dynamic topology, large and variable network size, and constrained mobility; these characteristics led to the need for efficient routing and resource saving VANET protocols, to fit with different VANET environments. These differences render traditional MANET's protocols unsuitable for VANET. The aim of this work is to give a survey of the VANETs routing mechanisms, this paper gives an overview of Vehicular ad hoc networks (VANETs) and the existing VANET routing protocols; mainly it focused on vehicle to vehicle (V2V) communication and protocols. The paper also represents the general outlines and goals of VANETs, investigates different routing schemes that have been developed for VANETs, as well as providing classifications of VANET routing protocols (focusing on two classification forms), and gives summarized comparisons between different classes in the context of their methodologies used, strengths, and limitations of each class scheme compared to other classes. Finally, it extracts the current trends and the challenges for efficient routing mechanisms in VANETs.


title: What You Need to Know About SDN Flow Tables

abstract: SDN deployments rely on switches that come from various vendors and differ in terms of performance and available features. Understanding these differences and performance characteristics is essential for ensuring successful deployments. In this paper we measure, report, and explain the performance characteristics of flow table updates in three hardware OpenFlow switches. Our results can help controller developers to make their programs efficient. Further, we also highlight differences between the OpenFlow specification and its implementations, that if ignored, pose a serious threat to network security and correctness.


title: Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis

abstract: This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectories, a set of hot regions (topics) that trajectory points most frequently present are first discovered using a sticky HDP-HMM; then, the major paths of the trajectories' transitions across different hot regions are extracted by recursively mining frequent subsequences of topics; and finally, paths are modeled using a hierarchical hidden Markov model (HHMM), where the intra-path dynamics is represented using an HMM, in which each state corresponds to a hot region, while the inter-path transition is assumed to be Markovian. The model could be used for behavior prediction, i.e. whenever a vehicle is detected in a scene, predicting which route it will probably follow and how its trajectory will probably develop over time, which is essential to interpreting the potential risks for longer time horizons. Experiments are conducted using a large set of vehicle trajectories collected from motorways in Beijing, and promising results are presented.


title: Global optimization and simulated annealing

abstract: In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of ℝ n in which some real valued functionf assumes its optimal (maximal or minimal) value.


title: QUIC: quadratic approximation for sparse inverse covariance estimation

abstract: The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program. In contrast to recent state-of-the-art methods that largely use first order gradient information, our algorithm is based on Newton's method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and present experimental results using synthetic and real-world application data that demonstrate the considerable improvements in performance of our method when compared to previous methods.


title: Integer linear programming models for 2-staged two-dimensional Knapsack problems

abstract: We are given a unique rectangular piece of stock material S, with height H and width W, and a list of m rectangular shapes to be cut from S. Each shape's type i (i = 1, ..., m) is characterized by a height \(\), a width \(\), a profit \(\), and an upper bound ub i indicating the maximum number of items of type i which can be cut. We refer to the Two-Dimensional Knapsack (TDK) as the problem of determining a cutting pattern of S maximizing the sum of the profits of the cut items. In particular, we consider the classical variant of TDK in which the maximum number of cuts allowed to obtain each item is fixed to 2, and we refer to this problem as 2-staged TDK (2TDK). For the 2TDK problem we present two new Integer Linear Programming models, we discuss their properties, and we compare them with other formulations in terms of the LP bound they provide. Finally, both models are computationally tested within a standard branch-and-bound framework on a large set of instances from the literature by reinforcing them with the addition of linear inequalities to eliminate symmetries.


title: Using fleets of electric-drive vehicles for grid support

abstract: Electric-drive vehicles can provide power to the electric grid when they are parked (vehicle-to-grid power). We evaluated the economic potential of two utility-owned fleets of battery-electric vehicles to provide power for a specific electricity market, regulation, in four US regional regulation services markets. The two battery-electric fleet cases are: (a) 100 Th!nk City vehicle and (b) 252 Toyota RAV4. Important variables are: (a) the market value of regulation services, (b) the power capacity (kW) of the electrical connections and wiring, and (c) the energy capacity (kWh) of the vehicle's battery. With a few exceptions when the annual market value of regulation was low, we find that vehicle-to-grid power for regulation services is profitable across all four markets analyzed. Assuming now more than current Level 2 charging infrastructure (6.6 kW) the annual net profit for the Th!nk City fleet is from US$ 7000 to 70,000 providing regulation down only. For the RAV4 fleet the annual net profit ranges from US$ 24,000 to 260,000 providing regulation down and up. Vehicle-to-grid power could provide a significant revenue stream that would improve the economics of grid-connected electric-drive vehicles and further encourage their adoption. It would also improve the stability of the electrical grid.


title: Capturing and analyzing low-level events from the code editor

abstract: In this paper, we present FLUORITE, a publicly available event logging plug-in for Eclipse which captures all of the low-level events when using the Eclipse code editor. FLUORITE captures not only what types of events occurred in the code editor, but also more detailed information such as the inserted and deleted text and the specific parameters for each command. This enables the detection of many usage patterns that could otherwise not be recognized, such as "typo correction" that requires knowing that the entered text is immediately deleted and replaced. Moreover, the snapshots of each source code file that has been opened during the session can be completely reproduced using the collected information. We also provide analysis and visualization tools which report various statistics about usage patterns, and we provide the logs in an XML format so others can write their own analyzers. FLUORITE can be used for not only evaluating existing tools, but also for discovering issues that motivate new tools.


title: Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View

abstract: In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.


title: Towards A Differential Privacy and Utility Preserving Machine Learning Classifier

abstract: Abstract Many organizations transact in large amounts of data often containing personal identifiable information (PII) and various confidential data. Such organizations are bound by state, federal, and international laws to ensure that the confidentiality of both individuals and sensitive data is not compromised. However, during the privacy preserving process, the utility of such datasets diminishes even while confidentiality is achieved--a problem that has been defined as NP-Hard. In this paper, we investigate a differential privacy machine learning ensemble classifier approach that seeks to preserve data privacy while maintaining an acceptable level of utility. The first step of the methodology applies a strong data privacy granting technique on a dataset using differential privacy. The resulting perturbed data is then passed through a machine learning ensemble classifier, which aims to reduce the classification error, or, equivalently, to increase utility. Then, the association between increasing the number of weak decision tree learners and data utility, which informs us as to whether the ensemble machine learner would classify more correctly is examined. As results, we found that a combined adjustment of the privacy granting noise parameters and an increase in the number of weak learners in the ensemble machine might lead to a lower classification error.


title: Perceptions of Need and the Ethicality of the Male Social Work Practice

abstract: A discussion of the ethical requirements of male social workers to actively support women in the struggle for gender equality is presented. Specifically, two questions are addressed: (1) Are males in the social work profession, who take no active position on sexism and equality, practicing unethically according to the National Association of Social Worker (NASW) Code of Ethics (1999) by supporting the patriarchal structure via inaction, while accepting the privileges it affords, including benefiting financially and professionally through the analysis and treatment of its effects? And, if so, (2) do male social work practitioners have a moral and ethical duty to actively support social change which would enhance equality between women and men for the benefit of their clients, co-workers, and selves? The cultural perception of social need is discussed as being inseparable from the perception of moral obligation and the two concepts are explored through the lenses of four paradigms: (a) structural functional...


title: Forward-Mode Automatic Differentiation in Julia

abstract: We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support for higher-order differentiation and differentiation using custom number types (including complex numbers). For gradient and Jacobian calculations, ForwardDiff provides a variant of vector-forward mode that avoids expensive heap allocation and makes better use of memory bandwidth than traditional vector mode. In our numerical experiments, we demonstrate that for nontrivially large dimensions, ForwardDiff's gradient computations can be faster than a reverse-mode implementation from the Python-based autograd package. We also illustrate how ForwardDiff is used effectively within JuMP, a modeling language for optimization. According to our usage statistics, 41 unique repositories on GitHub depend on ForwardDiff, with users from diverse fields such as astronomy, optimization, finite element analysis, and statistics.


title: Inflammatory markers are associated with risk of colorectal cancer and chemopreventive response to anti-inflammatory drugs.

abstract: BACKGROUND & AIMS ::: Aspirin and nonsteroidal anti-inflammatory drugs (NSAIDs) lower the risk of colorectal cancer (CRC). We investigated whether plasma inflammatory markers were associated with risk of CRC and if use of anti-inflammatory drugs was differentially associated with risk of CRC according to levels of inflammatory markers. ::: ::: ::: METHODS ::: We measured levels of high-sensitivity C-reactive protein (CRP), interleukin (IL)-6, and the soluble tumor necrosis factor receptor 2 (sTNFR-2) in blood samples from 32,826 women, collected from 1989 to 1990. Through 2004, we documented 280 cases of incident CRC; each case was matched for age to 2 randomly selected participants without cancer (controls). Information on anti-inflammatory drug (aspirin and NSAIDs) use was collected biennially. ::: ::: ::: RESULTS ::: Compared with women in the lowest quartile of plasma levels of sTNFR-2, women in the highest quartile had an increased risk of CRC (multivariate relative risk [RR], 1.67; 95% confidence interval [CI], 1.05-2.68; P for trend = .03). Among women with high baseline levels of sTNFR-2, those who initiated aspirin/NSAID use after blood collection had significant reductions in subsequent risk of CRC (multivariate RR, 0.39; 95% CI, 0.18-0.86). In contrast, among women with low baseline levels of sTNFR-2, initiation of aspirin/NSAID use was not associated with significant risk reduction (multivariate RR, 0.86; 95% CI, 0.41-1.79). Plasma levels of CRP and IL-6 were not significantly associated with CRC risk. ::: ::: ::: CONCLUSIONS ::: Plasma levels of sTNFR-2, but not CRP or IL-6, are associated with an increased risk of CRC. Anti-inflammatory drugs appear to reduce risk of CRC among women with high, but not low, baseline levels of sTNFR-2. Certain subsets of the population, defined by inflammatory markers, may obtain different benefits from anti-inflammatory drugs.


title: Improving Twitter Community Detection through Contextual Sentiment Analysis.

abstract: Works on Twitter community detection have yielded new ways to extract valuable insights from social media. Through this technique, Twitter users can be grouped into different types of communities such as those who have the same interests, those who interact a lot, or those who have similar sentiments about certain topics. Computationally, information is represented as a graph, and community detection is the problem of partitioning the graph such that each community is more densely connected to each other than to the rest of the network. It has been shown that incorporating sentiment analysis can improve community detection when looking for sentiment-based communities. However, such works only perform sentiment analysis in isolation without considering the tweet’s various contextual information. Examples of these contextual information are social network structure, and conversational, author, and topic contexts. Disregarding these information poses a problem because at times, context is needed to clearly infer the sentiment of a tweet. Thus, this research aims to improve detection of sentiment-based communities on Twitter by performing contextual sentiment analysis.


abstract: We present Multi-Relational Latent Semantic Analysis (MRLSA) which generalizes Latent Semantic Analysis (LSA). MRLSA provides an elegant approach to combining multiple relations between words by constructing a 3-way tensor. Similar to LSA, a lowrank approximation of the tensor is derived using a tensor decomposition. Each word in the vocabulary is thus represented by a vector in the latent semantic space and each relation is captured by a latent square matrix. The degree of two words having a specific relation can then be measured through simple linear algebraic operations. We demonstrate that by integrating multiple relations from both homogeneous and heterogeneous information sources, MRLSA achieves stateof-the-art performance on existing benchmark datasets for two relations, antonymy and is-a.


title: Constructions from Dots and Lines

abstract: Summary. A graph is a data structure composed of dots (i.e. vertices) and lines (i.e. edges). The dots and lines of a graph can be organized into intricate arrangements. The ability for a graph to denote objects and their relationships to one another allow for a surprisingly large number of things to be modeled as a graph. From the dependencies that link software packages to the wood beams that provide the framing to a house, most anything has a corresponding graph representation. However, just because it is possible to represent something as a graph does not necessarily mean that its graph representation will be useful. If a modeler can leverage the plethora of tools and algorithms that store and process graphs, then such a mapping is worthwhile. This article explores the world of graphs in computing and exposes situations in which graphical models are benecial.


title: [Diagnosis and treatment of tuberculosis].

abstract: Unfortunately, advances in the management of tuberculosis (TB) occur all too slowly. It has, however, been quite a few years since the last guidelines on the diagnosis and treatment of TB were published by the Spanish Society of Pulmonology and Thoracic Surgery (SEPAR). During the intervening period, not only have advances been made in the treatment and, above all, the diagnosis of TB, but the demographic profile of Spain has also changed significantly as a result of increased immigration. Today, 10% of the country’s population is foreign born and 30% of TB cases involve immigrant patients. In this context, we considered it both timely and essential to update the society’s guidelines and adapt the recommendations to take into account these changes in the composition of Spanish society.. To simplify access to the information, we decided to combine all the society’s recommendations on the diagnosis and treatment of TB in a single practice guideline based on the scientific evidence currently available. Recommendations are graded as A, B, C, or D. A grade A classification indicates that the supporting scientific evidence is good or very good (level 1 trials); grade B indicates that the evidence is reasonably good (level 2 trials); grade C that the supporting evidence is scant (individual case reports and case series); and grade D that the recommendation is based on expert opinion or consensus. We would like to thank SEPAR’s Scientific Committee for their confidence in the ability of this group of experts to compile the new TB guidelines, and we hope that the resulting document will be of use in improving the management of TB. DIAGNOSIS OF TUBERCULOUS INFECTION


Ownership of a queue for practical lock-free scheduling


title: Security for Industrial Communication Systems

abstract: Modern industrial communication networks are increasingly based on open protocols and platforms that are also used in the office IT and Internet environment. This reuse facilitates development and deployment of highly connected systems, but also makes the communication system vulnerable to electronic attacks. This paper gives an overview of IT security issues in industrial automation systems which are based on open communication systems. First, security objectives, electronic attack methods, and the available countermeasures for general IT systems are described. General security objectives and best practices are listed. Particularly for the TCP/IP protocol suite, a wide range of cryptography-based secure communication protocols is available. The paper describes their principles and scope of application. Next, we focus on industrial communication systems, which have a number of security-relevant characteristics distinct from the office IT systems. Confidentiality of transmitted data may not be required; however, data and user authentication, as well as access control are crucial for the mission critical and safety critical operation of the automation system. As a result, modern industrial automation systems, if they include security measures at all, emphasize various forms of access control. The paper describes the status of relevant specifications and implementations for a number of standardized automation protocols. Finally, we illustrate the application of security concepts and tools by brief case studies describing security issues in the configuration and operation of substations, plants, or for remote access.


title: Developmental Readiness for Leadership: The Differential Effects of Leadership Courses on Creating “Ready, Willing, and Able” Leaders

abstract: The development of effective leadership capacity involves multiple factors including increasing students’ leadership self-efficacy, motivation to lead, and leadership skills. This study of 165 undergraduate students enrolled in an introductory leadership theory course explores the degree to which students report changes in these three areas of leadership from the beginning to the end of the course. Our analysis showed two important findings. First, students report significant gains in leadership self-efficacy, transformational and transactional leadership skill, and each measured form of motivation to lead at the conclusion of the course. Second, a closer examination shows that student learning is not across-the-board but, rather, differentiated. Students experience significantly different outcomes depending on their levels of self-efficacy and motivation to lead when they enter the course. These findings not only have broad implications for the way colleges and universities structure curricula around leadership development, but they also inform theoretical model-building regarding the process of student leadership development. DOI: 1012806/V13/I3/R1 Journal of Leadership Education Summer 2014


title: Eye Movement Analysis for Activity Recognition Using Electrooculography

abstract: In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.


title: Predicting User's Political Party Using Ideological Stances

abstract: Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties' ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users' ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users' party prediction task.


title: All-IDB: The acute lymphoblastic leukemia image database for image processing

abstract: The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research.


title: A relationally parametric model of dependent type theory

abstract: Reynolds' theory of relational parametricity captures the invariance of polymorphically typed programs under change of data representation. Reynolds' original work exploited the typing discipline of the polymorphically typed lambda-calculus System F, but there is now considerable interest in extending relational parametricity to type systems that are richer and more expressive than that of System F. This paper constructs parametric models of predicative and impredicative dependent type theory. The significance of our models is twofold. Firstly, in the impredicative variant we are able to deduce the existence of initial algebras for all indexed=functors. To our knowledge, ours is the first account of parametricity for dependent types that is able to lift the useful deduction of the existence of initial algebras in parametric models of System F to the dependently typed setting. Secondly, our models offer conceptual clarity by uniformly expressing relational parametricity for dependent types in terms of reflexive graphs, which allows us to unify the interpretations of types and kinds, instead of taking the relational interpretation of types as a primitive notion. Expressing our model in terms of reflexive graphs ensures that it has canonical choices for the interpretations of the standard type constructors of dependent type theory, except for the interpretation of the universe of small types, where we formulate a refined interpretation tailored for relational parametricity. Moreover, our reflexive graph model opens the door to generalisations of relational parametricity, for example to higher-dimensional relational parametricity.


title: Does customer satisfaction lead to improved brand equity? An empirical examination of two categories of retail brands

abstract: Purpose – The objective of the present research is to examine the relationship between consumers' satisfaction with a retailer and the equity they associate with the retail brand.Design/methodology/approach – Retail brand equity is conceptualized as a four‐dimensional construct comprising: retailer awareness, retailer associations, retailer perceived quality, and retailer loyalty. Then the associative network memory model is applied from cognitive psychology to the specific context of the relationships between customer satisfaction and consumer‐based retailer equity. A survey was undertaken using a convenience sample of shopping mall consumers in an Australian state capital city. The questionnaire used to collect data included an experimental design such that two categories of retailers were included in the study: department stores and specialty stores, with three retailers representing each category. The relationship between consumer‐based retailer equity and customer satisfaction was examined using mult...


title: Effect of Lumbar Stabilization and Dynamic Lumbar Strengthening Exercises in Patients With Chronic Low Back Pain

abstract: Objective To compare the effects of lumbar stabilization exercises and lumbar dynamic strengthening exercises on the maximal isometric strength of the lumbar extensors, pain severity and functional disability in patients with chronic low back pain (LBP). Methods Patients suffering nonspecific LBP for more than 3 months were included prospectively and randomized into lumbar stabilization exercise group (n=11) or lumbar dynamic strengthening exercise group (n=10). Exercises were performed for 1 hour, twice weekly, for 8 weeks. The strength of the lumbar extensors was measured at various angles ranging from 0° to 72° at intervals of 12°, using a MedX. The visual analog scale (VAS) and the Oswestry Low Back Pain Disability Questionnaire (ODQ) were used to measure the severity of LBP and functional disability before and after the exercise. Results Compared with the baseline, lumbar extension strength at all angles improved significantly in both groups after 8 weeks. The improvements were significantly greater in the lumbar stabilization exercise group at 0° and 12° of lumbar flexion. VAS decreased significantly after treatment; however, the changes were not significantly different between the groups. ODQ scores improved significantly in the stabilization exercise group only. Conclusion Both lumbar stabilization and dynamic strengthening exercise strengthened the lumbar extensors and reduced LBP. However, the lumbar stabilization exercise was more effective in lumbar extensor strengthening and functional improvement in patients with nonspecific chronic LBP.


title: Interactive Proof-of-stake

abstract: The paper examines decentralized cryptocurrency protocols that are based on the use of internal tokens as identity tools. An analysis of security problems with popular Proof-of-stake consensus protocols is provided. A new protocol, Interactive Proof-of-stake, is proposed. The main ideas of the protocol are to reduce a number of variables a miner can iterate over to a minimum and also to bring a communication into block generation. The protocol is checked against known attacks. It is shown that Interactive Proof-of-stake is more secure than current pure Proof-of-stake protocols.


title: Mindfulness Meditation: A Preliminary Study on Meditation Practice During Everyday Life Activities and its Association with Well-Being

abstract: Past research has shown that mindfulness meditation is useful for the attenuation of psychological and physical suffering in clinical populations. In structured mindfulness-based interventions, patients engage in meditation exercises to refine their attentional skills and to learn to purposefully relate to the present moment experience in a non-judgemental manner. Following the development of such interventions, mindfulness has also received considerable attention in academic psychology, where it has been incorporated in the self-determination theory (SDT). According to SDT, the cultivation of mindfulness may warrant effective need gratification and consequently yield enhanced well-being in healthy individuals. In this context, in the current study, we examined the association between mindfulness meditation, self-reported trait mindfulness and their predictive value for psychological well-being in a non-clinical sample. Individuals who engaged in mindfulness meditation regularly (N = 30) were compared to individuals without meditation experience (N = 30) on various scales which assessed trait mindfulness and psychological well-being. Meditators reported higher emotional well-being, which was predicted by frequency and duration of practice. Especially those practitioners, who made efforts to implement mindfulness practice in activities of everyday life showed enhanced emotional adjustment. In an explorative analysis, mindfulness was identified as a putative partial mediator of the relationship between meditation practice and well-being. Despite methodological constraints, results of the current study suggest that mindfulness meditation, in a non-clinical context, is associated with increased psychological well-being, and as such worth to be explored in more detail by future research. The study and its results might be relevant for the clinical sector as well, since they provide some information on how individuals with e.g., subclinical residual symptoms can protect themselves complementarily to CBT, but without participating in a completely structured mindfulness-based intervention.


title: CosFace: Large Margin Cosine Loss for Deep Face Recognition

abstract: Face recognition has achieved revolutionary advancement owing to the advancement of the deep convolutional neural network (CNN). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, traditional softmax loss of deep CNN usually lacks the power of discrimination. To address this problem, recently several loss functions such as central loss \cite{centerloss}, large margin softmax loss \cite{lsoftmax}, and angular softmax loss \cite{sphereface} have been proposed. All these improvement algorithms share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we design a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as cosine loss by L2 normalizing both features and weight vectors to remove radial variation, based on which a cosine margin term \emph{$m$} is introduced to further maximize decision margin in angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. To test our approach, extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmark experiments, which confirms the effectiveness of our approach.


title: An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data

abstract: This paper deals with city-wide air quality estimation with limited air quality monitoring stations which are geographically sparse. Since air pollution is influenced by urban dynamics (e.g., meteorology and traffic) which are available throughout the city, we can infer the air quality in regions without monitoring stations based on such spatial-temporal (ST) heterogeneous urban big data. However, big data-enabled estimation poses three challenges. The first challenge is data diversity, i.e., there are many different categories of urban data, some of which may be useless for the estimation. To overcome this, we extend Granger causality to the ST space to analyze all the causality relations in a consistent manner. The second challenge is the computational complexity due to processing the massive volume of data. To overcome this, we introduce the non-causality test to rule out urban dynamics that do not “Granger” cause air pollution, and the region of influence (ROI), which enables us to only analyze data with the highest causality levels. The third challenge is to adapt our grid-based algorithm to non-grid-based applications. By developing a flexible grid-based estimation algorithm, we can decrease the inaccuracies due to grid-based algorithm while maintaining computation efficiency.


title: Advanced Volume Visualization for Interactive Extraction and Physics-based Modeling of Volume Data

abstract: This paper proposes an advanced volume representation of 3D geometries and presents its application for volume visualization of medical images. 3D geometry is represented using cross sections of grids and visualized volumetrically using a slice-based volume rendering method. The developed framework achieves real-time visualization on general purpose PCs and graphic hardware. As practical application of the framework, on-demand extraction on 3D reconstructed images and volumetric representation in physics-based simulation like deformation and cutting are introduced. Performance and effectiveness of the proposed methods are confirmed in preliminary use.


title: Active end-effector pose selection for tactile object recognition through Monte Carlo tree search

abstract: This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2–9 grasps and outperformed a greedy baseline.


Knowledge Sharing Self-Efficacy, Motivation and Sense of Community as Predictors of Knowledge Receiving and Giving Behaviors


title: Discovery of facial motions using deep machine perception

abstract: Deep, intuitive understanding of facial motions has the potential to provide an intelligent facial expression system as well as a unique encoding of the dynamics of facial actions. The most promising existing approaches rely on extracting hand crafted features; and existing approaches typically work best in constrained conditions and do not generalise well to varying environmental conditions which make them poorly suited to applications such as real-time human robot interactions. In this paper, we propose a multi-label deep learning based facial action detector, which along with a linear SVM classifier outperforms state of the art approaches such as HOG and LBP. We show that our approach can be generalized to other datasets by learning inner data structure, encoding facial actions, and providing a hierarchical representation of facial features. Our experimental results also demonstrate the efficiency of using image patches, which results in faster learning convergence while outperforms holistic approaches. We evaluate our proposed frame-work on the DISFA and CK+ datasets.


title: Automatic annotation of bioinformatics workflows with biomedical ontologies

abstract: Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 myExperiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.


title: Tracking of multiple maneuvering targets using multiscan JPDA and IMM smoothing [radar sensor example]

abstract: We consider the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal fixed-lag smoothing algorithm is developed by applying the basic interacting multiple model (IMM) approach and joint probabilistic data association (JPDA) technique to a state augmented system. But unlike the standard single scan JPDA approach, we exploit a multiscan JPDA (Mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example.