Personalized, interactive tag recommendation for flickr

We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.
 Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user's tagging history) can or cannot be improved by global schemes (using everybody's tagging history).


A review on software development security engineering using Dynamic System Method (DSDM)

Agile methodology such as Scrum, Extreme Programming (XP), Feature Driven Development (FDD) and the Dynamic System Development Method (DSDM) have gained enough recognition as efficient development process by delivering software fast even under the time constrains. However, like other agile methods DSDM has been criticized because of unavailability of security element in its four phases. In order to have a deeper look into the matter and discover more about the reality, we conducted a literature review. Our findings highlight that, in its current form, the DSDM does not support developing secure software. Although, there are a few researches on this topic about Scrum, XP and FDD but, based on our findings, there is no research on developing secure software using DSDM. Thus, in our future work we intend to propose enhanced DSDM that will cater the security aspects in software development.


A systematic literature review of nurse shortage and the intention to leave.

AIM
To present the findings of a literature review regarding nurses' intention to leave their employment or the profession.

BACKGROUND
The nursing shortage is a problem that is being experienced worldwide. It is a problem that, left unresolved, could have a serious impact on the provision of quality health care. Understanding the reasons why nurses leave their employment or the profession is imperative if efforts to increase retention are to be successful.

EVALUATION
Electronic databases were systematically searched to identify English research reports about nurses' intention to leave their employment or the profession. Key results concerning the issue were extracted and synthesized.

KEY ISSUES
The diversified measurement instruments, samples and levels of intention to leave caused difficulties in the attempt to compare or synthesize findings. The factors influencing nurses' intention to leave were identified and categorized into organizational and individual factors.

CONCLUSIONS
The reasons that trigger nurses' intention to leave are complex and are influenced by organizational and individual factors. Further studies should be conducted to investigate how external factors such as job opportunities correlate with nurses' intention to leave.

IMPLICATIONS FOR NURSING MANAGEMENT
The review provides insight that can be useful in designing and implementing strategies to maintain a sustainable workforce in nursing.


A model for play-based intervention for children with ADHD.

BACKGROUND/AIM
The importance of play in the social development of children is undisputed. Even though children with attention-deficit hyperactivity disorder (ADHD) experience serious social problems, there is limited research on their play. By integrating literature on ADHD with literature on play, we can postulate how play is influenced by the characteristics of ADHD. These postulations enabled us to propose a theoretical model (proposed model) to depict the interactive process between the characteristics of ADHD and factors that promote play. This paper presents the revised model and principles for intervention based on the results of a study investigating the play of children with ADHD (reported elsewhere).

METHODS
We tested the proposed model in a study comparing two groups of children (n = 350) between the ages of 5 and 11 years. One group consisted of children diagnosed with ADHD (n = 112) paired with playmates (n = 112) who were typically developing; the control group consisted of typically developing children paired with typically developing playmates (n = 126). The Test of Playfulness was administered, and the model was revised in line with the findings.

RESULTS AND CONCLUSIONS
The findings suggest difficulties in the social play and lack of interpersonal empathy in the play of children with ADHD. We draw on the revised model to propose preliminary principles for play-based interventions for children with ADHD. The principles emphasise the importance of capturing the motivation of children with ADHD, counteracting the effects of lack of interpersonal empathy, and considerations for including playmates in the intervention process.


Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning.

This prospective study with 464 older adolescents (14 to 19 years at Time 1; 16 to 21 years at Time 2) tested the structural paths of influence through which perceived self-efficacy for affect regulation operates in concert with perceived behavioral efficacy in governing diverse spheres of psychosocial functioning. Self-efficacy to regulate positive and negative affect is accompanied by high efficacy to manage one's academic development, to resist social pressures for antisocial activities, and to engage oneself with empathy in others' emotional experiences. Perceived self-efficacy for affect regulation essentially operated mediationally through the latter behavioral forms of self-efficacy rather than directly on prosocial behavior, delinquent conduct, and depression. Perceived empathic self-efficacy functioned as a generalized contributor to psychosocial functioning. It was accompanied by prosocial behavior and low involvement in delinquency but increased vulnerability to depression in adolescent females.


Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task

Making no claim of being exhaustive, a review of the most popular MFCC (Mel Frequency Cepstral Coefficients) implementations is made. These differ mainly in the particular approximation of the nonlinear pitch perception of human, the filter bank design, and the compression of the filter bank output. Then, a comparative evaluation of the presented implementations is performed on the task of text-independent speaker verification, by means of the well-known 2001 NIST SRE (speaker recognition evaluation) one-speaker detection database.


Personality, Culture, and System Factors - Impact on Affective Response to Multimedia

Whilst affective responses to various forms and genres of multimedia content have been well researched, precious few studies have investigated the combined impact that multimedia system parameters and human factors have on affect. Consequently, in this paper we explore the role that two primordial dimensions of human factors personality and culture in conjunction with system factors frame rate, resolution, and bit rate have on user affect and enjoyment of multimedia presentations. To this end, a two-site, cross-cultural study was undertaken, the results of which produced three predictve models. Personality and Culture traits were shown statistically to represent 5.6% of the variance in positive affect, 13.6% in negative affect and 9.3% in enjoyment. The correlation between affect and enjoyment, was significant. Predictive modeling incorporating human factors showed about 8%, 7% and 9% improvement in predicting positive affect, negative affect and enjoyment respectively when compared to models trained only on system factors. Results and analysis indicate the significant role played by human factors in influencing affect that users experience while watching multimedia.


Towards a Simple but Useful Ontology Design Pattern Representation Language

The need for a representation language for ontology design patterns has long been recognized. However, the body of literature on the topic is still rather small and does not sufficiently reflect the diverse requirements on such a language. Herein, we propose a simple but useful and extendable approach which is fully compatible with the Web Ontology Language and should be easy to adopt by the community.


Enhanced multisensory integration in older adults

Information from the different senses is seamlessly integrated by the brain in order to modify our behaviors and enrich our perceptions. It is only through the appropriate binding and integration of information from the different senses that a meaningful and accurate perceptual gestalt can be generated. Although a great deal is known about how such cross-modal interactions influence behavior and perception in the adult, there is little knowledge as to the impact of aging on these multisensory processes. In the current study, we examined the speed of discrimination responses of aged and young individuals to the presentation of visual, auditory or combined visual-auditory stimuli. Although the presentation of multisensory stimuli speeded response times in both groups, the performance gain was significantly greater in the aged. Most strikingly, multisensory stimuli restored response times in the aged to those seen in young subjects to the faster of the two unisensory stimuli (i.e., visual). The current results suggest that despite the decline in sensory processing that accompanies aging, the use of multiple sensory channels may represent an effective compensatory strategy to overcome these unisensory deficits.


Potential Vulnerabilities of Neuronal Reward, Risk, and Decision Mechanisms to Addictive Drugs

How do addictive drugs hijack the brain's reward system? This review speculates how normal, physiological reward processes may be affected by addictive drugs. Addictive drugs affect acute responses and plasticity in dopamine neurons and postsynaptic structures. These effects reduce reward discrimination, increase the effects of reward prediction error signals, and enhance neuronal responses to reward-predicting stimuli, which may contribute to compulsion. Addictive drugs steepen neuronal temporal reward discounting and create temporal myopia that impairs the control of drug taking. Tonically enhanced dopamine levels may disturb working memory mechanisms necessary for assessing background rewards and thus may generate inaccurate neuronal reward predictions. Drug-induced working memory deficits may impair neuronal risk signaling, promote risky behaviors, and facilitate preaddictive drug use. Malfunctioning adaptive reward coding may lead to overvaluation of drug rewards. Many of these malfunctions may result in inadequate neuronal decision mechanisms and lead to choices biased toward drug rewards.


Work Teams Applications and Effectiveness

" This article uses an ecological approach to analyze factors in the effectiveness of work teams--small groups of interdependent individuals who share responsibility for outcomes for their organizations. Applications include advice and involvement, as in quality control circles and committees; production and service, as in assembly groups and sales teams; projects and development, as in engineering and research groups; and action and negotiation, as in sports teams and combat units. An analytic framework depicts team effectiveness as interdependent with organizational context, boundaries, and team development. Key context factors include (a) organizational culture, (b) technology and task design, (c) mission clarity, (d) autonomy, (e) rewards, ( f ) performance feedback, (g) training/consultation, and (h) physical environment. Team boundaries may mediate the impact of organizational context on team development. Current research leaves unanswered questions but suggests that effectiveness depends on organizational context and boundaries as much as on internal processes. Issues are raised for research and practice. The terms work team and work group appear often in today's discussions of organizations. Some experts claim that to be effective modern firms need to use small teams for an increasing variety of jobs. For instance, in an article subtitled "The Team as Hero," Reich (1987) wrote, If we are to compete in today's world, we must begin to celebrate collective entrepreneurship, endeavors in which the whole of the effort is greater than the sum of individual contributions. We need to honor our teams more, our aggressive leaders and maverick geniuses less. (p. 78) Work teams occupy a pivotal role in what has been described as a management transformation (Walton, 1985), paradigm shift (Ketehum, 1984), and corporate renaissance (Kanter, 1983). In this management revolution, Peters (1988) advised that organizations use "multi-function teams for all development activities" (p. 210) and "organize every function into tento thirty-person, largely self-managing teams" (p. 296). Tornatzky (1986) pointed to new technologies that allow small work groups to take responsibility for whole products. Hackman (1986) predicted that, "organizations in the future will rely heavily on member self-management" (p. 90). Building blocks of such organizations are self-regulating work teams. But University of Tennessee University of Wisconsin--Eau Claire University o f Tennessee far from being revolutionary, work groups are traditional; "the problem before us is not to invent more tools, but to use the ones we have" (Kanter, 1983, p. 64). In this article, we explore applications of work teams and propose an analytic framework for team effectiveness. Work teams are defined as interdependent collections of individuals who share responsibility for specific outcomes for their organizations. In what follows, we first identify applications of work teams and then offer a framework for analyzing team effectiveness. Its facets make up topics of subsequent sections: organizational context, boundaries, and team development. We close with issues for research and practice. A p p l i c a t i o n s o f W o r k T e a m s Two watershed events called attention to the benefits of applying work teams beyond sports and mih'tary settings: the Hawthorne studies (Homans, 1950) and European experiments with autonomous work groups (Kelly, 1982). Enthusiasm has alternated with disenchantment (Bramel & Friend, 1987), but the 1980s have brought a resurgence of interest. Unfortunately, we have little evidence on how widely work teams are used or whether their use is expanding. Pasmore, Francis, Haldeman, and Shani (1982) reported that introduction of autonomous work groups was the most common intervention in 134 experiments in manufacturing firms. Production teams number among four broad categories of work team applications: (a) advice and involvement, (b) production and service, (c) projects and development, and (d) action and negotiation. Advice and Involvement Decision-making committees traditional in management now are expanding to first-line employees. Quality control (QC) circles and employee involvement groups have been common in the 1980s, often as vehicles for employee participation ( Cole, 1982 ). Perhaps several hundred thousand U.S. employees belong to QC circles (Ledford, Lawler, & Mohrman, 1988), usually first-line manufacturing employees who meet to identify opportunities for improvement. Some make and carry out proposals, but most have restricted scopes of activity and little working time, perhaps a few hours each month (Thompson, 1982). Employee involvement groups operate similarly, exploring ways to improve customer service (Peterfreund, 1982). 120 February 1990 • American Psychologist Copyright 1990 by the American Psyc2aological A~mciafion, Inc. 0003-066X/90/$00.75 Vol. 45, No. 2, 120-133 QC circles and employee involvement groups at times may have been implemented poorly (Shea, 1986), but they have been used extensively in some companies


Self-Adaptive Anytime Stream Clustering

Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.


Depth-disparity calibration for augmented reality on binocular optical see-through displays

We present a study of depth-disparity calibration for augmented reality applications using binocular optical see-through displays. Two techniques were proposed and compared. The "paired-eyes" technique leverages the Panum's fusional area to help viewer find alignment between the virtual and physical objects. The "separate-eyes" technique eliminates the need of binocular fusion and involves using both eyes sequentially to check the virtual-physical object alignment on retinal images. We conducted a user study to measure the calibration results and assess the subjective experience of users with the proposed techniques.


The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. C. Bishop Pattern Recognition and Machine Learning


Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

In this paper we introduce a template-based method for recognizing human actions called action MACH. Our approach is based on a maximum average correlation height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.


Optimal Brain Damage

We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application.


A new class of upper bounds on the log partition function

We introduce a new class of upper bounds on the log partition function of a Markov random field (MRF). This quantity plays an important role in various contexts, including approximating marginal distributions, parameter estimation, combinatorial enumeration, statistical decision theory, and large-deviations bounds. Our derivation is based on concepts from convex duality and information geometry: in particular, it exploits mixtures of distributions in the exponential domain, and the Legendre mapping between exponential and mean parameters. In the special case of convex combinations of tree-structured distributions, we obtain a family of variational problems, similar to the Bethe variational problem, but distinguished by the following desirable properties: i) they are convex, and have a unique global optimum; and ii) the optimum gives an upper bound on the log partition function. This optimum is defined by stationary conditions very similar to those defining fixed points of the sum-product algorithm, or more generally, any local optimum of the Bethe variational problem. As with sum-product fixed points, the elements of the optimizing argument can be used as approximations to the marginals of the original model. The analysis extends naturally to convex combinations of hypertree-structured distributions, thereby establishing links to Kikuchi approximations and variants.


A 3-dimensional sift descriptor and its application to action recognition

In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.


Continuous N-gram Representations for Authorship Attribution

This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.


Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data.


Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors

In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour. We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems. A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice. What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice. The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem. The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy. Our analysis reveals areas of strength and weakness of each method.


Privacy Aware Learning

We study statistical risk minimization problems under a privacy model in which the data is kept confidential even from the learner. In this local privacy framework, we establish sharp upper and lower bounds on the convergence rates of statistical estimation procedures. As a consequence, we exhibit a precise tradeoff between the amount of privacy the data preserves and the utility, as measured by convergence rate, of any statistical estimator or learning procedure.


Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator

This article presents the development of an autonomous motion planning algorithm for a soft planar grasping manipulator capable of grasp-and-place operations by encapsulation with uncertainty in the position and shape of the object. The end effector of the soft manipulator is fabricated in one piece without weakening seams using lost-wax casting instead of the commonly used multilayer lamination process. The soft manipulation system can grasp randomly positioned objects within its reachable envelope and move them to a desired location without human intervention. The autonomous planning system leverages the compliance and continuum bending of the soft grasping manipulator to achieve repeatable grasps in the presence of uncertainty. A suite of experiments is presented that demonstrates the system's capabilities.


Fetal left brachiocephalic vein in normal and abnormal conditions.

OBJECTIVES
To establish values of fetal left brachiocephalic vein (LBCV) dimensions during normal pregnancy and determine whether routine assessment of the LBCV may help in identifying fetuses with congenital abnormalities of this vessel.

METHODS
Fetal LBCV was assessed prospectively during ultrasound examinations in 431 normal singleton pregnancies. The visualization rate of the transverse view of the upper fetal chest at the level of drainage of the LBCV into the superior vena cava (SVC) by two-dimensional (2D) and 2D plus color Doppler ultrasound was evaluated. Reference ranges of LBCV diameter during non-complicated pregnancies were established. Interobserver and intraobserver measurement variability was analyzed. In addition, a retrospective review of the hospital medical records of 91 pregnancies with fetuses diagnosed with LBCV abnormalities was performed.

RESULTS
Sonographic assessment of the fetal LBCV was consistently achieved in the second and third trimesters and in some fetuses in the first trimester of pregnancy. In normal fetuses LBCV diameter increased significantly throughout pregnancy, with a mean value of 0.7 mm at 11 weeks and 4.9 mm at term. Dilation of the fetal LBCV was noted in five cases of intracranial arteriovenous malformation and six cases of supracardiac type total anomalous pulmonary venous connection. Abnormal course of the LBCV was noted in 12 fetuses. In 63 fetuses with a persistent left SVC and a right SVC the LBCV was absent.

CONCLUSION
This is the first study describing an effective sonographic approach for the assessment of fetal LBCV dimensions during pregnancy. The normative data may provide an additional means of detecting rare anomalies of systemic and pulmonary veins during pregnancy.


The importance of drawing in the mechanical design process

-This paper is a study on the importance of drawing ( both formal drafting and informal sketching) during the process of mechanical design. Five hypotheses, focused on the t~es of drawings, their necessity in mechanical problem solving, and their relation to the external representation medium, are presented and supported. Support is through referenced studies in other domains and the results of protocol studies performed on five mechanical designers. Videotapes of all the marks-on-paper made by designers in representative sections of the design process were studied in detail for their type and purpose. The resulting data is supportive of the hypotheses. These results also give requirements for future computer aided design tools and graphics education, and goals for further studies. I. I N T R O D U C T I O N The goal of this paper is to study the importance of drawing (both formal drafting and informal sketching) during the process of mechanical design. This goal can be extended to state that we intend to show the necessio" of drawing during all the developmental stages of a mechanical design. Through the information presented here, the requirements for future computer aided design tools, graphics education, and further studies will be developed. All mechanical engineers are taught drafting. Thus, most engineers are skilled at making and interpreting these formal mechanical drawings. These drawings are representations of a final design (the end product of the design process) and they are intended to archive the completed design and communicate it to other designers and manufacturing personnel. Additionally, engineers are notorious for not being able to think without making "'back-of-the-envelope" sketches of rough ideas. Sometimes these informal sketches serve to communicate a concept to a colleague, but more often they just help the idea take shape on paper. It is in considering how these sketches help an idea take form that gives a hint that drawing's role in engineering is more than just to archive a concept or to communicate with others. Understanding the use of both drafting and sketching in design is important to help formulate the future development of Computer Aided Design or Drafting (CAD) systems. As CAD evolves and becomes more "'intelligent,'" the question of what attributes these systems must have becomes more important. In the past, CAD system attributes have primarily been driven from developments in the computer industry, it is only through understanding drawing's importance in the design process that these systems can be based on design needs. Additionally, the pressures of CAD tool development, faculty time demands, and course expenses cause academic institutions to reevaluate the content of their "graphics'" courses. Understanding drawing's importance in the design process helps establish what skills need to be taught to engineers during their training. This paper is organized by first, in Section 2, clarifying the types of drawings used in mechanical design. The hypotheses to be addressed in this paper are given in Section 3. A discussion of research on the understanding of visual imagery to be used as a basis for arguments in support of the hypotheses is in Section 4. In Section 5 is a discussion of the results of data taken on how mechanical engineers use drawings during design. Lastly, in Section 6, is a discussion of how well the hypotheses have been supported and the implications of our findings on CAD development, educational requirements, and future research directions. 2. TYPES OF D R A W I N G S USED IN DESIGN Engineers make many types of marks-on-paper. In research, to be described in Section 5, we have broken down these marks into two main groupings: support notation and graphic representations. Support notation includes textual notes, lists, dimensions (including leaders and arrows), and calculations. Graphic representations include drawings of objects and their functions, and plots and charts. Mechanical design graphic representations are often scale drawings made with mechanical instruments or CAD computer systems. These drawings, made in accordance with a set of widely accepted rules, are defined as having been drafted. Sketches, on the other hand, are defined as "free-hand" drawings. They are usually not to scale and may use shorthand notations to represent both objects and their function. A differentiation must be made between the act of graphic representation and the medium on which it occurs. The medium, whether it be paper and pencil, a computer stylus on a tablet, chalk on a blackboard, or other medium may put interface restrictions on the representation. The following discussions are concerned with what is being represented, not with how the representation is made. However, the discussions point to the medium's restriction on representation and the need for improved interfaces. Another aspect of drawings to be considered is the level of abstraction of the information to be represented. During the design process, the design is refined


Anticipative Interaction Primitives for Human-Robot Collaboration

This paper introduces our initial investigation on the problem of providing a semi-autonomous robot collaborator with anticipative capabilities to predict human actions. Anticipative robot behavior is a desired characteristic of robot collaborators that lead to fluid, proactive interactions. We are particularly interested in improving reactive methods that rely on human action recognition to activate the corresponding robot action. Action recognition invariably causes delay in the robot’s response, and the goal of our method is to eliminate this delay by predicting the next human action. Prediction is achieved by using a lookup table containing variations of assembly sequences, previously demonstrated by different users. The method uses the nearest neighbor sequence in the table that matches the actual sequence of human actions. At the movement level, our method uses a probabilistic representation of interaction primitives to generate robot trajectories. The method is demonstrated using a 7 degree-offreedom lightweight arm equipped with a 5-finger hand on an assembly task consisting of 17 steps.


The Missing Link of Jewish European Ancestry: Contrasting the Rhineland and
the Khazarian Hypotheses

The question of Jewish ancestry has been the subject of controversy for over two centuries and has yet to be resolved. The "Rhineland hypothesis" depicts Eastern European Jews as a "population isolate" that emerged from a small group of German Jews who migrated eastward and expanded rapidly. Alternatively, the "Khazarian hypothesis" suggests that Eastern European Jews descended from the Khazars, an amalgam of Turkic clans that settled the Caucasus in the early centuries CE and converted to Judaism in the 8th century. Mesopotamian and Greco-Roman Jews continuously reinforced the Judaized empire until the 13th century. Following the collapse of their empire, the Judeo-Khazars fled to Eastern Europe. The rise of European Jewry is therefore explained by the contribution of the Judeo-Khazars. Thus far, however, the Khazars' contribution has been estimated only empirically, as the absence of genome-wide data from Caucasus populations precluded testing the Khazarian hypothesis. Recent sequencing of modern Caucasus populations prompted us to revisit the Khazarian hypothesis and compare it with the Rhineland hypothesis. We applied a wide range of population genetic analyses to compare these two hypotheses. Our findings support the Khazarian hypothesis and portray the European Jewish genome as a mosaic of Near Eastern-Caucasus, European, and Semitic ancestries, thereby consolidating previous contradictory reports of Jewish ancestry. We further describe a major difference among Caucasus populations explained by the early presence of Judeans in the Southern and Central Caucasus. Our results have important implications for the demographic forces that shaped the genetic diversity in the Caucasus and for medical studies.


Energy efficient wearable sensor node for IoT-based fall detection systems

Falls can cause serious traumas such as brain injuries and bone fractures, especially among elderly people. Fear of falling might reduce physical activities resulting in declining social interactions and eventually causing depression. To lessen the effects of a fall, timely delivery of medical treatment can play a vital role. In a similar scenario, an IoT-based wearable system can pave the most promising way to mitigate serious consequences of a fall while providing the convenience of usage. However, to deliver sufficient degree of monitoring and reliability, wearable devices working at the core of fall detection systems are required to work for a prolonged period of time. In this work, we focus on energy efficiency of a wearable sensor node in an Internet-of-Things (IoT) based fall detection system. We propose the design of a tiny, lightweight, flexible and energy efficient wearable device. We investigate different parameters (e.g. sampling rate, communication bus interface, transmission protocol, and transmission rate) impacting on energy consumption of the wearable device. In addition, we provide a comprehensive analysis of energy consumption of the wearable in different configurations and operating conditions. Furthermore, we provide hints (hardware and software) for system designers implementing the optimal wearable device for IoT-based fall detection systems in terms of energy efficiency and high quality of service. The results clearly indicate that the proposed sensor node is novel and energy efficient. In a critical condition, the wearable device can be used continuously for 76 h with a 1000 mAh li-ion battery.


Predicting function from sequence in venom peptide families

Toxins from animal venoms are small peptides that recognize specific molecular targets in the brains of prey or predators. Next generation sequencing has uncovered thousands of diverse toxin sequences, but the functions of these peptides are poorly understood. Here we demonstrate that the use of machine learning techniques on sequence-derived features enables high accuracy in the task of predicting a toxin’s functionality using only its amino acid sequence. Comparison of the performance of several learning algorithms in this prediction task demonstrates that both physiochemical properties of the amino acid residues in a sequence as well as noncontiguous sequence motifs can be used independently to model the sequence dependence of venom function. We rationalize the observed model performance using unsupervised learning and make broad predictions about the distribution of toxin functions in the venome. Keywords—Bioinformatics, machine learning, protein function prediction, venomics.


ParaPIM: a parallel processing-in-memory accelerator for binary-weight deep neural networks

Recent algorithmic progression has brought competitive classification accuracy despite constraining neural networks to binary weights (+1/-1). These findings show remarkable optimization opportunities to eliminate the need for computationally-intensive multiplications, reducing memory access and storage. In this paper, we present ParaPIM architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) sub-arrays to massively parallel computational units capable of running inferences for Binary-Weight Deep Neural Networks (BWNNs). ParaPIM's in-situ computing architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers, accelerate BWNNs inference, eliminate unnecessary off-chip accesses and provide ultra-high internal bandwidth. The device-to-architecture co-simulation results indicate ~4x higher energy efficiency and 7.3x speedup over recent processing-in-DRAM acceleration, or roughly 5x higher energy-efficiency and 20.5x speedup over recent ASIC approaches, while maintaining inference accuracy comparable to baseline designs.


Automated Transplantation and Differential Testing for Clones

Code clones are common in software. When applying similar edits to clones, developers often find it difficult to examine the runtime behavior of clones. The problem is exacerbated when some clones are tested, while their counterparts are not. To reuse tests for similar but not identical clones, Grafter transplants one clone to its counterpart by (1) identifying variations in identifier names, types, and method call targets, (2) resolving compilation errors caused by such variations through code transformation, and (3) inserting stub code to transfer input data and intermediate output values for examination. To help developers examine behavioral differences between clones, Grafter supports fine-grained differential testing at both the test outcome level and the intermediate program state level. In our evaluation on three open source projects, Grafter successfully reuses tests in 94% of clone pairs without inducing build errors, demonstrating its automated code transplantation capability. To examine the robustness of G RAFTER, we systematically inject faults using a mutation testing tool, Major, and detect behavioral differences induced by seeded faults. Compared with a static cloning bug finder, Grafter detects 31% more mutants using the test-level comparison and almost 2X more using the state-level comparison. This result indicates that Grafter should effectively complement static cloning bug finders.


Bone graphs: Medial shape parsing and abstraction

1077-3142/$ see front matter 2011 Elsevier Inc. A doi:10.1016/j.cviu.2010.12.011 ⇑ Corresponding author. E-mail address: dmac@cs.toronto.edu (D. Macrini) The recognition of 3-D objects from their silhouettes demands a shape representation which is stable with respect to minor changes in viewpoint and articulation. This can be achieved by parsing a silhouette into parts and relationships that do not change across similar object views. Medial descriptions, such as skeletons and shock graphs, provide part-based decompositions but suffer from instabilities. As a result, similar shapes may be represented by dissimilar part sets. We propose a novel shape parsing approach which is based on identifying and regularizing the ligature structure of a medial axis, leading to a bone graph, a medial abstraction which captures a more stable notion of an object’s parts. Our experiments show that it offers improved recognition and pose estimation performance in the presence of within-class deformation over the shock graph. 2011 Elsevier Inc. All rights reserved.


A Discrete Model for Inelastic Deformation of Thin Shells

We introduce a method for simulating the inelastic deformation of thin shells: we model plasticity and fracture of curved, deformable objects such as light bulbs, egg-shells and bowls. Our novel approach uses triangle meshes yet evolves fracture lines unrestricted to mesh edges. We present a novel measure of bending strain expressed in terms of surface invariants such as lengths and angles. We also demonstrate simple techniques to improve the robustness of standard timestepping as well as collisionresponse algorithms.


The estimation of the gradient of a density function, with applications in pattern recognition

Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistenby of the estimates. The results are generalized to obtain a simple mean-shift estimate that can be extended in a k-nearestneighbor approach. Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality problems, with the ultimate goal of providing further understanding of these problems in terms of density gradients.


NVIDIA Jetson Platform Characterization

This study characterizes the NVIDIA Jetson TK1 and TX1 Platforms, both built on a NVIDIA Tegra System on Chip and combining a quad-core ARM CPU and an NVIDIA GPU. Their heterogeneous nature, as well as their wide operating frequency range, make it hard for application developers to reason about performance and determine which optimizations are worth pursuing. This paper attempts to inform developers’ choices by characterizing the platforms’ performance using Roofline models obtained through an empirical measurement-based approach as well as through a case study of a heterogeneous application (matrix multiplication). Our results highlight a difference of more than an order of magnitude in compute performance between the CPU and GPU on both platforms. Given that the CPU and GPU share the same memory bus, their Roofline models’ balance points are also more than an order of magnitude apart. We also explore the impact of frequency scaling: build CPU and GPU Roofline profiles and characterize both platforms’ balance point variation, power consumption, and performance per watt as frequency is scaled. The characterization we provide can be used in two main ways. First, given an application, it can inform the choice and number of processing elements to use (i.e., CPU/GPU and number of cores) as well as the optimizations likely to lead to high performance gains. Secondly, this characterization indicates that developers can use frequency scaling to tune the Jetson Platform to suit the requirements of their applications. Third, given a required power/performance budget, application developers can identify the appropriate parameters to use to tune the Jetson platforms to their specific workload requirements. We expect that this optimization approach can lead to overall gains in performance and/or power efficiency without requiring application changes.


Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, image captioning or translation. The main difference is the implementation of covert and overt attention with a multi-dimensional LSTM network. Our principal contribution towards handwriting recognition lies in the automatic transcription without a prior segmentation into lines, which was critical in previous approaches. Moreover, the system is able to learn the reading order, enabling it to handle bidirectional scripts such as Arabic. We carried out experiments on the well-known IAM Database and report encouraging results which bring hope to perform full paragraph transcription in the near future.


Accidental displacement and migration of endosseous implants into
adjacent craniofacial structures: A review and update

OBJECTIVES
Accidental displacement of endosseous implants into the maxillary sinus is an unusual but potential complication in implantology procedures due to the special features of the posterior aspect of the maxillary bone; there is also a possibility of migration throughout the upper paranasal sinuses and adjacent structures. The aim of this paper is to review the published literature about accidental displacement and migration of dental implants into the maxillary sinus and other adjacent structures.

STUDY DESIGN
A review has been done based on a search in the main on-line medical databases looking for papers about migration of dental implants published in major oral surgery, periodontal, dental implant and ear-nose-throat journals, using the keywords "implant," "migration," "complication," "foreign body" and "sinus."

RESULTS
24 articles showing displacement or migration to maxillary, ethmoid and sphenoid sinuses, orbit and cranial fossae, with different degrees of associated symptoms, were identified. Techniques found to solve these clinical issues include Cadwell-Luc approach, transoral endoscopy approach via canine fossae and transnasal functional endoscopy surgery.

CONCLUSION
Before removing the foreign body, a correct diagnosis should be done in order to evaluate the functional status of the ostiomeatal complex and the degree of affectation of paranasal sinuses and other involved structures, determining the size and the exact location of the foreign body. After a complete diagnosis, an indicated procedure for every case would be decided.


Language and life history: a new perspective on the development and evolution of human language.

It has long been claimed that Homo sapiens is the only species that has language, but only recently has it been recognized that humans also have an unusual pattern of growth and development. Social mammals have two stages of pre-adult development: infancy and juvenility. Humans have two additional prolonged and pronounced life history stages: childhood, an interval of four years extending between infancy and the juvenile period that follows, and adolescence, a stage of about eight years that stretches from juvenility to adulthood. We begin by reviewing the primary biological and linguistic changes occurring in each of the four pre-adult ontogenetic stages in human life history. Then we attempt to trace the evolution of childhood and juvenility in our hominin ancestors. We propose that several different forms of selection applied in infancy and childhood; and that, in adolescence, elaborated vocal behaviors played a role in courtship and intrasexual competition, enhancing fitness and ultimately integrating performative and pragmatic skills with linguistic knowledge in a broad faculty of language. A theoretical consequence of our proposal is that fossil evidence of the uniquely human stages may be used, with other findings, to date the emergence of language. If important aspects of language cannot appear until sexual maturity, as we propose, then a second consequence is that the development of language requires the whole of modern human ontogeny. Our life history model thus offers new ways of investigating, and thinking about, the evolution, development, and ultimately the nature of human language.


Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescents

The present study explored the role of gender in the association between Internet addiction and depression. Three-wave longitudinal panel data were collected from self-reported questionnaires that were completed by 1715 adolescents in grades 6e8 in China. Cross-lagged structural equation modeling was used to examine the relationship between Internet addiction and depression. In male adolescents, depression was found to significantly predict subsequent Internet addiction, suggesting that depression was the cause of Internet addiction and supporting the mood enhancement hypothesis. In female adolescents, Internet addiction was found to significantly predict subsequent depression, indicating that Internet addiction leads to depression and supporting the social displacement hypothesis. These results indicate that the relationship between Internet addiction and depression depended on gender. In addition, it was found that males and females exhibit different behavioral patterns and motivations of Internet usage. Males were more likely to use the Internet for pleasure and less likely to surf the Internet to search for information, compared with females. Although both males and females were prone to surfing the Internet alone, males were more likely to go online with friends compared with females. These findings suggest that gender-specific preventative and interventional strategies should be developed to reduce Internet addiction. © 2016 Elsevier Ltd. All rights reserved.


Facebook, Twitter and Google Plus for Breaking News: Is There a Winner?

Twitter is widely seen as being the go to place for breaking news. Recently however, competing Social Media have begun to carry news. Here we examine how Facebook, Google Plus and Twitter report on breaking news. We consider coverage (whether news events are reported) and latency (the time when they are reported). Using data drawn from three weeks in December 2013, we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events. We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.


A Compact Circularly Polarized Cross-Shaped Slotted Microstrip Antenna

A compact cross-shaped slotted microstrip patch antenna is proposed for circularly polarized (CP) radiation. A symmetric, cross shaped slot is embedded along one of the diagonal axes of the square patch for CP radiation and antenna size reduction. The structure is asymmetric (unbalanced) along the diagonal axes. The overall size of the antenna with CP radiation can be reduced by increasing the perimeter of the symmetric cross-shaped slot within the first patch quadrant of the square patch. The performance of the CP radiation is also studied by varying the size and angle variation of the cross-shaped slot. A measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz is achieved with the CP cross-shaped slotted microstrip antenna, with an 18.0 MHz 10-dB return-loss bandwidth. The measured boresight gain is more than 3.8 dBic over the operating band, while the overall antenna volume is 0.273λo × 0.273λo × 0.013λo (λο operating wavelength at 910 MHz).


Linkages between Depressive Symptomatology and Internet Harassment among Young Regular Internet Users

Recent reports indicate 97% of youth are connected to the Internet. As more young people have access to online communication, it is integrally important to identify youth who may be more vulnerable to negative experiences. Based upon accounts of traditional bullying, youth with depressive symptomatology may be especially likely to be the target of Internet harassment. The current investigation will examine the cross-sectional relationship between depressive symptomatology and Internet harassment, as well as underlying factors that may help explain the observed association. Youth between the ages of 10 and 17 (N = 1,501) participated in a telephone survey about their Internet behaviors and experiences. Subjects were required to have used the Internet at least six times in the previous 6 months to ensure a minimum level of exposure. The caregiver self-identified as most knowledgeable about the young person's Internet behaviors was also interviewed. The odds of reporting an Internet harassment experience in the previous year were more than three times higher (OR: 3.38, CI: 1.78, 6.45) for youth who reported major depressive symptomatology compared to mild/absent symptomatology. When female and male respondents were assessed separately, the adjusted odds of reporting Internet harassment for males who also reported DSM IV symptoms of major depression were more than three times greater (OR: 3.64, CI: 1.16, 11.39) than for males who indicated mild or no symptoms of depression. No significant association was observed among otherwise similar females. Instead, the association was largely explained by differences in Internet usage characteristics and other psychosocial challenges. Internet harassment is an important public mental health issue affecting youth today. Among young, regular Internet users, those who report DSM IV-like depressive symptomatology are significantly more likely to also report being the target of Internet harassment. Future studies should focus on establishing the temporality of events, that is, whether young people report depressive symptoms in response to the negative Internet experience, or whether symptomatology confers risks for later negative online incidents. Based on these cross-sectional results, gender differences in the odds of reporting an unwanted Internet experience are suggested, and deserve special attention in future studies.


Exploration of adaptive gait patterns with a reconfigurable linkage mechanism

Legged robots are able to move across irregular terrains and some can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This paper reports a reconfigurable design approach to robotic legged locomotion that produces a wide variety of gait cycles, opening new possibilities for innovative applications. In this paper, we present a distance-based formulation and its application to solve the position analysis problem of a standard Theo Jansen mechanism. By changing the configuration of a linkage, our objective in this study is to identify novel gait patterns of interest for a walking platform. The exemplary gait variations presented in this work demonstrate the feasibility of our approach, and considerably extend the capabilities of the original design to not only produce novel cum useful gait patterns but also to realize behaviors beyond locomotion.


Neural Variational Inference and Learning in Belief Networks

•We introduce a simple, efficient, and general method for training directed latent variable models. – Can handle both discrete and continuous latent variables. – Easy to apply – requires no model-specific derivations. •Key idea: Train an auxiliary neural network to perform inference in the model of interest by optimizing the variational bound. – Was considered before for Helmholtz machines and rejected as infeasible due to high variance of inference net gradient estimates. •We make the approach practical using simple and general variance reduction techniques. •Promising document modelling results using sigmoid belief networks.


Efficient Estimation of Word Representations in Vector Space

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.


Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.


The Helmholtz Machine

Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.


A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion

In this paper, we present a flexible new technique for single viewpoint omnidirectional camera calibration. The proposed method only requires the camera to observe a planar pattern shown at a few different orientations. Either the camera or the planar pattern can be freely moved. No a priori knowledge of the motion is required, nor a specific model of the omnidirectional sensor. The only assumption is that the image projection function can be described by a Taylor series expansion whose coefficients are estimated by solving a two-step least-squares linear minimization problem. To test the proposed technique, we calibrated a panoramic camera having a field of view greater than 200 in the vertical direction, and we obtained very good results. To investigate the accuracy of the calibration, we also used the estimated omni-camera model in a structure from motion experiment. We obtained a 3D metric reconstruction of a scene from two highly distorted omnidirectional images by using image correspondences only. Compared with classical techniques, which rely on a specific parametric model of the omnidirectional camera, the proposed procedure is independent of the sensor, easy to use, and flexible.


The effective use of Benford’s Law to assist in detecting fraud in accounting data

Benford’s law has been promoted as providing the auditor with a tool that is simple and effective for the detection of fraud. The purpose of this paper is to assist auditors in the most effective use of digital analysis based on Benford’s law. The law is based on a peculiar observation that certain digits appear more frequently than others in data sets. For example, in certain data sets, it has been observed that more than 30% of numbers begin with the digit one. After discussing the background of the law and development of its use in auditing, we show where digital analysis based on Benford’s law can most effectively be used and where auditors should exercise caution. Specifically, we identify data sets which can be expected to follow Benford’s distribution, discuss the power of statistical tests, types of frauds that would be detected and not be detected by such analysis, the potential problems that arise when an account contains too few observations, as well as issues related to base rate of fraud. An actual example is provided demonstrating where Benford’s law proved successful in identifying fraud in a population of accounting data.


Systematic Thinking Fostered by Illustrations in Scientific Text

In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models.


Pixel-Wise Attentional Gating for Scene Parsing

To achieve dynamic inference in pixel labeling tasks, we propose Pixel-wise Attentional Gating (PAG), which learns to selectively process a subset of spatial locations at each layer of a deep convolutional network. PAG is a generic, architecture-independent, problem-agnostic mechanism that can be readily "plugged in" to an existing model with fine-tuning. We utilize PAG in two ways: 1) learning spatially varying pooling fields that improve model performance without the extra computation cost associated with multi-scale pooling, and 2) learning a dynamic computation policy for each pixel to decrease total computation (FLOPs) while maintaining accuracy. We extensively evaluate PAG on a variety of per-pixel labeling tasks, including semantic segmentation, boundary detection, monocular depth and surface normal estimation. We demonstrate that PAG allows competitive or state-of-the-art performance on these tasks. Our experiments show that PAG learns dynamic spatial allocation of computation over the input image which provides better performance trade-offs compared to related approaches (e.g., truncating deep models or dynamically skipping whole layers). Generally, we observe PAG can reduce computation by 10% without noticeable loss in accuracy and performance degrades gracefully when imposing stronger computational constraints.


Current Mode Control Integrated Circuit with High Accuracy Current Sensing Circuit for Buck Converter

A current mode control integrated circuit with accuracy current sensing circuit ( CSC ) for buck converter is presented in this the proposed accurate integrated current sensed inductor with the internal ramp be used for DC-DC converter feedback proposed CSC doesn't need an op amp to implement, and has been fabricated with a standard 0.35 mum CMOS process. Simulation result show that the switching converter can be operated up to 1 MHZ. The supply is suitable for signal cell lithium-ion battery supply power efficiency is over 85% for supply voltage from 2.5 V to 5 V and output current is 200 mA. The performance of the proposed circuit is the good compared to the other circuits.


Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach

Although the matrix completion paradigm provides an appealing solution to the collaborative filtering problem in recommendation systems, some major issues, such as data sparsity and cold-start problems, still remain open. In particular, when the rating data for a subset of users or items is entirely missing, commonly known as the  cold-start problem, the standard matrix completion methods are inapplicable due the non-uniform sampling of available ratings. In recent years, there has been considerable interest in dealing with cold-start users or items that are principally based on the idea of exploiting other sources of information to compensate for this lack of rating data. In this paper, we propose a novel and general algorithmic framework based on matrix completion that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, our proposed recommender algorithm, dubbed DecRec, decouples the following two aspects of the cold-start problem to effectively exploit the side information: (i) the completion of a rating sub-matrix, which is generated by excluding cold-start users/items from the original rating matrix; and (ii) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference prevents the error propagation of completion and transduction, and also significantly boosts the performance when appropriate side information is incorporated. The recovery error of the proposed algorithm is analyzed theoretically and, to the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees on performance. Additionally, we also address the problem where both cold-start user and item challenges are present simultaneously. We conduct thorough experiments on real datasets that complement our theoretical results. These experiments demonstrate the effectiveness of the proposed algorithm in handling the cold-start users/items problem and mitigating data sparsity issue.


Automatic query-based keyword and keyphrase extraction

Extracting keywords and keyphrases mainly for identifying content of a document, has an importance role in text processing tasks such as text summarization, information retrieval, and query expansion. In this research, we introduce a new keyword/keyphrase extraction approach in which both single and multi-document keyword/keyphrase extraction techniques are considered. The proposed approach is specifically practical when a user is interested in additional data such as keywords/keyphrases related to a topic or query. In the proposed approach, first a set of documents are retrieved based on user's query, then a single document keyword extraction method is applied to extract candidate keyword/keyphrases from each retrieved document. Finally, a new re-scoring scheme is introduced to extract final keywords/keyphrases. We have evaluated the proposed method based on the relationship between the final keyword/keyphrases with the initial user query, and based user's satisfaction. Our experimental results show how much the extracted keywords/keyphrases are relevant and wellmatched with user's need.


Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking

Next Point-of-Interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity, besides the transition pattern between POIs has not been well studied. In this paper, we proposed a twofold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely PlackettLuce model and cross entropy, to generate the likelihood of a ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. The experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-ofthe-art methods.


Crowdsourcing based spatial mining of urban emergency events using social media

With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of emergency management systems through modern data processing techniques. The past decade has witnessed the tremendous technical advances in Sensor Networks, Internet/Web of Things, Cloud Computing, Mobile/Embedded Computing, Spatial/Temporal Data Processing, and Big Data, and these technologies have provided new opportunities and solutions to emergency management. GIS models and simulation capabilities are used to exercise response and recovery plans during non-disaster times. They help the decision-makers understand near real-time possibilities during an event. In this paper, a crowdsourcing based model for mining spatial information of urban emergency events is introduced. Firstly, basic definitions of the proposed method are given. Secondly, positive samples are selected to mine the spatial information of urban emergency events. Thirdly, location and GIS information are extracted from positive samples. At last, the real spatial information is determined based on address and GIS information. At last, a case study on an urban emergency event is given.


Attitudes towards following meat, vegetarian and vegan diets: an examination of the role of ambivalence

Vegetarianism within the U.K. is growing in popularity, with the current estimate of 7% of the population eating a vegetarian diet. This study examined differences between the attitudes and beliefs of four dietary groups (meat eaters, meat avoiders, vegetarians and vegans) and the extent to which attitudes influenced intentions to follow each diet. In addition, the role of attitudinal ambivalence as a moderator variable was examined. Completed questionnaires were obtained from 111 respondents (25 meat eaters, 26 meat avoiders, 34 vegetarians, 26 vegans). In general, predictions were supported, in that respondents displayed most positive attitudes and beliefs towards their own diets, and most negative attitudes and beliefs towards the diet most different form their own. Regression analyses showed that, as predicted by the Theory of Planned Behaviour, attitudes, subjective norm and perceived behavioural control were significant predictors of intention to follow each diet (apart from the vegetarian diet, where subjective norm was non-significant). In each case, attitudinal ambivalence was found to moderate the attitude-intention relationship, such that attitudes were found to be stronger predictors at lower levels of ambivalence. The results not only highlight the extent to which such alternative diets are an interesting focus for psychological research, but also lend further support to the argument that ambivalence in an important influence on attitude strength.


A 29–30GHz 64-element Active Phased array for 5G Application

A 64-element 29–30GHz active phased array for 5G millimeter wave applications is presented in this paper. The proposed phased array composites of 64-element antennas, 64-chan-nel T/R modules, 4 frequency conversion links, beam controlling circuitry, power management circuits and cooling fans, and are integrated in a in a very compact size(135mmX 77mmX56mm). Hybrid integration of GaAs and Si circuits are employed to achieve better RF performance. The architecture of the proposed phased array and the detail design of the T/R modules and antennas are analyzed. By the OTA (over the air) measurement, the proposed phased array achieves a bandwidth of 1 GHz at the center frequency of 29.5GHz, and the azimuth beam-width is 12 deg with the scanning range of ±45deg. With the excitation of 800MHz 64QAM signals, the transmitter beam achieves a EVM of 5.5%, ACLR of −30.5dBc with the PA working at −10dB back off, and the measured saturated EIRP is 63 dBm.


mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices’ proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.


A Review of Scar Scales and Scar Measuring Devices

OBJECTIVE
Pathologic scarring affects millions of people worldwide. Quantitative and qualitative measurement modalities are needed to effectively evaluate and monitor treatments.

METHODS
This article reviews the literature on available tools and existent assessment scales used to subjectively and objectively characterize scar.

RESULTS
We describe the attributes and deficiencies of each tool and scale and highlight areas where further development is critical.

CONCLUSION
An optimal, universal scar scoring system is needed in order to better characterize, understand and treat pathologic scarring.


WNtags: A Web-Based Tool For Image Labeling And Retrieval With Lexical Ontologies

Ever growing number of image documents available on the Internet continuously motivates research in better annotation models and more efficient retrieval methods. Formal knowledge representation of objects and events in pictures, their interaction as well as context complexity becomes no longer an option for a quality image repository, but a necessity. We present an ontologybased online image annotation tool WNtags and demonstrate its usefulness in several typical multimedia retrieval tasks using International Affective Picture System emotionally annotated image database. WNtags is built around WordNet lexical ontology but considers Suggested Upper Merged Ontology as the preferred labeling formalism. WNtags uses sets of weighted WordNet synsets as high-level image semantic descriptors and query matching is performed with word stemming and node distance metrics. We also elaborate our near future plans to expand image content description with induced affect as in stimuli for research of human emotion and attention.


Paired Learners for Concept Drift

To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, since the stable learner performs worse than does there active learner when the target concept changes. While the method uses the reactive learner as an indicator of drift, it uses the stable learner to predict, since the stable learner performs better than does the reactive learner when acquiring target concept. Experimental results support these assertions. We evaluated the method by making direct comparisons to dynamic weighted majority, accuracy weighted ensemble, and streaming ensemble algorithm (SEA) using two synthetic problems, the Stagger concepts and the SEA concepts, and three real-world data sets: meeting scheduling, electricity prediction, and malware detection. Results suggest that, on these problems, paired learners outperformed or performed comparably to methods more costly in time and space.


Intersection-priority based optimal RSU allocation for VANET

Roadside Unit (RSU) is an essential unit in a vehicular ad-hoc network (VANET) for collecting and analyzing traffic data given from smart vehicles. Furthermore, RSUs can take part in controlling traffic flow for vehicle's secure driving by broadcasting locally analyzed data, forwarding some important messages, and communicating with other RSUs, and soon. In order to maximize the availability of RSUs in the VANET, RSUs need to be fully distributed over an entire area. Thus, RSUs can make the best use of all traffic data gathered from every intersection. In this paper, we provide intersection-priority based RSU placement methods to find the optimal number and positions of RSUs for the full distribution providing with a maximal connectivity between RSUs while minimizing RSU setup costs. We propose three optimal algorithms: greedy, dynamic and hybrid algorithms. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu/Jongrogu, YongsanGu, and GangnamGu in Seoul, each of which has a characteristic road style different than each other. We analyze how our algorithms work in such different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.


Repeated labeling using multiple noisy labelers

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon’s Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.


Improving literacy in developing countries using speech recognition-supported games on mobile devices

Learning to read in a second language is challenging, but highly rewarding. For low-income children in developing countries, this task can be significantly more challenging because of lack of access to high-quality schooling, but can potentially improve economic prospects at the same time. A synthesis of research findings suggests that practicing recalling and vocalizing words for expressing an intended meaning could improve word reading skills - including reading in a second language - more than silent recognition of what the given words mean. Unfortunately, many language learning software do not support this instructional approach, owing to the technical challenges of incorporating speech recognition support to check that the learner is vocalizing the correct word. In this paper, we present results from a usability test and two subsequent experiments that explore the use of two speech recognition-enabled mobile games to help rural children in India read words with understanding. Through a working speech recognition prototype, we discuss two major contributions of this work: first, we give empirical evidence that shows the extent to which productive training (i.e. vocalizing words) is superior to receptive vocabulary training, and discuss the use of scaffolding hints to ""unpack"" factors in the learner's linguistic knowledge that may impact reading. Second, we discuss what our results suggest for future research in HCI.


Making middleboxes someone else's problem: network processing as a cloud service

Modern enterprises almost ubiquitously deploy middlebox processing services to improve security and performance in their networks. Despite this, we find that today's middlebox infrastructure is expensive, complex to manage, and creates new failure modes for the networks that use them. Given the promise of cloud computing to decrease costs, ease management, and provide elasticity and fault-tolerance, we argue that middlebox processing can benefit from outsourcing the cloud. Arriving at a feasible implementation, however, is challenging due to the need to achieve functional equivalence with traditional middlebox deployments without sacrificing performance or increasing network complexity.
 In this paper, we motivate, design, and implement APLOMB, a practical service for outsourcing enterprise middlebox processing to the cloud.
 Our discussion of APLOMB is data-driven, guided by a survey of 57 enterprise networks, the first large-scale academic study of middlebox deployment. We show that APLOMB solves real problems faced by network administrators, can outsource over 90% of middlebox hardware in a typical large enterprise network, and, in a case study of a real enterprise, imposes an average latency penalty of 1.1ms and median bandwidth inflation of 3.8%.


Improving Intrusion Detection System based on Snort rules for network probe attack detection

Data and network system security is the most important roles. An organization should find the methods to protect their data and network system to reduce the risk from attacks. Snort Intrusion Detection System (Snort-IDS) is a security tool of network security. It has been widely used for protecting the network of the organizations. The Snort-IDS utilize the rules to matching data packets traffic. If some packet matches the rules, Snort-IDS will generate the alert messages. However, Snort-IDS contain many rules and it also generates a lot of false alerts. In this paper, we present the procedure to improve the Snort-IDS rules for the network probe attack detection. In order to test the performance evaluation, we utilized the data set from the MIT-DAPRA 1999, which includes the normal and abnormal traffics. Firstly, we analyzed and explored the existing the Snort-IDS rules to improve the proposed Snort-IDS rules. Secondly, we applied the WireShark software to analyze data packets form of attack in data set. Finally, the Snort-IDS was improved, and it can detect the network probe attack. This paper, we had classified the attacks into several groups based on the nature of network probe attack. In addition, we also compared the efficacy of detection attacks between Snort-IDS rules to be updated with the Detection Scoring Truth. As the experimental results, the proposed Snort-IDS efficiently detected the network probe attacks compared to the Detection Scoring Truth. It can achieve higher accuracy. However, there were some detecting alert that occur over the attack in Detection Scoring Truth, because some attack occur in several time but the Detection Scoring Truth indentify as one time.


Risk management applied to software development projects in incubated technology-based companies : literature review , classification , and analysis Gestão


Algebraic Distance on Graphs

Measuring the connection strength between a pair of vertices in a graph is one of the most important concerns in many graph applications. Simple measures such as edge weights may not be sufficient for capturing the effects associated with short paths of lengths greater than one. In this paper, we consider an iterative process that smooths an associated value for nearby vertices, and we present a measure of the local connection strength (called the algebraic distance, see [25]) based on this process. The proposed measure is attractive in that the process is simple, linear, and easily parallelized. An analysis of the convergence property of the process reveals that the local neighborhoods play an important role in determining the connectivity between vertices. We demonstrate the practical effectiveness of the proposed measure through several combinatorial optimization problems on graphs and hypergraphs.


Comparing Speech Recognition Systems (Microsoft API, Google API And CMU Sphinx)

The idea of this paper is to design a tool that will be used to test and compare commercial speech recognition systems, such as Microsoft Speech API and Google Speech API, with open-source speech recognition systems such as Sphinx-4. The best way to compare automatic speech recognition systems in different environments is by using some audio recordings that were selected from different sources and calculating the word error rate (WER). Although the WER of the three aforementioned systems were acceptable, it was observed that the Google API is superior.


Pellet: An OWL DL Reasoner

Reasoning capability is of crucial importance to many applications developed for the Semantic Web. Description Logics provide sound and complete reasoning algorithms that can effectively handle the DL fragment of the Web Ontology Language (OWL). However, existing DL reasoners were implemented long before OWL came into existence and lack some features that are essential for Semantic Web applications, such as reasoning with individuals, querying capabilities, nominal support, elimination of the unique name assumption and so forth. With these objectives in mind we have implemented an OWL DL reasoner and deployed it in various kinds of applications.


Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders

Normally, lesions are detected using supervised learning techniques that require labelled training data. We explore the use of Bayesian autoencoders to learn the variability of healthy tissue and detect lesions as unlikely events under the normative model. As a proof-of-concept, we test our method on registered 2D midaxial slices from CT imaging data. Our results indicate that our method achieves best performance in detecting lesions caused by bleeding compared to baselines.


Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.


A 3D Face Model for Pose and Illumination Invariant Face Recognition

Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.


Deep Learning Face Attributes in the Wild

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.


Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a self-regularization term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.


A Measure of Polarization on Social Media Networks Based on Community Boundaries

Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a longterm challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric – modularity – is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.


Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography.

OBJECTIVE
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements.

APPROACH
Eight human subjects were asked to imagine either 'left' or 'right' hand movement during one recording session to control the computer cursor using TCRE and disc electrodes.

MAIN RESULTS
The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%).

SIGNIFICANCE
This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.


Assessment of healthcare claims rejection risk using machine learning

Modern healthcare service records, called Claims, record the medical treatments by a Provider (Doctor/Clinic), medication advised etc., along with the charges, and payments to be made by the patient and the Payer (insurance provider). Denial and rejection of healthcare claims is a significant administrative burden and source of loss to various healthcare providers and payers as well. Automating the identification of Claims prone to denial by reason, source, cause and other deciding factors is critical to lowering this burden of rework. We present classification methods based on Machine Learning (ML) to fully automate identification of such claims prone to rejection or denial with high accuracy, investigate the reasons for claims denial and recommend methods to engineer features using Claim Adjustment Reason Codes (CARC) as features with high Information Gain. The ML engine reported is first of its kind in Claims risk identification and represents a novel, significant enhancement to the state of practice of using ML for automating and containing claims denial risks.