query: Implementation of Brain Breaks® in the Classroom and Effects on Attitudes toward Physical Activity in a Macedonian School Setting

positive: Supporting public health priorities: recommendations for physical education and physical activity promotion in schools. Physical activity (PA) provides numerous physiological and psychosocial benefits. However, lifestyle changes, including reduced PA opportunities in multiple settings, have resulted in an escalation of overweight and obesity and related health problems. Poor physical and mental health, including metabolic and cardiovascular problems is seen in progressively younger ages, and the systematic decline in school PA has contributed to this trend. Of note, the crowded school curriculum with an intense focus on academic achievement, lack of school leadership support, funding and resources, plus poor quality teaching are barriers to PA promotion in schools. The school setting and physical educators in particular, must embrace their role in public health by adopting a comprehensive school PA program. We provide an overview of key issues and challenges in the area plus best bets and recommendations for physical education and PA promotion in the school system moving forward.

positive: Integrating movement in academic classrooms: understanding, applying and advancing the knowledge base. In the context of comprehensive and coordinated approaches to school health, academic classrooms have gained attention as a promising setting for increasing physical activity and reducing sedentary time among children. The aims of this paper are to review the rationale and knowledge base related to movement integration in academic classrooms, consider the practical applications of current knowledge to interventions and teacher education, and suggest directions for future research. Specifically, this paper (i) situates movement integration amid policy and research related to children's health and the school as a health-promoting environment; (ii) highlights the benefits of movement integration; (iii) summarizes movement integration programs and interventions; (iv) examines factors associated with classroom teachers' movement integration; (v) offers strategies for translating research to practice and (vi) forwards recommendations for future inquiry related to the effectiveness and sustainability of efforts to integrate movement into classroom routines. This paper provides a comprehensive resource for developing state-of-the-art initiatives to maximize children's movement in academic classrooms as a key strategy for important goals in both education and public health.

positive: The “ What ” and “ Why ” of Goal Pursuits : Human Needs and the Self-Determination of Behavior Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.

positive: Barriers to Providing Physical Education and Physical Activity in Victorian State Secondary Schools An on-line questionnaire was completed by 115 physical education teachers to establish the barriers to their implementation of physical education in Victorian state secondary schools. In addition, the barriers perceived by teachers to impact on students’ participation in school-based physical education and physical activity were examined. The barriers to the provision of physical education were found to be largely institutional, although two-thirds of respondents recognised their own difficulties in engaging students when teaching as potential obstacles to student participation. Students were also perceived to be influenced by their own (45 per cent) and their peers (62 per cent) low levels of interest when choosing to participate. An awareness of these barriers has implications for physical education teaching, curriculum design, teacher training and adolescent participation in the school environment.

positive: Self-efficacy: toward a unifying theory of behavioral change. The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.

negative: Web-Based Ontology Languages and its Based Description Logics The main object of semantic web languages is to add semantics to the existing information on the Web. These web languages have been developed to represent or express ontologies. Therefore, these description languages provide richer constructors for forming complex class expressions and axioms. An ontology is expressed in a knowledge representation language, which provides a formal frame of semantics. Since description logic is the basis of most ontology language, it is appropriate to explain briefly the base of the description to understand the ontology language clearly. The aim of this paper is to provide a brief survey of state-of-the-art ontology languages which are used to express ontology over the Web is provided. Also the gaol of this paper is to provide a basic understanding of ontologies and description logics, which are the basis of ontology languages.

negative: Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.

negative: An Attention-based Regression Model for Grounding Textual Phrases in Images Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.

negative: Intelligent book positioning for library using RFID and book spine matching In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.

negative: An expanding reference library for Peer-to-Peer content Many Peer-to-Peer (P2P) networks have emerged as scalable platforms for enabling file-sharing and content-distribution applications. Due to their reliance on decentralized models encompassing millions of users, P2P networks are subjected to being infested with contraband material, copyright infringing content, as well as malicious viruses and programs. Moreover, the dynamics involved as users join and leave these networks make investigating them quite complex and time-dependent. To that end, this paper presents PxP, a tool that creates an expanding reference library for content that appears on P2P networks. PxP continuously searches the P2P networks for new files, downloads and fingerprints them, deletes them, and add their meta data to a public reference library. We envision that this library will aid investigators in criminal investigations that involve contraband material, civil investigations with copyright infringement content. In addition, this library can be used to speed up forensics examination of media through skipping over some of the files shared on P2P networks and in validating carving tools. We assess the performance of our framework through analysis, simulations and real experiments for content collected from the Gnutella P2P network.

negative: An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.

negative: An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

negative: Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.

negative: k-Nearest Neighbor Augmented Neural Networks for Text Classification In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.

negative: Active intelligent antenna system for car2car In this paper an innovative antenna system for car2car applications is described. The antenna system is based on an active antenna module containing the antennas as well as the complete car2car chip hardware and software. This module is very small, so that it can be integrated e.g. into a shark fin antenna on the cars rooftop. Moreover additional modules can be mounted somewhere else in the car and connected with the main module via Ethernet. This allows high flexibility and re-configurability of the system. Two test cars have been equipped with the new car2car demonstrator systems and measurements have been performed in the 3D measurement chamber as well as in dynamic outdoor scenarios. The new system shows excellent performance in measurements and field trials.

negative: GenomeGraphs: integrated genomic data visualization with R Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

negative: The effect of construal level on subjective probability estimates. In a series of studies, we examined novel predictions drawn from a conceptualization of probability as psychological distance. Manipulating construal level in a number of different ways and examining a variety of probability judgments, we found that participants led to adopt a high-level-construal mind-set made lower probability assessments than did those led to adopt a low-level-construal mind-set. Moreover, this occurred even when construal level was manipulated in a context separate from the judgment task and the manipulation was unrelated in content to the events being judged. These findings suggest that broad processing variables can exert a widespread influence on probability judgment.

negative: Auto-directed video stabilization with robust L1 optimal camera paths We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.

negative: Prediction , operations , and condition monitoring in wind energy Recent developments in wind energy research including wind speed prediction, wind turbine control, operations of hybrid power systems, as well as condition monitoring and fault detection are surveyed. Approaches based on statistics, physics, and data mining for wind speed prediction at different time scales are reviewed. Comparative analysis of prediction results reported in the literature is presented. Studies of classical and intelligent control of wind turbines involving different objectives and strategies are reported. Models for planning operations of different hybrid power systems including wind generation for various objectives are addressed. Methodologies for condition monitoring and fault detection are discussed. Future research directions in wind energy are proposed. 2013 Elsevier Ltd. All rights reserved.

negative: Learning Graphical State Transitions Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.

negative: Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing “keywords” (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded “understanding” of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (humancreated) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the endto-end task of image captioning.

negative: Scheduling a Major College Basketball Conference - Revisited Nemhauser and Trick presented the problem of finding a timetable for the 1997/98 Atlantic Coast Conference (ACC) in basketball. Their solution, found with a combination of integer programming and exhaustive enumeration, was accepted by the ACC. Finite-domain constraint programming is another programming technique that can be used for solving combinatorial search problems such as sports tournament scheduling. This paper presents a solution of round robin tournament planning based on finite-domain constraint programming. The approach yields a dramatic performance improvement, which makes an integrated interactive software solution feasible.

negative: Collection, exploration and analysis of crowdfunding social networks Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.

negative: Exploiting Emoticons in Polarity Classification of Text With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level – and, to a lesser extent, on a sentence level – significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text.

negative: Full-Bridge+SRT Hybrid DC/DC Converter for a 6.6-kW EV On-Board Charger This paper suggests a hybrid pulsewidth-modulation (PWM) dc/dc converter for an electric vehicle (EV) on-board charger (OBC). It is composed of a full-bridge (FB) converter and a resonant converter with secondary resonance tank (SRT). They are coupled in a single transformer so that any power distribution problem that causes a size increase in the transformer can be solved. Dual power flow of the proposed charger provides an increase in charger gain and a reduction of turn-off voltage spikes of the rectifying diodes caused by transformer leakage inductance and diode junction capacitance. These characteristics help reduce diode loss and remove dissipative snubber circuitry. In addition, this structure has the additional advantage of output inductor size due to the reduction of voltage variation applied to the inductor. The feasibility of the proposed charger is verified with a 6.6-kW prototype implemented by a design procedure.

negative: Improving Relation Extraction by Pre-trained Language Representations Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.

negative: Pronoun Translation and Prediction with or without Coreference Links The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronounfocused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: grammatical constraints from the hypothesized coreference links, and candidate translations from an SMT decoder. The system for the second sub-task avoids hypothesizing a coreference link, and uses instead a large set of source-side and target-side features from the noun phrases surrounding the pronoun to train a pronoun predictor.

negative: Learning Simpler Language Models with the Delta Recurrent Neural Network Framework Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and highperforming design that unifies previously proposed gated neural models. The DeltaRNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. In language modeling at the word and character levels, the models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, perform comparably to several state-of-the-art baselines. At the subword level, we find the Delta-RNN’s performance to be comparable to that of complex gated architectures.

negative: Modified reduced switch symmetrical multilevel inverter Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.

negative: Searchable Encryption over Feature-Rich Data Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.

negative:
CS Unplugged and Middle-School Students' Views, Attitudes, and Intentions Regarding CS Many students hold incorrect ideas and negative attitudes about computer science (CS). In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Tim Bell and his colleagues. These activities expose young people to central concepts in CS in an entertaining way without requiring a computer. The CS Unplugged activities have become more and more popular among CS educators and several activities are recommended in the ACM K-12 curriculum for elementary schools. CS Unplugged is used worldwide and has been translated into many languages.
 We examined the effect of the CS Unplugged activities on middle-school students’ ideas about CS and their desire to consider and study it in high school. The results indicate that following the activities the ideas of the students on what CS is about were partially improved, but their desire to study CS lessened.
 In order to provide possible explanations to these results, we analyzed the CS Unplugged activities to determine to what extent the objectives of CS Unplugged were addressed in the activities. In addition, we checked whether the activities were designed according to constructivist principles and whether they were explicitly linked to central concepts in CS. We found that only some of the objectives were addressed in the activities, that the activities do not engage with the students’ prior knowledge and that most of the activities are not explicitly linked to central concepts in CS. We offer suggestions for modifying the CS Unplugged activities so that they will be more likely to achieve their objectives.

negative: Effects of Fitness Applications with SNS: How Do They Influence Physical Activity Fitness applications with social network services (SNS) have emerged for physical activity management. However, there is little understanding of the effects of these applications on users’ physical activity. Motivated thus, we develop a theoretical model based on social cognitive theory of self-regulation to explain the effects of goal setting, self-tracking, and SNS (through social comparison from browsing others’ tracking data and social support from sharing one’s own tracking data) on physical activity. The model was tested with objective data from 476 Runkeeper users. Our preliminary results show that goal setting, number of uses, the proportion of lower-performing friends, and number of likes positively influence users’ physical activities, while the proportion of higher-performing friends has a negative effect. Moreover, the effect of the proportion of higher-performing friends is moderated by the average difference between the friends and the individual. The initial contributions of the study and remaining research plan are described.

negative:
Cyber Bullying Prevention: Intervention in Taiwan BACKGROUND
This study aimed to explore the effectiveness of the cyber bullying prevention WebQuest course implementation.

METHODOLOGY/FINDINGS
The study adopted the quasi-experimental design with two classes made up of a total of 61 junior high school students of seventh grade. The study subjects comprised of 30 students from the experimental group and 31 students from the control group. The experimental group received eight sessions (total 360 minutes) of the teaching intervention for four consecutive weeks, while the control group did not engage in any related courses. The self-compiled questionnaire for the student's knowledge, attitudes, and intentions toward cyber bullying prevention was adopted. Data were analysed through generalized estimating equations to understand the immediate results on the student's knowledge, attitudes, and intentions after the intervention. The results show that the WebQuest course immediately and effectively enhanced the knowledge of cyber bullying, reduced the intentions, and retained the effects after the learning. But it produced no significant impact on the attitude toward cyber bullying.

CONCLUSIONS/SIGNIFICANCE
The intervention through this pilot study was effective and positive for cyber bulling prevention. It was with small number of students. Therefore, studies with large number of students and long experimental times, in different areas and countries are warranted.

negative: Facebook and texting made me do it: Media-induced task-switching while studying Electronic communication is emotionally gratifying, but how do such technological distractions impact academic learning? The current study observed 263 middle school, high school and university students studying for 15 min in their homes. Observers noted technologies present and computer windows open in the learning environment prior to studying plus a minute-by-minute assessment of on-task behavior, off-task technology use and open computer windows during studying. A questionnaire assessed study strategies, task-switching preference, technology attitudes, media usage, monthly texting and phone calling, social networking use and grade point average (GPA). Participants averaged less than six minutes on task prior to switching most often due to technological distractions including social media, texting and preference for task-switching. Having a positive attitude toward technology did not affect being on-task during studying. However, those who preferred to task-switch had more distracting technologies available and were more likely to be off-task than others. Also, those who accessed Facebook had lower GPAs than those who avoided it. Finally, students with relatively high use of study strategies were more likely to stay on-task than other students. The educational implications include allowing students short ‘‘technology breaks’’ to reduce distractions and teaching students metacognitive strategies regarding when interruptions negatively impact learning. 2012 Elsevier Ltd. All rights reserved.

negative: Computational Thinking and Expository Writing in the Middle School To broaden participation in computing we need to look beyond traditional domains of inquiry and expertise. We present results from a demonstration project in which interactive journalism was used to infuse computational thinking into the standard curriculum and regular classroom experience at a middle school with a diverse population. Outcomes indicate that we were able to develop positive attitudes about computational thinking and programming among students and teachers who did not necessarily view themselves as “math types.” By partnering with language arts, technology and math teachers at Fisher Middle School, Ewing New Jersey, we introduced the isomorphism between the journalistic process and computational thinking to 7th and 8th graders. An intense summer institute, first with the teachers and then with students recruited from the school, immersed them in the “newsroom of the future” where they researched and wrote news stories, shot and edited video, and developed procedural animations in Scratch to support their storylines. An afterschool club sustained the experience. The teachers adapted interactive journalism and Scratch programming to enrich standard language arts curriculum and are infusing computational thinking in classroom experiences throughout the school.

negative: Increase Physical Fitness and Create Health Awareness through Exergames and Gamification - The Role of Individual Factors, Motivation and Acceptance Demographic change and the aging population push health and welfare system to its limits. Increased physical fitness and increased awareness for health issues will help elderly to live independently for longer and will thereby reduce the costs in the health care system. Exergames seem to be a promising solution for promoting physical fitness. Still, there is little evidence under what conditions Exergames will be accepted and used by elderly. To investigate promoting and hindering factors we conducted a user study with a prototype of an Exergame. We contrasted young vs. elderly players and investigated the role of gamer types, personality factors and technical expertise on the performance within the game and changes in the attitude towards individual health after the game. Surprisingly, performance within the game is not affected by performance motivation but by gamer type. More importantly, a universal positive effect on perceived pain is detected after the Exergame

negative:
Classroom Re-design to Facilitate Student Learning : A Case Study of Changes to a University Classroom This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning. Keywords: learning spaces, active learning, classrooms, teaching and learning environment, classroom design For a number of years there has been an on-going pedagogical shift in higher education away from a traditional content delivery model of instruction to more active models of learning in which students play more involved and interactive roles within the classroom (Cornell, 2002; Brown, 2006). This movement has been coupled with the recognition that the traditional university classroom, with its unidirectional design and tiered, fixed theatre-like seating, is insufficient to accommodate what have increasingly become more varied teaching and learning practices. This growing realization that as the nature of teaching and learning evolves so too must teaching and learning spaces has, in recent years, resulted in a heightened interest among scholars in the examination of classroom space and, more specifically, the inquiry into the connection between classroom design and pedagogy and learning (Brooks, 2011). This study presents findings from an on-going research project examining classroom spaces on a small university campus. The present case study, which is one component of the larger project, compares perceptions of a specific classroom as an effective learning space before and after relatively extensive changes were made to the physical environment of the room. These changes were guided by feedback from students and instructors as well as by existing literature on innovative classroom designs. In particular, we were interested in assessing if these changes 1
  University
  of
  Lethbridge,
  Lethbridge,
  Alberta,
  Canada


query: IntelliArm: An exoskeleton for diagnosis and treatment of patients with neurological impairments

positive: Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm Exoskeleton robots are promising assistive/rehabilitative devices that can help people with force deficits or allow the recovery of patients who have suffered from pathologies such as stroke. The key component that allows the user to control the exoskeleton is the human machine interface (HMI). Setting the HMI at the neuro-muscular level may lead to seamless integration and intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a model of the human muscle, the "myoprocessor," running in real-time and in parallel to the physiological muscle, that predicts joint torques as a function of the joint kinematics and neural activation levels. This paper presents the development of myoprocessors for the upper limb based on the Hill phenomenological muscle model. Genetic algorithms are used to optimize the internal parameters of the myoprocessors utilizing an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system

positive: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.

positive: Robot-aided neurorehabilitation of the upper extremities Task-oriented repetitive movements can improve muscle strength and movement co-ordination in patients with impairments due to neurological lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate and document the rehabilitation of movements. The paper provides an overview of existing devices that can support movement therapy of the upper extremities in subjects with neurological pathologies. The devices are critically compared with respect to technical function, clinical applicability, and, if they exist, clinical outcomes.

positive: A myosignal-based powered exoskeleton system Integrating humans and robotic machines into one system offers multiple opportunities for creating assistive technologies that can be used in biomedical, industrial, and aerospace applications. The scope of the present research is to study the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The Human–Machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor (Hill-based muscle model) which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked (worn) by the human operator. In the present setup the shoulder joint was kept fixed at given positions and the actuator was mounted on the exoskeleton elbow joint. The operator manipulated an external weight, located at the exoskeleton tip, while feeling a scaled-down version of the load. The remaining external load on the joint was carried by the exoskeleton actuator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental tests have shown that synthesizing the processed EMG signals as command signals with the external-load/human-arm moment feedback, significantly improved the mechanical gain of the system, while maintaining natural human control of the system, relative to other control algorithms that used only position or contact forces. The results indicated the feasibility of an EMG-based power exoskeleton system as an integrated human–machine system using highlevel neurological signals.

positive: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.

negative: Machine Learning in Medical Applications Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.

negative: High Performance Emulation of Quantum Circuits As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.

negative: Fire flame detection in video sequences using multi-stage pattern recognition techniques In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempospatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate. & 2012 Elsevier Ltd. All rights reserved.

negative: Prediction of click frauds in mobile advertising Click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. This paper proposes a novel framework for prediction of click fraud in mobile advertising which consists of feature selection using Recursive Feature Elimination (RFE) and classification through Hellinger Distance Decision Tree (HDDT).RFE is chosen for the feature selection as it has given better results as compared to wrapper approach when evaluated using different classifiers. HDDT is also selected as classifier to deal with class imbalance issue present in the data set. The efficiency of proposed framework is investigated on the data set provided by Buzzcity and compared with J48, Rep Tree, logitboost, and random forest. Results show that accuracy achieved by proposed framework is 64.07 % which is best as compared to existing methods under study.

negative: SlowFast Networks for Video Recognition We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report 79.0% accuracy on the Kinetics dataset without using any pre-training, largely surpassing the previous best results of this kind. On AVA action detection we achieve a new stateof-the-art of 28.3 mAP. Code will be made publicly available.

negative: Reducing the risk of query expansion via robust constrained optimization We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. This framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. Our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. In addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. We show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. Our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. We provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.

negative: Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities. Overcoming the technical and social challenges to broader data sharing is a common and important research objective, but increasing the supply and accessibility of scientific data is no guarantee data will be applied by scientists. Before reusing data created by others, scientists need to assess the data’s relevance, they seek confidence the data can be understood, and they must trust the data. Using interview data from earthquake engineering researchers affiliated with the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES), we examine how these scientists assess the reusability of colleagues’ experimental data for model validation.

negative: The Multidimensional Knapsack Problem: Structure and Algorithms Structure and Algorithms Jakob Puchinger NICTA Victoria Laboratory Department of Computer Science & Software Engineering University of Melbourne, Australia jakobp@csse.unimelb.edu.au Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria raidl@ads.tuwien.ac.at Ulrich Pferschy Department of Statistics and Operations Research University of Graz, Austria pferschy@uni-graz.at

negative: State of the Art: Embedding Security in Vehicles For new automotive applications and services, information technology (IT) has gained central importance. IT-related costs in car manufacturing are already high and they will increase dramatically in the future. Yet whereas safety and reliability have become a relatively well-established field, the protection of vehicular IT systems against systematic manipulation or intrusion has only recently started to emerge. Nevertheless, IT security is already the base of some vehicular applications such as immobilizers or digital tachographs. To securely enable future automotive applications and business models, IT security will be one of the central technologies for the next generation of vehicles. After a state-of-the-art overview of IT security in vehicles, we give a short introduction into cryptographic terminology and functionality. This contribution will then identify the need for automotive IT security while presenting typical attacks, resulting security objectives, and characteristic constraints within the automotive area. We will introduce core security technologies and relevant security mechanisms followed by a detailed description of critical vehicular applications, business models, and components relying on IT security. We conclude our contribution with a detailed statement about challenges and opportunities for the automotive IT community for embedding IT security in vehicles.

negative: Culture as situated cognition : Cultural mindsets , cultural fluency , and meaning making The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable

negative: Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

negative: A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography Limited-angle computed tomography (CT) is common in industrial applications, where incomplete projection data can cause artifacts. For objects made from homogeneous materials, we propose a joint reconstruction and segmentation method that performs joint image reconstruction and segmentation directly on the projection data. We describe an alternating minimization algorithm to solve the resulting optimization problem, and we modify the primal-dual hybrid gradient algorithm for the non-convex piecewise constant Mumford-Shah model, which is a popular approximation model in biomedical image segmentation. The effectiveness of the proposed approach is validated by simulation and by application to actual micro-CT data sets.

negative: Unsupervised document zone identification using probabilistic graphical models Document zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine learning methods, which require a large amount of annotated data, which is often difficult and expensive to obtain. In order to overcome this bottleneck, we propose graphical models based on the popular Latent Dirichlet Allocation (LDA) model. The first model, which we call zoneLDA aims to cluster the sentences into zone classes using only unlabelled data. We also study an extension of zoneLDA called zoneLDAb, which makes distinction between common words and non-common words within the different zone types. We present results on two different domains: the scientific domain and the technical domain. For the latter one we propose a new document zone classification schema, which has been annotated over a collection of 689 documents, achieving a Kappa score of 85%. Overall our experiments show promising results for both of the domains, outperforming the baseline model. Furthermore, on the technical domain the performance of the models are comparable to the supervised approach using the same feature sets. We thus believe that graphical models are a promising avenue of research for automatic document zoning.

negative: Violence during pregnancy in Jordan: its prevalence and associated risk and protective factors. This study estimates the lifetime prevalence of physical violence during pregnancy and examines risk and protective factors among women (N = 390) attending reproductive health clinics in Jordan. Approximately 15% reported physical violence during pregnancy. The husband was the sole perpetrator in 83% of the cases. A high frequency of quarreling, the husband's use of alcohol, attitudes supportive of a woman's duty to obey her husband, infrequent communication between the respondent and her family, and exposure to violence as a child increased the risk of violence. Consanguinity (marriage to a blood relative) and higher education levels were protective against violence during pregnancy.

negative: A study on lidar data forensics 3D LiDAR (Light Imaging Detection and Ranging) data has recently been used in a wide range of applications such as vehicle automation and crime scene reconstruction. Decision making in such applications is highly dependent on LiDAR data. Thus, it becomes crucial to authenticate the data before using it. Though authentication of 2D digital images and video has been widely studied, the area of 3D data forensic is relatively unexplored. In this paper, we investigate and identify three possible attacks on the LiDAR data. We also propose two novel forensic approaches as a countermeasure for such attacks and study their effectiveness. The first forensic approach utilises the density consistency check while the second method leverages the occlusion effect for revealing the forgery. Experimental results demonstrate the effectiveness of the proposed forgery attacks and raise the awareness against unauthenticated use of LiDAR data. The performance analyses of the proposed forensic approaches indicate that the proposed methods are very efficient and provide the detection accuracy of more than 95% for certain kinds of forgery attacks. While the forensic approach is unable to handle all forgery attacks, the study motivates to explore more sophisticated forensic methods for LiDAR data.

negative: Can we Evaluate the Quality of Generated Text? Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.

negative: Condensation methodologies for two-dimensional non-uniform constellations Two-dimensional non-uniform constellations (2D-NUC) have been adopted for the most recent terrestrial broadcasting system called ATSC 3.0. They are known for being more efficient than one-dimensional non uniform constellations (1D-NUC) and uniform constellations (UC). They can be used in any communication system with the bit-interleaved coded modulation (BICM) structure. However, one of the main challenges of such constellations is their design for optimal behaviour in a wide range of cases and the design of a demapper that exploits their advantages. This paper presents two different condensation methodologies to design efficient 2D-NUCs simple in design and with limited demapping complexity. The proposal provides a complexity reduction of the design and demapping processes in the range from 13% to 94%. The demapping stage provides performance losses less than 0.1 dB if compared with standard 2D-NUC demapping.

negative: 1 Text Mining Systems for Predicting Market Response to News : A Survey Several prototypes for predicting the short-term market reaction to news based on text mining techniques have been developed. However, no detailed comparison of the systems and their performances is available thus far. This paper describes the main systems developed and presents a framework for comparing the approaches. The prototypes differ in the text mining methods applied and the data sets used for performance evaluation. Some (mostly implicit) assumptions of these evaluations are rather unrealistic with respect to properties of financial markets and the performance results cannot be achieved in reality. Furthermore, the adequacy of applying text mining techniques for predicting stock price movements in general and approaches for dealing with existing problems are discussed.

negative: Fast routing in road networks with transit nodes. When you drive to somewhere far away, you will leave your current location via one of only a few important traffic junctions. Starting from this informal observation, we developed an algorithmic approach, transit node routing, that allows us to reduce quickest path queries in road networks to a small number of table lookups. For road maps of Western Europe and the United States, our best query times improved over the best previously published figures by two orders of magnitude. This is also more than one million times faster than the best known algorithm for general networks.

negative: Twin Networks: Using the Future as a Regularizer Being able to model long-term dependencies in sequential data, such as text, has been among the long-standing challenges of recurrent neural networks (RNNs). This issue is strictly related to the absence of explicit planning in current RNN architectures, more explicitly, the network is trained to predict only the next token given previous ones. In this paper, we introduce a simple way of biasing the RNNs towards planning behavior. Particularly, we introduce an additional neural network which is trained to generate the sequence in reverse order, and we require closeness between the states of the forward RNN and backward RNN that predict the same token. At each step, the states of the forward RNN are required to match the future information contained in the backward states. We hypothesize that the approach eases modeling of long-term dependencies thus helping in generating more globally consistent samples. The model trained with conditional generation achieved 4% relative improvement (CER of 7.3 compared to a baseline of 7.6).

negative: Design of a multi-DOF cable-driven mechanism of a miniature serial manipulator for robot-assisted minimally invasive surgery While multi-fingered robotic hands have been developed for decades, none has been used for surgical operations. μAngelo is an anthropomorphic master-slave system for teleoperated robot-assisted surgery. As part of this system, this paper focuses on its slave instrument, a miniature three-digit hand. The design of the mechanism of such a manipulator poses a challenge due to the required miniaturization and the many active degrees of freedom. As the instrument has a human-centered design, its relation to the human hand is discussed. Two ways of routing its cable-driven mechanism are investigated and the method of deriving the input-output functions that drive the mechanism is presented.

negative: German version of the Multidimensional Body-Self Relations Questionnaire - Appearance Scales (MBSRQ-AS): confirmatory factor analysis and validation. The Multidimensional Body-Self Relations Questionnaire (MBSRQ) is a widely used questionnaire that measures body image as a multidimensional construct. The Appearance Scales (AS) of the MBSRQ (Appearance Evaluation, Appearance Orientation, Body Areas Satisfaction, Overweight Preoccupation and Self-Classified Weight) are subscales which facilitate a parsimonious assessment of appearance-related aspects of body image. The current study tested the psychometric properties and factor structure of a German translation of the MBSRQ-AS. Participants were n=230 female patients with the SCID diagnosis of an eating disorder and n=293 female healthy controls. In a confirmatory factor analysis, convincing goodness-of-fit indices emerged. The subscales of the questionnaire yielded good reliability and convergent and discriminant validity coefficients, with most items showing excellent characteristics. Like the English version, the German adaptation of the questionnaire can be recommended for a multidimensional assessment of appearance-related aspects of body image in both research and clinical practice.

negative: “Anarchy is what states make of it: the social construction of power politics,” Wendt recasts the realist/liberal debate as an argument over the determinants of state action in the international system: i.e., whether state action is influenced by “structure” (anarchy and relative power) or “process” (interaction and learning). He puts forth a “constructivist” argument rejecting the realist belief that the structure of the international system – anarchy and “self-help” – forces states to “play competitive power politics.” Rather, Wendt argues that self-help and power politics do not follow “either logically or causally” from anarchy; if they exist, it is due to process, not structure (394). Thus, Wendt argues that state identities and interests are shaped and transformed within the international system, rather than (as the realists believe) existing as exogenous variables.

negative: Automatic scoring of pronunciation quality We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a variety of machine scores. We ®rst discuss issues related to the design of speech databases and the reliability of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce dierent machine scores that can be used as predictor variables. We validate these machine scores on the Voice Interactive Language Training System (VILTS) corpus, evaluating the pronunciation of American speakers speaking French and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a sucient amount of speech data is available. Ó 2000 Elsevier Science B.V. All rights reserved.

negative: Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

negative: Shoulder impingement revisited: evolution of diagnostic understanding in orthopedic surgery and physical therapy “Impingement syndrome” is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that “impingement syndrome” is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of “impingement syndrome”. Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of “anterior shoulder pain”. While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of “impingement syndrome”. For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.

negative: Parkinsons disease patients perspective on context aware wearable technology for auditive assistance In this paper we present a wearable assistive technology for the freezing of gait (FOG) symptom in patients with Parkinsons disease (PD), with emphasis on subjective user appreciation. Patients with advanced PD often suffer from FOG, which is a sudden and transient inability to move. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non+pharmacologic treatments are of special interest.

negative: Retrograde amnesia and remote memory impairment -The status of remote memory in case N.A., patients receiving bilateral ECT, and patients with KorsakoBsyndrome was assessed using seven different tests. The extent and pattern of remote memory impairment depended upon the etiology of amnesia, appearing as a relatively brief retrograde amnesia for N.A. and patients receiving ECT and as an extensive impairment covering many decades for patients with KorsakoB syndrome. Differences in remote memory impairment could not be explained by differences in the severity ofanterograde amnesia, indicating that amnesia is not a unitary disorder. We suggest that brief retrograde amnesia is typically present whenever amnesia occurs and that extensive remote memory impairment is a distinct entity related to superimposed cognitive deficits. These ideas and a review of previous studies lead to a comprehensive proposal for understanding retrograde amnesia and remote memory impairment. THE AMNESIC syndrome has been the subject of much interest in neuropsychology because of what it might reveal about the organization and neurological foundations of normal memory. The phenomenon of retrograde amnesia, that loss of memory can occur for the period before the precipitating incident, has had a particularly large impact on ideas about normal memory function [l-5]. However, conflicting views exist regarding both the extent and pattern of retrograde amnesia and these have been used to support different ideas about the nature of amnesia. Specifically, clinical assessment of memory loss in patients with medial temporal lobectomy has suggested that a relatively brief retrograde amnesia extends at least to several months and at most to a few years prior to surgery [6, 71. Formal testing of patients receiving bilateral electroconvulsive therapy (ECT) has revealed a brief retrograde amnesia affecting memory for public events and former television programs that occurred during the few years prior to treatment [8-lo]. These findings, together with studies of anterograde amnesia, are consistent with theories of amnesia that emphasize deficiency in the storage or consolidation of new information [l, 11, 121. By contrast, formal testing of patients with Korsakoff syndrome has revealed an extensive impairment of remote memory for public events [13-l 51, famous faces [2,13,15], and famous voices [16] that covers several decades. These *Now at Department of Psychology, Massachusetts Institute of Technology, Cambridge, MA 02139. ICorrespondence to Larry R. Squire, Veterans Administration Medical Center, 3350 La Jolla Village Drive, V-l 16A, San Diego, CA 92161, U.S.A.

negative: Neuroplasticity in the context of motor rehabilitation after stroke Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain–computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.

negative:
Review of Dercum’s disease and proposal of diagnostic criteria, diagnostic methods, classification and management UNLABELLED
DEFINITION AND CLINICAL PICTURE: We propose the minimal definition of Dercum's disease to be generalised overweight or obesity in combination with painful adipose tissue. The associated symptoms in Dercum's disease include fatty deposits, easy bruisability, sleep disturbances, impaired memory, depression, difficulty concentrating, anxiety, rapid heartbeat, shortness of breath, diabetes, bloating, constipation, fatigue, weakness and joint aches.

CLASSIFICATION
We suggest that Dercum's disease is classified into: I. Generalised diffuse form A form with diffusely widespread painful adipose tissue without clear lipomas, II. Generalised nodular form - a form with general pain in adipose tissue and intense pain in and around multiple lipomas, and III. Localised nodular form - a form with pain in and around multiple lipomas IV. Juxtaarticular form - a form with solitary deposits of excess fat for example at the medial aspect of the knee.

EPIDEMIOLOGY
Dercum's disease most commonly appears between the ages of 35 and 50 years and is five to thirty times more common in women than in men. The prevalence of Dercum's disease has not yet been exactly established.

AETIOLOGY
Proposed, but unconfirmed aetiologies include: nervous system dysfunction, mechanical pressure on nerves, adipose tissue dysfunction and trauma. DIAGNOSIS AND DIAGNOSTIC METHODS: Diagnosis is based on clinical criteria and should be made by systematic physical examination and thorough exclusion of differential diagnoses. Advisably, the diagnosis should be made by a physician with a broad experience of patients with painful conditions and knowledge of family medicine, internal medicine or pain management. The diagnosis should only be made when the differential diagnoses have been excluded.

DIFFERENTIAL DIAGNOSIS
Differential diagnoses include: fibromyalgia, lipoedema, panniculitis, endocrine disorders, primary psychiatric disorders, multiple symmetric lipomatosis, familial multiple lipomatosis, and adipose tissue tumours. GENETIC COUNSELLING: The majority of the cases of Dercum's disease occur sporadically. A to G mutation at position A8344 of mitochondrial DNA cannot be detected in patients with Dercum's disease. HLA (human leukocyte antigen) typing has not revealed any correlation between typical antigens and the presence of the condition. MANAGEMENT AND TREATMENT: The following treatments have lead to some pain reduction in patients with Dercum's disease: Liposuction, analgesics, lidocaine, methotrexate and infliximab, interferon α-2b, corticosteroids, calcium-channel modulators and rapid cycling hypobaric pressure. As none of the treatments have led to long lasting complete pain reduction and revolutionary results, we propose that Dercum's disease should be treated in multidisciplinary teams specialised in chronic pain.

PROGNOSIS
The pain in Dercum's disease seems to be relatively constant over time.

negative:
Single-stage posterior corpectomy and expandable cage placement for treatment of thoracic or lumbar burst fractures. STUDY DESIGN
A prospective study was performed.

OBJECTIVE
To assess an unusual technique for corpectomy and expandable cage placement via single-stage posterior approach in acute thoracic or lumbar burst fractures.

SUMMARY AND BACKGROUND DATA
Burst fractures represent 10% to 20% of all spine injuries at or near the thoracolumbar junction, and can cause neurologic complications and kyphotic deformity. The goal of surgical intervention is to decompress the neural elements, restore vertebral body height, correct angular deformity, and stabilize the columns of the spine.

METHODS
The study comprised 14 patients (8 women and 6 men aged 40.3 years) who had 1 spinal burst fracture between T8 and L4 and who underwent single-stage posterior corpectomy, circumferential reconstruction with expandable-cage placement, and transpedicle screwing between January 2003 and May 2005. Neurologic status was classified using the American Spinal Injury Association (ASIA) impairment scale and functional outcomes were analyzed using a visual analogue scale (VAS) for pain. The kyphotic angle (alpha) and lordotic angle (beta) were measured in the thoracic or thoracolumbar and lumbar regions, respectively. RESULTS.: The mean follow-up time was 24 months (range, 12-48 months). Neurologic status was in 7 patients (preop: ASIA-E, postop: unchanged), 2 patients (preop: ASIA-D, postop: 1 unchanged, 1 improved to ASIA-E), 3 patients (preop: ASIA-C, postop: 2 improved to ASIA-D, 1 improved to ASIA-E), 2 patients (preop: ASIA-B, postop: 1 improved to ASIA-C, 1 unchanged). The mean operative time was 187.8 minutes. The mean blood loss was 596.4 mL. Regarding postoperative complications, 1 patient experienced transient worsening of neurologic deficits and 1 patient developed pseudarthrosis. The mean preoperative VAS score was 8.21 and the mean postoperative VAS score was 2.66 (P < 0.05). The mean preoperative kyphotic angle for the 11 individuals with the thoracic or thoracolumbar burst fractures was 24.6 degrees and the mean preoperative lordotic angle for the 3 individuals with lumbar burst fractures was 10.6 degrees. The corresponding values at 12 months postsurgery were 17.1 degrees and 13.6 degrees.

CONCLUSION
This single-stage posterior approach for acute thoracic and lumbar burst fractures offers some advantages over the classic combined anterior-posterior approach. The results from this small series suggest that a single-stage posterior approach should be considered in select cases.

negative: Quality of life in the anxiety disorders: a meta-analytic review. There has been significant interest in the impact of anxiety disorders on quality of life. In this meta-analytic review, we empirically evaluate differences in quality of life between patients with anxiety disorders and nonclinical controls. Thirty-two patient samples from 23 separate studies (N=2892) were included in the analysis. The results yielded a large effect size indicating poorer quality of life among anxiety disorder patients vs. controls and this effect was observed across all anxiety disorders. Compared to control samples, no anxiety disorder diagnosis was associated with significantly poorer overall quality of life than was any other anxiety disorder diagnosis. Examination of specific domains of QOL suggests that impairments may be particularly prominent among patients with post-traumatic stress disorder. QOL domains of mental health and social functioning were associated with the highest levels of impairment among anxiety disorder patients. These findings are discussed in the context of future research on the assessment of quality of life in the anxiety disorders.