query: Movement segmentation using a primitive library

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

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

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

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

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

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

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

negative: Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour. We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems. A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice. What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice. The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem. The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy. Our analysis reveals areas of strength and weakness of each method.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

negative: Scap: stream-oriented network traffic capture and analysis for high-speed networks Many network monitoring applications must analyze traffic beyond the network layer to allow for connection-oriented analysis, and achieve resilience to evasion attempts based on TCP segmentation. However, existing network traffic capture frameworks provide applications with just raw packets, and leave complex operations like flow tracking and TCP stream reassembly to application developers. This gap leads to increased application complexity, longer development time, and most importantly, reduced performance due to excessive data copies between the packet capture subsystem and the stream processing module. This paper presents the Stream capture library (Scap), a network monitoring framework built from the ground up for stream-oriented traffic processing. Based on a kernel module that directly handles flow tracking and TCP stream reassembly, Scap delivers to user-level applications flow-level statistics and reassembled streams by minimizing data movement operations and discarding uninteresting traffic at early stages, while it inherently supports parallel processing on multi-core architectures, and uses advanced capabilities of modern network cards. Our experimental evaluation shows that Scap can capture all streams for traffic rates two times higher than other stream reassembly libraries, and can process more than five times higher traffic loads when eight cores are used for parallel stream processing in a pattern matching application.

negative: The design of early childhood makerspaces to support positive technological development: Two case studies Purpose – With the advent of the maker movement, there has been a new push to explore how spaces of learning ought to be designed. The purpose of this paper is to integrate three approaches for thinking about the role of design of the learning environment: the makerspace movement, Reggio Emilia’s Third Teacher approach, and the positive technological development (PTD) framework. Design/methodology/approach – This paper describes two case studies that involved the design of two different early childhood makerspaces (ECMSs) through a co-participatory design experience: the Kindergarten Creator Space at the International School of Billund in Denmark; and the ECMS at (removed for blind review), a resource library in Medford, MA. Findings – Based on the foundational education framework of PTD, and ideas from the field of interior design, this paper describes the design principles of several successful makerspaces, and case examples of children who use them. Originality/value – By grounding the theoretical discussion in three approaches, the authors aim to suggest design elements of physical spaces in schools and libraries that can promote young children’s learning through making. Recommendations are discussed for practitioners and researchers interested in ECMSs.

negative: Accelerating Apache Hive with MPI for Data Warehouse Systems Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.

negative: Driver fatigue detection based on saccadic eye movements The correct determination of driver's level of fatigue has been of vital importance for the safety of driving. There are various methods, such as analyzing facial expression, eyelid activity, and head movements to assess the fatigue level of drivers. This paper describes the design and prototype implementation of a driver fatigue level determination system based on detection of saccadic eye movements. Driver's eye movement speed is used to assess driver's fatigue level. The information about eyes is obtained via infrared led camera device. Movements of pupils were recorded in two driving scenarios with different traffic density. In the first scenario, the traffic density was set to low while the second scenario was based on high density and aggressive traffic. Based on the movements of pupils, the data on saccadic eye movement was analyzed to determine fatigue level of the driver. Acceleration, speed, and size of pupils at both traffic scenarios were compared with data mining techniques, such as segmentation adaptive peak, entropy, and data distribution analyses. Significantly different levels of fatigue were found between the tired and vigorous driver for the different types of scenarios.

negative: A Systematic Literature Review: Code Bad Smells in Java Source Code Code smell is an indication of a software designing problem. The presence of code smells can have a severe impact on the software quality. Smells basically refers to the structure of the code which violates few of the design principals and so has negative effect on the quality of the software. Larger the source code, more is its presence. Software needs to be reliable, robust and easily maintainable so that it can minimize the cost of its development as well as maintenance. Smells may increase the chances of failure of the system during maintenance. A SLR has been performed based on the search of digital libraries that includes the publications since 1999 to 2016. 60 research papers are deeply analyzed that are most relevant. The objective of this paper is to provide an extensive overview of existing research in the field of bad smells, identify the detection techniques and correlation between the detection techniques, in addition to find the name of the code smells that need more attention in detection approaches. This SLR identified that code clone (code smell) receives most research attention. Our findings also show that very few papers report on the impact of code bad smells. Most of the papers focused on the detection techniques and tools. A significant correlation between detection techniques has been calculated. There are four code smells that are not yet detected are Primitive Obsession, Inappropriate Intimacy, Incomplete library class and Comments.

negative: Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.

negative: Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.


query: A Survey of Artificial Intelligence for Cognitive Radios

positive: Genetic Algorithms in Search Optimization and Machine Learning This book brings together-in an informal and tutorial fashion-the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems...

positive: Metaheuristics in combinatorial optimization: Overview and conceptual comparison The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

positive: Cognitive radio: brain-empowered wireless communications Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

positive: Neural Networks: A Comprehensive Foundation El profesor Haykin ha escrito un libro muy interesante, que ya lleva su segunda edición, lo cual es extrafio dentro de la gran cantidad de libros que se producen en la actualidad. Es fácil encontrar en la literatura contemporánea la constante referencia que se hace a este libro; sin embargo, el libro no es lo que el título indica, ya que no es un libro ortodoxo de las teorías y técnicas que son conocidas como redes neuronales (también conocidas como sistemas conexionistas o sistemas neurocomputacionales), tampoco es un libro que presente los fundamentos o bases teóricas de las redes neuronales. neuronales, así como los grandes modelos de esta área de investigación, como son los perceptrones y las redes RBFN (Radial-Basis Function Networks). La presentación de estos cuatro capítulos es excelente.

negative: Engagement in Multimedia Training Systems Two studies examined user engagement in two type multimedia training systems -a more passive medium, videotape, and a less passive medium, interactive softw Each study compared engagement in three formats: the Text format contained text and still images, the Audio format contained audio and still images, and the Video format contained audio and video images. In both studies, engagement was lower in the Text condition than in the Video condition. However, there were no differences in engagement between Text and Audio in the videotapebased training, and no differences between Audio and Video in the computer-based training.

negative: AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.

negative: MODEL DIFFERENCES IN THE ECLIPSE MODELING FRAMEWORK Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining a fundamental importance and cannot be neglected in software modeling and development. In this respect, an interesting and useful operation between the designs of subsequent system versions is the difference management consisting in calculation, representation, and visualization. This work presents EMF Compare, an approach to model difference calculation and representation for the Eclipse modeling framework. Apart from leveraging the rank of model differences to that of first-class artifacts according to the “everything is a model” principle, the approach presents several properties which are discussed according to a conceptual framework.

negative: A Blended Deep Learning Approach for Predicting User Intended Actions User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

negative: 5G security architecture and light weight security authentication The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.

negative: OINT C LOUDS Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.

negative: Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

negative: Enterprise systems implementation and accounting benefits Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption. Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices. Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules. Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.

negative: Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

negative: Secure Logging as a Service—Delegating Log Management to the Cloud Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.

negative: Fast-and-Light Stochastic ADMM The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.

negative: Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70 Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.

negative: Severely suppressed bone turnover: a potential complication of alendronate therapy. Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.

negative:
Risk factors for shoulder and elbow injuries in adolescent baseball pitchers. BACKGROUND
There is little evidence supporting current safety recommendations for adolescent pitchers.

HYPOTHESIS
Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.

STUDY DESIGN
Case control study; Level of evidence, 3.

METHODS
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.

RESULTS
The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.

CONCLUSION
Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.

CLINICAL RELEVANCE
New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.

negative: Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.

negative: Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate? We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.

negative: The Polar Parallel Coordinates Method for Time-Series Data Visualization Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.

negative: Deep neural architectures for large scale android malware analysis Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.

negative: Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.

negative: Ranking scientific publications: the effect of nonlinearity Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

negative: GradNets: Dynamic Interpolation Between Neural Architectures In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.

negative: A Survey on NoSQL Stores Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.

negative: Distributed Manufacturing Scheduling Using Intelligent Agents reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's opti-mality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm , and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations , each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule—that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m. The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, …

negative: A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.

negative:
Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. PURPOSE OF THE RESEARCH
This study aimed to determine the prevalence of addiction to social networking sites/platforms (SNS) and its comorbidity with other behavioral addiction and affective disorder among college students in Singapore. 1110 college students (age: M=21.46, SD=1.80) in Singapore completed measures assessing online social networking, unhealthy food intake and shopping addiction as well as depression, anxiety and mania. Descriptive analyses were conducted to investigate the prevalence and comorbidity of behavioral addiction and affective disorder. Chi-square tests were used to examine gender differences.

THE PRINCIPAL RESULTS
The prevalence rates of SNS, food and shopping addiction were 29.5%, 4.7% and 9.3% respectively for the total sample. SNS addiction was found to co-occur with food addiction (3%), shopping addiction (5%), and both food and shopping addiction (1%). The comorbidity rates of SNS addiction and affective disorder were 21% for depression, 27.7% for anxiety, and 26.1% for mania. Compared with the total sample, students with SNS addiction reported higher comorbidity rates with other behavioral addiction and affective disorder. In general, females as compared to males reported higher comorbidity rates of SNS addiction and affective disorder.

MAJOR CONCLUSIONS
SNS addiction has a high prevalence rate among college students in Singapore. Students with SNS addiction were vulnerable to experience other behavior addiction as well as affective disorder, especially among females.

negative: Data fusion schemes for cooperative spectrum sensing in cognitive radio networks Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users. In this paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown through computer simulation that the soft combination scheme outperforms the hard one at the cost of more complexity; the quantized combination scheme provides a good tradeoff between detection performance and complexity. In the paper, we also analyze a quantized combination scheme based on a tree-bit quantization and compare its performance with some hard and soft combination schemes.

negative: Cooperative Sensing among Cognitive Radios Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.

negative: Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks In recent years, there has been a growing interest towards the application of artificial intelligence approaches in software engineering (SE) processes. In the specific area of SE for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and AI. However, just few significant results have been published. This paper briefly studies uncertainty in SASs and surveys techniques that have been developed to engineer SASs in order to tackle uncertainty. In particular, we highlight techniques that use AI concepts. We also report and discuss our own experience using Dynamic Decision Networks (DDNs) to model and support decision-making in SASs while explicitly taking into account uncertainty. We think that Bayesian inference, and specifically DDNs, provide a useful formalism to engineer systems that dynamically adapt themselves at runtime as more information about the environment and the execution context is discovered during execution. We also discuss partial results, challenges and future research avenues.

negative: Artificial Intelligence as an Effective Classroom Assistant The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.

negative: AI Methods in Algorithmic Composition: A Comprehensive Survey Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

negative: A Survey of Evaluation Methods and Measures for Interpretable Machine Learning The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. Researchers from different disciplines work together to define, design and evaluate interpretable intelligent systems for the user. Our work supports the different evaluation goals in interpretable machine learning research by a thorough review of evaluation methodologies used in machine-explanation research across the fields of human-computer interaction, visual analytics, and machine learning. We present a 2D categorization of interpretable machine learning evaluation methods and show a mapping between user groups and evaluation measures. Further, we address the essential factors and steps for a right evaluation plan by proposing a nested model for design and evaluation of explainable artificial intelligence systems.

negative: End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach. Keywords—Machine Learning, Software Radio, Protocol Recognition, Recurrent Neural Networks, LSTM, Protocol Learning, Traffic Classification, Cognitive Radio, Deep Learning