query: A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

positive: Building a Large Annotated Corpus of English: The Penn Treebank In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

positive: Visualizing and Understanding Convolutional Networks Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

positive: DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

positive: Enhanced LSTM for Natural Language Inference (1) Handcrafted features [BAPM15] 78.2 (2) LSTM [BGR+16] 80.6 (3) GRU [VKFU15] 81.4 (4) Tree CNN [MML+16] 82.1 (5) SPINN-PI [BGR+16] 83.2 (6) BiLSTM intra-Att [LSLW16] 84.2 (7) NSE [MY16a] 84.6 (8) Att-LSTM [RGH+15] 83.5 (9) mLSTM [WJ16] 86.1 (10) LSTMN [CDL16] 86.3 (11) Decomposable Att [PTDU16] 86.3 (12) Intra-sent Att+(11) [PTDU16] 86.8 (13) NTI-SLSTM-LSTM [MY16b] 87.3 (14) Re-read LSTM [SCSL16] 87.5 (15) btree-LSTM [PAD+16] 87.6

positive: A Convolutional Neural Network for Modelling Sentences The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

negative: The dynamics of organizational identity While many organizational researchers make reference to Mead’s theory of social identity, none have explored how Mead’s ideas about the relationship between the “I” and the “me” might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead’s “I” and “me” and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.

negative: Cross-modal plasticity: where and how? Animal studies have shown that sensory deprivation in one modality can have striking effects on the development of the remaining modalities. Although recent studies of deaf and blind humans have also provided convincing behavioural, electrophysiological and neuroimaging evidence of increased capabilities and altered organization of spared modalities, there is still much debate about the identity of the brain systems that are changed and the mechanisms that mediate these changes. Plastic changes across brain systems and related behaviours vary as a function of the timing and the nature of changes in experience. This specificity must be understood in the context of differences in the maturation rates and timing of the associated critical periods, differences in patterns of transiently existing connections, and differences in molecular factors across brain systems.

negative: A Machine Learning Approach to Twitter User Classification This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

negative: A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting Recognition Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

negative: #greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-theart face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.

negative: Feminism and interaction design This workshop is aimed at exploring the issues at the intersection of feminist thinking and human computer interaction. Both feminism and HCI have made important contributions to social science in the past several decades, but though their potential for overlap seem high, they have not engaged each other directly until recently. In this workshop we will explore diverse--and contentious--ways that feminist perspectives can support user research, design ideation and problem framing, sketching and prototyping, and design criticism and evaluation. The workshop will include fast-moving mini-panels and hands-on group exercises emphasizing feminist interaction criticism and design ideation.

negative: Scope+: a stereoscopic video see-through augmented reality microscope During the usage of conventional stereo microscope, repetitive head movement are often inevitable for the users to retrieve information away from the microscope. Moreover, risks of surgeons loosing focus and increasing fatigue could also arise during microsurgeries. Therefore, Scope+, a stereoscopic video see-through augmented reality system was created not only to solve the problems mentioned above, but also to improve users stereo microscopic experience.

negative: A New Approach to Linear Filtering and Prediction Problems AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems

negative: Radio Link Frequency Assignment The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NP-hard) optimization problem. In 1993, the CELAR (the French “Centre d'Electronique de l'Armement”) built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network Roisnel93. Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications). These problems should look very attractive to the CSP community: the problem is simple to represent, all constraints are binary and involve finite domain variables. They nevertheless have some of the flavors of real problems (including large size and several optimization criteria). This paper gives essential facts about the CELAR instances and also introduces the GRAPH instances which were generated during the CALMA project.

negative: A three-stage decision framework for multi-subject emotion recognition using physiological signals This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.

negative: Multimodal deep learning for robust RGB-D object recognition Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition. Our architecture is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. We focus on learning with imperfect sensor data, a typical problem in real-world robotics tasks. For accurate learning, we introduce a multi-stage training methodology and two crucial ingredients for handling depth data with CNNs. The first, an effective encoding of depth information for CNNs that enables learning without the need for large depth datasets. The second, a data augmentation scheme for robust learning with depth images by corrupting them with realistic noise patterns. We present state-of-the-art results on the RGB-D object dataset [15] and show recognition in challenging RGB-D real-world noisy settings.

negative: Replicating Milgram: Would people still obey today? The author conducted a partial replication of Stanley Milgram's (1963, 1965, 1974) obedience studies that allowed for useful comparisons with the original investigations while protecting the well-being of participants. Seventy adults participated in a replication of Milgram's Experiment 5 up to the point at which they first heard the learner's verbal protest (150 volts). Because 79% of Milgram's participants who went past this point continued to the end of the shock generator's range, reasonable estimates could be made about what the present participants would have done if allowed to continue. Obedience rates in the 2006 replication were only slightly lower than those Milgram found 45 years earlier. Contrary to expectation, participants who saw a confederate refuse the experimenter's instructions obeyed as often as those who saw no model. Men and women did not differ in their rates of obedience, but there was some evidence that individual differences in empathic concern and desire for control affected participants' responses.

negative: MaCow: Masked Convolutional Generative Flow Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MACOW), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MACOW enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.

negative: Spaceborne MIMO Synthetic Aperture Radar for Multimodal Operation In this paper, we introduce a novel multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) concept for multimodal operation. The proposed system employs waveforms based on the orthogonal frequency division multiplexing (OFDM) technique and digital beamforming (DBF) on receive. Thereby, it becomes feasible to maximize spatial degrees of freedom, which are necessary for the multimodal operation. The proposed MIMO SAR system produces multiple high-resolution wide-swath SAR imageries that are used for coherent postprocessing. Through this paper, we aim to open up a new perspective of using MIMO concept for a wide-swath SAR imaging with high resolution in interferometric and polarimetric modes, based on OFDM and DBF techniques. Therefore, this paper encompasses broad theoretical backgrounds and general system design issues as well as specific signal processing techniques and aspects.

negative: A Different Approach to the Management of Osteoarthritis in the Knee: Ultrasound Guided Genicular Nerve Block. A 61-year-old female was referred to our tertiary physical medicine and rehabilitation outpatient clinic with a 7-year history of severe left knee pain. The patient was a nonsmoker and obese, with no comorbidities, and reported that the left knee pain worsened when walking and descending stairs and particularly when climbing stairs. Radiography of the left knee revealed KellgrenLawrence grade 3 osteoarthritis (OA) with multiple osteophytes and definite joint space narrowing.

negative: Qualitative comparison of graph-based and logic-based multi-relational data mining: a case study The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of graph-based system, Subdue and the inductive logic programming system, CProgol. We identify three key factors for comparing graph-based and logic-based multi-relational data mining; namely, the ability to discover structurally large concepts, the ability to discover semantically complicated concepts and the ability to effectively utilize background knowledge. We perform an experimental comparison of Subdue and CProgol on the Mutagenesis domain and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue.

negative: Lung Nodule Detection Using Convolutional Neural Networks We present a novel procedure to apply deep learning techniques to medical image classification. With increasing popularity of computed tomography (CT) lung screening, fully manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung cancer detection is challenging because of the varying size, location, shape, and density of nodules. Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features. Convolutional neural network has demonstrated to learn discriminative visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database. We compare the performance of two 2.5D convolutional neural network models, two 3D convolutional neural network models, and one 3D convolutional neural networks with a 3D spatial transformer network module on the task of detecting lung nodules. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.

negative: Extreme Programming and Agile Software Development Methodologies As a stakeholder of a software project, how does this sound to you? You can have releases as often as you like. The small number of defects is unprecedented. All of the features in the system are the most valuable ones to your business. At anytime, you have access to complete, accurate information as the status of any feature and of the quality of the system as a whole. The team developing your project works in an energized space with constant communication about the project. You are not dependent on any one or even two programmers for the continued success of the project. If your needs change, the development team welcomes the change of direction. As a developer of a software project, how does this sound to you? No one estimates your tasks but you, period! You always have access to a customer to clarify details about the features you are implementing. You are free (and required) to clean up the code whenever necessary. You complete a project every two weeks. You can work in any part of the system that you wish. You can pick who will help you on any given task. You are not required to work constantly long hours. Does this sound to good to be true? Teams are achieving these advantages using a relatively new set of software methodologies. Collectively, they are referred to as Agile Software Development Methodologies. The most pervasive is Extreme Programming. This chapter will introduce you to these exciting, popular, yet controversial new approaches to software development. ____________________________________________________________________________

negative: Re-inventing PTPTN study loan with blockchain and smart contracts The issue of default payments from borrowers of the National Higher Education Fund Corporation (PTPTN) is worrisome. Many borrowers fail to pay their loans and claims that the PTPTN has poor management and filing system. This study proposed a prototype for managing study loan repayment utilizing blockchain and smart contracts. Borrowers have full access toward their accounts and ledgers while corporation filing and management system get automatically up-to-date with the assistance of smart contracts.

negative: Arousal-Biased Competition in Perception and Memory. Our everyday surroundings besiege us with information. The battle is for a share of our limited attention and memory, with the brain selecting the winners and discarding the losers. Previous research shows that both bottom-up and top-down factors bias competition in favor of high priority stimuli. We propose that arousal during an event increases this bias both in perception and in long-term memory of the event. Arousal-biased competition theory provides specific predictions about when arousal will enhance memory for events and when it will impair it, which accounts for some puzzling contradictions in the emotional memory literature.

negative: Innovation in Analogical Design: A Model-Based Approach Analogical reasoning, it is commonly accepted, plays an important role in innovation and creativity. Since design often is innovative, and can be creative, the design task provides a good context for exploring the role of analogy in innovation and creativity. In the IDEAL project, we are exploring the processes of innovation and creativity in the context of conceptual (or preliminary, qualitative) design of physical devices. In particular, we are developing a model-based approach to innovation and creativity in analogical design: our core hypothesis is that innovative analogical design involves both reasoning from past experiences in designing devices (in the form of analogs or cases) and comprehension of how those devices work (in the form of device models or theories). In this paper, we focus on the issues of understanding feedback on the performance of a design, discovery of new design constraints, and reformulation of the design problem, and describe how the model-based approach addresses these issues.

negative: Stronger generalization bounds for deep nets via a compression approach Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that’re orders of magnitude better in practice. These rely upon new succinct reparametrizations of the trained net — a compression that is explicit and efficient. These yield generalization bounds via a simple compression-based framework introduced here. Our results also provide some theoretical justification for widespread empirical success in compressing deep nets. Analysis of correctness of our compression relies upon some newly identified “noise stability”properties of trained deep nets, which are also experimentally verified. The study of these properties and resulting generalization bounds are also extended to convolutional nets, which had eluded earlier attempts on proving generalization.

negative: Automatic Text Classification: A Technical Review Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

negative: Control of the power in induction heating systems with L-LC resonant voltage source inverters This paper is a synthesis of the control circuit in an induction heating system with three-phase fully-controlled rectifier, single-phase voltage source inverter and L-LC resonant load. The attention is mainly directed on the control of the power transmitted to the induction coil. It is highlighted that the analytical design of a proportional integral derivative or proportional integral controller is practically impossible when the direct control of the current through inductor is desired. The two possibilities of power control taken into consideration are the inverter output current control and the inverter input voltage control. The design of the current and voltage controllers of proportional integral derivative type was carried out successfully based on Modulus Optimum criterion in Kessler variant. The performances of the control system in both variants of the power control were tested and validated by simulation under the Matlab-Simulink environment by using real parameters of induction coils and heated pipes from a leading Romanian manufacturer.

negative: "Exercise to be fit, not skinny": The effect of fitspiration imagery on women's body image. Fitspiration is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. The present study aimed to experimentally investigate the impact of fitspiration images on women's body image. Participants were 130 female undergraduate students who were randomly assigned to view either a set of Instagram fitspiration images or a control set of travel images presented on an iPad. Results showed that acute exposure to fitspiration images led to increased negative mood and body dissatisfaction and decreased state appearance self-esteem relative to travel images. Importantly, regression analyses showed that the effects of image type were mediated by state appearance comparison. Thus it was concluded that fitspiration can have negative unintended consequences for body image. The results offer support to general sociocultural models of media effects on body image, and extend these to "new" media.

negative: Modeling textual entailment with role semantic information In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution of frame semantic information to the recognition textual entailment and (ii) to present a robust system architecture that can serve as basis for future experiments, research, and improvement. Our work was carried out in the context of the textual entailment initiative, which since 2005 has set the stage for the broad investigation of inference in natural-language processing tasks, including empirical evaluation of its coverage and reliability. In short, textual entailment describes inferential relations between (entailing) texts and (entailed) hypotheses as interpreted by typical language users. This pre-theoretic notion captures a natural range of inferences as compared to logical entailment, which has traditionally been used within theoretical approaches to natural language semantics. Various methods for modeling textual entailment have been proposed in the literature, ranging from shallow techniques like lexical overlap to shallow syntactic parsing and the exploitation of WordNet relations. Recently, there has been a move towards more structured meaning representations. In particular, the level of predicate-argument structure has gained much attention, which seems to be a natural and straightforward choice. Predicate-argument structure allows annotating sentences or texts with nuclear meaning representations (“who did what to whom”), which are of obvious relevance for this task. For example, it can account for paraphrases like “Ghosts scare John” vs. “John is scared by ghosts”. In this thesis, we present an approach to textual entailment that is centered around the analysis of predicate-argument structure. It combines LFG grammatical analysis, predicate-argument structure in the FrameNet paradigm, and taxonomic information from WordNet into tripartite graph structures. By way of a declarative graph matching algorithm, the “structural and semantic” similarity of hypotheses and texts is computed and the result is represented as feature vectors. A supervised machine learning architecture trained on entailment corpora is used to check textual entailment for new text/hypothesis pairs. The approach is implemented in the SALSA RTE system, which successfully participated in the second and third RTE challenge. While system performance is on a par with that of comparable systems, the intuitively expected strong positive effect of using FrameNet information has not yet been confirmed. In order to evaluate different system components and to assess the potential contribution of FrameNet information for checking textual entailment, we conducted a number of experiments. For example, with the help of a gold-standard corpus, we

negative: Temporal Summaries of News Topics We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.

negative: Using link structure to infer opinions in social networks The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.

negative: Dynamic switching-based reliable flooding in low-duty-cycle wireless sensor networks Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.

negative: SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing Task 8 at SemEval 2014 defines BroadCoverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate–argument relationships for all content words, i.e. the semantic structure constituting the relational core of sentence meaning. In this task description, we position the problem in comparison to other sub-tasks in computational language analysis, introduce the semantic dependency target representations used, reflect on high-level commonalities and differences between these representations, and summarize the task setup, participating systems, and main results. 1 Background and Motivation Syntactic dependency parsing has seen great advances in the past decade, in part owing to relatively broad consensus on target representations, and in part reflecting the successful execution of a series of shared tasks at the annual Conference for Natural Language Learning (CoNLL; Buchholz & Marsi, 2006; Nivre et al., 2007; inter alios). From this very active research area accurate and efficient syntactic parsers have developed for a wide range of natural languages. However, the predominant data structure in dependency parsing to date are trees, in the formal sense that every node in the dependency graph is reachable from a distinguished root node by exactly one directed path. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and the proceedings footer are added by the organizers: http:// creativecommons.org/licenses/by/4.0/. Unfortunately, tree-oriented parsers are ill-suited for producing meaning representations, i.e. moving from the analysis of grammatical structure to sentence semantics. Even if syntactic parsing arguably can be limited to tree structures, this is not the case in semantic analysis, where a node will often be the argument of multiple predicates (i.e. have more than one incoming arc), and it will often be desirable to leave nodes corresponding to semantically vacuous word classes unattached (with no incoming arcs). Thus, Task 8 at SemEval 2014, Broad-Coverage Semantic Dependency Parsing (SDP 2014),1 seeks to stimulate the dependency parsing community to move towards more general graph processing, to thus enable a more direct analysis of Who did What to Whom? For English, there exist several independent annotations of sentence meaning over the venerable Wall Street Journal (WSJ) text of the Penn Treebank (PTB; Marcus et al., 1993). These resources constitute parallel semantic annotations over the same common text, but to date they have not been related to each other and, in fact, have hardly been applied for training and testing of datadriven parsers. In this task, we have used three different such target representations for bi-lexical semantic dependencies, as demonstrated in Figure 1 below for the WSJ sentence: (1) A similar technique is almost impossible to apply to other crops, such as cotton, soybeans, and rice. Semantically, technique arguably is dependent on the determiner (the quantificational locus), the modifier similar, and the predicate apply. Conversely, the predicative copula, infinitival to, and the vacSee http://alt.qcri.org/semeval2014/ task8/ for further technical details, information on how to obtain the data, and official results.

negative: Narrowband Internet of Things: Evolutions, Technologies, and Open Issues We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.

negative: Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.


query: AutoLock: Why Cache Attacks on ARM Are Harder Than You Think

positive: ARMageddon: Cache Attacks on Mobile Devices In the last 10 years, cache attacks on Intel x86 CPUs have gained increasing attention among the scientific community and powerful techniques to exploit cache side channels have been developed. However, modern smartphones use one or more multi-core ARM CPUs that have a different cache organization and instruction set than Intel x86 CPUs. So far, no cross-core cache attacks have been demonstrated on non-rooted Android smartphones. In this work, we demonstrate how to solve key challenges to perform the most powerful cross-core cache attacks Prime+Probe, Flush+Reload, Evict+Reload, and Flush+Flush on non-rooted ARM-based devices without any privileges. Based on our techniques, we demonstrate covert channels that outperform state-of-the-art covert channels on Android by several orders of magnitude. Moreover, we present attacks to monitor tap and swipe events as well as keystrokes, and even derive the lengths of words entered on the touchscreen. Eventually, we are the first to attack cryptographic primitives implemented in Java. Our attacks work across CPUs and can even monitor cache activity in the ARM TrustZone from the normal world. The techniques we present can be used to attack hundreds of millions of Android devices.

positive: Cache Template Attacks: Automating Attacks on Inclusive Last-Level Caches Recent work on cache attacks has shown that CPU caches represent a powerful source of information leakage. However, existing attacks require manual identification of vulnerabilities, i.e., data accesses or instruction execution depending on secret information. In this paper, we present Cache Template Attacks. This generic attack technique allows us to profile and exploit cachebased information leakage of any program automatically, without prior knowledge of specific software versions or even specific system information. Cache Template Attacks can be executed online on a remote system without any prior offline computations or measurements. Cache Template Attacks consist of two phases. In the profiling phase, we determine dependencies between the processing of secret information, e.g., specific key inputs or private keys of cryptographic primitives, and specific cache accesses. In the exploitation phase, we derive the secret values based on observed cache accesses. We illustrate the power of the presented approach in several attacks, but also in a useful application for developers. Among the presented attacks is the application of Cache Template Attacks to infer keystrokes and—even more severe—the identification of specific keys on Linux and Windows user interfaces. More specifically, for lowercase only passwords, we can reduce the entropy per character from log2(26) = 4.7 to 1.4 bits on Linux systems. Furthermore, we perform an automated attack on the Ttable-based AES implementation of OpenSSL that is as efficient as state-of-the-art manual cache attacks.

positive: TruSpy: Cache Side-Channel Information Leakage from the Secure World on ARM Devices As smart, embedded devices are increasingly integrated into our daily life, the security of these devices has become a major concern. The ARM processor family, which powers more than 60% of embedded devices, introduced TrustZone technology to offer security protection via an isolated execution environment called secure world. Caches in TrustZone-enabled processors are extended with a nonsecure (NS) bit to indicate whether a cache line is used by the secure world or the normal world. This cache design improves system performance by eliminating the need to perform cache flush during world switches; however, it also enables cache contention between the two worlds. In this work, we present TruSpy, the first study of timingbased cache side-channel information leakage of TrustZone. Our proposed attack exploits the cache contention between normal world and secure world to recover secret information from secure world. Two attacks are proposed in TruSpy, namely, the normal world OS attack and the normal world Android app attack. In the OS-based attack, the attacker is able to access virtual-to-physical address translation and high precision timers. In the Android app-based attack, these tools are unavailable to the attacker, so we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate that it is possible for a normal world attacker to steal a fine-grained secret from the secure world using a timing-based cache side-channel. We can recover the full AES encryption key via either the OS-based attack or the Android app-based attack. Since our zero permission TruSpy attack is based on the cache design in TrustZone enabled ARM processors, it poses a significant threat to a wide array of devices. To mitigate the newly discovered threat, we also propose both application-based and system-oriented countermeasures.

positive: Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.

positive: Cache Games -- Bringing Access-Based Cache Attacks on AES to Practice Side channel attacks on cryptographic systems exploit information gained from physical implementations rather than theoretical weaknesses of a scheme. In recent years, major achievements were made for the class of so called access-driven cache attacks. Such attacks exploit the leakage of the memory locations accessed by a victim process. In this paper we consider the AES block cipher and present an attack which is capable of recovering the full secret key in almost real time for AES-128, requiring only a very limited number of observed encryptions. Unlike previous attacks, we do not require any information about the plaintext (such as its distribution, etc.). Moreover, for the first time, we also show how the plaintext can be recovered without having access to the cipher text at all. It is the first working attack on AES implementations using compressed tables. There, no efficient techniques to identify the beginning of AES rounds is known, which is the fundamental assumption underlying previous attacks. We have a fully working implementation of our attack which is able to recover AES keys after observing as little as 100 encryptions. It works against the OpenS SL 0.9.8n implementation of AES on Linux systems. Our spy process does not require any special privileges beyond those of a standard Linux user. A contribution of probably independent interest is a denial of service attack on the task scheduler of current Linux systems (CFS), which allows one to observe (on average) every single memory access of a victim process.

negative: Using GLCM and Gabor filters for classification of PAN images In the present research we have used GLCM and Gabor filters to extract texture features in order to classify PAN images. The main drawback of GLCM algorithm is its time-consuming nature. In this work, we proposed a fast GLCM algorithm to overcome the mentioned weakness of traditional GLCM. The fast GLCM is capable of extracting approximately the same features as the traditional GLCM does, but in a really much less time (in the best case, 180 times faster, and in the worst case, 30 times faster). The other weakness of the traditional GLCM is its lower accuracies in the region near the class borders. As Gabor filters are more powerful in border regions, we have tried to combine Gabor features with GLCM features. In this way we would compensate the latter mentioned weakness of GLCM. Experimental results show good capabilities of the proposed fast GLCM and the feature fusion method in classification of PAN images.

negative: Exploring knowledge sharing in ERP implementation: an organizational culture framework This is a multi-site case study of firms that have implemented enterprise resource planning (ERP) systems. It examines eight dimensions of culture and their impact on how ERP implementation teams are able to effectively share knowledge across diverse functions and perspectives during ERP implementation. Through synthesizing the data, we develop a cultural configuration that shows the dimensions of culture that best facilitate knowledge sharing in ERP implementation. The results also indicate ways that firms may overcome cultural barriers to knowledge sharing. A model is developed that demonstrates the link between the dimensions of culture and knowledge sharing during ERP implementation. Possible research questions on which future research can be based are also identified. D 2004 Elsevier B.V. All rights reserved.

negative: External Data Access And Indexing In AsterixDB Traditional database systems offer rich query interfaces (SQL) and efficient query execution for data that they store. Recent years have seen the rise of Big Data analytics platforms offering query-based access to "raw" external data, e.g., file-resident data (often in HDFS). In this paper, we describe techniques to achieve the qualities offered by DBMSs when accessing external data. This work has been built into Apache AsterixDB, an open source Big Data Management System. We describe how we build distributed indexes over external data, partition external indexes, provide query consistency across access paths, and manage external indexes amidst concurrent activities. We compare the performance of this new AsterixDB capability to an external-only solution (Hive) and to its internally managed data and indexes.

negative: Realtime FPGA-based processing unit for a high-resolution automotive MIMO radar platform Next generations of high-resolution automotive radar sensors rely on powerful, real-time capable processing units. High sampling rates, large antenna arrays as well as hard real-time constraints require the use of both parallel architectures and high-bandwidth memory interfaces. This paper presents a novel architecture of a FPGA-based multiple-input multiple-output (MIMO) radar sensor processing unit. Sampling rates up to 250 MSPS and a maximum of 16 parallel receiving channels can be used, resulting in a maximum data rate of 56GBit/s. The processing chain consists of a flexible FIR filter, a range-Doppler processing unit using windowed FFTs and an ordered statistics constant-false-alarm-rate (OS-CFAR) unit for optimal target detection and data reduction. The realized target system is composed of a Virtex7-FPGA and a 1GByte SDRAM memory. The resource usage of the FPGA implementation is analyzed in order to provide estimations for future system designs. Finally, the resulting performance of the system is verified in connection with a prototype MIMO radar front-end. High-resolution measurements of moving scenes have been carried out to validate the correct operation of the system.

negative: Relation between language experiences in preschool classrooms and children's kindergarten and fourth-grade language and reading abilities. Indirect effects of preschool classroom indexes of teacher talk were tested on fourth-grade outcomes for 57 students from low-income families in a longitudinal study of classroom and home influences on reading. Detailed observations and audiotaped teacher and child language data were coded to measure content and quantity of verbal interactions in preschool classrooms. Preschool teachers' use of sophisticated vocabulary during free play predicted fourth-grade reading comprehension and word recognition (mean age=9; 7), with effects mediated by kindergarten child language measures (mean age=5; 6). In large group preschool settings, teachers' attention-getting utterances were directly related to later comprehension. Preschool teachers' correcting utterances and analytic talk about books, and early support in the home for literacy predicted fourth-grade vocabulary, as mediated by kindergarten receptive vocabulary.

negative: A tensor-based deep learning framework This paper presents an unsupervised deep learning framework that derives spatio-temporal features for human-robot interaction. The respective models extract high-level features from low-level ones through a hierarchical network, viz. the Hierarchical Temporal Memory (HTM), providing at the same time a solution to the curse of dimensionality in shallow techniques. The presented work incorporates the tensor-based framework within the operation of the nodes and, thus, enhances the feature derivation procedure. This is due to the fact that tensors allow the preservation of the initial data format and their respective correlation and, moreover, attain more compact representations. The computational nodes form spatial and temporal groups by exploiting the multilinear algebra, subsequently express the samples according to those groups in terms of proximity. This generic framework may be applied in a diverse of visual data, whilst it has been examined on sequences of color and depth images, exhibiting remarkable performance.

negative: On Resampling Algorithms for Particle Filters In this paper a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms with respect to their resampling quality and computational complexity. Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in terms of resampling quality and computational complexity.

negative: Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity Research has suggested that approach-related positive emotions are associated with greater left frontal brain activity and that withdrawal-related negative emotions are associated with greater right frontal brain activity. Different explanations have been proposed. One posits that frontal asymmetry is due to emotional valence (positivity/negativity), one posits that frontal asymmetry is due to motivational direction (approach/withdrawal), and one posits that frontal asymmetry is due to a combination of emotional valence and motivational direction (positive-approach/negative-withdrawal). Because research had confounded emotional valence and motivational direction, the theoretical explanation was muddled. Solely supporting the motivational direction model, recent research has revealed that anger and cognitive dissonance, emotions with negative valence and approach motivational tendencies, are related to relatively greater left frontal activity.

negative: A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.

negative: Motives for using Facebook, patterns of Facebook activities, and late adolescents' social adjustment to college. Previous studies have confirmed that Facebook, the leading social networking site among young people, facilitates social connections among college students, but the specific activities and motives that foster social adjustment remain unclear. This study examined associations between patterns of Facebook activity, motives for using Facebook, and late adolescents' social adjustment to the college environment. Anonymous self-report survey data from 193 mostly European American students (M age = 20.32; 54 % female) attending a major Midwestern university indicated that motives and activity patterns were associated directly with social adjustment, but the association between one activity, status updating, and social adjustment also was moderated by the motive of relationship maintenance. Findings provide a more comprehensive portrait of how Facebook use may foster or inhibit social adjustment in college.

negative: The eyeBook – Using Eye Tracking to Enhance the Reading Experience Introduction A rapid development of eye tracking technology has been observed in recent years. Today’s eye trackers can determine the current focus point of the eye precisely while being relatively unobtrusive in their application. Also, a variety of research and commercial groups has been working on this technology, and there is a growing interest for such devices on the market. Eye tracking has great potential and it can be assumed that it will advance further and might become a widespread technology used at a large number of personal or office computer workplaces. Approaches using simple webcams for eye tracking already exist, for example webcams integrated into laptop computers by default. Thus, they allow for new kinds of applications using eye gaze data. However, not only eye tracking technology is advancing rapidly to an easily usable state. Additionally, during the past 100 years researchers gathered a considerable amount of knowledge on eye movements, why and how they occur, and what they might mean. So, today, we have the technology and knowledge for tracking and analyzing eye movements, making an excellent starting point for sophisticated interactive gaze-based applications. Naive approaches where gaze data is directly employed for interacting with the system, e. g., pressing buttons on the screen with the “blink of an eye” generally have serious problems. Because the eyes are organs used for perceiving the world and not for manipulating the world, it is hard and against human nature to control eye movements deliberately. However, a highly promising approach is just to observe eye movements of the user during his or her daily work in front of the computer, to infer user intentions based on eye movement behavior, and to provide assistance where helpful. Gaze can be seen as a proxy for the user’s attention, and eye movements are known to be usually tightly coupled with cognitive processes in the brain, so that a great deal about those processes can be observed by eye tracking. For example, by interpreting eye movements, reading behavior of the user can be detected, which most likely entails cognitive processes of understanding with regard to the currently read text. In this paper we are focusing particularly on reading behavior since reading is probably the most common activity of knowledge workers sitting in front of a computer screen. We present an algorithm for online reading detection based on eye tracking data and introduce an application for assisted and augmented reading called the eyeBook. The idea behind the eyeBook is to create an interactive and entertaining reading experience. The system observes, which text parts are currently being read by the user on the screen and generates appropriate effects such as playing sounds, presenting

negative: CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.

negative: Research of Workflow Access Control Strategy based on Trust The traditional workflow access control strategy has often found to be inadequate to detect and restrain malicious behavior effectively. With the aim to solve this problem, this paper presents a new workflow access control model based on trust, and a new access control strategy with an authorization process. This strategy introduces user behavior evaluation, trust computation and role hierarchy into role access control strategy. Through the trust computation of user behavior, it can dynamically adjust user's role and permissions, realizing the dynamic authorization process. Theory analysis and simulation experiments show that this access control strategy is more sensitive in dynamic authorization, and it has fine-grained trust computation. Also this strategy can detect malicious behaviors in time, effectively restraining malicious behavior harming the system so enhancing the security of the system.

negative: Design of Metal Interconnects for Stretchable Electronic Circuits using Finite Element Analysis In this work, the design of flexible and stretchable interconnections is presented. These interconnections are done by embedding sinuous electroplated metallic wires in a stretchable substrate material. A silicone material was chosen as substrate because of its low stiffness and high elongation before break. Common metal conductors used in the electronic industry have very limited elastic ranges; therefore a metallization design is crucial to allow stretchability of the conductors going up to 100%. Different configurations were simulated and compared among them and based on these results, a horseshoe like shape was suggested. This design allows a large deformation with the minimum stress concentration. Moreover, the damage in the metal is significantly reduced by applying narrow metallization schemes. In this way, each conductor track has been split in four parallel lines of 15 mum and 15 mum space in order to improve the mechanical performance without limiting the electrical characteristics. Compared with the single copper or gold trace, the calculated stress was reduced up to 10 times.

negative: Using a Graph-based Coherence Model in Document-Level Machine Translation Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube (2016) with Docent1 (Hardmeier et al., 2012; Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.

negative: Post-Processing OCR Text using Web-Scale Corpora We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.

negative: Characteristics of personal space during obstacle circumvention in physical and virtual environments. It is not known how the flexible protective zone maintained around oneself during locomotion (personal space or PS; see [Gérin-Lajoie M, Richards CL, McFadyen BJ. The negotiation of stationary and moving obstructions during walking: anticipatory locomotor adaptations and preservation of personal space. Motor Control 2005;9:242-69]) is modulated with walking speed, whether both sides of the PS are symmetrical, and whether the circumvention of physical and virtual obstructions elicit the same use of such PS. Personal space was measured in ten adults as they circumvented a cylindrical obstacle that was stationary within their path. Both left and right passes were performed at natural self-selected, slow and fast walking speeds. The same circumvention task was also performed at natural speeds in an immersive virtual environment (VE) replicating the same obstruction scenario. The shape and size of PS were maintained across walking speeds, and a smaller PS was generally observed on the dominant side. The general shape and lateral bias of the PS were preserved in the VE while its size was slightly increased. The systematic behavior across walking speeds and types of environment and the lateral bias suggest that PS is used to control navigation. This study deepens our understanding of normal adaptive walking behavior and has implications for the development of better tools for the assessment and retraining of locomotor capacity in different populations, from people with walking deficits to elite athletes. Since the PS behavior was shown to be robust in the VE used for this study, the virtual reality technology is proposed as a promising platform for the development of such assessment and retraining applications.

negative: Multi-scale retinex for color image enhancement The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some \patho-logical" scenes that have very strong spectral characteristics in a single band.

negative: Vertical ridge augmentation in the esthetic zone. The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.

negative: A Lossy Image Codec Based on Index Coding In this paper we propose a new lossy image codec based on index coding. Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm (which is the state-of-the-art method today) use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The new algorithm combines the discrete wavelet transform, differential coding, variable-length coding of integers, ordered bit plane transmission, and adaptive arithmetic coding. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower bit rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. 456 1068.0314/96$5.00

negative: Extracting lines of curvature from noisy point clouds We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.

negative: Qualitative clinical evaluation of scapular dysfunction: a reliability study. The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.

negative: Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.

negative: Using Features From Pre-trained TimeNET For Clinical Predictions Predictive models based on Recurrent Neural Networks (RNNs) for clinical time series have been successfully used for various tasks such as phenotyping, in-hospital mortality prediction, and diagnostics. However, RNNs require large labeled data for training and are computationally expensive to train. Pre-training a network for some supervised or unsupervised tasks on a dataset, and then fine-tuning via transfer learning for a related end-task can be an efficient way to leverage deep models for scenarios that lack in either computational resources or labeled data, or both. In this work, we consider an approach to leverage a deep RNN – namely TimeNet [Malhotra et al., 2017] – that is pre-trained on a large number of diverse publicly available time-series from UCR Repository [Chen et al., 2015]. TimeNet maps varyinglength time series to fixed-dimensional feature vectors and acts as an off-the-shelf feature extractor. TimeNet-based approach overcome the need for hand-crafted features, and allows for use of traditional easy-to-train and interpretable linear models for the end-task, while still leveraging the features from a deep neural network. Empirical evaluation of the proposed approach on MIMIC-III1 data suggests promising direction for future exploration: our results are comparable to existing benchmarks while our models require lesser training and hyperparameter tuning effort.

negative: "You've Got E-Mail!" ... Shall I Deal With It Now? Electronic Mail From the Recipient's Perspective This article considers the nature of e-mail from the recipient’s perspective—what the seeminglyfreeandeasycommunicationreallycosts therecipient. Informationgathered by electronic monitoring software is shown to be at odds with the results of an online survey of e-mail users’ perceptions of their e-mail experience—users drastically underestimate the disruptive effects of e-mail. The conclusion is that the constant monitoring of e-mail actually reduces productivity and that there is a need for increased power, control,andawarenessonthepartof thee-mail recipient toensure thate-mail remainsatool rather than a tyrant. It is necesssary to alert the user of the true cost of e-mail alerts.

negative: On the Cache Behavior of SPLASH-2 Benchmarks on ARM and ALPHA Processors in Gem5 Full System Simulator Today cache size and hierarchy level of caches play an important role in improving computer performance. By using full system simulations of gem5, the variation in memory bandwidth, system bus throughput, L1 and L2 cache size misses are measured by running SPLASH-2 Benchmarks on ARM and ALPHA Processors. In this work we calculate cache misses, memory bandwidth and system bus throughput by running SPLASH2 benchmarks on gem5 Full System Mode. Our results show that L1 cache misses decrease as L1 cache size is varied from 16KB to 64KB. L1 cache misses are independent of L2 cache size after the program data resides in L2 cache. The memory bandwidth and system bus throughput decreases as L1 and L2 cache size increases.

negative: You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.

negative: Gadge me if you can: secure and efficient ad-hoc instruction-level randomization for x86 and ARM Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called Xifer, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that Xifer performs efficiently at load- and during run-time (1.2% overhead).

negative: RIC: Relaxed Inclusion Caches for mitigating LLC side-channel attacks Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed, preventing the attacker from replacing the victim's data from the local core caches thus protecting critical data from leakage. RIC improves performance (by about 10%) and retains snoop filtering capabilities of inclusive cache hierarchies, while requiring only minimal changes to the cache.

negative: A New Prime and Probe Cache Side-Channel Attack for Cloud Computing Cloud computing is considered one of the most dominant paradigms in the Information Technology (IT) industry nowadays. It supports multi-tenancy to fulfil future increasing demands for accessing and using resources provisioned over the Internet. However, multi-tenancy in cloud computing has unique vulnerabilities such as clients' co-residence and virtual machine physical co-residency. Physical co-residency of virtual machines can facilitate attackers with an ability to interfere with another virtual machine running on the same physical machine due to an insufficient logical isolation. In the worst scenario, attackers can exfiltrate sensitive information of victims on the same physical machine by using hardware side-channels. There are various types of side-channels attacks, which are classified according to hardware medium they target and exploit, for instance, cache side-channel attacks. CPU caches are one of the most hardware devices targeted by adversaries because it has high-rate interactions and sharing between processes. This paper presents a new Prime and Probe cache side-channel attack, which can prime physical addresses. These addresses are translated form virtual addresses used by a virtual machine. Then, time is measured to access these addresses and it will be varied according to where the data is located. If it is in the CPU cache, the time will be less than in the main memory. The attack was implemented in a server machine comparable to cloud environment servers. The results show that the attack needs less effort and time than other types and is easy to be launched.

negative: Cryptanalysis of DES Implemented on Computers with Cache This paper presents the results of applying an attack against the Data Encryption Standard (DES) implemented in some applications, using side-channel information based on CPU delay as proposed in [11]. This cryptanalysis technique uses side-channel information on encryption processing to select and collect effective plaintexts for cryptanalysis, and infers the information on the expanded key from the collected plaintexts. On applying this attack, we found that the cipher can be broken with 2 known plaintexts and 2 calculations at a success rate > 90%, using a personal computer with 600-MHz Pentium III. We discuss the feasibility of cache attack on ciphers that need many S-box look-ups, through reviewing the results of our experimental attacks on the block ciphers excluding DES, such as AES.

negative:
Return-oriented programming without returns We show that on both the x86 and ARM architectures it is possible to mount return-oriented programming attacks without using return instructions. Our attacks instead make use of certain instruction sequences that behave like a return, which occur with sufficient frequency in large libraries on (x86) Linux and (ARM) Android to allow creation of Turing-complete gadget sets.
 Because they do not make use of return instructions, our new attacks have negative implications for several recently proposed classes of defense against return-oriented programming: those that detect the too-frequent use of returns in the instruction stream; those that detect violations of the last-in, first-out invariant normally maintained for the return-address stack; and those that modify compilers to produce code that avoids the return instruction.