nils-herrmann/clean_scirepeval huggingface dataset:
Online Learning for Latent Dirichlet Allocation

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing

We introduce novel dynamic oracles for training two of the most accurate known shiftreduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fullysupervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.
CloseGraph: mining closed frequent graph patterns

Recent research on pattern discovery has progressed form mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging due to the presence of an exponential number of frequent subgraphs. Instead of mining all the subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g that has the same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods. Our performance study shows that CloseGraph not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns.
Functional MRI analysis of body and body part representations in the extrastriate and fusiform body areas.

This study examined the contributions of two previously identified brain regions-the extrastriate and fusiform body areas (EBA and FBA)-to the visual representation of the human form. Specifically we measured in these two areas the magnitude of fMRI response as a function of the amount of the human figure that is visible in the image, in the range from a single finger to the entire body. A second experiment determined the selectivity of these regions for body and body part stimuli relative to closely matched control images. We found a gradual increase in the selectivity of the EBA as a function of the amount of body shown. In contrast, the FBA shows a steplike function, with no significant selectivity for individual fingers or hands. In a third experiment we demonstrate that the response pattern seen in EBA does not extend to adjacent motion-selective human midtemporal area. We propose an interpretation of these results by analogy to nearby face-selective regions occipital face area (OFA) and fusiform face area (FFA). Specifically, we hypothesize that the EBA analyzes bodies at the level of parts (as has been proposed for faces in the OFA), whereas FBA (by analogy to FFA) may have a role in processing the configuration of body parts into wholes.
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics

The recent adaptation of deep neural networkbased methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. Nonetheless, progress on task-to-task transfer remains limited. In pursuit of efficient and robust generalization, we introduce the Schema Network, an objectoriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems.
Differentially private random decision forests using smooth sensitivity

We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries. To do so, we build an ensemble of random decision trees that avoids querying the private data except to find the majority class label in the leaf nodes. Rather than using a count query to return the class counts like the current state-ofthe-art, we use the Exponential Mechanism to only output the class label itself. This drastically reduces the sensitivity of the query – often by several orders of magnitude – which in turn reduces the amount of noise that must be added to preserve privacy. Our improved sensitivity is achieved by using “smooth sensitivity”, which takes into account the specific data used in the query rather than assuming the worst-case scenario. We also extend work done on the optimal depth of random decision trees to handle continuous features, not just discrete features. This, along with several other improvements, allows us to create a differentially private decision forest with substantially higher predictive power than the current state-of-the-art.
View-dependent displacement mapping

Significant visual effects arise from surface mesostructure, such as fine-scale shadowing, occlusion and silhouettes. To efficiently render its detailed appearance, we introduce a technique called view-dependent displacement mapping (VDM) that models surface displacements along the viewing direction. Unlike traditional displacement mapping, VDM allows for efficient rendering of self-shadows, occlusions and silhouettes without increasing the complexity of the underlying surface mesh. VDM is based on per-pixel processing, and with hardware acceleration it can render mesostructure with rich visual appearance in real time.
Unsupervised Control Through Non-Parametric Discriminative Rewards

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptuallyspecified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains – Atari, the DeepMind Control Suite and DeepMind Lab.
The SSN ontology of the W3C semantic sensor network incubator group

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
Nature over nurture: temperament, personality, and life span development.

Temperaments are often regarded as biologically based psychological tendencies with intrinsic paths of development. It is argued that this definition applies to the personality traits of the five-factor model. Evidence for the endogenous nature of traits is summarized from studies of behavior genetics, parent-child relations, personality structure, animal personality, and the longitudinal stability of individual differences. New evidence for intrinsic maturation is offered from analyses of NEO Five-Factor Inventory scores for men and women age 14 and over in German, British, Spanish, Czech, and Turkish samples (N = 5,085). These data support strong conceptual links to child temperament despite modest empirical associations. The intrinsic maturation of personality is complemented by the culturally conditioned development of characteristic adaptations that express personality; interventions in human development are best addressed to these.
An exact algorithm for solving the vertex separator problem

Given G = (V,E) a connected undirected graph and a positive integer β(|V |), the vertex sperator problem is to find a partition of V into noempty three classes A, B, C such that there is no edge between A and B, max{|A|, |B|} ≤ β(|V |) and |C| is minimum. In this paper we consider the vertex separator problem from a polyhedral point of view. We introduce new classes of valid inequalities for the associated polyhedron. Using a natural lower bound for the optimal solution, we present successful computational experiments.
A power primer.

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
ArgumenText: Searching for Arguments in Heterogeneous Sources

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice

Electronic networks of practice are computer mediated discussion forums focused on problems of practice that enable individuals to exchange advice and ideas with others based on common interests. However, why individuals help strangers in these electronic networks is not well under stood: there is no immediate benefit to the contri 1V. Sambamurthy and Mani Subramani were the accepting senior editors for this paper. butor, and free-riders are able to acquire the same knowledge as everyone else. To understand this paradox, we apply theories of collective action to examine how individual motivations and social capital influence knowledge contribution in elect ronic networks. This study reports on the activities of one electronic network supporting a professional legal association. Using archival, network, survey, and content analysis data, we empirically test a model of knowledge contribution. We find that people contribute their knowledge when they per ceive that it enhances their professional repu tations, when they have the experience to share, and when they are structurally embedded in the network. Surprisingly, contributions occur without regard to expectations of reciprocity from others or high levels of commitment to the network.
Foundations and Trends Vision for Robotics

Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of different tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing vision based control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.
Reducing Uncertainty of Low-Sampling-Rate Trajectories

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.
Pohlig-Hellman Applied in Elliptic Curve Cryptography

The Pohlig-Hellman algorithm is an algorithm that solves the discrete logarithm problem. The algorithm simplifies the problem by solving the elliptic curve discrete logarithm problem (ECDLP) in the prime subgroups of the point 〈P〉. The difficulty of solving the ECDLP in its prime order subgroups is no harder than solving the ECDLP in 〈P〉 [1, 3]. In our paper we will present the Pohlig-Hellman algorithm and its applications. We will discuss its complexity and how to construct the elliptic curves in order to defend against the Pohlig-Hellman attack. At the very end we will briefly discuss how to choose parameters such that the effectiveness of other attacks is also minimized.
Hybrid analog-digital backscatter: A new approach for battery-free sensing

After comparing the properties of analog backscatter and digital backscatter, we propose that a combination of the two can provide a solution for high data rate battery free wireless sensing that is superior to either approach on its own. We present a hybrid analog-digital backscatter platform that uses digital backscatter for addressability and control but switches into analog backscatter mode for high data rate transmission of sensor data. Using hybrid backscatter, we report the first digitally addressable real-time battery free wireless microphone. We develop the hybrid backscatter platform by integrating an electret microphone and RF switch with a digital RFID platform (WISP). The hybrid WISP operates by default in digital mode, transmitting and receiving digital data using the EPC Gen 2 RFID protocol but switching into analog mode to backscatter audio sensor data when activated by Gen 2 READ command. The data is recovered using a USRP-based Software Defined RFID reader. We report an operating range of 7.4 meters for the analog backscatter microphone and 2.7 meters for hybrid microphone with 26.7 dBm output power USRP-based RFID reader.
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data – as commonly encountered in robotics applications – and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.
Flying capacitor multilevel inverters and DTC motor drive applications

In this paper, the requirements imposed by a direct torque control (DTC) strategy on multilevel inverters are analyzed. A control strategy is proposed in order to fulfill those requirements when a flying-capacitor multilevel inverter is used. Simulation and practical results will confirm the performance of the proposed strategy when using the multilevel inverter to control an induction motor by the DTC principle. Also, the advantages of using a multilevel inverter with a DTC strategy are shown by simulation results.
Automatic Population of Structured Knowledge Bases via Natural Language Processing

The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one still being the free-text document. This motivates the need for Intelligent Web-reading Agents: hypothetically, they would skim through disparate Web sources corpora and generate meaningful structured assertions to fuel Knowledge Bases (KBs). Ultimately, comprehensive KBs, like Wikidata and DBpedia, play a fundamental role to cope with the issue of information overload. On account of such vision, this thesis depicts a set of systems based on Natural Language Processing (NLP), which take as input unstructured or semi-structured information sources and produce machine-readable statements for a target KB. We implement four main research contributions: (1) a one-step methodology for crowdsourcing the Frame Semantics annotation; (2) a NLP technique implementing the above contribution to perform N-ary Relation Extraction from Wikipedia, thus enriching the target KB with properties; (3) a taxonomy learning strategy to produce an intuitive and exhaustive class hierarchy from the Wikipedia category graph, thus augmenting the target KB with classes; (4) a recommender system that leverages a KB network to yield atypical suggestions with detailed explanations, serving as a proof of work for realworld end users. The outcomes are incorporated into the Italian DBpedia chapter, can be queried through its public endpoint, and/or downloaded as standalone data dumps.
Keystroke analysis of free text

Keystroke dynamics can be useful to ascertain personal identity even after an authentication phase has been passed, provided that we are able to deal with the typing rhythms of free text, chosen and entered by users without any specific constraint. In this paper we present a method to compare typing samples of free text that can be used to verify personal identity. We have tested our technique with a wide set of experiments on 205 individuals, obtaining a False Alarm Rate of less than 5&percent; and an Impostor Pass Rate of less than 0.005&percent;. Different trade-offs are, however, possible. Our approach can rely on what is typed by people because of their normal job, and a few lines of text, even collected in different working sessions, are sufficient to reach a high level of accuracy, which improves proportionally to the amount of available information: As a consequence, we argue that our method can be useful in computer security as a complementary or alternative way to user authentication and as an aid to intrusion detection.
Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations

There has been a growing interest in examining the factors that support or hinder one’s knowledge sharing behavior in the virtual communities. However, still very few studies examined them from both personal and environmental perspectives. In order to explore the knowledge sharing behaviors within the virtual communities of professional societies, this study proposed a social cognitive theory (SCT)-based model that includes knowledge sharing self-efficacy and outcome expectations for personal influences, and multidimensional trusts for environmental influences. The proposed research model was then evaluated with structural equation modeling, and confirmatory factor analysis was also applied to test if the empirical data conform to the proposed model.
A Comparative Study on Multi-person Tracking Using Overlapping Cameras

We present a comparative study for tracking multiple persons using cameras with overlapping views. The evaluated methods consist of two batch mode trackers (Berclaz et al, 2011, Ben-Shitrit et al, 2011) and one recursive tracker (Liem and Gavrila, 2011), which integrate appearance cues and temporal information differently. We also added our own improved version of the recursive tracker. Furthermore, we investigate the effect of the type of background estimation (static vs. adaptive) on tracking performance. Experiments are performed on two novel and challenging multi-person surveillance data sets (indoor, outdoor), made public to facilitate benchmarking. We show that our adaptation of the recursive method outperforms the other stand-alone trackers.