nils-herrmann/clean_scirepeval huggingface dataset:
Using Network Metrics in Soccer: A Macro-Analysis

The aim of this study was to propose a set of network methods to measure the specific properties of a team. These metrics were organised at macro-analysis levels. The interactions between teammates were collected and then processed following the analysis levels herein announced. Overall, 577 offensive plays were analysed from five matches. The network density showed an ambiguous relationship among the team, mainly during the 2nd half. The mean values of density for all matches were 0.48 in the 1st half, 0.32 in the 2nd half and 0.34 for the whole match. The heterogeneity coefficient for the overall matches rounded to 0.47 and it was also observed that this increased in all matches in the 2nd half. The centralisation values showed that there was no 'star topology'. The results suggest that each node (i.e., each player) had nearly the same connectivity, mainly in the 1st half. Nevertheless, the values increased in the 2nd half, showing a decreasing participation of all players at the same level. Briefly, these metrics showed that it is possible to identify how players connect with each other and the kind and strength of the connections between them. In summary, it may be concluded that network metrics can be a powerful tool to help coaches understand team's specific properties and support decision-making to improve the sports training process based on match analysis.
Learning latent block structure in weighted networks

Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network’s large-scale structure, which can facilitate its scientific interpretation and the prediction of unknown or future interactions. Popular approaches, including the stochastic block model, assume edges are unweighted, which limits their utility by discarding potentially useful information. We introduce the weighted stochastic block model (WSBM), which generalizes the stochastic block model to networks with edge weights drawn from any exponential family distribution. This model learns from both the presence and weight of edges, allowing it to discover structure that would otherwise be hidden when weights are discarded or thresholded. We describe a Bayesian variational algorithm for efficiently approximating this model’s posterior distribution over latent block structures. We then evaluate the WSBM’s performance on both edge-existence and edge-weight prediction tasks for a set of real-world weighted networks. In all cases, the WSBM performs as well or better than the best alternatives on these tasks. community detection, weighted relational data, block models, exponential family, variational Bayes.
Integrated Learning of Dialog Strategies and Semantic Parsing

Natural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of direct and indirect supervision. We present an approach to integrate the learning of both a dialog strategy using reinforcement learning, and a semantic parser for robust natural language understanding, using only natural dialog interaction for supervision. Experimental results on a simulated task of robot instruction demonstrate that joint learning of both components improves dialog performance over learning either of these components alone.
DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments

Dynamic Proof of Storage (PoS) is a useful cryptographic primitive that enables a user to check the integrity of outsourced files and to efficiently update the files in a cloud server. Although researchers have proposed many dynamic PoS schemes in singleuser environments, the problem in multi-user environments has not been investigated sufficiently. A practical multi-user cloud storage system needs the secure client-side cross-user deduplication technique, which allows a user to skip the uploading process and obtain the ownership of the files immediately, when other owners of the same files have uploaded them to the cloud server. To the best of our knowledge, none of the existing dynamic PoSs can support this technique. In this paper, we introduce the concept of deduplicatable dynamic proof of storage and propose an efficient construction called DeyPoS, to achieve dynamic PoS and secure cross-user deduplication, simultaneously. Considering the challenges of structure diversity and private tag generation, we exploit a novel tool called Homomorphic Authenticated Tree (HAT). We prove the security of our construction, and the theoretical analysis and experimental results show that our construction is efficient in practice.
Challenges to the Hypothesis of Extended

* In recent decades, an intriguing view of human cognition has garnered increasing support. According to this view, which I will call 'the hypothesis of extended cognition' ('HEC', hereafter), human cognitive processing literally extends into the environment surrounding the organism, and human cognitive states literally comprise—as wholes do their proper parts— elements in that environment; in consequence, while the skin and scalp may encase the human organism, they do not delimit the thinking subject. 1 The hypothesis of extended cognition should provoke our critical interest. Acceptance of HEC would alter our approach to research and theorizing in cognitive science and, it would seem, significantly change our conception of persons. Thus, if HEC faces substantive difficulties, these should be brought to light; this paper is meant to do just that, exposing some of the problems HEC must overcome if it is to stand among leading views of the nature of human cognition. The essay unfolds as follows: The first section consists of preliminary remarks, mostly about the scope and content of HEC as I will construe it.  II and III clarify HEC by situating it with respect to related theses one finds in the literature—the hypothesis of embedded cognition Association. I would like to express my appreciation to members of all three audiences for their useful feedback (especially William Lycan at the Mountain-Plains and David Chalmers at the APA), as well as to my conference commentators, Robert Welshon and Tadeusz Zawidzki. I also benefited from discussing extended cognition with 2 and content-externalism. The remaining sections develop a series of objections to HEC and the arguments that have been offered in its support. The first objection appeals to common sense: HEC implies highly counterintuitive attributions of belief. Of course, HEC-theorists can take, and have taken, a naturalistic stand. They claim that HEC need not be responsive to commonsense objections, for HEC is being offered as a theoretical postulate of cognitive science; whether we should accept HEC depends, they say, on the value of the empirical work premised upon it. Thus, I consider a series of arguments meant to show that HEC is a promising causal-explanatory hypothesis, concluding that these arguments fail and that, ultimately, HEC appears to be of marginal interest as part of a philosophical foundation for cognitive science. If the cases canvassed here are any indication, adopting HEC results in a significant loss of explanatory power or, at the …
Computing Matrix Functions ∗

The need to evaluate a function f (A) ∈ C n×n of a matrix A ∈ C n×n arises in a wide and growing number of applications, ranging from the numerical solution of differential equations to measures of the complexity of networks. We give a survey of numerical methods for evaluating matrix functions, along with a brief treatment of the underlying theory and a description of two recent applications. The survey is organized by classes of methods, which are broadly those based on similarity transformations, those employing approximation by polynomial or rational functions, and matrix iterations. Computation of the Fréchet derivative, which is important for condition number estimation, is also treated, along with the problem of computing f (A)b without computing f (A). A summary of available software completes the survey.
Mison: A Fast JSON Parser for Data Analytics

The growing popularity of the JSON format has fueled increased interest in loading and processing JSON data within analytical data processing systems. However, in many applications, JSON parsing dominates performance and cost. In this paper, we present a new JSON parser called Mison that is particularly tailored to this class of applications, by pushing down both projection and filter operators of analytical queries into the parser. To achieve these features, we propose to deviate from the traditional approach of building parsers using finite state machines (FSMs). Instead, we follow a two-level approach that enables the parser to jump directly to the correct position of a queried field without having to perform expensive tokenizing steps to find the field. At the upper level, Mison speculatively predicts the logical locations of queried fields based on previously seen patterns in a dataset. At the lower level, Mison builds structural indices on JSON data to map logical locations to physical locations. Unlike all existing FSM-based parsers, building structural indices converts control flow into data flow, thereby largely eliminating inherently unpredictable branches in the program and exploiting the parallelism available in modern processors. We experimentally evaluate Mison using representative real-world JSON datasets and the TPC-H benchmark, and show that Mison produces significant performance benefits over the best existing JSON parsers; in some cases, the performance improvement is over one order of magnitude.
What Will 5G Be?

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
POSTER: Mining with Proof-of-Probability in Blockchain

As interest in cryptocurrency has increased, problems have arisen with Proof-of-Work (PoW) and Proof-of-Stake (PoS) methods, the most representative methods of acquiring cryptocurrency in a blockchain. The PoW method is uneconomical and the PoS method can be easily monopolized by a few people. To cope with this issue, this paper introduces a Proof-of-Probability (PoP) method. The PoP is a method where each node sorts the encrypted actual hash as well as a number of fake hash, and then the first node to decrypt actual hash creates block. In addition, a wait time is used when decrypting one hash and then decrypting the next hash for restricting the excessive computing power competition. In addition, the centralization by validaters with many stakes can be avoided in the proposed PoP method.
High-Performance Pattern-Matching for Intrusion Detection

New generations of network intrusion detection systems create the need for advanced pattern-matching engines. This paper presents a novel scheme for pattern-matching, called BFPM, that exploits a hardware-based programmable statemachine technology to achieve deterministic processing rates that are independent of input and pattern characteristics on the order of 10 Gb/s for FPGA and at least 20 Gb/s for ASIC implementations. BFPM supports dynamic updates and is one of the most storage-efficient schemes in the industry, supporting two thousand patterns extracted from Snort with a total of 32 K characters in only 128 KB of memory.
Learning Foreign Language through an Interactive Multimedia Program: An Experimental Study on the Effects of the Relevance Component of the ARCS Model

This experimental study investigated effects of intrinsic motivation and embedded relevance enhancement within a computer-based interactive multimedia (CBIM) lesson for English as a foreign language (EFL) learners. Subjects, categorized as having a higher or lower level of intrinsic motivation, were randomly assigned to learn concepts related to criticism using a CBIM program featuring English language text, videos, and exercises either with or without enhanced relevance components. Two dependent variables, comprehension, as measured by a posttest, and perceptions of motivation, as measured by the Modified Instructional Material Motivation Survey (MIMMS), were assessed after students completed the CBIM program. Two-way ANOVA was used to analyze the collected data. The findings indicated that (a) the use of relevance enhancement strategies facilitated students’ language learning regardless of learners’ level of intrinsic motivation, (b) more highly intrinsically motivated students performed better regardless of the specific treatments they received, (c) the effects of the two variables were additive; intrinsically motivated students who learned from the program with embedded instructional strategies performed the best overall, and (d) there was no significant interaction between the two variables.
A Puppet Interface for Retrieval of Motion Capture Data

Intuitive and efficient retrieval of motion capture data is essential for effective use of motion capture databases. In this paper, we describe a system that allows the user to retrieve a particular sequence by performing an approximation of the motion with an instrumented puppet. This interface is intuitive because both adults and children have experience playacting with puppets and toys to express particular behaviors or to tell stories with style and emotion. The puppet has 17 degrees of freedom and can therefore represent a variety of motions. We develop a novel similarity metric between puppet and human motion by computing the reconstruction errors of the puppet motion in the latent space of the human motion and those of the human motion in the latent space of the puppet motion. This metric works even for relatively large databases. We conducted a user study of the system and subjects could find the desired motion with reasonable accuracy from a database consisting of everyday, exercise, and acrobatic behaviors.
DASS : Digital Advertising System Simulation

We describe a Digital Advertising System Simulation (DASS) for modeling advertising and its impact on user behavior. DASS is both flexible and general, and can be applied to research on a wide range of topics, such as digital attribution, ad fatigue, campaign optimization, and marketing mix modeling. This paper introduces the basic DASS simulation framework and illustrates its application to digital attribution. We show that common position-based attribution models fail to capture the true causal effects of advertising across several simple scenarios. These results lay a groundwork for the evaluation of more complex attribution models, and the development of improved models.
Zero Duality Gap in Optimal Power Flow Problem

The optimal power flow (OPF) problem is nonconvex and generally hard to solve. In this paper, we propose a semidefinite programming (SDP) optimization, which is the dual of an equivalent form of the OPF problem. A global optimum solution to the OPF problem can be retrieved from a solution of this convex dual problem whenever the duality gap is zero. A necessary and sufficient condition is provided in this paper to guarantee the existence of no duality gap for the OPF problem. This condition is satisfied by the standard IEEE benchmark systems with 14, 30, 57, 118, and 300 buses as well as several randomly generated systems. Since this condition is hard to study, a sufficient zero-duality-gap condition is also derived. This sufficient condition holds for IEEE systems after small resistance (10-5 per unit) is added to every transformer that originally assumes zero resistance. We investigate this sufficient condition and justify that it holds widely in practice. The main underlying reason for the successful convexification of the OPF problem can be traced back to the modeling of transformers and transmission lines as well as the non-negativity of physical quantities such as resistance and inductance.
vNMF: Distributed fault detection using clustering approach for network function virtualization

Network function virtualization introduces additional complexity for network management through the use of virtualization environments. The amount of managed data and the operational complexity increases, which makes service assurance and failure recovery harder to realize. In response to this challenge, the paper proposes a distributed management function, called virtualized network management function (vNMF), to detect failures related to virtualized services. vNMF detects the failures by monitoring physical-layer statistics that are processed with a self-organizing map algorithm. Experimental results show that memory leaks and network congestion failures can be successfully detected and that and the accuracy of failure detection can be significantly improved compared to common k-means clustering.
Visually guided grasping produces fMRI activation in dorsal but not ventral stream brain areas

Although both reaching and grasping require transporting the hand to the object location, only grasping also requires processing of object shape, size and orientation to preshape the hand. Behavioural and neuropsychological evidence suggests that the object processing required for grasping relies on different neural substrates from those mediating object recognition. Specifically, whereas object recognition is believed to rely on structures in the ventral (occipitotemporal) stream, object grasping appears to rely on structures in the dorsal (occipitoparietal) stream. We used functional magnetic resonance imaging (fMRI) to determine whether grasping (compared to reaching) produced activation in dorsal areas, ventral areas, or both. We found greater activity for grasping than reaching in several regions, including anterior intraparietal (AIP) cortex. We also performed a standard object perception localizer (comparing intact vs. scrambled 2D object images) in the same subjects to identify the lateral occipital complex (LOC), a ventral stream area believed to play a critical role in object recognition. Although LOC was activated by the objects presented on both grasping and reaching trials, there was no greater activity for grasping compared to reaching. These results suggest that dorsal areas, including AIP, but not ventral areas such as LOC, play a fundamental role in computing object properties during grasping.
PayLess: A low cost network monitoring framework for Software Defined Networks

Software Defined Networking promises to simplify network management tasks by separating the control plane (a central controller) from the data plane (switches). OpenFlow has emerged as the de facto standard for communication between the controller and switches. Apart from providing flow control and communication interfaces, OpenFlow provides a flow level statistics collection mechanism from the data plane. It exposes a high level interface for per flow and aggregate statistics collection. Network applications can use this high level interface to monitor network status without being concerned about the low level details. In order to keep the switch design simple, this statistics collection mechanism is implemented as a pull-based service, i.e. network applications and in turn the controller has to periodically query the switches about flow statistics. The frequency of polling the switches determines monitoring accuracy and network overhead. In this paper, we focus on this trade-off between monitoring accuracy, timeliness and network overhead. We propose PayLess - a monitoring framework for SDN. PayLess provides a flexible RESTful API for flow statistics collection at different aggregation levels. It uses an adaptive statistics collection algorithm that delivers highly accurate information in real-time without incurring significant network overhead. We utilize the Floodlight controller's API to implement the proposed monitoring framework. The effectiveness of our solution is demonstrated through emulations in Mininet.
Multi-Label Classification Using Conditional Dependency Networks

In this paper, we tackle the challenges of multilabel classification by developing a general conditional dependency network model. The proposed model is a cyclic directed graphical model, which provides an intuitive representation for the dependencies among multiple label variables, and a well integrated framework for efficient model training using binary classifiers and label predictions using Gibbs sampling inference. Our experiments show the proposed conditional model can effectively exploit the label dependency to improve multi-label classification performance.
Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of co-occurrences between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the document as follows. If the probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of bias of a distribution is measured by the χ2-measure. Our algorithm shows comparable performance to tfidf without using a corpus.
Being Accurate Is Not Enough: New Metrics for Disk Failure Prediction

Traditionally, disk failure prediction accuracy is used to evaluate disk failure prediction model. However, accuracy may not reflect their practical usage (protecting against failures, rather than only predicting failures) in cloud storage systems. In this paper, we propose two new metrics for disk failure prediction models: migration rate, which measures how much at-risk data is protected as a result of correct failure predictions, and mismigration rate, which measures how much data is migrated needlessly as a result of false failure predictions. To demonstrate their effectiveness, we compare disk failure prediction methods: (a) a classification tree (CT) model vs. a state-of-the-art recurrent neural network (RNN) model, and (b) a proposed residual life prediction model based on gradient boosted regression trees (GBRTs) vs. RNN. While prediction accuracy experiments favor the RNN model, migration rate experiments can favor the CT and GBRT models (depending on transfer rates). We conclude that prediction accuracy can be a misleading metric. Moreover, the proposed GBRT model offers a practical improvement in disk failure prediction in real-world data centers.