nils-herrmann/clean_scirepeval huggingface dataset:
Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems

Shared-memory systems such as regular desktops now possess enough memory to store large data. However, the training process for data classification can still be slow if we do not fully utilize the power of multi-core CPUs. Many existing works proposed parallel machine learning algorithms by modifying serial ones, but convergence analysis may be complicated. Instead, we do not modify machine learning algorithms, but consider those that can take the advantage of parallel matrix operations. We particularly investigate the use of parallel sparse matrix-vector multiplications in a Newton method for large scale logistic regression. Various implementations from easy to sophisticated ones are analyzed and compared. Results indicate that under suitable settings excellent speedup can be achieved.
Energy Conserving Routing in Wireless Ad-hoc Networks

An ad-hoc network of wireless static nodes is considered as it arises in a rapidly deployed, sensor based, monitoring system. Information is generated in certain nodes and needs to reach a set of designated gateway nodes. Each node may adjust its power within a certain range that determines the set of possible one hop away neighbors. Traffic forwarding through multiple hops is employed when the intended destination is not within immediate reach. The nodes have limited initial amounts of energy that is consumed in different rates depending on the power level and the intended receiver. We propose algorithms to select the routes and the corresponding power levels such that the time until the batteries of the nodes drain-out is maximized. The algorithms are local and amenable to distributed implementation. When there is a single power level, the problem is reduced to a maximum flow problem with node capacities and the algorithms converge to the optimal solution. When there are multiple power levels then the achievable lifetime is close to the optimal (that is computed by linear programming) most of the time. It turns out that in order to maximize the lifetime, the traffic should be routed such that the energy consumption is balanced among the nodes in proportion to their energy reserves, instead of routing to minimize the absolute consumed power.  energy-sensitive routing, wireless ad-hoc networks, sensor networks
Travel time estimation and order batching in a 2-block warehouse

The order batching problem (OBP) is the problem of determining the number of orders to be picked together in one picking tour. Although various objectives may arise in practice, minimizing the average throughput time of a random order is a common concern. In this paper, we consider the OBP for a 2-block rectangular warehouse with the assumptions that orders arrive according to a Poisson process and the method used for routing the orderpickers is the well-known S-shape heuristic. We first elaborate on the first and second moment of the order-picker's travel time. Then we use these moments to estimate the average throughput time of a random order. This enables us to estimate the optimal picking batch size. Results from simulation show that the method provides a high accuracy level. Furthermore, the method is rather simple and can be easily applied in practice.
Andragogy and Self-Directed Learning : Pillars of Adult Learning Theory

The central question of how adults learn has occupied the attention of scholars and practitioners since the founding of adult education as a professional field of practice in the 1920s. Some eighty years later, we have no single answer, no one theory or model of adult learning that explains all that we know about adult learners, the various contexts where learning takes place, and the process of learning itself. What we do have is a mosaic of theories, models, sets of principles, and explanations that, combined, compose the knowledge base of adult learning. Two important pieces of that mosaic are andragogy and self-directed learning. Other chapters in this volume focus on some of the newer approaches to understanding learning; the purpose of this chapter is to revisit two of the foundational theories of adult learning with an eye to assessing their “staying power” as important components of our present-day understanding of adult learning.
Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond

In their early days, brain-computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.
Breaking the Barrier of Transactions: Mining Inter-Transaction Association Rules

Most of the previous studies on mining association rules are on mining intro-transaction associations, i.e., the associations among items within the same transaction, where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, etc. In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional intratransaction associations to inter-transaction associations. Mining inter-transaction associations poses more challenges on efficient processing than mining intra-transaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. In this study, we introduce the notion of inter-transaction association rule, define its measurements: support and confidence, and develop an efficient algorithm, FITI (an acronym for “First Intra Then Inter”), for mining inter-transaction associations. We compare FITI with EH-Apriori, the best algorithm in our previous proposal, and demonstrate a substantial performance gain of FITI over EH-
Spatio-Temporal Join on Apache Spark

Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Apache Spark is one such open-source framework that is enjoying widespread adoption. Within this data space, it is important to note that most of the observational data (i.e., data collected by sensors, either moving or stationary) has a temporal component, or timestamp. In order to perform advanced analytics and gain insights, the temporal component becomes equally important as the spatial and attribute components. In this paper, we detail several variants of a spatial join operation that addresses both spatial, temporal, and attribute-based joins. Our spatial join technique differs from other approaches in that it combines spatial, temporal, and attribute predicates in the join operator.
 In addition, our spatio-temporal join algorithm and implementation differs from others in that it runs in commercial off-the-shelf (COTS) application. The users of this functionality are assumed to be GIS analysts with little if any knowledge of the implementation details of spatio-temporal joins or distributed processing. They are comfortable using simple tools that do not provide the ability to tweak the configuration of the
Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture

This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.
Onboard Reconfigurable Battery Charger for Electric Vehicles With Traction-to-Auxiliary Mode

This paper proposes a single-phase reconfigurable battery charger for an electric vehicle (EV) that operates in three different modes: grid-to-vehicle (G2V) mode, in which the traction batteries are charged from the power grid; vehicle-to-grid (V2G) mode, in which the traction batteries deliver part of the stored energy back to the power grid; and traction-to-auxiliary (T2A) mode, in which the auxiliary battery is charged from the traction batteries. When connected to the power grid, the battery charger works with a sinusoidal current in the ac side, for both G2V and V2G modes, and regulates the reactive power. When the EV is disconnected from the power grid, the control algorithms are modified, and the full-bridge ac-dc bidirectional converter works as a full-bridge isolated dc-dc converter that is used to charge the auxiliary battery of the EV, avoiding the use of an additional charger to accomplish this task. To assess the behavior of the proposed reconfigurable battery charger under different operation scenarios, a 3.6-kW laboratory prototype has been developed, and experimental results are presented.
Computer System Intrusion Detection : A Survey

The abil ity to detect intruders in computer systems increases in importance as computers are increasingly integrated into the systems that we rely on for the correct functioning of society. This paper reviews the history of research in intrusion detection as performed in software in the context of operating systems for a single computer, a distributed system, or a network of computers. There are two basic approaches: anomaly detection and misuse detection. Both have been practiced since the 1980s. Both have naturally scaled to use in distributed systems and networks.
Sensor Study for Monitoring Varroa Mites on Honey Bees ( Apis mellifera )

Rapid growth of parasites like Varroa destructor is one of the main reasons for elevated mortality of bee colonies. Beekeepers have to perform time consuming manual sampling and treatment to avoid colony losses. This work proposes a camera system, which can be mounted at the entrance of a standard honey bee hive and it is part of a two year ongoing project. The goal is to find a camera setup for detecting bees infected with V. destructor mites when they are entering or leaving, giving real time estimations of the V. destructor population inside the hive. To check the feasibility of the project a prototype was designed, implemented and tested. Recommendations for the automatic detection of Varroa mites are discussed and presented.
Geolocation of RF Emitters by Many UAVs

This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.
TopicViz: interactive topic exploration in document collections

Existing methods for searching and exploring large document collections focus on surface-level matches to user queries, ignoring higher-level semantic structure. In this paper we show how topic modeling - a technique for identifying latent themes across a large collection of documents - can support semantic exploration. We present TopicViz: an interactive environment which combines traditional search and citation-graph exploration with a force-directed layout that links documents to the latent themes discovered by the topic model. We describe usage scenarios in which TopicViz supports rapid sensemaking on large document collections.
Narcissism and self-esteem : Different motivations for selfie posting behaviors

Over the past few years, social networking site usage has increased rapidly, in particular as concerns photo-sharing and self-portrait photographs (so-called selfies). In the academic literature, some studies have recently analyzed the psychological antecedents of selfie posting behavior to better understand its underlying mechanism. Generally, scholars consider the use of selfies as a means of online self-presentation. In the present research, the effects of two personality traits known to impact self-presentation (i.e. narcissism and self-esteem) are investigated, considering the mediation effects of four selfie posting motives: attention-seeking, communication, archiving, and entertainment. Results on 237 participants show that selfie posting motives, narcissism and self-esteem are differently linked to frequencies depending on the type of selfie posted online (whether own, group, or partner). Moreover, the path analysis model shows that, even if positively correlated between them, self-esteem and narcissism have different motives underlying the use of selfies. In particular, narcissistic people engage in selfie posting behaviors because of a search for the attention of others and the urge to escape from boredom. Contrariwise, these are the two motives for which people with high self-esteem do not get involved in selfie-posting behaviors.  Roberta Biolcati, Department of Education Studies, University of Bologna, Via Filippo Re 6, 40126, Bologna, Italy E-mail: r.biolcati@unibo.it  Jens F. Binder, Nottingham Trent University, UK Additional information is available at the end of the article
Exact finite-state machine identification from scenarios and temporal properties

Finite-state models, such as finite-state machines (FSMs), aid software engineering in many ways. They are often used in formal verification and also can serve as visual software models. The latter application is associated with the problems of software synthesis and automatic derivation of software models from specification. Smaller synthesized models are more general and are easier to comprehend, yet the problem of minimum FSM identification has received little attention in previous research. This paper presents four exact methods to tackle the problem of minimum FSM identification from a set of test scenarios and a temporal specification represented in linear temporal logic. The methods are implemented as an open-source tool. Three of them are based on translations of the FSM identification problem to SAT or QSAT problem instances. Accounting for temporal properties is done via counterexample prohibition. Counterexamples are either obtained from previously identified FSMs, or based on bounded model checking. The fourth method uses backtracking. The proposed methods are evaluated on several case studies and on a larger number of randomly generated instances of increasing complexity. The results show that the Iterative SAT-based method is the leader among the proposed methods. The methods are also compared with existing inexact approaches, i.e., the ones which do not necessarily identify the minimum FSM, and these comparisons show encouraging results.
New complexity results about Nash equilibria

We provide a single reduction that demonstrates that in normal-form games: (1) it is NP-complete to determine whether Nash equilibria with certain natural properties exist (these results are similar to those obtained by Gilboa and Zemel [Gilboa, I., Zemel, E., 1989. Nash and correlated equilibria: Some complexity considerations. Games ]), (2) more significantly, the problems of maximizing certain properties of a Nash equilibrium are inapproximable (unless P =NP), and (3) it is #P-hard to count the Nash equilibria. We also show that determining whether a pure-strategy Bayes–Nash equilibrium exists in a Bayesian game is NP-complete, and that determining whether a pure-strategy Nash equilibrium exists in a Markov (stochastic) game is PSPACE-hard even if the game is unobserved (and that this remains NP-hard if the game has finite length). All of our hardness results hold even if there are only two players and the game is symmetric.   This work appeared as an oral presentation at the Second World Congress of the Game Theory Society (GAMES-04), and a short, early version was also presented at the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03). The material in this paper is based upon work supported by the National Science Foundation under grants IIS0234694, IIS-0427858, IIS-0234695, and IIS-0121678, as well as two Sloan Fellowships and an IBM PhD Fellowship.  Corresponding author at: Duke University, Department of Computer Science and Department of Economics, Box 90129, LRSC, Durham, NC 27708, USA. E-mail addresses: conitzer@cs.duke.edu (V. Conitzer), sandholm@cs.cmu.edu (T. Sandholm). 0899-8256/$ – see front matter  (2008) 621–641
LSTM CCG Parsing

We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies. Instead, all dependencies are implicitly encoded in an LSTM supertagger that assigns CCG lexical categories. The parser significantly outperforms all previously published CCG results, supports efficient and optimal A∗ decoding, and benefits substantially from semisupervised tri-training. We give a detailed analysis, demonstrating that the parser can recover long-range dependencies with high accuracy and that the semi-supervised learning enables significant accuracy gains. By running the LSTM on a GPU, we are able to parse over 2600 sentences per second while improving state-of-the-art accuracy by 1.1 F1 in domain and up to 4.5 F1 out of domain.
Probabilistic models of ranking novel documents for faceted topic retrieval

Traditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval models need to capture dependencies between documents. Such tasks require alternative evaluation and optimization methods that operate on different types of relevance judgments. We define faceted topic retrieval as a particular novelty-driven task with the goal of finding a set of documents that cover the different facets of an information need. A faceted topic retrieval system must be able to cover as many facets as possible with the smallest number of documents. We introduce two novel models for faceted topic retrieval, one based on pruning a set of retrieved documents and one based on retrieving sets of documents through direct optimization of evaluation measures. We compare the performance of our models to MMR and the probabilistic model due to Zhai et al. on a set of 60 topics annotated with facets, showing that our models are competitive.
Mining Graphs for Understanding Time-Varying Volumetric Data

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.
Special Perforated Steel Plate Shear Walls with Reduced Beam Section Anchor Beams . I : Experimental Investigation

This paper presents results of an experimental investigation of specially detailed ductile perforated steel plate shear walls SPSWs designed to accommodate utility passage, and having anchor beams with reduced beam sections connections. Single-story, single-bay SPSW frames are subjected to quasi-static cyclic loading up to their maximum strength and displacement capacity. The tested specimens also had low yield strength steel infill panels. Two specimens make allowances for penetration of the panel by utilities. The first, having multiple holes specially laid out in the steel panel, also has the characteristic of reduced panel strength and stiffness compared to the corresponding SPSW having a solid panel. The second such specimen utilizes quarter-circle cutouts in the panel corners, which are reinforced to transfer the panel forces to the adjacent framing. A SPSW with solid panel is also tested for reference purposes. All specimens resisted an imposed input history of increasing displacements to a minimum drift of 3%. The perforated panel reduced the elastic stiffness and overall strength of the specimen by 15% as compared with the solid panel specimen.  2009 135:3 211 CE Database subject headings: Steel plates; Beams; Anchors; Experimentation; Shear walls.
Compression of human body sequences using graph Wavelet Filter Banks

The next step in immersive communication beyond video from a single camera is object-based free viewpoint video, which is the capture and compression of a dynamic object such that it can be reconstructed and viewed from an arbitrary viewpoint. The moving human body is a particularly useful subclass of dynamic object for object-based free viewpoint video relevant to both telepresence and entertainment. In this paper, we compress moving human body sequences by applying recently developed Graph Wavelet Filter Banks to time-varying geometry and color signals living on a mesh representation of the human body. This model-based approach significantly outperforms state-of-the-art coding of the human body represented as ordinary depth plus color video sequences.