nils-herrmann/clean_scirepeval huggingface dataset:
A Simple and Efficient Replay Attack Prevention Scheme for LoRaWAN

In recent years, LPWAN technology, designed to realize low-power and long distance communication, has attracted much attention. Among several LPWAN technologies, Long Range (LoRa) is one of the most competitive physical layer protocol. Long Range Wide Area Network (LoRaWAN) is an upper layer protocol used with LoRa, and it provides several security functions including random number-based replay attack prevention system. According to recent studies, current replay attack prevention of LoRaWAN can mislead benign messages into replay attacks. To resolve this problem, several new replay attack prevention schemes have been proposed. However, existing schemes have limitations such as not being compatible with the existing packet structure or not considering an exceptional situation such as device reset. Therefore, in this paper, we propose a new LoRaWAN replay attack prevention scheme that resolves these problems. Our scheme follows the existing packet structure and is designed to cope with exceptional situations such as device reset. As a result of calculations, in our scheme, the probability that a normal message is mistaken for a replay attack is 60-89% lower than the current LoRaWAN. Real-world experiments also support these results.
An overview of semantic search evaluation initiatives

Recent work on searching the Semantic Web has yielded a wide range of approaches with respect to the underlying search mechanisms, results management and presentation, and style of input. Each approach impacts upon the quality of the information retrieved and the user’s experience of the search process. However, despite the wealth of experience accumulated from evaluating Information Retrieval (IR) systems, the evaluation of Semantic Web search systems has largely been developed in isolation from mainstream IR evaluation with a far less unified approach to the design of evaluation activities. This has led to slow progress and low interest when compared to other established evaluation series, such as TREC for IR or OAEI for Ontology Matching. In this paper, we review existing approaches to IR evaluation and analyse evaluation activities for Semantic Web search systems. Through a discussion of these, we identify their weaknesses and highlight the future need for a more comprehensive evaluation framework that addresses current limitations.
Automatic Chemical Design with Molecular Graph Variational Autoencoders

Traditional, more biochemically motivated approaches to chemical design and drug discovery are notoriously complex and costly processes. The space of all synthesizable molecules is far too large to exhaustively search any meaningful subset for interesting novel drug and molecule proposals, and the lack of any particularly informative and manageable structure to this search space makes the very task of defining interesting subsets a difficult problem in itself. Recent years have seen the proposal and rapid development of alternative, machine learning-based methods for vastly simplifying the search problem specified in chemical design and drug discovery. In this work, I build upon this existing literature exploring the possibility of automatic chemical design and propose a novel generative model for producing a diverse set of valid new molecules. The proposed molecular graph variational autoencoder model achieves comparable performance across standard metrics to the state-of-the-art in this problem area and is capable of regularly generating valid molecule proposals similar but distinctly different from known sets of interesting molecules. While an interesting result in terms of addressing one of the core issues with machine learning-based approaches to automatic chemical design, further research in this direction should aim to optimize for more biochemically motivated objectives and be more informed by the ultimate utility of such models to the biochemical field.
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP

The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP domain. In particular, large text corpora are necessary to build high-quality word embeddings, yet often large corpora that are suitably representative of the target clinical data are unavailable. This forces a choice between building embeddings from small clinical corpora and less representative, larger corpora. This paper explores this trade-off, as well as intermediate compromise solutions. Two standard clinical NLP tasks (the i2b2 2010 concept and assertion tasks) are evaluated with commonly used deep learning models (recurrent neural networks and convolutional neural networks) using a set of six corpora ranging from the target i2b2 data to large open-domain datasets. While combinations of corpora are generally found to work best, the single-best corpus is generally task-dependent.
A Three-Layer Architecture for E-Contract Enforcement in an E-Service Environment

In an e-service environment, contracts are important for attaining business process interoperability and enforcing their proper enactment.  This paper presents an architecture for e-contract enforcement with three layers, viz., document layer, business layer, and implementation layer. In the document layer, contracts are composed of different types of clauses. In the business layer, e-contract enforcement activities are defined through the realization of contract clauses as business rules in event-condition-action (ECA) form. In the implementation layer, cross-organizational e-contract enforcement interfaces are implemented with contemporary Enterprise Java Bean and Web services. We present a methodology for the engineering of econtracts enforcement from a high-level document-view down to the implementation layer based on this architecture, using a supply-chain example. As a result, econtracts can be seamlessly defined and enforced. Conceptual models of various layers are given in the Unified Modeling Language (UML).
High performance MIMO antenna for 5G wearable devices

In this paper, a dual-band 2×2 MIMO antenna for wearable device applications is presented to support the 5G radio frequency allocations at 3.3–3.6 and 4.5–5 GHz in China. The single antenna unit is composed of a monopole mode and a folded loop mode with a single feed to cover these two bands. Two identical antennas positioned at top and bottom sides of a smart watch sized PCB (40×40mm2) are used to demonstrate the MIMO performance. The simulation results from xFDTD show that these MIMO antennas can achieve over 85 % efficiency, more than 17 dB isolation and 0.02 correlation coefficient at both bands.
A Generic Approach for Extracting Aspects and Opinions of Arabic Reviews

New opportunities and challenges arise with the growing availability of online Arabic reviews. Sentiment analysis of these reviews can help the beneficiary by summarizing the opinions of others about entities or events. Also, for opinions to be comprehensive, analysis should be provided for each aspect or feature of the entity. In this paper, we propose a generic approach that extracts the entity aspects and their attitudes for reviews written in modern standard Arabic. The proposed approach does not exploit predefined sets of features, nor domain ontology hierarchy. Instead we add sentiment tags on the patterns and roots of an Arabic lexicon and used these tags to extract the opinion bearing words and their polarities. The proposed system is evaluated on the entity-level using two datasets of 500 movie reviews with accuracy 96% and 1000 restaurant reviews with accuracy 86.7%. Then the system is evaluated on the aspect-level using 500 Arabic reviews in different domains (Novels, Products, Movies, Football game events and Hotels). It extracted aspects, at 80.8% recall and 77.5% precision with respect to the aspects defined by domain experts.
Simulating Big Data Clusters for System Planning, Evaluation, and Optimization

With the fast development of big data technologies, IT spending on computer clusters is increasing rapidly as well. In order to minimize the cost, architects must plan big data clusters with careful evaluation of various design choices. Current capacity planning methods are mostly trial-and-error or high level estimation based. These approaches, however, are far from efficient, especially with the increasing hardware diversity and software stack complexity. In this paper, we present CSMethod, a novel cluster simulation methodology, to facilitate efficient cluster capacity planning, performance evaluation and optimization, before system provisioning. With our proposed methodology, software stacks are simulated by an abstract yet high fidelity model, Hardware activities derived from software operations are dynamically mapped onto architecture models for processors, memory, storage and networking devices. This hardware/software hybrid methodology allows low overhead, fast and accurate cluster simulation that can be easily carried out on a standard client platform (desktop or laptop). Our experimental results with six popular Hadoop workloads demonstrate that CSMethod can achieve an average error rate of less than six percent, across various software parameters and cluster hardware configurations. We also illustrate the application of the proposed methodology with two real-world use cases: Video-streaming service system planning and Terasort cluster optimization. All our experiments are run on a commodity laptop with execution speeds faster than native executions on a multi-node high-end cluster.
A UML profile for multidimensional modeling in data warehouses

The Multidimensional (MD) modeling, which is the foundation of data warehouses (DWs), MD databases, and On-Line Analytical Processing (OLAP) applications, is based on several properties different from those in traditional database modeling. In the past few years, there have been some proposals, providing their own formal and graphical notations, for representing the main MD properties at the conceptual level. However, unfortunately none of them has been accepted as a standard for conceptual MD modeling. In this paper, we present an extension of the Unified Modeling Language (UML) using a UML profile. This profile is defined by a set of stereotypes, constraints and tagged values to elegantly represent main MD properties at the conceptual level. We make use of the Object Constraint Language (OCL) to specify the constraints attached to the defined stereotypes, thereby avoiding an arbitrary use of these stereotypes. We have based our proposal in UML for two main reasons: (i) UML is a well known standard modeling language known by most database designers, thereby designers can avoid learning a new notation, and (ii) UML can be easily extended so that it can be tailored for a specific domain with concrete peculiarities such as the multidimensional modeling for data warehouses. Moreover, our proposal is Model Driven Architecture (MDA) compliant and we use the Query View Transformation (QVT) approach for an automatic generation of the implementation in a target platform. Throughout the paper, we will describe how to easily accomplish the MD modeling of DWs at the conceptual level. Finally, we show how to use our extension in Rational Rose for MD modeling.
Neurobiological Foundations of Acupuncture: The Relevance and Future Prospect Based on Neuroimaging Evidence

Acupuncture is currently gaining popularity as an important modality of alternative and complementary medicine in the western world. Modern neuroimaging techniques such as functional magnetic resonance imaging, positron emission tomography, and magnetoencephalography open a window into the neurobiological foundations of acupuncture. In this review, we have summarized evidence derived from neuroimaging studies and tried to elucidate both neurophysiological correlates and key experimental factors involving acupuncture. Converging evidence focusing on acute effects of acupuncture has revealed significant modulatory activities at widespread cerebrocerebellar brain regions. Given the delayed effect of acupuncture, block-designed analysis may produce bias, and acupuncture shared a common feature that identified voxels that coded the temporal dimension for which multiple levels of their dynamic activities in concert cause the processing of acupuncture. Expectation in acupuncture treatment has a physiological effect on the brain network, which may be heterogeneous from acupuncture mechanism. "Deqi" response, bearing clinical relevance and association with distinct nerve fibers, has the specific neurophysiology foundation reflected by neural responses to acupuncture stimuli. The type of sham treatment chosen is dependent on the research question asked and the type of acupuncture treatment to be tested. Due to the complexities of the therapeutic mechanisms of acupuncture, using multiple controls is an optimal choice.
Compare Stereo Patches Using Atrous Convolutional Neural Networks

In this work, we address the task of dense stereo matching with Convolutional Neural Networks (CNNs). Particularly, we focus on improving matching cost computation by better aggregating contextual information. Towards this goal, we advocate to use atrous convolution, a powerful tool for dense prediction task that allows us to control the resolution at which feature responses are computed within CNNs and to enlarge the receptive field of the network without losing image resolution and requiring learning extra parameters. Aiming to improve the performance of atrous convolution, we propose different frameworks for further boosting performance. We evaluate our models on KITTI 2015 benchmark, the result shows that we achieve on-par performance with fewer post-processing methods applied.
Disclosure Recipients' Social Reactions to Victims' Disclosures of Intimate Partner Violence.

Prior research indicates that victims of intimate partner violence (IPV) are most likely to disclose their victimization experiences to an informal support (e.g., friend, family), and that IPV disclosures are often met with both positive (e.g., empathic support) and negative (e.g., victim blame) reactions. However, research on social reactions to disclosure largely has neglected the perspectives of disclosure recipients. Guided by the attribution framework, the current study extends prior research by assessing factors (i.e., situation-specific, individual, relational, attributional, and emotional response) related to positive and negative reactions from the perspective of disclosure recipients ( N = 743 college students). Linear regression analyses indicated that positive social reactions were related to the victim being a woman, greater frequency of IPV victimization by the victim, greater frequency of IPV victimization by the disclosure recipient, less accepting attitudes toward IPV, a closer relationship with the victim, a less close relationship with the perpetrator, lower perceptions of victim responsibility, more empathy for the victim, and more emotional distress experienced by the disclosure recipient during the disclosure. Negative social reactions were associated with more accepting attitudes toward IPV, greater frequency of IPV victimization by the disclosure recipient, a less close relationship with the victim, higher perceptions of victim responsibility, and more emotional distress experienced by the disclosure recipient. Results suggest that programs to improve responses to victim disclosure should focus on decreasing IPV-supportive attitudes, increasing empathy, and assisting disclosure recipients in managing difficult emotional responses effectively.
Digit Ratio ( 2 D : 4 D ) and Cattell ’ s Personality Traits

The ratio between second and fourth finger (2D:4D) is sexually dimorphic; it is lower in men than in women. Studies using broad personality domains yielded correlations of 2D:4D with neuroticism, extraversion or agreeableness, but the obtained results have been inconsistent. We correlated 2D:4D of 184 women and 101 men with their scores in Cattell’s 16 Personality Factor (16PF) Questionnaire. We found women with a higher (more ‘feminine’) right hand 2D:4D to score lower in emotional stability and social boldness and higher in privateness. Mediator analysis showed emotional stability to be probably primarily correlated with 2D:4D and to act as a mediator between 2D:4D and social boldness. Privateness appears to be mediated by an even more complex path. We discuss the usefulness of primary-level personality questionnaires and mediator analyses in the investigation of psycho-morphological associations.
Machine-assisted Cyber Threat Analysis Using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together.
Safe Adaptive Importance Sampling

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants—using importance values defined by the complete gradient information which changes during optimization— enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed—in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.
FKRP (826C>A) frequently causes limb-girdle muscular dystrophy in German patients.

M C Walter,  A Schroers, H Schreiber, C O Hanemann, U Knirsch, A Rosenbohm, A Huebner, N Barisic, R Horvath, S Komoly, P Reilich, W Müller-Felber, D Pongratz, J S Müller, E A Auerswald, H Lochmüller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Is chess the drosophila of artificial intelligence? A social history of an algorithm.

Since the mid 1960s, researchers in computer science have famously referred to chess as the 'drosophila' of artificial intelligence (AI). What they seem to mean by this is that chess, like the common fruit fly, is an accessible, familiar, and relatively simple experimental technology that nonetheless can be used productively to produce valid knowledge about other, more complex systems. But for historians of science and technology, the analogy between chess and drosophila assumes a larger significance. As Robert Kohler has ably described, the decision to adopt drosophila as the organism of choice for genetics research had far-reaching implications for the development of 20th century biology. In a similar manner, the decision to focus on chess as the measure of both human and computer intelligence had important and unintended consequences for AL research. This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s. More broadly, it attempts to open up the virtual black box of computer software--and of computer games in particular--to the scrutiny of historical and sociological analysis.
Output ripple reduction of an automotive multi-phase bi-directional dc-dc converter

Bi-directional dc-dc converters for automotive applications typically are limited to generate only a small voltage ripple, especially when a ultra-capacitor with a limited ripple current capability is interfaced by the converter. Thus, to minimize the ripple quantities and the converter volume at the same time, interleaved multi-phase dc-dc converters are utilized. However, tolerances of the buck+boost inductors of the interleaved phases generate sub-harmonics in the ripple spectrum that are counterproductive to the advantage of ripple reduction. A method to cancel the fundamental frequency of the voltage ripple is proposed that is applicable to a converter consisting of three or more phases. It is shown that even for a low phase count the overall ripple amplitude is greatly reduced, only by adjustment of the phase shift angles. Experimental results carried out with a three-phase interleaved bi-directional dc-dc converter proof the concept functionality.
A Current Sharing Method Utilizing Single Balancing Transformer for a Multiphase LLC Resonant Converter With Integrated Magnetics

Integrated magnetics is applied to replace the three-discrete transformers by a single core transformer in a three-phase LLC resonant converter. The magnetic circuit of the integrated transformer is analyzed to derive coupling factors between the phases; these coupling factors are intentionally minimized to realize the magnetic behavior of the three-discrete transformers, with the benefit of eliminating the dead space between them. However, in a practical design, the transformer parameters in a multiphase LLC resonant converter are never exactly identical among the phases, leading to unbalanced current sharing between the paralleled modules. In this regard, a current balancing method is proposed in this paper. The proposed method can improve the current sharing between the paralleled phases relying on a single balancing transformer, and its theory is based on Ampere’s law, by forcing the sum of the three resonant currents to zero. Theoretically, if an ideal balancing transformer has been utilized, it would impose the same effect of connecting the integrated transformer in a solid star connection. However, as the core permeability of the balancing transformer is finite, the unbalanced current cannot be completely suppressed. Nonetheless, utilizing a single balancing transformer has an advantage over the star connection, as it keeps the interleaving structure simple which allows for traditional phase-shedding techniques, and it can be a solution for the other multiphase topologies where realizing a star connection is not feasible. Along with the theoretical discussion, simulation and experimental results are also presented to evaluate the proposed method considering various sources of the unbalance such as a mismatch in: 1) resonant and magnetizing inductances; 2) resonant capacitors; 3) transistor on-resistances of the MOSFETS; and 4) propagation delay of the gate drivers.