nils-herrmann/clean_scirepeval huggingface dataset:
Improving network connection locality on multicore systems

Incoming and outgoing processing for a given TCP connection often execute on different cores: an incoming packet is typically processed on the core that receives the interrupt, while outgoing data processing occurs on the core running the relevant user code. As a result, accesses to read/write connection state (such as TCP control blocks) often involve cache invalidations and data movement between cores' caches. These can take hundreds of processor cycles, enough to significantly reduce performance.
 We present a new design, called Affinity-Accept, that causes all processing for a given TCP connection to occur on the same core. Affinity-Accept arranges for the network interface to determine the core on which application processing for each new connection occurs, in a lightweight way; it adjusts the card's choices only in response to imbalances in CPU scheduling. Measurements show that for the Apache web server serving static files on a 48-core AMD system, Affinity-Accept reduces time spent in the TCP stack by 30% and improves overall throughput by 24%.
Learning Analytics with Excel Pivot Tables

Different actors like teachers, course designers and content providers need to gain more information about the way the resources provided with Moodle are used by the students so they can adjust and adapt their offer better. In this contribution we show that Excel Pivot Tables can be used to conduct a flexible analytical processing of usage data and gain valuable information. An advantage of Excel Pivot Tables is that they can be mastered by persons with good IT-skills but not necessarily computer scientists.
A survey of mathematical methods for indoor localization

This document provides a survey of the mathematical methods currently used for position estimation in indoor local positioning systems (LPS), particularly those based on radiofrequency signals. The techniques are grouped into four categories: geometry-based methods, minimization of the cost function, fingerprinting, and Bayesian techniques. Comments on the applicability, requirements, and immunity to nonline-of-sight (NLOS) propagation of the signals of each method are provided.
Evaluating and analyzing the performance of RPL in contiki

To meet the development of Internet of Things (IoT), IETF has proposed IPv6 standards working under stringent low-power and low-cost constraints. However, the behavior and performance of the proposed standards have not been fully understood, especially the RPL routing protocol lying at the heart the protocol stack. In this work, we make an in-depth study on a popular implementation of the RPL (routing protocol for low power and lossy network) to provide insights and guidelines for the adoption of these standards. Specifically, we use the Contiki operating system and COOJA simulator to evaluate the behavior of the ContikiRPL implementation. We analyze the performance for different networking settings. Different from previous studies, our work is the first effort spanning across the whole life cycle of wireless sensor networks, including both the network construction process and the functioning stage. The metrics evaluated include signaling overhead, latency, energy consumption and so on, which are vital to the overall performance of a wireless sensor network. Furthermore, based on our observations, we provide a few suggestions for RPL implemented WSN. This study can also serve as a basis for future enhancement on the proposed standards.
Schema clustering and retrieval for multi-domain pay-as-you-go data integration systems

A data integration system offers a single interface to multiple structured data sources. Many application contexts (e.g., searching structured data on the web) involve the integration of large numbers of structured data sources. At web scale, it is impractical to use manual or semi-automatic data integration methods, so a pay-as-you-go approach is more appropriate. A pay-as-you-go approach entails using a fully automatic approximate data integration technique to provide an initial data integration system (i.e., an initial mediated schema, and initial mappings from source schemas to the mediated schema), and then refining the system as it gets used. Previous research has investigated automatic approximate data integration techniques, but all existing techniques require the schemas being integrated to belong to the same conceptual domain. At web scale, it is impractical to classify schemas into domains manually or semi-automatically, which limits the applicability of these techniques. In this paper, we present an approach for clustering schemas into domains without any human intervention and based only on the names of attributes in the schemas. Our clustering approach deals with uncertainty in assigning schemas to domains using a probabilistic model. We also propose a query classifier that determines, for a given a keyword query, the most relevant domains to this query. We experimentally demonstrate the effectiveness of our schema clustering and query classification techniques.
Twenty-five pitfalls in the analysis of diffusion MRI data.

Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI.
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.
User Simulation in Dialogue Systems Using Inverse Reinforcement Learning

Spoken Dialogue Systems (SDS) are man-machine interfaces which use natural language as the medium of interaction. Dialogue corpora collection for the purpose of training and evaluating dialogue systems is an expensive process. User simulators aim at simulating human users in order to generate synthetic data. Existing methods for user simulation mainly focus on generating data with the same statistical consistency as in some reference dialogue corpus. This paper outlines a novel approach for user simulation based on Inverse Reinforcement Learning (IRL). The task of building the user simulator is perceived as a task of imitation learning.
Concentrated winding segmented rotor switched reluctance machine (SRM) using three-phase standard inverters

Two new topologies of three-phase segmented rotor switched reluctance machine (SRM) that enables the use of standard voltage source inverters (VSIs) for its operation are presented. The topologies has shorter end-turn length, axial length compared to SRM topologies that use three-phase inverters; compared to the conventional SRM (CSRM), these new topologies has the advantage of shorter flux paths that results in lower core losses. FEA based optimization have been performed for a given design specification. The new concentrated winding segmented SRMs demonstrate competitive performance with three-phase standard inverters compared to CSRM.
Emergent Language in a Multi-Modal, Multi-Step Referential Game

Inspired by previous work on emergent language in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal language significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.
Constructing a reassigning credit scoring model

Credit scoring model development became a very important issue as the credit industry has many competitions and bad debt problems. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. In order to solve the classification and decrease the Type I error of credit scoring model, this paper presents a reassigning credit scoring model (RCSM) involving two stages. The classification stage is constructing an ANN-based credit scoring model, which classifies applicants with accepted (good) or rejected (bad) credits. The reassign stage is trying to reduce the Type I error by reassigning the rejected good credit applicants to the conditional accepted class by using the CBR-based classification technique. To demonstrate the effectiveness of proposed model, RCSM is performed on a credit card dataset obtained from UCI repository. As the results indicated, the proposed model not only proved more accurate credit scoring than other four common used approaches, but also contributes to increase business revenue by decreasing the Type I and Type II error of scoring system.
Novelty based Ranking of Human Answers for Community Questions

Questions and their corresponding answers within a community based question answering (CQA) site are frequently presented as top search results forWeb search queries and viewed by millions of searchers daily. The number of answers for CQA questions ranges from a handful to dozens, and a searcher would be typically interested in the different suggestions presented in various answers for a question. Yet, especially when many answers are provided, the viewer may not want to sift through all answers but to read only the top ones. Prior work on answer ranking in CQA considered the qualitative notion of each answer separately, mainly whether it should be marked as best answer. We propose to promote CQA answers not only by their relevance to the question but also by the diversification and novelty qualities they hold compared to other answers. Specifically, we aim at ranking answers by the amount of new aspects they introduce with respect to higher ranked answers (novelty), on top of their relevance estimation. This approach is common in Web search and information retrieval, yet it was not addressed within the CQA settings before, which is quite different from classic document retrieval. We propose a novel answer ranking algorithm that borrows ideas from aspect ranking and multi-document summarization, but adapts them to our scenario. Answers are ranked in a greedy manner, taking into account their relevance to the question as well as their novelty compared to higher ranked answers and their coverage of important aspects. An experiment over a collection of Health questions, using a manually annotated gold-standard dataset, shows that considering novelty for answer ranking improves the quality of the ranked answer list.
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

We describe MITRE’s submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then finetuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.
Are All Training Examples Created Equal? An Empirical Study

Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images in four datasets of varying complexity. We find that in some cases, a small subsample is indeed sufficient for training. For other datasets, however, the relative differences in importance are negligible. These results have important implications for active learning on deep networks. Additionally, our analysis method can be used as a general tool to better understand diversity of training examples in datasets.
Packet Leashes : A Defense against Wormhole Attacks in Wireless Ad Hoc Networks

As mobile ad hoc network applications are deployed, security emerges as a central requirement. In this paper, we introduce the wormhole attack, a severe attack in ad hoc networks that is particularly challenging to defend against. The wormhole attack is possible even if the attacker has not compromised any hosts, and even if all communication provides authenticity and confidentiality. In the wormhole attack, an attacker records packets (or bits) at one location in the network, tunnels them to another location, and retransmits them there into the network. The wormhole attack can form a serious threat in wireless networks, especially against many ad hoc network routing protocols and location-based wireless security systems. For example, most existing ad hoc network routing protocols, without some mechanism to defend against the wormhole attack, would be unable to find routes longer than one or two hops, severely disrupting communication. We present a new, general mechanism, called packet leashes, for detecting and thus defending against wormhole attacks, and we present a specific protocol, called TIK, that implements leashes.
Click chain model in web search

Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents, which can be leveraged in numerous applications in search businesses. Due to the huge volume of click data, scalability is a must.
 We present the click chain model (CCM), which is based on a solid, Bayesian framework. It is both scalable and incremental, perfectly meeting the computational challenges imposed by the voluminous click logs that constantly grow. We conduct an extensive experimental study on a data set containing 8.8 million query sessions obtained in July 2008 from a commercial search engine. CCM consistently outperforms two state-of-the-art competitors in a number of metrics, with over 9.7% better log-likelihood, over 6.2% better click perplexity and much more robust (up to 30%) prediction of the first and the last clicked position.
Roles of Information Security Awareness and Perceived Fairness in Information Security Policy Compliance

Drawing on the Theory of Planned Behavior (TPB), this research investigates two factors that drive an employee to comply with requirements of the information security policy (ISP) of her organization with regards to protecting information and technology resources: an employee’s information security awareness (ISA) and her perceived fairness of the requirements of the ISP. Our results, which is based on the PLS analysis of data collected from 464 participants, show that ISA and perceived fairness positively affect attitude, and in turn attitude positively affects intention to comply. ISA also has an indirect impact on attitude since it positively influences perceived fairness. As organizations strive to get their employees to follow their information security rules and regulations, our study sheds light on the role of an employee’s ISA and procedural fairness with regards to security rules and regulations in the workplace.
A new I slotted compact microstrip antenna for L1 & L2 bands

A new high gain dual-band circularly polarized with a small frequency ratio is proposed for Global Positioning System (GPS) applications covering L1 and L2 frequencies of 1.575GHz and 1.227GHz. The antenna is consists of I slotted compact microstrip antenna with two truncated corners square patches that overlapped without an air-gap. The overall antenna size is 0.315λ<inf>0</inf> × 0.309λ<inf>0</inf> × 0.016λ<inf>0</inf> at 1.227GHz and 0.302λ<inf>0</inf> × 0.302λ<inf>0</inf> × 0.016λ<inf>0</inf> at 1.575GHz (where λ<inf>0</inf> corresponding to center frequency). The design achieved an impedance bandwidth of 20 MHz from 1.22 GHz to 1.24 GHz for the lower band, while upper band covers 97.88 MHz (from 1.562 to 1.66 GHz) with maximum gain of 7.27dBi and 6.87dBi in the lower and upper frequency band respectively. The bandwidth (VSWR ≤ 2) is 1.57% (1.222 GHz to 1.2414 GHz) for L2 and 6.27% (1.5617 GHz to 1.6626 GHz) for L1. The details of the antenna design are shown, and the results with low-profile characteristics make the antenna suitable for GPS applications.
Parameter-measurement and control of the synchronous reluctance machine including cross saturation

This paper presents a method for the measurement of the inductances Ld and Lq of the synchronous reluctance machine in all operating points with consideration of saturation and cross coupling. The measured inductances are used to calculate the currents necessary for optimum torque development in base speed and field weakening range. The implementation of these characteristics in a rotor-oriented control scheme and practical results are demonstrated. Additionally the measured characteristics are used for the development of a new parameter-based sensorless control scheme.
Anomaly detection in dynamic networks: a survey

Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time. In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state-of-the-art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two-stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two-tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories—community, compression, decomposition, distance, and probabilistic model based—we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches.
Analysis of lightning protection with transmission line arrester using ATP/EMTP: Case of an HV 220kV double circuit line

Over the last few decades, the electric utilities have seen a very significant increase in the application of metal oxide surge arresters on transmission lines in an effort to reduce lightning initiated flashovers, maintain high power quality and to avoid damages and disturbances especially in areas with high soil resistivity and lightning ground flash density. For economical insulation coordination in transmission and substation equipment, it is necessary to predict accurately the lightning surge overvoltages that occur on an electric power system.
PV cell modeling on single-diode equivalent circuit

PV cell model is necessary both for software and hardware simulators in analyzing and testing the performance of PV generation systems. To get the characteristic non-linear I-V Curve of a PV cell, 5 characteristic parameters Rs, Rsh, I0 and vt should be extracted from the three remarkable operation points of the manufacture's datasheets, Isc, Voc, IMPP and VMPP. This paper analyzes conventional three representative PV modeling algorithms by comparison, and proposes a novel PV modeling algorithm which is fast, accurate and applicable to all kinds of PV cells such as Cr-Si type and thin-film type PV cells. Proposed theory is verified by simulations for various PV cell types such as Cr-Si type and thin-film type PV cells.