nils-herrmann/clean_scirepeval huggingface dataset:
Persuasive Decision Support: Improving Reliance on Decision Support Systems

The primary role of a decision support system is to guide and support a decision maker. As reliance on a decision support system is largely discretionary the persuasiveness of the system becomes critically important. In this paper characteristics thought to affect systems persuasiveness are examined. This paper asserts that the target and source of a decision support message, along with the design of the message itself, act to influence the persuasiveness of the decision support provided. Using a purpose built experimental platform with seventy subjects the research finds that the persuasiveness of a decision support message is varied by the perceived difficulty of the task being undertaken, and the perceived usefulness of the decision support provided. The type of decisional guidance provided also affects persuasiveness of the system; in particular, providing suggestive decisional guidance is shown to significantly improve system persuasiveness. The implications of these findings relate to the appropriate design of decision support systems, and the contexts within which a decision support system can be expected to persuade decision makers to reply on the support provided.
Data mining approaches for network intrusion detection: from dimensionality reduction to misuse and anomaly detection

This paper describes the use of data mining techniques to solve three important issues in network intrusion detection problems. The first goal is finding the best dimensionality reduction algorithm which reduces the computational cost while still maintains the accuracy. We implement both feature extraction (Principal Component Analysis and Independent Component Analysis) and feature selection (Genetic Algorithm and Particle Swarm Optimization) techniques for dimensionality reduction. The second goal is finding the best algorithm for misuse detection system to detect known intrusion. We implement four basic machine learning algorithms (Naïve Bayes, Decision Tree, Nearest Neighbour and Rule Induction) and then apply ensemble algorithms such as bagging, boosting and stacking to improve the performance of these four basic algorithms. The third goal is finding the best clustering algorithms to detect network anomalies which contains unknown intrusion. We analyze and compare the performance of four unsupervised clustering algorithms (k-Means, k-Medoids, EM clustering and distance-based outlier detection) in terms of accuracy and false positives. Our experiment shows that the Nearest Neighbour (NN) classifier when implemented with Particle Swarm Optimization (PSO) as an attribute selection algorithm achieved the best performance, which is 99.71% accuracy and 0.27% false positive. The misuse detection technique achieves a very good performance with more than 99% accuracy when detecting known intrusion but it fails to accurately detect data set with a large number of unknown intrusions where the highest accuracy is only 63.97%. In contrast, the anomaly detection approach shows promising results where the distance-based outlier detection method outperforms the other three clustering algorithms with the accuracy of 80.15%, followed by EM clustering (78.06%), k-Medoids (76.71%), improved k-Means (65.40%) and k-Means (57.81%).
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous stateof-the-art methods, demonstrating the effectiveness of the attention mechanism.
Mumyo - evaluating and exploring the myo armband for musical interaction

The MYO armband from Thalmic Labs is a complete and wireless motion and muscle sensing platform. This paper evaluates the armband’s sensors and its potential for NIME applications. This is followed by a presentation of the prototype instrument MuMYO. We conclude that, despite some shortcomings, the armband has potential of becoming a new “standard” controller in the NIME community.
Performance Analysis of Private Blockchain Platforms in Varying Workloads

This paper conducts a performance analysis of two popular private blockchain platforms, Hyperledger Fabric and Ethereum (private deployment), to assess the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is said to be the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often- cited concern of current blockchain technology. Therefore, the goals of this preliminary performance analysis are twofold. First, a methodology for evaluating a blockchain platform is developed. Second, the analysis results are presented to inform practitioners in making decisions regarding adoption of blockchain technology in their IT systems. The experimental results, based on varying number of transactions, show that Hyperledger Fabric consistently outperforms Ethereum across all evaluation metrics which are execution time, latency and throughput. Additionally, both platforms are still not competitive with current database systems in term of performances in high workload scenarios.
A Lyapunov Analysis of Momentum Methods in Optimization

Momentum methods play a central role in optimization. Several momentum methods are provably optimal, and all use a technique called estimate sequences to analyze their convergence properties. The technique of estimate sequences has long been considered difficult to understand, leading many researchers to generate alternative, “more intuitive” methods and analyses. In this paper we show there is an equivalence between the technique of estimate sequences and a family of Lyapunov functions in both continuous and discrete time. This framework allows us to develop a simple and unified analysis of many existing momentum algorithms, introduce several new algorithms, and most importantly, strengthen the connection between algorithms and continuous-time dynamical systems.
Dr . Wheat : A Web-based Expert System for Diagnosis of Diseases and Pests in Pakistani Wheat

This paper presents a web-based expert system for wheat crop in Pakistan. Wheat is one of the major grain crops in Pakistan. It is cultivated in vast areas of Punjab followed by Sindh and ranked first as a cereal crop in the country. Our rule-based expert system covers two main classes of problems namely diseases and pests, normally encountered in wheat crop. The expert system is intended to help the farmers, researchers and students and provides an efficient and goal-oriented approach for solving common problems of wheat. The system gives results that are correct and consistent.
A Novel Drive Method for High-Speed Brushless DC Motor Operating in a Wide Range

In this paper, a novel drive method, which is different from the traditional motor drive techniques, for high-speed brushless DC (BLDC) motor is proposed and verified by a series of experiments. It is well known that the BLDC motor can be driven by either pulse-width modulation (PWM) techniques with a constant dc-link voltage or pulse-amplitude modulation (PAM) techniques with an adjustable dc-link voltage. However, to our best knowledge, there is rare study providing a proper drive method for a high-speed BLDC motor with a large power over a wide speed range. Therefore, the detailed theoretical analysis comparison of the PWM control and the PAM control for high-speed BLDC motor is first given. Then, a conclusion that the PAM control is superior to the PWM control at high speed is obtained because of decreasing the commutation delay and high-frequency harmonic wave. Meanwhile, a new high-speed BLDC motor drive method based on the hybrid approach combining PWM and PAM is proposed. Finally, the feasibility and effectiveness of the performance analysis comparison and the new drive method are verified by several experiments.
Selections: Internet Voting with Over-the-Shoulder Coercion-Resistance

We present Selections, a new cryptographic voting protocol that is end-to-end verifiable and suitable for Internet voting. After a one-time in-person registration, voters can cast ballots in an arbitrary number of elections. We say a system provides over-the-shoulder coercionresistance if a voter can undetectably avoid complying with an adversary that is present during the vote casting process. Our system is the first in the literature to offer this property without the voter having to anticipate coercion and precompute values. Instead, a voter can employ a panic password. We prove that Selections is coercion-resistant against a non-adaptive adversary. 1 Introductory Remarks From a security perspective, the use of electronic voting machines in elections around the world continues to be concerning. In principle, many security issues can be allayed with cryptography. While cryptographic voting has not seen wide deployment, refined systems like Prêt à Voter [11,28] and Scantegrity II [9] are representative of what is theoretically possible, and have even seen some use in governmental elections [7]. Today, a share of the skepticism over electronic elections is being apportioned to Internet voting.1 Many nation-states are considering, piloting or using Internet voting in elections. In addition to the challenges of verifiability and ballot secrecy present in any voting system, Internet voting adds two additional constraints: • Untrusted platforms: voters should be able to reliably cast secret ballots, even when their devices may leak information or do not function correctly. • Unsupervised voting: coercers or vote buyers should not be able to exert undue influence over voters despite the open environment of Internet voting. As with electronic voting, cryptography can assist in addressing these issues. The study of cryptographic Internet voting is not as mature. Most of the literature concentrates on only one of the two problems (see related work in Section 1.2). In this paper, we are concerned with the unsupervised voting problem. Informally, a system that solves it is said to be coercion-resistant.  One noted cryptographer, Ronald Rivest, infamously opined that “best practices for Internet voting are like best practices for drunk driving” . G. Danezis (Ed.): FC 2011, LNCS 7035,  2012.
A modelling and reasoning framework for social networks policies

Policy languages (such as privacy and rights) have had little impact on the wider community. Now that Social Networks have taken off, the need to revisit Policy languages and realign them towards Social Networks requirements has become more apparent. One such language is explored as to its applicability to the Social Networks masses. We also argue that policy languages alone are not sufficient and thus they should be paired with reasoning mechanisms to provide precise and unambiguous execution models of the policies. To this end we propose a computationally oriented model to represent, reason with and execute policies for Social Networks.
Divergence-free smoothed particle hydrodynamics

In this paper we introduce an efficient and stable implicit SPH method for the physically-based simulation of incompressible fluids. In the area of computer graphics the most efficient SPH approaches focus solely on the correction of the density error to prevent volume compression. However, the continuity equation for incompressible flow also demands a divergence-free velocity field which is neglected by most methods. Although a few methods consider velocity divergence, they are either slow or have a perceivable density fluctuation.
 Our novel method uses an efficient combination of two pressure solvers which enforce low volume compression (below 0.01%) and a divergence-free velocity field. This can be seen as enforcing incompressibility both on position level and velocity level. The first part is essential for realistic physical behavior while the divergence-free state increases the stability significantly and reduces the number of solver iterations. Moreover, it allows larger time steps which yields a considerable performance gain since particle neighborhoods have to be updated less frequently. Therefore, our divergence-free SPH (DFSPH) approach is significantly faster and more stable than current state-of-the-art SPH methods for incompressible fluids. We demonstrate this in simulations with millions of fast moving particles.
Using semi-supervised machine learning to address the Big Data problem in DNS networks

The problem of Big Data in cyber security (i.e., too much network data to analyze) compounds itself every day. Our approach is based on a fundamental characteristic of Big Data: an overwhelming majority of the network traffic in a traditionally secured enterprise (i.e., using defense-in-depth) is non-malicious. Therefore, one way of eliminating the Big Data problem in cyber security is to ignore the overwhelming majority of an enterprise's non-malicious network traffic and focus only on the smaller amounts of suspicious or malicious network traffic. Our approach uses simple clustering along with a dataset enriched with known malicious domains (i.e., anchors) to accurately and quickly filter out the non-suspicious network traffic. Our algorithm has demonstrated the predictive ability to accurately filter out approximately 97% (depending on the algorithm used) of the non-malicious data in millions of Domain Name Service (DNS) queries in minutes and identify the small percentage of unseen suspicious network traffic. We demonstrate that the resulting network traffic can be analyzed with traditional reputation systems, blacklists, or in-house threat tracking sources (we used virustotal.com) to identify harmful domains that are being accessed from within the enterprise network. Specifically, our results show that the method can reduce a dataset of 400k query-answer domains (with complete malicious domain ground truth) down to only 3% containing 99% of all malicious domains. Further, we demonstrate that this capability scales to 10 million query-answer pairs, which it can reduce by 97% in less than an hour.
A Graph Degeneracy-based Approach to Keyword Extraction

We operate a change of paradigm and hypothesize that keywords are more likely to be found among influential nodes of a graph-ofwords rather than among its nodes high on eigenvector-related centrality measures. To test this hypothesis, we introduce unsupervised techniques that capitalize on graph degeneracy. Our methods strongly and significantly outperform all baselines on two datasets (short and medium size documents), and reach best performance on the third one (long documents).
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).
Full-text based context-rich heterogeneous network mining approach for citation recommendation

Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.
Adaptive Algorithms for Acoustic Echo Cancellation: A Review

One of the major issues involved with communication is acoustic echo, which is actually a delayed version of sound reflected back to the source of sound hampering communication. Cancellation of these involve the use of acoustic echo cancellers involving adaptive filters governed by adaptive algorithms. This paper presents a review of some of the algorithms of acoustic echo cancellation covering their merits and demerits. Various algorithms like LMS, NLMS, FLMS, LLMS, RLS, AFA, LMF have been discussed.  Adaptive Filter, Acoustic Echo, LMS, NLMS, FX-LMS, AAF, LLMS, RLS.
Support vector machine with adaptive parameters in financial time series forecasting

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.
Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems

To evaluate a novel kind of textile electrodes based on woven fabrics treated with PEDOT:PSS, through an easy fabrication process, testing these electrodes for biopotential recordings.  Fabrication is based on raw fabric soaking in PEDOT:PSS using a second dopant, squeezing and annealing. The electrodes have been tested on human volunteers, in terms of both skin contact impedance and quality of the ECG signals recorded at rest and during physical activity (power spectral density, baseline wandering, QRS detectability, and broadband noise).  The electrodes are able to operate in both wet and dry conditions. Dry electrodes are more prone to noise artifacts, especially during physical exercise and mainly due to the unstable contact between the electrode and the skin. Wet (saline) electrodes present a stable and reproducible behavior, which is comparable or better than that of traditional disposable gelled Ag/AgCl electrodes.  The achieved results reveal the capability of this kind of electrodes to work without the electrolyte, providing a valuable interface with the skin, due to mixed electronic and ionic conductivity of PEDOT:PSS. These electrodes can be effectively used for acquiring ECG signals.  Textile electrodes based on PEDOT:PSS represent an important milestone in wearable monitoring, as they present an easy and reproducible fabrication process, very good performance in wet and dry (at rest) conditions and a superior level of comfort with respect to textile electrodes proposed so far. This paves the way to their integration into smart garments.
Surgery of the superficial musculoaponeurotic system: principles of release, vectors, and fixation.

The SMAS was described more than 25 years ago, yet its full potential in face-lift surgery has become appreciated only more recently. A reappraisal of the various aspects of SMAS surgery is now appropriate. These include aspects of its release from the deep fascia, the several considerations underlying the vectors of flap redistribution, and the rationale underlying the methods of flap fixation. These are unique, compared with the traditional considerations in subcutaneous face lifts and en bloc subperiosteal lifts.
Involuntary paretic wrist/finger flexion forces and EMG increase with shoulder abduction load in individuals with chronic stroke

Clinical observations of the flexion synergy in individuals with chronic hemiparetic stroke describe coupling of shoulder, elbow, wrist, and finger joints. Yet, experimental quantification of the synergy within a shoulder abduction (SABD) loading paradigm has focused only on shoulder and elbow joints. The paretic wrist and fingers have typically been studied in isolation. Therefore, this study quantified involuntary behavior of paretic wrist and fingers during concurrent activation of shoulder and elbow.



Eight individuals with chronic moderate-to-severe hemiparesis and four controls participated. Isometric wrist/finger and thumb flexion forces and wrist/finger flexor and extensor electromyograms (EMG) were measured at two positions when lifting the arm: in front of the torso and at maximal reaching distance. The task was completed in the ACT(3D) robotic device with six SABD loads by paretic, non-paretic, and control limbs.



Considerable forces and EMG were generated during lifting of the paretic arm only, and they progressively increased with SABD load. Additionally, the forces were greater at the maximal reach position than at the position front of the torso.



Flexion of paretic wrist and fingers is involuntarily coupled with certain shoulder and elbow movements.



Activation of the proximal upper limb must be considered when seeking to understand, rehabilitate, or develop devices to assist the paretic hand.