nils-herrmann/clean_scirepeval huggingface dataset:
The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers

Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6,996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis.
Maximum likelihood thresholding based on population mixture models

-Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of the conditional distribution in the population mixture model under the assumption of normal distributions with a common variance. It is also shown that Kittler and Illingworth's thresholding, which minimizes a criterion related to the average classification error rate assuming normal distribution with different variances, is equivalent to the maximization of the likelihood of the joint distribution in the population mixture model. A multi-thresholding algorithm based on Dynamic Programming is also presented. Thresholding Maximum likelihood Mixture models Dynamic programming
A Better Method to Analyze Blockchain Consistency

The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat  required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which  could not consider; We analyze a family of delaying attacks first presented in , and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb  system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.
ENHANCING THE PREDICTIVE POWER OF CYBERSICKNESS DOSE VALUE ( CSDV ) TO INCLUDE EFFECTS OF FIELD-OF-VIEW AND BINOCULAR VIEWS

The paper reports two experiments conducted to investigate the effects of display’s field-of-view (FOV) and binocular views on motion sickness symptoms experienced after exposure to a virtual reality simulation. The results are then used to enhance the scope of prediction of a previously reported cybersickness dose value (CSDV). Sickness symptoms of participants, without binocular view were not significantly different (n=20, p>0.2, ANOVA) while increases of FOV from 24°x18° to 48°x36° significantly increased the nausea ratings (n=24, p<0.05, ANOVA). The data collected from the experiment on display’s FOV were added to the CSDV regression model.
A Security Threat Analysis for the Routing Protocol for Low-Power and Lossy Networks (RPLs)

This document presents a security threat analysis for the Routing Protocol for Low-power and lossy networks (RPL, ROLL). The development builds upon previous work on routing security and adapts the assessments to the issues and constraints specific to low-power and lossy networks. A systematic approach is used in defining and evaluating the security threats. Applicable countermeasures are application specific and are addressed in relevant applicability statements.
JSpIRIT: a flexible tool for the analysis of code smells

Code smells are a popular mechanism to identify structural design problems in software systems. Since it is generally not feasible to fix all the smells arising in the code, some of them are often postponed by developers to be resolved in the future. One reason for this decision is that the improvement of the code structure, to achieve modifability goals, requires extra effort from developers. Therefore, they might not always spend this additional effort, particularly when they are focused on delivering customer-visible features. This postponement of code smells are seen as a source of technical debt. Furthermore, not all the code smells may be urgent to fix in the context of the system's modifability and business goals. While there are a number of tools to detect smells, they do not allow developers to discover the most urgent smells according to their goals. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The tool is flexible to allow developer s to add new smell detection strategies and to prioritize smells, and groups of smells, based on the confguration of their manifold criteria. To illustrate this flexibility, we present an application example of our tool. The results suggest that our tool can be easily extended to be aligned with the developer's goals.
s t )-r technologies social responses to communication c Defining Virtual Reality : Dimensions Determining Telepresence

Virtual reality (VR) is typically defined in terms of technological hardware. This paper attempts to cast a new, variable-based definition of virtual reality that can be used to classify virtual reality in relation to other media. The defintion of virtual reality is based on concepts of “presence” and “telepresence,” which refer to the sense of being in an environment, generated by natural or mediated means, respectively. Two technological dimensions that contribute to telepresence, vividness and interactivity, are discussed. A variety of media are classified according to these dimensions. Suggestions are made for the application of the new definition of virtual reality within the field of communication research.
A formal ontological perspective on the behaviors and functions of technical artifacts

In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the Functional Representation approach. We begin our formalization by reserving the term ‘behavior’ of a technical artifact as ‘the specific way in which the artifact occurs in an event.’ This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, e.g., input and output behaviors of artifacts, and then show that these ontologically characterizedconcepts give a general framework in which Chandrasekaran and Josephson’s meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson’s two meanings of artifact functions, namely, device-centric and environmental-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms ‘behavior’ and ‘function’ in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.
IDS rainStorm: visualizing IDS alarms

The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness. CR Categories: C.2.0 [Computer-Communication Networks]: General—Security and Protection C.2.3  Network Operations—Network Monitoring H.5.2 [Information Systems]: Information Interfaces and Presentation—
Jumping robots: a biomimetic solution to locomotion across rough terrain.

This paper introduces jumping robots as a means to traverse rough terrain; such terrain can pose problems for traditional wheeled, tracked and legged designs. The diversity of jumping mechanisms found in nature is explored to support the theory that jumping is a desirable ability for a robot locomotion system to incorporate, and then the size-related constraints are determined from first principles. A series of existing jumping robots are presented and their performance summarized. The authors present two new biologically inspired jumping robots, Jollbot and Glumper, both of which incorporate additional locomotion techniques of rolling and gliding respectively. Jollbot consists of metal hoop springs forming a 300 mm diameter sphere, and when jumping it raises its centre of gravity by 0.22 m and clears a height of 0.18 m. Glumper is of octahedral shape, with four 'legs' that each comprise two 500 mm lengths of CFRP tube articulating around torsion spring 'knees'. It is able to raise its centre of gravity by 1.60 m and clears a height of 1.17 m. The jumping performance of the jumping robot designs presented is discussed and compared against some specialized jumping animals. Specific power output is thought to be the performance-limiting factor for a jumping robot, which requires the maximization of the amount of energy that can be stored together with a minimization of mass. It is demonstrated that this can be achieved through optimization and careful materials selection.
Accelerated Proximal Gradient Methods for Nonconvex Programming

Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine learning. However, solving the nonconvex and nonsmooth optimization problems remains a big challenge. Accelerated proximal gradient (APG) is an excellent method for convex programming. However, it is still unknown whether the usual APG can ensure the convergence to a critical point in nonconvex programming. In this paper, we extend APG for general nonconvex and nonsmooth programs by introducing a monitor that satisfies the sufficient descent property. Accordingly, we propose a monotone APG and a nonmonotone APG. The latter waives the requirement on monotonic reduction of the objective function and needs less computation in each iteration. To the best of our knowledge, we are the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain O ( 1 k2 ) when the problems are convex, in which k is the number of iterations. Numerical results testify to the advantage of our algorithms in speed.
Attentive Recurrent Neural Network for Weak-supervised Multi-label Image Classification

Multi-label image classification is a fundamental and challenging task in computer vision, and recently achieved significant progress by exploiting semantic relations among labels. However, the spatial positions of labels for multi-labels images are usually not provided in real scenarios, which brings insuperable barrier to conventional models. In this paper, we propose an end-to-end attentive recurrent neural network for multi-label image classification under only image-level supervision, which learns the discriminative feature representations and models the label relations simultaneously. First, inspired by attention mechanism, we propose a recurrent highlight network (RHN) which focuses on the most related regions in the image to learn the discriminative feature representations for different objects in an iterative manner. Second, we develop a gated recurrent relation extractor (GRRE) to model the label relations using multiplicative gates in a recurrent fashion, which learns to decide how multiple labels of the image influence the relation extraction. Extensive experiments on three benchmark datasets show that our model outperforms the state-of-the-arts, and performs better on small-object categories and under the scenario with large number of labels.
Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

The availability of location information has become a key factor in today’s communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.
Control of the power in induction heating systems with L-LC resonant voltage source inverters

This paper is a synthesis of the control circuit in an induction heating system with three-phase fully-controlled rectifier, single-phase voltage source inverter and L-LC resonant load. The attention is mainly directed on the control of the power transmitted to the induction coil. It is highlighted that the analytical design of a proportional integral derivative or proportional integral controller is practically impossible when the direct control of the current through inductor is desired. The two possibilities of power control taken into consideration are the inverter output current control and the inverter input voltage control. The design of the current and voltage controllers of proportional integral derivative type was carried out successfully based on Modulus Optimum criterion in Kessler variant. The performances of the control system in both variants of the power control were tested and validated by simulation under the Matlab-Simulink environment by using real parameters of induction coils and heated pipes from a leading Romanian manufacturer.
Traveling Salesman Problems with Profits

T salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.
A Hybrid Framework for News Clustering Based on the DBSCAN-Martingale and LDA

Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of web articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of “noise”, i.e. news articles which are irrelevant to all other topics. In this context, we introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the well-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called “DBSCAN-Martingale”, which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot. We evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 web news articles, which are references to specific Wikipedia pages. Among twenty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information.
Lightweight DDoS flooding attack detection using NOX/OpenFlow

Distributed denial-of-service (DDoS) attacks became one of the main Internet security problems over the last decade, threatening public web servers in particular. Although the DDoS mechanism is widely understood, its detection is a very hard task because of the similarities between normal traffic and useless packets, sent by compromised hosts to their victims. This work presents a lightweight method for DDoS attack detection based on traffic flow features, in which the extraction of such information is made with a very low overhead compared to traditional approaches. This is possible due to the use of the NOX platform which provides a programmatic interface to facilitate the handling of switch information. Other major contributions include the high rate of detection and very low rate of false alarms obtained by flow analysis using Self Organizing Maps.
Neurologic foundations of spinal cord fusion (GEMINI).

Cephalosomatic anastomosis has been carried out in both monkeys and mice with preservation of brain function. Nonetheless the spinal cord was not reconstructed, leaving the animals unable to move voluntarily. Here we review the details of the GEMINI spinal cord fusion protocol, which aims at restoring electrophysiologic conduction across an acutely transected spinal cord. The existence of the cortico-truncoreticulo-propriospinal pathway, a little-known anatomic entity, is described, and its importance concerning spinal cord fusion emphasized. The use of fusogens and electrical stimulation as adjuvants for nerve fusion is addressed. The possibility of achieving cephalosomatic anastomosis in humans has become reality in principle.
Novel Electrodes for Underwater ECG Monitoring

We have developed hydrophobic electrodes that provide all morphological waveforms without distortion of an ECG signal for both dry and water-immersed conditions. Our electrode is comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS). For feasibility testing of the CB/PDMS electrodes, various tests were performed. One of the tests included evaluation of the electrode-to-skin contact impedance for different diameters, thicknesses, and different pressure levels. As expected, the larger the diameter of the electrodes, the lower the impedance and the difference between the large sized CB/PDMS and the similarly-sized Ag/AgCl hydrogel electrodes was at most 200 kΩ, in favor of the latter. Performance comparison of CB/PDMS electrodes to Ag/AgCl hydrogel electrodes was carried out in three different scenarios: a dry surface, water immersion, and postwater immersion conditions. In the dry condition, no statistical differences were found for both the temporal and spectral indices of the heart rate variability analysis between the CB/PDMS and Ag/AgCl hydrogel (p > 0.05) electrodes. During water immersion, there was significant ECG amplitude reduction with CB/PDMS electrodes when compared to wet Ag/AgCl electrodes kept dry by their waterproof adhesive tape, but the reduction was not severe enough to obscure the readability of the recordings, and all morphological waveforms of the ECG signal were discernible even when motion artifacts were introduced. When water did not penetrate tape-wrapped Ag/AgCl electrodes, high fidelity ECG signals were observed. However, when water penetrated the Ag/AgCl electrodes, the signal quality degraded to the point where ECG morphological waveforms were not discernible.