nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of point-to-point packet delay in an operational network

In this paper we perform a detailed analysis of point-to-point packet delay in an operational tier-1 network. The point-to-point delay is the time between a packet entering a router in one PoP (an ingress point) and its leaving a router in another PoP (an egress point). It measures the one-way delay experienced by packets from an ingress point to an egress point across an ISP's network and provides the most basic information regarding the delay performance of the ISP's network. Using packet traces captured in the operational network, we obtain precise point-to-point packet delay measurements and analyze the various factors affecting them. Through a simple, step-by-step, systematic methodology and careful data analysis, we identify the major network factors that contribute to point-to-point packet delay and characterize their effect on the network delay performance. Our findings are: 1) delay distributions vary greatly in shape, depending on the path and link utilization; 2) after constant factors dependent only on the path and packet size are removed, the 99th percentile variable delay remains under 1 ms over several hops and under link utilization below 90% on a bottleneck; 3) a very small number of packets experience very large delay in short bursts
Robust twin support vector machine for pattern classification

In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via second order cone programming formulations for classification, which can deal with data with measurement noise efficiently. Preliminary experiments confirm the robustness of the proposed method and its superiority to the traditional robust SVM in both computation time and classification accuracy. Remarkably, since there are only inner products about inputs in our dual problems, this makes us apply kernel trick directly for nonlinear cases. Simultaneously we does not need to solve the extra inverse of matrices, which is totally different with existing TWSVMs. In addition, we also show that the TWSVMs are the special case of our robust model and simultaneously give a new dual form of TWSVM by degenerating R-TWSVM, which successfully overcomes the existing shortcomings of TWSVM. & 2012 Elsevier Ltd. All rights reserved.
Multi-authority Attribute Based Encryption

In an identity based encryption scheme, each user is identified by a unique identity string. An attribute based encryption scheme (ABE), in contrast, is a scheme in which each user is identified by a set of attributes, and some function of those attributes is used to determine decryption ability for each ciphertext. Sahai and Waters introduced a single authority attribute encryption scheme and left open the question of whether a scheme could be constructed in which multiple authorities were allowed to distribute attributes [SW05]. We answer this question in
Predicting Students ’ Academic Performance using Artificial Neural Network : A Case Study of an Engineering Course

The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study an Artificial Neural Network (ANN) model, for predicting the likely performance of a candidate being considered for admission into the university was developed and tested. Various factors that may likely influence the performance of a student were identified. Such factors as ordinary level subjects’ scores and subjects’ combination, matriculation examination scores, age on admission, parental background, types and location of secondary school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained using data spanning five generations of graduates from an Engineering Department of University of Ibadan, Nigeria’s first University. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 70% of prospective students. (
Detection and Recognition of Painted Road Surface Markings

A method for the automatic detection and recognition of text and symbols painted on the road surface is presented. Candidate regions are detected as maximally stable extremal regions (MSER) in a frame which has been transformed into an inverse perspective mapping (IPM) image, showing the road surface with the effects of perspective distortion removed. Detected candidates are then sorted into words and symbols, before they are interpreted using separate recognition stages. Symbol-based road markings are recognised using histogram of oriented gradient (HOG) features and support vector machines (SVM). Text-based road signs are recognised using a third-party optical character recognition (OCR) package, after application of a perspective correction stage. Matching of regions between frames, and temporal fusion of results is used to improve performance. The proposed method is validated using a data-set of videos, and achieves F-measures of 0.85 for text characters and 0.91 for symbols.
Flexible isosurfaces: Simplifying and displaying scalar topology using the contour tree

The contour tree is an abstraction of a scalar field that encodes the nesting relationships of isosurfaces. We show how to use the contour tree to represent individual contours of a scalar field, how to simplify both the contour tree and the topology of the scalar field, how to compute and store geometric properties for all possible contours in the contour tree, and how to use the simplified contour tree as an interface for exploratory visualization.
Incorporate Group Information to Enhance Network Embedding

The problem of representing large-scale networks with low-dimensional vectors has received considerable attention in recent years. Except the networks that include only vertices and edges, a variety of networks contain information about groups or communities. For example, on Facebook, in addition to users and the follower-followee relations between them, users can also create and join groups. However, previous studies have rarely utilized this valuable information to generate embeddings of vertices. In this paper, we investigate a novel method for learning the network embeddings with valuable group information for large-scale networks. The proposed methods take both the inner structures of the groups and the information across groups into consideration. Experimental results demonstrate that the embeddings generated by the proposed methods significantly outperform state-of-the-art network embedding methods on two different scale real-world network
A Review on Road Accident in Traffic System using Data Mining Techniques

Road traffic accidents are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, road traffic accidents are among the leading cause of death and injury. The objective of this study is to evaluate a set of variable that contribute to the degree of accident severity in traffic crashes. The issue of traffic safety has raised great concerns across the sustainable development of modern traffic and transportation. The study on road traffic accident cause can identify the key factors rapidly, efficiently and provide instructional methods to the traffic accident prevention and road traffic accident reduction, which could greatly reduce personal casualty by road traffic accidents. Using the methods of traffic data analysis, can improve the road traffic safety management level effectively.
A dynamic predictive maintenance policy for complex multi-component systems

The use of prognostic methods in maintenance in order to predict remaining useful life is receiving more attention over the past years. The use of these techniques in maintenance decision making and optimization in multi-component systems is however a still underexplored area. The objective of this paper is to optimally plan maintenance for a multi-component system based on prognostic/predictive information while considering different component dependencies (i.e. economic, structural and stochastic dependence). Consequently, this paper presents a dynamic predictive maintenance policy for multi-component systems that minimizes the long-term mean maintenance cost per unit time. The proposed maintenance policy is a dynamic method as the maintenance schedule is updated when new information on the degradation and remaining useful life of components becomes available. The performance, regarding the objective of minimal long-term mean cost per unit time, of the developed dynamic predictive maintenance policy is compared to five other conventional maintenance policies, these are: block-based maintenance, age-based maintenance, age-based maintenance with grouping, inspection condition-based maintenance and continuous condition-based maintenance. The ability of the predictive maintenance policy to react to changing component deterioration and dependencies within a multi-component system is quantified and the results show significant cost
DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis

Bugs are prevalent. To improve software quality, developers often allow users to report bugs that they found using a bug tracking system such as Bugzilla. Users would specify among other things, a description of the bug, the component that is affected by the bug, and the severity of the bug. Based on this information, bug triagers would then assign a priority level to the reported bug. As resources are limited, bug reports would be investigated based on their priority levels. This priority assignment process however is a manual one. Could we do better? In this paper, we propose an automated approach based on machine learning that would recommend a priority level based on information available in bug reports. Our approach considers multiple factors, temporal, textual, author, related-report, severity, and product, that potentially affect the priority level of a bug report. These factors are extracted as features which are then used to train a discriminative model via a new classification algorithm that handles ordinal class labels and imbalanced data. Experiments on more than a hundred thousands bug reports from Eclipse show that we can outperform baseline approaches in terms of average F-measure by a relative improvement of 58.61%.
A New Type of Periodically Loaded Half-Mode Substrate Integrated Waveguide and Its Applications

A new type of half-mode substrate integrated waveguide (SIW) periodically loaded with different lumped elements and structures is proposed in this paper. The propagation constants, Bloch impedances, and voltage distributions of the Floquet TE-modes are all characterized by solving the eigenvalues of the generalized transmission matrix of a single period. Several typical capacitor-loaded, inductor-loaded, corrugated, and electromagnetic-bandgap (EBG)-loaded half-mode SIWs were fabricated to demonstrate their modal dispersion behavior. The measured and simulated S -parameters, group delays, and simulated electric field distributions in these novel structures are presented, with good agreements obtained for all samples. In particular, their unique controllable cutoff frequencies, multiple bandgaps, and separated monomode regions are also illuminated numerically and experimentally. To further show their applications, a tunable phase shifter is developed using a varactor-loaded structure, with its 3-dB bandwidth of 50% and a maximal tunable range of 88°. In addition, a bandpass filter based on EBG-loaded half-mode SIW is also realized and presented with its special characteristics. It demonstrates that the filter has a sharp upper slope side and a good performance of spurious suppression.
Deep Learning, Dark Knowledge, and Dark Matter

Particle colliders are the primary experimental instruments of high-energy physics. By creating conditions that have not occurred naturally since the Big Bang, collider experiments aim to probe the most fundamental properties of matter and the universe. These costly experiments generate very large amounts of noisy data, creating important challenges and opportunities for machine learning. In this work we use deep learning to greatly improve the statistical power on three benchmark problems involving: (1) Higgs bosons; (2) supersymmetric particles; and (3) Higgs boson decay modes. This approach increases the expected discovery significance over traditional shallow methods, by 50%, 2%, and 11% respectively. In addition, we explore the use of model compression to transfer information (dark knowledge) from deep networks to shallow networks.
EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters

Two complexity parameters of EEG, i.e. approximate entropy (ApEn) and Kolmogorov complexity (Kc) are utilized to characterize the complexity and irregularity of EEG data under the different mental fatigue states. Then the kernel principal component analysis (KPCA) and Hidden Markov Model (HMM) are combined to differentiate two mental fatigue states. The KPCA algorithm is employed to extract nonlinear features from the complexity parameters of EEG and improve the generalization performance of HMM. The investigation suggests that ApEn and Kc can effectively describe the dynamic complexity of EEG, which is strongly correlated with mental fatigue. Both complexity parameters are significantly decreased (P < 0.005) as the mental fatigue level increases. These complexity parameters may be used as the indices of the mental fatigue level. Moreover, the joint KPCA–HMM method can effectively reduce the dimensionality of the feature vectors, accelerate the classification speed and achieve higher classification accuracy (84%) of mental fatigue. Hence KPCA–HMM could be a promising model for the estimation of mental fatigue.
Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education. The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance
Adversarial Attacks and Defences: A Survey

Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.
Joint sizing and adaptive independent gate control for FinFET circuits operating in multiple voltage regimes using the logical effort method

FinFET has been proposed as an alternative for bulk CMOS in current and future technology nodes due to more effective channel control, reduced random dopant fluctuation, high ON/OFF current ratio, lower energy consumption, etc. Key characteristics of FinFET operating in the sub/near-threshold region are very different from those in the strong-inversion region. This paper first introduces an analytical transregional FinFET model with high accuracy in both sub- and near-threshold regimes. Next, the paper extends the well-known and widely-adopted logical effort delay calculation and optimization method to FinFET circuits operating in multiple voltage (sub/near/super-threshold) regimes. More specifically, a joint optimization of gate sizing and adaptive independent gate control is presented and solved in order to minimize the delay of FinFET circuits operating in multiple voltage regimes. Experimental results on a 32nm Predictive Technology Model for FinFET demonstrate the effectiveness of the proposed logical effort-based delay optimization framework.
Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious “populations”. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results.
An empirical study of best practices in virtual teams

This study explores the issue of effectiveness within virtual teams — groups of people who work together although they are often dispersed across space, time, and/or organizational boundaries. Due to the recent trend towards corporate restructuring, which can, in part, be attributed to an increase in corporate layoffs, mergers and acquisitions, competition, and globalization, virtual teams have become critical for companies to survive. Globalization of the marketplace alone, for that matter, makes such distributed work groups the primary operating units needed to achieve a competitive advantage in this ever-changing business environment. In an effort to determine the factors that contribute to/inhibit the success of a virtual team, a survey was distributed to a total of eight companies in the high technology, agriculture, and professional services industries. Data was then collected from 67 individuals who comprised a total of 12 virtual teams from these companies. Results indicated that several factors were positively correlated to the effectiveness of the participating teams. The teams’ processes and team members’ relations presented the strongest relationships to team performance and team member satisfaction, while the selection procedures and executive leadership styles also exhibited moderate associations to these measures of effectiveness. Analysis of predictor variables such as the design process, other internal group dynamics, and additional external support mechanisms, however, depicted weaker relations. Although the connections between the teams’ tools and technologies and communication patterns and the teams’ effectiveness measures did not prove significant, content analysis of the participants’ narrative responses to questions regarding the greatest challenges to virtual teams suggested otherwise. Beyond the traditional strategies used to enhance a team’s effectiveness, further efforts directed towards the specific technology and communication-related issues that concern dispersed team members are needed to supplement the set of best practices identified in the current study.
Optimized Invariant Representation of Network Traffic for Detecting Unseen Malware Variants

New and unseen polymorphic malware, zero-day attacks, or other types of advanced persistent threats are usually not detected by signature-based security devices, firewalls, or anti-viruses. This represents a challenge to the network security industry as the amount and variability of incidents has been increasing. Consequently, this complicates the design of learning-based detection systems relying on features extracted from network data. The problem is caused by different joint distribution of observation (features) and labels in the training and testing data sets. This paper proposes a classification system designed to detect both known as well as previouslyunseen security threats. The classifiers use statistical feature representation computed from the network traffic and learn to recognize malicious behavior. The representation is designed and optimized to be invariant to the most common changes of malware behaviors. This is achieved in part by a feature histogram constructed for each group of HTTP flows (proxy log records) of a user visiting a particular hostname and in part by a feature self-similarity matrix computed for each group. The parameters of the representation (histogram bins) are optimized and learned based on the training samples along with the classifiers. The proposed classification system was deployed on large corporate networks, where it detected 2,090 new and unseen variants of malware samples with 90% precision (9 of 10 alerts were malicious), which is a considerable improvement when compared to the current flow-based approaches or existing signaturebased web security devices.
An empirical study on the e-CRM performance influence model for service sectors in Taiwan

Electronic customer relationship management (e-CRM) emerges from the Internet and Web technology to facilitate the implementation of CRM. It focuses on Internet- or Web-based interaction between companies and their customers. Above all, e-CRM enables service sectors to provide appropriate services and products to satisfy the customers so as to retain customer royalty and enhance customer profitability. This research is to explore the key research issues about e-CRM performance influence for service sectors in Taiwan. A research model is proposed based on the widely applied technology-organization-environment (TOE) framework. Survey data from the questionnaire are collected to empirically assess our research model.