nils-herrmann/clean_scirepeval huggingface dataset:
Link prediction in multi-relational collaboration networks

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network.
NIST Net: a Linux-based network emulation tool

Testing of network protocols and distributed applications has become increasingly complex, as the diversity of networks and underlying technologies increase, and the adaptive behavior of applications becomes more sophisticated. In this paper, we present NIST Net, a tool to facilitate testing and experimentation with network code through emulation. NIST Net enables experimenters to model and effect arbitrary performance dynamics (packet delay, jitter, bandwidth limitations, congestion, packet loss and duplication) on live IP packets passing through a commodity Linux-based PC router. We describe the emulation capabilities of NIST Net; examine its architecture; and discuss some of the implementation challenges encountered in building such a tool to operate at very high network data rates while imposing minimal processing overhead. Calibration results are provided to quantify the fidelity and performance of NIST Net over a wide range of offered loads (up to 1 Gbps), and a diverse set of emulated performance dynamics.
Working and assembly modes of the agile eye

This paper deals with the in-depth kinematic analysis of a special parallel wrist, called the agile eye. The agile eye is a three-legged spherical parallel robot with revolute joints, in which all pairs of adjacent joint axes are orthogonal. Its most peculiar feature, demonstrated in this paper for the first time, is that its workspace is unlimited and flawed only by six singularity curves (instead of surfaces). These curves correspond to self-motions of the mobile platform and of the legs, or to a lockup configuration. This paper also demonstrates that the four solutions to the direct kinematics of the agile eye (assembly modes) have a simple direct relationship with the eight solutions to the inverse kinematics (working modes)
EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism

Signature-based network intrusion detection systems (NIDSs) have been widely deployed in current network security infrastructure. However, these detection systems suffer from some limitations such as network packet overload, expensive signature matching and massive false alarms in a large-scale network environment. In this paper, we aim to develop an enhanced filter mechanism (named EFM) to comprehensively mitigate these issues, which consists of three major components: a context-aware blacklist-based packet filter, an exclusive signature matching component and a KNN-based false alarm filter. The experiments, which were conducted with two data sets and in a network environment, demonstrate that our proposed EFM can overall enhance the performance of a signaturebased NIDS such as Snort in the aspects of packet filtration, signature matching improvement and false alarm reduction without affecting network security. a 2014 Elsevier Ltd. All rights reserved.
Continuously adaptive continuous queries over streams

We present a continuously adaptive, continuous query (CACQ) implementation based on the eddy query processing framework. We show that our design provides significant performance benefits over existing approaches to evaluating continuous queries, not only because of its adaptivity, but also because of the aggressive cross-query sharing of work and space that it enables. By breaking the abstraction of shared relational algebra expressions, our Telegraph CACQ implementation is able to share physical operators --- both selections and join state --- at a very fine grain. We augment these features with a grouped-filter index to simultaneously evaluate multiple selection predicates. We include measurements of the performance of our core system, along with a comparison to existing continuous query approaches.
An Analytics Approach for Proactively Combating Voluntary Attrition of Employees

We describe a framework for using analytics to proactively tackle voluntary attrition of employees. This is especially important in organizations with large services arms where unplanned departures of key employees can lead to big losses by way of lost productivity, delayed or missed deadlines, and hiring costs of replacements. By proactively identifying top talent at a high risk of voluntarily leaving, an organization can take appropriate action in time to actually affect such employee departures, thereby avoiding financial and knowledge losses. The main retention action we study in this paper is that of proactive salary raises to at-risk employees. Our approach uses data mining for identifying employees at risk of attrition and balances the cost of attrition/replacement of an employee against the cost of retaining that employee (by way of increased salary) to enable the optimal use of limited funds that may be available for this purpose, thereby allowing the action to be targeted towards employees with the highest potential returns on investment. This approach has been used to do a proactive retention action for several thousand employees across several geographies and business units for a large, Fortune 500 multinational company. We discuss this action and discuss the results to date that show a significant reduction in voluntary resignations of the targeted groups.
Efficient Natural Language Response Suggestion for Smart Reply

This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode messages into vectors which are optimized to give message-response pairs a high dot-product value. An optimized search finds response suggestions. The method is evaluated in a large-scale commercial e-mail application, Inbox by Gmail. Compared to a sequence-to-sequence approach, the new system achieves the same quality at a small fraction of the computational requirements and latency.
Multi-aspects Review Summarization Based on Identification of Important Opinions and their Similarity

The development of the Web services lets many users easily provide their opinions recently. Automatic summarization of enormous sentiments has been expected. Intuitively, we can summarize a review with traditional document summarization methods. However, such methods have not well-discussed “aspects”. Basically, a review consists of sentiments with various aspects. We summarize reviews for each aspect so that the summary presents information without biasing to a specific topic. In this paper, we propose a method for multiaspects review summarization based on evaluative sentence extraction. We handle three features; ratings of aspects, the tf -idf value, and the number of mentions with a similar topic. For estimating the number of mentions, we apply a clustering algorithm. By integrating these features, we generate a more appropriate summary. The experiment results show the effectiveness of our method.
Mean – Median Filtering For Impulsive Noise Removal

Abstrak –The objective of this paper is a new MeanMedian filtering for denoising extremely corrupted images by impulsive noise. Whenever an image is converted from one form to another, some of degradation occurs at the output. Improvement in the quality of these degraded images can be achieved by the application of Restoration and /or Enhancement techniques. Noise removing is one of the categories of Enhancement. Removing noise from the original signal is still a challenging problem. Mean filtering fails to effectively remove heavy tailed noise & performance poorly in the presence of signal dependent noise. The successes of median filters are edge preservation and efficient attenuation of impulsive noise. An important shortcoming of the median filter is that the output is one of the samples in the input window. Based on this mixture distributions are proposed to effectively remove impulsive noise characteristics. Finally, the results of comparative analysis of mean-median algorithm with mean, median filters for impulsive noise removal show a high efficiency of this approach relatively to other ones.
A novel circularly-polarised quad-band patch antenna for satellite applications

The design of a novel and versatile single-port quad-band patch antenna is presented. The antenna is capable of supporting a maximum of four operational sub-bands, with the inherent capability to enhance or suppress any resonance(s) of interest. In addition, circular-polarisation is also achieved at the low frequency band, to demonstrate the polarisation agility. A prototype model of the antenna has been fabricated and its performance experimentally validated. The antenna's single layer and low-profile configuration makes it suitable for mobile user terminals and its cavity-backed feature results in low levels of coupling.
Preventing Distributed Denial-of-Service Flooding Attacks With Dynamic Path Identifiers

In recent years, there are increasing interests in using path identifiers (<inline-formula> <tex-math notation="LaTeX">$\it PIDs$ </tex-math></inline-formula>) as inter-domain routing objects. However, the <inline-formula> <tex-math notation="LaTeX">$\it PIDs$ </tex-math></inline-formula> used in existing approaches are static, which makes it easy for attackers to launch the distributed denial-of-service (DDoS) flooding attacks. To address this issue, in this paper, we present the design, implementation, and evaluation of dynamic PID (D-PID), a framework that uses <inline-formula> <tex-math notation="LaTeX">$\it PIDs$ </tex-math></inline-formula> negotiated between the neighboring domains as inter-domain routing objects. In D-PID, the <inline-formula> <tex-math notation="LaTeX">$\it PID$ </tex-math></inline-formula> of an inter-domain path connecting the two domains is kept secret and changes dynamically. We describe in detail how neighboring domains negotiate <inline-formula> <tex-math notation="LaTeX">$\it PIDs$ </tex-math></inline-formula> and how to maintain ongoing communications when <inline-formula> <tex-math notation="LaTeX">$\it PIDs$ </tex-math></inline-formula> change. We build a 42-node prototype comprised of six domains to verify D-PID’s feasibility and conduct extensive simulations to evaluate its effectiveness and cost. The results from both simulations and experiments show that D-PID can effectively prevent DDoS attacks.
Chapter 3 Distinguishing the reflective , algorithmic , and autonomous minds : Is it time for a tri-process theory ?

In a recent book (Stanovich, 2004), I spent a considerable effort trying to work out the implications of dual process theory for the great rationality debate in cognitive science (see Cohen, 1981; Gigerenzer, 1996; Kahneman and Tversky, 1996; Stanovich, 1999; Stein, 1996). In this chapter, I wish to advance that discussion, first by discussing additions and complications to dual-process theory and then by working through the implications of these ideas for our view of human rationality.
An improvement to the SIFT descriptor for image representation and matching

0167-8655/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2013.03.021 ⇑ Corresponding author.  E-mail addresses: liugz.xjtu@gmail.com, liugz@xjtu Constructing proper descriptors for interest points in images is a critical aspect for local features related tasks in computer vision and pattern recognition. Although the SIFT descriptor has been proven to perform better than the other existing local descriptors, it does not gain sufficient distinctiveness and robustness in image match especially in the case of affine and mirror transformations, in which many mismatches could occur. This paper presents an improvement to the SIFT descriptor for image matching and retrieval. The framework of the proposed descriptor consists of the following steps: normalizing elliptical neighboring region, transforming to affine scale-space, improving the SIFT descriptor with polar histogram orientation bin, as well as integrating the mirror reflection invariant. A comparative evaluation of different descriptors is carried out showing that the present approach provides better results than the existing methods.
A Survey of Forecast Error Measures

Aug 7, 2013;  Sep 18, 2013;   This article reviews the common used forecast error measurements. All error measurements have been joined in the seven groups: absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures. The formulas are presented and drawbacks are discussed for every accuracy measurements. To reduce the impact of outliers, an Integral Normalized Mean Square Error have been proposed. Due to the fact that each error measure has the disadvantages that can lead to inaccurate evaluation of the forecasting results, it is impossible to choose only one measure, the recommendations for selecting the appropriate error measurements are given.
Wisconsin Card Sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging.

The Wisconsin Card Sorting Task (WCST) has been used to assess dysfunction of the prefrontal cortex and basal ganglia. Previous brain imaging studies have focused on identifying activity related to the set-shifting requirement of the WCST. The present study used event-related functional magnetic resonance imaging (fMRI) to study the pattern of activation during four distinct stages in the performance of this task. Eleven subjects were scanned while performing the WCST and a control task involving matching two identical cards. The results demonstrated specific involvement of different prefrontal areas during different stages of task performance. The mid-dorsolateral prefrontal cortex (area 9/46) increased activity while subjects received either positive or negative feedback, that is at the point when the current information must be related to earlier events stored in working memory. This is consistent with the proposed role of the mid-dorsolateral prefrontal cortex in the monitoring of events in working memory. By contrast, a cortical basal ganglia loop involving the mid-ventrolateral prefrontal cortex (area 47/12), caudate nucleus, and mediodorsal thalamus increased activity specifically during the reception of negative feedback, which signals the need for a mental shift to a new response set. The posterior prefrontal cortex response was less specific; increases in activity occurred during both the reception of feedback and the response period, indicating a role in the association of specific actions to stimuli. The putamen exhibited increased activity while matching after negative feedback but not while matching after positive feedback, implying greater involvement during novel than routine actions.
Using Abuse Case Models for Security Requirements Analysis

The relationships between the work products of a security engineering process can be hard to understand, even for persons with a strong technical background but little knowledge of security engineering. Market forces are driving software practitioners who are not security specialists to develop software that requires security features. When these practitioners develop software solutions without appropriate security-specific processes and models, they sometimes fail to produce effective solutions. We have adapted a proven object-oriented modeling technique, use cases, to capture and analyze security requirements in a simple way. We call the adaptation an abuse case model. Its relationship to other security engineering work products is relatively simple, from a
When the social mirror breaks: deficits in automatic, but not voluntary, mimicry of emotional facial expressions in autism.

Humans, infants and adults alike, automatically mimic a variety of behaviors. Such mimicry facilitates social functioning, including establishment of interpersonal rapport and understanding of other minds. This fundamental social process may thus be impaired in disorders such as autism characterized by socio-emotional and communicative deficits. We examined automatic and voluntary mimicry of emotional facial expression among adolescents and adults with autistic spectrum disorders (ASD) and a typical sample matched on age, gender and verbal intelligence. Participants viewed pictures of happy and angry expressions while the activity over their cheek and brow muscle region was monitored with electromyography (EMG). ASD participants did not automatically mimic facial expressions whereas the typically developing participants did. However, both groups showed evidence of successful voluntary mimicry. The data suggest that autism is associated with an impairment of a basic automatic social-emotion process. Results have implications for understanding typical and atypical social cognition.
Monocular 3D pose estimation and tracking by detection

Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions.
Information systems in the public sector: The e-Government enactment framework

Despite the burgeoning number of studies of public sector information systems, very few scholars have focussed on the relationship between e-Government policies and information systems choice and design. Drawing on Fountain’s (2001) technology enactment framework, this paper endeavours to conduct an in-depth investigation of the intricacies characterising the choice and design of new technologies in the context of e-Government reforms. By claiming that technologies are carriers of e-Government reform aims, this study investigates the logics embedded in the design of new technology and extant political interests and values inscribed in e-Government policies. The e-Government enactment framework is proposed as a theoretical and analytical approach to understand and study the complexity of these relationships which shape e-Government policies.
Are Passfaces More Usable Than Passwords? A Field Trial Investigation

The proliferation of technology requiring user authentication has increased the number of passwords which users have to remember, creating a significant usability problem. This paper reports a usability comparison between a new mechanism for user authentication Passfaces and passwords, with 34 student participants in a 3-month field trial. Fewer login errors were made with Passfaces, even when periods between logins were long. On the computer facilities regularly chosen by participants to log in, Passfaces took a long time to execute. Participants consequently started their work later when using Passfaces than when using passwords, and logged into the system less often. The results emphasise the importance of evaluating the usability of security mechanisms in field trials.
Evaluation of face recognition techniques using PCA, wavelets and SVM

In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy according to increasing dimension of training set, chosen feature extractor–classifier pairs and chosen kernel function for SVM classifier. As test set we used ORL face database which is known as a standard face database for face recognition applications including 400 images of 40 people. At the end of the overall separation task, we obtained the classification accuracy 98.1% with Wavelet–SVM approach for 240 image training set. As a special study of pattern recognition, face recognition has had crucial effects in daily life especially for security purposes. Face recognition task is actively being used at airports, employee entries , criminal detection systems, etc. For this task many methods have been proposed and tested. Most of these methods have trade off's like hardware requirements, time to update image database, time for feature extraction, response time. Generally face recognition methods are composed of a feature extractor (like PCA, Wavelet decomposer) to reduce the size of input and a classifier like Neural Networks, Support Vector Machines, Nearest Distance Classifiers to find the features which are most likely to be looked for. In this study, we chose wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA) as main techniques for data reduction and feature extraction. PCA is an efficient and long term studied method to extract feature sets by creating a feature space. PCA also has low computation time which is an important advantage. On the other hand because of being a linear feature extraction method, PCA is inefficient especially when nonlinearities are present in the underlying relationships (Kursun & Favorov, 2004). Wavelet decomposition is a multilevel dimension reduction process that makes time–space–frequency analysis. Unlike Fourier transform, which provides only frequency analysis of signals, wavelet transforms provide time–frequency analysis, which is particularly useful for pattern recognition (Gorgel, Sertbas, Kilic, Ucan, & Osman, 2009). In this study, we used available 40 classes in the ORL face recognition dataset (ORL Database of Faces, 1994). Eigenfaces and Discrete Wavelet Transform are used for feature extractor. For the classification step, we consider Support Vector Machines (SVM) and nearest distance classification …