nils-herrmann/clean_scirepeval huggingface dataset:
Spamming botnets: signatures and characteristics

In this paper, we focus on characterizing spamming botnets by leveraging both spam payload and spam server traffic properties. Towards this goal, we developed a spam signature generation framework called AutoRE to detect botnet-based spam emails and botnet membership. AutoRE does not require pre-classified training data or white lists. Moreover, it outputs high quality regular expression signatures that can detect botnet spam with a low false positive rate. Using a three-month sample of emails from Hotmail, AutoRE successfully identified 7,721 botnet-based spam campaigns together with 340,050 unique botnet host IP addresses.
 Our in-depth analysis of the identified botnets revealed several interesting findings regarding the degree of email obfuscation, properties of botnet IP addresses, sending patterns, and their correlation with network scanning traffic. We believe these observations are useful information in the design of botnet detection schemes.
Towards understanding the effects of individual gamification elements on intrinsic motivation and performance

Research on the effectiveness of gamification has proliferated over the last few years, but the underlying motivational mechanisms have only recently become object of empirical research. It has been suggested that when perceived as informational, gamification elements, such as points, levels and leaderboards, may afford feelings of competence and hence enhance intrinsic motivation and promote performance gains. We conducted a 2 4 online experiment that systematically examined how points, leaderboards and levels, as well as participants' goal causality orientation influence intrinsic motivation, competence and performance (tag quantity and quality) in an image annotation task. Compared to a control condition, game elements did not significantly affect competence or intrinsic motivation, irrespective of participants' causality orientation. However, participants' performance did not mirror their intrinsic motivation, as points, and especially levels and leaderboard led to a significantly higher amount of tags generated compared to the control group. These findings suggest that in this particular study context, points, levels and leaderboards functioned as extrinsic incentives, effective only for promoting perfor-
Survey on OFDMA based MAC protocols for the next generation WLAN

The physical (PHY) layer peak rate of the wireless local area network (WLAN) has been almost exponentially improved over the past 15 years since 1999. However, it is proved that the throughput is very low comparing to the PHY peak rate, and the media access control (MAC) efficiency is very low in the current WLANs specification, especially in dense deployment scenarios. Therefore, to achieve high MAC efficiency the IEEE Standards Association Standards Board (IEEE-SA) approves IEEE 802.11ax in March 2014, to draw up a brand new amendment for the next generation WLAN. One of the promising technologies to improve MAC efficiency is Orthogonal Frequency Division Multiple Access (OFDMA). In this paper, we firstly investigate the existing OFDMA based MAC protocols in the literature. Then, a framework of OFDMA based MAC protocol for the next generation WLAN is proposed. Finally, all of the existing OFDMA based MAC protocols listed in this paper are compared according to the proposed design issues. To the best of our knowledge this paper is the first survey focusing on OFDMA based MAC protocols for the next generation WLAN.
The Ideal Elf: Identity Exploration in World of Warcraft

In this study, we examine the identity exploration possibilities presented by online multiplayer games in which players use graphics tools and character-creation software to construct an avatar, or character. We predicted World of Warcraft players would create their main character more similar to their ideal self than the players themselves were. Our results support this idea; a sample of players rated their character as having more favorable attributes that were more favorable than their own self-rated attributes. This trend was stronger among those with lower psychological well-being, who rated themselves comparatively lower than they rated their character. Our results suggest that the game world allows players the freedom to create successful virtual selves regardless of the constraints of their actual situation.
Extracting Tactics from Cybersecurity Articles

Emerging cybersecurity threats can pose significant risk to organizations and their customers, often resulting in the compromise of confidential data and other devastating effects with substantial legal and business costs. By maintaining awareness of the modern threat landscape, at-risk organizations can evolve their defensive measures at the pace of the adversary and prevent successful attacks. One way such organizations accomplish this is by consuming research articles and extracting the attackers’ tactics from each document. Doing so allows these companies to prioritize their threat detection and prevention capabilities. However, this task is time and labor intensive and not all organizations are equipped to handle it successfully. We seek to address this problem by training an automated text classifier which can predict the attack tactics described in a given document. Specifically, our solution takes as input the text from a particular document and outputs a subset of up to seven attack tactics that the document describes, as illustrated in figure 1. The multi-label classifier is part of Magpie, an open-source text classification tool used by the European Organization for Nuclear Research (CERN). Magpie leverages the word2vec and convolutional neural network models to train on and subsequently classify text samples. We will describe these models and the overall classification scheme in greater detail in section IV. In the next section, we will describe some complementary text classification models.
Practical Secure Aggregation for Federated Learning on User-Held Data

Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors.
EGT for multiple view geometry and visual servoing: robotics vision with pinhole and panoramic cameras

The Epipolar Geometry Toolbox (EGT) for MATLAB is a software package targeted to research and education in computer vision and robotics visual servoing. It provides the user with a wide set of functions for designing multicamera systems for both pinhole and panoramic cameras. Functions include camera placement and visualization, computation, and estimation of epipolar geometry entities. The compatibility of EGT with the Robotics Toolbox enables users to address general vision-based control issues. Two applications of EGT to visual servoing tasks are examined in this article. Several epipolar geometry estimation algorithms have been implemented.
Untangling the links of parental responsiveness to distress and warmth to child outcomes.

This study demonstrated separate linkages between 2 features of positive parenting--responsiveness to distress and warmth--and different aspects of children's socio-emotional functioning, in a sample of 106 children (6-8 years old). As expected, mothers' and fathers' responsiveness to distress, but not warmth, predicted better negative affect regulation. Maternal responsiveness to distress also predicted children's empathy and prosocial responding. Maternal warmth, but not responsiveness to distress, was linked to better regulation of positive affect and (in boys only) to greater peer acceptance. Additionally, negative affect regulation mediated between maternal responsiveness to distress and children's empathic responding. Positive affect regulation mediated between maternal warmth and boys' peer acceptance. The findings support a differentiated approach to positive parenting.
EEG-correlated fMRI of human alpha activity

Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.
Plagiarism Detection: A Tool Survey and Comparison

We illustrate the state of the art in software plagiarism detection tools by comparing their features and testing them against a wide range of source codes. The source codes were edited according to several types of plagiarism to show the tools accuracy at detecting each type. The decision to focus our research on plagiarism of programming languages is two fold: on one hand, it is a challenging case-study since programming languages impose a structured writing style; on the other hand, we are looking for the integration of such a tool in an Automatic-Grading System (AGS) developed to support teachers in the context of Programming courses. Besides the systematic characterisation of the underlying problem domain, the tools were surveyed with the objective of identifying the most successful approach in order to design the aimed plugin for our AGS.
Intervention Studies on Forgiveness : A Meta-Analysis

79 A promising area of counseling research that emerged in the 1990s is the scientific investigation of forgiveness interventions. Although the notion of forgiving is ancient (Enright & the Human Development Study Group, 1991), it has not been systematically studied until relatively recently (Enright, Santos, & Al-Mabuk, 1989). Significant to counseling because of its interpersonal nature, forgiveness issues are relevant to the contexts of marriage and dating relationships, parent–child relationships, friendships, professional relationships, and others. In addition, forgiveness is integral to emotional constructs such as anger. As forgiveness therapies (Ferch, 1998;  1986) and the empirical study of these therapies (Freedman & Enright, 1996) begin to unfold, it is important to ask if these interventions can consistently demonstrate salient positive effects on levels of forgiveness and on the mental health of targeted clients. The purpose of this article is to analyze via meta-analysis the existing published interventions on forgiveness. Meta-analysis is a popular vehicle of synthesizing results across multiple studies. Recent successful uses of this method include the study by McCullough (1999), who analyzed five studies that compared the efficacy for depression of standard approaches with counseling with religion-accommo-dative approaches. Furthermore, in order to reach conclusions about the influence of hypnotherapy on treatment for clients with obesity, Allison and Faith (1996) used meta-analysis to examine six studies that compared the efficacy of using cognitive-behavioral therapy (CBT) alone with the use of CBT combined with hypnotherapy. Finally, Morris, Audet, Angelillo, Chalmers, and Mosteller (1992) used meta-analysis to combine the results of 10 studies with contradictory findings to show that the benefits of chlorinating drinking water far outweighed the risks. Although there may be some concern that using forgiveness as an intervention in counseling is in too early a stage of development and that too few studies exist for a proper meta-analysis, the effectiveness of these recent meta-analyses supports this meta-analytic investigation. Certainly any findings must be tempered with due caution. However, this analysis may serve as important guidance for the structure and development of future counseling studies of forgiveness. We first examine the early work in forgiveness interventions by examining the early case studies. From there, we define forgiveness, discuss the models of forgiveness in counseling and the empirically based interventions, and then turn to the meta-analysis. The early clinical case studies suggested that forgiveness might be helpful for people who have experienced deep emotional pain because of unjust treatment. For …
Wafer-scale VLSI implementations of pulse coupled neural networks

In this paper, we present a system architecture currently under development that will allow very large (>10 neurons, >10 synapses) reconfigurable networks to be built, in the form of interlinked dies on a single wafer. Reconfigurable routing and complex adaptation/plasticity across several timescales in neurons and synapses allow for the implementation of large-scale biologically realistic neuronal networks and behaviors. Compared to biology the network dynamics will be about 10 to 10 times faster, so that developmental plasticity can be studied in detail. neural VLSI hardware, system simulation, mapping algorithms, parallelization.
Efficient influence maximization in social networks

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of  and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching influence spread, (b) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned for a specific influence cascade model, it achieves almost matching influence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes.
 Based on our results, we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of  that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.
Grip and pinch strength: normative data for adults.

The primary purpose of this study was to establish clinical norms for adults aged 20 to 75+ years on four tests of hand strength. A dynamometer was used to measure grip strength and a pinch gauge to measure tip, key, and palmar pinch. A sample of 310 male and 328 female adults, ages 20 to 94, from the seven-county Milwaukee area were tested using standardized positioning and instructions. Right hand and left hand data were stratified into 12 age groups for both sexes. This stratification provides a means of comparing the score of individual patients to that of normal subjects of the same age and sex. The highest grip strength scores occurred in the 25 to 39 age groups. For tip, key, and palmar pinch the average scores were relatively stable from 20 to 59 years, with a gradual decline from 60 to 79 years. A high correlation was seen between grip strength and age, but a low to moderate correlation between pinch strength and age. The newer pinch gauge used in this study appears to read higher than that used in a previous normative study. Comparison of the average hand strength of right-handed and left-handed subjects showed only minimal differences.
Conspicuous Consumption and Household Indebtedness

Using a novel, large dataset of consumer transactions in Singapore, we study how conspicuous consumption affects household indebtedness. The coexistence of private housing (condominiums) and subsidized public housing (HDB) allows us to identify conspicuous consumers. Conditional on the same income and other socioeconomic characteristics, those who choose to reside in condominiums—considered a status good—are likely to be more conspicuous than their counterparts living in HDB units. We consistently find that condominium residents spend considerably more (by up to 44%) on conspicuous goods but not differently on inconspicuous goods. Compared with their matched HDB counterparts, more conspicuous consumers have 13% more credit card debt and 151% more delinquent credit card debt. Furthermore, the association between conspicuous consumption and credit card debt is concentrated among younger, male, single individuals. These results suggest that status-seeking-induced conspicuous consumption is an important determinant of household indebtedness.
Learning Robust Visual-Semantic Embeddings

Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our frame-work improves over the current state of the art on many of the considered tasks.
Enterprise Content Management - A Literature Review

Managing information and content on an enterprise-wide scale is challenging. Enterprise content management (ECM) can be considered as an integrated approach to information management. While this concept received much attention from practitioners, ECM research is still an emerging field of IS research. Most authors that deal with ECM claim that there is little scholarly literature available. After approximately one decade of ECM research, this paper provides an in-depth review of the body of academic research: the ECM domain, its evolution, and main topics are characterized. An established ECM research framework is adopted, refined, and explained with its associated elements and working definitions. On this basis, 68 articles are reviewed, classified, and concepts are derived. Prior research is synthesized and findings are integrated in a conceptcentric way. Further, implications for research and practice, including future trends, are drawn.
Mimetic Isomorphism and Technology Evaluation: Does Imitation Transcend Judgment?

Although contemporary technology adoption theories incorporate societal norms or peer references, it is unclear to what extent these factors influence choices. In this research, we apply institutional theory and the concept of mimetic isomorphism as peer influences to the technology evaluation process to determine the degree to which managers conform when selecting between competing information technologies. More specifically, we test if peer influence is sufficient to overcome a product evaluation where the choice is believed to be inferior. An experiment is conducted using the World Wide Web and a national sample of 348 senior information technology and business decision makers. Significant effects are found where inferior technologies are selected if respondents are informed that competitors have selected them. Further research is warranted to investigate the presence and extent of these effects but overall implications are that product evaluations may be more ornamental than substantive.