nils-herrmann/clean_scirepeval huggingface dataset:
Boon and Bane of IT Consumerization: The Burnout-Engagement-Continuum

IT consumerization refers to the diffusion of consumer IT in the workplace and is considered to influence work processes in organizations. While several studies provide evidence that IT consumerization can create significant value for an organization, Volkswagen and Daimler recently applied restriction for consumer IT use to prevent stress and burnout consequences. Analyzing the existing IS studies about stress outcomes, it is interesting to note that stress-reducing effects of IT are rarely considered. Thus, the foremost negatively associated relationship between IT and stress contradicts to the mostly positive perceptions of IT consumerization. Drawing on psychological burnout literature, we investigate individual factors that influence the effects of IT consumerization on burnout and engagement. To this end, we conduct a case study in a public sector organization. Our findings provide individual constructs that position IT consumerization effects along the burnoutengagement continuum.
stagNet: An Attentive Semantic RNN for Group Activity Recognition

Group activity recognition plays a fundamental role in a variety of applications, e.g. sports video analysis and intelligent surveillance. How to model the spatio-temporal contextual information in a scene still remains a crucial yet challenging issue. We propose a novel attentive semantic recurrent neural network (RNN), dubbed as stagNet, for understanding group activities in videos, based on the spatio-temporal attention and semantic graph. A semantic graph is explicitly modeled to describe the spatial context of the whole scene, which is further integrated with the temporal factor via structural-RNN. Benefiting from the ‘factor sharing’ and ‘message passing’ mechanisms, our model is capable of extracting discriminative spatio-temporal features and capturing inter-group relationships. Moreover, we adopt a spatio-temporal attention model to attend to key persons/frames for improved performance. Two widely-used datasets are employed for performance evaluation, and the extensive results demonstrate the superiority of our method.
Using High-Quality Resources in NLP: The Valency Dictionary of English as a Resource for Left-Associative Grammars

In Natural Language Processing (NLP), the quality of a system depends to a great extent on the quality of the linguistic resources it uses. Due to the unpredictable character of valency properties, a reliable source for information about valency is important for syntactic and semantic analysis. With this in mind, we discuss how the Valency Dictionary of English in machine-readable form can be used as a resource for NLP. We will show that the valency data can be integrated into a Left-Associative Grammar and thus can be used for accurately parsing natural language with a rule-based approach.
Modeling paddle-aided stair-climbing for a mobile robot based on eccentric paddle mechanism

To gain high mobility on challenging terrains, a mobile robot based on eccentric paddle mechanism (ePaddle) with locomotion versatility has been proposed. In this paper, a paddle-aided stair-climbing motion is presented for this ePaddle-based robot. The robot can roll on the stair as a traditional wheeled vehicle and also can climb up the stair under the help of its paddles. Robot-stair interaction modes are presented and typical feasible postures of the robot in stair-climbing are discussed. Frictional requirements for the robot to hold a desired posture are evaluated by modelling statics of the robot. Analyzed results reveal that two critical scenarios in wheeled mode occur when the front-wheel is at the bottom of the riser, and when the rear-wheel is at the top of the riser, respectively. In contrast, frictional requirements of the paddle-aided stair-climbing postures confirm that the robot can climb up the stair with all feasible postures by touching the stair with the paddle, which verifies the effectiveness of the proposed paddle-aided stair-climbing.
MockDroid: trading privacy for application functionality on smartphones

MockDroid is a modified version of the Android operating system which allows a user to 'mock' an application's access to a resource. This resource is subsequently reported as empty or unavailable whenever the application requests access. This approach allows users to revoke access to particular resources at run-time, encouraging users to consider the trade-off between functionality and the disclosure of personal information whilst they use an application. Existing applications continue to work on MockDroid, possibly with reduced functionality, since existing applications are already written to tolerate resource failure, such as network unavailability or lack of a GPS signal. We demonstrate the practicality of our approach by successfully running a random sample of twenty-three popular applications from the Android Market.
Towards a functional neuroanatomy of speech perception

The functional neuroanatomy of speech perception has been difficult to characterize. Part of the difficulty, we suggest, stems from the fact that the neural systems supporting 'speech perception' vary as a function of the task. Specifically, the set of cognitive and neural systems involved in performing traditional laboratory speech perception tasks, such as syllable discrimination or identification, only partially overlap those involved in speech perception as it occurs during natural language comprehension. In this review, we argue that cortical fields in the posterior-superior temporal lobe, bilaterally, constitute the primary substrate for constructing sound-based representations of speech, and that these sound-based representations interface with different supramodal systems in a task-dependent manner. Tasks that require access to the mental lexicon (i.e. accessing meaning-based representations) rely on auditory-to-meaning interface systems in the cortex in the vicinity of the left temporal-parietal-occipital junction. Tasks that require explicit access to speech segments rely on auditory-motor interface systems in the left frontal and parietal lobes. This auditory-motor interface system also appears to be recruited in phonological working memory.
Prolegomena to any future artificial moral agent

As arti® cial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an arti® cial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing arti® cial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a `moral Turing Test ' is considered and the computational di culties accompanying the diOE erent types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people and other sentient beings are great. The development of machines with enough intelligence to assess the eOE ects of their actions on sentient beings and act accordingly may ultimately be the most important task faced by the designers of arti® cially intelligent automata. 1.  A good web server is a computer that e ciently serves up html code. A good chess program is one that wins chess games. There are some grey areas and fuzzy edges, of course. Is a good chess program one that wins most games or just some ? Against just ordinary competitors or against world class players ? But in one sense of the question, it is quite clear what it means to build a good computer or write a good program. A good one is one that ful® lls the purpose we had in building it. However, if you wanted to build a computer or write a program that is good in a moral sense, that is a good moral agent, it is much less clear what would count as success. Yet as arti® cial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an arti® cial moral agent becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. How to curb these capacities for harm is …
Real-time Action Recognition with Dissimilarity-based Training of Specialized Module Networks

This paper addresses the problem of real-time action recognition in trimmed videos, for which deep neural networks have defined the state-of-the-art performance in the recent literature. For attaining higher recognition accuracies with efficient computations, researchers have addressed the various aspects of limitations in the recognition pipeline. This includes network architecture, the number of input streams (where additional streams augment the color information), the cost function to be optimized, in addition to others. The literature has always aimed, though, at assigning the adopted network (or networks, in case of multiple streams) the task of recognizing the whole number of action classes in the dataset at hand. We propose to train multiple specialized module networks instead. Each module is trained to recognize a subset of the action classes. Towards this goal, we present a dissimilarity-based optimized procedure for distributing the action classes over the modules, which can be trained simultaneously offline. On two standard datasets–UCF-101 and HMDB-51–the proposed method demonstrates a comparable performance, that is superior in some aspects, to the state-of-the-art, and that satisfies the real-time constraint. We achieved 72.5% accuracy on the challenging HMDB-51 dataset. By assigning fewer and unalike classes to each module network, this research paves the way to benefit from light-weight architectures without compromising recognition accuracy1.
Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines

This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM model over the SVR and came fourth in the track. We report a number of different evaluations using a finance specific word embedding model and reflect on the effects of using different evaluation metrics.
Mobile Agent Middleware for Mobile Computing

T he integration of the Internet with telecommunication networks promises a distributed computing infrastructure that provides globally available services. The Internet’s diffusion permits an almost ubiquitous availability of attachment points, allowing users access to its information services irrespective of their location. In addition, advances in cellular telecommunications and device miniaturization let increasing numbers of portable devices connect to the Internet. Several forms of mobility emerge in this scenario. User mobility requires providing users with a uniform view of their preferred working environments—user preferences and subscribed services—independent of their current positions in the network. Terminal mobility allows devices to transparently move and connect to different points of attachment. Mobile access is an emerging issue that involves the dynamic adaptation of mobile-aware resources and services that mobile users and terminals can automatically retrieve regardless of their current location. Mobile computing requires an advanced infrastructure that integrates suitable support protocols, mechanisms, and tools. This mobility middleware should dynamically reallocate and trace mobile users and terminals and permit communication and coordination of mobile entities. In addition, open and untrusted environments must overcome system heterogeneity and grant the appropriate security level. Solutions to these issues require compliance with standards to interoperate with different systems and legacy components and a reliable security infrastructure based on standard cryptographic mechanisms and tools. Many proposals suggest using mobile agent technology middleware to address these issues. A mobile agent (MA) moves entities in execution together with code and achieved state, making it possible to upgrade distributed computing environments without suspending service. We propose three mobile computing services: user virtual environment (UVE), mobile virtual terminal (MVT), and virtual resource management (VRM). UVE provides users with a uniform view of their working environments independent of current locations and specific terminals. MVT extends traditional terminal mobility by preserving the terminal execution state for restoration at new locations, including active processes and subscribed services. VRM permits mobile users and terminals to maintain access to resources and services by automatically requalifying the bindings and moving specific resources or services to permit load balancing and replication.
Project on Clustering Algorithms for Anti-Money Laundering Using Graph Theory and Social Network Analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This MasterThesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.
Feature Analysis for Computational Personality Recognition Using YouTube Personality Data set

It is an important yet challenging task to develop an intelligent system in a way that it automatically classifies human personality traits. Automatic classification of human traits requires the knowledge of significant attributes and features that contribute to the prediction of a given trait. Motivated by the fact that detection of significant features is an essential part of a personality recognition system, we present in this paper an in-depth analysis of audio visual, text, demographic and sentiment features for classification of multi-modal personality traits namely, extraversion, agreeableness, conscientiousness, emotional stability and openness to experience. We use the YouTube personality data set and use logistic regression model with a ridge estimator for the classification purpose. We experiment with audio-visual features, bag of word features, sentiment based and demographic features. Our results provide important insights about the significance of different feature types for personality classification task.
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities that characterize such scenes. This paper presents a novel approach that exploits rich mid-level convolutional features to categorize indoor scenes. Traditional convolutional features retain the global spatial structure, which is a desirable property for general object recognition. We, however, argue that the structure-preserving property of the convolutional neural network activations is not of substantial help in the presence of large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target data set but also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale data set of 1300 object categories that are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over the previous state-of-the-art approaches on five major scene classification data sets.
Better Summarization Evaluation with Word Embeddings for ROUGE

ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank co-
Content-based approach for Vietnamese spam SMS filtering

Short Message Service (SMS) spam is a serious problem in Vietnam because of the availability of very cheap prepaid SMS packages. There are some systems to detect and filter spam messages for English, most of which use machine learning techniques to analyze the content of messages and classify them. For Vietnamese, there is some research on spam email filtering but none focused on SMS. In this work, we propose the first system for filtering Vietnamese spam SMS. We first propose an appropriate preprocessing method since existing tools for Vietnamese preprocessing cannot give good accuracy on our dataset. We then experiment with vector representations and classifiers to find the best model for this problem. Our system achieves an accuracy of 94% when labelling spam messages while the misclassification rate of legitimate messages is relatively small, about only 0.4%. This is an encouraging result compared to that of English and can be served as a strong baseline for future development of Vietnamese SMS spam prevention systems.
The Lack of A Priori Distinctions Between Learning Algorithms

Plutowski, M., et al. (1994). Cross-validation estimates integrated mean squared error. In Advances in neural information processing systems 6, Cowan et al. some constant set by k and m. It's also true that E(C OTS | φ, d X) is not drastically different if one considers d X 's with a different m'. Accordingly, our summand doesn't vary drastically between d X 's of one m' and d X 's of another. Since n >> m and π(x) is uniform though, almost all of the terms in the sum have m' = m.
SuggestBot: using intelligent task routing to help people find work in wikipedia

Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.
Lung cancer cell identification based on artificial neural network ensembles

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.