nils-herrmann/clean_scirepeval huggingface dataset:
An empirical investigation of the factors that influence Internet user's ability to correctly identify a phishing website

The aim of this study is to report on the results of an empirical investigation of the various factors which have significant impacts on the Internet user’s ability to correctly identify a phishing website.  The research participants were Internet users who have had at least some experience of financial transactions over the Internet. This study conducted a quantitative research with the help of a structured survey questionnaire along with three experimental tasks. A total of 621 valid samples were collected and the multiple regression analysis technique was used to deduce the answers to the research question.  The results show that the model is useful and has explanatory power. And adjusted R computed as 0.927, means that 92.7 per cent of the variations in the Internet user’s ability to identify phishing website can be explained by the predictors selected for the model.  Future research should account for the Internet user’s general security practices and behaviour, attitude towards online financial activity, risk-taking ability or risk behaviour and their potential effects on Internet users’ ability to identify a phishing website.  The implications of this study provide the foundation for future research on the areas that intend to explain the Internet user’s necessity to take protection or avoid risky behaviour while performing financial transaction over the Internet.  This study provides the body of knowledge with an empirical analysis of impact of various factors on an Internet user’s ability to identify phishing websites. The results of this study can help practitioners create a more successful research model and help researchers better understand user behaviour on the Internet.
The impact of buccal corridors on smile attractiveness.

The aim of this study was to assess the impact of various sized buccal corridors (BCs) on smile attractiveness. One female smiling photograph, displaying first molar to first molar (M1-M1), was digitally altered to produce (1) smiles that filled 84, 88, 92, 96, and 100 per cent of the oral aperture; (2) second premolar to second premolar smiles (PM2-PM2) that filled 84, 88, 92, and 96 per cent of the oral aperture; and (3) smiles with asymmetrical BC that filled 88, 90, 94, and 96 per cent of the oral aperture. The 18 smiles produced were evaluated by 82 orthodontists (70 males and 12 females) and 94 laypeople (40 males and 54 females). Paired t-tests were used to evaluate differences within the orthodontist and laypeople groups: independent t-tests were used to compare the two groups. The effect of age and gender on the ratings was evaluated by two-way analysis of variance. Orthodontists and laypeople rated smiles with small BCs as significantly (P < 0.05) more attractive than those with large BCs. Orthodontists rated M1-M1 smiles as more attractive than PM2-PM2 smiles, whereas laypeople preferred PM2-PM2 smiles. Orthodontists rated only two of eight asymmetrical smiles as less attractive than would be expected for symmetrical smiles with similar arch widths; laypeople did not rate any asymmetrical smiles as less attractive than would be expected. Rater age and gender did not significantly influence the impact of BCs on smile attractiveness.
A Framework for Analyzing Big Social Data and Modelling Emotions in Social Media

Social media constitute a rich source of information. The analysis of the big volume of user-generated content in social media can provide meaningful information for gleaning people's emotions and deeper understanding public attitude and mood. In this work, we present a generic framework for the analysis of big social data and the modeling of public emotions and mood. The framework consists of two main parts. Initially an ensemble classifier schema, which combines a generic domain independent, knowledge-based tool with machine learning methods, is used to recognize emotional content in user generated social data. Then, a graph based method is utilized to model the emotional level of a topic based on the emotions recognized and then it creates the topic's emotional graph visualizing public emotions and mood on the topic. The results from the case study are quite encouraging.
Textile Electrodes for EEG Recording — A Pilot Study

The overall aim of our research is to develop a monitoring system for neonatal intensive care units. Long-term EEG monitoring in newborns require that the electrodes don't harm the sensitive skin of the baby, an especially relevant feature for premature babies. Our approach to EEG monitoring is based on several electrodes distributed over the head of the baby, and since the weight of the head always will be on some of them, any type of hard electrode will inevitably cause a pressure-point that can irritate the skin. Therefore, we propose the use of soft conductive textiles as EEG electrodes, primarily for neonates, but also for other kinds of unobtrusive long-term monitoring. In this paper we have tested two types of textile electrodes on five healthy adults and compared them to standard high quality electrodes. The acquired signals were compared with respect to morphology, frequency distribution, spectral coherence, correlation and power line interference sensitivity, and the signals were found to be similar in most respects. The good measurement performance exhibited by the textile electrodes indicates that they are feasible candidates for EEG recording, opening the door for long-term EEG monitoring applications.
Integration of independent component analysis and neural networks for ECG beat classification

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.
A Resource-Based Model For E-Commerce In Developing Countries

Previous efforts in electronic commerce (e-commerce) research in developing countries shows that there is an acute lack of theoretical frameworks and empirical evidence to understand how developing country firms realize electronic commerce benefits amidst their national constraints. This paper sets out to develop a theoretically abstracted but contextually grounded model of how developing country firms can orient their resources and realize these benefits amidst their national constraints. A review of e-commerce and strategy management literature to develop a resource – based model for e-commerce benefits was undertaken. The process-based model provides an understanding of how to identify, integrate, and reconfigure resources to achieve electronic commerce benefits; provides propositions that serves as theoretical platforms for future empirically grounded research on electronic commerce in developing country contexts and brings organizations closer to identifying and categorizing the strategic value of resources and the role managerial capabilities and intangible resources play in sustaining e-commerce benefits. Finally, our findings provides organizations the strategic options to address resources which have lost their value or have become less valuable to their strategic orientation in e-commerce adoption thereby serving as a starting point of examining e-commerce in developing countries through the theoretical lens of information systems and strategic management.
Behavior-Based Telecommunication Churn Prediction with Neural Network Approach

A behavior-based telecom customer churn prediction system is presented in this paper. Unlike conventional churn prediction methods, which use customer demographics, contractual data, customer service logs, call-details, complaint data, bill and payment as inputs and churn as target output, only customer service usage information is included in this system to predict customer churn using a clustering algorithm. It can solve the problems which traditional methods have to face, such as missing or non-reliable data and the correlation among inputs. This study provides a new way to solve traditional churn prediction problems.
DeepQA Jeopardy! Gamification: A Machine-Learning Perspective

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.
Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models

Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial attacks where miscreants can avoid detection by the classification algorithm with very few perturbations of the input data. Previous work has studied adversarial attacks against static analysisbased malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the antimalware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defensive strategies, the number of successfully crafted adversarial samples is reduced compared to a standard baseline system without any defenses. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we demonstrate that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier’s robustness to adversarial attacks.
Reforming education sector through Big Data

Big data, which is maturing over time, has a high impact in analyzing scenarios and coming up with decision making strategies pertaining to any sector. Consequently the number of applications are also increasing where in the past, they were limited to the confines of company sales departments. In lieu, this paper is primarily focused on applying Data Sciences within the education sector. We will be discussing the overall application to address higher-education issues. We will also present a framework for capturing, analyzing it and concluding on data in order to benefit decision-makers, thus ultimately manifesting in informing design and finalizing policy.
Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality

Media richness theory argues that performance improves when team members use “richer” media for equivocal tasks. This experiment studied the effects of media richness on decision making in two-person teams using “new media” (i.e., computer-mediated and video communication). Media richness was varied based on multiplicity of cues and immediacy of feedback. Subjects perceived differences in richness due to both cues and feedback, but matching richness to task equivocality did not improve decision quality, decision time, consensus change, or communication satisfaction. Use of media providing fewer cues (i.e., computer mediated communication) led to slower decisions and more so for the less equivocal task. In short, the results found no support for the central proposition of media richness theory; matching media richness to task equivocality did not improve performance.
Age-Related Macular Degeneration Detection and Stage Classification Using Choroidal OCT Images

Age-Related Macular Degeneration (AMD) is a progressive eye disease which damages the retina and causes visual impairment. Detecting those in the early stages at most risk of progression will allow more timely treatment and preserve sight. In this paper, we propose a machine learning based method to detect AMD and distinguish the di↵erent stages using choroidal images obtained from optical coherence tomography (OCT). We extract texture features using a Gabor filter bank and non-linear energy transformation. Then the histogram based feature descriptors are used to train the random forests, Support Vector Machine (SVM) and neural networks, which are tested on our choroid OCT image dataset with 21 participants. The experimental results show the feasibility of our method.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks

Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements, i.e., anytime property. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.
Overview and Framework for Data and Information Quality Research

Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. In this article, we present an overview of the evolution and current landscape of data and information quality research. We introduce a framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.
Infants' preferences for toys, colors, and shapes: sex differences and similarities.

Girls and boys differ in their preferences for toys such as dolls and trucks. These sex differences are present in infants, are seen in non-human primates, and relate, in part, to prenatal androgen exposure. This evidence of inborn influences on sex-typed toy preferences has led to suggestions that object features, such as the color or the shape of toys, may be of intrinsically different interest to males and females. We used a preferential looking task to examine preferences for different toys, colors, and shapes in 120 infants, ages 12, 18, or 24 months. Girls looked at dolls significantly more than boys did and boys looked at cars significantly more than girls did, irrespective of color, particularly when brightness was controlled. These outcomes did not vary with age. There were no significant sex differences in infants' preferences for different colors or shapes. Instead, both girls and boys preferred reddish colors over blue and rounded over angular shapes. These findings augment prior evidence of sex-typed toy preferences in infants, but suggest that color and shape do not determine these sex differences. In fact, the direction of influence could be the opposite. Girls may learn to prefer pink, for instance, because the toys that they enjoy playing with are often colored pink. Regarding within sex differences, as opposed to differences between boys and girls, both boys and girls preferred dolls to cars at age 12-months. The preference of young boys for dolls over cars suggests that older boys' avoidance of dolls may be acquired. Similarly, the sex similarities in infants' preferences for colors and shapes suggest that any subsequent sex differences in these preferences may arise from socialization or cognitive gender development rather than inborn factors.
Mindfulness-Based Cognitive Therapy and the Adult ADHD Brain: A Neuropsychotherapeutic Perspective

Attention-deficit/hyperactivity disorder (ADHD) is a recognized serious mental disorder that often persists into adulthood. The symptoms and impairments associated with ADHD often cause significant mental suffering in affected individuals. ADHD has been associated with abnormal neuronal activity in various neuronal circuits, such as the dorsofrontostriatal, orbitofrontostriatal, and frontocerebellar circuits. Psychopharmacological treatment with methylphenidate hydrochloride is recommended as the first-line treatment for ADHD. It is assumed that medication ameliorates ADHD symptoms by improving the functioning of the brain areas affected in the condition. However, side effects, contraindications, or non-response can limit the effectiveness of a psychopharmacological treatment for ADHD. It is therefore necessary to develop non-pharmacological interventions that target neuronal mechanisms associated with the condition in the same way as pharmacological treatment. We think that mindfulness meditation employed as a neuropsychotherapeutic intervention could help patients with ADHD to regulate impaired brain functioning and thereby reduce ADHD symptoms. In this paper, we highlight the mechanisms of such mindfulness meditation, and thus provide a rationale for further research and treatment development from a neuropsychotherapeutic perspective. We conclude that mindfulness meditation employed as a neuropsychotherapeutic intervention in therapy is a promising treatment approach in ADHD.
Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images

Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classificationbased, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy cmeans clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.
Genomics of Actinobacteria: tracing the evolutionary history of an ancient phylum.

Actinobacteria constitute one of the largest phyla among bacteria and represent gram-positive bacteria with a high G+C content in their DNA. This bacterial group includes microorganisms exhibiting a wide spectrum of morphologies, from coccoid to fragmenting hyphal forms, as well as possessing highly variable physiological and metabolic properties. Furthermore, Actinobacteria members have adopted different lifestyles, and can be pathogens (e.g., Corynebacterium, Mycobacterium, Nocardia, Tropheryma, and Propionibacterium), soil inhabitants (Streptomyces), plant commensals (Leifsonia), or gastrointestinal commensals (Bifidobacterium). The divergence of Actinobacteria from other bacteria is ancient, making it impossible to identify the phylogenetically closest bacterial group to Actinobacteria. Genome sequence analysis has revolutionized every aspect of bacterial biology by enhancing the understanding of the genetics, physiology, and evolutionary development of bacteria. Various actinobacterial genomes have been sequenced, revealing a wide genomic heterogeneity probably as a reflection of their biodiversity. This review provides an account of the recent explosion of actinobacterial genomics data and an attempt to place this in a biological and evolutionary context.