nils-herrmann/clean_scirepeval huggingface dataset:
Promoting transparency and accountability through ICTs , social media , and collaborative e-government

The purpose of this paper is to examine the ways in which governments build social media and information and communication technologies (ICTs) into e-government transparency initiatives, to promote collaboration with members of the public and the ways in members of the public are able to employ the same social media to monitor government activities.  This study used an iterative strategy that involved conducting a literature review, content analysis, and web site analysis, offering multiple perspectives on government transparency efforts, the role of ICTs and social media in these efforts, and the ability of e-government initiatives to foster collaborative transparency through embedded ICTs and social media.  The paper identifies key initiatives, potential impacts, and future challenges for collaborative e-government as a means of transparency.  The paper is one of the first to examine the interrelationships between ICTs, social media, and collaborative e-government to facilitate transparency.
Learning the speech front-end with raw waveform CLDNNs

Learning an acoustic model directly from the raw waveform has been an active area of research. However, waveformbased models have not yet matched the performance of logmel trained neural networks. We will show that raw waveform features match the performance of log-mel filterbank energies when used with a state-of-the-art CLDNN acoustic model trained on over 2,000 hours of speech. Specifically, we will show the benefit of the CLDNN, namely the time convolution layer in reducing temporal variations, the frequency convolution layer for preserving locality and reducing frequency variations, as well as the LSTM layers for temporal modeling. In addition, by stacking raw waveform features with log-mel features, we achieve a 3% relative reduction in word error rate.
Deep Learning for Skin Lesion Classification

Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns the representation of the image using Google’s pretrained CNN model known as Inception-v3 . After obtaining the representation vector for our input dermoscopic images we have trained two layer feed forward neural network to classify the images as malignant or benign. The system also classifies the images based on the cause of the cancer either due to melanocytic or non-melanocytic cells using a different neural network. These classification tasks are part of the challenge organized by International Skin Imaging Collaboration (ISIC) 2017. Our system learns to classify the images based on the model built using the training images given in the challenge and the experimental results were evaluated using validation and test sets. Our system has achieved an overall accuracy of 65.8% for the validation set.
Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation

Recommending new items to existing users has remained a challenging problem due to absence of user’s past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user’s previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for Top-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.
Playing Action Video Games Improves Visuomotor Control.

Can playing action video games improve visuomotor control? If so, can these games be used in training people to perform daily visuomotor-control tasks, such as driving? We found that action gamers have better lane-keeping and visuomotor-control skills than do non-action gamers. We then trained non-action gamers with action or nonaction video games. After they played a driving or first-person-shooter video game for 5 or 10 hr, their visuomotor control improved significantly. In contrast, non-action gamers showed no such improvement after they played a nonaction video game. Our model-driven analysis revealed that although different action video games have different effects on the sensorimotor system underlying visuomotor control, action gaming in general improves the responsiveness of the sensorimotor system to input error signals. The findings support a causal link between action gaming (for as little as 5 hr) and enhancement in visuomotor control, and suggest that action video games can be beneficial training tools for driving.
Predictive analytics on IoT

The next technological disruption in the field of Internet of Things (IoT) is the predictive analytics on IoT. The predictive analytics on IoT not only helps us in achieving the goal of smart IoT devices but also reduces the time required to configure the IOT appliances. The branch of predictive analytics came into existence very recently after extensive research in the field of Data Analytics. Although the IOT process generates enormous amount of data it is of no use until properly analyses and reviewed. After the analysis process the next step in the process is to implement the result in achieving higher efficiency. The process involves use of proper classification and clustering tool which helps us in managing big dataset generated by the IoT appliances. In this paper we have covered value points and challenges faced while applying predictive analytics on IoT so that the huge dataset generated by the IoT devices can be utilized to make life easier and accomplish the goal of smart automation.
Metallogeny of gold in relation to the evolution of the Nubian Shield in Egypt

Gold mineralization in the Eastern Desert of Egypt is confined, almost completely, to the basement rocks of the Nubian Shield that was cratonized during the Panafrican orogeny. Island-arc, orogenic and post-orogenic stages are indicated for the tectonic-magmatic evolution of the Nubian Shield in Late Proterozoic times. Different styles of gold mineralization recognised in the Eastern Desert are inferred to have developed during these stages. In the island-arc stage, which is characterized by volcanic and volcaniclastic rocks in an ensimatic environment, gold mineralization is hosted in stratiform to strata-bound Algoma-type BIF and associated tuffaceous sedimentary rocks. Both types represent exhalative deposits, formed during breaks in sub-marine basaltic and bastalic–andesite volcanic eruptions. The volcanic rocks have a tholeiitic affinity and reflect an immature arc stage. Gold hosted in massive-sulphide deposits within calc-alkaline rhyolites represents another style of gold mineralization connected with mature island arc stage. During the orogenic-stage, ophiolites and island arc volcanic and volcaniclastic rocks were thrust onto the Pre-Panafrican continental margin. Subduction was active beneath the continent while the thrusting was still operative. A phase of calc-alkaline magmatic activity developed during this stage and the compressional deformation event was synchronous with regional metamorphism (greenschist–amphibolite facies). Extensional shear fractures (brittle–ductile shear zones) were broadly contemporaneous with the intense compressional tectonic regime. These fractures opened spaces in which the mineralizing fluids penetrated. Gold mineralization associated with the orogenic-stage is represented by vein-type mineralization that constituted the main target for gold since Pharaonic times. Other styles of gold mineralization during this stage are represented by altered ophiolitic serpentinites (listwaenites), Gold mineralization associated with intrusion related deposits (possibly porphyry copper deposits), as well as, auriferous quartz veins at the contacts of younger gabbros and G-2 granites. The post-orogenic stage is characterized by the dominance of intra-plate magmatism. Small amounts of the element in disseminations, stockworks and quartz veins of Sn–W–Ta–Nb mineralization represent gold mineralization connected with this stage. The link between these tectonic–magmatic stages and gold mineralization can be used as a criterion at any exploration strategy for new targets of gold mineralization in Egypt. D 2002 Elsevier Science B.V. All rights reserved.
Dismemberment and disarticulation: A forensic anthropological approach.

The dismemberment of a corpse is fairly rare in forensic medicine. It is usually performed with different types of sharp tools and used as a method of concealing the body and thus erasing proof of murder. In this context, the disarticulation of body parts is an even rarer event. The authors present the analysis of six dismemberment cases (well-preserved corpses or skeletonized remains with clear signs of dismemberment), arising from different contexts and in which different types of sharp tools were used. Two cases in particular showed peculiar features where separation of the forearms and limbs from the rest of the body was performed not by cutting through bones but through a meticulous disarticulation. The importance of a thorough anthropological investigation is thus highlighted, since it provides crucial information on the manner of dismemberment/disarticulation, the types of tools used and the general context in which the crime was perpetrated.
How online reviews affect consumers in mobile APP store: A conceptual framework based on elaboration likelihood model

With the development of mobile Internet and the information technology, mobile APP is closely linked with people' s life. However, it seriously hinders the user to find a required APP that quantity overload and homogenization of APP in mobile application store. Based on the Elaboration Likelihood Model, this article has explored the effects of online reviews on APP discoverability under different degree of involvement. It is concluded that the quality of online reviews has a great influence on APP discoverability when the degree of user involvement is high, while the degree of user involvement is low, the quantity of online reviews has a great influence. This study fills up the shortcomings of the research on online reviews to APP discoverability. At the same time, this paper makes recommendations to the application store and the developers for improving APP discoverability, and to promote the continuous development of the application market.
Global warming and obesity: a systematic review.

Global warming and the obesity epidemic are two unprecedented challenges mankind faces today. A literature search was conducted in the PubMed, Web of Science, EBSCO and Scopus for articles published until July 2017 that reported findings on the relationship between global warming and the obesity epidemic. Fifty studies were identified. Topic-wise, articles were classified into four relationships - global warming and the obesity epidemic are correlated because of common drivers (n = 21); global warming influences the obesity epidemic (n = 13); the obesity epidemic influences global warming (n = 13); and global warming and the obesity epidemic influence each other (n = 3). We constructed a conceptual model linking global warming and the obesity epidemic - the fossil fuel economy, population growth and industrialization impact land use and urbanization, motorized transportation and agricultural productivity and consequently influences global warming by excess greenhouse gas emission and the obesity epidemic by nutrition transition and physical inactivity; global warming also directly impacts obesity by food supply/price shock and adaptive thermogenesis, and the obesity epidemic impacts global warming by the elevated energy consumption. Policies that endorse deployment of clean and sustainable energy sources, and urban designs that promote active lifestyles, are likely to alleviate the societal burden of global warming and obesity.
Predictors of longitudinal changes in older adults' physical activity engagement.

The study aimed to investigate factors influencing older adults' physical activity engagement over time. The authors analyzed 3 waves of data from a sample of Israelis age 75-94 (Wave 1 n = 1,369, Wave 2 n = 687, Wave 3 n = 154). Findings indicated that physical activity engagement declined longitudinally. Logistic regressions showed that female gender, older age, and taking more medications were significant risk factors for stopping exercise at Wave 2 in those physically active at Wave 1. In addition, higher functional and cognitive status predicted initiating exercise at Wave 2 in those who did not exercise at Wave 1. By clarifying the influence of personal characteristics on physical activity engagement in the Israeli old-old, this study sets the stage for future investigation and intervention, stressing the importance of targeting at-risk populations, accommodating risk factors, and addressing both the initiation and the maintenance of exercise in the face of barriers.
How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

Today, data availability has gone from scarce to superabundant. Technologies like IoT, trends in social media and the capabilities of smart-phones are producing and digitizing lots of data that was previously unavailable. This massive increase of data creates opportunities to gain new business models, but also demands new techniques and methods of data quality in knowledge discovery, especially when the data comes from different sources (e.g., sensors, social networks, cameras, etc.). The data quality process of the data set proposes conclusions about the information they contain. This is increasingly done with the aid of data cleaning approaches. Therefore, guaranteeing a high data quality is considered as the primary goal of the data scientist. In this paper, we propose a process for data cleaning in regression models (DC-RM). The proposed data cleaning process is evaluated through a real datasets coming from the UCI Repository of Machine Learning Databases. With the aim of assessing the data cleaning process, the dataset that is cleaned by DC-RM was used to train the same regression models proposed by the authors of UCI datasets. The results achieved by the trained models with the dataset produced by DC-RM are better than or equal to that presented by the datasets’ authors.
Gabor noise by example

Procedural noise is a fundamental tool in Computer Graphics. However, designing noise patterns is hard. In this paper, we present Gabor noise by example, a method to estimate the parameters of bandwidth-quantized Gabor noise, a procedural noise function that can generate noise with an arbitrary power spectrum, from exemplar Gaussian textures, a class of textures that is completely characterized by their power spectrum. More specifically, we introduce (i) bandwidth-quantized Gabor noise, a generalization of Gabor noise to arbitrary power spectra that enables robust parameter estimation and efficient procedural evaluation; (ii) a robust parameter estimation technique for quantized-bandwidth Gabor noise, that automatically decomposes the noisy power spectrum estimate of an exemplar into a sparse sum of Gaussians using non-negative basis pursuit denoising; and (iii) an efficient procedural evaluation scheme for bandwidth-quantized Gabor noise, that uses multi-grid evaluation and importance sampling of the kernel parameters. Gabor noise by example preserves the traditional advantages of procedural noise, including a compact representation and a fast on-the-fly evaluation, and is mathematically well-founded.
Computational Approaches to Storytelling and Creativity

naturally associate with computers. Yet over the last few years there has been a surge of research efforts concerning the combination of both subjects. This article tries to shed light on these efforts. In carrying out this program, one is handicapped by the fact that, as words, both creativity and storytelling are severely lacking in the precision one expects of words to be used for intellectual endeavor. If a speaker were to mention either word in front of an audience, each person listening would probably come up with a different mental picture of what is intended. To avoid the risks that such vagueness might lead to, an initial effort is made here to restrict the endeavor to those aspects that have been modeled computationally in some model or system. The article then proceeds to review some of the research efforts that have addressed these problems from a computational point of view.
Blockchains for Business Process Management - Challenges and Opportunities

Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.
Architectural Tactics for Big Data Cybersecurity Analytic Systems: A Review

Big Data Cybersecurity Analytics is increasingly becoming an important area of research and practice aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity Analytic Systems have been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective.  This paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for Big Data Cybersecurity Analytic Systems.  We used Systematic Literature Review (SLR) method for reviewing 74 primary studies selected using well-defined criteria.  Our findings are twofold: (i) identification of 12 most frequently reported quality attributes and the justification for their significance for Big Data Cybersecurity Analytic Systems; and (ii) identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with Big Data Cybersecurity Analytic systems. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each.  Our findings have revealed that (a) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support in the literature (b) empirical investigation is required to evaluate the impact of codified architectural tactics (c) a good deal of research effort should be invested to explore the trade-offs and dependencies among the identified tactics (d) there is a general lack of effective collaboration between academia and industry for supporting the field of Big Data Cybersecurity Analytic Systems and (e) more research is required on the comparative analysis among big data processing frameworks (i.e., Hadoop, Spark, and Storm) when used for Big Data Cybersecurity Analytic Systems.
Visual Madlibs: Fill in the blank Image Generation and Question Answering

In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset, the Visual Madlibs dataset, is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context. We provide several analyses of the Visual Madlibs dataset and demonstrate its applicability to two new description generation tasks: focused description generation, and multiple-choice question-answering for images. Experiments using joint-embedding and deep learning methods show promising results on these tasks.
Unraveling the mysteries of anxiety and its disorders from the perspective of emotion theory.

The ascendance of emotion theory, recent advances in cognitive science and neuroscience, and increasingly important findings from developmental psychology and learning make possible an integrative account of the nature and etiology of anxiety and its disorders. This model specifies an integrated set of triple vulnerabilities: a generalized biological (heritable) vulnerability, a generalized psychological vulnerability based on early experiences in developing a sense of control over salient events, and a more specific psychological vulnerability in which one learns to focus anxiety on specific objects or situations. The author recounts the development of anxiety and related disorders based on these triple vulnerabilities and discusses implications for the classification of emotional disorders.
Getting even or moving on? Power, procedural justice, and types of offense as predictors of revenge, forgiveness, reconciliation, and avoidance in organizations.

A field study and an experimental study examined relationships among organizational variables and various responses of victims to perceived wrongdoing. Both studies showed that procedural justice climate moderates the effect of organizational variables on the victim's revenge, forgiveness, reconciliation, or avoidance behaviors. In Study 1, a field study, absolute hierarchical status enhanced forgiveness and reconciliation, but only when perceptions of procedural justice climate were high; relative hierarchical status increased revenge, but only when perceptions of procedural justice climate were low. In Study 2, a laboratory experiment, victims were less likely to endorse vengeance or avoidance depending on the type of wrongdoing, but only when perceptions of procedural justice climate were high.
Kernel Graph Convolutional Neural Networks

Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during training. Unfortunately, they cannot handle irregular data such as graphs. We address this challenge by using graph kernels to embed meaningful local neighborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets. Code and data are publicly available.