nils-herrmann/clean_scirepeval huggingface dataset:
The Discipline and Practice of Qualitative Research

Writing about scientific research, including qualitative research, from the vantage point of the colonized, a position that she chooses to privilege, Linda Tuhiwai Smith (1999) states that “the term ‘research’ is inextricably linked to European imperialism and colonialism.” She continues, “The word itself is probably one of the dirtiest words in the indigenous world’s vocabulary. . . . It is implicated in the worst excesses of colonialism,” with the ways in which “knowledge about indigenous peoples was collected, classified, and then represented back to the West” (p. 1). This dirty word stirs up anger, silence, distrust. “It is so powerful that indigenous people even write poetry about research” (p. 1). It is one of colonialism’s most sordid legacies. Sadly, qualitative research, in many if not all of its forms (observation, participation, interviewing, ethnography), serves as a metaphor for colonial knowledge, for power, and for truth. The metaphor works this way. Research, quantitative and qualitative, is scientific. Research provides the foundation for reports about and representations of “the Other.” In the colonial context, research becomes an objective way of representing the dark-skinned Other to the white world. Colonizing nations relied on the human disciplines, especially sociology and anthropology, to produce knowledge about strange and foreign worlds. This close involvement with the colonial project contributed, in significant ways, to qualitative research’s long and anguished history, to its becoming a dirty word (for reviews, see in this volume Foley & Valenzuela, Chapter 9; Tedlock, Chapter 18). In sociology, the work of the “Chicago school” in the 1920s and 1930s established the importance of qualitative inquiry for the study of human group life. In anthropology during the same period, the discipline-defining studies of Boas, Mead, Benedict, Bateson, EvansPritchard, Radcliffe-Brown, and Malinowski charted the outlines of the fieldwork method (see Gupta & Ferguson, 1997; Stocking, 1986, 1989).
The data analytics transformation : Evidence from auditors , CFOs , and standard-setters

We investigate the use of data analytics internally by companies’ finance and accounting functions to prepare the financial statements and to detect fraud, as well as external auditors’ use of data analytics during the financial statement audit. Relying on Socio-technical theory, we examine how each of these groups use data analytics, how that usage affects their interactions with each other (i.e., client-auditor interactions), and the effect of rules and regulations on their use of data analytics. As such, we conducted 58 semi-structured interviews with prominent professionals from 15 companies, eight public accounting firms, and six standard-setters/regulators. Our sample also includes 12 client-auditor pairs (i.e., CFOs and their respective audit partners). Our findings suggest that most companies and their auditors have made changes to the financial reporting and audit processes to incorporate data analytics, with each group most often noting improved financial reporting quality or audit quality as a key benefit. Despite the benefits, both groups reported challenges that come with using data analytics, including finding employees with the right skillset, overcoming the financial cost, dealing with the lack of regulation/standards, and obtaining the data needed for analytics. Further, we leverage our client-auditor pairs to examine the effects of data analytics on the client-auditor relationship. Both parties believe the use of analytics has strengthened their relationship. However, we identify potential future conflicts regarding the audit fee model, as well as regulator concern over the increased business insights auditors are providing their clients as a result of analytics. Our study makes several important contributions to practice and theory, as we are among the first to empirically examine companies’ and audit firms’ recent and significant investment in developing data analytic tools.
<e-Adventure>: Introducing educational games in the learning process

Within the last years educational games have attracted some attention from the academic community. Multiple enhancements of the learning experience are usually attributed to educational games, although the most cited is their potential to improve students' motivation. In spite of these expected advantages, how to introduce video games in the learning process is an issue that is not completely clear yet, which reduces the potential impact of educational video games. Our goal at the <;e-UCM> research group is to identify the barriers that are limiting the integration of games in the learning process and propose approaches to tackle them. The result of this work is the <;e-Adventure> platform, an educational game authoring tool that aims to make of video games just another educational tool at the disposal of the instructors. In this paper we describe how <;e-Adventure> contributes to the integration of games in the learning process through three main focuses: reduction of the high development costs of educational games, involvement of instructors in the development process to enhance the educational value, and the production of the games using a white-box model. In addition we describe the current research that we are conducting using the platform as a test-bed.
CERAMICS IN RESTORATIVE AND PROSTHETIC DENTISTRY 1

This review is intended to provide the ceramic engineer with information about the history and current use of ceramics in dentistry, contemporary research topics, and potential research agenda. Background material includes intra-oral design considerations, descriptions of ceramic dental components, and the origin, composition, and microstructure of current dental ceramics. Attention is paid to efforts involving net-shape processing, machining as a forming method, and the analysis of clinical failure. A rationale is presented for the further development of all-ceramic restorative systems. Current research topics receiving attention include microstructure/processing/property relationships, clinical failure mechanisms and in vitro testing, wear damage and wear testing, surface treatments, and microstructural modifications. The status of the field is critically reviewed with an eye toward future work. Significant improvements seem possible in the clinical use of ceramics based on engineering solutions derived from the study of clinically failed restorations, on the incorporation of higher levels of “biomimicry” in new systems, and on the synergistic developments in dental cements and adhesive
Using Deep Learning for Image-Based Plant Disease Detection

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
A deep learning approach to traffic lights: Detection, tracking, and classification

Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometry which perceives traffic lights in real-time. Within the scope of this work, we present three major contributions. The first is an accurately labeled traffic light dataset of 5000 images for training and a video sequence of 8334 frames for evaluation. The dataset is published as the Bosch Small Traffic Lights Dataset and uses our results as baseline. It is currently the largest publicly available labeled traffic light dataset and includes labels down to the size of only 1 pixel in width. The second contribution is a traffic light detector which runs at 10 frames per second on 1280×720 images. When selecting the confidence threshold that yields equal error rate, we are able to detect traffic lights as small as 4 pixels in width. The third contribution is a traffic light tracker which uses stereo vision and vehicle odometry to compute the motion estimate of traffic lights and a neural network to correct the aforementioned motion estimate.
Detection and Localization of Multiple Spoofing Attackers in Wireless Networks

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.
Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.
Automatic breast ultrasound image segmentation: A survey

Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages. Keyword: breast ultrasound (BUS) images; breast cancer; segmentation; benchmark; early detection; computer-aided diagnosis (CAD)
Automatic Micro-expression Recognition from Long Video Using a Single Spotted Apex

Recently, micro-expression recognition has seen an increase of interest from psychological and computer vision communities. As microexpressions are generated involuntarily on a person’s face, and are usually a manifestation of repressed feelings of the person. Most existing works pay attention to either the detection or spotting of micro-expression frames or the categorization of type of micro-expression present in a short video shot. In this paper, we introduced a novel automatic approach to micro-expression recognition from long video that combines both spotting and recognition mechanisms. To achieve this, the apex frame, which provides the instant when the highest intensity of facial movement occurs, is first spotted from the entire video sequence. An automatic eye masking technique is also presented to improve the robustness of apex frame spotting. With the single apex, we describe the spotted micro-expression instant using a state-of-the-art feature extractor before proceeding to classification. This is the first known work that recognizes micro-expressions from a long video sequence without the knowledge of onset and offset frames, which are typically used to determine a cropped sub-sequence containing the micro-expression. We evaluated the spotting and recognition tasks on four spontaneous micro-expression databases comprising only of raw long videos – CASME II-RAW, SMICE-HS, SMIC-E-VIS and SMIC-E-NIR. We obtained compelling results that show the effectiveness of the proposed approach, which outperform most methods that rely on human annotated sub-sequences.
Quantitative RT-PCR: pitfalls and potential.

Reverse transcription PCR (RT-PCR) represents a sensitive and powerful tool for analyzing RNA. While it has tremendous potential for quantitative applications, a comprehensive knowledge of its technical aspects is required. Successful quantitative RT-PCR involves correction for experimental variations in individual RT and PCR efficiencies. This review addresses the mathematics of RT-PCR, choice of RNA standards (internal vs. external) and quantification strategies (competitive, noncompetitive and kinetic [real-time] amplification). Finally, the discussion turns to practical considerations in experimental design. It is hoped that this review will be appropriate for those undertaking these experiments for the first time or wishing to improve (or validate) a technique in what is frequently a confusing and contradictory field.
Toward simulating entire cities with behavioral models of traffic

Resilient transportation systems enable quick evacuation, rescue, distribution of relief supplies, and other activities for reducing the impact of natural disasters and for accelerating the recovery from them. The resilience of a transportation system largely relies on the decisions made during a natural disaster. We developed an agent-based traffic simulator for predicting the results of potential actions taken with respect to the transportation system to quickly make appropriate decisions. For realistic simulation, we govern the behavior of individual drivers of vehicles with foundational principles learned from probe-car data. For example, we used the probe-car data to estimate the personality of individual drivers of vehicles in selecting their routes, taking into account various metrics of routes such as travel time, travel distance, and the number of turns. This behavioral model, which was constructed from actual data, constitutes a special feature of our simulator. We built this simulator using the X10 language, which enables massively parallel execution for simulating traffic in a large metropolitan area. We report the use cases of the simulator in three major cities in the context of disaster recovery and resilient transportation.
Cyberbullying: The New Face of Workplace Bullying?

While the subject of cyberbullying of children and adolescents has begun to be addressed, less attention and research have focused on cyberbullying in the workplace. Male-dominated workplaces such as manufacturing settings are found to have an increased risk of workplace bullying, but the prevalence of cyberbullying in this sector is not known. This exploratory study investigated the prevalence and methods of face-to-face bullying and cyberbullying of males at work. One hundred three surveys (a modified version of the revised Negative Acts Questionnaire [NAQ-R]) were returned from randomly selected members of the Australian Manufacturing Workers' Union (AMWU). The results showed that 34% of respondents were bullied face-to-face, and 10.7% were cyberbullied. All victims of cyberbullying also experienced face-to-face bullying. The implications for organizations' "duty of care" in regard to this new form of bullying are indicated.
Attribute Discovery via Predictable Discriminative Binary Codes

We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.
Content-Driven Detection of Cyberbullying on the Instagram Social Network

We study detection of cyberbullying in photosharing networks, with an eye on developing earlywarning mechanisms for the prediction of posted images vulnerable to attacks. Given the overwhelming increase in media accompanying text in online social networks, we investigate use of posted images and captions for improved detection of bullying in response to shared content. We validate our approaches on a dataset of over 3000 images along with peer-generated comments posted on the Instagram photo-sharing network, running comprehensive experiments using a variety of classifiers and feature sets. In addition to standard image and text features, we leverage several novel features including topics determined from image captions and a pretrained convolutional neural network on image pixels. We identify the importance of these advanced features in assisting detection of cyberbullying in posted comments. We also provide results on classification of images and captions themselves as potential targets for cyberbullies.
DGWC: Distributed and generic web crawler for online information extraction

Online information has become important data source to analyze the public opinion and behavior, which is significant for social management and business decision. Web crawler systems target at automatically download and parse web pages to extract expected online information. However, as the rapid increasing of web pages and the heterogeneous page structures, the performance and the rules of parsing have become two serious challenges to web crawler systems. In this paper, we propose a distributed and generic web crawler system (DGWC), in which spiders are scheduled to parallel access and parse web pages to improve performance, utilized a shared and memory based database. Furthermore, we package the spider program and the dependencies in a container called Docker to make the system easily horizontal scaling. Last but not the least, a statistics-based approach is proposed to extract the main text using supervised-learning classifier instead of parsing the page structures. Experimental results on real-world data validate the efficiency and effectiveness of DGWC.
Predicting Early Academic Failure in High School From Prior Academic Achievement , Psychosocial Characteristics , and Behavior

The authors examined the differential effects of prior academic achievement, psychosocial, behavioral, demographic, and school context factors on early high school grade point average (GPA) using a prospective study of 4,660 middle-school students from 24 schools. The findings suggest that (a) prior grades and standardized achievement are the strongest predictors of high school GPA and (b) psychosocial and behavioral factors (e.g., motivation, self-regulation, and social control) add incremental validity to the prediction of GPA. When comparing the relative importance of each set of predictors (the dominance analysis technique), the variance accounted for by psychosocial and behavioral factors is comparable to that accounted for by prior grades. These findings highlight the importance of effective risk assessment based on multiple measures (i.e., academic, psychosocial, and behavioral) for the purpose of identifying risk, referring students to intervention, and improving academic success.
Dimensions of social media addiction among university students in Kuwait

This study aimed to examine social media addiction in a sample of university students. Based on the Internet addiction scale developed by Young (1996) the researcher used cross-sectional survey methodology in which a questionnaire was distributed to 1327 undergraduate students with their consent. Factor analysis of the self-report data showed that social media addiction has three independent dimensions. These dimensions were positively related to the users experience with social media; time spent using social media and satisfaction with them. In addition, social media addiction was a negative predictor of academic performance as measured by a student's GPA. Future studies should consider the cultural values of users and examine the context of social media usage.
A Maximum Power Point Tracking System With Parallel Connection for PV Stand-Alone Applications

This paper presents the analysis, design, and implementation of a parallel connected maximum power point tracking (MPPT) system for stand-alone photovoltaic power generation. The parallel connection of the MPPT system reduces the negative influence of power converter losses in the overall efficiency because only a part of the generated power is processed by the MPPT system. Furthermore, all control algorithms used in the classical series-connected MPPT can be applied to the parallel system. A simple bidirectional dc-dc power converter is proposed for the MPPT implementation and presents the functions of battery charger and step-up converter. The operation characteristics of the proposed circuit are analyzed with the implementation of a prototype in a practical application.