nils-herrmann/clean_scirepeval huggingface dataset:
Emotion analysis: A survey

Emotions form a very important and basic aspect of our lives. Whatever we do, whatever we say, somehow does reflect some of our emotions, though may not be directly. To understand the very fundamental behavior of a human, we need to analyze these emotions through some emotional data, also called, the affect data. This data can be text, voice, facial expressions etc. Using this emotional data for analyzing the emotions also forms an interdisciplinary field, called Affective Computing. Computation of emotions is a very challenging task, much work has been done but many more increments are also possible. With the advent of social networking sites, many people tend to get attracted towards analyzing this very text available on these various sites. Analyzing this data over the Internet means we are spanning across the whole continent, going through all the cultures and communities across. This paper summarizes the previous works done in the field of textual emotion analysis based on various emotional models and computational approaches used.
An eye tracking study of how font size and type influence online reading

In order to maximize online reading performance and comprehension, how should a designer choose typographical variables such as font size and font type? This paper presents an eye tracking study of how font size and font type affect online reading. In a between-subjects design, we collected data from 82 subjects reading stories formatted in a variety of point sizes, san serif, and serif fonts. Reading statistics such as reading speed were computed, and post-tests of comprehension were recorded. For smaller font sizes, fixation durations are significantly longer, resulting in slower reading – but not significantly slower. While there were no significant differences in serif vs. san serif fonts, serif reading was slightly faster. Significant eye tracking differences were found for demographic variables such as age group and whether English is the subject’s first language.
Big Data , Ethics , and the Social Implications of Knowledge Production

This position paper addresses current debates about data in general, and big data specifically, by examining the ethical issues arising from advances in knowledge production. Typically ethical issues such as privacy and data protection are discussed in the context of regulatory and policy debates. Here we argue that this overlooks a larger picture whereby human autonomy is undermined by the growth of scientific knowledge. To make this argument, we first offer definitions of data and big data, and then examine why the uses of data-driven analyses of human behaviour in particular have recently experienced rapid growth. Next, we distinguish between the contexts in which big data research is used, and argue that this research has quite different implications in the context of scientific as opposed to applied research. We conclude by pointing to the fact that big data analyses are both enabled and constrained by the nature of data sources available. Big data research will nevertheless inevitably become more pervasive, and this will require more awareness on the part of data scientists, policymakers and a wider public about its contexts and often unintended consequences.
Jpeg Image Compression Using Discrete Cosine Transform - A Survey

Due to the increasing requirements for transmission of images in computer, mobile environments, the research in the field of image compression has increased significantly. Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. When we compute the number of bits per image resulting from typical sampling rates and quantization methods, we find that Image compression is needed. Therefore development of efficient techniques for image compression has become necessary .This paper is a survey for lossy image compression using Discrete Cosine Transform, it covers JPEG compression algorithm which is used for full-colour still image applications and describes all the components of it.
Robust Smartphone App Identification via Encrypted Network Traffic Analysis

The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. In addition, the presence or absence of particular apps on a smartphone can inform an adversary, who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data, such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices, and across different versions of apps. In addition, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps, such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.
Quality Dimensions Trend Analysis in the Context of Evaluating E-government Services

Governments are increasingly realizing the importance of utilizing Information and Communication Technologies (ICT) as a tool to better address user’s/citizen’s needs. As citizen’s expectations grow, governments need to deliver services of high quality level to motivate more users to utilize these available e-services. In spite of this, governments still fall short in their service quality level offered to citizens/users. Thus understanding and measuring service quality factors become crucial as the number of services offered is increasing while not realizing what citizens/users really look for when they utilize these services. The study presents an extensive literature review on approaches used to evaluate e-government services throughout a phase of time. The study also suggested those quality/factors indicators government’s need to invest in of high priority in order to meet current and future citizen’s expectations of service quality.
Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels

Unsupervised semantic segmentation in the time series domain is a much-studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data. However, the current techniques have several shortcomings, which have limited the adoption of time series semantic segmentation beyond academic settings for three primary reasons. First, most methods require setting/learning many parameters and thus may have problems generalizing to novel situations. Second, most methods implicitly assume that all the data is segmentable, and have difficulty when that assumption is unwarranted. Finally, most research efforts have been confined to the batch case, but online segmentation is clearly more useful and actionable. To address these issues, we present an algorithm which is domain agnostic, has only one easily determined parameter, and can handle data streaming at a high rate. In this context, we test our algorithm on the largest and most diverse collection of time series datasets ever considered, and demonstrate our algorithm's superiority over current solutions. Furthermore, we are the first to show that semantic segmentation may be possible at superhuman performance levels.
SAFE-STORAGE GUN LAWS : ACCIDENTAL DEATHS , SUICIDES , AND CRIME

It is frequently assumed that safe-storage gun laws reduce accidental gun deaths and total suicides, while the possible impact on crime rates is ignored. We find no support that safe-storage laws reduce either juvenile accidental gun deaths or suicides. Instead, these storage requirements appear to impair people’s ability to use guns defensively. Because accidental shooters also tend to be the ones most likely to violate the new law, safe-storage laws increase violent and property crimes against law-abiding citizens with no observable offsetting benefit in terms of reduced accidents or suicides.
maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are an effective technique for partitioning an image or volume into locally similar regions, and are a common building block for the development of detection, segmentation and analysis methods. We introduce maskSLIC an extension of SLIC to create supervoxels within regions-of-interest, and demonstrate, on examples from 2-dimensions to 4-dimensions, that maskSLIC overcomes issues that affect SLIC within an irregular mask. We highlight the benefits of this method through examples, and show that it is able to better represent underlying tumour subregions and achieves significantly better results than SLIC on the BRATS 2013 brain tumour challenge data (p=0.001) – outperforming SLIC on 18/20 scans. Finally, we show an application of this method for the analysis of functional tumour subregions and demonstrate that it is more effective than voxel clustering.
Learnable Crawling : An Efficient Approach to Topic-specific Web Resource Discovery

The rapid growth of the Internet has put us into trouble when we need to find information in such a large network of databases. At present, using topic-specific web crawler becomes a way to seek the information. The main characteristic of topic-specific web crawler is trying to select and retrieve only the relevant web pages in each crawling processes. There are many previous researches focusing on the topic-specific web crawling. However, no one has ever mentioned about how the crawler does in the next crawling. In this paper, we present an algorithm that covers the detail of both the first and the next crawling. For efficient result of the next crawling, we keep the log of previous crawling to build some knowledge bases: seed URLs, topic keywords and URL prediction. These knowledge bases are used to build the experiences of the crawler to produce the result of the next crawling in a more efficient way.
Investigations of metastable Ca2IrO4 epitaxial thin-films: systematic comparison with Sr2IrO4 and Ba2IrO4

We have synthesized thermodynamically metastable Ca2IrO4 thin-films on YAlO3 (110) substrates by pulsed laser deposition. The epitaxial Ca2IrO4 thin-films are of K2NiF4-type tetragonal structure. Transport and optical spectroscopy measurements indicate that the electronic structure of the Ca2IrO4 thin-films is similar to that of Jeff = 1/2 spin-orbit-coupled Mott insulator Sr2IrO4 and Ba2IrO4, with the exception of an increased gap energy. The gap increase is to be expected in Ca2IrO4 due to its increased octahedral rotation and tilting, which results in enhanced electron-correlation, U/W. Our results suggest that the epitaxial stabilization growth of metastable-phase thin-films can be used effectively for investigating layered iridates and various complex-oxide systems.
A prototype Malayalam to Sign Language Automatic Translator

Sign language, which is a medium of communication for deaf people, uses manual communication and body language to convey meaning, as opposed to using sound. This paper presents a prototype Malayalam text to sign language translation system. The proposed system takes Malayalam text as input and generates corresponding Sign Language. Output animation is rendered using a computer generated model. This system will help to disseminate information to the deaf people in public utility places like railways, banks, hospitals etc. This will also act as an educational tool in learning Sign Language.
Hardware and Software Support for Virtualization

is book focuses on the core question of the necessary architectural support provided by hardware to efficiently run virtual machines, and of the corresponding design of the hypervisors that run them. Virtualization is still possible when the instruction set architecture lacks such support, but the hypervisor remains more complex and must rely on additional techniques. Despite the focus on architectural support in current architectures, some historical perspective is necessary to appropriately frame the problem. e first half of the book provides the historical perspective of the theoretical framework developed four decades ago by Popek and Goldberg. It also describes earlier systems that enabled virtualization despite the lack of architectural support in hardware. As is often the case, theory defines a necessary—but not sufficient—set of features, and modern architectures are the result of the combination of the theoretical framework with insights derived from practical systems. e second half of the book describes state-of-the-art support for virtualization in both x86-64 and ARM processors. is book includes an in-depth description of the CPU, memory, and I/O virtualization of these two processor architectures, as well as case studies on the Linux/KVM, VMware, and Xen hypervisors. It concludes with a performance comparison of virtualization on current-generation x86and ARM-based systems across multiple hypervisors.
Establishing confidence in the output of qualitative research synthesis: the ConQual approach

The importance of findings derived from syntheses of qualitative research has been increasingly acknowledged. Findings that arise from qualitative syntheses inform questions of practice and policy in their own right and are commonly used to complement findings from quantitative research syntheses. The GRADE approach has been widely adopted by international organisations to rate the quality and confidence of the findings of quantitative systematic reviews. To date, there has been no widely accepted corresponding approach to assist health care professionals and policy makers in establishing confidence in the synthesised findings of qualitative systematic reviews.



A methodological group was formed develop a process to assess the confidence in synthesised qualitative research findings and develop a Summary of Findings tables for meta-aggregative qualitative systematic reviews.



Dependability and credibility are two elements considered by the methodological group to influence the confidence of qualitative synthesised findings. A set of critical appraisal questions are proposed to establish dependability, whilst credibility can be ranked according to the goodness of fit between the author's interpretation and the original data. By following the processes outlined in this article, an overall ranking can be assigned to rate the confidence of synthesised qualitative findings, a system we have labelled ConQual.



The development and use of the ConQual approach will assist users of qualitative systematic reviews to establish confidence in the evidence produced in these types of reviews and can serve as a practical tool to assist in decision making.
Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning

Zero-shot methods in language, vision and other domains rely on a cross-space mapping function that projects vectors from the relevant feature space (e.g., visualfeature-based image representations) to a large semantic word space (induced in an unsupervised way from corpus data), where the entities of interest (e.g., objects images depict) are labeled with the words associated to the nearest neighbours of the mapped vectors. Zero-shot cross-space mapping methods hold great promise as a way to scale up annotation tasks well beyond the labels in the training data (e.g., recognizing objects that were never seen in training). However, the current performance of cross-space mapping functions is still quite low, so that the strategy is not yet usable in practical applications. In this paper, we explore some general properties, both theoretical and empirical, of the cross-space mapping function, and we build on them to propose better methods to estimate it. In this way, we attain large improvements over the state of the art, both in cross-linguistic (word translation) and cross-modal (image labeling) zero-shot experiments.
Pediatric Oral Motor Feeding Assessments: A Systematic Review.

To describe the clinical properties and psychometric soundness of pediatric oral motor feeding assessments.



A systematic search was conducted using Medline, CINAHL, EMBASE, PsycInfo, and HAPI databases. Assessments were analyzed for their clinical and psychometric characteristics.



12 assessment tools were identified to meet the inclusion/exclusion criteria. Clinical properties varied from assessments evaluating oral-motor deficits, screening to identify feeding problems, and monitoring feeding progress. Most assessments were designed for children with developmental disabilities or cerebral palsy. Eleven assessments had psychometric evidence, of these nine had reliability and validity testing (Ability for Basic Feeding and Swallowing Scale for Children, Behavioral Assessment Scale of Oral Functions in Feeding, Dysphagia Disorder Survey, Functional Feeding Assessment-modified, Gisel Video Assessment, Montreal Children's Hospital Feeding Scale, Oral Motor Assessment Scale, Schedule for Oral Motor Assessment, and Screening Tool of Feeding Problems Applied to Children). The Brief Assessment of Motor Function-Oral Motor Deglutition and the Pediatric Assessment Scale for Severe Feeding Problems had reliability testing only. The Slurp Test was not tested for any psychometric properties. Overall, psychometric evidence was inconsistent and inadequate for the evaluative tools.
Single Image Segmentation with Estimated Depth

A novel framework for automatic object segmentation is proposed that exploits depth information estimated from a single image as an additional cue. For example, suppose that we have an image containing an object and a background with a similar color or texture to the object. The proposed framework enables us to automatically extract the object from the image while eliminating the misleading background. Although our segmentation framework takes a form of a traditional formulation based on Markov random fields, the proposed method provides a novel scheme to integrate depth and color information, which derives objectness/backgroundness likelihood. We also employ depth estimation via supervised learning so that the proposed method can work even if it has only a single input image with no actual depth information. Experimental results with a dataset originally collected for the evaluation demonstrate the effectiveness of the proposed method against the baseline method and several existing methods for salient region detection.
Real-time eye blink and wink detection for object selection in HCI systems

This paper presents an approach for real-time detection of three types of eye blinks: eye blink (blinking both eyes simultaneously), left and right winks. The process of blink detection has been divided into four parts viz. face localization in facial images acquired through a video camera, eye pair localization, pixels’ motion analysis using optical flow technique, and classification of eye blinks. Blink detection has been performed using a video camera and MATLAB software with image processing and computer vision toolbox. The algorithm takes about 60 ms time for processing a frame and 250 ms time for confirmation and classification of the detected blink. An experiment was conducted to evaluate the performance of the proposed approach in which 10 users voluntarily participated. The performance of the proposed method has been tested under two lighting conditions: natural lighting conditions and controlled lighting conditions. Also, the performance has been tested by varying the distance of the user from the camera. Here, it is observed that the system gives best performance when used under controlled lighting conditions and the user sitting at a distance of about 0.5 m. Accuracy of the proposed approach has been found to be 96, 92 and 88% for detection of eye blink, left wink and right wink, respectively. The proposed method has also been tested on ZJU dataset where it has given precision, detection accuracy and false alarm rate of values 94.11, 91.2 and 1.54%, respectively. The proposed system has been used and evaluated for performing various mouse analogous functions using eye blinks and winks. It has given an accuracy of 90, 80 and 90% in performing left click, double click, and right click operations, respectively.
Micro-expression recognition: an updated review of current trends, challenges and solutions

Micro-expression (ME) recognition has attracted numerous interests within the computer vision circle in different contexts particularly, localization, magnification, and recognition. Challenges in these areas remain relevant due to the nature of ME’s split-second transition with minute intensity levels. In this paper, a comprehensive state-of-the-art analysis of ME recognition and detection challenges are provided. Contemporary solutions are categorized into low-level, mid-level, and high-level solutions with a review of their characteristics and performances. This paper also provides possible extensions to basic methods, highlight, and predict emerging trends. A thorough analysis of mainstream ME datasets is also provided by elucidating each of their advantages and limitations. This survey gives readers an understanding of ME recognition and an appreciation of future research direction in ME recognition systems.
Genetic improvement of Pacific white shrimp [Penaeus (Litopenaeus) vannamei]: perspectives for genomic selection

The uses of breeding programs for the Pacific white shrimp [Penaeus (Litopenaeus) vannamei] based on mixed linear models with pedigreed data are described. The application of these classic breeding methods yielded continuous progress of great value to increase the profitability of the shrimp industry in several countries. Recent advances in such areas as genomics in shrimp will allow for the development of new breeding programs in the near future that will increase genetic progress. In particular, these novel techniques may help increase disease resistance to specific emerging diseases, which is today a very important component of shrimp breeding programs. Thanks to increased selection accuracy, simulated genetic advance using genomic selection for survival to a disease challenge was up to 2.6 times that of phenotypic sib selection.
Information-based functional brain mapping.

The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.