nils-herrmann/clean_scirepeval huggingface dataset:
A postmodern Pandora's box: anti-vaccination misinformation on the Internet.

The Internet plays a large role in disseminating anti-vaccination information. This paper builds upon previous research by analyzing the arguments proffered on anti-vaccination websites, determining the extent of misinformation present, and examining discourses used to support vaccine objections. Arguments around the themes of safety and effectiveness, alternative medicine, civil liberties, conspiracy theories, and morality were found on the majority of websites analyzed; misinformation was also prevalent. The most commonly proposed method of combating this misinformation is through better education, although this has proven ineffective. Education does not consider the discourses supporting vaccine rejection, such as those involving alternative explanatory models of health, interpretations of parental responsibility, and distrust of expertise. Anti-vaccination protestors make postmodern arguments that reject biomedical and scientific "facts" in favour of their own interpretations. Pro-vaccination advocates who focus on correcting misinformation reduce the controversy to merely an "educational" problem; rather, these postmodern discourses must be acknowledged in order to begin a dialogue.
Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due, in part, to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, named the Markov chain Hebbian learning algorithm. The algorithm pursues efficient use in memory during training in that: 1) the weight matrix has ternary elements (−1, 0, 1) and 2) each update follows a Markov chain—the upcoming update does not need past weight values. The algorithm was verified by two proof-of-concept tasks: image (MNIST and CIFAR-10 datasets) recognition and multiplication table memorization. Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans’ mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into memory-based arithmetic.
BPM Governance: An Exploratory Study in Public Organizations

Business Process Management is a widely known approach focused on aligning processes of an organization in order to achieve improved efficiency and client satisfaction. Governance is an important requirement to enable successful BPM initiatives. This paper provides a qualitative empirical study to investigate what BPM governance elements are adopted by teams conducting early BPM initiatives in public organizations. The results suggest that early BPM adopters in public sector face several barriers due to difficulties in acquiring professionals with BPM expertise, bureaucracy and legislation rigidity, among others. In particular, committed sponsorship and monitoring were appointed as important BPM governance facilitators by participants of the study. Findings also show that further empirical studies are needed to increase the body of evidence in this field.
The Big-five Trait Taxonomy: History, Measurement, and Theoretical Perspectives

2 Taxonomy is always a contentious issue because the world does not come to us in neat little packages (S. Personality has been conceptualized from a variety of theoretical perspectives, and at various levels of Each of these levels has made unique contributions to our understanding of individual differences in behavior and experience. However, the number of personality traits, and scales designed to measure them, escalated without an end in sight (Goldberg, 1971). Researchers, as well as practitioners in the field of personality assessment, were faced with a bewildering array of personality scales from which to choose, with little guidance and no overall rationale at hand. What made matters worse was that scales with the same name often measure concepts that are not the same, and scales with different names often measure concepts that are quite similar. Although diversity and scientific pluralism are useful, the systematic accumulation of findings and the communication among researchers became difficult amidst the Babel of concepts and scales. Many personality researchers had hoped that they might devise the structure that would transform the Babel into a community speaking a common language. However, such an integration was not to be achieved by any one researcher or by any one theoretical perspective. As Allport once put it, " each assessor has his own pet units and uses a pet battery of diagnostic devices " (1958, p. 258). What personality psychology needed was a descriptive model, or taxonomy, of its subject matter. One of the central goals of scientific taxonomies is the definition of overarching domains within which large numbers of specific instances can be understood in a simplified way. Thus, in personality psychology, a taxonomy would permit researchers to study specified domains of personality characteristics, rather than examining separately the thousands of particular attributes that make human beings individual and unique. Moreover, a generally accepted taxonomy would greatly facilitate the accumulation and communication of empirical findings by offering a standard vocabulary, or nomenclature. After decades of research, the field is approaching consensus on a general taxonomy of personality traits, the " Big Five " personality dimensions. These dimensions do not represent a particular theoretical perspective but were derived from analyses of the natural-language terms people use to describe themselves 3 and others. Rather than replacing all previous systems, the Big Five taxonomy serves an integrative function because it can represent the various and diverse systems of personality …
A Survey on Parts of Speech Tagging for Indian Languages

Part of speech (POS) tagging is basically the process of automatically assigning its lexical category to each word according to its context and definition. Each word of sentence is marked in croups as corresponding to a particular part of speech like noun, verb, adjective and adverb. POS serves as a first step in natural language process applications like information extraction, parsing, and word sense disambiguation etc. this paper presents a survey on Part of Speech taggers used for Indian languages. The main problem of tagging is to find proper way to tag each word according to particular part of speech. Very less work has been done for POS tagging on Indian languages mainly due to morphologically richness. In this paper, various techniques are discussed that are used for development of POS
Intelligent Web Crawling

Web crawling, a process of collecting web pages in an automated manner, is the primary and ubiquitous operation used by a large number of web systems and agents starting from a simple program for website backup to a major web search engine. Due to an astronomical amount of data already published on the Web and ongoing exponential growth of web content, any party that want to take advantage of massive-scale web data faces a high barrier to entry. We start with background on web crawling and the structure of the Web. We then discuss different crawling strategies and describe adaptive web crawling techniques leading to better overall crawl performance. We finally overview some of the challenges in web crawling by presenting such topics as collaborative web crawling, crawling the deep Web and crawling multimedia content. Our goals are to introduce the intelligent systems community to the challenges in web crawling research, present intelligent web crawling approaches, and engage researchers and practitioners for open issues and research problems. Our presentation could be of interest to web intelligence and intelligent agent technology communities as it particularly focuses on the usage of intelligent/adaptive techniques in the web crawling domain.
A Systematic Review of Service Level Management in the Cloud

Cloud computing make it possible to flexibly procure, scale, and release computational resources on demand in response to workload changes. Stakeholders in business and academia are increasingly exploring cloud deployment options for their critical applications. One open problem is that service level agreements (SLAs) in the cloud ecosystem are yet to mature to a state where critical applications can be reliably deployed in clouds. This article systematically surveys the landscape of SLA-based cloud research to understand the state of the art and identify open problems. The survey is particularly aimed at the resource allocation phase of the SLA life cycle while highlighting implications on other phases. Results indicate that (i) minimal number of SLA parameters are accounted for in most studies; (ii) heuristics, policies, and optimisation are the most commonly used techniques for resource allocation; and (iii) the monitor-analysis-plan-execute (MAPE) architecture style is predominant in autonomic cloud systems. The results contribute to the fundamentals of engineering cloud SLA and their autonomic management, motivating further research and industrial-oriented solutions.
Transcending transmedia: emerging story telling structures for the emerging convergence platforms

Although the current paradigm for expanded participatory storytellling is the "transmedia" exploitation of the same storyworld on multiple platforms, it can be more productive to think of the digital medium as a single platform, combining all the functionalities we now associate with networked computers, game consoles, and conventionally delivered episodic television to enhance the traditional strengths and cultural functions of television. By looking at the how the four affordances of the digital medium the procedural, participatory, encyclopedic, and spatial apply to three core characteristics of television moving images, sustained storytelling, and individual viewing in the presence of a mass audience -- we can identify areas of emerging conventions and widening opportunity for design innovation
An Overview of Evolutionary Algorithms in Multiobjective Optimization

The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.
Enhancing Automated Decision Support across Medical and Oral Health Domains with Semantic Web Technologies

Research has shown that the general health and oral health of an individual are closely related. Accordingly, current practice of isolating the information base of medical and oral health domains can be dangerous and detrimental to the health of the individual. However, technical issues such as heterogeneous data collection and storage formats, limited sharing of patient information and lack of decision support over the shared information are the principal reasons for the current state of affairs. To address these issues, the following research investigates the development and application of a cross-domain ontology and rules to build an evidence-based and reusable knowledge base consisting of the inter-dependent conditions from the two domains. Through example implementation of the knowledge base in Protégé, we demonstrate the effectiveness of our approach in reasoning over and providing decision support for cross-domain patient information.
Joint Text Embedding for Personalized Content-based Recommendation

Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published everyday. However, most existing recommendation techniques, such as matrix factorization based methods, mainly rely on interaction histories to learn representations of items. While latent factors of items can be learned eectively from user interaction data, in many cases, such data is not available, especially for newly emerged items. In this work, we aim to address the problem of personalized recommendation for completely new items with text information available. We cast the problem as a personalized text ranking problem and propose a general framework that combines text embedding with personalized recommendation. Users and textual content are embedded into latent feature space. e text embedding function can be learned end-to-end by predicting user interactions with items. To alleviate sparsity in interaction data, and leverage large amount of text data with lile or no user interactions, we further propose a joint text embedding model that incorporates unsupervised text embedding with a combination module. Experimental results show that our model can signicantly improve the eectiveness of recommendation systems on real-world datasets.
Content-Based Music Information Retrieval (CB-MIR) and Its Applications toward the Music Industry: A Review

A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the signal-processing techniques to extract useful features for performing specific tasks mentioned above and discusses their strengths as well as weaknesses. This article also points to some general research issues in CB-MIR and probable approaches toward their solutions so as to improve the efficiency of the existing CB-MIR systems.
SkiMap++: Real-Time Mapping and Object Recognition for Robotics

We introduce SkiMap++, an extension to the recently proposed SkiMap mapping framework for robot navigation . The extension deals with enriching the map with semantic information concerning the presence in the environment of certain objects that may be usefully recognized by the robot, e.g. for the sake of grasping them. More precisely, the map can accommodate information about the spatial locations of certain 3D object features, as determined by matching the visual features extracted from the incoming frames through a random forest learned off-line from a set of object models. Thereby, evidence about the presence of object features is gathered from multiple vantage points alongside with the standard geometric mapping task, so to enable recognizing the objects and estimating their 6 DOF poses. As a result, SkiMap++ can reconstruct the geometry of large scale environments as well as localize some relevant objects therein (Fig.1) in real-time on CPU. As an additional contribution, we present an RGB-D dataset featuring ground-truth camera and object poses, which may be deployed by researchers interested in pursuing SLAM alongside with object recognition, a topic often referred to as Semantic SLAM.
Outpatient appointment scheduling given individual day-dependent no-show predictions

This paper examines the combined use of predictive analytics, optimization, and overbooking to schedule outpatient appointments in the presence of no-shows. We tackle the problem of optimally overbooking appointments given no-show predictions that depend on the individual appointment characteristics and on the appointment day. The goal is maximizing the number of patients seen while minimizing waiting time and overtime. Our analysis leads to the definition of a near-optimal and simple heuristic which consists of giving same-day appointments to likely shows and future-day appointments to likely no-shows. We validate our findings by performing extensive simulation tests based on an empirical data set of nearly fifty thousand appointments from a real outpatient clinic. The results suggest that our heuristic can lead to a substantial increase in performance and that it should be preferred to open access under most parameter configurations. Our paper will be of great interest to practitioners who want to improve their clinic performance by using individual no-show predictions to guide appointment scheduling.
Exploring sub-20nm FinFET design with Predictive Technology Models

Predictive MOSFET models are critical for early stage design-technology co-optimization and circuit design research. In this work, Predictive Technology Model files for sub-20nm multi-gate transistors have been developed (PTM-MG). Based on MOSFET scaling theory, the 2011 ITRS roadmap and early stage silicon data from published results, PTM for FinFET devices are generated for 5 technology nodes corresponding to the years 2012-2020 on the ITRS roadmap.
Identification of autism spectrum disorder using deep learning and the ABIDE dataset

The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.
An Improved Algorithm for Harris Corner Detection

Corner points are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this novel we discuss the theory of the Harris corner detection and indicate its disadvantage. Then it proposes an improved algorithm of Harris detection algorithm based on the neighboring point eliminating method. It reduces the time of the detection, and makes the corners distributing more homogenous so that avoids too many corners stay together. Experimental results show that the algorithm can detect corner more equality distributing, and can be used in some fact applications such as image registration well. Key words-Corner point; Harris corner; neighboring point; homogenous distributing 1. Introduce Corner point is a maximum curvature point of the two-dimensional image brightness change or the edge of sharp curves in a image, which haves widely used in such as the camera calibration, virtual scene reconstruction, motion estimation, image registration and more computer vision tasks. These points keep important features of image and can also be effective in reducing the amount of data information, allow real-time processing possible. Now, there are two corner detection methods: based on the image edge method and based on the image gray scale method. The former always need to encode the image edge, which is heavily dependent on image segmentation and edge extraction, have large difficulty and calculate, and if the detected area haves some change, it will lead to the failure of the operation, therefore, this method is not used widely. The latter method detect corner by calculating the curvature and gradient, it avoid these disadvantage. In currently, it haves Moravec operator, Harris operator, Susan operator and so on. Experimental shows that Harris corner detection method haves the best result in above mentioned. But in fact applications, Harris algorithm must to give a proper threshold T, and the algorithm based on the provided threshold to detect the ideal corner. T always depend on the image, specifically it is difficult to determine under the different colors. And it gets the ideal T after user blind set T more times. In addition, the points which have the larger eigenvalues are concentrated in some regions cause the detect corners are non-uniform distribution; and if T dropped, even though the overall distribution of the corner towards reasonable, but it also leads to the points close together, have the clustering phenomenon, it will affect the later analysis and processing. 2. Harris Corner Detection Algorithm 2.1 Detection Theory Harris corner detection algorithm is based on the point feature extraction of signal. It makes the window ω (usually rectangular area) to move infinitesimal displacement to any direction, and the variation of gray can be defined as: − = + + 2 , , , , ] [ v u v y u x v u y x I I w E + + = v u v u y x O yY xX w , 2 2 2 , )] , ( [ 2 2 2 By Cxy Ax + + = T y x M y x ) , ( ) , ( = (1) Where X and Y are one-order gray-level gradient, they can be got by convolution with 978-1-4244-4131-0/09/$25.00
A NEW EMOTION DATABASE : CONSIDERATIONS , SOURCES AND SCOPE

Research on the expression of emotion is underpinned by databases. Reviewing available resources persuaded us of the need to develop one that prioritised ecological validity. The basic unit of the database is a clip, which is an audiovisual recording of an episode that appears to be reasonably selfcontained. Clips range from 10 – 60 secs, and are captured as MPEG files. They were drawn from two main sources. People were recorded discussing emotive subjects either with each other, or with one of the research team. We also recorded extracts from television programs where members of the public interact in a way that at least appears essentially spontaneous. Associated with each clip are two additional types of file. An audio file (.wav format) contains speech alone, edited to remove sounds other than the main speaker. An interpretation file describes the emotional state that observers attribute to the main speaker, using the FEELTRACE system to provide a continuous record of the perceived ebb and flow of emotion. Clips have been extracted for 100 speakers, with at least two for each speaker (one relatively neutral and others showing marked emotions of different kinds).