nils-herrmann/clean_scirepeval huggingface dataset:
Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media

The web is a huge source of valuable information. However, in recent times, there is an increasing trend towards false claims in social media, other web-sources, and even in news. Thus, factchecking websites have become increasingly popular to identify such misinformation based on manual analysis. Recent research proposed methods to assess the credibility of claims automatically. However, there are major limitations: most works assume claims to be in a structured form, and a few deal with textual claims but require that sources of evidence or counter-evidence are easily retrieved from the web. None of these works can cope with newly emerging claims, and no prior method can give user-interpretable explanations for its verdict on the claim’s credibility. This paper overcomes these limitations by automatically assessing the credibility of emerging claims, with sparse presence in web-sources, and generating suitable explanations from judiciously selected sources. To this end, we retrieve diverse articles about the claim, and model the mutual interaction between: the stance (i.e., support or refute) of the sources, the language style of the articles, the reliability of the sources, and the claim’s temporal footprint on the web. Extensive experiments demonstrate the viability of our method and its superiority over prior works. We show that our methods work well for early detection of emerging claims, as well as for claims with limited presence on the web and social media.
Silent speech interfaces

The possibility of speech processing in the absence of an intelligible acoustic signal has given rise to the idea of a ‘silent speech’ interface, to be used as an aid for the speech-handicapped, or as part of a communications system operating in silence-required or high-background-noise environments. The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies. A concluding section underlining some of the common challenges faced by silent speech interface researchers, and ideas for possible future directions, is also provided.
Analyzing Factors Affecting Users ’ Behavior Intention to Use Social Media : Twitter Case

Advancement of technology and the Internet proliferation have visible effects in the world. One of the most important effects is the increased social media usage among the Internet users. For this purpose, factors having impacts on users’ behavior intention to social media usage are investigated within the scope of the study. A research model based on technology acceptance model 2 is proposed and revised by the taking social media platforms into consideration. The effects of perceived ease of use, perceived usefulness, social influence, facilitating conditions, playfulness, and trust are measured. Data collected from 462 respondents are analyzed by structural equation modeling technique. According to results of the measurement and structural model validities, a fit and acceptable model is achieved to measure the related effects of variables. The results of study reveal the both direct and indirect positive impacts of the factors on users’ behavior intention to use social media.
Following My Head and My Heart: Integrating Preschoolers' Empathy, Theory of Mind, and Moral Judgments.

Associations among hypothetical, prototypic moral, and conventional judgments; theory of mind (ToM); empathy; and personal distress were examined in 108 socioeconomically diverse preschoolers (Mage  = 42.94 months, SD = 1.42). Repeated measures analysis of covariance with empathy, false beliefs, and their interaction as covariates indicated that empathy was significantly associated with judgments of greater moral but not conventional transgression severity, particularly for psychological harm, and with deserved punishment for unfairness. False beliefs were associated with (combined) moral criterion judgments of rule and authority independence and inalterability. Empathy also was positively associated with criterion judgments but only for children low in ToM. Personal distress was unrelated to judgments. Results demonstrate the importance of both affective and cognitive processes in preschoolers' moral judgments.
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images.



In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules.



With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images.



Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.
Efficient Harmful Email identification Using Neural Network

Phishing is a form of online fraud that aims to steal a user’s sensitive information such as online banking passwords or credit card numbers. In this paper, we present a technique to quickly detect suspected email using Neural Network Pruning approach. The goal is to determine whether the email is suspected or legitimate. A Multilayer feedforward neural network with Pruning Strategy is used for Feature Extraction and extracted features are used for identifying email as phishing email. Pruning Strategy extracts important features which are playing a key role in identifying phishing mail which looks similar to a legitimate one. To verify the feasibility of the proposed approach experimental evaluation has been performed using a dataset composed of phishing emails along with legitimate emails. The experimental results are satisfactory in terms of false positives and false negatives. The results of conducted test indicated good identification rate with very short processing time.
RUM Extractor: A Facebook Extractor for Data Analysis

Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.
Robotic Cutting : Mechanics and Control of Knife Motion

Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by the knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.
System architecture of a wireless body area sensor network for ubiquitous health monitoring

Recent technological advances in sensors, low-power microelectronics and miniaturization, and wireless networking enabled the design and proliferation of wireless sensor networks capable of autonomously monitoring and controlling environments. One of the most promising applications of sensor networks is for human health monitoring. A number of tiny wireless sensors, strategically placed on the human body, create a wireless body area network that can monitor various vital signs, providing real-time feedback to the user and medical personnel. The wireless body area networks promise to revolutionize health monitoring. However, designers of such systems face a number of challenging tasks, as they need to address often quite conflicting requirements for size, operating time, precision, and reliability.
A deep neural network approach for sentence boundary detection in broadcast news

This paper presents a deep neural network (DNN) approach to sentence boundary detection in broadcast news. We extrac t prosodic and lexical features at each inter-word position i n the transcripts and learn a sequential classifier to label these positions as either boundary or non-boundary. This work is rea lized by a hybrid DNN-CRF (conditional random field) architec ture. The DNN accepts prosodic feature inputs and non-linea rly maps them into boundary/non-boundary posterior probabili ty outputs. Subsequently, the posterior probabilities are co mbined with lexical features and the integrated features are model ed by a linear-chain CRF. The CRF finally labels the inter-word positions as boundary or non-boundary by Viterbi decoding. Ex periments show that, as compared with the state-of-the-art DTCRF approach , the proposed DNN-CRF approach achieves 16.7% and 4.1% reduction in NIST boundary detection error in reference and speech recognition transcripts, respective ly.
LHCP and RHCP Substrate Integrated Waveguide Antenna Arrays for Millimeter-Wave Applications

Left-hand and right-hand circularly polarized (LHCP and RHCP) substrate integrated waveguide (SIW) antenna arrays are presented at the 28-GHz band for millimeter-wave (mm-wave) applications. Two types of circularly polarized (CP) antenna elements are designed to achieve respective LHCP and RHCP performance. Eight-element LHCP and RHCP antenna arrays have been implemented with feeding networks and measured. Based on the measurement, the LHCP and RHCP antenna arrays have impedance bandwidths of 1.54 and 1.7 GHz within $| S_{\rm{11}}| < - \text{10 dB}$, whereas 3-dB axial-ratio bandwidths are 1.1 and 1.3 GHz, respectively. Each of the fabricated LHCP and RHCP antenna arrays is accomplished with a gain up to 13.09 and 13.52 dBi. Most of the measured results are validated with the simulated ones. The proposed CP antenna arrays can provide low-cost, broadband characteristics, and high-gain radiation performance with CP properties for mm-wave applications.
Successful failure: what Foucault can teach us about privacy self-management in a world of Facebook and big data

The “privacy paradox” refers to the discrepancy between the concern individuals express for their privacy and the apparently low value they actually assign to it when they readily trade personal information for low-value goods online. In this paper, I argue that the privacy paradox masks a more important paradox: the self-management model of privacy embedded in notice-and-consent pages on websites and other, analogous practices can be readily shown to underprotect privacy, even in the economic terms favored by its advocates. The real question, then, is why privacy self-management occupies such a prominent position in privacy law and regulation. Borrowing from Foucault’s late writings, I argue that this failure to protect privacy is also a success in ethical subject formation, as it actively pushes privacy norms and practices in a neoliberal direction. In other words, privacy self-management isn’t about protecting people’s privacy; it’s about inculcating the idea that privacy is an individual, commodified good that can be traded for other market goods. Along the way, the self-management regime forces privacy into the market, obstructs the functioning of other, more social, understandings of privacy, and occludes the various ways that individuals attempt to resist adopting the market-based view of themselves and their privacy. Throughout, I use the analytics practices of Facebook and social networking sites as a sustained case study of the point.
Emotions recognition by speechand facial expressions analysis

In this paper, we propose a bimodal emotion recognition system using the combination of facial expressions and speech signals. The models obtained from a bimodal corpus with six acted emotions and ten subjects were trained and tested with different classifiers, such as Support Vector Machine, Naive Bayes and K-Nearest Neighbor. In order to fuse visual and acoustic information, two different approaches were implemented: feature level fusion and match score level fusion. Comparative studies reveal that the performance and the robustness of emotion recognition systems can be improved by the use of fusion-based techniques. Further, the fusion performed at the feature level showed better results than the one performed at the score level.
Low Cost Heart Multi Parameter Monitoring and Arrhythmia Analysis Using MATLAB

Recent years percentage of heart related diseases are in peak limit and to analysis heart diseases, ECG (electrocardiogram) one of best method to acquisition of ECG signals.The ECG devices are huge demand in market but when we deal with the cost, size and power its required to be minimized.In this paper present the ECG, Blood pressure, stethoscope signal acquisition using MPM board with C8051F380 development board and compare its power consumption with previous reference works. Output of MPM board is stored as .txt format and displayed the signals in MATLAB GUI.Finally processing of ECG .txt file using the pan Tompkins algorithm to track the QRS value and analyzing the arrhythmia using MATLAB graphical user interface (GUI).
Comparison of price change and health message interventions in promoting healthy food choices.

This study examines the feasibility and effectiveness of an environmental intervention for improving diet by comparing the impact of health messages, lowered prices, and their combination on the purchase of healthy food items in a restaurant. Price decreases alone, rather than a combination of price decreases and health messages, were associated with a higher level of increased purchases of some healthy food items as compared with control items over a 4-month period. Price decreases may be a more powerful means than health messages of increasing consumption of healthy foods. Health messages may have paradoxical effects if foods labeled as healthy are assumed to taste bad.
Performance of elasticsearch in cloud environment with nGram and non-nGram indexing

The fact that technology have changed the lives of human beings cannot be denied. It has drastically reduced the effort needed to perform a particular task and has increased the productivity and efficiency. Computers especially have been playing a very important role in almost all fields in today's world. They are used to store large amount of data in almost all sectors, be it business and industrial sectors, personal lives or any other. The research areas of science and technology uses computers to solve complex and critical problems. Information is the most important requirement of each individual. In this era of quick-growing and huge data, it has become increasingly illogical to analyse it with the help of traditional techniques or relational databases. New big data instruments, architectures and designs have come into existence to give better support to the requirements of organizations/institutions in analysing large data. Specifically, Elasticsearch, a full-text java based search engine, designed keeping cloud environment in mind solves issues of scalability, search in real time, and efficiency that relational databases were not able to address. In this paper, we present our own experience with Elasticsearch an open source, Apache Lucene based, full-text search engine that provides near real-time search ability, as well as a RESTful API for the ease of user in the field of research.
Content-Based Music Information Retrieval: Current Directions and Future Challenges

The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.
Multi-Label Classification by Semi-Supervised Singular Value Decomposition

Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.
Gut microbes and the brain: paradigm shift in neuroscience.

The discovery of the size and complexity of the human microbiome has resulted in an ongoing reevaluation of many concepts of health and disease, including diseases affecting the CNS. A growing body of preclinical literature has demonstrated bidirectional signaling between the brain and the gut microbiome, involving multiple neurocrine and endocrine signaling mechanisms. While psychological and physical stressors can affect the composition and metabolic activity of the gut microbiota, experimental changes to the gut microbiome can affect emotional behavior and related brain systems. These findings have resulted in speculation that alterations in the gut microbiome may play a pathophysiological role in human brain diseases, including autism spectrum disorder, anxiety, depression, and chronic pain. Ongoing large-scale population-based studies of the gut microbiome and brain imaging studies looking at the effect of gut microbiome modulation on brain responses to emotion-related stimuli are seeking to validate these speculations. This article is a summary of emerging topics covered in a symposium and is not meant to be a comprehensive review of the subject.
Synchronising movements with the sounds of a virtual partner enhances partner likeability

Previous studies have demonstrated that synchronising movements with other people can influence affiliative behaviour towards them. While research has focused on synchronisation with visually observed movement, synchronisation with a partner who is heard may have similar effects. We replicate findings showing that synchronisation can influence ratings of likeability of a partner, but demonstrate that this is possible with virtual interaction, involving a video of a partner. Participants performed instructed synchrony in time to sounds instead of the observable actions of another person. Results show significantly higher ratings of likeability of a partner after moving at the same time as sounds attributed to that partner, compared with moving in between sounds. Objectively quantified synchrony also correlated with ratings of likeability. Belief that sounds were made by another person was manipulated in Experiment 2, and results demonstrate that when sounds are attributed to a computer, ratings of likeability are not affected by moving in or out of time. These findings demonstrate that interaction with sound can be experienced as social interaction in the absence of genuine interpersonal contact, which may help explain why people enjoy engaging with recorded music.
WebWinnow: leveraging exploit kit workflows to detect malicious urls

Organized cybercrime on the Internet is proliferating due to exploit kits. Attacks launched through these kits include drive-by-downloads, spam and denial-of-service. In this paper, we tackle the problem of detecting whether a given URL is hosted by an exploit kit. Through an extensive analysis of the workflows of about 40 different exploit kits, we develop an approach that uses machine learning to detect whether a given URL is hosting an exploit kit. Central to our approach is the design of distinguishing features that are drawn from the analysis of attack-centric and self-defense behaviors of exploit kits. This design is based on observations drawn from exploit kits that we installed in a laboratory setting as well as live exploit kits that were hosted on the Web. We discuss the design and implementation of a system called WEBWINNOW that is based on this approach. Extensive experiments with real world malicious URLs reveal that WEBWINNOW is highly effective in the detection of malicious URLs hosted by exploit kits with very low false-positives.