nils-herrmann/clean_scirepeval huggingface dataset:
Secure and anonymous decentralized Bitcoin mixing

The decentralized digital currency Bitcoin presents an anonymous alternative to the centralized banking system and indeed enjoys widespread and increasing adoption. Recent works, however, show how users can be reidentified and their payments linked based on Bitcoin’s most central element, the blockchain, a public ledger of all transactions. Thus, many regard Bitcoin’s central promise of financial privacy as broken. In this paper, we propose CoinParty, an efficient decentralized mixing service that allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. CoinParty, through a novel combination of decryption mixnets with threshold signatures, takes a unique place in the design space of mixing services, combining the advantages of previously proposed centralized and decentralized mixing services in one system. Our prototype implementation of CoinParty scales to large numbers of users and achieves anonymity sets by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain. CoinParty can easily be deployed by any individual group of users, i.e., independent of any third parties, or provided as a commercial or voluntary service, e.g., as a community service by privacy-aware organizations.
Stress Detection and Management: A Survey of Wearable Smart Health Devices

In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.
A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.
Examination of the Correlation Between Internet Addiction and Social Phobia in Adolescents.

This was a descriptive and cross-sectional study conducted with adolescents to examine the correlation between Internet addiction and social phobia. The population of the study consisted of 24,260 students aged between 11 and 15 years. Sampling method was used from population with known number, and 1,450 students were calculated as sample of the study. In this study, 13.7% of the adolescents had an Internet addiction, and 4.2% spent more than 5 hr on the computer every day. There was a positive correlation between Internet addiction and social phobia. The form of time spent on Internet was examined in terms of addiction and social phobia; although Internet addiction was related to games, dating sites, and web surfing, social phobia was related to homework, games, and web surfing. It was hypothesized that adolescents with social phobia were Internet addicts, and the participants used the Internet to spend time rather than socialize.
A weight-neutral versus weight-loss approach for health promotion in women with high BMI: A randomized-controlled trial

Weight loss is the primary recommendation for health improvement in individuals with high body mass index (BMI) despite limited evidence of long-term success. Alternatives to weight-loss approaches (such as Health At Every Size - a weight-neutral approach) have been met with their own concerns and require further empirical testing. This study compared the effectiveness of a weight-neutral versus a weight-loss program for health promotion. Eighty women, aged 30-45 years, with high body mass index (BMI ≥ 30 kg/m(2)) were randomized to 6 months of facilitator-guided weekly group meetings using structured manuals that emphasized either a weight-loss or weight-neutral approach to health. Health measurements occurred at baseline, post-intervention, and 24-months post-randomization. Measurements included blood pressure, lipid panels, blood glucose, BMI, weight, waist circumference, hip circumference, distress, self-esteem, quality of life, dietary risk, fruit and vegetable intake, intuitive eating, and physical activity. Intention-to-treat analyses were performed using linear mixed-effects models to examine group-by-time interaction effects and between and within-group differences. Group-by-time interactions were found for LDL cholesterol, intuitive eating, BMI, weight, and dietary risk. At post-intervention, the weight-neutral program had larger reductions in LDL cholesterol and greater improvements in intuitive eating; the weight-loss program had larger reductions in BMI, weight, and larger (albeit temporary) decreases in dietary risk. Significant positive changes were observed overall between baseline and 24-month follow-up for waist-to-hip ratio, total cholesterol, physical activity, fruit and vegetable intake, self-esteem, and quality of life. These findings highlight that numerous health benefits, even in the absence of weight loss, are achievable and sustainable in the long term using a weight-neutral approach. The trial positions weight-neutral programs as a viable health promotion alternative to weight-loss programs for women of high weight.
Functions of Social Networking Services

Social Networking Services (SNS) are the fastest g rowing type of social software – both in the Internet and in compa ny-wide Intranets. Due to the fact that SNS have emerged just recently and the de v lopment speed of the services is enormous, there exist large gaps in res ea ch about this type of service. For example, so far there has been no atte mpt to identify and categorize the basic functionalities of SNS. This is the goal f this contribution. Six groups of functionalities for SNS are proposed and their c ategorization is motivated. The identification of a distinct set of SNS functio ns will facilitate the modularization and integration of different social network applications.
Online communication among adolescents: an integrated model of its attraction, opportunities, and risks.

Adolescents far outnumber adults in their use of e-communication technologies, such as instant messaging and social network sites. In this article, we present an integrative model that helps us to understand both the appeal of these technologies and their risks and opportunities for the psychosocial development of adolescents. We first outline how the three features (anonymity, asynchronicity, and accessibility) of online communication stimulate controllability of online self-presentation and self-disclosure among adolescents. We then review research on the risks and opportunities of online self-presentation and self-disclosure for the three components of adolescents' psychosocial development, including identity (self-unity, self-esteem), intimacy (relationship formation, friendship quality, cyberbullying), and sexuality (sexual self-exploration, unwanted sexual solicitation). Existing research suggests several opportunities of online communication, such as enhanced self-esteem, relationship formation, friendship quality, and sexual self-exploration. It also yields evidence of several risks, including cyberbullying and unwanted sexual solicitation. We discuss the shortcomings of existing research, the possibilities for future research, and the implications for educators and health care professionals.
Teaching 3D geometry to deformable part models

Current object class recognition systems typically target 2D bounding box localization, encouraged by benchmark data sets, such as Pascal VOC. While this seems suitable for the detection of individual objects, higher-level applications such as 3D scene understanding or 3D object tracking would benefit from more fine-grained object hypotheses incorporating 3D geometric information, such as viewpoints or the locations of individual parts. In this paper, we help narrowing the representational gap between the ideal input of a scene understanding system and object class detector output, by designing a detector particularly tailored towards 3D geometric reasoning. In particular, we extend the successful discriminatively trained deformable part models to include both estimates of viewpoint and 3D parts that are consistent across viewpoints. We experimentally verify that adding 3D geometric information comes at minimal performance loss w.r.t. 2D bounding box localization, but outperforms prior work in 3D viewpoint estimation and ultra-wide baseline matching.
Neurobiology of Schizophrenia

With its hallucinations, delusions, thought disorder, and cognitive deficits, schizophrenia affects the most basic human processes of perception, emotion, and judgment. Evidence increasingly suggests that schizophrenia is a subtle disorder of brain development and plasticity. Genetic studies are beginning to identify proteins of candidate genetic risk factors for schizophrenia, including dysbindin, neuregulin 1, DAOA, COMT, and DISC1, and neurobiological studies of the normal and variant forms of these genes are now well justified. We suggest that DISC1 may offer especially valuable insights. Mechanistic studies of the properties of these candidate genes and their protein products should clarify the molecular, cellular, and systems-level pathogenesis of schizophrenia. This can help redefine the schizophrenia phenotype and shed light on the relationship between schizophrenia and other major psychiatric disorders. Understanding these basic pathologic processes may yield novel targets for the development of more effective treatments.
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate

CHOLMOD is a set of routines for factorizing sparse symmetric positive definite matrices of the form A or AAT, updating/downdating a sparse Cholesky factorization, solving linear systems, updating/downdating the solution to the triangular system Lx = b, and many other sparse matrix functions for both symmetric and unsymmetric matrices. Its supernodal Cholesky factorization relies on LAPACK and the Level-3 BLAS, and obtains a substantial fraction of the peak performance of the BLAS. Both real and complex matrices are supported. CHOLMOD is written in ANSI/ISO C, with both C and MATLABTM interfaces. It appears in MATLAB 7.2 as x = A\b when A is sparse symmetric positive definite, as well as in several other sparse matrix functions.
Static analysis of cable-driven manipulators with non-negligible cable mass

This paper addresses the static analysis of cable-driven robotic manipulators with non-negligible cable mass. An approach to computing the static displacement of a homogeneous elastic cable is presented. The resulting cable-displacement expression is used to solve the inverse kinematics of general cable-driven robotic manipulators. In addition, the sag-induced stiffness of the cables is derived. Finally, two sample robotic manipulators with dimensions and system parameters similar to a large scale cable-driven manipulator currently under development are analyzed. The results show that cable sag can have a significant effect on both the inverse kinematics and stiffness of such manipulators.
A multi-mode cavity filter with Jerusalem Cross structure resonator

In this paper, a novel multi-mode dielectric resonator sandwiched by a Jerusalem Cross metal structure in the middle is proposed. This resonator has one degenerate dual modes and another nearby mode. These three modes are smartly used in a cylindrical cavity to generate two transmission zeros and one transmission pole. Therefore a single-cavity multi-mode bandpass filter is designed. In order to excite the three modes, two ports are placed orthogonally and another two tuning screws are needed to provide similar capacitance effects as the port pins. Simulation results show that the cavity with the resonator successfully becomes a bandpass filter and measurement results also prove this concept.
Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate

The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety.  Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus.  We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics.  We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse.  Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.
A Tutorial on H.264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance

The evolution of digital video technology and the continuous improvements in communication infrastructure is propelling a great number of interactive multimedia applications, such as real-time video conference, web video streaming and mobile TV, among others. The new possibilities on interactive video usage have created an exigent market of consumers, which demands the best video quality wherever they are and whatever their network support is (Schwarz et al., 2006). On this purpose, the transmitted video must match the receiver’s characteristics such as the required bit rate, resolution and frame rate, thus aiming to provide the best quality subject to receiver’s and network’s limitations. Besides, the same link is often used to transmit to either restricted devices such as small cell phones, or to high-performance equipments, e.g. HDTV workstations. In addition, the stream should adapt to wireless lossy networks (Ohm, 2005). Based on this reasoning, these heterogeneous and non-deterministic networks represent a great problem for traditional video encoders which do not allow for on-the-fly video streaming adaptation. To circumvent this drawback, the concept of scalability for video coding has been lately proposed as an emergent solution for supporting, in a given network, endpoints with distinct video processing capabilities. The principle of a scalable video encoder is to break the conventional single-stream video in a multi-stream flow, composed by distinct and complementary components, often referred to as layers (Huang et al., 2007).  1 illustrates this concept by depicting a transmitter encoding the input video sequence into three complementary layers. Therefore, receivers can select and decode different number of layers – each corresponding to distinct video characteristics – in accordance with the processing constraints of both the network and the device itself. The layered structure of any scalable video content can be defined as the combination of a base layer and several additional enhancement layers. The base layer corresponds to the lowest supported video performance, whereas the enhancement layers allow for the refinement of A Tutorial on H.264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance 1
Rethinking project management : A structured literature review with a critical look at the brave new world

This paper presents the results of a structured review of the rethinking project management (RPM) literature based on the classification and analysis of 74 contributions and in addition takes a critical look at this brave new world. Through the analysis, a total of 6 overarching categories emerged: contextualization, social and political aspects, rethinking practice, complexity and uncertainty, actuality of projects and broader conceptualization. These categories cover a broad range of different contributions with diverse and alternative perspectives on project management. The early RPM literature dates back to the 1980s, while the majority was published in 2006 onwards, and the research stream appears to be still active. A critical look at this brave new world exhibits the overall challenge for RPM to become much more diffused and accepted.  APM and IPMA. All rights reserved.
Modular Forms of Weight One

The first three sections are preliminary. The fourth section contains the statement of the main theorem and some corollaries. The proof occupies sections 5 through 9. The idea is as follows: one begins by constructing, for each prime number `, a representation of Gal(Q/Q) in characteristic ` (see §6); we then show that the image of Gal(Q/Q) is “small” under each of these representations, which permits us to lift this to characteristic 0 and obtain the complex representation we seek. The “smallness” in question itself results from an increase of the average of the eigenvalues of the Hecke operators (Rankin, §5). Section 9 contains an estimate of the coefficients of the modular forms of weight one. We note that we have used an essential point (§6, Thm. 6.1) of some results demonstrated by one of us (P. Deligne), but of which no complete proof has been published; this proof should appear in SGA 5, so we ask that the reader accept this for now.
Filtering spams using the minimum description length principle

Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle. The proposed model is fast to construct and incrementally updateable. Additionally, we offer an analysis concerning the measurements usually employed to evaluate the quality of the anti-spam classifiers. In this sense, we present a new measure in order to provide a fairer comparison. Furthermore, we conducted an empirical experiment using six well-known, large and public databases. Finally, the results indicate that our approach outperforms the state-of-the-art spam filters.
Multi-label classification by exploiting label correlations

a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China
Evaluating faces on trustworthiness: an extension of systems for recognition of emotions signaling approach/avoidance behaviors.

People routinely make various trait judgments from facial appearance, and such judgments affect important social outcomes. These judgments are highly correlated with each other, reflecting the fact that valence evaluation permeates trait judgments from faces. Trustworthiness judgments best approximate this evaluation, consistent with evidence about the involvement of the amygdala in the implicit evaluation of face trustworthiness. Based on computer modeling and behavioral experiments, I argue that face evaluation is an extension of functionally adaptive systems for understanding the communicative meaning of emotional expressions. Specifically, in the absence of diagnostic emotional cues, trustworthiness judgments are an attempt to infer behavioral intentions signaling approach/avoidance behaviors. Correspondingly, these judgments are derived from facial features that resemble emotional expressions signaling such behaviors: happiness and anger for the positive and negative ends of the trustworthiness continuum, respectively. The emotion overgeneralization hypothesis can explain highly efficient but not necessarily accurate trait judgments from faces, a pattern that appears puzzling from an evolutionary point of view and also generates novel predictions about brain responses to faces. Specifically, this hypothesis predicts a nonlinear response in the amygdala to face trustworthiness, confirmed in functional magnetic resonance imaging (fMRI) studies, and dissociations between processing of facial identity and face evaluation, confirmed in studies with developmental prosopagnosics. I conclude with some methodological implications for the study of face evaluation, focusing on the advantages of formally modeling representation of faces on social dimensions.