nils-herrmann/clean_scirepeval huggingface dataset:
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both languageagnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of “social agony” to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models.
Anonymity for Decentralized Electronic Cash Systems

In 2008 Bitcoin was introduced as the first decentralized electronic cash system and it has seen widespread adoption since it became fully functional in 2009. This thesis describe the Bitcoin system, anonymity aspects for Bitcoin and how we can use cryptography to improve anonymity by a scheme called Zerocoin. The Bitcoin system will be described with focus on transactions and the blockchain where all transactions are recorded. We look more closely into anonymity in terms of address unlinkability and illustrate how the anonymity provided is insufficient by clustering addresses. Further we describe Zerocoin, a decentralized electronic cash scheme designed to cryptographically improve the anonymity guarantees in Bitcoin by breaking the link between individual Bitcoin transactions. We detail the construction of Zerocoin, provide security analysis and describe how it integrates into Bitcoin.
Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision

This paper presents a lightweight stereo vision-based driving lane detection and classification system to achieve the ego-car’s lateral positioning and forward collision warning to aid advanced driver assistance systems (ADAS). For lane detection, we design a self-adaptive traffic lanes model in Hough Space with a maximum likelihood angle and dynamic pole detection region of interests (ROIs), which is robust to road bumpiness, lane structure changing while the ego-car’s driving and interferential markings on the ground. What’s more, this model can be improved with geographic information system or electronic map to achieve more accurate results. Besides, the 3-D information acquired by stereo matching is used to generate an obstacle mask to reduce irrelevant objects’ interfere and detect forward collision distance. For lane classification, a convolutional neural network is trained by using manually labeled ROI from KITTI data set to classify the left/right-side line of host lane so that we can provide significant information for lane changing strategy making in ADAS. Quantitative experimental evaluation shows good true positive rate on lane detection and classification with a real-time (15Hz) working speed. Experimental results also demonstrate a certain level of system robustness on variation of the environment.
Phase Repeatable Synthesizers as a New Harmonic Phase Standard for Nonlinear Network Analysis

In this paper, the synthesized phase reference standard is introduced as a new viable alternative to comb generator-based phase references for nonlinear vector network analyzer measurements. Emphasis is put on the achievable higher specific output power of a frequency picket of interest for a denser spacing in the reference frequency grid. Supporting calculations on network analyzer IF filter bandwidths and limits for a denser grid spacing for comb-based phase references are presented. The concept is evaluated with a reference prototype covering the frequency range of 54 MHz to 6.8 GHz requiring only a 10-MHz reference signal and no network analyzer generators for stimulus, thus enabling even 2-port network analyzers without direct receiver and generator access to perform nonlinear reflection measurements. Furthermore, a method for generating a low-jitter trigger signal for the oscilloscope in absence of the comb generator pulse is shown. The synthesized phase standard implementation and the characterization setup are described in high detail to facilitate reproducibility of the results. Characterization of the new phase standard is performed in a frequency hopping pattern for amplitude and phase values of the reference pickets with a sampling oscilloscope-based setup. Startup and steady state measurements are provided. A steady-state relative phase repeatability better than 2° over power cycles and drift of 0.089 °/h at 4 GHz referenced to a synthesized 1-GHz fundamental is achieved.
A Chebyshev-Davidson Algorithm for Large Symmetric Eigenproblems

A polynomial filtered Davidson-type algorithm is proposed for symmetric eigenproblems, in which the correction-equation of the Davidson approach is replaced by a polynomial filtering step. The new approach has better global convergence and robustness properties when compared with standard Davidson-type methods. The typical filter used in this paper is based on Chebyshev polynomials. The goal of the polynomial filter is to amplify components of the desired eigenvectors in the subspace, which has the effect of reducing both the number of steps required for convergence and the cost in orthogonalizations and restarts. Numerical results are presented to show the effectiveness of the proposed approach.
Towards the Personal Equation of Interaction: The impact of personality factors on visual analytics interface interaction

These current studies explored the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered 3 psychometric measures designed to assess Locus of Control, Extraversion, and Neuroticism. Participants were then asked to complete multiple procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, results analyses demonstrated personality factors also predicted the number of insights participants reported while completing the tasks in each interface. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction.
Chatbol , a chatbot for the Spanish “ La Liga ”

This work describes the development of a social chatbot for the football domain. The chatbot, named chatbol, aims at answering a wide variety of questions related to the Spanish football league “La Liga”. Chatbol is deployed as a Slack client for text-based input interaction with users. One of the main Chatbol’s components, a NLU block, is trained to extract the intents and associated entities related to user’s questions about football players, teams, trainers and fixtures. The information for the entities is obtained by making sparql queries to Wikidata site in real time. Then, the retrieved data is used to update the specific chatbot responses. As a fallback strategy, a retrieval-based conversational engine is incorporated to the chatbot system. It allows for a wider variety and freedom of responses, still football oriented, for the case when the NLU module was unable to reply with high confidence to the user. The retrieval-based response database is composed of real conversations collected both from a IRC football channel and from football-related excerpts picked up across movie captions, extracted from the OpenSubtitles database.
C + + Template Instantiation and Concepts in CLANG

In modern C++ development, templates are essential to achieve maintainable and reusable code. On compilation of the source code, the compilers have to perform semantic checks of the template definition and instantiation, to satisfy the rules of the C++ standard and to make sure, the code related to the templates is correct. This task can be accomplished in different ways. In this paper, the process of the semantic verification of C++ tenmplates in CLANG is analyzed. The mechanism of checking the template definition and instantiation is explored separately by examining selected functions from the CLANG source code. Beside this, statements about the code quality and implementation are given. In addition, an analysis of the capability of CLANG to support Concepts, a type-system for template parameters, originally proposed for the C++0x standard is included in this paper. Based on the findings of the template definition and instantiation semantics, necessary changes to the code of CLANG are described as well as possible problems that can appear.
Generating What-If Scenarios for Time Series Data

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.
Modeling Sustainable Food Systems

The processes underlying environmental, economic, and social unsustainability derive in part from the food system. Building sustainable food systems has become a predominating endeavor aiming to redirect our food systems and policies towards better-adjusted goals and improved societal welfare. Food systems are complex social-ecological systems involving multiple interactions between human and natural components. Policy needs to encourage public perception of humanity and nature as interdependent and interacting. The systemic nature of these interdependencies and interactions calls for systems approaches and integrated assessment tools. Identifying and modeling the intrinsic properties of the food system that will ensure its essential outcomes are maintained or enhanced over time and across generations, will help organizations and governmental institutions to track progress towards sustainability, and set policies that encourage positive transformations. This paper proposes a conceptual model that articulates crucial vulnerability and resilience factors to global environmental and socio-economic changes, postulating specific food and nutrition security issues as priority outcomes of food systems. By acknowledging the systemic nature of sustainability, this approach allows consideration of causal factor dynamics. In a stepwise approach, a logical application is schematized for three Mediterranean countries, namely Spain, France, and Italy.
Investigations on linear transformations for speaker adaptation and normalization

This thesis deals with linear transformations at various stages of the automatic speech recognition process. In current state-of-the-art speech recognition systems linear transformations are widely used to care for a potential mismatch of the training and testing data and thus enhance the recognition performance. A large number of approaches has been proposed in literature, though the connections between them have been disregarded so far. By developing a unified mathematical framework, close relationships between the particular approaches are identified and analyzed in detail. Mel frequency Cepstral coefficients (MFCC) are commonly used features for automatic speech recognition systems. The traditional way of computing MFCCs suffers from a twofold smoothing, which complicates both the MFCC computation and the system optimization. An improved approach is developed that does not use any filter bank and thus avoids the twofold smoothing. This integrated approach allows a very compact implementation and needs less parameters to be optimized. Starting from this new computation scheme for MFCCs, it is proven analytically that vocal tract normalization (VTN) equals a linear transformation in the Cepstral space for arbitrary invertible warping functions. The transformation matrix for VTN is explicitly calculated exemplary for three commonly used warping functions. Based on some general characteristics of typical VTN warping functions, a common structure of the transformation matrix is derived that is almost independent of the specific functional form of the warping function. By expressing VTN as a linear transformation it is possible, for the first time, to take the Jacobian determinant of the transformation into account for any warping function. The effect of considering the Jacobian determinant on the warping factor estimation is studied in detail. The second part of this thesis deals with a special linear transformation for speaker adaptation, the Maximum Likelihood Linear Regression (MLLR) approach. Based on the close interrelationship between MLLR and VTN proven in the first part, the general structure of the VTN matrix is adopted to restrict the MLLR matrix to a band structure, which significantly improves the MLLR adaptation for the case of limited available adaptation data. Finally, several enhancements to MLLR speaker adaptation are discussed. One deals with refined definitions of regression classes, which is of special importance for fast adaptation when only limited adaptation data are available. Another enhancement makes use of confidence measures to care for recognition errors that decrease the adaptation performance in the first pass of a two-pass adaptation process.
Competence in College Students : The Role of Psychological Flexibility

Psychological flexibility (PF), the core process of Acceptance and Commitment Therapy (ACT; a third-wave cognitive-behavioral therapy), is the ability to stay focused on the present moment and intentionally engage in value-driven behavior despite experiencing difficult thoughts or feelings. This multifaceted construct includes components that target processes occurring both internally (e.g., cognitive processes) and behaviorally (e.g., value-consistent actions). Psychological flexibility has been applied to studies of adjustment in non-clinical samples and may be beneficial for college students as individuals navigate novel developmental stressors. Despite evidence suggesting the benefits of PF for psychological distress, additional work is needed to examine the potential of PF to foster adaptive functioning. The current study builds on previous research by a) conceptualizing distinct internal and behavioral components of PF as promotive factors and b) emphasizing competence-focused outcomes. This research examined the influence of components of PF over the course of an academic semester. A sample of college students (N = 250) completed self-report measures online at the beginning (Time 1) and end (Time 2) of a college semester. Measures included components of PF, competence, and demographic and academic information. Structural equation modeling was used to examine associations between components of PF at Time 1 on competence at Time 2, while accounting for the influence of competence at Time 1. Findings suggested that within the social domain, value-consistent action at Time 1 was associated with increased social competence at Time 2. Additional results indicated that baseline competence accounted for associations between components of PF and Time 2 competence. Implications for the dissemination of ACT-informed efforts to promote positive adjustment among college students are reviewed, and future research directions are discussed.
Consistent Individualized Feature Attribution for Tree Ensembles

Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a feature’s assigned importance when the true impact of that feature actually increases. This is a fundamental problem that casts doubt on any comparison between features. To address it we turn to recent applications of game theory and develop fast exact tree solutions for SHAP (SHapley Additive exPlanation) values, which are the unique consistent and locally accurate attribution values. We then extend SHAP values to interaction effects and define SHAP interaction values. We propose a rich visualization of individualized feature attributions that improves over classic attribution summaries and partial dependence plots, and a unique “supervised” clustering (clustering based on feature attributions). We demonstrate better agreement with human intuition through a user study, exponential improvements in run time, improved clustering performance, and better identification of influential features. An implementation of our algorithm has also been merged into XGBoost and LightGBM, see http://github.com/slundberg/shap for details.  Scott M. Lundberg, Gabriel G. Erion, and Su-In Lee. 2018. Consistent Individualized Feature Attribution for Tree Ensembles. In Proceedings of ACM (KDD’18). ACM, New York, NY, USA, 9 pages.
Self-Esteem as an Interpersonal Monitor : The Sociometer Hypothesis

Five studies tested hypotheses derived from the sociometer model of self-esteem according to which the self-esteem system monitors others' reactions and alerts the individual to the possibility of social exclusion. Study 1 showed that the effects of events on participants' state self-esteem paralleled their assumptions about whether such events would lead others to accept or reject them. In Study 2, participants' ratings of how included they felt in a real social situation correlated highly with their self-esteem feelings. In Studies 3 and 4, social exclusion caused decreases in self-esteem when respondents were excluded from a group for personal reasons, but not when exclusion was random, but this effect was not mediated by self-presentation. Study 5 showed that trait self-esteem correlated highly with the degree to which respondents generally felt included versus excluded by other people. Overall, results provided converging evidence for the sociometer model.
Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.
Wideband Coaxial to Ridge Gap Waveguide Transition

Recently, ridge gap waveguides are considered as guiding structures in high-frequency applications. One of the major problems facing this guiding structure is the limited ability of using all the possible bandwidths due to the limited bandwidth of the transition to the coaxial lines. Here, a review of the different excitation techniques associated with this guiding structure is presented. Next, some modifications are proposed to improve its response in order to cover the possible actual bandwidth. The major aim of this paper is to introduce a wideband coaxial to ridge gap waveguide transition based on five sections of matching networks. The introduced transition shows excellent return loss, which is better than 15 dB over the actual possible bandwidth for double transitions.
Labial fusion first diagnosed during pregnancy with voiding difficulty and its management: a case report.

Labial fusion is described as partial or complete adherence of the labia minora. Adhesions of the labia are extremely rare in the reproductive population with only a few cases described in the literature and none reported with pregnancy.



A 24-year-old woman who had extensively fused labia with a pinhole opening at the upper midline with menstrual delay was diagnosed at six weeks of pregnancy. The case and its management are presented.



The condition was treated surgically with complete resolution of the urinary symptoms.
Common purslane: a source of omega-3 fatty acids and antioxidants.

omega-3 fatty acids, alpha-tocopherol, ascorbic acid, beta-carotene and glutathione determined in leaves of purslane (Portulaca oleracea), grown in both a controlled growth chamber and in the wild, were compared in composition to spinach. Leaves from both samples of purslane contained higher amounts of alpha-linolenic acid (18:3w3) than did leaves of spinach. Chamber-grown purslane contained the highest amount of 18:3w3. Samples from the two kinds of purslane contained higher leaves of alpha-tocopherol, ascorbic acid and glutathione than did spinach. Chamber-grown purslane was richer in all three and the amount of alpha-tocopherol was seven times higher than that found in spinach, whereas spinach was slightly higher in beta-carotene. One hundred grams of fresh purslane leaves (one serving) contain about 300-400 mg of 18:3w3; 12.2 mg of alpha-tocopherol; 26.6 mg of ascorbic acid; 1.9 mg of beta-carotene; and 14.8 mg of glutathione. We confirm that purslane is a nutritious food rich in omega-3 fatty acids and antioxidants.
Security Shortcomings and Countermeasures for the SAE J1939 Commercial Vehicle Bus Protocol

In the recent years, countless security concerns related to automotive systems were revealed either by academic research or real life attacks. While current attention was largely focused on passenger cars, due to their ubiquity, the reported bus-related vulnerabilities are applicable to all industry sectors where the same bus technology is deployed, i.e., the CAN bus. The SAE J1939 specification extends and standardizes the use of CAN to commercial vehicles where security plays an even higher role. In contrast to empirical results that attest such vulnerabilities in commercial vehicles by practical experiments, here, we determine that existing shortcomings in the SAE J1939 specifications open road to several new attacks, e.g., impersonation, denial of service (DoS), distributed DoS, etc. Taking the advantage of an industry-standard CANoe based simulation, we demonstrate attacks with potential safety critical effects that are mounted while still conforming to the SAE J1939 standard specification. We discuss countermeasures and security enhancements by including message authentication mechanisms. Finally, we evaluate and discuss the impact of employing these mechanisms on the overall network communication.