nils-herrmann/clean_scirepeval huggingface dataset:
On Being Told How We Feel: How Algorithmic Sensor Feedback Influences Emotion Perception

Algorithms and sensors are increasingly deployed for highly personal aspects of our everyday lives. Recent work suggests people have imperfect understanding of system outputs, often assuming sophisticated capabilities and deferring to feedback. We explore how people construe algorithmic interpretations of emotional data in personal informatics systems. A survey (n=188) showed strong interest in automatic stress and emotion tracking, but many respondents expected these systems to provide objective measurements for their emotional experiences. A second study examined how algorithmic sensor feedback influences emotional self-judgments, by comparing three system framings of physiological ElectroDermal Activity data (EDA): Positive ("alert and engaged"), Negative ("stressed"), and Control (no frame) in a mixed-methods study with 64 participants. Despite users reporting strategies to test system outputs, users still deferred to feedback and their perceived emotions were significantly influenced by feedback frames. Some users overrode personal judgments, believing the system had access to privileged information about their emotions. Based on these findings, we explore design implications for personal informatics including risks of users trusting systems that seemingly "unlock" hidden aspects of the self. We propose design approaches that provide opportunities for future emotion-monitoring systems to exploit these framing effects, and for users to more actively construe emotional states.
Enhanced autonomous resource selection for LTE-based V2V communication

Vehicle-to-vehicle (V2V) communication has the potential to improve road safety significantly by providing connectivity between vehicles to exchange information messages. V2V has recently gained considerable attention in part due to the standardization work that has started for Long Term Evolution (LTE) under the Third Generation Partnership Project (3GPP). Efficient and reliable distributed allocation of time-frequency resources among different vehicle user equipments (VUEs) remains a challenge particularly in dense urban environments. In this work, a new autonomous resource selection scheme for urban V2V communication scenario is proposed. Specifically, the proposed scheme relies on resource partitioning based on VUE heading direction along with a sensing-based collision avoidance mechanism, which aims to alleviate the potential interference between VUEs due to resource collision and in-band emission (IBE). The performance of the two-stage autonomous resource selection scheme is evaluated by system-level simulations in urban scenarios, where the results show a significant performance gain in comparison to existing approaches.
Applying machine learning to software fault-proneness prediction

The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository.
Solar-wind hybrid energy system using MPPT

Energy demand is ever increasing in the world, searching for fossil fuel is done on priority basis. These fuels are not sustainable, they pollute the environment. Shortage of fossil fuels resources and adverse environment affects made use of Renewable Energy Sources (RES) as Solar energy and Wind energy essential. Solar energy and Wind energy are natural resources which are not depleted by use and are more popular. Availability and ease to obtain electric power made Solar and Wind power as alternative energy sources. Solar energy and Wind energy combined to form Solar-Wind Hybrid Power System (SWHPS), which will enhance the qualities of each other and another. To reduce the power demand on the conventional power generation sector, the optimized utilization of these natural resources is essential to produce power. Various methodologies are in practice for generation of power using Solar-Wind Hybrid System with Maximum Power Point Tracking (MPPT). Constant voltage method is used for maximum power transfer. This method should have some key features to increase the stability and efficiency.
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.



We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.



We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.



Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.



Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.
Beam selection for performance-complexity optimization in high-dimensional MIMO systems

Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.
ERC A Theory of Equity , Reciprocity and Competition

We demonstrate that a simple model, constructed on the premise that people are motivated by both their pecuniary payoff and their relative payoff standing, explains behavior in a wide variety of laboratory games. Included are games where equity is thought to be a factor, such as ultimatum, two-period alternating offer, and dictator games; games where reciprocity is thought to play a role, such as the prisoner’s dilemma and the gift exchange game; and games where competitive behavior is observed, such as Bertrand and Cournot markets, and the guessing game.
Bitcoin Network Measurements for Simulation Validation and Parameterization

Bitcoin is gaining increasing popularity nowadays, even though the crypto-currencies field has plenty of digital currencies that have emerged before the adoption of Bitcoin idea. Bitcoin is a decentralized digital currency which relies on set of miners to maintain a distributed public ledger and peer-to-peer network to broadcast transactions. In this paper, we analyse how transaction validation is achieved by the transaction propagation round trip and how transaction dissemination throughout the network can lead to inconsistencies in the view of the current transactions ledger by different nodes. We then measure the transaction propagation delay in the real Bitcoin network and how it is affected by the number of nodes and network topology. This measurement enables a precise validation of any simulation model of the Bitcoin network. Large-scale measurements of the real Bitcoin network are performed in this paper. This will provide an opportunity to parameterise any model of the Bitcoin
Fusion of AV features and external information sources for event detection in team sports video

The use of AV features alone is insufficient to induce high-level semantics. This article proposes a framework that utilizes both internal AV features and various types of external information sources for event detection in team sports video. Three schemes are also proposed to tackle the asynchronism between the fusion of AV and external information. The framework is extensible as it can provide increasing functionalities given more detailed external information and domain knowledge. By demonstrating its effectiveness on soccer and American football, we believe that with the availability of appropriate domain knowledge, the framework is applicable to other team sports.
Development of a Facebook Addiction Scale.

The Bergen Facebook Addiction Scale (BFAS), initially a pool of 18 items, three reflecting each of the six core elements of addiction (salience, mood modification, tolerance, withdrawal, conflict, and relapse), was constructed and administered to 423 students together with several other standardized self-report scales (Addictive Tendencies Scale, Online Sociability Scale, Facebook Attitude Scale, NEO-FFI, BIS/BAS scales, and Sleep questions). That item within each of the six addiction elements with the highest corrected item-total correlation was retained in the final scale. The factor structure of the scale was good (RMSEA = .046, CFI = .99) and coefficient alpha was .83. The 3-week test-retest reliability coefficient was .82. The scores converged with scores for other scales of Facebook activity. Also, they were positively related to Neuroticism and Extraversion, and negatively related to Conscientiousness. High scores on the new scale were associated with delayed bedtimes and rising times.
Hyper-parameter selection in non-quadratic regularization-based radar image formation

We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein’s unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) “Backhoe Data Dome,” to demonstrate and discuss the effectiveness of these methods.
Dose-dependent pharmacokinetics of benzoic acid following oral administration of sodium benzoate to humans

Plasma concentration-time data for benzoic and hippuric acids and urinary excretion-time data for hippuric acid were analyzed simultaneously after oral doses of 40, 80 or 160 mg/kg sodium benzoate administered at least one week apart to 6 healthy subjects. The mean AUCs of benzoic acid after the doses of 80 and 160 mg/kg of sodium benzoate were 3.7- and 12.0-times greater, respectively, than after 40 mg/kg. However, the mean AUC of hippuric acid was roughly proportional to the benzoate doses. The observed data were explained by a one-compartment model with first-order rate absorption and Michaelis-Menten elimination of benzoic acid, together with a one-compartment model with first-order elimination for hippuric acid. Although the maximum rate of biotransformation of benzoic acid to hippuric acid varied between 17.2 and 28.8 mg·kg−1·h−1 among the six individuals, the mean value (23.0 mg·kg−1·h−1) was fairly close to that provided by daily maximum dose (0.5 g·kg−1·day−1) recommended in the treatment of hyperammonaemia in patients with inborn errors of ureagenesis. The individual maximum rate of metabolism can be estimated from the urinary excretion rate of hippuric acid 1.5 to 3 h after the single oral dose of 80 to 160 mg·kg−1 sodium benzoate. The justification of this concept requires further studies in patients with inborn errors of urea synthesis.
Prediction of heart disease using multilayer perceptron neural network

In medical field the diagnosis of heart disease is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the prediction of heart disease that is highly accurate, effective and helpful in early diagnosis. In this paper we present a prediction system for heart disease using multilayer perceptron neural network. The neural network in this system accepts 13 clinical features as input and it is trained using back-propagation algorithm to predict that there is a presence or absence of heart disease in the patient with highest accuracy of 98% comparative to other systems. The accuracy thus obtained with this system shows that it is better and efficient than other systems.
Exploring Users' Attitudes and Intentions toward the Adoption of Cloud Computing in Saudi Arabia: an Empirical Investigation

Over the past few years, cloud computing has evolve d as one of the major advances in the field of Information Technology (IT) utilizing third-party s ervices. Therefore, trust in cloud vendors as well as the determination of potential risks, such as privacy a nd security issues, are crucial for ensuring the su ccessful adoption of an appropriate cloud. Prior research ha ve ddressed the technical aspects of cloud-based environments, such as cloud virtualization, scalabi lity and security. Nevertheless, it is argued that the biggest obstacle of cloud computing is not technolo gical, rather it is perceptual or attitudinal. The adoption of cloud computing has been central to several scho larly research areas, particularly user acceptance. This study presents an extended Technology Acceptance Mo del (TAM), which integrates Trust (TR), Anxiety (ANX) and Perceived Risk (PR), to investigate users ’ attitudes and intentions toward the adoption of c l ud computing. The proposed model was empirically exami ned using the Structural Equation Model (SEM) to analyze data gathered by a survey of both IT profes sionals and end users. The results herein suggested that trust, anxiety and PR can be successfully integrate d within the TAMs. Trust has demonstrated to have a strong positive influence on Perceived Ease of Use (PEOU), but it had no significant effects on Perceived Use fuln ss (PU). Both anxiety and PR were found to have signif icant negative effects on PEOU and PU. In addition, Behavioral Intention (BI) to use the cloud can be p r dicted by trust, attitudes and PU, as PR was show n t have no significant effect on BI. The proposed model, including PR, trust and anxiety , has been demonstrated to be a true predictor of user intentions, toward t he use of cloud computing, within the context of Sa udi Arabia.
Very high frequency half bridge DC/DC converter

This paper presents the first, off chip, class DE (resonant half bridge) converter working in the Very High Frequency (VHF) range. The benefits of using half bridge circuits both in the inverter and rectifier part of a VHF resonant dc/dc converter are analyzed and design equations for all components in the power stage are given. The circuit has been simulated to verify the accuracy of the presented equations and an efficiency of 89% has been shown. A prototype has been implemented with self-oscillating resonant gate drives driving the switches. The prototype has been used to drive an LED string and shows an efficiency of 85% at 29 MHz with 130 V input and 13.4 W output. The efficiency was above 82% in the range 110-150 V input with output power between 10.3 W and 16.5 W.
Robustness of factors influencing social media usage/adoption amongst SMEs in developing countries: a case of Pakistan

The purpose of this study is to analyze the durability of the factors influencing social media usage or adoption by the SMEs of Pakistan. Considering the literature, this study integrates some factors in the model of TAM (Technology Acceptance Model) to analyze the durability of factors influencing the usage/adoption of famous social media i.e. Facebook by the SMEs in Pakistan. Using the bootstrapping technique through computer software (smartPLS3), we statistically analyzed the hypothesis developed for the survey-based data of 95 SMEs. Empirical findings show that the factors integrated in PU-Perceived Usefulness (Increase in Sales & Increase in Customers) are significantly impactful in Facebook usage/adoption by the SMEs presently as well as in the long run. Except Cost Effectiveness, other two factors integrated in PEOU-Previewed Ease of Use (i.e. Communication and Smartphones usage) are also the influencing factors not only presently but in long term as well. There are limited studies that focus on the technological factors such as diffusion of digital devices other than personal and organizational factors to assess the influencing factors of Facebook usage/adoption by the SMEs. Therefore we attempt to fill this gap in the literature and add smartphone diffusion as one of the core factors influencing Facebook adoption by the SMEs.
Data mining in software engineering

The increased availability of data created as part of the sof tware development process allows us to apply novel analysis techniques on the data and use the results to guide t he process’s optimization. In this paper we describe variou s data sources and discuss the principles and techniques of data mi ning as applied on software engineering data. Data that can b e mined is generated by most parts of the development process: requi rements elicitation, development analysis, testing, debu gging, and maintenance. Based on this classification we survey the mini ng approaches that have been used and categorize them accord ing to the corresponding parts of the development process and th e task they assist. Thus the survey provides researchers wit h a concise overview of data mining techniques applied to softw are engineering data, and aids practitioners on the selecti on of appropriate data mining techniques for their work.
Transferable Semi-Supervised Semantic Segmentation

The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (PNet). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.