nils-herrmann/clean_scirepeval huggingface dataset:
Cellphones in public: social interactions in a wireless era

Cellphones provide a unique opportunity to examine how new media both reflect and affect the social world. This study suggests that people map their understanding of common social rules and dilemmas onto new technologies. Over time, these interactions create and reflect a new social landscape. Based upon a year-long observational field study and in-depth interviews, this article examines cellphone usage from two main perspectives: how social norms of interaction in public spaces change and remain the same; and how cellphones become markers for social relations and reflect tacit pre-existing power relations. Informed by Goffman’s concept of cross talk and Hopper’s caller hegemony, the article analyzes the modifications, innovations and violations of cellphone usage on tacit codes of social interactions.
DIGITAL PHEROMONES FOR AUTONOMOUS COORDINATION OF SWARMING UAV'S

Modern UAV’s reduce the threat to human operators, but do not decrease the manpower requirements. Each aircraft requires a flight crew of one to three, so deploying large numbers of UAV’s requires committing and coordinating many human warfighters. Insects perform impressive feats of coordination without direct inter-agent coordination, by sensing and depositing pheromones (chemical scent markers) in the environment [14]. We have developed a novel technology for coordinating the movements of multiple UAV’s based on a computational analog of pheromone dynamics. The control logic is simple enough that it can be executed autonomously by a UAV, enabling a single human to monitor an entire swarm of UAV’s. This paper describes the technology, its application to UAV coordination, and the results we have obtained.
A Koch-Shaped Log-Periodic Dipole Array (LPDA) Antenna for Universal Ultra-High-Frequency (UHF) Radio Frequency Identification (RFID) Handheld Reader

In this communication, we propose a printed log-periodic dipole array (LPDA) antenna covering the universal ultra-high-frequency (UHF) radio frequency identification (RFID) band of operation. Koch-shaped dipoles together with closely-coupled parasitic elements as directors were utilized for compactness in size and improvement in front-to-back ratio (F/B ratio). The experimental results demonstrate that the proposed antenna features good impedance match, moderate gain, better than 80% total radiation efficiency and excellent front-to-back ratio over the universal UHF RFID band from 840 MHz to 960 MHz. The antenna is well suitable for applications in RFID handheld readers.
Stress Tensor Field Visualization for Implant Planning in Orthopedics

We demonstrate the application of advanced 3D visualization techniques to determine the optimal implant design and position in hip joint replacement planning. Our methods take as input the physiological stress distribution inside a patient's bone under load and the stress distribution inside this bone under the same load after a simulated replacement surgery. The visualization aims at showing principal stress directions and magnitudes, as well as differences in both distributions. By visualizing changes of normal and shear stresses with respect to the principal stress directions of the physiological state, a comparative analysis of the physiological stress distribution and the stress distribution with implant is provided, and the implant parameters that most closely replicate the physiological stress state in order to avoid stress shielding can be determined. Our method combines volume rendering for the visualization of stress magnitudes with the tracing of short line segments for the visualization of stress directions. To improve depth perception, transparent, shaded, and antialiased lines are rendered in correct visibility order, and they are attenuated by the volume rendering. We use a focus+context approach to visually guide the user to relevant regions in the data, and to support a detailed stress analysis in these regions while preserving spatial context information. Since all of our techniques have been realized on the GPU, they can immediately react to changes in the simulated stress tensor field and thus provide an effective means for optimal implant selection and positioning in a computational steering environment.
Privacy-Preserving ECG Classification With Branching Programs and Neural Networks

Privacy protection is a crucial problem in many biomedical signal processing applications. For this reason, particular attention has been given to the use of secure multiparty computation techniques for processing biomedical signals, whereby nontrusted parties are able to manipulate the signals although they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs (a particular kind of decision tree) and the latter relying on neural networks. The paper deals with all the requirements and difficulties related to working with data that must stay encrypted during all the computation steps, including the necessity of working with fixed point arithmetic with no truncation while guaranteeing the same performance of a floating point implementation in the plain domain. A highly efficient version of the underlying cryptographic primitives is used, ensuring a good efficiency of the two proposed methods, from both a communication and computational complexity perspectives. The proposed systems prove that carrying out complex tasks like ECG classification in the encrypted domain efficiently is indeed possible in the semihonest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards.
Role of stress, arousal, and coping skills in primary insomnia.

Although stress is often presumed to cause sleep disturbances, little research has documented the role of stressful life events in primary insomnia. The present study examined the relationship of stress and coping skills, and the potential mediating role of presleep arousal, to sleep patterns in good sleepers and insomnia sufferers.



The sample was composed of 67 participants (38 women, 29 men; mean age, 39.6 years), 40 individuals with insomnia and 27 good sleepers. Subjects completed prospective, daily measures of stressful events, presleep arousal, and sleep for 21 consecutive days. In addition, they completed several retrospective and global measures of depression, anxiety, stressful life events, and coping skills.



The results showed that poor and good sleepers reported equivalent numbers of minor stressful life events. However, insomniacs rated both the impact of daily minor stressors and the intensity of major negative life events higher than did good sleepers. In addition, insomniacs perceived their lives as more stressful, relied more on emotion-oriented coping strategies, and reported greater presleep arousal than good sleepers. Prospective daily data showed significant relationships between daytime stress and nighttime sleep, but presleep arousal and coping skills played an important mediating role.



The findings suggest that the appraisal of stressors and the perceived lack of control over stressful events, rather than the number of stressful events per se, enhance the vulnerability to insomnia. Arousal and coping skills play an important mediating role between stress and sleep. The main implication of these results is that insomnia treatments should incorporate clinical methods designed to teach effective stress appraisal and coping skills.
Evaluation of Machine Translation and its Evaluation

Evaluation of MT evaluation measures is limited by inconsistent human judgment data. Nonetheless, machine translation can be evaluated using the well-known measures precision, recall, and their average, the F-measure. The unigrambased F-measure has significantly higher correlation with human judgments than recently proposed alternatives. More importantly, this standard measure has an intuitive graphical interpretation, which can facilitate insight into how MT systems might be improved. The relevant software is publicly available from http://nlp.cs.nyu.edu/GTM/.
Energy Efficient Scientific Computing on FPGAs using OpenCL

An indispensable part of our modern life is scientific computing which is used in large-scale high-performance systems as well as in low-power smart cyber-physical systems. Hence, accelerators for scientific computing need to be fast and energy efficient. Therefore, partial differential equations (PDEs), as an integral component of many scientific computing tasks, require efficient implementation. In this regard, FPGAs are well suited for data-parallel computations as they occur in PDE solvers. However, including FPGAs in the programming flow is not trivial, as hardware description languages (HDLs) have to be exploited, which requires detailed knowledge of the underlying hardware. This issue is tackled by OpenCL, which allows to write standardized code in a C-like fashion, rendering experience with HDLs unnecessary. Yet, hiding the underlying hardware from the developer makes it challenging to implement solvers that exploit the full FPGA potential. Therefore, we propose in this work a comprehensive set of generic and specific optimization techniques for PDE solvers using OpenCL that improve the FPGA performance and energy efficiency by orders of magnitude. Based on these optimizations, our study shows that, despite the high abstraction level of OpenCL, very energy efficient PDE accelerators on the FPGA fabric can be designed, making the FPGA an ideal solution for power-constrained applications.
A Ka Dual-Band Circular Waveguide Polarizer

The design of a Ka dual-band circular polarizer by inserting a dielectric septum in the middle of the circular waveguide is discussed here. The dielectric septum is located in fixing slots, and by adjusting the dimension of the dual-compensation slots which are built in the orthogonal plane, the phase difference of 90deg at the center frequency for the dual-band can be achieved. Furthermore, the gradual changing structures at both ends of the dielectric septum are built for impedance matching for both Ex and Ey polarizations. The simple structure of this kind of polarizer can reduce the influence of manufacturing inaccuracy in the Ka-band. The measured phase difference is within 90degplusmn 4.5deg for both bands. In addition, the return losses for both Ex and Ey polarizations are better than -15 dB.
Future memory technology including emerging new memories

There have been concerns about how far we can extend the so far so successful conventional semiconductor memories Such as DRAM, SRAM and Flash memory and what will be the future directions of memory development. In this article, we will review the key technical limits of conventional memory scaling and the directions to overcome the problem. In addition, we will review the technical challenges and opportunities of emerging. new memories such as ferroelectric RAM (FRAM), magnetic RAM (MRAM) and phase change RAM (PRAM) which has been recently focused as candidates for ideal memory which can solve the problems of conventional memories.
Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks

Driver’s status is crucial because one of the main reasons for motor vehicular accidents is related to driver’s inattention or drowsiness. Drowsiness detector on a car can reduce numerous accidents. Accidents occur because of a single moment of negligence, thus driver monitoring system which works in real-time is necessary. This detector should be deployable to an embedded device and perform at high accuracy. In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed. Main contribution of our paper is compression of heavy baseline model to a light weight model deployable to an embedded board. Moreover, minimized network structure was designed based on facial landmark input to recognize whether driver is drowsy or not. The proposed model achieved an accuracy of 89.5% on 3-class classification and speed of 14.9 frames per second (FPS) on Jetson TK1.
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs

We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approximate message passing (AMP) algorithms. In particular, we model the angle-delay coefficients using a Bernoulli–Gaussian-mixture distribution with unknown parameters and use the expectation-maximization forms of the generalized AMP and vector AMP algorithms to simultaneously learn the distributional parameters and compute approximately minimum mean-squared error (MSE) estimates of the channel coefficients. We design a training sequence that allows fast, fast Fourier transform based implementation of these algorithms while minimizing peak-to-average power ratio at the transmitter, making our methods scale efficiently to large numbers of antenna elements and delays. We present the results of a detailed simulation study that compares our algorithms to several benchmarks. Our study investigates the effect of SNR, training length, training type, ADC resolution, and runtime on channel estimation MSE, mutual information, and achievable rate. It shows that, in a mmWave MIMO system, the methods we propose to exploit joint angle-delay sparsity allow 1-bit ADCs to perform comparably to infinite-bit ADCs at low SNR, and 4-bit ADCs to perform comparably to infinite-bit ADCs at medium SNR.
A class-feature-centroid classifier for text categorization

Automated text categorization is an important technique for many web applications, such as document indexing, document filtering, and cataloging web resources. Many different approaches have been proposed for the automated text categorization problem. Among them, centroid-based approaches have the advantages of short training time and testing time due to its computational efficiency. As a result, centroid-based classifiers have been widely used in many web applications. However, the accuracy of centroid-based classifiers is inferior to SVM, mainly because centroids found during construction are far from perfect locations.
 We design a fast Class-Feature-Centroid (CFC) classifier for multi-class, single-label text categorization. In CFC, a centroid is built from two important class distributions: inter-class term index and inner-class term index. CFC proposes a novel combination of these indices and employs a denormalized cosine measure to calculate the similarity score between a text vector and a centroid. Experiments on the Reuters-21578 corpus and 20-newsgroup email collection show that CFC consistently outperforms the state-of-the-art SVM classifiers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.
On the design of NFC antennas for contactless payment applications

The increasing interest in using Near Field Communications (NFC) technology  at 13.5MHz is growing rapidly in the area of contactless payments, as well as numerous other applications, between devices that are within 10cm distance apart. However, there is growing concern that the use of such devices for contactless payments invites problems with regards to using metallic objects in the vicinity of the two devices to act as “rogue” antennas, which eavesdrop information during a financial transaction is taking place. This paper presents aspects of designing H-antennas both for the two devices communicating while also identifying the means by which rogue antennas can be created by exploiting real life metallic structures. In this paper, a shopping trolley is taken as an example.
Television Advertising and Online Shopping

M multitasking competes with television advertising for consumers’ attention, but may also facilitate immediate and measurable response to some advertisements. This paper explores whether and how television advertising influences online shopping. We construct a massive data set spanning $3.4 billion in spending by 20 brands, measures of brands’ website traffic and transactions, and ad content measures for 1,224 commercials. We use a quasi-experimental design to estimate whether and how TV advertising influences changes in online shopping within two-minute pre/post windows of time. We use nonadvertising competitors’ online shopping in a difference-in-differences approach to measure the same effects in two-hour windows around the time of the ad. The findings indicate that television advertising does influence online shopping and that advertising content plays a key role. Action-focus content increases direct website traffic and sales. Information-focus and emotion-focus ad content actually reduce website traffic while simultaneously increasing purchases, with a positive net effect on sales for most brands. These results imply that brands seeking to attract multitaskers’ attention and dollars must select their advertising copy carefully.
Community detection in large‐scale networks: a survey and empirical evaluation

Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the ground-truth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa.
Developing a Multi-Criteria Decision Making Model for PESTEL Analysis

Although the conceptual structure and nature of PESTEL (Political, Economic, Socio-cultural, Technological, Environment and Legal) analysis requires an integrated approach to analysis, the technical framework of PESTEL does not adequately support such an approach. PESTEL analysis, as it stands, mainly provides a general idea about the macro environmental conditions and situation of a company. This study presents a model to address problems encountered in the measurement and evaluation process of PESTEL analysis. The integrated structure of PESTEL factors and sub-factors were modeled by AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process) techniques. Relationships between PESTEL factors were determined by DEMATEL (Decision Making Trial and Evaluation Laboratory). Global weights of the sub-factors were calculated using ANP. The PESTEL analysis model proposed in the study could determine the extent to which the macro environment of a company provides suitable conditions to achieve the aims of the company.
Person re-identification by pose priors

The person re-identification problem is a well known retrieval task that requires finding a person of interest in a network of cameras. In a real-world scenario, state of the art algorithms are likely to fail due to serious perspective and pose changes as well as variations in lighting conditions across the camera network. The most effective approaches try to cope with all these changes by applying metric learning tools to find a transfer function between a camera pair. Unfortunately, this transfer function is usually dependent on the camera pair and requires labeled training data for each camera. This might be unattainable in a large camera network. In this paper, instead of learning the transfer function that addresses all appearance changes, we propose to learn a generic metric pool that only focuses on pose changes. This pool consists of metrics, each one learned to match a specific pair of poses. Automatically estimated poses determine the proper metric, thus improving matching. We show that metrics learned using a single camera improve the matching across the whole camera network, providing a scalable solution. We validated our approach on a publicly available dataset demonstrating increase in the re-identification performance.
Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes

Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error. This is a 56% reduction of mean square error compared to the state of the art (i.e., the CKD-EPI equation: a glomerular filtration rate estimator). We also perform feature selection to determine the most relevant attributes for detecting CKD and rank them according to their predictability. We identify new predictive attributes which have not been used by any previous GFR estimator equations. Finally, we perform a cost-accuracy tradeoff analysis to identify a new CKD detection approach with high accuracy and low cost.
A Proposed Classification of Data Mining Techniques in Credit Scoring

Credit scoring has become very important issue due to the recent growth of the credit industry, so the credit department of the bank faces a large amount of credit data. Clearly it is impossible analyzing this huge amount of data both in economic and manpower terms, so data mining techniques were employed for this purpose. So far many data mining methods are proposed to handle credit scoring problems that each of them, has some prominences and limitations than the others, but there is no a comprehensive reference introducing most used data mining method in credit scoring problem. The aim of this study is providing a comprehensive literature survey related to applied data mining techniques in credit scoring context. Such reference can help the researchers to be aware of most common methods in credit scoring evaluation, find their limitations, improve them and suggest new method with better capabilities. At the end we notice the limitation of the most proposed methods and suggest the more applicable method than other proposed.