nils-herrmann/clean_scirepeval huggingface dataset:
Daily Fluctuations in Smartphone Use, Psychological Detachment, and Work Engagement: The Role of Workplace Telepressure

Today's work environment is shaped by the electronic age. Smartphones are important tools that allow employees to work anywhere and anytime. The aim of this diary study was to examine daily smartphone use after and during work and their association with psychological detachment (in the home domain) and work engagement (in the work domain), respectively. We explored whether workplace telepressure, which is a strong urge to respond to work-related messages and a preoccupation with quick response times, promotes smartphone use. Furthermore, we hypothesized that employees experiencing high workplace telepressure would have more trouble letting go of the workday during the evening and feel less engaged during their workday to the extent that they use their smartphone more intensively across domains. A total of 116 employees using their smartphones for work-related purposes completed diary questionnaires on five workdays (N = 476 data points) assessing their work-related smartphone use, psychological detachment after work, and engagement during work. Workplace telepressure was measured as a between-individual variable and only assessed at the beginning of the study, as well as relevant control variables such as participants' workload and segmentation preference (a preference for work and home domains to be as segmented as possible). Multilevel path analyses revealed that work-related smartphone use after work was negatively related to psychological detachment irrespective of employees' experienced workplace telepressure, and daily smartphone use during work was unrelated to work engagement. Supporting our hypothesis, employees who reported high telepressure experienced less work engagement on days that they used their smartphone more intensively during work. Altogether, intensive smartphone use after work hampers employees' psychological detachment, whereas intensive smartphone use during work undermines their work engagement only when employees experience high workplace telepressure as well. Theoretical and practical implications of these findings are discussed.
Circuit Design of a High Speed and Low Power CMOS Continuous-time Current Comparator

Current comparator is a fundamental component of current-mode analog integrated circuits. A novel high-performance continuous-time CMOS current comparator is proposed in this paper, which comprises one CMOS complementary amplifier, two resistive-load amplifiers and two CMOS inverters. A MOS resistor is used as the CMOS complementary amplifier’s negative feedback. Because the voltage swings of the CMOS complementary amplifier are reduced by low input and output resistances, the delay time of the current comparator is shortened. Its power consumption can be reduced rapidly with the increase of input current. Simulation results based on 1.2 μm CMOS process model show the speed of the novel current comparator is comparable with those of the existing fastest CMOS current comparators, and its power consumption is the lowest, so it has the smallest power-delay product. Furthermore, the new current comparator occupies small area and is process-robust, so it is very suitable to high-speed and low-power applications.
Wireless Location in IS – 95 CDMA Cellular Radio Systems

Research and development of technologies supporting subscriber location identification has rapidly increased around the world. A technology demonstrator was developed for wireless location estimation in IS-95 CDMA wireless cellular systems. Field measurements were used to evaluate the performance of the developed technology demonstrator. Interim results, in terms of mean standard deviation of range estimates, are presented. In the analysis it was observed that indoor scenarios resulted in a higher standard deviation of range estimates than in outdoor scenarios. It is shown that the mean standard deviation of range estimates may be used as an indicator of NLoS propagation.
Deep Learning of Convolutional Auto-Encoder for Image Matching and 3D Object Reconstruction in the Infrared Range

Performing image matching in thermal images is challenging due to an absence of distinctive features and presence of thermal reflections. Still, in many applications, infrared imagery is an attractive solution for 3D object reconstruction that is robust against low light conditions. We present an image patch matching method based on deep learning. For image matching in the infrared range, we use codes generated by a convolutional auto-encoder. We evaluate the method in a full 3D object reconstruction pipeline that uses infrared imagery as an input. Image matches found using the proposed method are used for estimation of the camera pose. Dense 3D object reconstruction is performed using semi-global block matching. We evaluate on a dataset with real and synthetic images to show that our method outperforms existing image matching methods on the infrared imagery. We also evaluate the geometry of generated 3D models to demonstrate the increased reconstruction accuracy.
Segregated Systems of Human Brain Networks

The organization of the brain network enables its function. Evaluation of this organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting-state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which correspond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and important features to the brain network that are central to its function and behavior.
I Am Fine but You Are Not: Optimistic Bias and Illusion of Control on Information Security

Information security is a critically important issue in current networked business and work environments. While there is extensive publicity on the increasing incidents of numerous information security breaches and their serious consequences, recent surveys and research on information security repeatedly identify the low levels of user and managerial awareness as a key obstacle to achieving a good information security posture. The main motivation of our research emanates from this contradicting phenomenon: increased vulnerability to information security breaches yet the low level of user and managerial awareness on information security threats. In this research, we study this dissonance by addressing a cognitive bias, optimistic bias, that is, the tendency of people to believe that negative events are less likely to happen to them than to others and that positive events are more likely to happen to them than others. Using a survey, we find that users demonstrate optimistic bias in their risk perceptions associated with information security. This self-serving bias is also found to be related to a perception of controllability with information security threats. These results have practical implications for designing security awareness programs by suggesting that risk communication and management efforts are likely to fail unless they consider this bias.
Data Stream Clustering Algorithms: A Review

Data stream mining has become a research area of some interest in recent years. The key challenge in data stream mining is extracting valuable knowledge in real time from a massive, continuous, dynamic data stream in only a single scan. Clustering is an efficient tool to overcome this problem. Data stream clustering can be applied in various fields such as financial transactions, telephone records, sensor network monitoring, telecommunications, website analysis, weather monitoring, and e-business. Data stream clustering presents some challenges; it needs to be done in a short time frame with limited memory using a single-scan process. Moreover, because data stream outliers are hidden, clustering algorithms must be able to detect outliers and noise. In addition, the algorithms have to handle concept drift and detect arbitrary shaped clusters. Several algorithms have been proposed to overcome these challenges. This paper presents a review of five types of data stream clustering approaches: partitioning, hierarchical, density-based, grid-based and model-based. The different data stream clustering algorithms in the literature by considering their respective advantages and disadvantages are discussed.
A Bridging Model for Parallel Computation

The success of the von Neumann model of sequential computation is attributable to the fact that it is an efficient bridge between software and hardware: high-level languages can be efficiently compiled on to this model; yet it can be effeciently implemented in hardware. The author argues that an analogous bridge between software and hardware in required for parallel computation if that is to become as widely used. This article introduces the bulk-synchronous parallel (BSP) model as a candidate for this role, and gives results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
A novel change detection framework based on deep learning for the analysis of multi-temporal polarimetric SAR images

Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked auto-encoder stage is used to learn an efficient representation of the multi-temporal polarimetric information. Then a label aggregation is performed in the feature space before classification by a multi-layer perceptron. The proposed methodology is validated on a L-band UAVSAR dataset acquired over Los Angeles, CA and performs accurately and effectively with a low false alarm rate.
Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System?

Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU and the related NIST metric, are becoming increasingly important in MT. Yet, their behaviors are not fully understood. In this paper, we analyze some flaws in the BLEU/NIST metrics. With a better understanding of these problems, we can better interpret the reported BLEU/NIST scores. In addition, this paper reports a novel method of calculating the confidence intervals for BLEU/NIST scores using bootstrapping. With this method, we can determine whether two MT systems are significantly different from each other.
A Solution for Privacy-Preserving Data Manipulation and Query on NoSQL Database

Privacy of data owners and query users is vital in modern clouding data management. Many researches have been done on cloud security, but most of them are focused on the privacy of data owners or of query users separately. How to protect the privacy of the data owners and users simultaneously is a great challenge. In this paper, a solution of data storage and query protocol based on classical homomorphic encryption scheme is given to preserve privacy of both data owners and query users. Our main efforts are put on NoSQL database which is less structural than relational database. Storage and indexing structure on NoSQL database, query protocol are proposed, and algorithms for updating and querying are also given. To implement our solution, Berkley DB, an excellent storage solution for NoSQL database is chosen and data are encrypted/decrypted using Elgamal and Paillier encryption system, using basic Java package. Experiments are done under different parameters in order to achieve better efficiency.
Interactive HDR Environment Map Capturing on Mobile Devices

Real world illumination, captured by digitizing devices, is beneficial to solve many problems in computer graphics. Therefore, practical methods for capturing this illumination are of high interest. In this paper, we present a novel method for capturing environmental illumination by a mobile device. Our method is highly practical as it requires only a consumer mobile phone and the result can be instantly used for rendering or material estimation. We capture the real light in high dynamic range (HDR) to preserve its high contrast. Our method utilizes the moving camera of a mobile phone in auto-exposure mode to reconstruct HDR values. The projection of the image to the spherical environment map is based on the orientation of the mobile device. Both HDR reconstruction and projection run on the mobile GPU to enable interactivity. Moreover, an additional image alignment step is performed. Our results show that the presented method faithfully captures the real environment and that the rendering with our reconstructed environment maps achieves high quality, comparable to reality.
Wireless power transfer via strongly coupled magnetic resonances.

Using self-resonant coils in a strongly coupled regime, we experimentally demonstrated efficient nonradiative power transfer over distances up to 8 times the radius of the coils. We were able to transfer 60 watts with approximately 40% efficiency over distances in excess of 2 meters. We present a quantitative model describing the power transfer, which matches the experimental results to within 5%. We discuss the practical applicability of this system and suggest directions for further study.
Wideband Excitation Technology of ${\rm TE}_{20}$ Mode Substrate Integrated Waveguide (SIW) and Its Applications

The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE20 mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE20 mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE20 mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE20 mode excitation structures and TE20 mode characteristics. The 180 ° out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 °. The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 °, respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE20 mode in the SIW.
Beware and be aware: capture of spatial attention by fear-related stimuli in neglect.

Stimuli with threat significance may be privileged in summoning attention, allowing fast detection even outside the field of attention. We studied patients with unilateral neglect and visual extinction, who usually remain unaware of contralesional stimuli presented together with concurrent ipsilesional stimuli, to learn whether emotional stimuli might differentially be affected by contralesional extinction. Pictures of spiders or flowers with similar features were presented in right, left, or both fields. On bilateral trials, the patients detected emotional stimuli (spiders) on the left side much more often than neutral pictures (flowers). While mechanisms of spatial attention are impaired after parietal damage in neglect patients, intact visual pathways to the ventral temporal lobe and amygdala might still mediate distinct mechanisms of emotional attention.
Exploiting the web of data in model-based recommender systems

The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.
Maximizing the Spread of Influence through a Social Network

Suppose we want to market a product or promote an idea or behavior in a society. In order to do so, we can “target” individuals, say by offering free samples of the product or explaining an idea, such as the consequences of drug abuse to teenagers (or college students). These people can then influence other people by word of mouth, and those people in turn can influence more people, and so on, creating a cascade of recommendations. The problem becomes how to find a given number of individuals who together will have the greatest influence. This is the “Influence Maximization Problem”, which we shall formalize and discuss later.
Comparison of some well-known PID tuning formulas

Criteria based on disturbance rejection and system robustness are proposed to assess the performance of PID controllers. A simple robustness measure is defined and the integral gains of the PID controllers are shown to be a good measure for disturbance rejection. An analysis of some well-known PID tuning formulas reveals that the robustness measure should lie between 3 and 5 to have a good compromise between performance and robustness.
Social media mining for drug safety signal detection

Adverse Drug Reactions (ADRs) represent a serious problem all over the world. They may complicate a patient's medical conditions and increase the morbidity, even mortality. Drug safety currently depends heavily on post-marketing surveillance, because pre-marketing review process cannot identify all possible adverse drug reactions in that it is limited by scale and time span. However, current post-marketing surveillance is conducted through centralized volunteering reporting systems, and the reporting rate is low. Consequently, it is difficult to detect the adverse drug reactions signals in a timely manner. To solve this problem, many researchers have explored methods to detect ADRs in electronic health records. Nevertheless, we only have access to electronic health records form particular health units. Aggregating and integrating electronic health records from multiple sources is rather challenging. With the advance of Web 2.0 technologies and the popularity of social media, many health consumers are discussing and exchanging health-related information with their peers. Many of this online discussion involve adverse drug reactions. In this work, we propose to use association mining and Proportional Reporting Ratios to mine the associations between drugs and adverse reactions from the user contributed content in social media. We have conducted an experiment using ten drugs and five adverse drug reactions. The FDA alerts are used as the gold standard to test the performance of the proposed techniques. The result shows that the metrics leverage, lift, and PRR are all promising to detect the adverse drug reactions reported by FDA. However, PRR outperformed the other two metrics.
Learning Fine-grained Features via a CNN Tree for Large-scale Classification

We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure among a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models.
Single camera lane detection and tracking

In this paper we present a method to detect and track straight lane boundaries, using a forward-looking single camera mounted on-board. The method proved to be robust, even under varying lighting conditions, and in the presence of heavy shadowing cast by vegetation, vehicles, bridges, etc. Moreover, false positive hardly occur. The lane markings are being continuously detected even when the vehicle is performing maneuvers such as excursion or lane change. The performance is achieved by a novel combination of methods, namely, by first finding vanishing point candidates for the lane boundaries, and later by the careful selection of line segments given a vanishing point, and finally by using a smart heuristics to select the whole set of four lane boundaries. Suppression of false positives is further achieved by tracking the vanishing point, and by constraining the lane width based on recent history.
Risks and benefits of social media for children and adolescents.

Resources • AAP Internet safety resources site, http:// safetynet.aap.org • CDC Social Media Tools Guidelines & Best Practices site,  SocialMedia/Tools/ guidelines/ • Common Sense Media (2009). Is technology changing childhood? A national poll on teens and social networking. R e t r i e v e d f r om http://www.commonsensemedia.org/teensocial-media. FACEBOOK, TWITTER, AND YouTube bring benefits to children and teenagers, including enhancing communication, broadening social connections, and learning technical skills, but can also expose them to risks, such as cyberbullying, “Facebook depression,” and “sexting,” according to a new report by the American Academy of Pediatrics (AAP; O'Keeffe, Clarke-Pearson, & Council on Communications and Media, 2011). The new report outlines the latest research on one of the most common activities among today's children and teenagers and describes how health care providers can help families understand these sites and encourage their healthy use. The report considers any site that allows social interaction as a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. The abundance of these sites has grown exponentially in recent years. According to a poll cited in the report, more than half of American teenagers log onto their favorite social media site at least once a day, whereas 22% do so at least 10 times a day; 75% of teenagers now own cell phones, with 54% of them using them for texting, 24% for instant messaging, and 25% for social media access. According to the authors of the new report, increase in social media has been so rapid and their presence in children's everyday life is now so pervasive that, for some teens, social media is the primary way they interact socially, and a large part of this generation's social and emotional development is occurring while on the Internet and on cell phones. Because of their limited capacity for self-regulation and susceptibility to peer pressure,