nils-herrmann/clean_scirepeval huggingface dataset:
Attention training in socially anxious children: a multiple baseline design analysis.

Current evidence has established an association between anxiety and attentional threat biases. Emerging research suggests these attentional biases may play a causal role in anxiety development. Clinical researchers have begun to develop treatments specifically designed to address these attentional processes. As such, Attention training (ATT) has been shown to be effective in the treatment of anxiety in adults. The current study represents an early attempt to implement ATT to treat social anxiety disorder (SOC) in children. Two boys meeting criteria for DSM-IV SOC participated in the study, along with their parents. Both boys received 10, 10-min sessions of ATT. A multiple-baseline design was used. Following treatment, both boys evidenced reductions in social anxiety. The current study provides preliminary evidence regarding the utility of ATT for treatment of childhood SOC. Future research is needed to further examine the use of this treatment strategy with youth and to explore the mechanisms of change.
Mediators of the association between narcissism and compulsive buying: the roles of materialism and impulse control.

Building upon past research about the guiding values and self-regulation difficulties of people with narcissistic personalities, this study tested a model of the association between narcissism and compulsive consumption. In data obtained from a sample of undergraduate consumers (N=238) with varying degrees of spending problems, positive associations emerged between narcissism, materialism, and compulsive buying. Impulse control was negatively correlated with each of these variables. Mediation tests revealed that both impulse control and materialism accounted for significant portions of the shared variance between narcissism and compulsive consumption. These findings highlight the importance of both personal values and impulse control as correlates of addictive buying. They also add to growing evidence that people who are relatively narcissistic are poor self-regulators who may be at risk of developing a variety of addictive behaviors.
Synthesizing tests for detecting atomicity violations

Using thread-safe libraries can help programmers avoid the complexities of multithreading. However, designing libraries that guarantee thread-safety can be challenging. Detecting and eliminating atomicity violations when methods in the libraries are invoked concurrently is vital in building reliable client applications that use the libraries. While there are dynamic analyses to detect atomicity violations, these techniques are critically dependent on effective multithreaded tests. Unfortunately, designing such tests is non-trivial. In this paper, we design a novel and scalable approach for synthesizing multithreaded tests that help detect atomicity violations. The input to the approach is the implementation of the library and a sequential seed testsuite that invokes every method in the library with random parameters. We analyze the execution of the sequential tests, generate variable lock dependencies and construct a set of three accesses which when interleaved suitably in a multithreaded execution can cause an atomicity violation. Subsequently, we identify pairs of method invocations that correspond to these accesses and invoke them concurrently from distinct threads with appropriate objects to help expose atomicity violations. We have incorporated these ideas in our tool, named Intruder, and applied it on multiple open-source Java multithreaded libraries. Intruder is able to synthesize 40 multithreaded tests across nine classes in less than two minutes to detect 79 harmful atomicity violations, including previously unknown violations in thread-safe classes. We also demonstrate the effectiveness of Intruder by comparing the results with other approaches designed for synthesizing multithreaded tests.
Attentional Pooling for Action Recognition

We introduce a simple yet surprisingly powerful model to incorporate attention in action recognition and human object interaction tasks. Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the same. It leads to significant improvements over state of the art base architecture on three standard action recognition benchmarks across still images and videos, and establishes new state of the art on MPII dataset with 12.5% relative improvement. We also perform an extensive analysis of our attention module both empirically and analytically. In terms of the latter, we introduce a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods (typically used for fine-grained classification). From this perspective, our attention formulation suggests a novel characterization of action recognition as a fine-grained recognition problem.
Comparison between SPM and IPM motor drives for EV application

Vehicles make use of permanent magnet synchronous traction motors for their high torque density and efficiency. A comparison between interior permanent magnet (IPM) and surface mounted permanent magnet (SPM) motors is carried out, in terms of performance at given inverter ratings. A simplified analytical approach is proposed and its results are confirmed by actual motor design, validated through FEA. The main result of the proposed analysis is to put in evidence the different behavior of IPM and SPM motors in terms of overload capability: as expected, the overload current results in a power overload of the IPM motor, while in the SPM motor the maximum power is upper limited irrespectively of the current overload.
Alike people, alike interests? Inferring interest similarity in online social networks

Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users’ interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users’ interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users’ interests are unknown. Relying on a large Facebook dataset, which contains 479, 048 users and 5, 263, 351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services.
A survey of traffic sign recognition

Advanced Driver Assistance Systems (ADAS) refer to various high-tech in-vehicle systems that are designed to increase road traffic safety by helping drivers gain better awareness of the road and its potential hazards as well as other drivers around them. The design of traffic sign recognition, one important subsystem of ADAS, has been a challenge problem for many years and hence become an important and active research topic in the area of intelligent transport systems. The realization of a real-time traffic sign recognition system is usually divided into three stages: detection, tracking and classification. This paper introduces the main difficulties in road sign recognition and briefly surveys the state-of-the-art technologies in this field with further discussions on the potential trend of development of road sign recognition.
Control of Grid Connected PV Array Using P&O MPPT Algorithm

The renewable energy is becoming moremainstream and accessible. This has been made possible due toan increase in environmental awareness coupled with thepopular demand to cut back on the greenhouse emissions. We inthis project propose a grid connected PV system. The aim of theproject is to implement a complete distributed energy resourcesystem (DER). The project will feature a PV module, which willbe controlled and optimized by means of a maximum powerpoint tracking (MPPT) algorithm. A boost converter along witha single phase grid tie inverter will be used to increase theoutput voltage and to convert it to AC. A phase locked loopcircuit will be used to integrate the single phase inverter withthe grid. A control methodology consisting of PI controllers isemployed for operating the PV at the MPPT point bycontrolling the switching of the boost converter and also for theoperation of the single phase inverter and its integration withthe grid. The parameters of these controllers are tuned to givethe best results. This will be followed by a detailed mathematicaland engineering analysis for the simulated results. The validityof the proposed scheme will be verified by simulation using thePSIM software.
Localization and map-building of mobile robot based on RFID sensor fusion system

In this paper, we propose an improved localization system for an indoor mobile robot using RFID (Radio Frequency IDentification) system and wheel encoders. Nowadays, RFID technology is widely used in the robot field. We investigate recent RFID localization system based on (passive) tag-floor for mobile robot, and analyze the problems and limitation of previous researches. First, RFID and wheel encoder localization systempsilas uncertainty, which may result in inaccurate location data, is modeled. And then, the algorithm for estimating each uncertainty is proposed for localization. Finally, a proposed algorithm successfully demonstrated through simulation experiments conducted under certain assumption.
Provision of Load Frequency Control by PHEVs, Controllable Loads, and a Cogeneration Unit

This paper presents a method for tracking a secondary frequency control (Load Frequency Control) signal by groups of plug-in hybrid electric vehicles (PHEVs), controllable thermal household appliances under a duty-cycle coordination scheme, and a decentralized combined-heat-and-power generation unit. The distribution of the control action on the participating units is performed by an aggregator utilizing a Model Predictive Control strategy which allows the inclusion of unit and grid constraints. In addition to the individual dynamic behavior, the varying availability of the units during the day is taken into account. The proposed methodology, easily extendable to larger networks, is evaluated on a four-bus system corresponding to a medium-voltage distribution grid and illustrates a possible operation mode of an aggregator in the power system.
Sensitivity and diagnosticity of NASA-TLX and simplified SWAT to assess the mental workload associated with operating an agricultural sprayer.

The objectives of the present study were: a) to investigate three continuous variants of the NASA-Task Load Index (TLX) (standard NASA (CNASA), average NASA (C1NASA) and principal component NASA (PCNASA)) and five different variants of the simplified subjective workload assessment technique (SSWAT) (continuous standard SSWAT (CSSWAT), continuous average SSWAT (C1SSWAT), continuous principal component SSWAT (PCSSWAT), discrete event-based SSWAT (D1SSWAT) and discrete standard SSWAT (DSSWAT)) in terms of their sensitivity and diagnosticity to assess the mental workload associated with agricultural spraying; b) to compare and select the best variants of NASA-TLX and SSWAT for future mental workload research in the agricultural domain. A total of 16 male university students (mean 30.4 +/- 12.5 years) participated in this study. All the participants were trained to drive an agricultural spraying simulator. Sensitivity was assessed by the ability of the scales to report the maximum change in workload ratings due to the change in illumination and difficulty levels. In addition, the factor loading method was used to quantify sensitivity. The diagnosticity was assessed by the ability of the scale to diagnose the change in task levels from single to dual. Among all the variants of NASA-TLX and SSWAT, PCNASA and discrete variants of SSWAT showed the highest sensitivity and diagnosticity. Moreover, among all the variants of NASA and SSWAT, the discrete variants of SSWAT showed the highest sensitivity and diagnosticity but also high between-subject variability. The continuous variants of both scales had relatively low sensitivity and diagnosticity and also low between-subject variability. Hence, when selecting a scale for future mental workload research in the agricultural domain, a researcher should decide what to compromise: 1) between-subject variability or 2) sensitivity and diagnosticity.  The use of subjective workload scales is very popular in mental workload research. The present study investigated the different variants of two popular workload rating scales (i.e. NASA-TLX and SSWAT) in terms of their sensitivity and diagnositicity and selected the best variants of each scale for future mental workload research.
A wireless slanted optrode array with integrated micro leds for optogenetics

This paper presents a wireless-enabled, flexible optrode array with multichannel micro light-emitting diodes (μ-LEDs) for bi-directional wireless neural interface. The array integrates wirelessly addressable μ-LED chips with a slanted polymer optrode array for precise light delivery and neural recording at multiple cortical layers simultaneously. A droplet backside exposure (DBE) method was developed to monolithically fabricate varying-length optrodes on a single polymer platform. In vivo tests in rat brains demonstrated that the μ-LEDs were inductively powered and controlled using a wireless switched-capacitor stimulator (SCS), and light-induced neural activity was recorded with the optrode array concurrently.
Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex

Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.
Mapping moods: Geo-mapped sentiment analysis during hurricane sandy

Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users’ concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users’ locations, but also based on the distance from the disaster.
A new linearization method of unbalanced electrical distribution networks

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.
Cybersecurity Games and Investments: A Decision Support Approach

In this paper we investigate how to optimally invest in cybersecurity controls. We are particularly interested in examining cases where the organization suffers from an underinvestment problem or inefficient spending on cybersecurity. To this end, we first model the cybersecurity environment of an organization. We then model non-cooperative cybersecurity control-games between the defender which abstracts all defense mechanisms of the organization and the attacker which can exploit different vulnerabilities at different network locations. To implement our methodology we use the SANS Top 20 Critical Security Controls and the 2011 CWE/SANS top 25 most dangerous software errors. Based on the profile of an organization, which forms its preferences in terms of indirect costs, its concerns about different kinds of threats and the importance of the assets given their associated risks we derive the Nash Equilibria of a series of control-games. These game solutions are then handled by optimization techniques, in particular multi-objective, multiple choice Knapsack to determine the optimal cybersecurity investment. Our methodology provides security effective and cost efficient solutions especially against commodity attacks. We believe our work can be used to advise security managers on how they should spend an available cybersecurity budget given their organization profile.
Sparsity Invariant CNNs

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth completion from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising over 94k depth annotated RGB images. Our dataset allows for training and evaluating depth completion and depth prediction techniques in challenging real-world settings and is available online at: www.cvlibs.net/datasets/kitti.
The origins and the future of microfluidics

The manipulation of fluids in channels with dimensions of tens of micrometres — microfluidics — has emerged as a distinct new field. Microfluidics has the potential to influence subject areas from chemical synthesis and biological analysis to optics and information technology. But the field is still at an early stage of development. Even as the basic science and technological demonstrations develop, other problems must be addressed: choosing and focusing on initial applications, and developing strategies to complete the cycle of development, including commercialization. The solutions to these problems will require imagination and ingenuity.
An Out-of-band Authentication Scheme for Internet of Things Using Blockchain Technology

While the rapid development of IoT devices is changing our daily lives, some particular issues hinder the massive deployment of IoT devices. For example, current network ID management system cannot handle so many new terminals; there is no agreed security standards for IoT manufacturers to follow when designing their products. The whole IoT industry is expecting the breakthrough in network infrastructure and the development of novel security mechanisms that can enable the flexible, secure and reliable access and management of IoT devices. Bitcoin, first released in 2009, breeds the decentralized Blockchain technology. The decentralization, anonymity and proof of security characteristics of Blockchain can prevent collusion and single point failure of a centralized server. We believe that the application of Blockchain into the IoT system can clear the obstacles facing the development of IoT architecture and security. To this end, we propose an out-of-band two-factor authentication scheme for IoT devices based on Blockchain infrastructure. We implemented the IoT and Blockchain integrated system with Eris Blockchain and equivalent computing devices to emulate IoT devices. The overheads to run Blockchain and smart contract services on the emulator devices are measured. The BeagleBone Black and Raspberry Pi 3 nodes have an average memory usage of 29.5M, and the CPU usage of 29.55% and 13.35%, respectively.
Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.