nils-herrmann/clean_scirepeval huggingface dataset:
Moral disengagement in ethical decision making: a study of antecedents and outcomes.

This article advances understanding of the antecedents and outcomes of moral disengagement by testing hypotheses with 3 waves of survey data from 307 business and education undergraduate students. The authors theorize that 6 individual differences will either increase or decrease moral disengagement, defined as a set of cognitive mechanisms that deactivate moral self-regulatory processes and thereby help to explain why individuals often make unethical decisions without apparent guilt or self-censure (Bandura, 1986). Results support 4 individual difference hypotheses, specifically, that empathy and moral identity are negatively related to moral disengagement, while trait cynicism and chance locus of control orientation are positively related to moral disengagement. Two additional locus of control orientations are not significantly related to moral disengagement. The authors also hypothesize and find that moral disengagement is positively related to unethical decision making. Finally, the authors hypothesize that moral disengagement plays a mediating role between the individual differences they studied and unethical decisions. Their results offer partial support for these mediating hypotheses. The authors discuss the implications of these findings for future research and for practice.
Tracking drone orientation with multiple GPS receivers

Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.
On the instability of sensor orientation in gait verification on mobile phone

Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security strength on mobile devices. However, the existing approaches are obtrusive since the user is required to perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is understood to be a reliable profile for effective implicit authentication, recent studies have been conducted in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the instability of sensor's orientation which mostly happens in the reality. A flexible solution taking advantages of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of the biometric gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45% and accuracy rate of 99.14% in terms of the verification and identification aspects being achieved, respectively.
On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch

We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective in tests on constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are selected and bred to reduce their constraint violations. Interbreeding between populations is completely indirect, that is, only through their offspring that happen to migrate to the other population. We introduce an empirical measure of distance, and apply it between individuals and between population centroids to monitor the progress of evolution. We find that the centroids of the two populations approach each other and stabilize. This is a valuable characterization of convergence. We find the infeasible population influences, and sometimes dominates, the genetic material of the optimum solution. Since the infeasible population is not evaluated by the objective function, it is free to explore boundary regions, where the optimum is likely to be found. Roughly speaking, the No Free Lunch theorems for optimization show that all blackbox algorithms (such as Genetic Algorithms) have the same average performance over the set of all problems. As such, our algorithm would, on average, be no better than random search or any other blackbox search method. However, we provide two general theorems that give conditions that render null the No Free Lunch results for the constrained optimization problem class we study. The approach taken here thereby escapes the No Free Lunch implications, per se.
Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited]

A torus-topology photonic data center network with smart flow control is presented, where optical packet switching (OPS), optical circuit switching (OCS), and virtual OCS (VOCS) schemes are all enabled on a unified hardware platform via the combination of hybrid optoelectronic routers (HOPR) and a centralized network controller. An upgraded HOPR is being developed with the goal of handling 100 Gbps optical packets with a high energy efficiency of 90 mW/Gbps and low latency of less than 100 ns. The architecture of HOPR and its enabling technologies are reviewed, including a label processor, optical switch, and optoelectronic shared buffer. We also explain the concept and operation mechanisms of both the data and control planes in the hybrid OPS/OCS/VOCS torus network. The performance of these three transmission modes is evaluated by numerical simulations for a network composed of 4096 HOPRs.
Power Control in D2D-Based Vehicular Communication Networks

This paper studies how to efficiently apply device-to-device (D2D) communications underlaying a cellular system to support vehicle-to-vehicle (V2V) connection (termed D2D-V). By considering the geographic features of the D2D-V system, we propose a D2D-V grouping, reuse channel selection (RS), and power control (PC) framework to achieve the optimal performance of the D2D-V system, in terms of either maximized sum rate or maximized minimally achievable rate. First, in a full channel state information (CSI) scenario, we apply difference of two convex functions (D.C.) programming to obtain the optimal PC. However, because the CSI between some terminals is hard to obtain and some interference can be appropriately inhibited by taking advantage of the vehicles' geographic features, we make a series of simplifications to the PC problem to reduce the requirement of full CSI, the dependence of centralized control, and the computational complexity. The suitable conditions of each simplification are elaborated. Each step of our simplifications are shown as a certain tradeoff between performance and complexity. Furthermore, we provide an investigation into the service quality that one vehicle can achieve when it passes through the covered segment of the highway and propose the location-service curve to portray this. We use numerical simulations to verify the accuracy and feasibility of each step of our suboptimal PC and specific features embedded in the location-service curve.
To Lock, Swap, or Elide: On the Interplay of Hardware Transactional Memory and Lock-Free Indexing

The release of hardware transactional memory (HTM) in commodity CPUs has major implications on the design and implementation of main-memory databases, especially on the architecture of highperformance lock-free indexing methods at the core of several of these systems. This paper studies the interplay of HTM and lockfree indexing methods. First, we evaluate whether HTM will obviate the need for crafty lock-free index designs by integrating it in a traditional B-tree architecture. HTM performs well for simple data sets with small fixed-length keys and payloads, but its benefits disappear for more complex scenarios (e.g., larger variable-length keys and payloads), making it unattractive as a general solution for achieving high performance. Second, we explore fundamental differences between HTM-based and lock-free B-tree designs. While lock-freedom entails design complexity and extra mechanism, it has performance advantages in several scenarios, especially high-contention cases where readers proceed uncontested (whereas HTM aborts readers). Finally, we explore the use of HTM as a method to simplify lock-free design. We find that using HTM to implement a multi-word compare-and-swap greatly reduces lockfree programming complexity at the cost of only a 10-15% performance degradation. Our study uses two state-of-the-art index implementations: a memory-optimized B-tree extended with HTM to provide multi-threaded concurrency and the Bw-tree lock-free B-tree used in several Microsoft production environments.
SVM Based Effective Malware Detection System

Malware is coined as an instance of malicious code that has the potential to harm a computer or network. Recent years have encountered massive growth in malwares as existing signature based malware detection approaches are becoming ineffective and intractable. Cyber criminals and malware developers have adapted code obfuscation techniques which undermines the effectiveness of malware defense mechanism. Hence we propounded a system which focuses on static analysis in addition with automated behavior analysis in emulated environment generating behavior reports to investigate malwares. The proposed method uses programs as opcode density histograms and reduces the explosion of features. We employed eigen vector subspace analysis to filter and diminish the misclassification and interference of features. Our system uses a hybrid approach for discovering malware based on support vector machine classifier so that potential of malware detection system can be leveraged to combat with diverse forms of malwares while attaining high accuracy and low false alarms. Behavior Analysis, Static Analysis, Opcode Extraction, Malware Detection, Support Vector Machine.
Learning Plannable Representations with Causal InfoGAN

In recent years, deep generative models have been shown to ‘imagine’ convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans – a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation3.
Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI

Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called “Meta BCI”.
Suppressing the azimuth ambiguities in synthetic aperture radar images

This paper proposes a method for removing the azimuth ambiguities from synthetic aperture radar ( S A R ) images. The basic idea is to generate a two-dimensional reference function for S A R processing which provides, in addition to the matched filtering for the unaliased part of the received signal, the deconvolution of the azimuth ambiguities. This approach corresponds to an ideal filter concept, where an ideal impulse response function is obtained even in the presence of severe phase and amplitude errors. Modeling the sampled azimuth signal shows that the absolute phase value of the ambiguities cannot easily be determined due to their undersampling. The concept of the ideal filter is then extended to accommodate the undefined phase of the ambiguities and also the fading of the azimuth signal. Raw data from the E-SAR system have been used to verify the improvement in image quality by using the new method. It has a substantial significance in enabling the pulse-repetition frequency (PRF) constraints in the S A R system design to be relaxed and also for improving S A R image quality and interpretation.
Performance Evaluation Methodology for Historical Document Image Binarization

Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.
A critical review of Richard Lynn's reports on reaction time and race.

In the early 1990s, psychologist Richard Lynn published papers documenting average reaction times and decision times in samples of nine-year-olds taken from across the world. After summarizing these data, Lynn interpreted his results as evidence of national and racial differences in decision time and general intelligence. Others have also interpreted Lynn's data as evidence of racial differences in decision time and intelligence. However, comparing Lynn's summaries with his original reports shows that Lynn misreported and omitted some of his own data. Once these errors are fixed the rankings of nations in Lynn's datasets are unstable across different decision time measures. This instability, as well as within-race heterogeneity and between-race overlap in decision times, implies that Lynn's reaction time data do not permit generalizations about the decision times and intelligence of people of different races.
Social engineering attack framework

The field of information security is a fast growing discipline. Even though the effectiveness of security measures to protect sensitive information is increasing, people remain susceptible to manipulation and the human element is thus a weak link. A social engineering attack targets this weakness by using various manipulation techniques in order to elicit sensitive information. The field of social engineering is still in its infancy stages with regards to formal definitions and attack frameworks. This paper proposes a social engineering attack framework based on Kevin Mitnick's social engineering attack cycle. The attack framework addresses shortcomings of Mitnick's social engineering attack cycle and focuses on every step of the social engineering attack from determining the goal of an attack up to the successful conclusion of the attack. The authors use a previously proposed social engineering attack ontological model which provides a formal definition for a social engineering attack. The ontological model contains all the components of a social engineering attack and the social engineering attack framework presented in this paper is able to represent temporal data such as flow and time. Furthermore, this paper demonstrates how historical social engineering attacks can be mapped to the social engineering attack framework. By combining the ontological model and the attack framework, one is able to generate social engineering attack scenarios and to map historical social engineering attacks to a standardised format. Scenario generation and analysis of previous attacks are useful for the development of awareness, training purposes and the development of countermeasures against social engineering attacks.
A Fraud Resilient Medical Insurance Claim System

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.
Radar cross section and detection of small unmanned aerial vehicles

Commercial availability of small UAV's and their capabilities raises the question of the ability to defend against these assets, which could be used, or misused, as the air threat. This paper brings results of the series of experiments with the commercial quadcopter DJI Phantom 2 Vision that were performed by Department of Air Defense Systems/University of Defense Brno members in laboratory (anechoic chamber) and in the exterior as well. Terrain tests were done using military Doppler radar, with respect to the laboratory-measured radar cross section. Besides this, the visibility, audibility and other aspects of possible misuse of the drone were determined and analyzed for to find possible and identify optimal means for their detection.
Genetic and environmental influences on sexual orientation and its correlates in an Australian twin sample.

We recruited twins systematically from the Australian Twin Registry and assessed their sexual orientation and 2 related traits: childhood gender nonconformity and continuous gender identity. Men and women differed in their distributions of sexual orientation, with women more likely to have slight-to-moderate degrees of homosexual attraction, and men more likely to have high degrees of homosexual attraction. Twin concordances for nonheterosexual orientation were lower than in prior studies. Univariate analyses showed that familial factors were important for all traits, but were less successful in distinguishing genetic from shared environmental influences. Only childhood gender nonconformity was significantly heritable for both men and women. Multivariate analyses suggested that the causal architecture differed between men and women, and, for women, provided significant evidence for the importance of genetic factors to the traits' covariation.
Transient response and dynamic power dissipation comparison of various Dickson charge pump configurations based on charge transfer switches

Dickson charge pump is a switches-capacitor network providing a voltage gain and having open-circuit resistance dependent on the topology frequency and the size of storage capacitors. In this paper, the current status of research and development in the field of Dickson charge pump is extensively reviewed. Dickson charge pump is inductor-less DC to DC converter which uses a capacitor for its energy storage. The aim of this paper is to compare various techniques of Dickson charge pump currently explored in industry and academia. The parameters especially efficiency and voltage are compared. We have also discussed and evaluated the optimised criteria of capacitor sizing according to clock signals provided such that the charge pump performance could be properly optimised. We have simulated the transient response of DCP and provided the comprehensive study.
Effectiveness of Chinese massage therapy (Tui Na) for chronic low back pain: study protocol for a randomized controlled trial

Low back pain is a common, disabling musculoskeletal disorder in both developing and developed countries. Although often recommended, the potential efficacy of massage therapy in general, and Chinese massage (tuina) in particular, for relief of chronic low back pain (CLBP) has not been fully established due to inadequate sample sizes, low methodological quality, and subclinical dosing regimens of trials to date. Thus, the purpose of this randomized controlled trial (RCT) is to evaluate the comparative effectiveness of tuina massage therapy versus conventional analgesics for CLBP.



The present study is a single center, two-arm, open-label RCT. A total of 150 eligible CLBP patients will be randomly assigned to either a tuina treatment group or a conventional drug control group in a 1:1 ratio. Patients in the tuina group receive a 20 minutes, 4-step treatment protocol which includes both structural and relaxation massage, administered in 20 sessions over a period of 4 weeks. Patients in the conventional drug control group are instructed to take a specific daily dose of ibuprofen. The primary outcome measure is the change from baseline back pain and function, measured by Roland-Morris Disability Questionnaire, at two months. Secondary outcome measures include the visual analogue scale, Japanese orthopedic association score (JOAS), and McGill pain questionnaire.



The design and methodological rigor of this trial will allow for collection of valuable data to evaluate the efficacy of a specific tuina protocol for treating CLBP. This trial will therefore contribute to providing a solid foundation for clinical treatment of CLBP, as well as future research in massage therapy.