nils-herrmann/clean_scirepeval huggingface dataset:
(Im)maturity of judgment in adolescence: why adolescents may be less culpable than adults.

A crucial step in the establishment of effective policies and regulations concerning legal decisions involving juveniles is the development of a complete understanding of the many factors-psychosocial as well as cognitive-that affect the evolution of judgment over the course of adolescence and into adulthood. This study examines the influence of three psychosocial factors (responsibility, perspective, and temperance) on maturity of judgment in a sample of over 1,000 participants ranging in age from 12 to 48 years. Participants completed assessments of their psychosocial maturity in the aforementioned domains and responded to a series of hypothetical decision-making dilemmas about potentially antisocial or risky behavior. Socially responsible decision making is significantly more common among young adults than among adolescents, but does not increase appreciably after age 19. Individuals exhibiting higher levels of responsibility, perspective, and temperance displayed more mature decision-making than those with lower scores on these psychosocial factors, regardless of age. Adolescents, on average, scored significantly worse than adults, but individual differences in judgment within each adolescent age group were considerable. These findings call into question recent arguments, derived from studies of logical reasoning, that adolescents and adults are equally competent and that laws and social policies should treat them as such.
Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems

Energy-harvesting technology provides a promising platform for future IoT applications. However, since communication is very expensive in these devices, applications will require inference “beyond the edge” to avoid wasting precious energy on pointless communication. We show that application performance is highly sensitive to inference accuracy. Unfortunately, accurate inference requires large amounts of computation and memory, and energy-harvesting systems are severely resource-constrained. Moreover, energy-harvesting systems operate intermittently, suffering frequent power failures that corrupt results and impede forward progress. This paper overcomes these challenges to present the first full-scale demonstration of DNN inference on an energyharvesting system. We design and implement SONIC, an intermittence-aware software system with specialized support for DNN inference. SONIC introduces loop continuation, a new technique that dramatically reduces the cost of guaranteeing correct intermittent execution for loop-heavy code like DNN inference. To build a complete system, we further present GENESIS, a tool that automatically compresses networks to optimally balance inference accuracy and energy, and TAILS, which exploits SIMD hardware available in some microcontrollers to improve energy efficiency. Both SONIC & TAILS guarantee correct intermittent execution without any hand-tuning or performance loss across different power systems. Across three neural networks on a commercially available microcontroller, SONIC & TAILS reduce inference energy by 6.9× and 12.2×, respectively, over the state-of-the-art.
Loneliness, social contacts and Internet addiction: A cross-lagged panel study

This study aims to examine the causal priority in the observed empirical relationships between Internet addiction and other psychological problems. A cross-lagged panel survey of 361 college students in Hong Kong was conducted. Results show that excessive and unhealthy Internet use would increase feelings of loneliness over time. Although depression had a moderate and positive bivariate relationship with Internet addiction at each time point, such a relationship was not significant in the cross-lagged analyses. This study also found that online social contacts with friends and family were not an effective alternative for offline social interactions in reducing feelings of loneliness. Furthermore, while an increase in face-to-face contacts could help to reduce symptoms of Internet addiction, this effect may be neutralized by the increase in online social contacts as a result of excessive Internet use. Taken as a whole, findings from the study show a worrisome vicious cycle between loneliness and Internet addiction.
Software Defect Prediction via Convolutional Neural Network

To improve software reliability, software defect prediction is utilized to assist developers in finding potential bugs and allocating their testing efforts. Traditional defect prediction studies mainly focus on designing hand-crafted features, which are input into machine learning classifiers to identify defective code. However, these hand-crafted features often fail to capture the semantic and structural information of programs. Such information is important in modeling program functionality and can lead to more accurate defect prediction.In this paper, we propose a framework called Defect Prediction via Convolutional Neural Network (DP-CNN), which leverages deep learning for effective feature generation. Specifically, based on the programs' Abstract Syntax Trees (ASTs), we first extract token vectors, which are then encoded as numerical vectors via mapping and word embedding. We feed the numerical vectors into Convolutional Neural Network to automatically learn semantic and structural features of programs. After that, we combine the learned features with traditional hand-crafted features, for accurate software defect prediction. We evaluate our method on seven open source projects in terms of F-measure in defect prediction. The experimental results show that in average, DP-CNN improves the state-of-the-art method by 12%.
Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.
Learning Cross-View Binary Identities for Fast Person Re-Identification

In this paper, we propose to learn cross-view binary identities (CBI) for fast person re-identification. To achieve this, two sets of discriminative hash functions for two different views are learned by simultaneously minimising their distance in the Hamming space, and maximising the cross-covariance and margin. Thus, similar binary codes can be found for images of a same person captured at different views by embedding the images into the Hamming space. Therefore, person re-identification can be solved by efficiently computing and ranking the Hamming distances between the images. Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-ofthe-art re-identification approaches but is at least 2200 times faster.
A survey on virtual machine scheduling in cloud computing

Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Resource scheduling of multiple virtual machines (VMs) is the core of Infrastructure as a Service (IaaS). We aimed to study various algorithms and schedulers in VM sheduling, and identify research directions in this area. In this paper, the related model and characteristics of VM scheduling are described. Then a variety of VM schedulers are reviewed and classified, also the features of these schedulers are expressed.
A Fast Stereo Visual-Inertial Odometry for MAVs

Motion estimation is one of the key perception problems for Micro Aerial Vehicles (MAVs). Recent developments based on visual-inertial sensors exhibit promising performance. In this paper, we propose a fast stereo-inertial odometry approach leveraging the advantages of both visual feature tracking and visual inertial fusion. Instead of tracking visual feature on every frame, our method estimates short-term motion with inertial-assisted flow tracking. By that, the frame-rate of visual feature detection and tracking is reduced to save computational power. Also, we design an Extended Kalman Filter (EKF) to achieve full state estimation, including position, velocity and attitude. Our method is able to estimate camera motion with less computational effort, while maintaining comparative state estimation performance. Experiments on the Euroc dataset which is captured onboard a MAV show the good performance of our method.
Least Cost Rumor Blocking in Social Networks

In many real-world scenarios, social network serves as a platform for information diffusion, alongside with positive information (truth) dissemination, negative information (rumor) also spread among the public. To make the social network as a reliable medium, it is necessary to have strategies to control rumor diffusion. In this article, we address the Least Cost Rumor Blocking (LCRB) problem where rumors originate from a community Cr in the network and a notion of protectors are used to limit the bad influence of rumors. The problem can be summarized as identifying a minimal subset of individuals as initial protectors to minimize the number of people infected in neighbor communities of Cr at the end of both diffusion processes. Observing the community structure property, we pay attention to a kind of vertex set, called bridge end set, in which each node has at least one direct in-neighbor in Cr and is reachable from rumors. Under the OOAO model, we study LCRB-P problem, in which α (0 <; α <; 1) fraction of bridge ends are required to be protected. We prove that the objective function of this problem is submodular and a greedy algorithm is adopted to derive a (1-1/e)-approximation. Furthermore, we study LCRB-D problem over the DOAA model, in which all the bridge ends are required to be protected, we prove that there is no polynomial time o(ln n)-approximation for the LCRB-D problem unless P = NP, and propose a Set Cover Based Greedy (SCBG) algorithm which achieves a O(ln n)-approximation ratio. Finally, to evaluate the efficiency and effectiveness of our algorithm, we conduct extensive comparison simulations in three real-world datasets, and the results show that our algorithm outperforms other heuristics.
The effects of modern mathematics computer games on mathematics achievement and class motivation

This study examined the effects of a computer game on students' mathematics achievement and motivation, and the role of prior mathematics knowledge, computer skill, and English language skill on their achievement and motivation as they played the game. A total of 193 students and 10 teachers participated in this study. The teachers were randomly assigned to experimental and control groups. A mixed method of quantitative and interviews were used with Multivariate Analysis of Co-Variance to analyze the data. The results indicated significant improvement of the achievement of the experimental versus control group. No significant improvement was found in the motivation of the groups. Students who played the games in their classrooms and school labs reported greater motivation compared to the ones who played the games only in the school labs. Prior knowledge, computer and English language skill did not play significant roles in achievement and motivation of the experimental group.
A Pattern Reconfigurable U-Slot Antenna and Its Applications in MIMO Systems

A new compact pattern reconfigurable U-slot antenna is presented. The antenna consists of a U-slot patch and eight shorting posts. Each edge of the square patch is connected to two shorting posts via PIN diodes. By switching between the different states of the PIN diodes, the proposed antenna can operate in either monopolar patch or normal patch mode in similar frequency ranges. Therefore, its radiation pattern can be switched between conical and boresight patterns electrically. In addition, the plane with the maximum power level of the conical pattern can be changed between two orthogonal planes. Owing to a novel design of the switch geometry, the antenna does not need dc bias lines. The measured overlapping impedance bandwidth (|S11| <; -10 dB) of the two modes is 6.6% with a center frequency of 5.32 GHz. The measured radiation patterns agree well with simulated results. The antennas are incorporated in a 2 × 2 multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system to demonstrate the improvement in system capacity. In the real-time MIMO-OFDM channel measurement, it is shown that compared to omnidirectional antennas, the pattern reconfigurable antennas can enhance the system capacity, with 17% improvement in a line-of-sight (LOS) scenario and 12% in a non-LOS (NLOS) scenario at a signal-to-noise ratio (SNR) of 10 dB.
Agricultural aid to seed cultivation: An Agribot

Machine intelligence is a developing technology which has made its way to various fields of engineering and technology. Robots are slowly being implemented in the field of agriculture, very soon AgriBots are to take over the agricultural fields and be used for various difficult and tiresome tasks involving agriculture. They have become the inevitable future of agriculture. This paper proposes an idea that will help in effective cultivation of vast areas of land left uncultivated or barren. Numerous farmers are dying during hill farming mainly due to falling from heights, which can be reduced by this technological effort. The proposed work will help in cultivation in remote areas and increase green cover as well as help farmers in harsh environments. The Agricultural Aid to Seed Cultivation (AASC) robot will be an unmanned aerial vehicle equipped with a camera, a digital image processing unit and a seed cultivation unit. A quadcopter is chosen as an aerial vehicle is independent of the form and shape of the ground and is not deterred by these factors while providing high mobility and reliability. The research aims about the new technology which can be suitable for any kind of remote farming.
Tomographic reconstruction of piecewise smooth images

In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it is necessary to consider the estimation of reconstructed images when the problem is under-constrained, i.e., when a unique solution does not exist. To resolve ambiguities among solutions, it is necessary to place additional constraints on the reconstructed image. In this paper, we present a surface evolution technique to model the reconstructed image as piecewise smooth. We model the reconstructed image as two regions that are each smoothly varying in intensity and are separated by a smooth surface. We define a cost functional to penalize deviation from piecewise smoothness while ensuring that the projections of the estimated image match the measured projections. From this functional, we derive an evolution for the modeled image intensity and an evolution for the surface, thereby defining a variational tomographic estimation technique. We show example reconstructions to highlight the performance of the proposed method on real medical images.
An Energy-Efficient , Wireless Top-of-Rack to Top-of-Rack Datacenter Network using 60 GHz Links

Datacenters have become the digital backbone of the modern society and consume enormous amounts of power. Significant portion of the power consumption is due to the power hungry switching fabric necessary for communication in the datacenter. Additionally, the complex cabling in traditional datacenters pose design and maintenance challenges and increase the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Recent research on wireless datacenters have proposed interconnecting rows of racks at the top-of-rack (ToR) level via wireless links to eliminate the need for complex network of power hungry routers and cables. Links are established using either highly directional phased array antennas or narrow-beam horned antennas between those ToR entities. ToR-to-ToR wireless links have also been used to augment existing wired networks, improving overall performance characteristics. All these wireless approaches advocate the use of 60GHz line-of-sight (LoS) communication paths between antennas for the establishment of reliable wireless channels. In this work, we explore the feasibility of a ToR-to-ToR wireless network for a small to medium-scale datacenter from the perspective of system-level performance. We evaluate a ToR-to-ToR wireless datacenter network (DCN) for network-level data rate and overall power consumption and compare it to a traditional fat-tree based DCN. We find that the ToR-to-ToR wireless DCN can sustain similar data rates for typical query based applications and consume less power compared to that of traditional datacenters. KeywordsWireless datacenter; Energy Efficiency; 60GHz; IEEE 802.11ad; Top-of-Rack;
A novel method for eye tracking and blink detection in video frames

This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.
Magic decorator: automatic material suggestion for indoor digital scenes

Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.
A Shooting Training and Instructing System Based on Image Analysis

The main shortcoming of the traditional way to train shooting athletes is that the effect is hard to be evaluated without live firing. We have designed and implemented a computer-aided shooting training and instructing system that can help coaches and athletes do more quality than quantity work. The minimum system consists of 6 components: a gun, a laser aiming device, a target, a camera, an image card and a computer. It works as follows: the camera snaps a target image, then the aiming point which is marked by the laser spot is extracted from the image and the corresponding score is calculated and printed in the screen in real time. The trace of the aiming point in a training session is also drawn in the screen. The system can prompt the best trigger moment. Based on the recorded performance data in the whole training session, the coach can make objective evaluation on the training effect and give pointed instructions to the athlete. The evaluation and instructions can be made by the expert system instead. Up to 16 targets can be processed simultaneously by one system.
Multiple HOG templates for gait recognition

In gait recognition field, template-based approaches such as Gait Energy Image (GEI) and Chrono-Gait Image (CGI) can achieve good recognition performance with low computational cost. Meanwhile, CGI can preserve temporal information better than GEI. However, they pay less attention to the local shape features. To preserve temporal information and generate more abundant local shape features, we generate multiple HOG templates by extracting Histogram of Oriented Gradients (HOG) of GEI and CGI templates. Experiments show that compared with several published approaches, our proposed multiple HOG templates achieve better performance for gait recognition.
Bearing fault diagnosis based on improved LMD

A new time-frequency analysis method named Local mean decomposition (LMD) was studied, which can adaptively decompose complicated component signal into a set of product functions (PF). The PF whose instantaneous frequency has physical significance was the product of an envelope signal and a frequency modulated signal. In order to solve the shortage moving average algorithm in LMD method, the author puts forward to using hermite interpolation instead of moving average algorithm. Through the analysis on simulation signals, we found that the improved LMD method is better than the LMD method both in analysis accuracy and computation time. According to the modulating characteristics of the roller bearing fault vibration signals, a fault diagnosis method for bearings based on improved LMD was proposed. The analysis results show the method can be used in the fault diagnosis.
Understanding microblog continuance usage intention: an integrated model

The purpose of this paper is to explore the factors affecting users’ continuous microblog usage intention. In recent years, the number of microblog users has gradually declined. This research can reveal microblog users’ needs and provide the improvement direction of microblog services.  By integrating Wixom and Todd’s theoretical framework, the Uses and Gratifications Theory and the DeLone and McLean Information System Success Model, a conceptual model is proposed. In this model, gratification is defined as a kind of behavioral attitude, and satisfaction is viewed as an object-based attitude. The survey data were collected online and analyzed using the partial least squares method.  The results suggest that users’ continuance intention (behavioral intention) is jointly determined by users’ gratification (behavioral-based attitude) and their habitual microblog usage behavior. Likewise, gratification is positively affected by satisfaction (object-based attitude) which is a joint function of system quality and information quality (object-based beliefs).  In this research, Wixom and Todd’s principle is applied as the basic theoretical framework; gratification is viewed as a behavior attitude and user satisfaction is identified as an object-based attitude. This research model is a new lens for continuance usage research.