nils-herrmann/clean_scirepeval huggingface dataset:
Association between body mass index and health outcomes among adolescents: the mediating role of traditional and cyber bullying victimization

It is well-documented that obese children and adolescents tend to experience a variety of negative physical and psychological health consequences. Despite the association between obesity and physical and psychological well-being, few studies have examined the role of off-line and on-line forms of bullying victimization in this link. The main objective of the current study is to investigate the direct and mediating effects of traditional and cyber bullying victimization in explaining the relationship between the body mass index (BMI) and physical/psychological distress.



A nationally representative sample of 10,160 school children (mean age = 12.95 ± 1.75) were collected from the 2009 Health Behavior in School-aged Children (HBSC) study. Data were collected on body mass index, physical and psychological health, bullying victimization experience, and demographic information. A seemingly unrelated regression (SUR) was employed to assess and compare the indirect effects in multiple mediation models.



While a significant direct association was found between BMI and both physical and psychological health, the indirect effect of BMI on physical distress was significant only via traditional bullying victimization. Both forms of bullying victimization had a mediating impact between BMI and psychological distress. However, the indirect effect on psychological distress was manifested through a negative mediating role of cyberbullying victimization. The negative relation between cyberbullying victimization and psychological distress warrants further exploration.



Obesity represents a serious risk to adolescent health and well-being, both physically and psychologically. If becoming a victim of traditional bullying mediates (specifically exacerbates) the level of physical and psychological distress among obese and overweight adolescents, health professionals need to focus on raising awareness of the importance of weight-based victimization for children and adolescents with obesity. School administrators and teachers could increase the efforts to identify school-age children who are stigmatized for their weight and recommend coping strategies for distressed victims of traditional and cyberbullying.
High-Speed Flight of Quadrotor Despite Loss of Single Rotor

In order to achieve high-speed flight of a damaged quadrotor with complete loss of a single rotor, a multiloop hybrid nonlinear controller is designed. By fully making use of sensor measurements, the model dependence of this control method is reduced, which is conducive to handling disturbance from the unknown aerodynamic effects. This controller is tested on a quadrotor vehicle with one rotor completely removed in the high-speed condition. Free flights are performed in the Open Jet Facility, a large-scale wind tunnel. Over 9 m/s flight speed is reached for the damaged quadrotor in these tests. In addition, several high-speed spin-induced aerodynamic effects are discovered and analyzed.
Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery

In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.
Lipedema: an overview of its clinical manifestations, diagnosis and treatment of the disproportional fatty deposition syndrome - systematic review.

Lipedema is a disproportionate, symmetrical fatty swelling characterized by pain and bruising existing almost exclusively among women. We undertook a systematic review of the available literature about lipedema, given the lack of knowledge and little evidence about this disorder especially among obesity experts. Diagnosis of lipedema is usually based on clinical features. Symmetrical edema in the lower limbs with fatty deposits located to hips and thighs usually appears at puberty and often affects several members of the same family. Main disorders considered for differential diagnosis are lymphedema, obesity, lipohypertrophy and phlebedema. Treatment protocols comprise conservative (decongestive lymphatic therapy) and surgical (liposuction) approaches. Early diagnosis and treatment are mandatory for this disorder otherwise gradual enlargement of fatty deposition causes impaired mobility and further comorbidities like arthrosis and lymphatic insufficiency.
Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment

This paper presents a sophisticated vision-aided flocking system for unmanned aerial vehicles (UAVs), which is able to operate in GPS-denied unknown environments for exploring and searching missions, and also able to adopt two types of vision sensors, day and thermal cameras, to measure relative motion between UAVs in different lighting conditions without using wireless communication. In order to realize robust vision-aided flocking, an integrated framework of tracking-learning-detection on the basis of multifeature coded correlation filter has been developed. To achieve long-term tracking, a redetector is trained online to adaptively reinitialize target for global sensing. An advanced flocking strategy is developed to address the autonomous multi-UAVs' cooperative flight. Light detection and ranging (LiDAR)-based navigation modules are developed for autonomous localization, mapping, and obstacle avoidance. Flight experiments of a team of UAVs have been conducted to verify the performance of this flocking system in a GPS-denied environment. The extensive experiments validate the robustness of the proposed vision algorithms in challenging scenarios.
UP-Hist Tree: An Efficient Data Structure for Mining High Utility Patterns from Transaction Databases

High-utility itemset mining is an emerging research area in the field of Data Mining. Several algorithms were proposed to find high-utility itemsets from transaction databases and use a data structure called UP-tree for their working. However, algorithms based on UP-tree generate a lot of candidates due to limited information availability in UP-tree for computing utility value estimates of itemsets. In this paper, we present a data structure named UP-Hist tree which maintains a histogram of item quantities with each node of the tree. The histogram allows computation of better utility estimates for effective pruning of the search space. Extensive experiments on real as well as synthetic datasets show that our algorithm based on UP-Hist tree outperforms the state of the art pattern-growth based algorithms in terms of the total number of candidate high utility itemsets generated that needs to be verified.
Event-Based Mobile Social Networks: Services, Technologies, and Applications

Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.
Anaerobic biotechnology for industrial wastewater treatment.

Microbiological formation of methane has been occurring naturally for ages in such diverse habitats as marshes, rice paddies, benthic deposits, deep ocean trenches, hot springs, trees, cattle, pigs, iguanas, termites, and human beings (Mah and Smith, 1981; Steggerda and Dimmick, 1966; Prins, 1979; Balch et al., 1979). In the past decade, interest in anaerobic biotechnology has grown considerably, both in the harnessing of the process for industrial wastewater treatment and in the bioconversion of crop-grown biomass to methane (Sheridan, 1982; Chynoweth and Srivastava, 1980).
Parallel $(k)$-Clique Community Detection on Large-Scale Networks

The analysis of real-world complex networks has been the focus of recent research. Detecting communities helps in uncovering their structural and functional organization. Valuable insight can be obtained by analyzing the dense, overlapping, and highly interwoven k-clique communities. However, their detection is challenging due to extensive memory requirements and execution time. In this paper, we present a novel, parallel k-clique community detection method, based on an innovative technique which enables connected components of a network to be obtained from those of its subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight upper bounds on its worst case time and space complexities are given as well. Experiments on real-world networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism (i.e., a linear speedup). Comparisons with other state-of-the-art k-clique community detection methods show dramatic reductions in execution time and memory footprint. An open-source implementation of the method is also made publicly available.
Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach

This paper presents a novel histogram thresholding – fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve the compactness of the clusters forming these uniform regions. Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.
linalg: Matrix Computations in Apache Spark

We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark comes with the mllib.linalg library, which provides abstractions and implementations for distributed matrices. Using these abstractions, we highlight the computations that were more challenging to distribute. When translating single-node algorithms to run on a distributed cluster, we observe that often a simple idea is enough: separating matrix operations from vector operations and shipping the matrix operations to be ran on the cluster, while keeping vector operations local to the driver. In the case of the Singular Value Decomposition, by taking this idea to an extreme, we are able to exploit the computational power of a cluster, while running code written decades ago for a single core. We conclude with a comprehensive set of benchmarks for hardware accelerated matrix computations from the JVM, which is interesting in its own right, as many cluster programming frameworks use the JVM.
Dependency grammars as Haskell programs

In the paper we try to show that a lazy functional language such as Haskell is a convenient framework not only for implementing dependency parsers but also for expressing dependency grammars directly in the programming language in a compact, readable and mathematically clean way. The parser core, supplying necessary types and functions, is presented together with two examples of grammars: one trivial and one more elaborate, allowing to express a range of complex grammatical constraints such as long distance agreement. The complete Haskell code of the parser core as well the grammar examples is included.
ShakeIn: Secure User Authentication of Smartphones with Single-Handed Shakes

Smartphones have been widely used with a vast array of sensitive and private information stored on these devices. To secure such information from being leaked, user authentication schemes are necessary. Current password/pattern-based user authentication schemes are vulnerable to shoulder surfing attacks and smudge attacks. In contrast, stroke/gait-based schemes are secure but inconvenient for users to input. In this paper, we propose ShakeIn, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone. With embedded motion sensors, ShakeIn can effectively capture the unique and reliable biometrical features of users about how  they shake. In this way, even if an attacker sees a user shaking his/her phone, the attacker can hardly reproduce the same behavior. Furthermore, by allowing users to customize the way they shake the phone, ShakeIn endows users with the maximum operation flexibility. We implement ShakeIn and conduct both intensive trace-driven simulations and real experiments on 20 volunteers with about 530,555 shaking samples collected over multiple months. The results show that ShakeIn achieves an average equal error rate of 1.2 percent with a small number of shakes using only 35 training samples even in the presence of shoulder-surfing attacks.
Deep Location-Specific Tracking

Convolutional Neural Network (CNN) based methods have shown significant performance gains in the problem of visual tracking in recent years. Due to many uncertain changes of objects online, such as abrupt motion, background clutter and large deformation, the visual tracking is still a challenging task. We propose a novel algorithm, namely Deep Location-Specific Tracking, which decomposes the tracking problem into a localization task and a classification task, and trains an individual network for each task. The localization network exploits the information in the current frame and provides a specific location to improve the probability of successful tracking, while the classification network finds the target among many examples generated around the target location in the previous frame, as well as the one estimated from the localization network in the current frame. CNN based trackers often have massive number of trainable parameters, and are prone to over-fitting to some particular object states, leading to less precision or tracking drift. We address this problem by learning a classification network based on 1 × 1 convolution and global average pooling. Extensive experimental results on popular benchmark datasets show that the proposed tracker achieves competitive results without using additional tracking videos for fine-tuning. The code is available at https://github.com/ZjjConan/DLST
An intentional AI for hanabi

Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication theory and psychology research. To demonstrate that our agent performs better with a human cooperator we ran an experiment in which 224 participants played one or more games of Hanabi with different AIs, and will show that our AI scores higher than previously published work in such a setting.
A prototype real-time intrusion-detection expert system

This paper describes the design and implementation of a prototype intrusion-detection expert system (IDES) developed at SRI International. IDES is based on the concept that an intrusion manifests itself as a departure from expected behavior for a user. The prototype monitors users on a remote system using audit records which characterize their activities. It adaptively learns normal behavior of each user and detects and reports anomalous user behavior in real-time.
Evolutionary games and spatial chaos

MUCH attention has been given to the Prisoners' Dilemma as a metaphor for the problems surrounding the evolution of coopera-tive behaviour1–6. This work has dealt with the relative merits of various strategies (such as tit-for-tat) when players who recognize each other meet repeatedly, and more recently with ensembles of strategies and with the effects of occasional errors. Here we neglect all strategical niceties or memories of past encounters, considering only two simple kinds of players: those who always cooperate and those who always defect. We explore the consequences of placing these players in a two-dimensional spatial array: in each round, every individual 'plays the game' with the immediate neighbours; after this, each site is occupied either by its original owner or by one of the neighbours, depending on who scores the highest total in that round; and so to the next round of the game. This simple, and purely deterministic, spatial version of the Prisoners' Dilemma, with no memories among players and no strategical elaboration, can generate chaotically changing spatial patterns, in which cooperators and defectors both persist indefinitely (in fluctuating proportions about predictable long-term averages). If the starting configurations are sufficiently symmetrical, these ever-changing sequences of spatial patterns—dynamic fractals—can be extraordinarily beautiful, and have interesting mathematical properties. There are potential implications for the dynamics of a wide variety of spatially extended systems in physics and biology.
The future of parking: A survey on automated valet parking with an outlook on high density parking

In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.
Fast obstacle detection using U-disparity maps with stereo vision

This paper introduces a computationally efficient, approach for obstacle detection in driving assistance applications, based on stereovision. The proposed approach involves three different steps aiming an increased quality of the results. The first step relies on the basics: obstacles correspond to peak regions in the u-disparity map. By applying the model of the stereo system, the peaks are detected with an adaptive threshold. The adaptive thresholding will calculate the accumulation of points required for an obstacle based on its distance (disparity) and will be related to the characteristics of the stereovision system. The second and third steps consist of refining the result of the previous, vertically respectively horizontally. This is necessary in order to fill out unmarked pixel regions which are classified as belonging to obstacles. The second step iterates vertically and propagates the obstacle label to neighbor pixels. The third step improves obstacle regions horizontally, with points that do not belong to the road. The solution is fast and reliable, on various scenarios, as every step is an improvement of the standard U-disparity approach for obstacle detection.
ON MEANING-PRESERVING ADVERSARIAL PERTUR-

Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance. However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output. Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general. Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs. Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.
Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D

Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. The PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors, as well as methods for refining initial alignments using different variants of the well-known iterative closest point (ICP) algorithm. This article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3-D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in the PCL. These examples include dense red-green-blue-depth (RGB-D) point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3-D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3-D scanner on a microaerial vehicle (MAV).