nils-herrmann/clean_scirepeval huggingface dataset:
Ontology expansion: appending with extracted sub-ontology

The reuse of ontologies existing on the Web should be required to speed up ontology construction process. However, current ontology mapping approaches assume that two input ontologies are given not discovered from available semantic data of the web. In this paper, we present a new approach, which integrates ontology selection, mapping, and merging processes in order to minimise human mediation. Our ontology selection mechanism accepts a hand-crafted ontology as query ontology to search already constructed relevant ontologies. In addition, we develop a ranking method based on syntactic and semantic structure of classes to provide the best search result to the user.
Video-based facial expression recognition using histogram sequence of local Gabor binary patterns from three orthogonal planes

Video-based facial expression recognition has received significant attention in recent years due to its widespread applications. One key issue for video-based facial expression analysis in practice is how to extract dynamic features. In this paper, a novel approach is presented using histogram sequence of local Gabor binary patterns from three orthogonal planes (LGBP-TOP). In this approach, every facial expression sequence is firstly convolved with the multi-scale and multi-orientation Gabor filters to extract the Gabor Magnitude Sequences (GMSs). Then, we use local binary patterns from three orthogonal planes (LBP-TOP) on each GMS to further enhance the feature extraction. Finally, the facial expression sequence is modeled as a histogram sequence by concatenating the histogram pieces of all the local regions of all the LGBP-TOP maps. For recognition, Support Vector Machine (SVM) is exploited. Our experimental results on the extended Cohn-Kanade database (CK+) demonstrate that the proposed method has achieved the best results compared to other methods in recent years.
Improving information security awareness and behaviour through dialogue, participation and collective reflection. An intervention study

The paper discusses and evaluates the effects of an information security awareness programme. The programme emphasised employee participation, dialogue and collective reflection in groups. The intervention consisted of small-sized workshops aimed at improving information security awareness and behaviour. An experimental research design consisting of one survey before and two after the intervention was used to evaluate whether the intended changes occurred. Statistical analyses revealed that the intervention was powerful enough to significantly change a broad range of awareness and behaviour indicators among the intervention participants. In the control group, awareness and behaviour remained by and large unchanged during the period of the study. Unlike the approach taken by the intervention studied in this paper, mainstream information security awareness measures are typically top-down, and seek to bring about changes at the individual level by means of an expert-based approach directed at a large population, e.g. through formal presentations, e-mail messages, leaflets and posters. This study demonstrates that local employee participation, collective reflection and group processes produce changes in short-term information security awareness and behaviour. a 2009
Real-Time Video Streaming over NS 3-based Emulated LTE Networks

In this paper, we present a developed NS-3 based emulation platform for evaluating and optimizing the performance of the LTE networks. The developed emulation platform is designed to provide real-time measurements. Thus it eliminates the need for the high cost spent on real equipment. The developed platform consists of three main parts, which are video server, video client(s), and NS-3 based simulation environment for LTE network. Using the developed platform, the server streams video clips to the existing clients going through the LTE simulated network. We utilize this setup to evaluate multiple cases such as mobility and handover. Moreover, we use it for evaluating multiple streaming protocols such as UDP, RTP, and Dynamic Adaptive Streaming over HTTP (DASH). DASH, Emulation, LTE, NS-3, Real-time, RTP, UDP.
3D Reconstruction in the Presence of Glass and Mirrors by Acoustic and Visual Fusion

We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras or even to our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces. The key technical challenge is the sparse sampling rate from the acoustic sensor, which only takes one point measurement per frame. To address this challenge, we take advantage of the fact that the large scale glass structures in indoor environments are usually either piece-wise planar or a simple parametric surface. Based on these assumptions, we have developed a novel sensor fusion algorithm that first segments the (hybrid) depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. We validated our algorithms with a number of challenging cases, including multiple panes of glass, mirrors, and even a curved glass cabinet.
Use of Tethered Small Unmanned Aerial System at Berkman Plaza II Collapse

A tethered Small Unmanned Aerial System (sUAS) provided structural forensic inspection of the collapsed Berkman Plaza II six-story parking garage. The sUAS, an iSENSYS IP3 miniature helicopter, was tethered to meet US Federal Aviation Administration (FAA) requirements for unregulated flight below 45 m (150 ft). This created new platform control, human-robot interaction, and safety issues in addition to the challenges posed by the active, city environment. A new technique, viewpoint-oriented Cognitive Work Analysis (CWA), was used to generate the 4:1 human-robot crew organization and operational protocol. The sUAS over three flights was able to provide useful imagery to structural engineers that had been difficult to obtain from manned helicopters due to dust obscurants. Based on these flights this work shows that tethered operations decreases team effectiveness, increases overall safety liability, and in general is not a recommended solution for sUAS flight.
A Cascaded Inception of Inception Network With Attention Modulated Feature Fusion for Human Pose Estimation

Accurate keypoint localization of human pose needs diversified features: the high level for contextual dependencies and the low level for detailed refinement of joints. However, the importance of the two factors varies from case to case, but how to efficiently use the features is still an open problem. Existing methods have limitations in preserving low level features, adaptively adjusting the importance of different levels of features, and modeling the human perception process. This paper presents three novel techniques step by step to efficiently utilize different levels of features for human pose estimation. Firstly, an inception of inception (IOI) block is designed to emphasize the low level features. Secondly, an attention mechanism is proposed to adjust the importance of individual levels according to the context. Thirdly, a cascaded network is proposed to sequentially localize the joints to enforce message passing from joints of stand-alone parts like head and torso to remote joints like wrist or ankle. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both MPII and
From Contacts to Graphs: Pitfalls in Using Complex Network Analysis for DTN Routing

Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict which nodes are likely to deliver a content or bring it closer to the destination. One promising way of predicting future contact opportunities is to aggregate contacts seen in the past to a social graph and use metrics from complex network analysis (e.g., centrality and similarity) to assess the utility of a node to carry a piece of content. This aggregation presents an inherent tradeoff between the amount of time-related information lost during this mapping and the predictive capability of complex network analysis in this context. In this paper, we use two recent DTN routing algorithms that rely on such complex network analysis, to show that contact aggregation significantly affects the performance of these protocols. We then propose simple contact mapping algorithms that demonstrate improved performance up to a factor of 4 in delivery ratio, and robustness to various connectivity scenarios for both protocols.
Allosteric Coupling of Drug Binding and Intracellular Signaling in the A<ce:inf loc=post>2A</ce:inf> Adenosine Receptor

Signaling across cellular membranes, the 826 human G protein-coupled receptors (GPCRs) govern a wide range of vital physiological processes, making GPCRs prominent drug targets. X-ray crystallography provided GPCR molecular architectures, which also revealed the need for additional structural dynamics data to support drug development. Here, nuclear magnetic resonance (NMR) spectroscopy with the wild-type-like A2A adenosine receptor (A2AAR) in solution provides a comprehensive characterization of signaling-related structural dynamics. All six tryptophan indole and eight glycine backbone 15N-1H NMR signals in A2AAR were individually assigned. These NMR probes provided insight into the role of Asp522.50 as an allosteric link between the orthosteric drug binding site and the intracellular signaling surface, revealing strong interactions with the toggle switch Trp 2466.48, and delineated the structural response to variable efficacy of bound drugs across A2AAR. The present data support GPCR signaling based on dynamic interactions between two semi-independent subdomains connected by an allosteric switch at Asp522.50.
Risk Factors of Internet Addiction among Internet Users: An Online Questionnaire Survey

Internet addiction (IA) has become a major public health issue worldwide and is closely linked to psychiatric disorders and suicide. The present study aimed to investigate the prevalence of IA and its associated psychosocial and psychopathological determinants among internet users across different age groups.



The study was a cross-sectional survey initiated by the Taiwan Suicide Prevention Center. The participants were recruited from the general public who responded to the online questionnaire. They completed a series of self-reported measures, including Chen Internet Addiction Scale-revised (CIAS-R), Five-item Brief Symptom Rating Scale (BSRS-5), Maudsley Personality Inventory (MPI), and questions about suicide and internet use habits.



We enrolled 1100 respondents with a preponderance of female subjects (85.8%). Based on an optimal cutoff for CIAS-R (67/68), the prevalence rate of IA was 10.6%. People with higher scores of CIAS-R were characterized as: male, single, students, high neuroticism, life impairment due to internet use, time for internet use, online gaming, presence of psychiatric morbidity, recent suicide ideation and past suicide attempts. Multiple regression on IA showed that age, gender, neuroticism, life impairment, internet use time, and BSRS-5 score accounted for 31% of variance for CIAS-R score. Further, logistic regression showed that neuroticism, life impairment and internet use time were three main predictors for IA. Compared to those without IA, the internet addicts had higher rates of psychiatric morbidity (65.0%), suicide ideation in a week (47.0%), lifetime suicide attempts (23.1%), and suicide attempt in a year (5.1%).



Neurotic personality traits, psychopathology, time for internet use and its subsequent life impairment were important predictors for IA. Individuals with IA may have higher rates of psychiatric morbidity and suicide risks. The findings provide important information for further investigation and prevention of IA.
Vitreoretinal instruments: vitrectomy cutters, endoillumination and wide-angle viewing systems

There have been many advances in vitreoretinal surgery since Machemer introduced the concept of pars plana vitrectomy, in 1971. Of particular interest are the changes in the vitrectomy cutters, their fluidics interaction, the wide-angle viewing systems and the evolution of endoillumination through the past decade and notably in the last few years. The indications of 27-gauge surgery have expanded, including more complex cases. Cut rates of up to 16,000 cuts per minute are already available. New probe designs and pump technology have allowed duty cycle performances of near 100% and improved flow control. The smaller vitrectomy diameter can be positioned between narrow spaces, allowing membrane dissection and serving as a multifunctional instrument. Enhanced endoillumination safety can be achieved by changing the light source, adding light filters, increasing the working distance and understanding the potential interactions between light and vital dyes commonly used to stain the retina. Wide-angle viewing systems (contact, non-contact or a combination of both) provide a panoramic view of the retina. Non-contact systems are assistant-independent, while contact systems may be associated with better image resolution. This review will cover some current aspects on vitrectomy procedures, mainly assessing vitrectomy cutters, as well as the importance of endoillumination and the use of wide-angle viewing systems.
Biomechanical, anthropometric, and psychological determinants of barbell back squat strength.

Previous investigations of strength have only focused on biomechanical or psychological determinants, while ignoring the potential interplay and relative contributions of these variables. The purpose of this study was to investigate the relative contributions of biomechanical, anthropometric, and psychological variables to the prediction of maximum parallel barbell back squat strength. Twenty-one college-aged participants (male = 14; female = 7; age = 23 ± 3 years) reported to the laboratory for two visits. The first visit consisted of anthropometric, psychometric, and parallel barbell back squat one-repetition maximum (1RM) testing. On the second visit, participants performed isometric dynamometry testing for the knee, hip, and spinal extensors in a sticking point position-specific manner. Multiple linear regression and correlations were used to investigate the combined and individual relationships between biomechanical, anthropometric, and psychological variables and squat 1RM. Multiple regression revealed only one statistically predictive determinant: fat free mass normalized to height (standardized estimate ± SE = 0.6 ± 0.3; t(16) = 2.28; p = 0.037). Correlation coefficients for individual variables and squat 1RM ranged from r = -0.79-0.83, with biomechanical, anthropometric, experiential, and sex predictors showing the strongest relationships, and psychological variables displaying the weakest relationships. These data suggest that back squat strength in a heterogeneous population is multifactorial and more related to physical rather than psychological variables.
A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks †

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work-Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.
On Jigsaw Sudoku Puzzles and Related Topics Bachelor Thesis

Using memoization and various other optimization techniques, the number of dissections of the n × n square into n polyominoes of size n is computed for n ≤ 8. On this task our method outperforms Donald Knuth’s Algorithm X with Dancing Links. The number of jigsaw sudoku puzzle solutions is computed for n ≤ 7. For every jigsaw sudoku puzzle polyomino cover with n ≤ 6 the size of its smallest critical sets is determined. Furthermore it is shown that for every n ≥ 4 there exists a polyomino cover that does not allow for any sudoku puzzle solution. We give a closed formula for the number of possible ways to fill the border of an n × n square with numbers while obeying Latin square constraints. We define a cannibal as a nonempty hyperpolyomino that disconnects its exterior from its interior, where the interior is exactly the size of the hyperpolyomino itself, and we present the smallest found cannibals in two and three dimensions.
A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies

Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a "smart" neuroprosthetic system for treatment of neurologic and psychiatric disorders.
Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems

We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.
An electronic ballast to control an induction lamp

The use of electromagnetic induction lamps without electrodes has increased because of their long life and energy efficiency. The control of the ignition and luminosity of the lamp is provided by an electronic ballast. Beyond that, the electronic ballast also provides a power factor correction, allowing the minimizing of the lamps impact on the quality of service of the electrical network. The electronic ballast includes several blocks, namely a bridge rectifier, a power factor correcting circuit (PFC), an asymmetric half-bridge inverter with a resonant filter on the inverter output, and a circuit to control the conduction time ot the ballast transistors. Index Terms – SEPIC, PFC, electrodeless lamp, ressonant filter,
Current-fed DC/DC converters for high voltage gain and low voltage high current applications: An overview of topologies and modulation techniques

Emerging applications such as solar cells, fuel cells, energy storage, UPS, etc. require voltage gain and are low voltage high current specifications. This paper thoroughly studies and reviews high-frequency current-fed converters for low voltage high current applications. Hard-switching, dissipative or passive snubber based, active-clamped, naturally-clamped, impulse commutated, and parasitic assisted current-fed topologies are studied, compared, and evaluated. Individual merits, demerits, unique attributes and features of the various categories of the current-fed topologies are reported. Detailed comparison and critical evaluation are presented for the selection and design of a topology for the given application and specifications.