nils-herrmann/clean_scirepeval huggingface dataset:
Cyberbullying and Primary-School Aged Children : The Psychological Literature and the Challenge for Sociology

Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted OPEN ACCESS Societies 2015, 5 493 impacts, understanding and behaviours or engaged children’s independent voice. This review highlights current gaps in our knowledge about younger children’s experiences with this form of bullying, and the importance of employing cross-disciplinary and developmentally appropriate methodologies to inform future research.
Are effective teachers like good parents? Teaching styles and student adjustment in early adolescence.

This study examined the utility of parent socialization models for understanding teachers' influence on student adjustment in middle school. Teachers were assessed with respect to their modeling of motivation and to Baumrind's parenting dimensions of control, maturity demands, democratic communication, and nurturance. Student adjustment was defined in terms of their social and academic goals and interest in class, classroom behavior, and academic performance. Based on information from 452 sixth graders from two suburban middle schools, results of multiple regressions indicated that the five teaching dimensions explained significant amounts of variance in student motivation, social behavior, and achievement. High expectations (maturity demands) was a consistent positive predictor of students' goals and interests, and negative feedback (lack of nurturance) was the most consistent negative predictor of academic performance and social behavior. The role of motivation in mediating relations between teaching dimensions and social behavior and academic achievement also was examined; evidence for mediation was not found. Relations of teaching dimensions to student outcomes were the same for African American and European American students, and for boys and girls. The implications of parent socialization models for understanding effective teaching are discussed.
No . 6100 A Theory of Crowdfunding – A Mechanism Design Approach with Demand Uncertainty and Moral Hazard

Crowdfunding provides innovation in enabling entrepreneurs to contract with consumers before investment. Under aggregate demand uncertainty, this improves screening for valuable projects. Entrepreneurial moral hazard and private cost information threatens this benefit. Despite these threats, popular crowdfunding schemes are able to implement optimal screening mechanisms. Crowdfunding’s after-markets enable consumers to actively implement deferred payments and thereby optimally manage the entrepreneur’s incentives. Efficiency is sustainable only if expected returns exceed an agency cost associated with the entrepreneurial incentive problems. By reducing demand uncertainty, crowdfunding promotes welfare and complements traditional entrepreneurial financing which focuses on controlling moral hazard.
Machine Learning Combining with Visualization for Intrusion Detection: A Survey

Intrusion detection is facing great challenges as network attacks producing massive volumes of data are increasingly sophisticated and heterogeneous. In order to gain much more accurate and reliable detection results, machine learning and visualization techniques have been respectively applied to intrusion detection. In this paper, we review some important work related to machine learning and visualization techniques for intrusion detection. We present a collaborative analysis architecture for intrusion detection tasks which integrate both machine learning and visualization techniques into intrusion detection. We also discuss some significant issues related to the proposed collaborative analysis architecture.
The new blocs on the block: using community forums to foster new neighbourhoods

Research has consistently shown that online tools increase social capital. In the context of neighbourhoods Hampton and Wellman have shown that in newly developed areas residents effectively used mailing lists to connect with each other, circulate information, and ask for help. The research question of whether similar findings would hold in the larger context of a city for a long period of time is still open. To tackle this research question, we have gathered the complete dataset of the most popular neighbourhood online forum in Dublin. In this dataset, we have people sharing a common purpose (blocs) who live in the same neighbourhood and interact online to ask for help, engage in local activities, and, more generally, have a better understanding of their physical community. Our analysis highlights the particularly concentrated usage in newly established developments where a pre-existing community may be absent. Additionally, these communications provide a valuable resource to understand local issues relevant to the community.
Compact UWB Bandpass Filter With Ultra Narrow Notched Band

A compact ultra-wideband (UWB) bandpass filter with an ultra narrow notched band is proposed using a hybrid microstrip and coplanar waveguide (CPW) structure. The CPW detached-mode resonator (DMR) composed of a quarter-wavelength (¿/4) nonuniform CPW resonator with a short-stub and a ¿/4 single-mode CPW resonator (SMCR) can allocate three split frequencies at the lower end, middle, higher end of the UWB passband. The conventional broadside-coupled microstrip/CPW structure is introduced to improve the bandwidth enhancement around the split frequencies, which leads to good UWB operation. To avoid the interferences such as WLAN signals, the ¿/4 meander slot-line structure embedded in the DMR is employed to obtain the notched band inside the UWB passband. The design is then verified by experiment. Good passband and stopband performances are achieved. Specifically, the fabricated filter has a 10 dB notched fractional bandwidth (FBW) of 2.06% at the notched center frequency of 5.80 GHz.
Using eye tracking to understand learners' reading process through the concept-mapping learning strategy

The author used an eye-tracking methodology to exam ine the influence of the concept-mapping learning strategy on learners perfo rming an English reading task. Eighty-six freshmen enrolled in English courses participated i n this control-group pretest-posttest experiment, and received either traditional or conc ept-mapping instruction for learning English reading skills. A concept-mapping strategy was introduced to the learners in the experimental group to improve their reading ability . The results of independent t tests, in which eye-tracking data on fixation time and fixati on path were used, indicated that the participants who received concept-mapping instructi on exhibited shorter fixation times on the core concept and other content in the text, compare d with the group that received traditional instruction. Moreover, the experimental group demon strated longer and irregular rereading paths than the control group (CG) did. These result s indicated that concept mapping serves as a reference to assist average readers to improve an d to identify primary ideas that clarify the meaning of an article.
DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a broad range of systems (from embedded systems to data centers). At the same time, a small set of machine-learning algorithms (especially Convolutional and Deep Neural Networks, i.e., CNNs and DNNs) are proving to be state-of-the-art across many applications. As architectures evolve towards heterogeneous multi-cores composed of a mix of cores and accelerators, a machine-learning accelerator can achieve the rare combination of efficiency (due to the small number of target algorithms) and broad application scope.
 Until now, most machine-learning accelerator designs have focused on efficiently implementing the computational part of the algorithms. However, recent state-of-the-art CNNs and DNNs are characterized by their large size. In this study, we design an accelerator for large-scale CNNs and DNNs, with a special emphasis on the impact of memory on accelerator design, performance and energy.
 We show that it is possible to design an accelerator with a high throughput, capable of performing 452 GOP/s (key NN operations such as synaptic weight multiplications and neurons outputs additions) in a small footprint of 3.02 mm2 and 485 mW; compared to a 128-bit 2GHz SIMD processor, the accelerator is 117.87x faster, and it can reduce the total energy by 21.08x. The accelerator characteristics are obtained after layout at 65 nm. Such a high throughput in a small footprint can open up the usage of state-of-the-art machine-learning algorithms in a broad set of systems and for a broad set of applications.
(Artificial) Mind over Matter: Integrating Humans and Algorithms in Solving Matching Problems

1  Schema matching is a data integration task that provides correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources (e.g., attributes in database schemata). The research of data integration and schema matching spans over multiple decades, holds both theoretical and practical appeal, and enjoys a continued interest by researchers and practitioners  and books [6, 18] for an overview). In recent years, there has been an evolution of data accumulation, management, analytics, and visualization (also known as big data). Big data is primarily about collecting volumes of data in an increased velocity from various sources and varying veracity. Big data is characterized by technological advancements such as Internet of things (accumulation), cloud computing (management), and deep learning (analytics). Putting it all together provides us with a new, exciting, and challenging research agenda. Schema matching problems have been historically defined as a semi-automated task in which correspondences are generated by matching algorithms and subsequently validated by a single human expert. The reason for that is the inherent assumption that humans “do it better," which is not necessarily the case [28]. This research is devoted to the investigation of respective roles of humans and machines in achieving cognitive tasks. We focus on matching as a concrete task where the traditional roles of humans and machines are subject to change due to the availability of data and advances in machine learning. The rest of this paper is organized as follows. Preliminaries are given in Section 2 followed the model and approach (Section 3). We conclude in 4 with our main contributions and results.
Adversarial Robustness: Softmax versus Openmax

Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwise correctly recognized inputs. Various approaches have been developed for efficiently generating these so-called adversarial examples, but those mostly rely on ascending the gradient of loss. In this paper, we introduce the novel logits optimized targeting system (LOTS) to directly manipulate deep features captured at the penultimate layer. Using LOTS, we analyze and compare the adversarial robustness of DNNs using the traditional Softmax layer with Openmax, which was designed to provide open set recognition by defining classes derived from deep representations, and is claimed to be more robust to adversarial perturbations. We demonstrate that Openmax provides less vulnerable systems than Softmax to traditional attacks, however, we show that it can be equally susceptible to more sophisticated adversarial generation techniques that directly work on deep representations.
An improved analytical IGBT model for loss calculation including junction temperature and stray inductance

An improved analytical model suitable for IGBT modules is proposed in this paper to calculate the power losses with high accuracy and short calculation time. In this model, the parameters varying with junction temperature of the modules, such as di/dt in the turn-on period and dv/dt in the turn-off period, are discussed and derived according to several equivalent models. In addition, the parasitic inductance in the circuit including the emitter and collector inductance in the power circuits and the gate inductance in the driving loop are considered in this model. Based on this proposed model, the simulation switching waveforms of collector currents and collector-emitter voltages are provided to verify the model. Meanwhile, the calculated power losses are confirmed to be precise by comparing with measurement results.
Aircraft angle of attack and air speed detection by redundant strip pressure sensors

Air speed and angle of attack are fundamental parameters in the control of flying bodies. Conventional detection techniques use sensors that may protrude outside the aircraft, be too bulky for installation on small UAVs, or be excessively intrusive. We propose a novel readout methodology where flight parameters are inferred from redundant pressure readings made by capacitive strip sensors directly applied on the airfoil skin. Redundancy helps lower the accuracy requirements on the individual sensors. A strategy for combining sensor data is presented with an error propagation analysis. The latter enables foreseeing the precision by which the flight parameters can be detected. The methodology has been validated by fluid dynamic simulation and a sample case is illustrated.
Digital Ordering System for Restaurant Using Android

Nowadays web services technology is widely used to integrate heterogeneous systems and develop new applications. Here an application of integration of hotel management systems by web services technology is presented. Digital Hotel Management integrates lots of systems of hotel industry such as Ordering System Kitchen Order Ticket (KOT), Billing System, Customer Relationship Management system (CRM) together. This integration solution can add or expand hotel software system in any size of hotel chains environment. This system increases quality and speed of service. This system also increases attraction of place for large range of customers. Implementing this system gives a cost-efficient opportunity to give your customers a personalized service experience where they are in control choosing what they want, when they want it – from dining to ordering to payment and feedback. We are implementing this system using android application for Tablet PC’s. The front end will be developed using JAVA Android and the backend will work on MySQL database.  Data Flow Diagram.  Digital Ordering System for Restaurant Using KOT: Kitchen Order Ticket Android UML: Unified Modeling Language.
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox

To enable complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to train complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the serving and management of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of targeting advertisements, recommending products, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at “Big Data” scale.
A comprehensive survey on vehicular Ad Hoc network

Vehicular ad hoc networks (VANETs) are classified as an application of mobile ad hoc network (MANET) that has the potential in improving road safety and in providing travellers comfort. Recently VANETs have emerged to turn the attention of researchers in the field of wireless and mobile communications, they differ from MANET by their architecture, challenges, characteristics and applications. In this paper we present aspects related to this field to help researchers and developers to understand and distinguish the main features surrounding VANET in one solid document, without the need to go through other relevant papers and articles starting from VANET architecture and ending up with the most appropriate simulation tools to simulate VANET protocols and applications. & 2013 Elsevier Ltd. All rights reserved.
Low power/low voltage high speed CMOS differential track and latch comparator with rail-to-rail input

A new CMOS differential latched comparator suitable for low voltage, low-power application is presented. The circuit consists of a constant-gm rail-to-rail common-mode operational transconductance amplifier followed by a regenerative latch in a track and latch configuration to achieve a relatively constant delay. The use of a track and latch minimizes the total number of gain stages required for a given resolution. Potential offset from the constant-gm differential input stage, estimated as the main source of offset, can be minimized by proper choice of transistors sizes. Simulation results show that the circuit requires less than 86 μA with a supply voltage of 1.65 V in a standard CMOS 0.18 μm digital process. The average delay is less than 1 ns and is approximately independent of the common-mode input voltage.
Computing Visual Correspondence with Occlusions using Graph Cuts

Several new algorithms for visual correspondence based on graph cuts [7, 14, 17] have recently been developed. While these methods give very strong results in practice, they do not handle occlusions properly. Specifically, they treat the two input images asymmetrically, and they do not ensure that a pixel corresponds to at most one pixel in the other image. In this paper, we present a new method which properly addresses occlusions, while preserving the advantages of graph cut algorithms. We give experimental results for stereo as well as motion, which demonstrate that our method performs well both at detecting occlusions and computing disparities.
An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

Thepickup and delivery problem with time windows is the problem of serving a number of transportation requestsusing a limited amount of vehicles. Each request involves mo ving a number of goods from a pickup location to a delivery location. Our task is to constr uc routes that visit all locations such that corresponding pickups and deliveries are placed on the same rout and such that a pickup is performed before the corresponding delivery. The routes must also sat isfy time window and capacity constraints. This paper presents a heuristic for the problem based on an ex te sion of theLarge Neighborhood Search heuristic previously suggested for solving the vehicle rou ting problem with time windows. The proposed heuristic is composed of a number of competing sub-heuristi cs which are used with a frequency corresponding to their historic performance. This general framework i s denotedAdaptive Large Neighborhood Search . The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for mor e than 50% of the problems. The computational experiments indicate that it is advantag eous to use several competing sub-heuristics instead of just one. We believe that the proposed heuristic i very robust and is able to adapt to various instance characteristics.
Process Authentication for High System Assurance

This paper points out the need in modern operating system kernels for a process authentication mechanism, where a process of a user-level application proves its identity to the kernel. Process authentication is different from process identification. Identification is a way to describe a principal; PIDs or process names are identifiers for processes in an OS environment. However, the information such as process names or executable paths that is conventionally used by OS to identify a process is not reliable. As a result, malware may impersonate other processes, thus violating system assurance. We propose a lightweight secure application authentication framework in which user-level applications are required to present proofs at runtime to be authenticated to the kernel. To demonstrate the application of process authentication, we develop a system call monitoring framework for preventing unauthorized use or access of system resources. It verifies the identity of processes before completing the requested system calls. We implement and evaluate a prototype of our monitoring architecture in Linux. The results from our extensive performance evaluation show that our prototype incurs reasonably low overhead, indicating the feasibility of our approach for cryptographically authenticating applications and their processes in the operating system.