nils-herrmann/clean_scirepeval huggingface dataset:
Feeling cybervictims' pain-The effect of empathy training on cyberbullying.

As the world's population increasingly relies on the use of modern technology, cyberbullying becomes an omnipresent risk for children and adolescents and demands counteraction to prevent negative (online) experiences. The classroom-based German preventive intervention "Medienhelden" (engl.: "Media Heroes") builds on previous knowledge about links between cognitive empathy, affective empathy, and cyberbullying, among others. For an evaluation study, longitudinal data were available from 722 high school students aged 11-17 years (M = 13.36, SD = 1.00, 51.8% female) before and six months after the implementation of the program. A 10-week version and a 1-day version were conducted and compared with a control group (controlled pre-long-term-follow-up study). Schools were asked to randomly assign their participating classes to the intervention conditions. Multi-group structural equation modeling (SEM) showed a significant effect of the short intervention on cognitive empathy and significant effects of the long intervention on affective empathy and cyberbullying reduction. The results suggest the long-term intervention to be more effective in reducing cyberbullying and promoting affective empathy. Without any intervention, cyberbullying increased and affective empathy decreased across the study period. Empathy change was not generally directly linked to change in cyberbullying behavior. "Media Heroes" provides effective teaching materials and empowers schools to address the important topic of cyberbullying in classroom settings without costly support from the outside.
Smart-Contract Based System Operations for Permissioned Blockchain

Enterprises have paid attention to blockchain (BC), recently permissioned BC characterized with smart-contract, where busi-ness transactions among inter-authorized companies (forming consortium) can automatically be executed based on distributed consensus protocol over user-defined business logics pre-built with program codes. A single BC system will be built across mul-tiple management domains having different operational policies, e.g., datacenter of each organization; this will trigger a problem that its system operations (e.g., backup) will become time-consuming and costly due to the difficulty in unifying and/or adjusting operational policy, schedule, etc. Toward solving the problem, we propose an operations execution method for BC systems; a primary idea is to define operations as smart-contract so that unified and synchronized cross-organizational operations can be executed effectively by using BC-native features. We de-sign the proposed method as hybrid architecture including in-BC consensus establishment and out-BC event-based instruction execution, in order to be adaptable to the recent heterogeneous BC architecture. Performance evaluation using a prototype with Hyperledger Fabric v1.0 shows that the proposed method can start executing operations within 5 seconds. Furthermore, cost evaluation using model-based estimation shows that the total yearly cost of monthly operations on a 5-organizational BC sys-tem could be reduced by 61 percent compared to a conventional manual method.
Work in progress: Teaching computational thinking in middle and high school

Computational thinking (CT) does not require profound knowledge of computer science (CS). Vice versa, it helps students organize and build up new knowledge around the core CS concepts they encounter on the daily basis. Furthermore, CT provides a set of problem-solving skills and enhances analytical abilities that are crucial for the young generation to succeed in modern world pervaded with technology. Teaching the principles of computing to students with non-computing tracks of study provides them with a competitive advantage and breaks down stereotypes around CS. Our approach is to introduce a topic related to world issues and students' interests while concealing its relation to CS until students' interest and attention are captured. The approach takes advantage of pattern-matching abilities that are naturally wired in our brain to improve understanding and make the obtained knowledge persist in memory. The paper discusses some of the modules developed as part of the Inspire-CT project that can be delivered in one or part of a class period at high school or middle school.
BLE device indoor localization based on RSS fingerprinting mapped by propagation modes

Nowadays, Bluetooth Low Energy (BLE) technology has a great attention in the field of wireless localization techniques, especially in the case of indoor scenarios. Study presented in this paper explores BLE localization performance in indoor scenarios based on a received signal strength (RSS). Firstly, we present a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in the office environment. Secondly, an appropriate measurement workplace and its setup is proposed to measure RSS values. Obtained results are used to create an RSS-fingerprinting map. Furthermore, accuracy of position determination is calculated and evaluated. Results show advantage of BLE technology for indoor localization purposes (without calibration measurement). However, its performance highly depends on the number of considered BLE nodes and on applied evaluation method (e.g. number of considered sectors).
Adolescent development and the regulation of youth crime.

Elizabeth Scott and Laurence Steinberg explore the dramatic changes in the law's conception of young offenders between the end of the nineteenth century and the beginning of the twenty-first. At the dawn of the juvenile court era, they note, most youths were tried and punished as if they were adults. Early juvenile court reformers argued strongly against such a view, believing that the justice system should offer young offenders treatment that would cure them of their antisocial ways. That rehabilitative model of juvenile justice held sway until a sharp upswing in youth violence at the end of the twentieth century led both public opinion and public policy toward a view that youths should be held to the same standard of criminal accountability as adults. Lawmakers seemed to lose sight of developmental differences between adolescents and adults. But Scott and Steinberg note that lawmakers and the public appear now to be rethinking their views once more. A justice system that operates on the principle of "adult time for adult crime" now seems to many to take too little note of age and immaturity in calculating criminal punishment. In 2005 the United States Supreme Court abolished the juvenile death penalty as cruel and unusual punishment, emphasizing that the immaturity of adolescents made them less culpable than adult criminals. In addition, state legislatures recently have repealed or moderated some of the punitive laws they recently enacted. Meanwhile, observe the authors, public anger has abated and attitudes toward young offenders have softened somewhat. In response to these changes, Scott and Steinberg argue that it is appropriate to reexamine juvenile justice policy and to devise a new model for the twenty-first century. In this article, they propose what they call a developmental model. They observe that substantial new scientific evidence about adolescence and criminal activity by adolescents provides the building blocks for a new legal regime superior to today's policy. They put adolescent offenders into an intermediate legal category-neither children, as they were seen in the early juvenile court era, nor adults, as they often are seen today. They observe that such an approach is not only more compatible than the current regime with basic principles of fairness at the heart of the criminal law, but also more likely to promote social welfare by reducing the social cost of juvenile crime.
Data augmentation for deep convolutional neural network acoustic modeling

This paper investigates data augmentation based on label-preserving transformations for deep convolutional neural network (CNN) acoustic modeling to deal with limited training data. We show how stochastic feature mapping (SFM) can be carried out when training CNN models with log-Mel features as input and compare it with vocal tract length perturbation (VTLP). Furthermore, a two-stage data augmentation scheme with a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Improved performance has been observed in experiments conducted on the limited language pack (LLP) of Haitian Creole in the IARPA Babel program.
Impact of maternal probiotic-supplemented dietary counselling on pregnancy outcome and prenatal and postnatal growth: a double-blind, placebo-controlled study.

The perinatal nutritional environment impacts upon the health and well-being of mother and child also in the long term. The aim of the present study was to determine the safety and efficacy of perinatal probiotic-supplemented dietary counselling by evaluating pregnancy outcome and fetal and infant growth during the 24 months' follow-up. Altogether, 256 women were randomised at their first trimester of pregnancy into a control and a dietary intervention group. The intervention group received intensive dietary counselling provided by a nutritionist and were further randomised, double-blind to receive probiotics (Lactobacillus rhamnosus GG and Bifidobacterium lactis Bb12; diet/probiotics) or placebo (diet/placebo). Firstly, probiotic intervention reduced the frequency of gestational diabetes mellitus (GDM); 13 % (diet/probiotics) v. 36 % (diet/placebo) and 34 % (control); P = 0.003. Secondly, the safety of this approach was attested by normal duration of pregnancies with no adverse events in mothers or children. No significant differences in prenatal or postnatal growth rates among the study groups were detected. Thirdly, distinctive effects of the two interventions were detected; probiotic intervention reduced the risk of GDM and dietary intervention diminished the risk of larger birth size in affected cases; P = 0.035 for birth weight and P = 0.028 for birth length. The results of the present study show that probiotic-supplemented perinatal dietary counselling could be a safe and cost-effective tool in addressing the metabolic epidemic. In view of the fact that birth size is a risk marker for later obesity, the present results are of significance for public health in demonstrating that this risk is modifiable.
Evaluating Knowledge Representation and Reasoning Capabilites of Ontology Specification Languages

The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness of “ traditional” ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and “web-based” ontology languages. As a result of this study, we conclude that different needs in KR and reasoning may exist in the building of an ontology-based application, and these needs must be evaluated in order to choose the most suitable ontology language(s).
A Novel Cross Layer Intrusion Detection System in MANET

Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.
Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans.

This article considers the role of the hippocampus in memory function. A central thesis is that work with rats, monkeys, and humans--which has sometimes seemed to proceed independently in 3 separate literatures--is now largely in agreement about the function of the hippocampus and related structures. A biological perspective is presented, which proposes multiple memory systems with different functions and distinct anatomical organizations. The hippocampus (together with anatomically related structures) is essential for a specific kind of memory, here termed declarative memory (similar terms include explicit and relational). Declarative memory is contrasted with a heterogeneous collection of nondeclarative (implicit) memory abilities that do not require the hippocampus (skills and habits, simple conditioning, and the phenomenon of priming). The hippocampus is needed temporarily to bind together distributed sites in neocortex that together represent a whole memory.
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics

It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called Sentiment Utility Logistic Model (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have about the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user's potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty & spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.
Explaining and Predicting the Impact of Branding Alliances and Web Site Quality on Initial Consumer Trust of E-Commerce Web Sites

Trust is a crucial factor in e-commerce. However, consumers are less likely to trust unknown Web sites. This study explores how less-familiar e-commerce Web sites can use branding alliances and Web site quality to increase the likelihood of initial consumer trust. We use the associative network model of memory to explain brand knowledge and to show how the mere exposure effect can be leveraged to improve a Web site’s brand image. We also extend information integration theory to explain how branding alliances are able to increase initial trust and transfer positive effects to Web sites. Testing of our model shows that the most important constructs for increasing initial trust in our experimental context are branding and Web site quality. Finally, we discuss future research ideas, limitations, implications, and ideas for practitioners.
Sex differences in the human olfactory system

The olfactory system (accessory) implicated in reproductive physiology and behavior in mammals is sexually dimorphic. These brain sex differences present two main characteristics: they are seen in neural circuits related to sexual behavior and sexual physiology and they take one of two opposite morphological patterns (male>female or female>male). The present work reports sex differences in the olfactory system in a large homogeneous sample of men (40) and women (51) using of voxel-based morphology. Gray matter concentration showed sexual dimorphism in several olfactory regions. Women have a higher concentration in the orbitofrontal cortex involving Brodmann's areas 10, 11 and 25 and temporomedial cortex (bilateral hippocampus and right amygdala), as well as their left basal insular cortex. In contrast, men show a higher gray matter concentration in the left entorhinal cortex (Brodmann's area 28), right ventral pallidum, dorsal left insular cortex and a region of the orbitofrontal cortex (Brodmann's area 25). This study supports the hypothesis that the mammalian olfactory system is a sexually dimorphic network and provides a theoretical framework for the morphofunctional approach to sex differences in the human brain.
Detection of Anatomic Structures in Human Retinal Imagery

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.
Compact Multi-signatures for Smaller Blockchains

We construct new multi-signature schemes that provide new functionality. Our schemes are designed to reduce the size of the Bitcoin blockchain, but are useful in many other settings where multi-signatures are needed. All our constructions support both signature compression and public-key aggregation. Hence, to verify that a number of parties signed a common message m, the verifier only needs a short multisignature, a short aggregation of their public keys, and the message m. We give new constructions that are derived from Schnorr signatures and from BLS signatures. Our constructions are in the plain public key model, meaning that users do not need to prove knowledge or possession of their secret key. In addition, we construct the first short accountable-subgroup multisignature (ASM) scheme. An ASM scheme enables any subset S of a set of n parties to sign a message m so that a valid signature discloses which subset generated the signature (hence the subset S is accountable for signing m). We construct the first ASM scheme where signature size is only O(κ) bits over the description of S , where κ is the security parameter. Similarly, the aggregate public key is only O(κ) bits, independent of n. The signing process is non-interactive. Our ASM scheme is very practical and well suited for compressing the data needed to spend funds from a t-of-n Multisig Bitcoin address, for any (polynomial size) t and n.
Design of Traffic Lights Controlling System Based on PLC and Configuration Technology

A kind of traffic lights controlling system by using PLC was designed, and the automatic control of the traffic lights was performed by software. In the system, the original relay wiring was replaced by the program, and the hardware and software resources of PLC were used reasonably. The normally running and emergency transport were introduced in detail, and that the emergencies from the east-west and from the south-north can interlock. The traffic lights signal system was displayed with two seven-segment digital tubes in countdown order; meanwhile the wiring of hardware and PLC ladder diagram was discussed in details. The remote monitoring system of traffic lights at the crossroads was designed with configuration software MCGS, which monitored traffic lights in real-time and improved the reliability greatly. At the same time, we can change the states of the traffic lights by the configuration picture.
Classification of Liver Cancer using Artificial Neural Network and Support Vector Machine

The purpose of this study is to compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for liver cancer classification. The performance of both models is compared and validated on BUPA Liver Disorder Dataset in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The comparative results show that the SVM classifier outperforms ANN classifier where SVM gives an accuracy of 63.11%, specificity of 100% and AUC of 68.34% where as ANN gives classification accuracy of 57.28%, specificity of 32.56 and AUC of 53.78. This result indicates that the classification capability of SVM is better than ANN and may potentially fill in a critical gap in the use of current or future classification algorithms for liver cancer.