nils-herrmann/clean_scirepeval huggingface dataset:
A belief-desire-intention model for blog users' negative emotional norm compliance: Decision-making in crises

The purpose of this paper is to understand blog users’ negative emotional norm compliance decision-making in crises (blog users’-NNDC).  A belief– desire–intention (BDI) model to evaluate the blog users’-NNDC (the BDI-NNDC model) was developed. This model was based on three social characteristics: self-interests, expectations and emotions. An experimental study was conducted to evaluate the efficiency of the BDI-NNDC model by using data retrieved from a popular Chinese social network called “Sina Weibo” about three major crises.  The BDI-NNDC model strongly predicted the Blog users’-NNDC. The predictions were as follows: a self-interested blog user posted content that was targeting his own interests; a blogger with high expectations wrote and commented emotionally negative blogs on the condition that the numbers of negative posts increased, while he ignored the norm when there was relatively less negative emotional news; and an emotional blog user obeyed the norm based on the emotional intentions of the blogosphere in most of the cases.  The BDI-NNDC model can explain the diffusion of negative emotions by blog users during crises, and this paper shows a way to bridge the social norm modelling and the research of blog users’ activity and behaviour characteristics in the context of “real life” crises. However, the criterion for differentiating blog users according to social characteristics needs to be further revised, as the generalizability of the results is limited by the number of cases selected in this study.  The current method could be applied to predict emotional trends of blog users who have different social characteristics and it could support government agencies to build strategic responses to crises. The authors thank Mr Jon Walker and Ms Celia Zazo Seco in this work for their dedication and time. This paper is supported by the Key project of National Social Science Foundation under contract No. 13&ZD174; National Natural Science Foundation of China under contract No. 71273132, 71303111, 71471089, 71403121, 71503124 and 71503126; National Social Science Foundation under contract No. 15BTQ063; “Fundamental Research Funds for the Central Universities”, No: 30920140111006; Jiangsu “Qinlan” project (2016); Priority Academic Program Development of Jiangsu Higher Education Institutions; and Hubei Collaborative Innovation Center for Early Warning and Emergency Response Research project under contract JD20150401. The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0264-0473.htm
“ Sustainable Packaging Logistics ” . The link between Sustainability and Competitiveness in Supply Chains

Packaging is one element that can support and promote improvements and innovations in sustainable management of supply chains. This multifunctional vision has brought about the approach “sustainable packaging logistics” (SPL), which refers to the integration of packaging design, logistics management and new product development. The main aim of this paper is twofold. Firstly, to characterise the main aspects associated with SPL as a preliminary step towards constructing a conceptual model that can be validated in a quantitative way. Secondly, to carry out an exploratory study in the Spanish toy manufacturing sector in order to identify whether SPL deployment promotes changes and innovations in packaging that can lead to improved sustainability. The literature review was conducted via content analysis, and the methodology used to carry out empirical analysis was an electronic questionnaire dealing with aspects of SPL. The number of toy manufacturers participating in the study was 70. The results analysis indicates that further development of the main aspects of SPL actively contributes to better sustainable performance in almost all items considered in the study. The SPL approach is considered particularly innovative, because there is a scarcity of literature that deals jointly with packaging, logistics and sustainability from an empirical standpoint, rather than just the analysis of case studies.
Real-time single channel EEG motor imagery based Brain Computer Interface

This paper presents a motor imagery based Brain Computer Interface (BCI) that uses single channel EEG signal from the C3 or C4 electrode placed in the motor area of the head. Time frequency analysis using Short Time Fourier Transform (STFT) is used to compute spectrogram from the EEG data. The STFT is scaled to have gray level values on which Grey Co-occurrence Matrix (GLCM) is computed. Texture descriptors such as correlation, energy, contrast, homogeneity and dissimilarity are calculated from the GLCM matrices. The texture descriptors are used to train a logistic regression classifier which is then used to classify the left and right motor imagery signals. The single-channel motor imagery classification system is tested offline with different subjects. The average offline accuracy is 87.6%. An online BCI system is implemented in openViBE with the single channel classification scheme. The stimuli presentations and feedback are implemented in Python and integrated with the openViBe BCI system.
An analytical model for software defined networking: A network calculus-based approach

Software defined networking (SDN) and OpenFlow as the outcome of recent research and development efforts provided unprecedented access into the forwarding plane of networking elements. This is achieved by decoupling the network control out of the forwarding devices. This separation paves the way for a more flexible and innovative networking. While SDN concept and OpenFlow find their ways into commercial deployments, performance evaluation of the SDN concept and its scalability, delay bounds, buffer sizing and similar performance metrics are not investigated in recent researches. In spite of usage of benchmark tools (like OFlops and Cbench), simulation studies and very few analytical models, there is a lack of analytical models to express the boundary condition of SDN deployment. In this work we present a model based on network calculus theory to describe the functionality of an SDN switch and controller. To the best of our knowledge, this is for the first time that network calculus framework is utilized to model the behavior of an SDN switch in terms of delay and queue length boundaries and the analysis of the buffer length of SDN controller and SDN switch. The presented model can be used for network designers and architects to get a quick view of the overall SDN network deployment performance and buffer sizing of SDN switches and controllers.
Gait analysis and machine learning classification on healthy subjects in normal walking

Gait analysis and machine learning classification on healthy subjects in normal walking Tomohiro Shirakawa, Naruhisa Sugiyama, Hiroshi Sato, Kazuki Sakurai & Eri Sato To cite this article: Tomohiro Shirakawa, Naruhisa Sugiyama, Hiroshi Sato, Kazuki Sakurai & Eri Sato (2015): Gait analysis and machine learning classification on healthy subjects in normal walking, International Journal of Parallel, Emergent and Distributed Systems,  To link to this article: http://dx.doi.org/10.1080/17445760.2015.1044007
Polyphenol-enriched oolong tea increases fecal lipid excretion

To assess possibility of polyphenol-enriched oolong tea to reduce dietary lipid absorption in humans.Twelve healthy adult subjects, three males and nine females, aged (mean±s.d.) 22.0±1.8 years, respectively, were randomly divided into two groups. The participants were followed a double-blind placebo-controlled crossover design, including 7-day washout periods and 10-day treatment periods. During the treatment periods, subjects were given about 38 g of lipids from potato chips (19 g each within 30 min after lunch and dinner) and total 750 ml beverages (placebo- or polyphenol-enriched oolong tea) at three meals. Blood samples were collected for biochemical examination at days 8, 18, 25 and 35 of the study period. On the last 3 days of each treatment period, feces were collected to measure the excretion of lipids.Lipid excretion into feces was significantly higher in the polyphenol-enriched oolong tea period (19.3±12.9 g/3day) than in the placebo period (9.4±7.3 g/3day) (P<0.01). Cholesterol excretion tended to increase in polyphenol-enriched oolong tea period (1.8±1.2 g/3day) compared with that of placebo (1.2±0.6 g/3day) (P=0.056).Conclusions:The results of this study indicated that polyphenol-enriched oolong tea could increase lipid excretion into feces when subjects took high-lipid diet.
Non-local NetVLAD Encoding for Video Classification

This paper describes our solution for the 2 YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics and Moments, the YouTube8M challenge provides pre-extracted visual and audio features instead of raw videos. In this challenge, the submitted model is restricted to 1GB, which encourages participants focus on constructing one powerful single model rather than incorporating of the results from a bunch of models. Our system fuses six different sub-models into one single computational graph, which are categorized into three families. More specifically, the most effective family is the model with non-local operations following the NetVLAD encoding. The other two family models are Soft-BoF and GRU, respectively. In order to further boost single models performance, the model parameters of different checkpoints are averaged. Experimental results demonstrate that our proposed system can effectively perform the video classification task, achieving 0.88763 on the public test set and 0.88704 on the private set in terms of GAP@20, respectively. We finally ranked at the fourth place in the YouTube-8M video understanding challenge.
On the Bragg Scattering Observed in L-Band Synthetic Aperture Radar Images of Flooded Rice Fields

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machineplanted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20–25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields. key words: synthetic aperture radar (SAR), Bragg scattering, rice plants, JERS-1 SAR, Pi-SAR
Ask, and shall you receive? Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject’s emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.
Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes <underline>D</underline>ifferentiated cloud-<underline>A</underline>ssisted <underline>VI</underline>deo <underline>S</underline>treaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.
Autosomal recessive brachyolmia: early radiological findings

Brachyolmia (BO) is a heterogeneous group of skeletal dysplasias with skeletal changes limited to the spine or with minimal extraspinal features. BO is currently classified into types 1, 2, 3, and 4. BO types 1 and 4 are autosomal recessive conditions caused by PAPSS2 mutations, which may be merged together as an autosomal recessive BO (AR-BO). The clinical and radiological signs of AR-BO in late childhood have already been reported; however, the early manifestations and their age-dependent evolution have not been well documented. We report an affected boy with AR-BO, whose skeletal abnormalities were detected in utero and who was followed until 10 years of age. Prenatal ultrasound showed bowing of the legs. In infancy, radiographs showed moderate platyspondyly and dumbbell deformity of the tubular bones. Gradually, the platyspondyly became more pronounced, while the bowing of the legs and dumbbell deformities of the tubular bones diminished with age. In late childhood, the overall findings were consistent with known features of AR-BO. Genetic testing confirmed the diagnosis. Being aware of the initial skeletal changes may facilitate early diagnosis of PAPSS2-related skeletal dysplasias.
Development of IoT device for traffic management system

Traffic congestion is a major issue that happens across urban cities around the world. In 2015, annual economic losses of Malaysia caused by traffic congestion is estimated about RM20 billion. Fixed cycle traffic light system (TLS) is first introduced on road intersection to solve traffic congestion. However, fixed cycle TLS in unable to cope with dramatically increase of registered vehicles. In this paper, development of Internet of Things (IoT) device for traffic management system is proposed. An Intel Edison collects real-time traffic flow and communicate with Microsoft Azure IoT cloud server. The cloud server assign priorities to each road bound based on their current traffic volume. Green light phase time (GLPT) is then calculated utilizing a dynamic algorithm. Simulation results showed that dynamic cycle TLS reduces queue length and waiting time on the road intersection by 68% and 67% respectively. Additionally, a monitoring application is designed to ease traffic officer in monitoring real-time traffic flow.
Gender differences in autoimmune disease

Autoimmune diseases are a range of diseases in which the immune response to self-antigens results in damage or dysfunction of tissues. Autoimmune diseases can be systemic or can affect specific organs or body systems. For most autoimmune diseases there is a clear sex difference in prevalence, whereby females are generally more frequently affected than males. In this review, we consider gender differences in systemic and organ-specific autoimmune diseases, and we summarize human data that outlines the prevalence of common autoimmune diseases specific to adult males and females in countries commonly surveyed. We discuss possible mechanisms for sex specific differences including gender differences in immune response and organ vulnerability, reproductive capacity including pregnancy, sex hormones, genetic predisposition, parental inheritance, and epigenetics. Evidence demonstrates that gender has a significant influence on the development of autoimmune disease. Thus, considerations of gender should be at the forefront of all studies that attempt to define mechanisms that underpin autoimmune disease.
ADANA: Active Name Disambiguation

Name ambiguity has long been viewed as a challenging problem in many applications, such as scientific literature management, people search, and social network analysis. When we search a person name in these systems, many documents (e.g., papers, web pages) containing that person's name may be returned. It is hard to determine which documents are about the person we care about. Although much research has been conducted, the problem remains largely unsolved, especially with the rapid growth of the people information available on the Web. In this paper, we try to study this problem from a new perspective and propose an ADANA method for disambiguating person names via active user interactions. In ADANA, we first introduce a pairwise factor graph (PFG) model for person name disambiguation. The model is flexible and can be easily extended by incorporating various features. Based on the PFG model, we propose an active name disambiguation algorithm, aiming to improve the disambiguation performance by maximizing the utility of the user's correction. Experimental results on three different genres of data sets show that with only a few user corrections, the error rate of name disambiguation can be reduced to 3.1%. A real system has been developed based on the proposed method and is available online.
Improving Internet Security Through Social Information and Social Comparison : A Field Quasi-Experiment

Cybersecurity is a national priority in this big data era. Because of negative externalities and the resulting lack of economic incentives, companies often underinvest in security controls, despite government and industry recommendations. Although many existing studies on security have explored technical solutions, only a few have looked at the economic motivations. To fill the gap, we propose an approach to increase the incentives of organizations to address security problems. Specifically, we utilize and process existing security vulnerability data, derive explicit security performance information, and disclose the information as feedback to organizations and the public. We regularly release information on the organizations with the worst security behaviors, imposing reputation loss on them. The information is also used by organizations for self-evaluation in comparison to others. Therefore, additional incentives are solicited out of reputation concern and social comparison. To test the effectiveness of our approach, we conducted a field quasi-experiment for outgoing spam for 1,718 autonomous systems in eight countries and published SpamRankings.net, the website we created to release information. We found that the treatment group subject to information disclosure reduced outgoing spam approximately by 16%. We also found that the more observed outgoing spam from the top spammer, the less likely an organization would be to reduce its own outgoing spam, consistent with the prediction by social comparison theory. Our results suggest that social information and social comparison can be effectively leveraged to encourage desirable behavior. Our study contributes to both information architecture design and public policy by suggesting how information can be used as intervention to impose economic incentives. The usual disclaimers apply for NSF grants 1228990 and 0831338.
FCART: A New FCA-based System for Data Analysis and Knowledge Discovery

We introduce a new software system called Formal Concept Analysis Research Toolbox (FCART). Our goal is to create a universal integrated environment for knowledge and data engineers. FCART is constructed upon an iterative data analysis methodology and provides a built-in set of research tools based on Formal Concept Analysis techniques for working with object-attribute data representations. The provided toolset allows for the fast integration of extensions on several levels: from internal scripts to plugins. FCART was successfully applied in several data mining and knowledge discovery tasks. Examples of applying the system in medicine and criminal investigations are considered.
Personal Health Information Recommender: implementing a tool for the empowerment of cancer patients

Nowadays, patients have a wealth of information available on the Internet. Despite the potential benefits of Internet health information seeking, several concerns have been raised about the quality of information and about the patient's capability to evaluate medical information and to relate it to their own disease and treatment. As such, novel tools are required to effectively guide patients and provide high-quality medical information in an intelligent and personalised manner. With this aim, this paper presents the Personal Health Information Recommender (PHIR), a system to empower patients by enabling them to search in a high-quality document repository selected by experts, avoiding the information overload of the Internet. In addition, the information provided to the patients is personalised, based on individual preferences, medical conditions and other profiling information. Despite the generality of our approach, we apply the PHIR to a personal health record system constructed for cancer patients and we report on the design, the implementation and a preliminary validation of the platform. To the best of our knowledge, our platform is the only one combining natural language processing, ontologies and personal information to offer a unique user experience.
Ant colony optimization for finding the global minimum

The ant colony optimization (ACO) algorithms are multi-agent systems in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. This paper presents the ACO based algorithm to find global minimum. Algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was experimented on test problems, and successful results were obtained. The algorithm was compared with other methods which had been experimented on the same test problems, and observed to be better.
Chaotic Autonomous Mobile Robot for Military Missions

This work presents an autonomous mobile robot in order to cover an unknown terrain “randomly”, namely entirely, unpredictably and evenly. This aim is very important, especially in military missions, such as the surveillance of terrains, the terrain exploration for explosives and the patrolling for intrusion in military facilities. The “heart” of the proposed robot is a chaotic motion controller, which is based on a chaotic true random bit generator. This generator has been implemented with a microcontroller, which converts the produced chaotic bit sequence, to the robot's motion. Experimental results confirm that this approach, with an appropriate sensor for obstacle avoidance, can obtain very satisfactory results in regard to the fast scanning of the robot’s workspace with unpredictable way.  Autonomous mobile robot, terrain coverage, microcontroller, random bit generator, nonlinear system, chaos, Logistic map.