nils-herrmann/clean_scirepeval huggingface dataset:
A multi user tabletop display with enhanced mobile visuals for teaching and collaborative training: faculty poster abstract

Advances in technology provide access to cost effective user interfaces that change the way people interact and carry out their daily activities. Massive use of smartphones, tablets, and other portable computing devices is reshaping the world of learning. Novel educational tools provide means to visualize and interact with compelling media, creating a virtual and augmented reality that can greatly enhance the knowledge transfer  Education is also benefiting from various collaborative scenarios where students work together to overcome challenges while improving cognitive and social skills  Indeed, as a way to improve learning, Information and Communication Technology (ICT) has already become an indispensable component of modern educational systems [2] ICT brings a wealth of online features such as short messaging and chats, forums and groups, IP voice and video calls, cloud based interactions and shared storage, etc. but it also forces students to focus on the screens of their computing devices and use the content to perform instruction bound and other tasks. Therefore, despite all benefits that ICT brings to education, it could also have a negative impact on the learning process as it might restrict natural interactions thus isolating students and limiting their experience  Here, we investigate the effectiveness of using heterogeneous computing paradigms (mobile devices and tabletop computers) in a collaborative learning environment.
Negotiating Compensation for Indians with Idi Amin's Government

Throughout the 19th century and up to the First World War, India was the hub of the expanding British Empire. India had the experience and provided the structural administrative model for ruling over other British tropical conquests. Until the colonial office and the colonial service came to be established, the governance of Kenya and Uganda were guided as much from Calcutta as from Whitehall. There were definite periods of intensified Indian migration to Africa, the first following the construction of the railway. The second phase came during the inter war years and a third spurt took place after the Second World War. The cumulative outcome was that there
ACO based embedded system testing using UML Activity Diagram
Venue: 2016 IEEE Region 10 Conference (TENCON)

This paper proposed a model based technique for test scenario generation using Activity Diagram (AD) We transform an AD specification into an intermediate graph called Activity Interaction Graph (AIG) using the proposed parser. After that, we apply combination of BFS and DFS algorithms for generating test scenarios. Then, we apply an algorithm called ACOToTSP (Ant Colony Optimization for Test Scenarios Prioritization) algorithm on the generated test scenarios with respect to some decision and concurrent criteria, for prioritizing the test scenarios. This approach generates test scenarios according to forks, Joins, and merge point's strength in the activity diagram.
Test case generation for concurrent systems using UML activity diagram
Venue: 2016 IEEE Region 10 Conference (TENCON)

Concurrency problems, such as deadlocks, should be identified before the design process begins. This is very difficult as larger and more complex concurrent systems are being developed. Here we propose an approach, based on the analysis of Unified Modelling Language (UML) that represents the behavioural properties of the system. In this work, we have presented an approach for generating a minimum test suite with maximum coverage. It builds an Input/Output Activity Diagram (IOAD) using UML activity diagram which helps to remove deadlock and reduce complexity in solving complex concurrent systems. It is also user friendly and generates effective test cases.
Mitochondrial dysfunction in inflammatory responses and cellular senescence: pathogenesis and pharmacological targets for chronic lung diseases
Venue: British journal of pharmacology

Mitochondria are dynamic organelles, which couple the various cellular processes that regulate metabolism, cell proliferation and survival. Environmental stress can cause mitochondrial dysfunction and dynamic changes including reduced mitochondrial biogenesis, oxidative phosphorylation and ATP production, as well as mitophagy impairment, which leads to increased ROS, inflammatory responses and cellular senescence. Oxidative stress, inflammation and cellular senescence all have important roles in the pathogenesis of chronic lung diseases, such as chronic obstructive pulmonary disease, pulmonary fibrosis and bronchopulmonary dysplasia. In this review, we discuss the current state on how mitochondrial dysfunction affects inflammatory responses and cellular senescence, the mechanisms of mitochondrial dysfunction underlying the pathogenesis of chronic lung diseases and the potential of mitochondrial transfer and replacement as treatments for these diseases.
Nuclear Receptors as Therapeutic Targets in Liver Disease: Are We There Yet?
Venue: Annual review of pharmacology and toxicology

Nuclear receptors (NR) are ligand modulated transcription factors that play diverse roles in cell differentiation, development, proliferation, and metabolism and are associated with numerous liver pathologies such as cancer, steatosis, inflammation, fibrosis, cholestasis, and xenobiotic/drug induced liver injury. The network of target proteins associated with NRs is extremely complex, comprising coregulators, small noncoding microRNAs, and long noncoding RNAs. The importance of NRs as targets of liver disease is exemplified by the number of NR ligands that are currently used in the clinics or in clinical trials with promising results. Understanding the regulation by NR during pathophysiological conditions, and identifying ligands for orphan NR, points to a potential therapeutic approach for patients with liver diseases. An overview of complex NR metabolic networks and their pharmacological implications in liver disease is presented here.
Prolonging network lifetime by electing suitable cluster head by dynamic weight adjustment for weighted clustering algorithm in MANET
Venue: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)

Mobile Ad hoc network has taken the lead in the field of wireless communication. MANET has supported mobility, scalability and extendibility of the network over the air. Connectivity in wireless medium has several costs for communication. Costs such as life of connections, packets routing, information delay, security over the air and trusted source and receivers needs extra care in communication. Flat topology was not able to support scalability of the mobile nodes in the wireless network. To overcome this, hierarchy topology has been proposed which overcome the scalability problem. One of the hierarchy topology can be consider as clustering. Clustering of mobile nodes solves basic three problems which are (a) expanding the network (b) communication stays within the cluster so that other neighboring cluster remains unawares of the communication and (c) maintenance of routing become much easier. Clustering has mainly two processes which are (a) cluster formation and (b) cluster maintenance. Weighted clustering is one of the clustering schemes and this paper has described the constraints on weights which is fixed and not varying with the dynamics [Degree Difference Nv, Sum of Distances Dv, Mobility (Mv) and Power (Pv) of the nodes] of the mobile node in the network. Weight plays major role to select the best stable cluster head as it defines and support the dynamics of the mobile nodes. Algorithm depends on the minimum weight among the mobile nodes, which become the cluster head. Choosing cluster head is an important task of the clustering which takes in cluster formation and also in maintenance phase as well. This paper describes the dynamic weight adjustments by using method of soft computing. Soft computing has non deterministic algorithm such as fuzzy logic and neural networks. Weight based clustering algorithm shows the nature of artificial neural network with fuzzy behavior on their node dynamics. Since less computation and fast clustering is goal of any clustering based algorithm, so proposed work is intended to select the best cluster head by choosing the appropriate weights for mobile nodes with less computation overhead. Choosing and selecting intermediate weights impact overall performance in election of cluster head selection process and consequently impact the overall network stability. Weight correction model helps in prolonging network lifetime and reduce load on selected cluster head.
Trust Factor Based Secure Congestion Control For Vanet

Weight based clustering algorithm in ad hoc network is an on demand clustering algorithm for multi hop packet radio network. These types of networks are ad hoc networks and dynamic in nature due to mobility of nodes. Clustering in mobile ad hoc network can be defined as various partitions into various groups. It is an important concept of VANNET, because clustering makes it possible to guarantee of system performance, such as throughput delay and also security issues. So in the algorithm they select the cluster head whose performance is well and formation of cluster in ad hoc network. The cluster head is elected based on weight factor, so it is called weighted clustering algorithm. Weighted clustering algorithm is basically appropriate cluster selection in wireless adhoc network where it is necessary to provide robustness in face of topological changes caused by node motion, node failure and node insertion or node removal. The WCA select the cluster head which has lowest weight among the nodes and some other factor also consider the election for cluster head. The cluster head, form a dominant set in the network, determine the topology and its stability. The weighted clustering algorithm takes into consideration the ideal degree, transmission power, mobility and battery power of nodes. Traffic efficiency applications are becoming increasingly popular over the road networks in the last few years. This type of applications aims mainly at increasing the traffic fluency over the road network, which minimizes the travel time of each vehicle towards its targeted destinations. The Vehicular Ad Hoc Networks (VANETs) technology has been utilized to design these applications. Communications between vehicles, V2V, and between vehicles and installed Road Side Units (RSUs) V2I, helped designing these applications. Malicious, selfish and intruder drivers can take advantages of other cooperative drivers and use their trust. a Secure Congestion control protocol. This protocol aims to guarantee integrity and authenticity of transmitted data. It is designed to provide the security requirements of traffic efficiency protocols that have been proposed using the technology of VANETs. SCOOL also aims to preserve the privacy of the cooperative vehicles and drivers. The main contribution of work is new strategy for clustering a wireless ad hoc network and improvement in WCA. Author derived a simple stability model and a load balancing clustering scheme. They showed that the algorithm outperform in term of cluster formation and stability. Main idea of approach is to avoid cluster re election and reduce the computation and communication cost. The improved weighted clustering algorithm, the goals of algorithm are maintaining stable clustering structure, minimizing the overhead for clustering set up, maximize lifetime of nodes in the system and achieving good performance. Keyword Weight based clustering, SCOOL, WCA, VANETs.
Compressing Convolutional Neural Networks in the Frequency Domain
Venue: KDD

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers, hindering many applications such as image and speech recognition on mobile phones and other devices. In this paper, we present a novel net work architecture, Frequency Sensitive Hashed Nets (FreshNets) which exploits inherent redundancy in both convolutional layers and fully connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back propagation. To further reduce model size, we allocate fewer hash buckets to high frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to better compressed performance than several relevant baselines.
Artificial intelligence in service oriented software design
Venue: Eng. Appl. Artif. Intell.

Service Oriented Architecture (SOA) has gained considerable popularity for the development of distributed enterprise wide applications within the software industry. The SOA paradigm promotes the reusability and integrability of software in heterogeneous environments by means of open standards. Most software companies capitalize on SOA by discovering and composing services already accessible over the Internet, whereas other organizations need internal control of applications and develop new services with quality attribute properties tailored to their particular environment. Therefore, based on architectural and business requirements, developers can elaborate different alternatives within a SOA framework to design their software applications. Each of these alternatives will imply trade offs among quality attributes, such as performance, dependability and availability, among others. In this context, Artificial Intelligence (AI) can assist developers in dealing with service oriented design with the positive impact on scalability and management of generic quality attributes. In this paper, we offer a detailed, conceptualized and synthesized analysis of AI research works that have aimed at discovering, composing, or developing services. We also identify open research issues and challenges in the aforementioned research areas. The results of the characterization of 69 contemporary approaches and potential research directions for the areas are also shown. It is concluded that AI has aimed at exploiting the semantic resources and achieving quality attribute properties so as to produce flexible and adaptive to change service discovery, composition, and development.
Interventions for Adolescent Substance Abuse: An Overview of Systematic Reviews
Venue: The Journal of adolescent health official publication of the Society for Adolescent Medicine

Many unhealthy behaviors often begin during adolescence and represent major public health challenges. Substance abuse has a major impact on individuals, families, and communities, as its effects are cumulative, contributing to costly social, physical, and mental health problems. We conducted an overview of systematic reviews to evaluate the effectiveness of interventions to prevent substance abuse among adolescents. We report findings from a total of 46 systematic reviews focusing on interventions for smoking/tobacco use, alcohol use, drug use, and combined substance abuse. Our overview findings suggest that among smoking/tobacco interventions, school based prevention programs and family based intensive interventions typically addressing family functioning are effective in reducing smoking. Mass media campaigns are also effective given that these were of reasonable intensity over extensive periods of time. Among interventions for alcohol use, school based alcohol prevention interventions have been associated with reduced frequency of drinking, while family based interventions have a small but persistent effect on alcohol misuse among adolescents. For drug abuse, school based interventions based on a combination of social competence and social influence approaches have shown protective effects against drugs and cannabis use. Among the interventions targeting combined substance abuse, school based primary prevention programs are effective. Evidence from Internet based interventions, policy initiatives, and incentives appears to be mixed and needs further research. Future research should focus on evaluating the effectiveness of specific interventions components with standardized intervention and outcome measures. Various delivery platforms, including digital platforms and policy initiative, have the potential to improve substance abuse outcomes among adolescents; however, these require further research.
The Adolescent Substance Abuse Goal Commitment (ASAGC) Questionnaire: An Examination of Clinical Utility and Psychometric Properties.
Venue: Journal of substance abuse treatment

Commitment to change is an innovative potential mediator or mechanism of behavior change that has not been examined in adolescents with substance use disorders (SUD) The Adolescent Substance Abuse Goal Commitment (ASAGC) questionnaire is a 16 item measure developed to assess an individual's commitment to his/her stated treatment goal. The objectives of this study are to explore the research and clinical utility of the commitment construct as measured by the ASAGC. During sessions 3 and 9 of a 10 week SUD treatment, therapists completed the ASAGC for 170 13 18 year old adolescents. An exploratory factor analysis was conducted on the ATAGC items. Concurrent validity with related constructs, self efficacy and motivation for change, was examined as well. At both sessions, the factor analysis resulted in two scales Commitment to Recovery and Commitment to Harm Reduction. The ASAGC scales were found to demonstrate a high level of internal consistency (alpha coefficients ranged from .92 to .96 over time) In contrast to the Commitment to Harm Reduction scale, the Commitment to Recovery scale consistently correlated with scales from the Situational Confidence Questionnaire assessing self efficacy, evidencing concurrent validity. Similarly, the Commitment to Recovery scale was related to the Problem Recognition Questionnaire, providing further evidence of the validity of the ASAGC. The ASAGC is a reliable and valid clinical research instrument for the assessment of adolescents' commitment to their substance abuse treatment goal. Clinical researchers may take advantage of the clinical utility of the ASAGC including its ability to differentiate between commitment to abstinence versus commitment to harm reduction.
Substance abuse and personality disorder comorbidity in adolescent outpatients: are girls more severely ill than boys?
Venue: Child and Adolescent Psychiatry and Mental Health

AbstractBackgroundSubstance use disorders (SUDs) constitute a major health problem and are associated with an extensive psychiatric comorbidity. Personality disorders (PDs) and SUDs commonly co occur. Comorbid PD is characterized by more severe addiction problems and by an unfavorable clinical outcome. The present study investigated the prevalence of SUDs, PDs and common Axis I disorders in a sample of adolescent outpatients. We also investigated the association between PDs and SUDs, and how this association was influenced by adjustment for other Axis I disorders, age and gender.MethodsThe sample consisted of 153 adolescents, aged 14 17 years, who were referred to a non specialized mental health outpatient clinic with a defined catchment area. SUDs and other Axis I conditions were assessed using the mini international neuropsychiatric interview. PDs were assessed using the structured interview for DSM IV personality.Results18.3 of the adolescents screened positive for a SUD, with no significant gender difference. There was a highly significant association between number of PD symptoms and having one or more SUDs; this relationship was practically unchanged by adjustment for gender, age and presence of Axis I disorders. For boys, no significant associations between SUDs and specific PDs, conduct disorder (CD) or attention deficit hyperactivity disorder (ADHD) were found. For girls, there were significant associations between SUD and BPD, negativistic PD, more than one PD, CD and ADHD.ConclusionsWe found no significant gender difference in the prevalence of SUD in a sample of adolescents referred to a general mental health outpatient clinic. The association between number of PD symptoms and having one or more SUDs was practically unchanged by adjustment for gender, age and presence of one or more Axis I disorders, which suggested that having an increased number of PD symptoms in itself may constitute a risk factor for developing SUDs in adolescence. The association in girls between SUDs and PDs, CD and ADHD raises the question if adolescent girls suffering from these conditions may be especially at risk for developing SUDs. In clinical settings, they should therefore be monitored with particular diligence with regard to their use of psychoactive substances.
An Empirical Study on Recommendation with Multiple Types of Feedback
Venue: KDD

User feedback like clicks and ratings on recommended items provides important information for recommender systems to predict users' interests in unseen items. Most systems rely on models trained using a single type of feedback, e.g. ratings for movie recommendation and clicks for online news recommendation. However, in addition to the primary feedback, many systems also allow users to provide other types of feedback, e.g. liking or sharing an article, or hiding all articles from a source. These additional feedback potentially provides extra information for the recommendation models. To optimize user experience and business objectives, it is important for a recommender system to use both the primary feedback and additional feedback. This paper presents an empirical study on various training methods for incorporating multiple user feedback types based on LinkedIn recommendation products. We study three important problems that we face at LinkedIn: (1) Whether to send an email based on clicks and complaints, (2) how to rank updates in LinkedIn feeds based on clicks and hides and (3) how jointly optimize for viral actions and clicks in LinkedIn feeds. Extensive offline experiments on historical data show the effectiveness of these methods in different situations. Online A/B testing results further demonstrate the impact of these methods on LinkedIn production systems.
"Somewhere in the Middle is the Optimal Balance" A Subject Specialist Negotiating Business and Language in Teaching Business Writing

This article reports on a case study of a subject specialist Business English teacher who was appointed to teach business writing at a Chinese university. It draws on a variety of data collected from the study participant, including his course syllabus and lecture notes, his feedback on student assignments, classroom observations, interviews and email exchanges with him, student evaluation of his teaching, and assessment of his students' assignments by other business professionals. A constant comparative thematic analysis of the data identifies six major themes regarding his approach to teaching business writing, including Gravitation Towards Business Skills, Instilling the Mind sets and Attitudes, Incidental Teaching of Language, Classroom Management, Image of the Business World, and Positive Reception of his Teaching. These themes are discussed to illuminate several key issues in ESP instruction and teacher development, including content and language integrated learning, motivation, researcher practitioner collaboration, and strategies for team teaching.