nils-herrmann/clean_scirepeval huggingface dataset:
Object Fusion Tracking Based on Visible and Infrared Images Using Fully Convolutional Siamese Networks
Venue: 2019 22th International Conference on Information Fusion (FUSION)
Year: 2019

Visual tracking is of great importance and thus has attracted a lot of interests in recent years. However, tracking based on visible images may fail when visible images are not reliable, for example when the illumination conditions are poor or in foggy day. Infrared images reveal thermal information thus are insensitive to these factors. Due to their complementary features, object fusion tracking based on visible and infrared images has attracted great attention recently. In this paper, a pixel level fusion tracking method based on fully convolutional Siamese Networks, which has shown great potential in RGB object tracking, is proposed. Visible and infrared images are firstly fused and then tracking is performed based on fused images. Extensive experiments on a large dataset which contains challenging scenarios have been conducted to evaluate tracking performances. The results clearly indicate that the proposed fusion tracking method can improve tracking performance compared to methods based on images of single modality.
Cloud based big data analytics for customer insight driven design innovation in SMEs
Venue: Int. J. Inf. Manag.
Year: 2020

Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud based approach may be the most appropriate way of giving access to big data analytics techniques.
Effectiveness of Internet based cognitive behaviour therapy for depression in routine psychiatric care.
Venue: Journal of affective disorders
Year: 2014

Efficacy of guided Internet based cognitive behaviour therapy (ICBT) for depression has been demonstrated in several randomised controlled trials. Knowledge on the effectiveness of the treatment, i.e. how it works when delivered within routine care, is however scarce. The aim of this study was to investigate the effectiveness of ICBT for depression.  We conducted a cohort study investigating all patients (N=1203) who had received guided ICBT for depression between 2007 and 2013 in a routine care setting at an outpatient psychiatric clinic providing Internet based treatment. The primary outcome measure was the Montgomery Asberg Depression Rating Scale Self rated (MADRS S) RESULTS Patients made large improvements from pre treatment assessments to post treatment on the primary outcome (effect size d on the MADRS S=1.27, 99% CI, 1.14 1.39) Participants were significantly improved in terms of suicidal ideation and sleep difficulties. Improvements were sustained at 6 month follow up.  Attrition was rather large at 6 month follow up. However, additional data was collected through telephone interviews with dropouts and advanced statistical models indicated that missing data did not bias the findings.  ICBT for depression can be highly effective when delivered within the context of routine psychiatric care. This study suggests that the effect sizes are at least as high when the treatment is delivered in routine psychiatric care by qualified staff as when delivered in a controlled trial setting.
Performance analysis of a metaheuristic algorithm for the line haul feeder vehicle routing problem
Venue: Journal on Vehicle Routing Algorithms
Year: 2018

The line haul feeder vehicle routing problem (LFVRP) is a rather new and therefore hardly investigated problem and can be seen as vehicle routing problem with synchronisation constraints. Synchronisation takes place if two or more vehicles (small and large vehicles) meet at the same place and at the same time and perform a transshipment of goods. Small vehicles are used to serve customers which are difficult to access (e.g. due to traffic jams, narrow streets or limited parking space) The downside is their limited capacity. To avoid the journey back to the depot, the small vehicle can meet with a large vehicle at a customer with enough space for reloading. Thus, the small vehicle uses the large vehicle as virtual depot and is able to save time and reduce costs. In Brandstatter and Reimann ( 2018) we already introduced two promising solution approaches to master the LFVRP the Linkage and Split Approach. Both approaches were shown to find high quality solutions in short time. Moreover, we demonstrated the advantage of the LFVRP strategy when compared with the heterogeneous fleet vehicle routing problem. With this paper, we propose and justify several improvements to our original algorithms. These include the application of Ant Colony Optimization, different local search operators, as well as the exact solution of a sub problem. To highlight the performance contribution of these modifications, we perform a thorough and extensive computational experiment. We evaluate each algorithmic component individually as well as their combination and statistically validate the benefits. Overall, we find that the final algorithm improves our previous approaches by almost 9% at a computational cost of a few seconds.
Boceprevir for untreated chronic HCV genotype 1 infection.
Venue: The New England journal of medicine
Year: 2011

Peginterferon ribavirin therapy is the current standard of care for chronic infection with hepatitis C virus (HCV) The rate of sustained virologic response has been below 50% in cases of HCV genotype 1 infection. Boceprevir, a potent oral HCV protease inhibitor, has been evaluated as an additional treatment in phase 1 and phase 2 studies.  We conducted a double blind study in which previously untreated adults with HCV genotype 1 infection were randomly assigned to one of three groups. In all three groups, peginterferon alfa 2b and ribavirin were administered for 4 weeks (the lead in period) Subsequently, group 1 (the control group) received placebo plus peginterferon ribavirin for 44 weeks; group 2 received boceprevir plus peginterferon ribavirin for 24 weeks, and those with a detectable HCV RNA level between weeks 8 and 24 received placebo plus peginterferon ribavirin for an additional 20 weeks; and group 3 received boceprevir plus peginterferon ribavirin for 44 weeks. Nonblack patients and black patients were enrolled and analyzed separately.  A total of 938 nonblack and 159 black patients were treated. In the nonblack cohort, a sustained virologic response was achieved in 125 of the 311 patients (40% in group 1, in 211 of the 316 patients (67% in group 2 (P<0.001) and in 213 of the 311 patients (68% in group 3 (P<0.001) In the black cohort, a sustained virologic response was achieved in 12 of the 52 patients (23% in group 1, in 22 of the 52 patients (42% in group 2 (P=0.04) and in 29 of the 55 patients (53% in group 3 (P=0.004) In group 2, a total of 44% of patients received peginterferon ribavirin for 28 weeks. Anemia led to dose reductions in 13% of controls and 21% of boceprevir recipients, with discontinuations in 1% and 2% respectively.  The addition of boceprevir to standard therapy with peginterferon ribavirin, as compared with standard therapy alone, significantly increased the rates of sustained virologic response in previously untreated adults with chronic HCV genotype 1 infection. The rates were similar with 24 weeks and 44 weeks of boceprevir.
Fuzzy rough Neural Network Feature Selection
Year: 2009

For the sake of measuring fuzzy uncertainty and rough uncertainty of real datasets,the fuzzy rough membership function(FRMF) defined in fuzzy rough set is introduced.A new fuzzy rough neural network(FRNN) is constructed based on neural network implementation of FRMF.FRNN has the merits of quick learning and good classification performance.A new neural network feature selection algorithm based on FRNN is designed.The input nodes are pruned according to the descent of accuracies and at the same time the search of optimal feature subset is realized.The test results on UCI datasets show that the algorithm is quick and effective,and has better selection precision and generalization ability than RBF feature selection.
SAARC s Dilemma under the Prism of Regional Security Complex Theory
Year: 2017

South Asian Association for Regional Cooperation (SAARC) has not been emerged as an effective regional organization such as the EU and ASEAN. Despite taking several initiatives in this regard, it has not achieved the desired objectives set for at the time of its establishment. Over the thirty years it has become hostage chiefly by the Pakistan India antagonistic relationship. On the basis of interviews of the Key Informant (KIs) this article investigates the causes for the lack of momentum of SAARC and explores the prospects for SAARC as vibrant economic group of the South Asian nations. Furthermore, the study finds that lack of implementation of SAARC declarations, conflicting issues between India and other neighbouring countries and strict SAARC visa regime are important barriers in enhancement of regional co operation in South Asia. The article recommends that SAARC countries may enhance regional cooperation by managing bilateral conflicts particularly between Pakistan India. The article also discovers that bilateral economic co operation between Pakistan and India has spillover effect on the enhancement of regional economic cooperation in South Asia.
Audio Visual Sentiment Analysis for Learning Emotional Arcs in Movies
Venue: 2017 IEEE International Conference on Data Mining (ICDM)
Year: 2017

Stories can have tremendous power not only useful for entertainment, they can activate our interests and mobilize our actions. The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon. In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by using deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large scale datasets, after which they can be used to compute separate audio and visual emotional arcs. We then crowdsource annotations for 30 second video clips extracted from highs and lows in the arcs in order to assess the micro level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings. These annotations are also used to combine the audio and visual predictions. Next, we look at macro level characterizations of movies by investigating whether there exist 'universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.
The Power and Compassion of Rational Emotive Behavior Therapy (REBT)
Year: 2019

This chapter demonstrates the effectiveness of Rational Emotive Behavior Therapy (REBT) for enabling people to enhance their lives and create greater happiness. It is the pioneering approach developed by Albert Ellis PhD, the late husband of the author, that heralded in the cognitive revolution in psychotherapy, and which influenced approaches that followed it such as Cognitive Behavior Therapy, Positive Psychology and more. It is a holistic approach that reminds us of the inseparable interplay of thoughts, emotions and behaviors, and in addition to being an evidence based therapy, it can also be a way of life for those who choose to apply it as such. By practicing its principles which encourage us to create healthy emotions in response to adverse situations, we are also guided to experience and express unconditional acceptance of ourselves, others, and life, and to practice daily gratitude. In so doing we can feel compassion and empathy for those who suffer, including ourselves, experience greater joy and minimize emotional suffering in our lives.
Do Language Models Perform Generalizable Commonsense Inference?
Venue: FINDINGS
Year: 2021

Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs) However, there is a lack of understanding on their generalization to multiple CKGs, unseen relations, and novel entities. This paper analyzes the ability of LMs to perform generalizable commonsense inference, in terms of knowledge capacity, transferability, and induction. Our experiments with these three aspects show that: (1) LMs can adapt to different schemas defined by multiple CKGs but fail to reuse the knowledge to generalize to new relations. (2) Adapted LMs generalize well to unseen subjects, but less so on novel objects. Future work should investigate how to improve the transferability and induction of commonsense mining from LMs.1
Do the socially rich get richer? A nuanced perspective on social network site use and online social capital accrual.
Venue: Psychological bulletin
Year: 2019

The benefits of using social network sites (SNS) have spurred heated debate in academia and popular culture alike. This study sought to address the debate by formulating a new, nuanced framework highlighting two conceptual distinctions: (a) preference for versus problem in one's interpersonal relations, and (b) SNS use versus the benefits of such use. Mixed effects meta analysis was performed in 178 independent samples from seven regions worldwide (n 108,068; age range 13 68) Eligible studies were those that examined an association between at least one common proxy measure of the socially rich (vs. poor; i.e. extraversion, social anxiety, or loneliness) and a criterion measure (i.e. SNS use or online social capital) The results revealed a complex picture. SNS use was positively correlated with both extraversion and social anxiety, although the social anxiety SNS use correlation was significant for adult samples rather than adolescent samples. Online social capital was positively correlated with extraversion but inversely correlated with loneliness. Our conclusion is that extraverted individuals use SNS to enhance their opportunities for social interactions and can acquire more online social resources, whereas adults who are socially anxious use SNS to compensate for their social deficits but such effort is unrelated to online social resource accumulation. Individuals who feel lonely tend to obtain few such resources. However, most of the studies examined the leisure use of Facebook. We advocate more thorough testing of our hypotheses in future research on therapeutic SNS use and/or the use of SNS other than Facebook.
Genette et les SIC, une filiation inversee
Year: 2019

Genette est il un "classique" un "fondamental" pour les sciences de l'information et de la communication (SIC) Cet article analyse un corpus d'articles et de theses de doctorat dans ce domaine pour decrire les citations des ecrits de Genette faites par cette communaute scientifique entre 1980 et 2018. Contre ses propres predictions, les categories de Genette sont devenues des "concepts nomades" notamment dans l'analyse des medias et des discours sociaux. Le but de cette recherche est de caracteriser quantitativement et qualitativement les usages de la reference a Genette par les analystes de la communication quotidienne et mediatique, quand ils attirent vers eux les categories theoriques de Genette. L'article montre que ce nomadisme conceptuel affleure aux relations paradoxales entre une poetique visant la litterarite et un usage de la poetique comme instrument d'analyse des discours sociaux.
Lazy Learning Based Segregation of Top 3 South Indian Languages with LSF A Feature
Venue: RTIP2R
Year: 2018

Identification of language from voice signals is known as automatic language identification. It is very important for speech recognition in multi lingual countries like India where people use more than a single language while talking. Language identification for South Indian languages is difficult for a person without prior knowledge. In this paper, the top 3 most spoken languages of South India namely Telugu, Tamil and Kannada has been distinguished with the help of line spectral frequency based features namely LSF A (Line spectral frequency Approximation) Experiments have been performed on multiple datasets having as many as 21700 clips and a highest accuracy of 99.70% has been obtained with a lazy learning based classifier.
Dyslipidemia and its Correlation with Creatinine Clearence in Chronic Kidney Disease
Year: 2019

Dyslipidemia is recognized risk factors for progression of renal diseases. This altered lipid metabolism contribute to pathogenesis of cardiovascular disease as well as to the progression of renal disease.  To measure the lipid profile in patients with Chronic kidney disease and to correlate HDL TGL levels with creatinine clearance.  The biochemical parameters Blood urea Creatinine, Total Cholesterol, Triglycerides, HDL and LDL were measured in 50 cases 50 controls.  There is significantly higher serum TGL and VLDL levels and lower HDL levels in the cases than controls (p<0.05) There is higher TGL/HDL and LDL/HDL ratio in cases than controls (p<0.05).There is a serial increase in serum TGL and VLDL levels and a serial decrease in HDL levels with declining renal function (p<0.05)
Immigration and Wage Dynamics: Evidence from the Mexican Peso Crisis
Venue: Journal of Political Economy
Year: 2020

How does the US labor market absorb low skilled immigration? In the short run, high immigration locations see their low skilled labor force increase, native low skilled wages decrease, and the relative price of rentals increase. Internal relocation dissipates this shock spatially. In the long run, the only lasting consequences are (a) worse labor market conditions for low skilled natives who entered the labor force in high immigration years, and (b) lower housing prices in high immigrant locations, when immigrant workers disproportionately enter the construction sector and lower construction costs. I use a quantitative dynamic spatial equilibrium many region model to obtain the policy relevant counterfactuals.
Combining Text Embedding and Knowledge Graph Embedding Techniques for Academic Search Engines
Venue: Semdeep/NLIWoD@ISWC
Year: 2018

The past decades have witnessed a rapid increase in the global scientific output as measured by published papers. Exploring a scientific field and searching for relevant papers and authors seems like a needle in a haystack problem. Although many academic search engines have been developed to accelerate this retrieval process, most of them rely on content based methods and feature engineering. In this work, we present an entity retrieval prototype system on top of IOS Press LD Connect which utilizes both textual and structure information. Paragraph vector and knowledge graph embedding are used to embed papers and entities into low dimensional hidden space. Next, the semantic similarity between papers and entities can be measured based on the learned embedding models. Two benchmark datasets have been collected from Semantic Scholar and DBLP to evaluate the performance of our entity retrieval models. Results show that paragraph vectors are effective at capturing the similarity and relatedness among papers and knowledge graph embedding models can preserve the inherent structure of the original knowledge graph and hence assist in link prediction tasks such as co author inference.
Heat Exchanger Network synthesis considering prohibited and restricted matches
Year: 2021

In the industry, there are frequently prohibited and restricted matches in the Heat Exchanger Networks (HENs) These matches could cause risk cost when failures happen. Some matches of streams can be entirely prohibited, but some may only have a certain level of restriction. The margins are set up, based on the risk cost assessment, whether to exchange the heat and by which type of equipment between specific streams. This study proposes a method based on the Advanced Grid Diagram to benefit from an optimised synthesis plan for HEN considering prohibited and restricted matches. A pre designed HEN could be determined in the synthesis stage, not considering the prohibited or restricted matches. A follow up topology evolution is performed to trade off the prohibitions and evaluate the restrictions. A Benefit Diagram has been developed to show how many economic benefits can be gained by implementing new heat exchangers. It is used for comparison between allowing all restricted streams and avoiding restricted streams to find the potential risk cost margin. The method is applied to a case study to analyse the restricted and prohibited matches. Optimal HEN design results offer the designers a guide to evaluate to which extent should implement the restricted matches.