nils-herrmann/clean_scirepeval huggingface dataset:
'How corporate social responsibility can be integrated into corporate sustainability: a theoretical review of their relationships'
Year: 2018

The notion that corporations are expected to have social and environmental performance standards and practices while meeting their financial and legal obligations is commonly referred to as corporate social responsibility (CSR) or corporate sustainability (CS) Previous studies have discussed whether the two terms are converging or should be used as stand alone concepts. After reviewing the trends in development of CSR and CS, and the various viewpoints on the relationships between the two, we propose a relationship model to better understand how CSR can be integrated into CS as either a transitional stage or ultimate goal for a corporation. This study aims to provide researchers and practitioners with further insights on the adoption of CSR and CS. The model is intended to provide a plausible view of the relationships between CSR and CS to help to lessen the confusion and ease the communication both between and within these fields.
A Brief Overview of Amblyaudia
Year: 2015

Amblyaudia, a recent subcategory of auditory processing disorder, is characterized by asymmetrical auditory processing of an individual's ears. Amblyaudia can result in speech comprehension difficulties, reading difficulties, information processing deficits, and inattention. These difficulties can be mistakenly attributed to Attention Deficit Hyperactivity Disorder (ADHD) Specific Learning Disorders (SLD) depression, anxiety disorders, and communication disorders. Unfortunately, traditional hearing tests do not place the two ears in competition and cannot detect asymmetry. Therefore, students who exhibit these difficulties and have normal performance on traditional hearing tests should be also evaluated for amblyaudia with dichotic listening tests. Amblyaudia can be addressed through dichotic listening tasks that strengthen the non dominant ear, as well as minor adjustments to the classroom environment. This paper will examine the current literature on amblyaudia and provide a brief overview of the causes, diagnosis, treatments, and prognosis.
WHAT IS PERFORMANCE STUDIES
Year: 2017

The Department of Performance Studies integrates artistic and analytical approaches to a wide range of performance texts, events, and processes. The courses explore an interdisciplinary range of literary, cultural, and personal texts in performance. The department has particular strengths in the study of literature through solo performance; the ensemble adaptation and staging of poetic, narrative, and nonfictional texts; intercultural performance; performance art; cultural studies and the ethnography of performance; and performance theory and criticism. Internships and field study for performance studies majors extend and deepen their classroom work with experiential learning. Extracurricular work provides students with a variety of opportunities to perform, adapt, and direct, enabling their creative work to reach an audience outside the classroom.
A study of user profile representation for personalized cross language information retrieval
Venue: Aslib J. Inf. Manag.
Year: 2016

With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross language information retrieval (CLIR) systems through the means of personalized query expansion.  The user profiles consist of weighted terms computed by using frequency based methods such as tf idf and BM25, as well as various latent semantic models trained on monolingual documents and cross lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods.  Experimental results suggest that latent semantic weighted user profile representation techniques are superior to frequency based methods, and are particularly suita.
Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts
Venue: J. Am. Medical Informatics Assoc.
Year: 2020

The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.  Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts' embedding vectors. Performance was compared with 2 traditional path based (shortest path and Leacock Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus.  Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec.  Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path based measurements and cui2vec, and can be further enhanced by combining with graph embedding.
COVID 19 vaccine acceptance among healthcare workers in India: Results from a cross sectional survey
Venue: medRxiv
Year: 2021

Remarkable scientific progress has enabled expeditious development of effective vaccines against COVID 19. While healthcare workers (HCWs) have been at the frontlines for the pandemic response, vaccine acceptance amongst them needs further study.  A web based survey to assess vaccine acceptance and preparedness in India was disseminated to HCWs working in various settings between January and February 2021, shortly after the launch of Indias vaccination campaign. Descriptive statistics were used to examine respondent demographics and Likert scale responses. Binomial logistic regression analyses were used to identify factors associated with vaccine acceptance.  The survey yielded 624 respondents from 25 states and five union territories in India; 53.5% were male, and median age was 37 years (IQR 32 46) Amongst all respondents, 84.1% (525/624) supported COVID 19 vaccines, and 63.2% (141/223) of those unvaccinated at the time of survey administration were willing to accept a vaccine. Reliability on government sources, healthcare providers or scientific journal articles for COVID 19 related information was reported by 66.8% while confidence in social media for this information was reported by only 4.5% Factors independently associated with vaccine acceptance included advancing age (aOR 3.50 [95% CI, 1.04 11.76] for those above 45 years) evidence of vaccine effectiveness and safety (aOR 3.78 [95% CI 1.15 12.38] and provision of free/no cost vaccine (aOR 2.63 [95% CI, 1.06 6.50] Most respondents (80% were confident about their hospital being equipped to efficiently rollout COVID 19 vaccines to the general population.  Overall attitudes towards COVID 19 vaccination and preparedness were positive among HCWs in India, although acceptance was lower among healthier and younger HCWs. Data availability on vaccine safety and effectiveness, and cost considerations were important for acceptance. Targeted interventions are needed to improve vaccine acceptance amongst HCWs, since they are critical in promoting vaccine acceptance amongst the general population.
An emerging novel goose astrovirus associated with gosling gout disease, China
Venue: Emerging Microbes Infections
Year: 2018

Since the first isolation from human, astroviruses have been detected in many species. Wide host range and occasional cross transmission of astrovirus pose a risk for zoonotic infection. Here, novel astroviruses were identified from goslings with recent epidemic gout disease in China. A virus, designated as GD, was efficiently isolated from a diseased gosling using LMH cells. Genome of GD amplified using 5' and 3' RACE was 7183nt in full length. Sequence analysis revealed the genome of GD was <60.8% homology with others deposited in Genbank. Moreover, GD could be neutralized by goose convalescent sera, and the gout associated symptom in goslings could be reproduced by GD infection. Our data demonstrated the goose astrovirus could be one of the causative agents of the ongoing gosling gout disease in China. The identification of the goose astrovirus not only diversified the astrovirus species, but also broadened the disease patterns caused by astroviruses.
What Drives Business Model Adaptation? The Impact of Opportunities, Threats and Strategic Orientation
Year: 2017

Business models change as managers not only innovate business models, but also engage in more mundane adaptation in response to external changes, such as changes in the level or composition of demand. However, little is known about what causes such business model adaptation. We employ threat rigidity as well as prospect theory to examine business model adaptation in response to external threats and opportunities. Additionally, drawing on the behavioural theory of the firm, we argue that the past strategic orientation of a firm creates path dependencies that influence the propensity of the firm to adapt its business model. We test our hypotheses on a sample of 1196 Norwegian companies, and find that firms are more likely to adapt their business model under conditions of perceived threats than opportunities, and that strategic orientation geared towards market development is more conducive to business model adaptation than an orientation geared towards defending an existing market position.
Comprehensive evaluation of agricultural circular economy in Hebei province based on rough set theory
Venue: 2016 International Conference on Machine Learning and Cybernetics (ICMLC)
Year: 2016

According to this paper, the agricultural circular economy evaluation system is constructed by four aspects, which contains reducing resources input, environmental security and quality, economic and social development standard and recycle of resource. There are fourteen indicators, which is based on the agricultural circular economy concept and the basic principles. Meanwhile, this paper's target is to point out which one is relatively important indicator on the development of agricultural circular economy in Hebei Province, by the rough set theory, on the basis of the fact, we make further comprehensive evaluation about the development of agricultural circular economy for each city of Hebei Province. The result shows that the agricultural added value per unit area is an important indicator to measure the development potential and development power of agricultural circular economy. Shijiazhuang ranks the first in comprehensive indicators of the development of agricultural circular economy, but it also needs to reduce the using intensity of plastic film, whereas the Zhangjiakou is the worst, the main reasons are the abilities of the economic development and the recycle of resources are relatively worse. The using intensity of plastic film can achieve effective reaction in every city with technological innovation, waste resource recycling and status of effective governance at the end. Hengshui, Chengde and other places still need to continue to strengthen technological innovation, to realize the recycling and reusing of waste resources and improve the end of the effective governance of waste, for promoting the healthy development of agricultural recycling economy.
Cardiac physiology: comparison of an e learning and classroom based resource for first year medical students
Venue: Journal of Biological Education
Year: 2019

Medical education is moving towards more interactive teaching methods. During Year 1 at Manchester Medical School (MMS) students study the heart in preparation for the hospital based years of study. Cardiovascular disease (CVD) is the main cause of mortality worldwide and thus, the content studied in Year 1 is essential for practising clinicians. This study produced an e learning resource (ELR) and a classroom based lesson (CBL) which both aimed to increase students' understanding of CVD by using 'patient' centred activities. Confidence surveys pre and post resource were used to assess their efficacy, while questionnaires and verbal feedback assessed the resources' enjoyability. It was hypothesised the resources would increase participants' confidence in their understanding of CVD. As hypothesised, Year 1 students' confidence significantly increased after both resources (Wilcoxon matched pairs signed rank test, n 22 for ELR and n 13 for CBL, P 0.05) and feedback suggested students found the resources enjoyable and engaging. Students reported greater confidence increases in their pharmacology knowledge and communication skills after the CBL vs ELR, whilst confidence in their anatomy and physiology knowledge had greater increases after the ELR. These findings support studies that suggest interactive learning is effective and suggest different teaching methods could be used to provide a well rounded education.
Motion tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography
Venue: IEEE Transactions on Information Technology in Biomedicine
Year: 2010

This paper addresses the design considerations and critical evaluation of a novel embodiment for wearable photoplethysmography (PPG) comprising a magnetic earring sensor and wireless earpiece. The miniaturized sensor can be worn comfortably on the earlobe and contains an embedded accelerometer to provide motion reference for adaptive noise cancellation. The compact wireless earpiece provides analog signal conditioning and acts as a data forwarding device via a radio frequency transceiver. Using Bland Altman and correlation analysis, we evaluated the performance of the proposed system against an FDA approved ECG measurement device during daily activities. The mean standard deviation (SD) of the differences between heart rate measurements from the proposed device and ECG (expressed as percentage of the average between the two techniques) along with the 95% limits of agreement (LOA 1.96 SD) was 0.62% 4.51% (LOA 8.23% and 9.46% 0.49% 8.65% 17.39% and 16.42% and 0.32% 10.63% 21.15% and 20.52% during standing, walking, and running, respectively. Linear regression indicated a high correlation between the two measurements across the three evaluated conditions (r 0.97, 0.82, and 0.76, respectively with p 0.001) The new earring PPG system provides a platform for comfortable, robust, unobtrusive, and discreet monitoring of cardiovascular function.
Multi digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Venue: ICLR
Year: 2014

Recognizing arbitrary multi character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub problem in this domain viz. recognizing arbitrary multi digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\ accuracy in recognizing complete street numbers. We show that on a per digit recognition task, we improve upon the state of the art, achieving $97.84\ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.
Bipolar Disorder after Traumatic Brain Injury: Ethiopian Perspective
Year: 2019

Traumatic brain injury (TBI) can result in a variety of neuropsychiatric disturbances and may cause vulnerability to psychiatric disorders, with latency periods of over 10 years. We report the case of a 26 year old man with no psychiatric antecedents who developed manic and aggressive behavior after traumatic brain injury (TBI)  Findings on psychiatric evaluation encompassing detailed history and mental state examination, neuropsychological test battery, baseline laboratory examination and imaging exams suggested the presence of Bipolar I Disorder; Current manic Episode, Severe, with psychotic features due to TBI.  This case demonstrates that TBI may cause vulnerability to psychiatric disorders, with long latency periods, and that its course may be exacerbated with existing psychosocial stressor. Evidences suggest valproate as the primary pharmacological intervention in conjunction with cognitive and physical rehabilitation, and family and personal support.
Constructing Linear Sized Spectral Sparsification in Almost Linear Time
Year: 2017

We present an almost linear time algorithm for constructing a spectral sparsifier with the number of edges linear in its number of vertices. This improves all previous constructions of linear sized spectral sparsifiers, which requires O(n2) time. A key ingredient in our algorithm is a novel combination of two techniques used in literature for constructing spectral sparsifiers: random sampling by effective resistance, and adaptive construction based on barrier functions.
Psychodynamische Hintergrunde von Suizid und Suizidversuch
Year: 2014

Es geht in diesem Artikel nur um die verschiedenen Typen konfliktpsychologischer Hintergrunde, die gerade den einen Suizid( versuch) in seiner psychologischen Bedeutung vom anderen unterscheiden, nicht um allgemein Suizide auszeichnende Faktoren. Ich mochte mich hierzu der Typologie von Riemann (2011) bedienen, wie er sie als Grundformen der Angst beschrieben hat. Er unterscheidet zwei Achsen, auf denen jeweils zwei extreme Pole angesiedelt sind: Angst vor Nahe (Hingabe) versus Angst vor Distanz (Selbstwerdung) ist die eine Achse und Angst vor Veranderung versus Angst vor Endgultigkeit (Festlegung, Stillstand, Dauer) ist die andere. Jeder Mensch ist auf den beiden Achsen verortet und nur die Nahe zu den extremen Polen gewinnt neurosenpsychologische Bedeutung. Die extreme Ausformung der Angst vor Nahe ist die schizophrene (schizoide) die der Angst vor Distanz die depressive Personlichkeitsstruktur. Auf der anderen Achse ist die extreme Ausformung der Angst vor Veranderung die zwangsneurotische, die der Angst vor Stillstand die hysterische Personlichkeit. Die Schizophrenie (oder die dem Normalfall nahere Schizoidie) ist nach Fenichel (1945, deutsch 2005) durch extrem unterschiedliche Auspragungen gekennzeichnet. Gemeinsam sei jedoch allen Erscheinungsformen die Angst, dass die Welt sich auflost, es keine Kontakte, keine Menschen und auch die eigene Person nicht mehr gibt. Auftauchende Kontakte werden eher als bedrohlich erlebt, vermieden oder zerstort. Ablehnung und Ausgrenzung werden phantasiert und auch herbeigefuhrt. Eine Vorstellungswelt, in der es so etwas wie Gern gemocht Werden, Integriertsein in eine Gruppe und Familie nicht gibt, lasst die eigene Existenz sinnlos erscheinen. Im Fall paranoider Auspragung kommt die Angst vor einem ubermachtigen Verfolger hinzu. Bei schizoid strukturierten Personen ware der Suizid die Beendigung einer unertraglichen Angst, das Beenden des Konflikts zwischen Sehnsucht nach Kontakt und Unfahigkeit hierzu sowie die Chance, sich der Bedrohung durch Verfolger zu entziehen. Suizid ist eine wesentliche Todesursache bei Schizophrenen, wobei gewaltsame Suizidmethoden uberwiegen. Der schizophrene Suizid stellt Autoaggression, Selbstbestrafung, blinde Abfuhr aggressiver Spannungen, omnipotente Beherrschung der Situation (wenn alle anderen Vorstellungen von eigener Allmacht an der Realitat gescheitert sind, bleibt der Suizid immer noch der Bereich, in dem man sich nicht von anderen von seinem Vorhaben abbringen lassen muss) Wunsch nach Zasur und aktives einem Unheil Zuvorkommen dar.
Lyapunov guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile Edge Computing Networks
Venue: ArXiv
Year: 2020

Opportunistic computation offloading is an effective method to improve the computation performance of mobile edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi user MEC network with time varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi stage stochastic mixed integer non linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL) Specifically, LyDROO first applies Lyapunov optimization to decouple the multi stage stochastic MINLP into deterministic per frame MINLP subproblems. By doing so, it guarantees to satisfy all the long term constraints by solving the per frame subproblems that are much smaller in size. Then, LyDROO integrates model based optimization and model free DRL to solve the per frame MINLP problems with low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low execution latency that is particularly suitable for real time implementation in fast fading environments.