nils-herrmann/clean_scirepeval huggingface dataset:
Strategy for Data Center Optimization Improve Data Center capability to meet business opportunities
Venue: 2018 2nd International Conference on I SMAC (IoT in Social, Mobile, Analytics and Cloud) (I SMAC)I SMAC (IoT in Social, Mobile, Analytics and Cloud) (I SMAC) 2018 2nd International Conference on
Year: 2018

Considering current evolving technology and the way data are growing, IT consulting and outsourcing industry expected to be strategic partner for technology innovation in addition to support on going business with reduced operational cost. Data Center is backbone for digital economy, big data, cloud, artificial intelligence, IoT or wearable technology. Data growth and on demand data access changed the focus of data center as storage and disaster recovery to access data instantly from cloud without compromising security controls and data quality. These technology transformations create demand for latency. Every organization like Facebook, Equinix, Amazon, and Google are having their own data centers and expanding their business on cloud services. Data Center plays major critical on success of digital business. It is important to find possible options to optimize infrastructure and improve efficiency and productivity of Data Center. At the same time, we need to make sure that environment is up and running without compromising quality and security of data. This paper gives few solutions to get more from Data Center, reduce operational cost and optimize infrastructure utilization.
Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
Venue: ArXiv
Year: 2021

In this paper, we develop a safe decision making method for self driving cars in a multi lane, single agent setting. The proposed approach utilizes deep reinforcement learning (RL) to achieve a high level policy for safe tactical decision making. We address two major challenges that arise solely in autonomous navigation. First, the proposed algorithm ensures that collisions never happen, and therefore accelerate the learning process. Second, the proposed algorithm takes into account the unobservable states in the environment. These states appear mainly due to the unpredictable behavior of other agents, such as cars, and pedestrians, and make the Markov Decision Process (MDP) problematic when dealing with autonomous navigation. Simulations from a well known self driving car simulator demonstrate the applicability of the proposed method
Relationship between gender and psychotic symptoms in cocaine dependent and methamphetamine dependent participants.
Venue: Gender medicine
Year: 2010

It has been well documented that cocaine and methamphetamine use can lead to the onset of psychotic symptoms similar to schizophrenia. However, the research and literature on gender differences and stimulant induced psychosis have been mixed.  The primary aim of this study was to investigate gender differences in the reporting of psychotic symptoms in cocaine versus methamphetamine dependent individuals.  Participants were recruited from the Los Angeles, California, community via radio and newspaper advertisements. All met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria for cocaine or methamphetamine dependence, and all reported either methamphetamine or cocaine as their primary drug of abuse. During a screening interview, participants answered questions from the Psychotic Symptom Assessment Scale, which characterizes various types of psychotic symptoms during drug use "while high" or during periods of nonuse "while abstinent"  Participants included 42 cocaine dependent individuals (27 men, 15 women) and 43 methamphetamine dependent individuals (25 men, 18 women) Among cocaine users, there were no significant differences between men and women with regard to ethnicity, years of use, route of administration, and amount used in the past week, though they differed significantly with regard to age (P 0.029) In the "while abstinent" condition, women were significantly more likely than men to report experiencing auditory hallucinations (13% vs 0% respectively; P 0.050) and tactile hallucinations (20% vs 0% P 0.016) whereas men were more likely to report delusions of grandeur (48% vs 6% P 0.006) During the "while high" condition, women were significantly more likely than men to report delusions of grandeur (13% vs 0% respectively; P 0.050) tactile hallucinations (33% vs 0% P 0.001) and olfactory hallucinations (13% vs 0% P 0.050) Among methamphetamine users, there were no significant differences between men and women with regard to age, ethnicity, years of use, route of administration, or amount used in the past week. In the "while abstinent" condition, women were significantly more likely than men to report feeling that something was wrong with the way a part of their body looked (72% vs 32% respectively; P 0.009) olfactory hallucinations (39% vs 8% P 0.010) and dressing inappropriately (22% vs 0% P 0.010) During the "while high" condition, women were more likely than men to report delusions of grandeur (33% vs 16% respectively; P 0.030) paranoia (50% vs 16% P 0.017) and tactile hallucinations (61% vs 32% P 0.050)  The findings of the present study revealed that cocaine and methamphetamine dependent women were more likely than their male counterparts to report experiencing various psychotic symptoms. This information may be useful for clinicians and mental health professionals, who should take these symptoms into account as potential barriers that may impede effective treatment.
Learned image representations for visual recognition
Year: 2016

This thesis addresses the problem of extracting image structures for representing images effectively in order to solve visual recognition tasks. Problems from diverse research areas (medical imaging, material science and food processing) have motivated large parts of the methodological development. The solutions are inspired by and extend state of the art techniques for describing and learning image content. More specifically, the thesis explores two approaches to constructing image representations, namely feature engineering and feature learning. In the feature engineering approach, we devise a new image representation for texture like patterns based on count statistics of second order image structure. We demonstrate the discriminative capabilities of this representation on medical images and perform both cell classification and mitosis detection. Moreover, we develop an object identification method based on vector quantized local image descriptors allowing us to distinguish individual meat cuts along a production line and trace them in a non intrusive manner. In the feature learning approach, we propose to solve the task of segmenting scanning electron microscopy images of calcite crystals by learning a meaningful pixel description to facilitate the actual segmentation. Finally, we present a new unsupervised generative image model addressing the problem of pixel based similarity measures for images. We propose a scheme for employing feature based similarity measures and demonstrate how this improves the ability to learn high level concepts in images of faces. The thesis argues in favor of learning features and presents new methods for domains with limited amounts of labeled data allowing feature learning to be applied more broadly.
BIOMIMETIC MULTI SCALE DAMAGE IMMUNITY FOR CONSTRUCTION MATERIALS: M4L PROJECT OVERVIEW
Year: 2013

This paper presents a vision of a sustainable and resilient built environment that is comprised of materials and structures that continually monitor, regulate, adapt and repair themselves without the need for external intervention. In this way, these self?healing materials and intelligent structures will significantly enhance durability and serviceability, improve safety and reduce maintenance costs. The conglomerate materials that form the basis of the majority of such construction materials (concrete, grouts, mortars, hydraulically bound materials, grouted soils etc) are extremely complex multiphase composites with multi?scale internal structures that exhibit a hierarchy of multi?dimensional, time?dependent damage mechanisms. For example, in cementitious composites nano?scale damage occurs during hydration and the strength development phase, while medium?term damage due to chemical attack also leads to the formation of defects in its structure. Other short?term factors can also produce dislocations at the nano scale. In time, this nano?damage grows to form micro cracks which eventually coalesce to form networks of meso?cracks which in turn lead to debonding between the paste and aggregate particles, followed by a discrete number of visible macro?cracks which so often lead to corrosion of the steel reinforcement. Hence, it is evident that to truly achieve a self?healing cementitious composite, a system is needed that can act at both the different time and length scales at which the damage can form. This paper presents a newly funded research project, M4L: Materials for life, that is addressing this complex problem by taking advantage of innovations in allied scientific disciplines to pave the way for the development of a new generation of versatile and robust construction materials.
Relationship between Online Repurchase Intention and E Satisfaction: Quantitative Research Study Based on Young People in Western Province in Sri Lanka
Year: 2020

The purpose of this study is to identify the relationship between online repurchase intention and security, trust, ease of use (EOU) and privacy concerns mediated by e satisfaction. Since there are few studies that explain online repurchase intention in developing countries. This study is based on Sri Lankan online shoppers. A survey method is used for respondents' age between 18 32 years. To test the hypothesis of this study, a questionnaire is distributed to participants. The data are analyzed using SPSS software which is used to test the model and the hypothesis. The results showed that ease of use and privacy concerns are the factors that have the greatest impact on consumers' buying behavior. In addition, online satisfaction is important for the group in this study and the results show that it impacts on the relationship between repurchase intention and other factors. This study presents the impact of security, trust, ease of use and privacy concerns on the online repurchase intention of the younger generation. Further, the mediation effect of e satisfaction is also analyzed.
Financial Decision, Innovation, Profitability and Company Value: Study on Manufacturing Company Listed in Indonesian Stock Exchange
Year: 2015

This study aims to describe the relationship between financial decisions, innovation, enterprise profitability and the value of the company. Based on the research objectives, this research is a causality research. The data used are secondary data for a 5 years period, obtained through several sources such as Indonesian Capital Market Directory (ICMD) and the websites of 22 companies. The results show that (1) Investment decision affects company profitability positively and significantly, (2) Investment decision affects company value positively and significantly, (3) Capital structure affects company profitability positively and significantly, (4) Capital structure affects company value positively and significantly, (5) Dividend policy affects company profitability positively and significantly, (6) Dividend policy does not affect company value, (7) Innovation affects company profitability positively and significantly, (8) Innovation affects company value positively and significantly, and (9) Profitability affects company value positively and significantly.
Full text Search for Verifiable Credential Metadata on Distributed Ledgers
Venue: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)
Year: 2019

Self sovereign Identity (SSI) powered by distributed ledger technologies enables more flexible and faster digital identification workflows, while at the same time limiting the control and influence of central authorities. However, a global identity solution must be able to handle myriad credential types from millions of issuing organizations. As metadata about types of digital credentials is readable by everyone on the public permissioned ledger with Hyperledger Indy, anyone could find relevant and trusted credential types for their use cases by looking at the records on the blockchain. To this date, no efficient full text search mechanism exists that would allow users to search for credential types in a simple and efficient fashion tightly integrated into their applications. In this work, we propose a full text search framework based on the publicly available metadata on the Hyperledger Indy ledger for retrieving matching credential types. The proposed solution is able to find credential types based on textual input from the user by using a full text search engine and maintaining a local copy of the ledger. Thus, we do not need to rely on information about credentials coming from a very large candidate pool of third parties we would need to trust, such as the website of a company displaying its own identifier and a list of issued credentials. We have also proven the feasiblity of the concept by implementing and evaluating a prototype of the full text credential metadata search service.
From crowdsourced rankings to affective ratings
Venue: 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
Year: 2014

Automatic prediction of emotions requires reliably annotated data which can be achieved using scoring or pairwise ranking. But can we predict an emotional score using a ranking based annotation approach? In this paper, we propose to answer this question by describing a regression analysis to map crowdsourced rankings into affective scores in the induced valence arousal emotional space. This process takes advantages of the Gaussian Processes for regression that can take into account the variance of the ratings and thus the subjectivity of emotions. Regression models successfully learn to fit input data and provide valid predictions. Two distinct experiments were realized using a small subset of the publicly available LIRIS ACCEDE affective video database for which crowdsourced ranks, as well as affective ratings, are available for arousal and valence. It allows to enrich LIRIS ACCEDE by providing absolute video ratings for the whole database in addition to video rankings that are already available.
Sarcasm Detection with Sentiment Semantics Enhanced Multi level Memory Network
Venue: Neurocomputing
Year: 2020

Sarcasm detection is a challenging natural language processing task for sentiment analysis. Existing deep learning based sarcasm detection models have not fully considered sentiment semantics, even though sentiment semantics is necessary to improve the performance of sarcasm detection. To deal with the problem, we propose a multi level memory network using sentiment semantics to capture the features of sarcasm expressions. In our model, we use the first level memory network to capture sentiment semantics, and use the second level memory network to capture the contrast between sentiment semantics and the situation in each sentence. Moreover, we use an improved convolutional neural network to improve the memory network in the absence of local information. The experimental results on the Internet Argument Corpus (IAC V1 and IAC V2) and Twitter dataset demonstrate the effectiveness of our model.
What is the most appropriate dose of N acetylcysteine after massive acetaminophen overdose?
Venue: Clinical toxicology
Year: 2019

While the traditional intravenous N acetylcysteine (NAC) dosing regimen works well for the vast majority of acetaminophen overdoses, there may be cases of massive overdose where additional NAC may be necessary. Recent evidence suggests that patients with acetaminophen concentrations above the "300 line" develop hepatotoxicity at a higher rate than those below the 300 line, suggesting that an increase of dose may be beneficial at this cut off. Additional clinical data suggest a further increase in doses at the 450 line and 600 lines. I propose a strategy for step wise increases in NAC dosing in response to high acetaminophen concentrations at the 300 450 and 600 lines after acute massive acetaminophen overdoses.
Cold start Sequential Recommendation via Meta Learner
Venue: AAAI
Year: 2021

This paper explores meta learning in sequential recommendation to alleviate the item cold start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold start items, which are prevalent in those scenarios. As there is generally no side information in the setting of sequential recommendation task, previous cold start methods could not be applied when only user item interactions are available. Thus, we propose a Meta learning based Cold Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold start problem in sequential recommendation. This task is non trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold start item with the potential user. Besides, our framework can be painlessly integrated with neural network based models. Extensive experiments conducted on three real world datasets verify the superiority of Mecos, with the average improvement up to 99% 91% and 70% in HR@10 over state of the art baseline methods.
Exploring The effects Of An online asynchronous mindfulness meditation intervention with nursing students On Stress, mood, And Cognition: A descriptive study.
Venue: Nurse education today
Year: 2016

Nurses returning to school while working, raising families, and maintaining other roles, can experience stress, mood changes and cognition disturbance that negatively impact their academic success.  To explore the effect of an online mindfulness meditation intervention with distance nursing students on stress, mood and cognition.  A 24 week descriptive study.  An 8 week online intervention was offered to all undergraduate and graduate nursing students, of three nursing programs of a middle sized university in mid Atlantic US.  A total of 26 nursing students completed the study.  An 8 week online, asynchronous mindfulness intervention was provided through the learning management system with a 16 week follow up.  Perceived Stress Scale (PSS) Hospital Anxiety and Depression Scale (HADS) and Attention Network Test (ANT) RESULTS Stress was significantly reduced ( A decreasing trend for anxiety was noted with significant difference between time points  ability to shift attention, attention selection, concentration, and accuracy improved.  Findings from this study may illuminate the usefulness of a mindfulness based stress reduction program offered to distance nursing students. Further studies are needed to better demonstrate the effectiveness of the intervention.
The gut microbiome modulates gut brain axis glycerophospholipid metabolism in a region specific manner in a nonhuman primate model of depression
Venue: Molecular Psychiatry
Year: 2020

Emerging research demonstrates that microbiota gut brain (MGB) axis changes are associated with depression onset, but the mechanisms underlying this observation remain largely unknown. The gut microbiome of nonhuman primates is highly similar to that of humans, and some subordinate monkeys naturally display depressive like behaviors, making them an ideal model for studying these phenomena. Here, we characterized microbial composition and function, and gut brain metabolic signatures, in female cynomolgus macaque Macaca fascicularis displaying naturally occurring depressive like behaviors. We found that both microbial and metabolic signatures of depressive like macaques were significantly different from those of controls. The depressive like monkeys had characteristic disturbances of the phylum Firmicutes. In addition, the depressive like macaques were characterized by changes in three microbial and four metabolic weighted gene correlation network analysis (WGCNA) clusters of the MGB axis, which were consistently enriched in fatty acyl, sphingolipid, and glycerophospholipid metabolism. These microbial and metabolic modules were significantly correlated with various depressive like behaviors, thus reinforcing MGB axis perturbations as potential mediators of depression onset. These differential brain metabolites were mainly mapped into the hippocampal glycerophospholipid metabolism in a region specific manner. Together, these findings provide new microbial and metabolic frameworks for understanding the MGB axis' role in depression, and suggesting that the gut microbiome may participate in the onset of depressive like behaviors by modulating peripheral and central glycerophospholipid metabolism.
Vimentin Intermediate Filaments as Potential Target for Cancer Treatment
Venue: Cancers
Year: 2020

Intermediate filaments constitute the third component of the cellular skeleton. Unlike actin and microtubule cytoskeletons, the intermediate filaments are composed of a wide variety of structurally related proteins showing distinct expression patterns in tissues and cell types. Changes in the expression patterns of intermediate filaments are often associated with cancer progression; in particular with phenotypes leading to increased cellular migration and invasion. In this review we will describe the role of vimentin intermediate filaments in cancer cell migration, cell adhesion structures, and metastasis formation. The potential for targeting vimentin in cancer treatment and the development of drugs targeting vimentin will be reviewed.
Burden, Perceived Social Support and Quality of Life among Elderly Caregivers in Johor Bahru
Year: 2019

Caregiver burden in caring for the elderly is perceived as an important and serious problem that the family is experiencing. The outcomes of taking care of the elderly with cognitive or and physical impairment will be either negative or positive. The objectives of the study were to assess the level of burden and associated factors among family caregivers, as well as to investigate the variables correlated to burden.  The study was performed in a cross sectional survey among 335 caregivers who cared for the elderly in Johor Bahru, Malaysia. The Zarit Burden Interview (ZBI) was used to measure the level of burden. The level of functional dependence of the elderly was assessed by using The Barthel Index (BI) and Lawton Brody Index (LBI) The perceived Social Support was measured by using the Multidimensional Scale of Perceived Social Support (MSPSS) and WHOQOL Bref scale for quality of life of the caregivers. The data was analysed using the Software Package for Statistical Analysis (SPSS 22) Informed concerns were taken from participants and Ethical permission was obtained from authority concerned. Collection of data was done through convenience sampling.  The result of the study showed that 93% of the participants experienced burden with mean ZBI score of 34.24 (SD 10.96) 60.8% experienced mild to moderate burden, 31.4% at moderate to severe level of burden and 0.6% at severe level and 7.2% did not experience burden. The emotional burden represented the most important domain of burden with a mean value of 9.58 (SD 4.76) while burden in loss of control was the least significant with a mean value of 1.84 (SD 0.86) The other domains of burden are; burden in relationship (M 8.04 SD 3.08) social and family life burden (M 5.98 SD3.07) and financial burden (M1.96  This study contributed to improving knowledge concerning symptom arising from burden and emotional distress experienced by the caregivers of elderly individuals. Family caregivers can be taught about problem solving, skill building training and self regulation of emotional response. Caregivers need relevant information and education and health personnel need to assist them in these issues in order to equip them in administering quality care in the community.