nils-herrmann/clean_scirepeval huggingface dataset:
Systematic literature study for dimensional classification of success factors affecting process improvement in global software development: client vendor perspective
Venue: IET Softw.
Year: 2018

The majority of organisations are globalising their software development activities by following the ideas of global software development (GSD) The motivation behind the adoption of GSD phenomena are the list of benefits gained by the software industry. However, there are different challenges face by the GSD organisations, particularly the issues related to software process improvement (SPI) The aim of this study is the identification and classification into categories of the success factors that can impact SPI initiatives taken in GSD organisations. The systematic literature review (SLR) method has been used to extract the success factors from the literature. SLR phases, 'planning, conducting, and reporting the review' have been followed to perform this study. Totally, 15 success factors were identified and classified into the six main categories. The authors have also reported the critical success factors of SPI, i.e. management commitment, staff involvement, roles and responsibilities, communication, and resources allocation. This article also reported the similarities and differences between the success factors classified on the bases of client vendor organisation and size of the organisation. The identified factors can contribute towards the implementation of SPI programme in both client and vendor GSD organisations because these factors represent key areas of process improvement.
Prevalence of Functional Limitation in COVID 19 Recovered Patients Using the Post COVID 19 Functional Status Scale
Venue: JNMA; journal of the Nepal Medical Association
Year: 2021

COVID 19 is an emerging global health pandemic causing tremendous morbidity and mortality worldwide. Chronic symptoms progressing to poor functional status have been reported in a substantial proportion of COVID 19 patients worldwide. This study aimed to determine the prevalence of functional limitation in COVID 19 recovered patients using the post COVID 19 functional status scale.  A descriptive cross sectional study was conducted at Tribhuvan University Teaching Hospital. COVID 19 recovered patients with reverse transcription polymerase chain reaction negative status were included and assessed using the post COVID 19 functional status scale. Data entry and analysis was done in Statistical Package for the Social Sciences version 20.0. Descriptive statistics were performed.  A total of 106 patients were included for the final analysis. More than half of the patients (56.6% reported having no functional limitation (grade 0) while the prevalence of some degree of functional limitation was observed in 46 (43.4% patients (grade 1 to 4)  Some form of functional limitation should be anticipated after COVID 19 infection. Post COVID 19 functional status scale can be a valuable tool in determining the prevalence of functional limitation in COVID 19 recovered patients in acute health care settings. It can potentially guide in planning rehabilitative measures in post acute care management of COVID 19 survivors.
The Road from Host Defense Peptides to a New Generation of Antimicrobial Drugs
Venue: Molecules
Year: 2018

Host defense peptides, also called antimicrobial peptides (AMPs) whose protective action has been used by animals for millions of years, fulfill many requirements of the pharmaceutical industry, such as: (1) broad spectrum of activity; (2) unlike classic antibiotics, they induce very little resistance; (3) they act synergically with conventional antibiotics; (4) they neutralize endotoxins and are active in animal models. However, it is considered that many natural peptides are not suitable for drug development due to stability and biodisponibility problems, or high production costs. This review describes the efforts to overcome these problems and develop new antimicrobial drugs from these peptides or inspired by them. The discovery process of natural AMPs is discussed, as well as the development of synthetic analogs with improved pharmacological properties. The production of these compounds at acceptable costs, using different chemical and biotechnological methods, is also commented. Once these challenges are overcome, a new generation of versatile, potent and long lasting antimicrobial drugs is expected.
Multi Criteria Evaluation Framework in Selection of Accelerated Bridge Construction (ABC) Method
Year: 2018

Accelerated Bridge Construction (ABC) is bridge construction that uses innovative planning, design and construction methods in a safe and cost effective manner, which reduces construction mobility and environmental impacts, and contributes to city sustainable planning and development. To deal with the pressing need to support the decisions associated with the selection between the ABC and conventional bridge construction, this paper presents the development of a multi criteria evaluation framework. Methods are developed and identified to estimate the construction, agency, and user costs associated with the construction methods. A novel model was developed to allow the estimation of the construction and agency costs of ABC relative to conventional construction. This paper also demonstrates the estimation of user costs, including those associated with mobility, reliability, safety, and emissions, utilizing combinations of the proposed prediction method. The paper then compares the use of the return on investment and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) Multi Criteria Decision Making (MCDM) evaluation approaches in the decision to select between ABC and conventional bridge construction. The results from the employment of the two approaches to a case study demonstrate the advantage of using the TOPSIS approach, which is also applicable in the urban planning process.
Morton's neuroma: review of anatomy, pathomechanism, and imaging.
Venue: Clinical radiology
Year: 2020

Morton's neuroma is a commonly encountered cause of forefoot pain, which may limit weight bearing activities and footwear choices. Although the aetiology and pathomechanism of this condition is controversial, the histological endpoint is well established as benign perineural fibrosis of a common plantar digital nerve, typically within the third intermetatarsal space. The diagnosis of Morton's neuroma is mainly based on characteristic symptoms and clinical findings, but may be confirmed by ultrasonography. Although ultrasound is a highly accurate diagnostic tool for Morton's neuroma, it is subject to interoperator variability due to differences in technique and level of experience. In this paper, the authors review the anatomy of the common plantar digital nerves and surrounding structures in the forefoot, which are deemed relevant to the understanding of Morton's neuroma, especially from a sonographic point of view. Several theories of the pathomechanism of Morton's neuroma are briefly discussed. The main purpose of this article is to illustrate the ultrasound techniques for evaluating Morton's neuroma and performing ultrasound guided corticosteroid injections.
Disambiguation of Chinese Polyphones in an End to End Framework with Semantic Features Extracted by Pre Trained BERT
Venue: INTERSPEECH
Year: 2019

Grapheme to phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text to speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end to end framework to predict the pronunciation of polyphonic character, which accepts sentence containing polyphonic character as input in the form of Chinese character sequence without the necessity of any preprocessing. The proposed method consists of a pre trained bidirectional encoder representations from Transformers (BERT) model and a neural network (NN) based classifier. The pre trained BERT model extracts semantic features from raw Chinese character sequence and the NN based classifier predicts the polyphonic character's pronunciation according to BERT output. To explore the impact of contextual information on polyphone disambiguation, three different classifiers are investigated: a fully connected network based classifier, a long short term memory (LSTM) network based classifier and a Transformer block based classifier. Experimental results demonstrate the effectiveness of the proposed end to end framework for polyphone disambiguation and the semantic features extracted by BERT can greatly enhance the performance.
Attention enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.
Venue: Physics in medicine and biology
Year: 2019

A deeply supervised attention enabled boosted convolutional neural network (DAB CNN) is presented as a superior alternative to current state of the art convolutional neural networks (CNN) for semantic CT segmentation. Spatial attention gates were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB CNN is compared to seven architectures: a variation of U Net (UNet) attention enabled U Net (A UNet) boosted U Net (B UNet) deeply supervised U Net (D UNet) U Net with ResNeXt blocks (ResNeXt) life long learning segmentation CNN (LL CNN) and deeply supervised attention enabled U Net (DA UNet) The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. 120 patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparator p values indicate that DAB CNN achieved significantly superior dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention enabled boosted convolutional neural networks using deep supervision are capable of achieving superior prediction accuracy compared to current state of the art automatic segmentation methods.
Serpina3n: Potential drug and challenges, mini review
Venue: Journal of drug targeting
Year: 2019

Serpina3n is a secretory serine protease inhibitor belonging to clade "a" exhibiting unique structural and physiological characteristics, playing significant roles ranging from complement cascade, apoptosis, wound healing to Alzheimer by inhibiting a wide range of proteases. Recently studies have reported its significant roles during various pathologies. Although its full range of potential applications are yet to reveal, its reported implications particularly in CNS insults are making it potential therapeutical approach. Here we aim to draw together the literature shedding light on the potential therapeutical applications of Serpina3n/SERPINA3 in various diseases and a brief comparison between murine Serpina3n and its human ortholog SERPINA3 (a1 antichymotrypsin) accounting their biological roles and significance.
Ethical consideration of the research proposal and the informed consent process: An online survey of researchers and ethics committee members in Thailand
Venue: Accountability in research
Year: 2019

Researchers designing and conducting studies using human data should consider the values and principles of ethical conduct. Research ethics committees (RECs) typically evaluate the ethical acceptability of research proposals. Sometimes, differences arise between how researchers and RECs interpret ethical principles, and how they decide what constitutes ethical conduct. This study aimed to explore the opinions of these two groups about the importance of core ethical issues in the proposal and in the informed consent process. An anonymous online questionnaire was distributed to a target population in health related academic/research institutes across Thailand; 219 researchers and 72 REC members participated. Significantly, more REC members than researchers attributed the highest importance to three core ethical considerations risk/benefit, vulnerability, and confidentiality/privacy. For the informed consent process, significant differences were found for communication of risks, decision making authority for consent, process for approaching study participants, and availability of a contact for study deviations/violations. The different ratings indicate differences in the groups' perspectives on ethical principles, which may affect focal congruence on ethical issues in the proposal. Communication of these findings should help close gaps between REC and researcher perceptions. Further study should investigate how RECs and researchers perceive equivocal ethics terms.
Big data methods in the social sciences
Venue: Current Opinion in Behavioral Sciences
Year: 2017

Big data methods are attractive to the social sciences and all the sciences because they can address data sets where the number of variables can far exceed the number of cases being analyzed. Generally speaking, big data methods seek to detect stable and potentially complex clusters and/or predictions in the data, while also taking aggressive steps not to capitalize on chance in doing so. On this latter point, big data methods often perturb some combination of the sample, the variables, and the model to ensure that solutions are robust, where traditional statistical methods rarely do so, with some exceptions (e.g. cross validation approaches, latent variable models)
Emotional eye movement generation based on Geneva Emotion Wheel for virtual agents
Venue: J. Vis. Lang. Comput.
Year: 2012

Eye movement plays an important role in face to face communication in that it conveys nonverbal information and emotional intent beyond speech. Our research proposes a computational framework that enables virtual agents to convey different emotional expressions to users through eye movement. We build this framework based on eye movement parameters derived from the Kohn Canade AU(action unit) Coded Facial Expression Database as well as real time eye movement data. We also describe a rule based approach to generate emotional eye movement based on the Geneva Emotion Wheel. We then present one experiment in which subjects evaluated the emotional eye movement generated by this framework. When our proposed model was employed, the results showed a higher rate of recognition of the agent intended emotion, proving the validity of our approach.
Analisis Pengaruh Kegagalan Sistem Pendingin Genset Caterpillar 3500 Series
Venue: Jurnal Rekayasa Material, Manufaktur dan Energi
Year: 2019

When the generator set is operating it is often overheated, the load does not match the capacity of the power expended. An early indication of a high cooling system temperature and this affects the reliability of the generator set that is operating. Overheating can be the initial cause of damage to engine components and can cause fatalities. To find out the root cause of damage to overheating in the cooling system must be analyzed. The damage analysis method is to know the generator set operating system and ensure the loading conditions and temperature measurements on the radiator. The main cause of overheating is lack of radiator water, where the lack of water is caused by leakage on the radiator cap, radiator core, dead end radiator tube, radiator blocking, water leakage from the water pump, leakage from the pipe or radiator hose. To minimize damage to the steps that must be done is, to recondition the radiator in case of damage to the core, radiator tube, and if there is a water leak from the water pump then what must be done is repair or replacement of the water pump. While damage to the pipe or hose must be replaced and if the radiator water is contaminated with oil, flushing of the radiator water must be carried out, doing regular cleaning of the radiator core. From the analysis and some cooling system maintenance actions, to minimize the occurrence of overheating periodic maintenance must be done
TEACHING PROFICIENCY THROUGH READING AND STORYTELLING (TPRS) AS A TECHNIQUE TO FOSTER STUDENTS' SPEAKING SKILL
Year: 2017

Teaching Proficiency through Reading and Storytelling (TPRS) is quite essential to improve EFL learners' speaking ability. It can be done successfully by means of the basic concept of TPRS known as comprehensible input in second language acquisition (SLA) This paper presents a study on learners' speaking ability through TPRS making use of three important steps; Showing, Telling, and Reading. This is a quantitative study using quasi experimental as the two intact groups are used; experimental and control group. The data are obtained from pre test, post test and questionnaires viewed from both students' and teachers' perspective. The data from pre test and post test are analyzed by using independent sample t test The experimental and control are ascertained to be homogenous in term of English performance from the pre test analysis. The post test are carried out from both groups after the treatment and the the result of the test are compared in order to prove if the null hypothesis is rejected indicating that there is significant difference performance between the two groups. The result of the study is expected to be beneficial for English teachers, EFL learners, and further researchers.  Speaking Skill, Teaching Proficiency through Reading and Storytelling (TPRS)
Smart Ration Distribution and Controlling
Year: 2015

This paper proposes the advanced Ration Distribution System, named as "Smart Ration Distribution and Controlling" Huge amount of Govt. money get wasted due to corruption in the conventional Ration Distribution System. This paper implements a simple PDA device (personal data assistant) with RFID tag used as an e ration card in place of a conventional ration card. This PDA device is similar to the ticketing machine used by bus conductor or bank pigmy agent and the e ration card is similar to swipe card. The Subscriber has to use this card instead of a traditional ration card to get ration from the dealer. Efforts are put together from our side to combat corruption and to have better management of public distribution system. he ration distribution system is one of the largest government's economic policies in India. Its main motto is to provide food grains (sugar, wheat, rice, kerosene etc. to the people at affordable rates. The network of the ration shops is spread all over in India to provide food security to the people. This distribution of ration is controlled and monitored by central government, along with the state government. But it has so many limitations. Most of the ration shopkeepers keep fake ration cards with them. Due to fake ration cards, the dealer receives the extra ration from higher authority and he sales it into the open market. The dealer may not provide a sufficient amount of food grains to consumers. Most of the time people are not aware of the availability of ration in ration shop. The dealer may sale ration at higher rates than recommended by the government or he may do wrong entries in register. In this way, in the current situation we are facing problem of corruption in public distribution system. There is no such effective system through which government gets acknowledgement of consumption of food grains by people. ORGANIZATION OF PAPER The paper possesses three sections. First section consists of abstract and brief introduction about paper. The second section describes the development of hardware and software in detail. And last section covers the future scope along with conclusions and references for further details
Learning Depth Guided Convolutions for Monocular 3D Object Detection
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020

3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo LiDAR representation, and then apply existing 3D point cloud based object detectors. However, their results depend heavily on the accuracy of the estimated depth maps, resulting in suboptimal performance. In this work, instead of using pseudo LiDAR representation, we improve the fundamental 2D fully convolutions by proposing a new local convolutional network (LCN) termed Depth guided Dynamic Depthwise Dilated LCN (D4LCN) where the filters and their receptive fields can be automatically learned from image based depth maps, making different pixels of different images have different filters. D4LCN overcomes the limitation of conventional 2D convolutions and narrows the gap between image representation and 3D point cloud representation. Extensive experiments show that D4LCN outperforms existing works by large margins. For example, the relative improvement of D4LCN against the state of the art on KITTI is 9.1% in the moderate setting. D4LCN ranks 1st on KITTI monocular 3D object detection benchmark at the time of submission (car, December 2019) The code is available at https:/github.com/dingmyu/D4LCN.
Generalized Translation Based Embedding of Knowledge Graph
Venue: IEEE Transactions on Knowledge and Data Engineering
Year: 2020

Knowledge graphs are useful for many AI tasks but often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. TransE is one of such models and the first translation based method. TransE is well known because the principle of TransE can effectively capture the rules of a knowledge graph although it seems very simple. However, TransE has problems with its regularization and an unchangeable ratio of negative sampling. In this paper, we generalize TransE to solve these problems by proposing knowledge graph embedding on a Lie group (KGLG) and the Weighted Negative Part (WNP) method for the objective function of translation based models. KGLG is the novel translation based method which embeds entities and relations of a knowledge graph on any Lie group. It allows us not to employ regularization during training of the model if we choose a compact lie group for the embedding space. The WNP method is for changing the ratio of negative sampling, which enhances translation based models. Our approach outperforms other state of the art approaches such as TransE, DistMult, and ComplEx on a standard link prediction task. We show that TorusE, KGLG on a torus, is scalable to large size knowledge graphs and faster than the original TransE.
A Consolidated Framework for Implementing Robotic Process Automation Projects
Venue: BPM
Year: 2020

Robotic process automation (RPA) is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes. In contrast to other process automation technologies, RPA only accesses the presentation layer of IT systems and imitates human behavior. Due to the novelty of this approach and the varying approaches when implementing the technology, up to 50% of RPA projects fail. To tackle this issue, we use a design science research approach to develop a framework for the initiation of RPA projects. We analyzed a total of 23 case studies of RPA implementation projects to derive a preliminary sequential model. We then used expert interviews to validate and refine the model. The result is a consolidated framework with variable stages, that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments. We conclude the paper with a discussion and an outlook on research opportunities on adapting and scaling RPA technology in projects.