Reviewerly/exHarmony huggingface dataset:
Critical Success Factor of Agile Methodology in Software Industry of Nepal

In the modern world, software plays an integral role, following an effective methodology while developing these software have become crucial to accomplish the project goal. Agile approach is a methodology which have been introduced as an effective approach for developing software to increase productivity and efficacy of software development process compare to traditional methodologies. The rapid deployment of solutions, early and continuous delivery of valuable product to the customers, resource utilization, rapid issue detection, customer focus, effective collaboration and communication, adapting changes and flexibility are the key principles of Agile. Meeting the deliverables of the project without compromising its scope, quality and timeline within a limited budget is a key to success. Use of effective project management methodology and identification of critical success factors to achieve project success is essential to understand for any organization whether it is software or non-software.
An outlier ensemble for unsupervised anomaly detection in honeypots data

Nowadays, computers, as well as smart devices, are connected through communication networks making them more vulnerable to attacks. Honeypots are proposed as deception tools but usually used as part of a proactive defense strategy. Hence, this article demonstrates how honeypots data can be analyzed in an active defense strategy. Furthermore, anomaly detection based on unsupervised machine learning techniques allows to build autonomous systems and to detect unknown anomalies without the need for prior knowledge. However, the unsupervised techniques applied for honeypots data analysis do not value the advantages of these tools’ data, particularly the high probability that they include a large number of previously unseen anomalies with unexpected and diverse patterns. Therefore, in the present work, the aim is to improve the unsupervised anomaly detection in honeypots data by varying the data feature subset and the parameterization of the anomaly detection algorithm. To this purpose, an outlier ensemble with LOF (Local Outlier Factor) as a base algorithm is proposed. The ensemble outperforms existing solutions as depicted in the experiments where a detection rate higher than 92% is achieved.
Teenager Health Oriented Data Security and Privacy Protection Research for Smart Wearable Device

In recent years, the fitness problems of adolescents have attracted increasing attention from all walks of life. It has become an irreversible trend to use new technical means to supervise and guide young students’ behaviour. Smart wearable devices have become a better choice because of its simple usage and convenient wearing. But its lightness also brings the shortcoming of being vulnerable to attack. This research proposes a scheme to protect the privacy of smart wearable users. There are two main aspects: (1) Perturb the data collected by smart wearing devices before sending information to prevent third-party servers from leakage of users’ privacy; and a scheme of data reconstruction is proposed using SVD (Singular Value Decomposition) method to decompose eigenvalues; (2) During data publishing, we proposed a personalized k-anonymity publishing scheme based on entropy weight method, which has brilliant adaptability as well as preventing privacy leakage. Experiments show that the proposed method can effectively improve the safety of smart wearable devices, so that adolescents and parents can use it safely.
How Human Factors Can Help Preserve Democracy in the Age of Pandemics

Objective To describe user-centered voting systems that would support the safe conduct of voting in a pandemic environment. Background The COVID-19 pandemic has complicated our democratic processes. Voters and poll workers feel threatened by the potential dangers of voting in business-as-usual polling stations. Indeed, significant problems were encountered in the recent 2020 primary elections in Wisconsin, where the National Guard had to be mobilized because so few poll workers reported to work, and more than 90% of polling places had to remain closed. Method We describe a number of possible user-centered solutions that would help protect voters and poll workers in times of pandemic, and also report the results of a survey that asked voters and poll workers about what kinds of systems might make them willing to vote. Results Political as well as safety considerations will need to be considered as these safer voting solutions are designed since, surprisingly, the kinds of solutions preferred depend on the political affiliation of the voters. Conclusion Human factors professionals have a large role to play in realizing the safe, successful implementation of these user-centered systems. Good human factors analysis can help minimize the risk to voters and poll workers. Moreover, human factors methods can help safeguard democracy by creating safe and well-engineered environments that are conducive to voting in the age of pandemics. Application Creating safe and effective voting solutions that protect voters and poll workers during pandemic outbreaks is crucial to the preservation of democracy.
Before and after enforcement of GDPR

The European Union's (EU) General Data Protection Regulation (GDPR) was put in force on 25th May 2018. It is not known how many personal data protection requests the national authority in Croatia had received before and after GDPR, and how many of those were related to research.We obtained data from the Croatian Personal Data Protection Agency (CPDPA) about requests/complaints related to personal data protection that were received specifically from academic/research institutions, specifically the number and type of all cases/requests between the years 2015-2019.In 2018, CPDPA had a dramatic increase in the number of requests in the post-GDPR period, compared to the pre-GDPR period of the same year. In 2019, CPDPA received 2718 requests/complaints; less than in the year 2018. From 2015 to 2019, CPDPA received only 37 requests related to research.Very few requests about personal data protection from academic and research institutions in Croatia were submitted to the national Croatian data protection authority. Future studies could explore whether researchers have sufficient awareness and knowledge about personal data protection related to research, to adequately implement the GDPR regulations.
Achieving guidance in applied machine learning through software engineering techniques

Development of machine learning (ML) applications is hard. Producing successful applications requires, among others, being deeply familiar with a variety of complex and quickly evolving application programming interfaces (APIs). It is therefore critical to understand what prevents developers from learning these APIs, using them properly at development time, and understanding what went wrong when it comes to debugging. We look at the (lack of) guidance that currently used development environments and ML APIs provide to developers of ML applications, contrast these with software engineering best practices, and identify gaps in the current state of the art. We show that current ML tools fall short of fulfilling some basic software engineering gold standards and point out ways in which software engineering concepts, tools and techniques need to be extended and adapted to match the special needs of ML application development. Our findings point out ample opportunities for research on ML-specific software engineering.
Utilising E-Learning to Assist Primary School ESL Pupils in Learning to Spell during COVID-19 Pandemic: A Literature Review

Learning to spell is important in English Language learning.However, primary school ESL pupils struggled with it.Various factors influence the pupils' difficulties in spelling.In order to improve their ability in learning to spell, a new approach should be employed, parallel to Information and Communication Technology (ICT) in the 21 st century learning.The unprecedented COVID-19 pandemic created a new norm in the society in which one of the affected sectors is education.Following, e-learning becomes a necessity to transcend knowledge.This paper imparts a literature review on the 1) challenges faced by the primary school ESL pupils in learning to spell and 2) measures to overcome the challenges faced by the primary school ESL pupils in learning to spell via e-learning during COVID-19 pandemic.
A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations.

As local explanations of black box models are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, local explanations generated by existing techniques are often prone to high variance. Further, these techniques are computationally inefficient, require significant hyper-parameter tuning, and provide little insight into the quality of the resulting explanations. We identify lack of uncertainty modeling as a main cause of these challenges and develop a novel set of tools for analyzing explanation uncertainty in a Bayesian framework. In particular, we estimate credible intervals (CIs) that capture the uncertainty associated with each feature importance in local explanations. These credible intervals are tight when we have high confidence in the feature importances of a local explanation. The CIs are also informative both for estimating how many perturbations we need to sample -- sampling can proceed until the CIs are sufficiently narrow -- and where to sample -- sampling in regions with high predictive uncertainty leads to faster convergence. We instantiate this framework to generate Bayesian versions of LIME and KernelSHAP. Experimental evaluation with multiple real world datasets and user studies demonstrate the efficacy of our framework and the resulting explanations.
DEVELOPMENT OF SCIENTIFIC INQUIRY SKILLS OF SCIENCE TEACHING THROUGH ARGUMENT-FOCUSED VIRTUAL LABORATORY APPLICATIONS

This research designed within the scope of a TUBITAK 4005 project aimed to give science teachers training on argumentation-based learning, to provide them with scientific inquiry experience, to ensure that they gain skills to develop argumentation-based experimental learning methods. Throughout the research, 100 science teachers were given a four-day (28 hours in total) training program in virtual labs that would enable them to make scientific inquiries through argumentation-based experimental activities developed in accordance with the science curriculum. The “Opinion Scale for the Virtual Lab” and the “Principles of Scientific Inquiry- Teacher” were applied to the participants as a pre-test and post-test. Following the implementation phase, the “Assessing Argumentation-based Science Learning Activities- Scale” was applied to the participating students and teachers. In addition, semi-structured interviews were conducted with randomly selected 15 teachers and 15 students. When all the obtained data were evaluated qualitatively and quantitatively, a statistically moderate effect was determined on the teachers' post-test scores from the “Opinion Scale for the Virtual Lab” and the “Principles of Scientific Inquiry- Teacher.” Also, the participating students and teachers mostly expressed positive views in the “Assessing Argumentation-based Science Learning Activities- Scale” applied after the implementation phase. Keywords: argumentation-based experimental learning methods, mixed-method, scientific inquiry, semi-structured interviews, virtual laboratory.
The Use of Social Media to Assist Writing Skills among Secondary Pupils

Social media has become the most crucial online platform which functions to help connect people.People use social media for different purposes; meanwhile, pupils use social media widely in their daily routine and learning activities including writing.Hence, this study attempts to seek the use of social media to assist in writing skills among secondary school pupils.Data was collected via a questionnaire survey from 40 students in one secondary school.This study shows that respondents realized the benefit of using social media to assist writing but they did not spend much time on writing.The results of this study are hoped to identify whether social media can be one of the methods to assist in English writing skills.
Ontology-guided Semantic Composition for Zero-shot Learning

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering.
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization

Pre-trained language models such as BERT have exhibited remarkable performances in many tasks in natural language understanding (NLU). The tokens in the models are usually fine-grained in the sense that for languages like English they are words or sub-words and for languages like Chinese they are characters. In English, for example, there are multi-word expressions which form natural lexical units and thus the use of coarse-grained tokenization also appears to be reasonable. In fact, both fine-grained and coarse-grained tokenizations have advantages and disadvantages for learning of pre-trained language models. In this paper, we propose a novel pre-trained language model, referred to as AMBERT (A Multi-grained BERT), on the basis of both fine-grained and coarse-grained tokenizations. For English, AMBERT takes both the sequence of words (fine-grained tokens) and the sequence of phrases (coarse-grained tokens) as input after tokenization, employs one encoder for processing the sequence of words and the other encoder for processing the sequence of the phrases, utilizes shared parameters between the two encoders, and finally creates a sequence of contextualized representations of the words and a sequence of contextualized representations of the phrases. Experiments have been conducted on benchmark datasets for Chinese and English, including CLUE, GLUE, SQuAD and RACE. The results show that AMBERT can outperform BERT in all cases, particularly the improvements are significant for Chinese. We also develop a method to improve the efficiency of AMBERT in inference, which still performs better than BERT with the same computational cost as BERT.
Video transcoding optimization based on input perceptual quality

Today's video transcoding pipelines choose transcoding parameters based on rate-distortion curves, which mainly focus on the relative quality difference between original and transcoded videos. By investigating the recently released YouTube UGC dataset, we found that human subjects were more tolerant to changes in low quality videos than in high quality ones, which suggests that current transcoding frameworks can be further optimized by considering perceptual quality of the input. In this paper, an efficient machine learning metric is proposed to detect low quality inputs, whose bitrate can be further reduced without sacrificing perceptual quality. To evaluate the impact of our method on perceptual quality, we conducted a crowd-sourcing subjective experiment, and provided a methodology to evaluate statistical significance among different treatments. The results show that the proposed quality guided transcoding framework is able to reduce the average bitrate up to 5% with insignificant perceptual quality degradation.
CRUPA: collusion resistant user revocable public auditing of shared data in cloud

Abstract Cloud repository is one of the most important services afforded by Cloud Computing where information is preserved, maintained, archived in distant servers and made available to the users over the Internet. Provided with the cloud repository facilities, customers can organize themselves as a cluster and distribute information with one another. In order to allow public integrity auditing on the information stored in semi-trusted cloud server, customers compute the signatures for every chunk of the shared information. When a malicious client is repudiated from the group, the chunks that were outsourced to the cloud server by this renounced customer need to be verified and re-signed by the customer present in the cluster (i.e., the straightforward approach) which results in huge transmission and reckoning cost for the customer. In order to minimize the burden of customers present in the cluster, in the existing scheme Panda, the semi-trusted Cloud Service Provider (CSP) is allowed to compute the R e − s i g n key. Further, the CSP audits and re-signs the revoked customer chunks by utilizing the R e − s i g n key. So, it is easy for the CSP by colluding with the revoked customer to find the secret keys of the existing customer. We introduce a novel Collusion Resistant User Revocable Public Auditing of Shared Data in Cloud ( CRUPA ) by making use of the concept of regression technique. In order to secure the secret keys of the existing customers from the CSP, we have allowed the information proprietor to compute the R e − s i g n key using the regression technique. Whenever the information proprietor revokes the customer from the cluster, the information proprietor computes the R e − s i g n key using the regression technique and sends to the CSP. Further, the CSP audits and re-signs the revoked customer chunks using the R e − s i g n key. The R e − s i g n key computed by the information proprietor using regression method is highly secure and the malicious CSP cannot find the private information of the customers in the cluster. Besides, our mechanism achieves significant improvement in the computation cost of the R e − s i g n key by information proprietor. Further, the proposed scheme is collusion resistant, supports effective and secure customer repudiation, multi-information proprietor batch auditing and is scalable.
MultiGBS: A multi-layer graph approach to biomedical summarization

Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values.
Minting Mechanism for Proof of Stake Blockchains

As an alternative for the computational waste generated by proof-of-work (PoW) blockchains, proof-of-stake (PoS) systems gained a lot of popularity, being adopted by many existing cryptocurrencies. Unfortunately, as we show, PoS-based currencies, where newly minted coins are assigned to the slot leader, inevitably incentivises coin hoarding, as players maximise their utility by holding their stakes and not trading. As a result, existing PoS-based cryptocurrencies do not mimic the properties of fiat currencies, but are rather regarded as investment vectors. In this work we initiate the study of minting mechanisms in cryptocurrencies as a primitive on its own right, and as a first step to a solution to mitigate coin hoarding in PoS currencies we propose a novel minting mechanism based on waiting-time first-price auctions. Our main technical tool is a protocol to run an auction over any blockchain. Moreover, our protocol is the first to securely implement an auction without requiring a semi-trusted party, i.e., where every miner in the network is a potential bidder. Our approach is generically applicable and we show that it is incentive-compatible with the underlying blockchain, i.e., the best strategy for a player is to behave honestly. Our proof-of-concept implementation shows that our system is efficient and scales to tens of thousands of bidders.
A Framework of Utilizing Big Data of Social Media to Find Out the Habits of Users Using Keyword

Optimal search in search engines is very urgent, especially search performs on social media search engine. Very big data in social media have not been not used as many as expected, making the existed data are only limited to the data themselves. However, using keywords on social media search engines could only produces incomplete and inaccurate data thereby the search results only have limited usage. This paper contains a framework using keywords in social media search engines that aims to gain users' habits by utilizing social media data. The framework offered is in the form of steps to optimize the search engines, so that the optimal social media search engine could be used as an entry point to find the desired data on specific social media user. The step is initialized by analyzing existing posts to get more specific and accurate data from social media users. The social media system then could use those specific keywords to identify the identity of a specific user in the information space that can be accessed by the search engines.
Preserving employee trust during crisis

Employees’ trust in their organization is vital during crises and disruption. It powerfully facilitates employees’ ability to respond constructively to crises and change, and it underpins organizational agility and resilience. Yet it is during such episodes that trust is most threatened. During the COVID-19 pandemic, this conundrum has organizational leaders asking, How can we preserve employee trust in the face of the financial and other challenges posed by the outbreak? In this article, we synthesize and extrapolate from related research on trust to delineate the key practical actions that leaders can take to preserve trust. The research shows that during crises, employee trust can not only be preserved, it can even be enhanced.