Reviewerly/exHarmony huggingface dataset:
Logic Rule Guided Attribution with Dynamic Ablation

With the increasing demands for understanding the internal behaviors of deep networks, Explainable AI (XAI) has been made remarkable progress in interpreting the model's decision. A family of attribution techniques has been proposed, highlighting whether the input pixels are responsible for the model's prediction. However, the existing attribution methods suffer from the lack of rule guidance and require further human interpretations. In this paper, we construct the 'if-then' logic rules that are sufficiently precise locally. Moreover, a novel rule-guided method, dynamic ablation (DA), is proposed to find a minimal bound sufficient in an input image to justify the network's prediction and aggregate iteratively to reach a complete attribution. Both qualitative and quantitative experiments are conducted to evaluate the proposed DA. We demonstrate the advantages of our method in providing clear and explicit explanations that are also easy for human experts to understand. Besides, through the attribution on a series of trained networks with different architectures, we show that more complex networks require less information to make a specific prediction.
An Online Integrated Development Environment (IDE) by using Lightweight Framework Render Studio

The task plans to work with an internet based IDE utilizing web advancements. We had numerous internet based IDE's for other programming dialects like C, Python, and so on. Thus, we intended to plan an internet based IDE for Web advancements and named it as "Render Studio". It is predominantly for amateurs. The Render Studio gives you a spotless connection point to the execution of the HTML code, CSS code, and JavaScript code all the while, which gives you a moment result and helps in decreasing the existence intricacy. IDE is the Integrated Development Environment which gives the climate to execute a specific program of any language. One point of the IDE is to lessen the setup important to sort out numerous improvement utilities. All things considered, it gives similar arrangement of abilities as one strong unit. Decreasing arrangement time can increment engineer efficiency, particularly in situations where figuring out how to utilize the IDE is quicker than physically coordinating and realizing the singular apparatuses in general. More tight joining of all advancement errands can possibly work on by and large efficiency past assisting with arrangement assignments. On the web or electronic IDE is a web-based compiler which permits you to aggregate source code and execute it online in more than one language. The product will order the program and return the result to the client.
Preface

The 5 th International Conference on Mechanical, Automotive and Aerospace Engineering (ICMAAE) was initially scheduled to take place as a face-to-face conference in 11-13 August 2020 in Kuala Lumpur, Malaysia. However, due to the covid19 pandemic, the conference was finally conducted virtually a year later in 22 – 23 June 2021. Nevertheless, the conference has attracted fellow researchers, academicians, and industry professionals from 13 countries, including Bahrain, Bangladesh, Canada, India, Indonesia, Malaysia, Morocco, Nigeria, Pakistan, Saudi Arabia, Tunisia, UAE, UK and USA, to share their experiences and ideas while renewing old friendships and making new acquaintances. The topics of the conference cover a wide range of fields in mechanical, automotive, and aerospace engineering. The conference received 134 papers where 96 papers were accepted for presentation. Among these papers, 25 papers are selected for publication in IOP Conference Series: Materials Science and Engineering. The other papers are submitted for publication in four indexed journals including CFD Letters, FME Transactions, IIUM Engineering Journal and Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. The success of ICMAAE 2021 depends completely on the effort, talent, and energy of researchers in the field of Mechanical, Automotive and Aerospace engineering who have submitted papers on a variety of topics. We are indeed glad at the favourable response received from the scientific community around the world. List of ICMAAE 2021 Conference Organizing Committee is available in this pdf.
Task and Sales Manager Web Application

The purpose of the Web Application and Sales Manager Web application is to automate the existing hands-on system with the help of fully computerized software and software, to meet their needs, so that their important data / information is stored longer and easily accessible. and similar fraud. The required software and hardware are easily accessible and easy to operate. This application can lead to a flawless, secure, reliable, and fast management system. It can help the user to focus on other tasks instead of focusing on keeping records. Thus, it will help to plan for better utilization of resources. An organization can store computer records without unnecessary access. That means you don't have to be distracted by useless information, while being able to access information. Basically, the project outlines how to manage efficiency and better customer service.
Immersive Experience in Virtual Reality Horror Game

In this study, we proposed an analysis framework that examines participants' immersive experience processes from three dimensions: (1) the state of enjoyment, (2) transformation of the awareness of time, and (3) a sense of spatial integration. We found that the balance of skills and challenges is threshold factor affecting participants’ overall enjoyment state and experience of transforming the time awareness during a VR gaming process. When considering the influence of social interaction on the three dimension of enjoyment, time and space, a new emerged element of emotion connection suggests that an autotelic experience – the feel of intrinsically rewarding during the process of interacting with other partners is a threshold factor affecting participants’ overall enjoyment. Further, that the mechanism by which fear is transformed into enjoyment corresponds to the element of a sense of control in the state of flow.
Assessment of the share of women in the scientific and pedagogical staff: data from modern countries

Introduction. Currently, higher education is important in the training of highly qualified specialists, the creation of new knowledge, and the introduction of innovations. Objectives. The purpose of our study was to evaluate the indicators characterizing the gender structure of scientific and pedagogical personnel in higher educational institutions of various countries. These indicators were the share of female teachers in the total number of teachers of higher educational institutions. Methodology. We used data from the UNESCO Institute for Statistics for 107 countries. We used the density functions of the normal distribution as models. Results. The results of the computational experiment showed that the share of female teaching staff in the number of scientific and pedagogical staff on average in the countries under consideration was 41.6%. Women make up more than half of the teaching staff in twenty-four countries. The predominance of men as teachers of higher education institutions was in seventy-eight countries. The countries with the maximum and minimum values of such indicators as the share of female teachers in the scientific and pedagogical staff were identified. A significant difference was proved for groups of countries with maximum and minimum values of indicators. Contribution. The proposed methodological approach and the results obtained have a scientific novelty, since the assessment of the territorial features of the gender structure of teachers of higher educational institutions has not previously been paid attention in scientific research.
Using Temporal Data Mining on Patient Data for Clinical Decision Making in the Care of the Sick Newborn.

In a neonatal intensive care unit, streaming healthcare data comes from many sources, but humans are unable to understand relationships between data variables. Data mining and analysis are just beginning to get utilized in critical care. We present a case study using electronic medical record data in the neonatal intensive care unit and explore possible avenues of advancement using temporal data analytics.Electronic medical record data were collected for physiological monitor data. Heart rate, respiratory rate, oxygen saturation and temperature data were retrospectively analyzed by temporal data mining. Three premature babies were selected and data de-identified. The first case of a urinary tract infection showed nursing ability to synthesize data streams coming from a patient. For the second case of necrotizing enterocolitis, Temporal-Data-Mining analysis of combinations of clinical events based on deviations from the mean showed specific heuristic biomarkers related to events before discovery of necrotizing enterocolitis. Specific sequences 6-event and 5-event in length were identified with nursing unease at clinical deterioration, which were 100- and 87-times unlikely to occur randomly with 99.5% confidence. No such sequences were found in the rest of the 37 days for the second case and entire 133 days of stay in the third case of an uneventful premature baby.Temporal data mining is a possible clinical tool in providing useful information in the neonatal intensive care unit for diagnosis of adverse clinical occurrences such as necrotizing enterocolitis. There is the possibility of changing the clinical paradigm of episodic watchfulness to constant vigilance using real-time data gathering.
When Secure Data Sharing Meets Blockchain: Overview, Challenges and Future Prospects

Promoting data sharing to recreate greater value has been an important development trend in recent years. However, the drawbacks of centralized storage, data leakage risk, and the lack of trust mechanisms among entities exposed by traditional data sharing models have seriously reduced users' willingness to share data and hindered the maximization of data value. Blockchain technology, which has emerged and gradually popularized, provides new ideas for secure data sharing by virtue of its decentralization, traceability, trustlessness and programmability, but a series of problems such as compatibility, performance, security and privacy have also brought serious challenges to it. According to the research results in recent years, we conduct an overview of the layered technology architecture of blockchain-based secure data sharing model by particularly emphasizing on the distributed storage technology, data sharing mechanisms and associated cryptography algorithms. And we also expound the challenges faced by the current research as well as the prospect of future research directions.
Urban Renewal Design Based on Integration of Database Information under Data Mining

It is necessary to integrate the data reflecting the spatial organization and residents’ behavior characteristics in design of urban space renewal. Accordingly, based on the current principles of urban space design, a design platform for urban space renewal under the background of big data is proposed in this paper. Through the analysis of platform requirements and the design of organizational structure, the information integration of urban spatial database is mainly studied, where the database features are extracted through data mining, and the information entropy detection algorithm is used to implement data clustering, so as to rationally allocate urban spatial information and schedule resources and provide new ideas for urban design.
DIC Using Low Speed Cameras on a Scaled Wind Turbine Blade

During operation, wind turbine blades are subjected to varying loads, making them vulnerable, reducing performance, and increasing mechanical failures. Furthermore, blade maintenance is both time-consuming and costly. Optical methods, such as DIC, are attractive to measure deformations on this kind of structures because they do not require electrical wiring, they avoid mass loading effects, and they can be easily configured to measure large test articles. In this paper, a novel optical technique based on DIC is described for performing full-field measurements by using contact-less techniques. In this paper, the possibility of using low speed cameras DIC to perform modal analysis and to validate and update finite element models is investigated, testing a scaled wind turbine blade in different conditions. The results obtained with DIC are compared with the ones obtained with accelerometers and correlated with the numerical solutions.
Big Data with IoT to Develop Sustainability

Things- or internet-oriented perspective, the Internet of Things (IoT) is a collection of unrelated networks that are linked and addressed by a common communications protocol [1]. When using sensors that send complex data at fast speeds via the industrial Internet, it might be challenging to give accurate and practical insights. Since the machine's underlying dynamic patterns vary over time due to a variety of variables, including degradation, processing large amounts of data is a significant problem. The actionable model must now be updated because it has become dated as a result. In the article, a brand-new deep learning technique called Gaussian-dependent dynamic probabilistic clustering (GDPC) is proposed. Models for usage in complicated situations that are built on the fusion and optimization of three well-known techniques. The Page-Hinkley test with the Chernoff constraint had been used to detect concept drifts, and the expectation-maximization (EM) approach had been used to estimate parameter values. In contrast to previous irregular models, GDPC's model gives membership probabilities to clusters. When a concept drift can be identified using a Brier score analysis, this can discover the robustness and evolution of the instance assignment. Additionally, the technique uses a tiny quantity of data, which significantly minimises the amount of processing power needed to determine whether the model should be changed. The method may be evaluated on artificial data and data streams from a test bed where different operational circumstances are automatically recognised with satisfactory results in terms of classification accuracy, sensitivity, and characteristics [2]. The typical behaviour of private autos is derived from trajectory data.
Comparison of the Most Important Models of Investments in Cyber and Information Security

Purpose of the article: Cyber security has become a key factor in determining the success or failure of companies that rely on information systems. However, this entails considerable investment. Typical investments in information technology aim to create value, while investments in cyber security aim to minimise losses caused by cyber-attacks. In the case of investment in cyber and information security, therefore, we must evaluate the amount of loss that will never actually occur. This is a complicated problem, and several approaches have been proposed over the years to estimate the cost-benefit balance of security investments.Methodology/methods: This paper is based on previous research (Podešva et al., 2021), where two most used methods of the ROI / ROSI (Bojanc, 2008) and Gordon-Loe model (Gordon, Loeb, 2002) were identified in the field of investments in cyber and information security. Both methods are described and the advantages and limitations for further research are identified.Scientific aim: The main goal is to select the most suitable method for further research in the field of investment in cyber and information security.Findings: ROI / ROSI does not seem suitable for further research because it only tells us what percentage of return on in-vestment will be provided during a given period. The separate use of this method (ROI / ROSI) provides us with very limited results and it is necessary to combine it with other methods. On the other hand, the Gordon-Loeb model is much more complex despite several limitations, especially for coefficients ʎ and t. Further research will therefore focus on the constant t (probability of attack on a given information set) and its value will be modelled based on the SIR epidemic model on network with standard incidents (Podešva, Koch 2019).Conclusions: At present, there is no standardised approach to decision-making and the size of investments in cyber and in-formation security. This is a very complex issue, and it is very difficult to find one universal model. Nevertheless, there are several models that help in this decision-making process, and as the most appropriate method for further research is Gordon Loe model
Analysis of research trends in work family balance policy using text mining

The purpose of this study is to confirm the achievements of existing studies by analyzing the research trends of the past 20 years of Work Family Balance Policy(WFBP), and to suggest future research directions. For this purpose, 157 papers in 22 journals with an impact factor of 1.0 or higher for the last 5 years as of 2020 were analyzed using text mining techniques in the journals registered in the field of social welfare of the National Research Foundation of Korea. According to Results First, an average of 8 papers have been published every year since 2001, mainly ‘family’, ‘women’, ‘policy’, ‘family-friendly’, ‘family-friendly policy’, ‘childcare’, ‘leave of absence’, ‘work family balance’, ‘work family conflict’, and ‘low fertility’. Second, as a result of semantic network analysis, ‘work family balance’ was highly related to ‘gender role’, ‘family policy’, ‘low fertility’, ‘support policy’, ‘family-friendly policy’, and ‘parental leave’. In addition, ‘father’ was found to be highly related to ‘family-friendly’, ‘work family conflict’, ‘family-friendly policy’, and ‘low fertility’ was highly related to ‘childcare policy’ and ‘education for children’. Finally, as a result of the topic modeling analysis, three topics could be identified, and topic 1 was ‘WFBP and gender’, topic 2 was ‘family-friendly policy’, and topic 3 was ‘childcare policy and welfare state ideology’. Based on the results of this analysis, the research trends of WFBP are summarized as follows. First, there is an effort to have a balanced perspective on gender effects in the study of WFBP. Second, WFBP research is deeply related to the low fertility situation, and it is intended to lay the foundation for fertility-related policies and childcare and childcare policies.&#x0D;
Software Defect Estimation Using Machine Learning

Abstract: In recent years, software has become increasingly vital in our daily lives. We come across it in various forms like Alexa to automatic mops. Building a software is a huge task, it involves many stages. The developer is required to build or design a software within the given deadline and with the allotted budget. During which few erroneous decisions might be taken which lead to poor logic, improper design etc. That would make the user dissatisfied. In order to let the device achieve the users desired outcome we must build a flawless software. To build one as that, one must predict if any defects are present. As a result, we employ seven machine learning algorithms to assist us in predicting any software flaws
Analyzing Group and Individual Contributions within Group Programming

In this work, we focus on developing resources for group programming and software engineering education which are important topics within Computer Science curriculums. Our goal is to help educators to be able to understand and assess their students' software projects. To achieve this goal, my advisor and I have developed a web-based application that processes student code repositories and visualizes the resulting data. In our analysis, we use coding metrics such as the number of commits made and number of lines coded (this includes insertions and deletions) along with commit frequency. From this analysis, we are able to curate specific information to help the educator answer questions about fairness, timeliness, consistency, and overall contribution from each student. In the future, we hope to make our application available for all educators.
Korean Part-Of-Speech Tagging by using Head-Tail Tokenization

Korean part-of-speech taggers decompose a compound morpheme into unit morphemes and attach part-of-speech tags. So, here is a disadvantage that part-of-speech for morphemes are over-classified in detail and complex word types are generated depending on the purpose of the taggers. When using the part-of-speech tagger for keyword extraction in deep learning based language processing, it is not required to decompose compound particles and verb-endings. In this study, the part-of-speech tagging problem is simplified by using a Head-Tail tokenization technique that divides only two types of tokens, a lexical morpheme part and a grammatical morpheme part that the problem of excessively decomposed morpheme was solved. Part-of-speech tagging was attempted with a statistical technique and a deep learning model on the Head-Tail tokenized corpus, and the accuracy of each model was evaluated. Part-of-speech tagging was implemented by TnT tagger, a statistical-based part-of-speech tagger, and Bi-LSTM tagger, a deep learning-based part-of-speech tagger. TnT tagger and Bi-LSTM tagger were trained on the Head-Tail tokenized corpus to measure the part-of-speech tagging accuracy. As a result, it showed that the Bi-LSTM tagger performs part-of-speech tagging with a high accuracy of 99.52% compared to 97.00% for the TnT tagger.
Extending Tapis Workflow Management Framework with Elastic Google Cloud Distributed System using CloudyCluster by Omnibond

The goal of a robust cyberinfrastructure (CI) ecosystem is to catalyse discovery and innovation. Tapis does this through offering a sustainable production-quality set of API services to support modern science and engineering research, which increasingly span geographically distributed data centers, instruments, experimental facilities, and a network of national and regional CI. Leveraging frameworks, such as Tapis, enables researchers to accomplish computational and data-intensive research in a secure, scalable, and reproducible way and allows them to focus on their research instead of the technology needed to accomplish it.
AI predicts crime a week in advance

The location of crimes could be accurately predicted, but bias concerns persist
Artificial Intelligence to Classify and Detect Masquerading Users on HPC Systems from Shell Histories

Modern high-performance computing (HPC) systems are typically accessed through interactive Linux shell sessions. Comprised of login and compute nodes, the system is accessed by researchers who will first access a login node and be provided a bash shell session. By default, bash shell histories are recorded up to a certain number of commands. Since 2013, Arizona State University HPC clusters have enabled a login-sourced shell utility to enable session histories to be recorded to a hidden user home directory. These HPC shell histories are typically utilized to help diagnose researcher issues as they arise and have proved to be invaluable in that respect. However, these histories may have additional value by being able to provide characterizations of how researchers engage with HPC systems, or perhaps be leveraged to foster collaboration or improve the HPC research cycle. This study documents a novel analysis of these prospective datasets by training two different machine learning methods on typical shell behavior as to detect a masquerading user.