Reviewerly/exHarmony huggingface dataset:
Accurate and Language Agnostic Code Clone Detection by Measuring Edit Distance of ANTLR Parse Tree

In spite of significant research done in the past 3 decades introducing more than 250 clone detection tools/ techniques for finding the same language clones, there exists no single framework to detect and classify all 4 basic types of clones with great accuracy (precision and recall). In this paper, we propose an accurate and language agnostic technique to classify 4 types of clones. The method first generates an ANTLR parse tree for the input program file using freely available ANTLR grammar files then finds the edit distance between the two parse trees using the Levenshtein distance algorithm and converts the edit distance into similarity using. We obtained 100% precision and recall in detecting type 1 &amp; 2 clone types and achieve 98.50 and 98.12 respectively for type 3 and 4 clone types for our datasets containing microprograms of C, CPP, and Java. This paper provides evidence that the Levenshtein distance on ANTLR parse tree is the good choice to build a complete and accurate software clone detector and act as proper validation tools to detect code plagiarism.
On the Evolutionary Synthesis of Fault-Resilient Digital Circuits

In the event of an upset, fault-resilient circuits maintain correct functionality allowing the system to remain fully operational or at least operate with a graceful degradation. Every circuit has a certain level of inherent resilience to faults. Often times, this inherent resilience to faults is insufficient for the given application. This is because conventional synthesis tools generally only focus on optimizing a circuit with respect to area, power, or timing budgets. There is a wide range of applications where faulty circuit behavior can lead to fatal results. Fault injection analyses are reported and show that even a single fault can be critical to the desired circuit operation. To which end, this article presents synthesis of fault-resilient (SYFR) circuits, an evolutionary method for automated synthesis of increased fault-resilience digital circuits suitable for fine-grained use. Test results for synthesis of up to 60 input circuits with SYFR are reported. SYFR can be repeatedly applied to a circuit to obtain various design tradeoffs between fault resilience and implementation costs. SYFR can also be flexibly applied to build circuits, which are selectively fault resilient, i.e., their tolerance to faults is workload aware. In addition, a novel population seeding mechanism to reduce the design space is introduced and experimentally validated. In summary, this article demonstrates that SYFR can be considered a competitive synthesis methodology for constructing fault-resilient circuits.
A Constructive Deep Learning Applied Agent Mining for Supported Categorization Model to Forecast the Hypokinetic Rigid Syndrome (HRS) Illness

The sufferer with Hypokinetic Rigid Syndrome (HRS) illness runs into enormous neurological mess. The former forecast and improving the categorization precision are the complete solution to increase the sufferer existence. To forecast the illness in preliminary phase is painstaking and very difficult. Consequently, the efficient and suitable forecast of the illness is considerably confronting the specialists still. Sorting these concerns and to improve the forecast accurateness, the following work foresee the deep learning applied agent mining move toward the forecaster classification. Paralleling the performance measure in the important regular factors like exactness, determination, exactitude and fault pace are outlined. The Kaggle provides the required figure deposit for the implementation. And finally a most favorable precision is achieved, while operating the categorization model in MATLAB environment. The projected model provides an improved settlement with the contrast of established methods.
A Discrepant Information Selection Mechanism for Cooperative Sensors

Aiming at the discrepancy of information sources caused by different fields of view of cooperative sensors, this paper proposes a fusion rule based on a new information-theoretic cost function. Specifically, the optimal function is proposed based on the principle of Minimum Discrimination Information (MDI), where the fusion weights are the functions of the label set. The solution of the optimal function exhibits the information selection mechanism, i.e., the GLMB components of the global multitarget density are selected among the local GLMB components based on their weights. Besides, to evaluate the performance of the proposed method, we also propose the overlapping indicator which assesses the similarity among multiple FoVs of cooperative sensors. The simulation results demonstrate that the proposed method achieves robust performance under different values of the overlapping indicator.
Information Security Policy Compliance: An Exploration of User Behaviour and Organizational Factors

Organizations are adopting security policies to protect critical information, despite the fact that IT users frequently violate these standards. Numerous factors affecting user compliance have been identified in previous research, but few research findings have addressed human and organizational factors. The purpose of this study is to ascertain the human and organizational factors that influence user adherence to policies. The variables examined included leadership, organizational commitment, rewards, awareness, behavioral intentions, and habits, all of which were gender-related. This is a quantitative study that includes offline and online surveys of university users in Indonesia. The findings indicated that the awareness variable had the greatest effect, while the reward variable had no effect. This research is expected to contribute to an understanding of how to comply with information security policies.
An Automated Approach to Web Offensive Security

Web Application Penetration testing is a popular approach that aims at discovering vulnerabilities by emulating real attacks. Experts often use a variety of publicly available attack tools, define attack methodologies and orchestrate them throughout the separate phases of testing. In doing so, they leverage personal experience and intuition, making any automation effort very challenging. In this paper, we propose the design and implementation of a framework for Web Penetration Testing that allows for the integration, as well as orchestration, of several types of attacks. We identify the generic tasks performed during a penetration test.Then, we provide a way to integrate attacks that implement such tasks in a component responsible for executing them. A further component holds the logic that decides which task to execute and aggregates the results of completed tasks. We also define the communication protocol between the two components to enable the orchestration of tasks across all phases of a testing campaign. As a concrete example of the application of the proposed framework, we show how it is possible to integrate several types of attacks, as well as embed an ad hoc defined behavioral model in order to discover cross-site scripting vulnerabilities.
A SWOT Analysis of Software Requirement Validation Techniques

Existing software requirement validation (SRV) techniques are theoretical concepts with no real-life application. No report on what could be considered as the best of the SRV techniques. Our study focus on systematic literature review of existing SRV techniques, emphasizing on their strengths, weaknesses, opportunities and threats (SWOT) as well as the involvement of end-users in SRV process. We opined to identify SRV technique(s) that could be considered best and user-centric. Four hundred and twenty-four articles were identified initially but after applying the exclusion criteria, 59 articles were identified for review. We defined one generic research question (GRQ) and five specific research questions (SRQi, i = 1,2,3,4,5) to guide our review and provide the required details for SWOT analysis of SRV techniques. A software is yet to be developed for users’ requirements validation both on a small and larg scale and none of the SRV techniques incorporate user story framework.
Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/AI Converged Cluster

As the complexity of deep learning (DL) networks and training data grows enormously, methods that scale with computation are becoming the future of artificial intelligence (AI) development.In this regard, the interplay between machine learning (ML) and high-performance computing (HPC) is an innovative paradigm to speed up the efficiency of AI research and development.However, building and operating an HPC/AI converged system require broad knowledge to leverage the latest computing, networking, and storage technologies.Moreover, an HPC-based AI computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources.In this regard, we introduce a technique for building and operating a high-performance AI-computing environment with the latest technologies.Specifically, an HPC/AI converged system is configured inside Gwangju Institute of Science and Technology (GIST), called GIST AI-X computing cluster, which is built by leveraging the latest Nvidia DGX servers, high-performance storage and networking devices, and various open source tools.Therefore, it can be a good reference for building a small or middlesized HPC/AI converged system for research and educational institutes.In addition, we propose a resource allocation method for DL jobs to efficiently utilize the computing resources with multi-agent deep reinforcement learning (mDRL).Through extensive simulations and experiments, we validate that the proposed mDRL algorithm can help the HPC/AI converged cluster to achieve both system utilization and power consumption improvement.By deploying the proposed resource allocation method to the system, total job completion time is reduced by around 20% and inefficient power consumption is reduced by around 40%.
Identifying the right Wavelet and Solving the Computational Problems Based on Electromagnetic Wave Equations

Fast and accurate response to computational problems is the need of the industry. Hence this research study aims at finding the correct wavelet for quick and correct solutions to computational problems with electromagnetic (EM) wave equations. Three separate computational problems were addressed utilizing the suitable wavelet technique after the proper wavelet has been provided. Error analysis has also been discussed in this paper.
Damage Identification of Selected Car Parts Using Image Classification and Deep Learning

This study presents the use of image classification and deep learning in the field of insurance claims and management for the identification and assessment of damaged vehicle parts. Vehicular insurance claims on require appraisers to decide the damage of the vehicles. A two-level machine learning-based system was developed to classify different car parts (front bumper, rear bumper, and car wheels), and to detect the presence of any damages. The image dataset used in the study was obtained from a Google image. This dataset is used for training and validation of the convolutional neural network (CNN) model. The first model yields a training accuracy of 94.84% and validation accuracy of 81.25% for car parts classification. The second model yields a training accuracy of 97.16% and validation accuracy of 49.28% for damage identification.
Text Matching application for integration between industry and education

A new talent training mode of collaboration between industry and education was proposed, which aims to reduce the gaps of talent definition between enterprise demand and college education. We formulates scenario as a text matching task of similarity between courses details and job position information, developed a ERNIE+DPCNN based model for the task. The model has been benchmarked on LCQMC dataset, the results show that it achieves the art-of state performance of Acc 0.8809, Recall 0.8745, and F1 score 0.8739, We conducted many experiments about the text similarity calculation between domains of industry and educations. The experiment results show our approach has much potential to optimize the curriculum system while applying in this field.
Evaluating CMMN execution capabilities: An empirical assessment based on a Smart Farming case study

When integrating technology in every-day activities, new challenges arise. IoT systems have made their way in everyday life, resulting in smart environments enabling humans to make decisions in a more knowledgeable fashion. As smart systems become more complex, the process of using them becomes knowledge-intensive. This type of processes heavily depend on knowledge and experience of humans, that may work in a highly automated environment. In 2016, Case Management Model and Notation (CMMN) was introduced as a standard for modeling and automating human-centric processes. However, existing CMMN execution platforms have not met the full potential of the standard yet. In the paper, we aim to evaluate CMMN execution capabilities based on the experience obtained using two popular, advanced CMMN execution platforms. The evaluation is performed in the context of a smart farming case study based on twenty-five requirements imposed by knowledge-intensive processes, already identified in the literature.
Participative Cognitive Mapping as a Multidisciplinary Approach for Managing Complex Systems

The fundamental methodological challenge of dealing with a complex system is the need for multidisciplinarity. The paper develops the approach to combine epistemologically different methods of cognitive maps, influence diagrams, fuzzy logic, Bayesian networks, using the capabilities of human-machine interaction and artificial intelligence in the process of strategic management of complexity. The study is designed to update the participative cognitive mapping to account for system dynamics that include both the human and machine procedures. For this purpose, the paper discusses the opportunities to integrate artificial intelligence and closely related big data analysis in the participative cognitive mapping algorithm. The proposed procedural framework based on the participative cognitive mapping with human-machine interaction for strategic decision-making in complex organizational systems extends capabilities of the strategic management procedure as a foundation for the development of programs and projects of change. It combines different methodologies such as hybrid and fuzzy cognitive maps, Bayesian networks, influence diagrams, it integrates procedures implemented by humans and computers in a sequential manner. For scientists, this multidisciplinary framework provides the way for doing active research through participation in management procedures, for practice managers to apply cognitive mapping for the strategy development and implementation.
An Adaptive System for a Real-Time Matching Application

In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and manage which function with different locations and contents is most valuable to the user's experience without using computationally time-consuming optimization calculations. To address this, this paper is focusing on self-adaptive software technology. The authors built a software adaptation mechanism that can be immediately calculated online using a performance characteristic map and threshold judgment with a learning function and sequential updates. The results confirmed the effectiveness of the mechanism in an application that supports personnel exchange events.
CHATBOT EFFECTIVENESS AND USER EXPERIENCE

Artificial intelligence and its use is an integral part of modern business development, that makes it possible for companies to operate successfully in a competitive environment. This study identifies factors that influence consumer satisfaction with the use of chat robots (chatbots) in order to enable companies to improve their use and become more competitive. The study uses both secondary data analysis and expert interviews, chat bot user mapping and a survey. Within the framework of the research, the situation in the field of using chatbots in Latvia and in the world is described. The study finds that personalized chatbots are more efficient than neutral chatbots and provide a higher level of consumer satisfaction. In order to achieve the goal of the study, three tasks were set:1) to analyze the theoretical basis of chat bot implementation; 2) to describe the use of chat bots and the contribution to the company; 3)to explore user satisfaction with chatbots. A monographic or descriptive method was used to analyse the theoretical aspects of chatbots and their use, secondary data analysis and expert interviews were used to describe the situation, but in order to study consumer attitudes using chatbots, user sorting and surveys were conducted.
A 2021 Review on Recent Advancements in Robotic-assisted Minimally Invasive Surgery: The Philippines Perspective

This paper provides an overview of the recent advancements in robot-assisted minimally invasive surgery (RMIS) from the perspective of the Philippines. The rapid development of AI technology and assistive robots along with its integration into the healthcare industry will be discussed. A brief introduction of the evolution of conventional surgery into minimally invasive surgery and (RMIS) will be tackled. This is then followed by the robotic surgery standards. A number of existing robotic surgical platforms are then explored along with their background, features, and FDA approval status. Systems that will be focused on are the Senhance Surgical Robotic System, Symani Surgical System, and the Hugo RAS System. Current trends in surgical systems would also be tackled to highlight features and techniques that seem to work the best with every advancement in robotic surgery. Trends such as soft robotics, sensors, computer vision and augmented reality, autonomy, training and techniques will be focused on. The paper concludes by discussing the more evident integration of RMIS in surgery operations. In addition, trends addressing the shortcomings of RMIS to provide an overall greater experience is mentioned. Lastly, more studies are being made to further improve and provide ease of control on the side of the surgeon during operations.
How does Knowledge Spur the Productivity of Private Egyptian Firms? An Instrumental Variable Approach

This study dissects firm knowledge into two main types: Internal firm Knowledge (Knowledge workers) and External firm Knowledge (Relational Knowledge with other firms). The major goal is to investigate how each type affects the productivity of the firms. This article also examines how this effect differs among Egyptian firms in the agriculture, manufacturing, and service sectors. The authors use firm-level data in Egypt on the sectoral level. The properties of instrumental variables regression using two-stage least-squares (2SLS) estimation are adopted to overcome endogeneity and omitted variable bias in the empirical estimations. The study's findings reveal that (a) the effects of Internal and External knowledge on the firm productivity are sector-specific. (b) Knowledge-workers and relational knowledge are two times more effective for agriculture than manufacturing and service firms. (c) External knowledge plays a vital role in increasing productivity relative to internal knowledge for the manufacturing sector. (d) Finally, internal and external knowledge has the same effect on the service firms.
Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.
Assessing Activity of Daily Living through Technology-Enabled Tools: Mobility and Nutrition Assessment: MiranaBot: A Nutrition Assessment Use Case

Abstract Activities of Daily Living (ADLs) refers to the fundamental skills required to care for one-self and live independently and includes dressing, feeding, personal hygiene, continence and transferring. Assessing ADLs is therefore essential, especially for vulnerable population who may need assistance in performing these activities. As current validated scales to measure ADLs capacity are often dependent of an informant or a caregiver and are mainly performed in the controlled settings of the hospital, using technology-enabled tools could benefit individual’s health in terms of disease prevention and treatment but would also enhance individual’s quality of life and independence. This chapter presents 4 standard validated scales for ADLs and the current research activities on the use of technologies to assess one’s ability to perform ADLs, mainly indoor-outdoor mobility and nutrition. A nutrition assessment use case through a conversational agent is presented in the second part of the chapter. Future opportunities for technology-enabled ADL assessment are discussed.
Analyzing the Preferences and Personal Needs of Teenage Readers to Make Book Recommendations

Reading is one of the main sources of learning, especially for young readers such as teens. Promoting good reading habits among teens is essential, given the enormous influence of reading on teenagers’ development as learners and members of society. For this reason, it is imperative to motivate young readers to read by offering them appealing books to read so that they can enjoy reading and gradually establish a reading habit during their formative years that can aid in enhancing their learning attitude. Books, which provide an indispensable source of reading materials, broaden the horizons of teenagers and allow them to learn from different disciplines, expand their perspective in decision making, and gain knowledge. These days there are a wide variety of books available on the market for teenagers, parents, and librarians to choose from. Due to the diversity of books, choosing a desirable book from a set of unfamiliar books to read is time-consuming and there is no guarantee of the satisfaction of its content. Existing book recommender systems, however, either focus on general audience or very young readers, such as children, and thus might not meet the specific needs of the particular group of users whom we target, i.e., teenagers. To make appropriate recommendations on books that are appealing to teenagers, we propose a book recommender system, called TBRec. TBRec recommends books to teenagers based on their personal preferences and needs that are determined by using various book features. These features, which include book genres, topic relevance, predicted user ratings, and readability levels, have significant impact on the readers’ preference and satisfaction on a book. These distinguished parts of a book identify the type, subject area, (un)likeness, and complexity of the book content. Experimental results reveal that TBRec outperforms Amazon, Barnes & Noble, and LibraryThing in making book recommendations for teenagers, and the results are statistically significant.