Reviewerly/exHarmony huggingface dataset:
Robust twin depth support vector machine based on average depth

As one of the classical machine learning algorithms, twin support vector machine (TWSVM) can construct two nonparallel hyperplanes, which keeps the hyperplane close to points of one class, but far away from the other class. Being different from the classical SVM algorithm, TWSVM can reduce the computational complexity by replacing one large quadratic programming problems (QPPs) with a pair of smaller ones. Although compared to SVM classifier, TWSVM is four times faster, it is still insufficient in resisting noise or outliers. In this paper, we propose a twin depth support vector machine (TDSVM), a binary SVM classifier that considers the influence of depth when calculating the distance. A novel average depth is proposed and applied on TWSVM to construct a robust SVM framework, which can identify outliers in the dataset. By strengthening the center and weakening the edge, a better generalization performance is achieved, and the SRM principle is also implemented. TDSVM can be applied to any place where TWSVM can be applied, which is also useful for reducing the influence of outliers or noise on data and obtaining more robust results. Finally, the advantages of the method are verified theoretically and empirically by experiments. Experimental results on eight UCI datasets and one synthetic dataset demonstrate the effectiveness and robustness of TDSVM. The classification accuracy of TDSVM is better than other compared algorithms on almost every dataset, whether a certain percentage of Gaussian noise is introduced or not.
A Low-power Neural 3D Rendering Processor with Bio-inspired Visual Perception Core and Hybrid DNN Acceleration

This paper presents a low-power neural 3D rendering processor which can support both inference (INF) and training of the deep neural network (DNN). The processor is realized with four key features: 1) bio-inspired visual perception core (VPC), 2) neural engines using hybrid sparsity exploitation, 3) dynamic neural network allocation (DNNA) core with centrifugal-sampling (CS), and 4) hierarchical weight memory (HWM) with input-channel (iCh) pre-fetcher. Thanks to the VPC and the proposed DNN acceleration architecture, it can improve throughput by 4174x and demonstrates> 30 FPS rendering while consuming 133 mW power.
On the effect of uncompensated latencies on trajectory reconstruction for surveillance performance monitoring

In this paper we present the outcomes of the validation of a new error model for ADS-B data that could be used to improve the Opportunity traffic reconstructor (OTR) module of the EUROCONTROL SASS-C suite. The new error model simplifies former models considering just a time bias due to uncompensated latencies, thus neglecting space bias terms representing the drift caused by inertial navigation systems. Moreover, the error model computes the ADS-B error covariance matrix from the reported positions, leading to a more accurate estimation of the measurement noise. Several numerical experiments have been conducted using a test bench implemented in Matlab® to showcase the potential improvement provided by the proposed new error model.
RODAD: Resilience Oriented Decentralized Anomaly Detection for Urban Air Mobility Networks

Urban air mobility (UAM) helps ease traffic congestion and offers cleaner, faster, and safer transportation, especially for densely populated areas. Recent events have shown that modern unmanned aerial vehicles (UAVs) are vulnerable to attacks through buggy or malicious devices, which raise concerns regarding performance, security, and privacy on UAM networks. Existing Air Traffic Service (ATS) providers mainly rely on a centralized system (e.g., Information Display System) for data aggregation, sharing, and security policy enforcement; and it incurs critical issues related to a bottleneck of data analysis, provenance, and consistency in terms of less efficiency with large computational resources, and high false positive with low flexibility. In this paper, we develop a Resilience Oriented Decentralized Anomaly Detection (RODAD) framework to maximize UAM capability to secure data access among aircraft and ATS service providers based on microservices technologies in an edge-fog-cloud computing paradigm. Machine learning based anomaly detection (MLAD) is developed to detect anomaly behaviors (e.g., aircraft route anomaly) against both single-feature and multi-feature spoofing attacks across avionics mission data. Two GPS spoofing attack scenarios (e.g., restricted and generalized) with four attacking types (e.g., continuous, interim, biased, random) are crafted for the performance evaluation. A hardware-in-the-loop (HITL) implementation is also developed to demonstrate the effectiveness of RODAD for supporting real-time resilient analysis. Our experiments validate the performance of RODAD in detection accuracy and efficiency against spoofing attacks for UAM.
Enhancing Dynamic Profiles on Voting Advice Applications Using Social Media Analysis

Voting advice applications are web-based tools designed to guide citizens toward the parties and candidates close to their political preferences. However, they require the generation of candidates' profiles, and they must fill out a questionnaire scientists have designed. Since this procedure is time-consuming and expensive, we propose a data-driven model which can facilitate the construction of the profiles using the recent developments in the Natural Language Processing domain. To this end, we first gather the politicians' Twitter data and YouTube videos. Then we clean the data and translate the tweets and video transcripts. We aim to use a question-answering (QA) model, which takes a question and a context and predicts the answer using the context. Our Twitter and YouTube data is significant. Therefore it is not immediately suitable for QA models. According to the topics of the questions in VAAs, we create shorter context vectors using manually defined keywords. Then we apply QA models to the tweet and YouTube data. Our experiments show that this approach can automatically find correct answers in the data and help human annotators.
Knowledge And Awareness Of The Use Of Artificial Intelligence In Digital Pathology

Artificial intelligence (AI) is the intelligence displayed by machines that mimic human-like functions such as “learning” and "problem-solving". Digital pathology is a sub- field of pathology that involves the management of data based on information generated from digitized specimen slides. The primary goal of this survey was to evaluate the knowledge and awareness of AI in digital pathology. A self-structured questionnaire consisting of 10 close-ended questions was used and circulated among 335 randomly selected dental undergraduates in Chennai via Google Forms. The obtained data were then subjected to statistical analysis. Results indicate that 73.43% of the participants were aware of AI, while 26.57% were not. In terms of AI usage in pathology, 43.88% opted for early diagnosis, 24.78% opted for easy lab work, 27.46% opted for improved analysis, and 3.88% felt that the usage of AI in pathology is not specific. Additionally, 80.30% of the participants felt that AI in digital pathology helps in early diagnosis of breast and prostate cancer, while 19.70% felt that it doesn’t. Based on the statistical results, we can conclude that the participants had an average level of knowledge and awareness regarding AI in digital pathology.
Improved and Automatic Classification of Polyp for Colorectal Cancer

The CRC (Colorectal Cancer) is the lethal disease now a day in all over the world. To detect the polyp and predict about colorectal cancer increased the rate of patient survival. An improved and automated detection system is developed for the detection and classification of polyp. The images based dataset plays a crucial role in the field of medical by applying Deep learning with TL approaches the proposed research implements two models: ShuffelNet and ResNet-50. These models enhanced the accuracy and sort outset the gaps of formal work. The prior work were expensive, time taken, use less size of data and no of classes was also low. There are different parameters applied to achieve the results. Before training models dataset was processed and changed the dimension of all classes in to 224 x 224. Both models were altered according to the problem like the last 3 layers of the models. The accuracy achieved was 98.36 by using above mentioned models respectively. This method indicates the high accuracy which will helpful in the reduction of mortality rate of colorectal cancer.
The Impact of Digital marketing and Digital Procurement on Operations Performance

The use of digital marketing by entrepreneurs to connect with consumers and a wide audience is not new in this technological era. Examples include attracting clients through email, content marketing, search engines, social media, and many more channels. Utilizing digital marketing is a vital approach that businesses may utilize to preserve and survive their enterprise. By utilizing this marketing tactic, business owners can cut operating costs while also indirectly increasing their company’s profit. However, after a deep review regarding digital marketing and digital procurement and their impact on operations performance highlighted the key factors that shows significant impact theoretically and empirically. A quantitative technique used to analyze proposed model. Valid data of 188 employees working in food manufacturing firms located in Dubai UAE, has used to determine the variables. SmartPLS used to evaluate model validity, correlation and hypothesis testing. Moreover, a positive significant relationship revealed among each variable. The food manufacturing companies can get benefit from current research to distinguish the key areas to be focused to keep outstanding operations performance of the organization.
The Impact of Implementing Mind Mapping Technology on Raising the Achievement of Curriculum Processing Unit in Teaching Methodologies &amp; Styles Course

This article aims to pinpoint the impact of implementing the mind mapping technology as one of the active learning models on raising the achievement of the curriculum processing unit in the Teaching Methodologies & Styles Course on classroom teacher department taught to students in the first semester at the faculty of Arts at the Al-Zaytoonah University of Jordan. To achieve the study objectives, the semi-experimental approach is used. The study sample consists of two groups selected by the purposeful method, where the experimental group taught using mind mapping, while the control group taught using the traditional method. The internal validity of the participants of the two groups is determined, and a pre-test is conducted to ensure that the two groups are equal in the study achievement. After implementing the study and ensuring the validity and reliability of the study instrument, the achievement test is conducted at the end of the first semester of the academic year 2021/2022. The findings indicate that there is a statistically significant difference at the level of statistical significance (α = 0.05) between the means of the scores of the students of the experimental and control groups in the post-application in the achievement test in favor of the experimental group. The findings also demonstrate a statistically significant difference at the level of statistical significance (α = 0.05) between the means of the scores of the experimental group students in the pre and post-application in the achievement test in favor of the post-application. In light of these results, the study recommends using mind mapping in university teaching.
The Role of Big Data tools and Supply Chain Capabilities in Promoting Supply Chain Sustainability: Insights using Balanced Scorecard Approach

This research paper examines the role of big data tools and supply chain capabilities in promoting supply chain sustainability, with a particular focus on the mediation impact of the balanced scorecard approach. Using a quantitative Supply chain search design, data is collected from the hardware manufacturing industry in the UAE through a survey. The study finds that supply chain capabilities have a direct impact on the balanced scorecard, which, in turn, has a direct impact on supply chain sustainability. The results also demonstrate that the balanced scorecard mediates the relationship between supply chain sustainability and supply chain capabilities. The study's findings have significant implications for organizations seeking to improve their sustainability performance by enhancing their supply chain capabilities and leveraging big data tools to achieve more effective supply chain management. These results provide a valuable contribution to the literature on sustainable supply chain management, and the study's practical implications have important implications for managers and policymakers alike.
An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide Information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial Information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity.
The Nexus between Digital Innovation and Digital Entrepreneurship in the Strategic Transformation

While the technologies that drive computers, robots, and other smart equipment are rapidly improving, becoming more powerful, and changing businesses faster than in the past, digital technologies and how we use them are changing the face of business. It is now possible to achieve a significant competitive advantage through digital innovation and transformation. Whereas, anything novel and distinctive about organization in the digital age is covered by the category of digital entrepreneurship considered as significant as needed. Keeping these concepts in priority this research was aimed to investigate the node of digital innovation and digital entrepreneurship on strategic transformation. Employed a quantitative approach with convenient random sampling technique. 184 respondent’s data from retail industry UAE were considered for data analysis. SmartPLS 4.0 used to assess the hypothesized model of the research. Reliability, validity, discriminant validity and hypothesis testing were performed to assess the model. The results showed that digital innovation and digital entrepreneurship have a positive, significant influence on strategic transformation.
Shared Leadership Practices on the Role of the Employees’ Effectiveness

Recently, the theme of shared leadership (also termed distributed or collective leadership) has emerged in the literature. This theme offers a shift in perspective on leadership from a traditional view of an individual-level phenomenon focusing on the leader as a person and, to a dynamic, and group-level leadership phenomenon (Pearce, 2004). Under conditions of shared leadership, responsibilities are scattered among individual team members (Carson, Tesluk, and Marrone, (2007). This research paper tries to address the problem of the effect of shared leadership on the effectiveness of employees. The methodology of the present research study is questionnaire-based. The questionnaire has been distributed and collected data from the employees working in the organization. A total of 150 questionnaires were distributed. Out of these 150 questionnaires, 133 participants responded to the questionnaire. The findings indicate one of the great qualities of shared leadership, which is prominent in organizational culture. The paper discusses the limitations and scope of future research
Comparative Evaluation of the Maintainability of RESTful and SOAP-WSDL Web Services

This research aims to recognize between RESTful and SOAP-WSDL web services that are more modifiable and maintainable. A web service that is easily adaptable and maintainable will undoubtedly reduce costs and increase the quality of service. Web services are based on Service Oriented Architecture, which empowers communication between two applications. Web services enable sending any information through encryption over the network. It also allows the integration of two separate applications over the Web.
An Investigation of Operative Data Safety and Authentication Schemes for Benign Cloud Environments

The term "cloud computing" refers to a new computing architecture in which data is stored and processed over the Internet and accessed by anybody or any organisation. That makes it easier to afford keeping it around and less expensive to maintain. In spite of the many advantages cloud users enjoy, the cloud has a number of security issues. When sensitive information is stored in the cloud without encryption, it can be easily accessed by prying eyes. One of the most useful and essential uses of the cloud is for data outsourcing. However, due to the outsourcing company's lack of physical access to the data, the data may not be reliable in all cases. The Remote Data Verification (RDV) method is a helpful approach for ensuring the authenticity and accuracy of data stored on a central or decentralised server. Using a variety of visual cryptographic approaches and Securing Portable Document Format files, the suggested method safeguards data storage and simplifies computation. For the purpose of securing information stored in the cloud, we employ Extended Visual Cryptography. The proposed approach has been proved to be efficient in terms of complexity and resistant to several attacks in experiments.
Examining the Growth of Online Entrepreneurship Effects on Family Income in Jordan during the Corona Virus

According to the significant impacts of social media and the internet on all facets of our lives in general and the business world in particular, many business owners and entrepreneurs who are looking to expand their clientele or start their own ventures have moved to the virtual world, particularly when they want to advance their careers. For the reasons mentioned above, this article aims to ascertain how social media networks impact business operations, with a focus on how they affect entrepreneurship growth. Facebook and Instagram are the two most useful social media platforms. The findings indicate that Facebook and Instagram have a significant impact on increasing entrepreneurship and household income in Jordan.
Paper Book Recommendation System by using Neural Network

Data mining is the concept of collecting raw data and convert into useful information. Data mining is used for everything from what customers want to buy a thing and which thing is recommended to customers. In this paper, the proposed algorithm used is Neural Network. A Neural Network algorithm is used to recognize the hidden relationship between the data set by adopting the process of how the human brain performs functioning. In this research paper, a different algorithm is also used, and compare the result of this algorithm. The algorithm is applied to the data set of books which may result in the book being recommended or not on the different features and also the Accuracy of the algorithm.
Impact of Robo-advisors and Artificial Intelligence on Customer Service Performance at Personal Finance Industry

As a response to market demands, the financial sector has placed a strong emphasis on streamlining decision-making processes, maximizing procedural efficiency through collaboration with the financial technology (fintech) sector, robotization, and the practicing artificial intelligence. These efforts are also becoming a key component of how firms providing financial services to compete on the market. The customer service performance makes the assumption that customers would priorities their demands and make decisions in order to maximize their personal happiness. The goal of this research is to identify the empirical findings about robo-advisors and artificial adoption that influence customer satisfaction with personal financial planning made possible by contemporary technology. PLS-SEM, validity and reliability test and Spearman's rho correlation were used for quantitative analysis of robo-advisor in order to compare data from different finance companies. 39 finance companies were approached to collect data online. A valid sample size of 166 employees used for data analysis. SmartPLS 4 software used to measure the hypothesized model. Findings revealed significant relationship of each construct.
Analysis On Perception And Psychosocial Effect Of Online Teaching And Regular Contact Classes Among The Parents Of Children Under 10 Years

The technology has advanced so much that education can be taught at ease online. Schools have started providing education through the internet. This mainly aims for a wide range of connections with people and also to meet the financial crisis. Parents of the children undergoing online education had been constantly under stress due to the target and work which their child had to meet up. The teachers who provide online education should be qualified accordingly so that the children get quality education.To know and analyse the perception and psychosocial effect of online classes among the parents of children under 10 years. The survey was conducted by preparing questionnaires pertaining to the topic and circulated through online google forms and the responses obtained and the data was analysed.it was observed that the participants felt stressed about the online classes, their ward felt lethargic and lazy due to the online classes, they spent time with their ward during online class and sat beside them, helped them with their homework and many preferred online classes. The p-values are in the range 0.000- 0.05 i.e less than 0.05 and is statistically significant. It can be concluded from the result that the parents feel stressful about their child’s online presence and also it affects their mental health and psychosocial well-being.
Investigating the Role of Artificial Intelligence in Embedded Finance on Improving a Nonfinancial Customer Experience

Financial services are changing as a result of artificial intelligence (AI), which is upending conventional business practices and client interactions. Recent developments in the area of artificial intelligence systems have altered both the consumer lifestyle and the worldwide landscape of embedded financial services. In this exploratory study, the role of artificial intelligence and embedded finance evaluated to improve the nonfinancial customer experience. In order to empirically investigate the model construct, a quantitative technique was employed to gather data from public and private banks located in Sharjah, UAE. Convenient random sampling technique adopted. 208 valid responses were utilized for data analysis with SmartPLS 4.0 in order to assess the model reliability, discriminant reliability, hypothesis testing and variance prediction. The statistical research showed that using embedded finance and artificial intelligence to enhance the non-financial customer experience had a positive and significant impact. The results of the empirical analysis were presented in line with prior studies that looked into significant influence. Additionally, embedded finance promotes client non-financial experiences more effectively.