Reviewerly/exHarmony huggingface dataset:
Data-compatibility Analysis Based on Expectation-Maximization Algorithm with Unknown Noise Statistics

In this paper, data-compatibility analysis (DCA) problem is investigated with both the measurement noise and input noise considered. The expectation-maximization (EM) algorithm is exploited to deal with the unknown noise statistics. Motivated by the fact that although there exists an excellent EM based estimator for dynamic systems, it cannot be used in the context of DCA due to the presence of non-additive input noises, a generalized EM based estimator is developed to reconstruct the flight path, as well as to estimate the sensor model parameters. Making full use of the special dependencies among the state components and the fact that the associated dynamic system has an affine nonlinear structure, the generalized estimator retains many advantages of the existing EM based one, such as the simple optimization process for the unknowns. Experimental results show that it can give more accurate results than the classical output error method and the existing EM based one in the case of high input noise.
HADPoS: Improvement of DPoS Consensus Mechanism Based on Heat Attenuation

Blockchain was known by Satoshi Nakamoto in 2008 because of bitcoin. After more than a decade of development, blockchain has been widely applied in medical, digital finance, and electronic evidence, and others. At present, there are dozens of different consensus algorithms for different application context, such as proof of work, proof of stake, delegate proof of stake (DPoS), and proof of reputation. The DPoS consensus mechanism selects proxy nodes for block generation and confirmation through node voting, which can achieve fast block verification and adapt to a wide range of business areas. Aiming at the problems of DPoS consensus mechanism, such as giant monopoly, inactive voting, and high centralization, we puts forward the heat attenuation mechanism. The heat attenuation mechanism mildly punishes those nodes that maintain high ranking for a long time, which reduces the phenomenon of giant monopoly and enhances the degree of decentralization of the whole network. This article proposes the heat attenuation mechanism and the benefit distribution scheme. The heat attenuation mechanism improves the degree of decentralization of the blockchain network. The benefit distribution scheme makes sure that every node participating in the voting can get income. Finally, the feasibility and correctness of the consensus algorithm is proved by simulation experiments of the blockchain system.
Binary Classification for High Dimensional Data Using Supervised Non-parametric Ensemble Method

High dimensional data for classification does create many difficulties for machine learning algorithms. The generalization can be done using ensemble learning methods such as bagging based supervised nonparametric random forest algorithm. In this paper we solve the problem of binary classification for high dimensional data using random forest for polycystic ovary syndrome dataset. We have performed the implementation and provided a detailed visualization of the data for general inference. The training accuracy that we have achieved is 95.6% and validation accuracy over 91.74% respectively.
Analysis of Python Libraries for Artificial Intelligence

Python libraries are a collection of essential functions that eliminate the need for users to develop code from scratch. Python is a plethora of libraries that serve a range of purposes and it has become a necessity to have sound knowledge of the best ones. Human and machine data production greatly outpaces humans’ ability to absorb, assess, and make complicated decisions based on that data. AI (Artificial Intelligence) is the foundation of all computer learning and the future of all intricate decision making. These technologies are being looked upon as tools and techniques to make this world a better place. It’s application ranges from various fields like healthcare, finance, transport, manufacturing, fraud detection and so on which evidently depicts its potential to transform the future. This paper intends to well verse the readers with the top libraries used to implement concepts of Artificial Intelligence like Machine Learning, Data Science, Deep Learning, Data Visualization and so on. It provides meticulous and unambiguous details about the essential building blocks necessary to execute and perform such ideas. It also includes a comparative analysis of various libraries to provide a detailed understanding and overview of them.
Investigating the factors of customer experiences using real-life text-based banking chatbot: a qualitative study in Norway

In recent times, banks have increasingly started using chatbots to offer round-the-clock customer service. However, customers experience with this type of technology is not well understood. The aim of this study was to get an in-depth understanding of factors affecting customer experience with a banking chatbot. Eight participants interacted with a real-life banking chatbot to complete a simple task (order a credit/debit card) and a complex task (apply for a housing loan). Semi-structured interviews were then conducted to examine chatbot-related factors (ease of use, miscommunication errors and human-likeness) and user-related factors (perceptions, future behaviors). The findings indicate that the human-like factors like a human personality, use of emojis, willingness to help, and polite communication style, have a positive impact of customer experience with banking chatbots. The chatbot's ability to understand questions was a critical factor. Miscommunication errors have negative impact, especially when the task is a simple one. Takeaway from this study is that banks should inform customers about the limits of the chatbot's abilities. In addition, they should communicate that the chatbot is safe to use for complex tasks. Successful development and implementation of chatbots for customer service require a customer centric approach from banks.
Uncover Social Media Interactions On Cryptocurrencies Using Social Set Analysis (SSA)

Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide, where millions of users share tweets continuously every second. By leveraging 1724328 tweets, this research-in-progress paper aims to understand the dynamics of social media users’ interactions on cryptocurrencies using social set analysis (SSA). The findings reveal that Twitter users are more positive about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The research-in-progress paper also contributes to demonstrating the effectiveness of the social set analysis framework to analyse and visualize a big social media data.
A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case

In this paper, we are proposing a blockchain-based architectural model to ensure the integrity of healthcare-sensitive data in an AI-based medical research context. In our approach, we will use the HL7 FHIR standardized data structure to ensure the interoperability of our approach with the existing hospital information systems (HIS). Indeed, structuring the data coming from several heterogeneous sources would enhance its quality. In addition, a standardised data structure would help establish a more accurate security and data protection model throughout the process of data collection cleansing and processing. Hence, we designed our architecture to be interoperable with all FHIR-based HISs to add a trust layer to the current medical research process. In this paper we are to achieve our goal, we will combine continua healthcare IoT architecture and Hyperledger fabric architecture. Our trust layer model is composed of four components: (1) an architecture that integrates with the HL7 FHIR data exchange framework, which extends an open protocol that supports efficient standards-based healthcare data exchange (2) a blockchain layer to support access control and auditing of FHIR health records that are stored in the data exchange network databases; (3) a distributed architecture consisting of multiple trusted nodes ensure privacy protection for health data; and (4) an application programming interface (API) will be available for use by the network.
Virtual Reality in Depressive and Anxiety Symptomatology – Contributions to REVIDA project from a mobile app mapping

The worldwide prevalence of mental health diseases is alarming. 792 million individuals have a mental disorder such as anxiety or depression. Treatment varies among the types of illness. They can be expensive and, in order to be effective, must address a combination of psychotherapy and medications. The drugs used for treating symptoms present a risk of negative side effects. Studies have shown the benefits of other co-therapies such as physical exercise. In this case, the ability to simulate reality can strongly increase the introduction of other psychological therapies and treatment results can be improved through new immersive experiences. Virtual Reality Exposure Therapy has demonstrated its effectiveness by allowing patients to gradually face fearful stimuli or stressful situations. Recent studies showed that the use of Virtual Reality is effective and safe for mental health. Particularly, in the scenario of how Virtual Reality simulation could increase empathy and behaviour change. This article presents a preliminary study of the REVIDA project: It aims to investigate through Benchmarking and SWOT Analysis the possible integration of Virtual Reality in mental illness monitoring in Portugal. Results show that there exist several new opportunities for using Virtual Reality technology in mental health and wellbeing.
FLEXI - A Conceptual Model for Enterprise Cyber Resilience

The growing increase in cyberattacks overwhelms the ability of organizations to reliably contain and maintain their operations. In this sense, current good practices and standards create a false sense of security that is usually exploited by attackers to create areas of instability that end up compromising not only their operations but also their promise of value. Therefore, the FLEXI model and its basic practices are proposed as an unconventional alternative that prepares the organization to assume the uncertainty and instability as the new normal of its operation, which is to learn, cushion and rebound in the face of a successful cyber event, where the challenge is to overcome the vision of silos in the organization to build a collective defense that generates greater uncertainty to the adversary's risk model and a better organizational posture in the face of the inevitability of failure.
Agile teams’ assignment model for Scaling Agile

Adherence to the schedule for completion of a project is one of the basic requirements for the success of a management team. Failure to meet this schedule can have a negative impact on both the business side and the reputation of the company, this issue is a constant challenge in project management. This challenge becomes even greater with agile project management. Agile methodology has increasingly become the method of choice for project delivery. However, in this type of project, uncertainty increases in terms of execution time due to the uncertainty that arises from the agile characteristics that are so valued. Optimized assignment of agile teams based on their performance to project tasks can reduce this uncertainty and increase the probability of success. However, it appears that the tools available in the literature have very limited applicability to this problem. In this paper, we present a model for agile team assignment that uses the Program Evaluation Review Technique (PERT) along with the effective risk model and the Monte Carlo model. An illustrative case study was used to illustrate the application of the proposed model. The results show that the project execution time is highly dependent on the performance of the agile teams and their respective assignment to the project tasks. In practice, the proposed model enables the determination of the execution time of a given agile project considering the performance of the respective teams.
Intelligent Identification of Hate Speeches to address the increased rate of Individual Mental Degeneration

Hate speech is a public statement that demonstrates resentment or provokes disturbance towards a person or group often based upon race, age, religion, sexual orientation, minority group, psychical disability, political belief, etc. Such an act is a leading cause of mental degeneration in individuals observed throughout the world. We have witnessed an upsurge in the spreading of hateful speech through videos in recent times due to increased social media usage. Researchers are working on this issue because it has become more frequent on several social media platforms, and it leads to low self-esteem and has significant negative impacts on human life. In this work, we focus on collecting data from such videos as nowadays people are sharing numerous videos of this negative nature on platforms like Facebook and YouTube. The audio data of these videos were then converted into text to build the dataset, and we applied some classifier models to our dataset. In this paper, we utilized a transfer learning Bidirectional Encoder Representations from Transformers (BERT) model that gives state-of-the-art outcomes. More precisely, we fine-tuned our model based on transfer learning to evaluate BERT's capacity to capture hostile contexts inside YouTube videos. We examined Fine–Tuning BERT; with different learning rates and listed the outcomes. We train the BERT by freezing all the hyperparameters but with various random seeds to evaluate our suggested Fine-tuning approach. Compared to previous methodologies that used our dataset, our proposition fared better in terms of accuracy and execution time.
Using Extended Reality in Experiential Learning for Hospitality Management Education

Training qualified human resources is a challenge in all areas of knowledge. In particular, in the areas where the training environment of the graduates is not easily laboratoryable and/or possible to emulate, internships are often used in the work context, where an On-the-Job Training is provided. Higher education all over the world has sought to innovate in the way it mixes education. Accordingly, the digital has populated teaching-learning strategies in an attempt to make teaching more universal, more ubiquitous and more personalized, without losing rigor or the necessary requirements. In this path of innovation, e-learning or b-learning mechanisms were and still are used. However, we believe that a more decisive step must be taken by incorporating Extended Reality tools to develop and evolve the way in which competences can be acquired and evaluated, both in the classroom and in home study - a new approach at the service of the teacher and of the student. In this context, this work analyzes the role of Extended Reality in Human Resources training in the hospitality sector. In addition, a conceptual model is proposed to implement the use of Extended Reality in the context of higher education in the field of hospitality, in a conceptualization approach. Finally, future work is referred concerning the validation of the model and drawn technological considerations for the model implementation.
An analysis of Botswana's cybercrime legislation

Botswana has identified development of legal frameworks as one of its cybersecurity strategies. One issue that government is dealing with when it comes to cybercrime legislation is the overuse of technology related terminology. Therefore, in this study the cybercrime act of Botswana was examined alongside that of South Africa and the United Kingdom (UK), to assess its use of technology jargon. The legislation from these countries was analyzed quantitatively using frequency of terms, and feature co-occurrence. The analysis performed in this study was done through the statistical and analytical lens of R. It was discovered in this study that the UK cybercrime law uses less technology specific terms compared to the Botswana and the South African cybercrime acts. Hence it is adaptable and remains relevant to the ever-evolving technological environment.
Diseases Prediction from Officially Anonymized Medical and Healthcare Big Data

With the advanced technologies for ubiquitous computing, the large sensitive big data is valuable to explore the associations of data for epidemiology. New data privacy regulations allow sharing anonymization of data for relatively low cost than that of the conventional cohort studies. In this paper, We analyze the disease risk using Anonymously Processed Information to predict the diseases risk using the anonymously processed information of medical examination and health insurance claim data consisting of 200,000 and 290,000 individuals. We aim to demonstrate that using Anonymously Processed Information should be an additional methodology to accompany conventional cohort data analysis. We analyze a diseases risk by performing a logistic regression and calculating the relative risk, and develop some machine-learning models that predict the likelihood of a disease in three years, given an individual medical examination. Finally, we reveal the reduction in accuracy of the analysis as a result of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -anonymizing data.
A Comparison of Linear Rank and Tournament for Parent Selection in a Genetic Algorithm Solving a Dynamic Travelling Salesman Problem

We compare the two parent selection methods “linear rank” and “tournament” in a Genetic Algorithm applied to a dynamic Travelling Salesman Problem (TSP). The inherent dynamics of the problem is considered by temporarily doubling the costs between two randomly selected cities. In our experiments we take into account tournament selection with tournament sizes of 3, 5, and 10. A larger tournament size results in as good a performance as with linear rank selection in a small-scale dynamic TSP, whereas smaller tournament sizes better preserve the diversity of the population and avoid getting stuck in local optima. However, the assumption that tournament is superior to linear rank on a dynamic TSP could neither be confirmed nor falsified in the applied testcases.
Online News Extraction and Multiclass Classification Using Linear Support Vector Machines

Online news articles, blogs, sites are a rich source of diverse text-based data. However, the data contained in these sources cannot be manually extricated, recorded, and listed because it comes in colossal size. Accurate mapping of precise news into their corresponding category is challenging in these times. Several methods have been proposed over time for news classification when training documents for each predefined class are present readily, however such methods were tried and tested upon a small dataset. With the underlying research, the aim is to propose a method that can be used when lakhs and lakhs of instances are present. This research analysis involves the task of news classification using multiclass classifiers - OneVsRest and OneVsOne classifiers over the Linear Support Vector Classification to learn the performance of multiclass news categorization. The proposed methodology “Keyword Based Classification Technique (KBCT)” in this study was executed and concluded using Python and deployed using Google Colaboratory. The result was expressed using four distinguished news classes over a multivariate dataset of 422419 instances from the uci-news-aggregator dataset. The OneVsRestClassifier's accuracy was computed to be 95.76% that was 0.09% more than the OneVsOneClassifier's accuracy of 95.67%. The proposed prototype was compared with some of the related studies and algorithms, and the outcomes produced by the OneVsRest model were the most optimum in terms of accuracy.
Concepts and Relationships in Safety and Security Ontologies: A Comparative Study

Safety and security ontologies quickly become essential support for integrating heterogeneous knowledge from various sources. Today, there is little standardization of ontologies and almost no discussion of how to compare concepts and their relationships, establish a general approach to create relationships or model them in general. However, concepts with similar names are not semantically similar or compatible in some cases. In this case, the problem of correspondence arises among the concepts and relationships found in the ontologies. To solve this problem, a comparison between the Hazard Ontology (HO) and the Combined Security Ontology (CSO) is proposed, in which the value of equivalence between their concepts and their relationships was extracted and analyzed. Although the HO covers the concepts related to the safety domain and the CSO includes securityrelated concepts, both are based on the Unified Foundational Ontology (UFO). For this study, HO and CSO were compared, and the results were summarized in the form of comparison tables. Our main contribution involves the comparisons among the concepts in HO and CSO to identify equivalences and differences between the two. Due to the increasing number of ontologies, their mapping, merging, and alignment are primary challenges in bridging the gaps that exist between the safety and security domains.
A Memory Protection Strategy for Resource Constrained Devices in Safety Critical Applications

In modern safety-related applications, software has achieved an increasingly critical role. Their safety-critical nature, however, requires special attention: industry-specific functional-safety standards guide designers, developers, integrators, and testers during all phases of the software life-cycle and the final artifacts undergo a rigorous certification process.In the field, it is not uncommon to find very resource-constrained devices performing real-time sensing and actuating tasks. Although these devices, typically microcontroller units, offer a rich plethora of on-chip devices for communication, sensing, and interaction with the physical world, they often have quite reduced computational capabilities, and barely provide memory protection functionalities, relying solely upon rudimentary Memory Protection Units (MPUs). In this perspective, guaranteeing fault-confinement through spatial isolation – i.e., the isolation between the memory used by each of the tasks, as mandated by in force regulations – is quite challenging.In this paper, we present an MPU-based memory management and protection strategy that enables achieving spatial isolation in multi-application real-time operating systems (RTOS) tailored for safety-critical domains, while allowing a good degree of flexibility and combinability. Furthermore, we discuss the implementation of the proposed strategy as part of a RTOS from the industry domain, in order to provide a case-study pertaining to its actual implementation.