Reviewerly/exHarmony huggingface dataset:
Implementation and Evaluation of the Lightweight Cipher SPARKLE

In recent years, lightweight cryptography, which can be used with small circuit size, low latency, and low power consumption, has been attracting attention. NIST is currently conducting the Lightweight Cryptography (LWC) competition to determine the standard for lightweight cryptography. SPARKLE, the target of this study, is a lightweight cryptography that was selected as a finalist in the NIST LWC. On the other hand, it is very important to verify the tamper-resistance of cryptographic algorithms against implementation attacks such as side-channel attacks. In this study, we evaluate the tamper resistance of SPARKLE.
Graph Structure Exploration for Reinforcement Learning State Embedding – Train Tetris Agent with Graph Neural Network

Artificial intelligence has been developed in many fields due to the rapid spread of the Internet worldwide and the rapid development of computer computing power in the last decade. During this development, many branches of AI, such as computer vision, natural language processing, graph deep learning, and reinforcement learning, have been proposed and studied, making AI ubiquitous in people's lives today. In recent years, cross-domain applications have been a very hot topic, where the combination of graph deep learning and reinforcement learning has achieved good results in several fields. In our previous work, we used graph deep learning as an encoder to encode game states on Tetris game and fed it into a reinforcement learning algorithm for training game agents with success. In this paper, we build on our previous work and explore the impact of different graph construction methods on the performance of the game Agent. We compare the performance between graph structures with different number of edges and finally find that there are cases where the Game Agent can maintain good performance even when using graph structures with significantly fewer edges.
A Literature Review on the Various Trends of Digital Forensics Usage in Combating Cybercrimes

Different cybercrimes have been identified with instances of ransomware, online scams, Denial of Service (DoS) and many more. Wherein in the present, a variety of solutions have also emerged to be able to combat cybercrime. In the Philippines, cybercrime has increased throughout the past few years with most of them being online scams. Digital forensics focuses on locating, acquiring, processing, analyzing, and reporting on electronically stored data. Its main goal is to extract data from different devices such as computers or smartphones, process it into actionable intelligence, and can then be used in the court of law. The researchers aim to know the effectiveness of digital forensics to fight cybercrime using the different emerging trends regarding digital forensics such as cloud, social media, IoT, and network forensics. For the Philippines, having sufficient infrastructure is a possible and rational solution since globally, technology keeps evolving and so do cyber-attacks and crimes. With this, one may be able to mitigate possible attacks and damages throughout the country.
No Reference Stereoscopic Video Quality Assessment Considering Self-Attention and Different Resolution Level

In this paper, we propose a no reference stereoscopic video quality assessment (SVQA) method that takes self-attention fusion and different resolution level into account, which reduces loss of detail information. Firstly, considering that left and right branches have semantically inconsistent information, we build a self-attention feature fusion (SAFF) module, which maintains high resolution in both channel and spatial dimensions in the feature fusion process. Secondly, we design a multi-scale module with asymmetric convolution (MSMAC) to obtain multi-scale information and to enhance representation ability of square convolution. Finally, we fuse and weight the features of different resolution levels and then get the final quality score with regressing weighted features. We also consider the depth information between left and right videos and construct the disparity branch. Experimental results on two public stereoscopic video quality databases show that our proposed method outperforms other methods.
Identifying Users based on their Activity Pattern using Machine Learning

Biometric technology is a very advanced security method that is difficult to duplicate. Recently, fingerprint and face recognition have become popular with the public through their practical use in unlocking smartphones. Research on activity-based biometrics using data from smartphones, smartwatches, and small sensors has been conducted. This study presents a machine learning (ML)-based approach for automatically identifying users based on their daily activity patterns. We used a publicly available human activity dataset, which was collected from eight subjects using on-body three wearable sensors (accelerometer, gyroscope, and magnetometer). First, we extract 21 time and frequency-domain features. Secondly, more efficient features were selected using a minimum redundancy-maximum relevance (mRMR)-based feature selection method. Thirdly, four ML-based approaches, namely random forest, decision tree, 1-dimensional convolutional network, and extra tree (ET) were implemented for user identification. Our experimental results illustrated that the mRMR-based ET classifier produced a higher recognition accuracy rate of 99.5%. Our results indicate that our proposed method can more effectively identify users based on their daily living activities.
Image Recognition Accuracy, Number of Parameters and Computational Complexity Using Channel Reduction by Dimensional Compression and Attention Function

Currently, due to the development of cloud services, artificial intelligence programs are being used in various places, and various machine learning models have been proposed. AI is also required to be realized on the edge side. However, on the edge side, the energy supply is severely limited, so an extremely energy-efficient AI is required. It is research. Currently, with the development of cloud services, artificial intelligence programs are used in various places, and various machine learning models have been proposed. There is a problem of time, and AI is required to be realized on the edge side as well. However, AI with extremely high energy efficiency is required due to the strong limitation of energy supply on the edge side. This paper is based on channel reduction by dimensional compression by GAP of SE blocks and channel attention function by weighting by sigmoid.
Satellite Imagery Processing using NDVI for the Detection of Illegal Mining in Chaspa, Puno - Peru

Peru is a country that distributes its economic activity in the export of oil, natural gas, precious metals and mining, the latter being often extracted illegally, which causes havoc and damage to a certain geographical area. The Peruvian jungle and high Andean areas are areas of difficult access, where the communication signal is poor, this can cause that the authorities fail to supervise and control efficiently, therefore, illegal mining often expands damaging the environment, for this reason it is proposed remote monitoring of the area of Chaspa, Puno using satellite images. In this research, an image processing algorithm using NDVI was developed to locate the damages done in that geographical area from the year 2017 to 2022. The implementation of the algorithm was performed using computer vision, employing an EarthExplorer Data Set and LANDSAT 7 satellite images. Achieving the detection of the variation in vegetation in the period 2017 - 2022 by means of coloration indicators. This algorithm can be taken as a reference to analyze other places affected by illegal mining or even to map new areas as a future projection, these images can be derived to jurisdictional entities for their respective action and contribution to the improvement and reduction of illegal mining in Peru or in other countries.
Development of a Cloud Business Process Architecture Using the Riva Method

This paper aims to introduce a new artifact in the cloud computing (CC) discipline that directs and supports management in organizations in choosing the right approach to embrace CC. This artifact is a business process architecture (BPA) that presents and simplifies the field and generates a bird’s-eye view for its users. It also solves technological preparedness, complexity, and compatibility problems that companies meet while transforming into CC. The artifact contains the core elements of CC and identifies its main processes. A qualitative approach is adopted for data collection and analysis using semi-structured interviews with experts in addition to the literature review of CC resources. The Riva BPA is nominated as an object-based BPA method to build this artifact. Riva’s primary fundamental step is finding the essential business entities (EBEs) of the domain they have agreed on, cloud computing. These EBEs are the cornerstones that present the CC discipline and are utilized to continue the development of a CC business process architecture (CCBPA). EBEs are translated into Riva units of work (UOWs) and their dependent 1st and 2nd cut process architectures. The final CCBPA has been evaluated by validating its main elements by experts in the cloud computing domain. Eventually, the new CCBPA shows advantages in understanding the CC discipline and presenting an approach for organizations to prepare and plan the host of cloud technology in their business, in addition to its support of integration between cloud computing and other related disciplines.
Artificial Intelligence in Marketing: Concerns and Solutions

This paper studied the implementation of artificial intelligence (AI) in marketing while taking the privacy and security problems into consideration. AI has been implemented in many fields to use human intelligent machines to perform our daily activities including education, traffic, public security, social governance, healthcare, finance, and building smart societies. The success of AI in many industries encouraged marketers to use it in marketing. AI is used to improve marketing in websites and social media applications and to build models that can formulate marketing strategies and human intelligent decision-making. Several studies proved the success of implementing AI in marketing. However, there are also risks and issues related to it. The main problem associated with AI in marketing is the privacy of the data collected from the customers, which forced some organizations to prevent implementing AI in their marketing strategy to save their reputation. On the other hand, other organizations relied on AI to improve their marketing and gain a competitive advantage in the market. Several solutions are suggested to prevent or mitigate data privacy and security problems. Based on the findings of this paper, companies should keep the customers data local while advertising, isolate the sensitive and confidential data from the cloud or use edge technology as a replacement for cloud storing, ensure that all software interacting with AI are secure, use authentication methods, and limit the access for customers’ confidential data for certain employees only.
The Economics of Blockchain Within Africa

Abstract Blockchain technology offers the potential to transform economic activity and improve living standards in Africa by overcoming information asymmetry problems, property rights and governance barriers. Blockchain innovations have the ability to boost levels of productivity and unlock capital flows to underserved sectors, in addition to leveraging the increasing returns of information as an input to production in order to spur economic growth. This background paper discusses the recent advances in blockchain technology within Africa, and investigates the underlying economic principles of blockchain networks, both universally and in an African context. It reviews the enabling infrastructure and supporting technology, including digital identity. Finally, it gives policy recommendations to increase the uptake of blockchain technology on the continent.Keywords: blockchain, data governance, Africa.
Understanding Artificial Intelligence Through Its Applications and Concerns

It is no secret that artificial intelligence has become incredibly popular in today’s world. However, as different researchers offer different explanations of what artificial intelligence is, the term and its associated concepts remain ambiguous. The literature devoted to explaining artificial intelligence (AI) and its applications was reviewed, focusing on how AI forms are used to understand the basic principles of AI creation and operation and develop a deeper knowledge of the construct. With this purpose in mind, the main AI approaches and AI families and their applications in today’s most developing industries were discussed. The findings show that there is no universal way of describing AI and that AI can best be understood by looking into its applications. It was found that most AI applications deploy the AI machine learning (ML) system, and some applications use deep machine learning and neural network techniques to analyze live and historical information. The findings further show that these neural networks are complex algorithms that require initial human training and supervision to link things together. Nevertheless, they can continuously build up new information from sensors, correlate data, and analyze problems in a hierarchical way that mimics the human brain. Moreover, it was also revealed that the euphoria over AI applications needs to be tempered as they can have unintended negative consequences for businesses and cause individual harm if not implemented with care. Biased AI applications risk compliance and governance breaches and damage to the corporate brand. To mitigate the risks, a growing number of organizations have been working on ethical AI principles and frameworks to ensure responsible AI use.
Data Regulation in Africa: Free Flow of Data, Open Data Regimes and Cybersecurity

Abstract This chapter seeks to address the concerns associated with data regulation on the African continent. In particular, the paper zooms in on three major aspects of data regulation that hold the reigns to the potential development of the continent. These are the free flow of data, the adoption of open data regimes and cybersecurity. This will be in the general context of Africa, with a focus on regulatory instruments from the different bodies at continental and sub-regional level as well as some national legislation from countries that have developed any legislative instruments that address the same concerns. Emphasis will also be paid to the strides that have been taken by the European Union, the first continental body that has taken a geographically concerted approach to comprehensive data regulation. The aim is to draw lessons from such efforts with the intention of determining an appropriate African centred approach to data regulation, particularly in the context of increased inter-African trade as envisaged by the Agreement on the African Free Continental Trade Area and an enhanced digital economy as motivated for in the Digital Transformation Strategy for Africa (2020–2030).
Analyzing the Effects of Digital Communication on Project Management in Bahrain During the COVID-19 Pandemic (a Case Study)

During the COVID-19 pandemic, the government of Bahrain took many actions to ensure social distancing. Turning to digital communication was a significant step taken by many organizations, public and private, to keep everyone safe, along with continuous achievement and development. A case study was conducted with the Bahrain Tender Board on e-tendering system users, including employees of the Bahrain Tender Board, purchasing authorities, suppliers, and contractors. This study investigated the causes of the increase in the tender’s value in Bahrain during the pandemic while shifting to the e-tendering system. These factors included safety and security, cost, ease of use, and accessibility. To collect data, a survey was distributed to a sample of e-tendering system users, and 127 responses were received. A five-level Likert scale was used in the questionnaire, and the data were analyzed with SPSS and Microsoft Excel. The findings from the study demonstrated that the mentioned factors had an impact on the e-tendering process, and digitalization resulted in a reduction in suppliers’ participation and increased the value of tenders in the Kingdom of Bahrain. The researcher provided recommendations, such as focusing on digitalization, coping with technology that is improving day by day, and new innovations and ideas. Furthermore, some digital communication factors should be handled with utmost care, such as the safety and security of users and accessibility. The study showed that these factors were highly effective for successful project management.
Artificial Intelligence in Marketing and Organizational Decision-Making: Some Challenges and Concerns

Artificial intelligence (AI) has proven to be crucial for competing in today’s global economy. This study investigated the role of AI in marketing and other business decision-making by reviewing the literature on the incorporation of AI in these business areas. The study covered the marketing functions that were impacted by AI, which include promotion, marketing information management, customer service management, marketing decision-making, marketing operations, pricing and product place management, financing and securing transaction, and transportation. In this context, it was found that marketing decision-making is likely to be improved if supported by intelligent systems, such as AI. However, these systems can only offer options constrained by the information that was made available to them and by their capacity of producing knowledge from this data. It was also found that AI has already shown its ability to secure adequate cash and budget for marketing efforts, as well as facilitate the checkout and payment process. Moreover, the study investigated research findings regarding AI support for strategic decision-making and its impact on organizational structure, as well as some of the challenges and concerns that AI poses. Other findings suggest that leaders should guide their workforce in the implementation of AI and should themselves have thorough technological literacy and competence. There was sufficient evidence to suggest that the incorporation of AI into business functions has significantly enhanced performance, particularly, in marketing and that the latter will have a promising future pairing with AI.
Geochemical Orientation Study of Stream Sediment Samples in the Southern Part of Nuggihalli Schist Belt: Ore Mineral Phases and Their Implications on the Bedrock Potential for Ores

Stream sediment sampling is a significant tool in geochemical exploration.The stream sediment composition reflects the bedrock geology, overburden cover, and metalliferous mineralization.This research article focuses on assessing selected trace element concentrations in stream sediments and interpreting their inter-element relationships using multivariate statistical methods.Tagadur Ranganathaswamy Gudda and its surroundings in the Nuggihalli schist belt of southern India have been investigated in the present work.The geology of the study area is complex, with a diverse range of litho units and evidence of strong structural deformation.The area is known for its mineralization potential for chromite, vanadiferous titanomagnetite, and sulfides.The topography of the region is characterized by an undulating terrain with a radial drainage pattern.Most part of the schist belt is soil covered except the Tagadur Ranganathaswamy Gudda area.For this study, a discrete stream sediment sampling method was adopted to collect the samples.Stream sediment samples were collected using a discrete sampling method and analyzed for trace elements using an ICP-AES spectrophotometer: Fe, Cr, Ti, V, Cu, Ni, Zn, Pb, Mn, Cd, and As have been analyzed.The analytical data were statistically treated using the SPSS software, including descriptive statistics, normalization of data using natural log transformation, and factor analysis with varimax rotation.The transformed data showed a log-normal distribution, indicating the presence of geochemical anomalies.The results of the study provide valuable insights into the geochemical processes and mineralization potential of the study area.The statistical analysis helps in understanding the inter-element relationships and identifying element groups and How to cite this paper:
DeFi Security: Turning The Weakest Link Into The Strongest Attraction

The innovation we pioneer, focused on blockchain security, is called the Safe-house. The Safe-house is a piece of engineering sophistication that utilizes existing blockchain principles to bring about greater security when customer assets are moved around. The Safe-house is badly needed since there are many ongoing hacks and security concerns in the DeFi space right now. The safe-house provides a first level of security that caters to, a significant extent, the enhanced fund movement needs for today's DeFi protocols and blockchain investment funds. To improve the safety of the safe-house even further, we adapt the one time password (OPT) concept to operate using blockchain technology. Well suited to blockchain cryptographic nuances, our secondary advancement can be termed the one time next time password (OTNTP) mechanism. The OTNTP is designed to complement the Safe-house making it even more safe. We provide a detailed discussion including the corresponding mathematical formulations and pointers for technological implementation.We discuss how the safe-house can also participate with other external yield generation protocols in a secure way.
A Case Study of a Critical Discourse Analysis of Political Discourse: Boris Johnson's Resignation Speech

The paper is a study of qualitative with descriptive data presentation. The author uses power to invest the pronouns of Boris Johnson's resignation speech. The Cognition Theory by (Teun A Van Dijk, 2009) was used. Textually, social cognition and social context are three component aspects in revealing the implied meaning of the use of personal pronouns in Boris Johnson's Resignation speech. The data was collected from Boris Johnson's Resignation speech on the 7th of July,2022. This transcript was collected from the media. The study's objective is to describe the personal pronouns used by Boris Johnson in his Resignation speech to find out the unexposed meaning of the relied personal pronouns. In this current paper, different pronouns (I, we, us, me, I'm) were used, and each has a specific function and target, and I have found that each personal pronoun used by Boris Johnson refers to a certain ideology and strategy. The reason behind using such pronouns is to attract the audience and supports them to achieve the wanted target in the policy. Also, Johnson tried to convince supports that he and his party in the right way.
Introduction

This chapter explores the Artificial Intelligence research area, highlighting the different approaches to Machine Learning and the types of problems that each one solves. The concept of Reinforcement Learning is presented playfully through examples, and its framework is explained in detail. In addition, relevant historical milestones that influenced several areas of knowledge and contributed to the development of AI research are addressed.
Recent Applications and Future Research

In this chapter, the most recent applications of Reinforcement Learning (RL) and how this technique impacts various areas of knowledge are explored. Examples of RL applications in areas such as robotics, games, education, and quantum mechanics are presented. In addition, this chapter explores the main benefits and challenges that RL applications present in different areas and analyzes the prospects of using this technique in each of these fields.