Reviewerly/exHarmony huggingface dataset:
The MEV Saga: Can Regulation Illuminate the Dark Forest?

In this article, we develop an interdisciplinary analysis of MEV which desires to merge the gap that exists between technical and legal research supporting policymakers in their regulatory decisions concerning blockchains, DeFi and associated risks. Consequently, this article is intended for both technical and legal audiences, and while we abstain from a detailed legal analysis, we aim to open a policy discussion regarding decentralized governance design at the block building layer - as the place where MEV occurs. Maximal Extractable Value or MEV has been one of the major concerns in blockchain designs as it creates a centralizing force which ultimately affects user’s transactions. In this article, we dive into the technicality behind MEV, where we explain the concept behind the novel Proposal Builder Separation design (PBS) as an effort by Flashbots to increase decentralization through modularity. We underline potential vulnerability factors under the PBS design, which open space for MEV extracting adversarial strategies by inside participants. We discuss the shift of trust from validators to builders in PoS blockchains such as Ethereum, acknowledging the impact that the later ones may have on users’ transactions (in terms of front-running) and censorship resistance (in terms of transaction inclusion). We recognize that under PBS, centralized (dominant) entities such as builders could potentially harm users by extracting MEV via front-running strategies. Finally, we suggest adequate design and policy measures which could potentially mitigate these negative effects while protecting blockchain users.
Human Activity Identification and Suspicious Behaviour Detection System

Recognizing human action has been one of the biggest challenges in computer vision for the past two decades. Recently, it has become feasible to extract precise and cost-effective skeleton information. Our proposed system utilizes a cut-based framework to identify human actions using skeleton data. By using a single stationary camera as input, this system can recognize various continuous human activities in real-time, including raising or waving one or more hands, sitting down, and bending over. The recognition process is based on machine learning. Firstly, a dataset with the human body's coordinates is created. Then, a training model is developed using Logistic Regression and an outcome to be achieved. Finally, the model is utilized to identify human activities such as sitting, running, and waking up, as well as recognizing suspicious behaviour.
Semantic Matching Through Knowledge Graphs: A Smart City Case

Using selected building blocks of the Semantic Web vision to enhance domain-specific modeling methods is advancing as a new research area. The study at hand aims to contribute to this novel area of research by proposing a Semantic Matching Model that serves as a foundation for matching the purpose of a certain modeling method with the intention of future users. Within this matching model, Linked Open Data is utilized to semantically align user inputs in the form of words or phrases with resources from the Semantic Web, which can then form the basis for semantic enrichment of model instances. An experimental proof-of-concept is provided in the form of a smart city-related implementation scenario. In this scenario, tour models are first matched with relevant information from the Semantic Web and then translated into semantic-rich knowledge graphs that enable the publication of the enriched models.
The Strategy of Applying e-learning within (30) days in the Iraqi Ministry of Education in Light of the Corona Pandemic and the Deterioration of the Economic Situation for the Academic Year (2019-2020) and the Extent to which this Strategy can be Applied in the Future for Similar Cases

The outbreak of the Corona epidemic in all countries of the world has paralyzed almost all human activities, including the education sector, and the countries of the world are in trouble with the high number of injuries and deaths, including Iraq, in addition to the drop in global oil prices, which is the main source of national income in Iraq. As we are on the cusp of a new academic year, we must find solutions to continue the educational process, and to reduce the presence of large numbers of students in the classroom, in compliance with the directives of the Ministry of Health, in addition to taking into account the financial aspect of the Ministry of Education. This proposed project for e-learning achieves the desired goals of the study, first, dividing roles and tasks between the Ministry of Education and the general directorates of education and schools, and secondly, producing an electronic platform for the Iraqi Ministry of Education in record time, and third: it does not cost the Ministry of Education large sums of money.
Blockchain Governance Design a Computer Science Perspective

Blockchain-based DAOs and governance frameworks have emerged, however limited research has been done on the governance foundations of blockchain networks. In blockchain networks decisions are made through a collaborative and consensus building design mechanism. Such a governance process is complex, dynamic, and challenging. This paper presents blockchain governance design from a computer science perspective. We do so by exploring concepts such as decentralization, blockchain governance, Decentralized Autonomous Organization (DAO) and a novel modeling approach on blockchain governance namely DECENT. In this paper, we presented why conceptual modeling is a design requirement for blockchain governance. Researchers can use the DECENT modeling approach as a reference framework for blockchain governance design, such as empirical and comparative case studies.
Securing Account Hijacking Security Threats in Cloud Environment Using Artificial Neural Networks

Cloud computing has become increasingly popular and successful due to recent technological advances. It offers storage and computing resources as needed. Cloud computing provides financial advantages for users. But, most cloud service provider companies’ funds are used in cloud data security. In the present work, the threats related to cloud account hijacking have been illustrated and the model to secure from this threat has been presented. We have used Feed-Forward Back-Propagation Algorithm for storage in cloud environment.
Metaverse in Robotics—A Hypothetical Walkthrough to the Future

The Metaverse is a parallel, virtual realm that people may freely explore through avatar representation. Gaming companies such as Rockstar, Second Life, and Unity were early adopters of the Metaverse in order to attract customers. Since this endeavour has proven to be successful, other industries as diverse as education and entertainment are eager to follow suit. In order to maintain their top position among the rapidly growing user applications and software companies, even well-known social media developers have begun to invest significantly in the effective growth of the Metaverse era. The Metaverse has recently emerged as a research subject and expectation, simultaneously, throughout the ages robotics has captured people’s imagination greatly. This research aims to provide light on the potential superpower and effect of integrating two developing technologies, the Metaverse and Robotics.
Optimization of Fuzzy Controllers Using Distributed Bioinspired Methods with Random Parameters

The optimization of fuzzy controllers is a task that requires a high demand for computational resources since it is necessary to carry out a large number of simulations to evaluate the operation of the controller. This is time-consuming work, and because of this, distributed and asynchronous bio-inspired algorithms have been proposed recently to make the execution achieve acceptable results in terms of scaling and optimization improvement. These algorithms use multiple populations and processors to search algorithms in parallel on each population. A problem with these algorithms is finding the ideal configuration that we will use to execute the algorithms, such as the number of populations, the number of processors needed, and particularly the parameters that affect the exploration and exploitation in the search. In this work, we compare homogeneous configurations for all populations against heterogeneous configurations in which we randomly define the parameters. We want to evaluate if this strategy helps us to minimize the evaluation time and minimize the RMSE. We will use genetic and particle swarm optimization algorithms in this experiment. As a case study, we applied the algorithms to optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot. Results show that even when we use random parameters in our algorithms, we continue to obtain an RMSE similar to the previous results. We also observe a reduction in execution time.
Particle Swarm Optimization Algorithm with Improved Opposition-Based Learning (IOBL-PSO) to Solve Continuous Problems

This paper presents a particle swarm optimization algorithm with improved opposition-based learning (IOBL-PSO) to solve continuous optimization problems. IOBL-PSO adds this mechanism at the end of each iteration to introduce randomness in the search process, improving the quality and the efficiency of the algorithm and to avoiding premature convergence. In such a way, the update mechanism of the particle positions in the proposed algorithm is more flexible. To validate the feasibility of this approach, the performance of the proposed algorithm was compared with the performances of standard PSO and the Variable Velocity particle swarm optimization (VVS-PSO). Nine benchmark functions were solved, and the non-parametric Wilcoxon test was applied to validate the significance of the observed differences. The experimental results show that IOBL-PSO outperforms to the other two variants in comparison. Furthermore, the proposed algorithm obtains better results in all the instances used in terms of quality.
Recovering from Population Extinction in the Animal Life Cycle Algorithm (ALCA)

In previous work, we introduced an algorithm inspired by the biological Life Cycle of animal species, consisting of the stages: birth, growth, reproduction, and death. As in nature, population individuals grow older and age. In this algorithm’s reproduction process, couples must match by mutual attraction, where sometimes individuals won’t procreate offspring because of the mate’s low appeal. As time passes, the environment kills its individuals either because of low fitness or age, causing occasional population extinction. When this condition presents, it is required to create a new population if evaluations are available and termination conditions are favorable to continue. Some alternatives we explored to restart this new population are the random generation of a new set of candidate solutions and the creation of a new set of candidate solutions based on either: a mix of elements from the best historically found candidate solutions set (Elite); the mutation with uniform modification from the Elite or the historically best-found solution (Champion); crossing the Champion with the Elite; the projection of the Elite towards the Champion; based on random use of the previous alternatives. In this paper, we present one of the most promising alternatives to solve population extinction caused by nature’s pressure in the Life Cycle algorithm: the projection of the Elite towards the Champion, where we compare the results obtained with the latter and the random use of all the alternatives, using classic benchmark functions for optimization for comparison.
Mechanisms for Implementing Managerial Innovations at the University

The authors propose to classify managerial innovations according to the management cycle model, highlighting innovations related to goal setting, forecasting, planning, organization and coordination, motivation, and control. The authors emphasize that managerial innovations meet opportunities and limitations when implemented and suggest considering them based on a new theory of production factors in combination with K. Lewin’s force field model. It gives the possibility to anticipate the upraise of negative factors and make timely management decisions aimed at finding and using the necessary resources for innovation. Using examples from the practice of implementing innovation at the university, the research substantiates the conclusion about the possibility of synergetic coexistence of two mechanisms for implementing innovation—“top-down” (on the initiative of management) and “bottom-up” (on the initiative of employees). The advantages of the first approach are high manageability and a clear relationship with the strategic goals of the university. Its disadvantage is the high duration of the process. The advantages of the second mechanism are its flexibility and implementation time. The disadvantage is possible inconsistency with the goals of the university’s strategic development.
Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Feedback Control Techniques

This work focuses on the trajectory tracking control of Wheeled Mobile Robots (WMRs) through neural networks and feedback control techniques. We employ different neural network architectures to analyze the environment, divide the road into segments, and generate reference trajectories for the WMR. For control purposes, we consider the nonlinear kinematic model of the WMR considering the nonholonomic constraints. Then, this kinematic model is fully linearized through a dynamic controller, which accomplishes the trajectory tracking objective. The reference trajectory procedure and control scheme combination are used to attain the desired trajectory tracking objective. To this end, we utilize a numerical simulator for a scale vehicle, namely the Autominy car. Numerical simulations using Matlab and the Robotic Operative System (ROS) demonstrate the performance of the proposed control methodology.
Multidimensional Space

Nowadays, many data analysis problems involve multidimensional space as in factor analysis, principal component analysis and multidimensional scaling.
An Evolutionary Bilevel Optimization Approach for Neuroevolution

Convolutional neural networks (CNN) have been extensively studied and achieved significant progress on a variety of computer vision tasks in recent years. However, the design of their architectures remains challenging due to the computational cost and the number of parameters used. Neuroevolution has offered various evolutionary algorithms to provide a suitable option for designing CNNs. Moreover, modeling such a design as a bilevel optimization problem has recently attracted the interest of researchers and practitioners because it can be seen as a hierarchical task. This work precisely addresses it as a bilevel optimization problem. Unlike existing approaches, the upper level minimizes the complexity of the network (described by the number of its parameters), while the lower level optimizes the topology of the network structure for maximum accuracy. The results suggest that based on user preferences, the bilevel optimization approach can report neural architecture with higher accuracy values or simpler convolutional neural networks.
A New Continuous Mycorrhiza Optimization Nature-Inspired Algorithm

In this paper we present a new algorithm Continuous Mycorrhiza Optimization Algorithm (CMOA) inspired by the symbiosis of plant roots and the Mycorrhiza Network (MN). Mycorrhiza Networks are fungal hyphae (Ectomycorrhizae) that connect the roots of at least two plants, this type of networks is very important ecologically because they facilitate the transfer of resources between plants and improve the growth, survival, regeneration and colonization of forests. The transfer of resources between plants that may or may not be of the same species are carbon, nitrogen and other minerals, and the transfer from the plants to the MN would basically be carbon, and water from the MN to the plants. In addition, this equipment created by the plants and the MN has a defense system of biochemical signals that travel through the network to warn of possible disturbances such as wind, fire, logging, flooding, predators, pests, etc. To model this ecological environment, we used the Lotka-Volterra (LV) system of continuous equations: LV Predator–Prey Model, LV Cooperative Model and LV Competitive Model, certainly these LV models are represented in this ecosystem, we performed experiments with mathematical functions and checked their effectiveness comparing with other similar researches.
An SBIR Case Study

This chapter shares a case study from the author from beginning to successful grant awards. This example shows the need for persistence and how the process can be lengthy.
Cybersecurity Disclosure and Corporate Reputation

This chapter emphasizes the importance of cybersecurity for a corporation as today's organizations are more vulnerable than ever and their enemies are in the form of viruses and malware. The work provides evidence that cybersecurity can have an impact on brand value, market value, and overall corporate reputation. It focuses on depicting the global scenario with reference to cybersecurity disclosures by corporations and how it is important in today's digitized era where data is the most valuable and vulnerable asset. With rapid digitalization, cybersecurity has become a major concern for all businesses, especially when there is financial and reputational damage to cybersecurity breaches and incidents. Even in the absence of clear cybersecurity laws and regulations, corporations are opting for voluntary disclosure. Existing literature explains this as an attempt to mitigate any potential risk or occurred risk through increased transparency which will build the trust of all stakeholders.
Imperatives of Risk Analysis and Asset Management on Cyber Security in a Technology-Driven Economy

As the relevance of the digital economy increases, the need for secured cyberspace increases. Cyber threats are inescapable in digital progress. In the growth of cyber dominance, cybersecurity is necessary and very essential. Risk analysis is the process of assessing the likelihood of an adverse event occurring within the corporate, government, or environmental sectors. This chapter, therefore, explores the imperatives of risk analysis and asset management on cyber security in a technology-driven economy from the existing body of knowledge in the field. The chapter is divided into five sections, where the first section has to do with the introduction. The remaining four sections deal with the concepts of risk and risk analysis, asset management, factors determining attainment of continuous improvement of an asset performance management (APM) solution, cyber-crime and cyber-security, and conclusion and recommendations.
HES

The COVID-19 pandemic has led to the implementation of digital health surveillance systems worldwide, including Turkey's Hayat Eve Sığar (HES) platform. While effective in monitoring and tracing the virus, these systems also raise concerns about data privacy and cybersecurity risks. This chapter examines potential risks such as data breaches, malware attacks, unauthorized access, misuse of personal health information, and inadequate security protocols. To mitigate these risks, healthcare organizations must implement strong security measures and adopt a culture of security with a focus on continuous improvement and proactive risk management. The HES in Turkey provides a valuable case study for understanding these risks and measures to mitigate them. The chapter highlights the growing trend of digital health services invading individuals' private spaces and regulating their lives, emphasizing the importance of data privacy and security in safeguarding personal health information.
Pragmatic Risk-Based Approach to Cybersecurity

Sometimes security and technology professionals confuse their state of compliance with their security posture. While an organization can meet the requirements to any regulatory standard (HIPAA, SOC, etc.), doing so should not be construed as meeting the requirements to defend a potential cyberattack, provide data protection during business processing, or maintain a highly secure development environment. In this chapter, the authors discuss how security and compliance can co-exist. They associate each one of these with controls that are either derived from formal frameworks or meet custom operational or other requirements of an organization. They explore how each control needs to be implemented with a risk perspective in mind, and finally, they suggest methods on how to manage such a control catalog.
Zero Day Vulnerability Assessment

In this technology-driven era, software development and maintenance are rapidly growing domains and are predestined to thrive over the coming decade. But the growing demand for software solutions also brings its own implications, and software vulnerabilities are the most crucial of these. Software vulnerabilities can be referred to as weaknesses or shortcomings of software solutions, which increase the risks of exploitation of resources and information. In the past few years, the number of exploits has been increasing rapidly, reaching an all-time high in 2021, affecting more than 100 million people worldwide. Even with the presence of existing vulnerability management models and highly secure tools and frameworks, software vulnerabilities are harder to identify and resolve as they may not be independent, and resolving them may cause other vulnerabilities. Moreover, a majority of the exploits are caused by known vulnerabilities and zero-day vulnerabilities.
An interestingness measure for knowledge bases

Association rule mining and logical rule mining both aim to discover interesting relationships in data or knowledge. In association rule mining, relationships are identified based on the occurrence of items in a dataset, while in logical rule mining, relationships are determined based on logical relationships between atoms in a knowledge base. Association rule mining has been widely studied in transactional databases, mainly for market basket analysis. Confidence has become the most widely used interesting measure to assess the strength of a rule. Many other interestingness measures have been proposed since confidence can be insufficient to filter negatively associated relationships. Recently, logical rule mining has become an important area of research, as new facts can be inferred by applying discovered logical rules. They can be used for reasoning, identifying potential errors in knowledge bases, and to better understand data. However, there are currently only a few measures for logical rule mining. Furthermore, current measures do not consider relations that can have several objects, called quasi-functions, which can dramatically alter the interestingness of the rule. In this paper, we focus on effectively assessing the strength of logical rules. We propose a new interestingness measure that takes into account two categories of relations, functions and quasi-functions, to assess the degree of certainty of logical rules. We compare our proposed measure with a widely used measure on both synthetic test data and real knowledge bases. We show that it is more effective in indicating rule quality, making it an appropriate interestingness measure for logical rule evaluation.