Reviewerly/exHarmony huggingface dataset:
High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system

Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo's ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders.
AGI-agent cognitive architecture AGICA - axiomatic approach.

For the last half of the century there were proposed and modeled several dozen cognitive architectures as the models of mind. As one of the results of this Standard Model of the Mind was proposed and discussed in 2017. It accumulated lessons learned in one structure. In the articles published in 2016-2018, the author formulated main definitions of the concepts of Artificial General Intelligence (AGI): AGI-Individual Type, AGI-Collective Type, AGI-Consciousness, AGI-Thought, AGI-Knowledge, AGI-Emotions. The author’s approach belongs to the direction Embodied Cognition in Cognitive Science and is following “Axiomatic Approach” in Artificial Intelligence. The definitions proposed by the author are of constructive type from mathematical point of view and can be modeled by the existing software &amp;amp; hardware tools and methods. In this article the author is proposing AGI-Agent cognitive architecture AGICA as detailed modification of Standard Model of the Mind. It can be used in the development of universal operating system for AGI-robots.
Identification on the Need of English Public Speaking Class for Students at the Tourism Destination Study Program

This study aims to identify the need of English public speaking skill for students at the Tourism Destination Study Program and formulate the required solution. This study was motivated by recent student events called “Seminar Pariwisata” or Tourism Seminar where all the students had to present their projects from the Integrated Field Practicum and also “Duta Polije”, a contest where campus ambassadors were selected. This study was an aftermath evaluation following the conducted events with the hope to bring an improvement in the future for both the students and the study program. This was a descriptive-qualitative study and the data were collected through observation, documentation and interview. The results of the study show that there is a need in providing students with English public speaking skill through intensive public speaking classes, to support them especially in non-academic activities. This is also useful to equip them with additional competencies in order to help them in the workplace after graduation, especially to have a career in the tourism business. The class can be conducted during the running academic year by integrating the class with English courses, by adding the public speaking class at the speaking skill section of the RPS (Semester Teaching Plan) and BKPM (Student’s Workbook) of English courses. This idea is suggested to be taken into consideration to create a better educational supports for the students as well as for the development of the study program in the future.
Constrained Global Optimization by Smoothing

This paper proposes a novel technique called "successive stochastic smoothing" that optimizes nonsmooth and discontinuous functions while considering various constraints. Our methodology enables local and global optimization, making it a powerful tool for many applications. First, a constrained problem is reduced to an unconstrained one by the exact nonsmooth penalty function method, which does not assume the existence of the objective function outside the feasible area and does not require the selection of the penalty coefficient. This reduction is exact in the case of minimization of a lower semicontinuous function under convex constraints. Then the resulting objective function is sequentially smoothed by the kernel method starting from relatively strong smoothing and with a gradually vanishing degree of smoothing. The finite difference stochastic gradient descent with trajectory averaging minimizes each smoothed function locally. Finite differences over stochastic directions sampled from the kernel estimate the stochastic gradients of the smoothed functions. We investigate the convergence rate of such stochastic finite-difference method on convex optimization problems. The "successive smoothing" algorithm uses the results of previous optimization runs to select the starting point for optimizing a consecutive, less smoothed function. Smoothing provides the "successive smoothing" method with some global properties. We illustrate the performance of the "successive stochastic smoothing" method on test-constrained optimization problems from the literature.
Synergizing Generative AI and Cybersecurity: Roles of Generative AI Entities, Companies, Agencies, and Government in Enhancing Cybersecurity

&lt;p&gt;The digital landscape of the modern world has witnessed a remarkable evolution over the past few decades, with technological advancements permeating every facet of our lives. While these innovations have brought forth unprecedented convenience and connectivity, they have also exposed society to new vulnerabilities. Cybercrimes have surged in both frequency and sophistication, punctuating the digital era with high-profile incidents that have shaken industries and nations. Recent history serves as a stark reminder of the potential havoc that cybercriminals can unleash upon critical infrastructure, as exemplified by the notorious Colonial Pipeline breach, where a malevolent actor manipulated digital systems to demand ransom in the form of cryptocurrency.&lt;/p&gt; &lt;p&gt;Traditionally, cybercriminal endeavors necessitated a specialized skill set and an in-depth understanding of intricate technological nuances. However, the landscape has transformed dramatically with the emergence of Generative Artificial Intelligence (AI). Previously the domain of highly specialized engineers, the tools required to orchestrate cybercrimes have become increasingly accessible due to the proliferation of advanced AI models such as ChatGPT and other modern Large Language Models (LLMs). These AI-driven capabilities have lowered the entry barrier for potential wrongdoers, enabling individuals with even basic technical aptitude to partake in cybercriminal activities.&lt;/p&gt; &lt;p&gt;A cursory glance at contemporary news headlines underscores the growing ubiquity of cybercrimes. A relentless surge in cyberattacks serves as an alarming indication of the escalating threat posed by malicious actors in the digital realm. As each year unfolds, instances of cybercrime proliferate, impacting individuals, corporations, and governments alike. This trend signals a pressing need to comprehend the intersection between Generative AI and cybersecurity – a convergence that holds the potential to reshape the dynamics of digital malfeasance and defense.&lt;/p&gt; &lt;p&gt;This research paper embarks on a journey to explore the intricate relationship between Generative AI and cybersecurity. Delving into the realm of AI-driven creativity and manipulation, we examine how the advent of Generative AI technologies has facilitated a paradigm shift in the landscape of cyber threats. As we navigate through this exploration, we unravel the challenges and opportunities that arise from this dynamic interplay. By delving into case studies, examining emerging trends, and scrutinizing potential countermeasures, this paper aims to shed light on the novel dimensions of cybersecurity in the era of Generative AI. Through a comprehensive analysis, we aim to equip readers with an informed understanding of the evolving cybersecurity landscape and the critical role that Generative AI plays therein.&lt;/p&gt;
Level of voluntary disclosure and market value: An applied study on companies listed in Amman Stock Exchange

Voluntary disclosure is viewed as an essential communication tool through which the company's ideas can be promoted to potential stakeholders, which contributes to achieving the company's growth and sustainability. This study came to reveal the reality of voluntary disclosure in the Jordanian industrial companies and its impact on its market value. The descriptive-analytical approach was adopted, where content analysis of the financial reports published by 72 industrial companies listed on the Amman Stock Exchange during the period (2016-2022). The study found that the level of disclosure of non-financial items included the aspects of the board of directors, social responsibility, and community policy were high, which reached 56%, compared to the level of disclosure of financial items, which reached 43%. This indicates that companies pay more attention to disclosing non-financial items than financial items. In addition, the study indicated that companies with a high level of voluntary disclosure have a better market value compared to those companies that have not disclosed or have a lower level of disclosure. Moreover, increasing voluntary disclosure of strategic information, financial information, and non-financial information increases the market-to-book value, because voluntary disclosure enhances the information available to investors about shares, which in turn improves the market value.
The influence of agile HRMS on the organizational performance: The case of Dubai government

In today's rapidly changing and interconnected world, governments are aware that they need to adapt their operations to effectively navigate the complexities they face. To address these challenges and operate more efficiently, governments must forge partnerships, embrace innovation, demonstrate effective leadership, and, most importantly, cultivate a skilled workforce. Recognizing this need, this study focuses on examining the impact of Agile HRMS on organizational performance within the Dubai government. To investigate this relationship, a survey was conducted involving 107 employees from various government departments in Dubai. The results of the survey revealed significant and positive correlations between all the dimensions of Agile HRMS (namely, Agile talent acquisition, Agile employee engagement, and Agile learning and development) and organizational performance. These findings underscore the significance of adopting Agile HRMS approaches in enhancing the professional development and effectiveness of government operations. Therefore, it is recommended that policymakers within the government sector adopt and integrate Agile HRMS practices, as doing so can create an environment conducive to increased productivity and success across these organizations.
Virtual Mobility as Innovative Approach in Education

Modern realities requires new and innovative approaches in organization of university educational process. Academic mobility is one of these innovative approaches, equipping students with the new comprehension of global educational and modern world in general, which is characterized by free movement of people and ides. Virtual mobility is one of the forms of academic mobility, which cannot substitute the physical one, but can enhance organization of digital educational formats, open for joint of work of students from geographically remoted countries and universities. Virtual mobility was born on the cross-section of several initiatives – development of electronic tools in education, digital formats of education and international cooperation in high education. The article summarizes the experience of the Students’ Mobility Capacity Building in Higher Education in Ukraine and Serbia (MILETUS) project, co-funded by the Erasmus+ Program of European Union and implemented in 2019-19, as well as other similar initiatives. The results and outcomes of these projects confirm that mobility is very relevant element of today’s education, which enriches educational experience and provides global competence, while in the current pandemic realities virtual mobility is a valuable alternative to the physical one.
Quality of information sharing as a moderator: An investigation of the relationship between supply chain management strategies and competitive advantage in Saudi Arabian manufacturing companies

The purpose of this research is to investigate the influence of supply chain management methods on competitive advantage in Saudi Arabian manufacturing firms, with a particular emphasis on the function of information sharing quality as a moderating variable. The data received from a sample of Saudi manufacturing enterprises were analyzed using a structural equation modelling technique. According to the findings, customer relationships, the level of information sharing, and strategic supplier partnerships all have strong beneficial impacts on competitive advantage. However, the influence of information sharing quality alone on competitive advantage was shown to be statistically negligible. These results emphasize the necessity of developing strong customer relationships, encouraging effective information exchange procedures, and creating strategic collaborations with suppliers to gain a competitive edge. The research adds to the current literature by providing insights unique to Saudi Arabian industrial firms. The results are important for managers and decision-makers developing competitive supply chain management strategies. Future studies may investigate other factors and dimensions, as well as perform longitudinal studies, to better understand the structures of supply chain management and competitive advantage in the Saudi context.
The moderating impact of cloud computing on the relationship between the reliability of accounting information systems and credit granting decisions in Jordanian banks

The study explored trust in the accounting information system and tested its compatibility with the credit granting decision level. The study also hypothesized that whenever the confidence in the current accounting information system is high, the level of the decision to grant credit is high, and vice versa. To test this assumption, the study used partial least squares structural equation modelling (PLS-SEM). This method is usually preferred when the goal of the research is to develop theory and explain variance or predict structures. The survey design approach was adopted in the commercial banks in Jordan, where 225 valid questionnaires were retrieved for statistical analysis. The results demonstrate a direct and significant effect on GCD in commercial banks. Moreover, the results of this paper show that cloud computing (CC) mediates the relationship between the independent variable (Accounting Information System Trust (AIS-Trust) with the dependent variable (Grant Credit Decision (GCD)).
An Efficient Image Deblurring Network with a Hybrid Architecture

Blurring is one of the main degradation factors in image degradation, so image deblurring is of great interest as a fundamental problem in low-level computer vision. Because of the limited receptive field, traditional CNNs lack global fuzzy region modeling, and do not make full use of rich context information between features. Recently, a transformer-based neural network structure has performed well in natural language tasks, inspiring rapid development in the field of defuzzification. Therefore, in this paper, a hybrid architecture based on CNN and transformers is used for image deblurring. Specifically, we first extract the shallow features of the blurred images using a cross-layer feature fusion block that emphasizes the contextual information of each feature extraction layer. Secondly, an efficient transformer module for extracting deep features is designed, which fully aggregates feature information at medium and long distances using vertical and horizontal intra- and inter-strip attention layers, and a dual gating mechanism is used as a feedforward neural network, which effectively reduces redundant features. Finally, the cross-layer feature fusion block is used to complement the feature information to obtain the deblurred image. Extensive experimental results on publicly available benchmark datasets GoPro, HIDE, and the real dataset RealBlur show that the proposed method outperforms the current mainstream deblurring algorithms and recovers the edge contours and texture details of the images more clearly.
Explainable Approaches for Forecasting Building Electricity Consumption

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.
Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques

With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools.
Application of Machine Learning and Deep Learning Models for Prostate Cancer Diagnosis using Medical Images: A Systematic Review

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications. &#x0D; Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them. &#x0D; Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made. &#x0D; Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.
Recursive trimmed filter in eliminating high density impulse noise from digital image

Advances in technology have made it easier to share media over the Internet. In the process of media sharing, a media may receive noise or interference that results in loss of information. In this paper, a new method to remove Salt and Pepper noise from images based on recursive method will be presented. The first stage is to recognize the noise from the damaged image, the damaged pixels will be replaced by the mean of the surrounding window, the difference with other methods is the use of recursive approach that aims to minimize the size of the window in the recovery process.
What does the population in Niger think about a military government?

Nigeriens generally do not have a strong aversion toward military rule. In fact, 50 percent of them approved of military rule in 2022. Moreover, 69 percent of Nigeriens agreed with a statement in support of military intervention when the democratically elected government was corrupt. My regression results showed that socioeconomic deprivation negatively correlated with support for both military rule and military intervention when the democratically elected government was corrupt. This suggests that the poor prefer democracy to an authoritarian regime. The regression results also showed that political instability, which I measured using the incidence of violent conflict in the country’s regions, increased the likelihood of supporting both military rule and military intervention.