Reviewerly/exHarmony huggingface dataset:
Distance learning expert and leader

The paper deals with the theoretical foundations of training experts and leaders of distance learning in order to improve their training program. An expert is a specialist who has an experience in developing distance courses, conducting distance learning classes and has and uses the skills of a content curator. Leaders create the conditions for innovative changes that allow people and organizations to share their vision and move in its direction, contribute to the management and implementation of ideas. The paper describes the basic skills to be developed by an expert and a leader, provides typical mistakes of distance course developers. Training of experts and leaders was based on the distance course ”Distance Course Examination” for experienced developers of distance learning courses and administrators of distance learning systems from different organizations. During the training, students analyzed the purpose of learning according to the Bloom’s taxonomy and its accessibility, the complexity of the text and its design, tasks and learning activities, test quality and evaluation system as well as other elements responsible for the quality of learning. During the pilot training, the results were obtained in the form of reports of students about the examination of the distance course as well as analysis of this work was done. 47 teachers of educational institutions of Ukraine were trained and only 12 teachers successfully completed their training. Based on the results of training and taking into account the experience of emergency training during the pandemic and military actions in Ukraine, the course program has been refined. It is planned to use a team of experts, ”playing” coaches and new methods for conducting distance learning. Particular attention is paid to training experts, leaders and measuring their competencies.
Libraries and librarians’ roles in ensuring cyberethical behaviour

Purpose This paper aims to explore the role of libraries and librarians in promoting appropriate cyberethical behaviour among postgraduate students in Nigeria. Design/methodology/approach Using content analysis and a review of relevant literature, this study examines the challenges that librarians face in enforcing cyberethics and highlights the strategies that libraries can adopt to promote responsible online behaviour. Findings This paper identifies the lack of awareness among postgraduate students regarding cybersecurity risks and the need for training and resources for librarians as key challenges in promoting cyberethics. Originality/value This study recommends that libraries provide access to training resources and develop policies and guidelines to promote cyberethics among postgraduate students. Overall, this paper argues that libraries and librarians can play a critical role in promoting responsible online behaviour and ensuring the safety and security of postgraduate students in Nigeria.
Federated learning for minimizing nonsmooth convex loss functions

Federated learning possesses a distributed learning framework for protecting privacy where local clients need to collaboratively train a shared model via a central server.Many existing methods in the literature of federated learning are analyzed under some smoothness conditions of loss functions and need access to gradient information to perform local optimization algorithms such as stochastic gradient descent or dual average.Some methods even need a strong convexity of the loss function.However, in many situations of real world applications such as readability of texts, first-order gradient information is difficult to obtain, and the strong smoothness and strong convexity of loss functions are not satisfied.We consider methods to overcome such situations.This paper aims at providing an understanding of federated learning in the situation that the loss functions are nonsmooth and gradient information is unavailable, also a strong convexity condition is not needed.Based on Nesterov's zeroth-order (gradient-free) techniques, we propose a zeroth-order stochastic federated learning method.Constant and decreasing step size strategies are considered.Moreover, a new type of approximating sequence is proposed in federated learning for strictly decreasing step sizes.Expected error bounds for the proposed approximating sequence and learning rates of the proposed method are derived under some selection rules of the step sizes and smoothing parameters.
Diagnostic ability of deep learning in detection of pancreatic tumour

Abstract Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.
Inter-layer Scheduling Space Definition and Exploration for Tiled Accelerators

With the continuous expansion of the DNN accelerator scale, inter-layer scheduling, which studies the allocation of computing resources to each layer and the computing order of all layers in a DNN, plays an increasingly important role in maintaining a high utilization rate and energy efficiency of DNN inference accelerators. However, current inter-layer scheduling is mainly conducted based on some heuristic patterns. The space of inter-layer scheduling has not been clearly defined, resulting in significantly limited optimization opportunities and a lack of understanding on different inter-layer scheduling choices and their consequences.
Image encryption based on 2DNA encoding and chaotic 2D logistic map

Abstract DNA has recently been investigated as a possible medium concerning ultra-compact information storage and ultra-scale computation. The development of secure image encryption systems has recently received a certain effective and new direction from chaos-based cryptographic algorithms. This paper proposes a novel image encryption algorithm, 2DNALM , based on double-dynamic DNA sequence encryption and a chaotic 2D logistic map. The three phases regarding the suggested approach are as follows: the first phase involves permuting the positions of the pixels using a position key-based scrambling operation. The second phase involves double DNA encoding on scrambled images using various rules by DNA cryptography concept to produce an encoded image, and in the final step, an image which has been encoded is encrypted using XOR operation and chaotic keys created through a chaotic 2D logistic map. The entropy analysis and experimental findings show that the suggested scheme exhibits great encryption and withstands several common attacks.
MODELLING THE DESIGN OF MODELS: AN EXAMPLE USING CRISP-DM

Abstract Design is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.
Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect on the task outcome and the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social and task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.
To disclose or not disclose, is no longer the question – effect of AI-disclosed brand voice on brand authenticity and attitude

Purpose This paper aims to explore consumer perception of “brand voice” authenticity, brand authenticity and brand attitude when the source of text is disclosed as either artificial intelligence (AI)-generated or human-written. Design/methodology/approach A 3 × 3 experimental design using Adidas marketing texts disclosed as either “AI” or “human”, or not disclosed was applied to data gathered online from 624 English-speaking students. Findings Text disclosed as AI-generated is not perceived as less authentic than that disclosed as human-written. No negative effect on brand voice authenticity and brand attitude results if an AI-source is disclosed. Practical implications Findings offer brand managers the potential for cost and time savings but emphasise the strong effect of AI technology on perceived brand authenticity and brand attitude. Originality/value Results show that brands can afford to be transparent in disclosing the use of AI to support brand voice as communicated in product description or specification or in chatbot text.
Defi-crime: condition, trends and criminological models

Objective : to develop a criminological concept of DeFi-crime and measures to deter it. Methods : dialectical materialism and the general scientific methods of cognition based on it, used in the Russian criminology. Results : a criminological concept of DeFi-crime as a type of digital crime was developed, i.e. a new specific criminological theory of DeFi-crime (DeFi-criminology) within the structure of digital criminology was proposed. The information factors influencing the DeFi-crime state, structure and trends were determined, and measures for its deterrence by criminological and technological means were proposed. Scientific novelty : the presented work is the first and only study conducted within the framework of digital criminology, reflecting the state, trends and structure of DeFi-crime, revealing the main causes that form it, as well as describing options for deterring this type of digital crime by technological and criminological means. Practical significance : the study results can be used in law enforcement activities when assessing potential threats from DeFicrime and developing measures to reduce them; in educational activities – in the process of teaching courses in criminology, cybercriminology, digital criminology; in research activities – when obtaining additional knowledge on certain types of DeFi–crime: DeFi-theft and DeFi-fraud during their further research in DeFi-criminology.
A Fuzzy Approach to Educational Grading Systems “Fuzzy Logic Based Grade Card”

This study aims at developing a fuzzy approach for the educational grading systems. Through this study a fuzzy logic-based grading card is suggested. The grading system based on crisp approach just deals with numbers. Fuzziness, being an important property of language, motivates us to work and study in this fuzzy environment. Before discussing the actual grade card some introduction about the key concepts is given for the readers. We have also analyzed the difference in grading and evaluating systems followed from the decades and the new fuzzy logic-based evaluation system. It also includes connectivity levels, advantages, and disadvantages between both evolution methods. The future scope of the fuzzy grading system is also discussed. The report will conclude with the answer to the question, “Is a fuzzy logic- based grade card worth for the educational grading systems?”. Moreover, towards the end, suggestions will be provided on how to bring more of these fuzzy approaches into education systems.
PyVBMC: Efficient Bayesian inference in Python

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018(Acerbi, , 2020)).VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy.Specifically, VBMC computes:
The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective

To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine. Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine. The discussion mainly centers on the applications of classical models in this field and addresses the limitations and challenges of these basic models. In this paper, the more comprehensive overview of the classical structural modules in convolutional neural networks is described in detail from the perspective of cybernetical intelligence in deep medicine. The results and data of major research contents of deep learning are consolidated and summarized. There are some problems in machine learning internationally, such as insufficient research techniques, unsystematic research methods, incomplete research depth, and incomplete evaluation research. Some suggestions are given in our review to solve the problems existing in the deep learning models. Cybernetical intelligence has proven to be a valuable and promising avenue for advancing various fields, including deep medicine and personalized medicine.
Practical Related-Key Forgery Attacks on Full-Round TinyJAMBU-192/256

TinyJAMBU is one of the finalists in the NIST lightweight cryptography competition. It is considered to be one of the more efficient ciphers in the competition and has undergone extensive analysis in recent years as both the keyed permutation as well as the mode are new designs. In this paper we present a related-key forgery attack on the updated TinyJAMBU-v2 scheme with 256- and 192-bit keys. We introduce a high probability related-key differential attack where the differences are only introduced into the key state. Therefore, the characteristic is applicable to the TinyJAMBU mode and can be used to mount a forgery attack. The time and data complexity of the forgery are 233 using 214 related-keys for the 256-bit key version, and 243 using 216 related-keys for the 192-bit key version.For the 128-bit key we construct a related-key differential characteristic on the full keyed permutation of TinyJAMBU with a probability of 2−16. We extend the relatedkey differential characteristics on TinyJAMBU to practical-time key-recovery attacks that extract the full key from the keyed permutation with a time and data complexity of 224, 221, and 219 for respectively the 128-, 192-, and 256-bit key variants.All characteristics are experimentally verified and we provide key nonce pairs that produce the same tag to show the feasibility of the forgery attack. We note that the designers do not claim related-key security, however, the attacks proposed in this paper suggest that the scheme is not key-commiting, which has been recently identified as a favorable property for AEAD schemes.
HUBO and QUBO models for prime factorization

The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers [Formula: see text] and [Formula: see text] satisfying [Formula: see text]. This paper presents a prime factorization method using a D-Wave quantum computer that could threaten the RSA cryptosystem in the future. The starting point for this method is very simple, representing two prime numbers as qubits. Then, we set the difference between the product of the two prime numbers expressed in qubits and N as a cost function, and we find the solution when the cost function is minimized. D-Wave's quantum annealer can find the minimum value of any quadratic problem. However, the cost function must be a higher-order unconstrained optimization (HUBO) model because it contains second- or higher-order terms. We used a hybrid solver accessible via Leap, D-Wave's real-time quantum cloud service, and the dimod package provided by the D-Wave Ocean software development kit (SDK) to solve the HUBO problem. We also successfully factorized 102,454,763 with 26 logical qubits. In addition, we factorized 1,000,070,001,221 using the range-dependent Hamiltonian algorithm.
Enhancing student research activities through virtual chemical laboratories: a case study on the topic of Solutions

This paper explores the significance of student research activities in fostering the key competencies essential for the future specialists in the field of chemistry. Specifically, it emphasizes the importance of primary school students' ability to solve experimental problems in chemistry and highlights the role of virtual chemical laboratories in facilitating the development of these skills. The concept of ``experimental chemical problem"'' is thoroughly analyzed, providing a comprehensive understanding of its essence. Moreover, the paper delves into the concept of ``virtual chemical laboratories'', discussing their primary types, advantages, and disadvantages, which define the methodical boundaries for their implementation in chemistry education, particularly in supporting educational chemical experiments.Furthermore, the study scrutinizes the major benefits and limitations of virtual chemical laboratories regarding the modeling of chemical processes necessary for creating virtual experimental problems in chemistry. The distinctive features of VLab, a virtual chemical laboratory, are elucidated, shedding light on its operational essence and the process of designing virtual laboratory work within it. Notably, two types of virtual chemical laboratories, namely distance laboratories and imitation laboratories, are identified as integral components for supporting students' research activities. The synergistic combination of these laboratory types, particularly in the study of the topic ``Solutions'', offers an opportunity to harness the advantages of each type and elevate the level of support for students' research activities during the learning process.To exemplify the practical implementation of this approach, the paper presents developed virtual chemical works, providing insights into their essence and purpose. Drawing from the successful integration of virtual chemical laboratories in diverse educational institutions, the paper justifies the assumption regarding the effectiveness of utilizing the developed virtual experimental chemical problems to foster students' research activities within the context of studying the topic ``Solutions''. This research contributes to the field of educational technology by providing evidence-based insights into the potential of virtual chemical laboratories for enhancing student engagement and competency development in chemistry education.
Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment

Abstract With the increasing rise of distributed system technologies, one of the most pressing problems facing the digital world is ensuring the security of sensitive and confidential data during transport and storage, which is also regarded as one of the most critical difficulties facing cloud computing. Numerous techniques exist for enhancing data security in the cloud computing storage environment. Encryption is the most important method of data protection. Consequently, several accessible encryption strategies are utilized to provide security, integrity, and authorized access by employing modern cryptographic algorithms. Cloud computing is an innovative paradigm widely accepted as a platform for storing and analysing user data. The cloud is accessible via the internet, exposing the data to external and internal threats. Cloud Service Providers (CSPs) must now implement a secure architecture to detect cloud intrusions and safeguard client data from hackers and attackers. This paper combines Stochastic Gradient Descent long short-term memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. User registration, intrusion detection, and intrusion prevention are the three phases of the planned system. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. In the data access phase, cloud data access is managed by authenticating the authorized user with the Blowfish encryption algorithm. Comparing the proposed classifier to existing classifiers demonstrates that it detects abnormal access accurately. The experimental outcomes enhanced data security, which can be utilized to protect cloud computing applications. The experimental results of the suggested SGD-LSTM algorithm indicated a high level of protection, as well as a considerable improvement in security and execution speed when compared to algorithms that are often used in cloud computing.