Reviewerly/exHarmony huggingface dataset:
Comparing the performance of different optimizers in convolutional neural networks for leukocyte classification

This paper investigates the effectiveness of different optimizers for leukocyte classification in convolutional neural networks. We compare the performance of optimization functions supported by TensorFlow. Our experiment shows that “Adam” optimizer achieves the highest accuracy of 0.9844, followed by “RMS Prop” with 0.9776. The lowest accuracy of 0.4158 was achieved by “Gradient Descent”. Our study demonstrates the importance of selecting optimal optimizer for best performance in leukocyte classification tasks using CNNs on blood images.
Verification of serialising instructions for security against transient execution attacks

Abstract Transient execution attacks such as Spectre and Meltdown exploit speculative execution in modern microprocessors to leak information via cache side‐channels. Software solutions to defend against many transient execution attacks employ the lfence serialising instruction, which does not allow instructions that come after the lfence to execute out‐of‐order with respect to instructions that come before the lfence . However, errors and Trojans in the hardware implementation of lfence can be exploited to compromise the software mitigations that use lfence . The aforementioned security gap has not been identified and addressed previously. The authors provide a formal method solution that addresses the verification of lfence hardware implementation. The authors also show how hardware Trojans can be designed to circumvent lfence and demonstrate that their verification approach will flag such Trojans as well. The authors have demonstrated the efficacy of our approach using RSD, which is an open source RISC‐V based superscalar out‐of‐order processor.
EDUCATIONAL SUPREMATISM: FRAMELESS SPASE IN EDUCATION

Objective. To improve the training programs for psychiatry interns.&#x0D; Materials. The bibliographic method and the structural-logical analysis method were used. The systematic approach was the methodical basis.&#x0D; Results. In order to help document the multidimensional achievements of interns in 2021, an experimental version of an educational portfolio was introduced at the department meeting. The portfolio consists of a title page and four appendices (information on scientific research activity, lecture attendance, organizational practice and work as part of a multidisciplinary team at a medical institution). Each intern is required to document their own achievements, which can be confirmed by signatures of lecturers, certificates, testimonials from practice supervisors, publications, etc.&#x0D; Conclusion. During the implementation process, we have substantiated the methodological foundations of the "Portfolio" technology, described the advantages and disadvantages of this method.
Cyber security industry clusters

Drıven by national security interests and private sector advocacy, the cybersecurity industry plays an important role in our shared economic and cultural reality. A key premise of this report is that the cybersecurity industry does not emerge in a vacuum, but rather is influenced by the environmental conditions that exist at a particular place and time. Industry can be seen as a manifestation of two broad interactions: industrial clustering processes. Through comprehensive mapping, we show that cybersecurity clusters are layered across large, diverse urban areas, within complex, multimodal transportation networks, with nearby universities, and across household income sectors.
ADMINISTRATIVE STRUCTURE OF INSTITUTIONS OF PUBLIC EDUCATION OF THE USSR IN THE 1920s

The article examines the issue of the management system of the first Soviet pedagogical institutions of higher education in the 1920s - institutes of public education. On the basis of normative documents that established the management system of each higher education institution, the scope of duties of the rector, political commissar, activities of institute and faculty councils, commissions, general meetings, etc. were revealed.
Social Distancing Detector Using YOLO3 Algorithm

In 2019 our world was introduced by a pandemic named as corona which had taken a lot of lives, because of which our India has suffered a lot. So we decided to make something which can help peoples to follow the norms of government and can take care of others as well as themselves. Hence we had decided to work on this project. The social distancing among persons are the best solution for avoiding the COVID and thus this paper is based on similar aspects. The paper proposes a solution for maintaining the distance among person as per guidelines by using YOLO based algorithm and proposed a solution as “Social Distancing Detector”. Social distance detector is used to provide information about people who disobey the norms of maintaining the distance between any two persons and provide demarcation by means of red rectangular box for those who are not maintaining the proper distance among themselves whereas segregating other with green rectangle boxes. Thus, social distancing can be monitored through remote location and having clear demarcation.
HTSE: hierarchical time-surface model for temporal knowledge graph embedding

Representation learning based on temporal knowledge graphs (TKGs) has attracted widespread interest, and temporal knowledge graph embedding (TKGE) expresses time entity and relation tokens and exhibit strong dynamics. Despite the significance of the dynamics and the persistent updates in TKGs, most studies have been devoted to static knowledge graphs. Moreover, previous temporal works ignored the semantic hierarchies observed in knowledge modelling cases, which are common in real-world applications. Inaccurate semantic expressions caused by incomplete projections might not capture complex topological structures very well. To solve this problem, a novel hierarchical time-surface embedding (HTSE) model is proposed for the representation learning of entities, relations and time. Specifically, a unified relation-oriented hierarchical space aims to distinguish relations at different semantic levels of a hierarchy, and entities can naturally reflect the corresponding hierarchy. Then, a time surface aims to enhance the temporal characteristics, and quadruples are learned through exponential mapping and tangent planes in the time surface. According to extensive experiments, HTSE can achieve remarkable performance on five benchmark datasets, outperforming baseline models for time scope prediction, temporal link prediction and hierarchical relation embedding tasks.Furthermore, the qualitative analysis is used to demonstrate the explainable strategy for hierarchical embeddings and their significance in TKGs.
De-identification techniques to preserve privacy in medical records

The COVID-19 pandemic has required a collective global effort to be faced. The need to rapidly exchange clinical information to advance medical investigations has highlighted the importance of clinical de-identification techniques to make protected health information in electronic health records shareable and publishable while fully complying with privacy regulations. In this study, a comparative analysis is provided regarding the performance of language models with respect to the Italian language, employing the SIRM COVID-19 de-identification corpus data set, built in accordance with the US HIPAA regulations and labeled consistently with the 2014 English i2b2 data set for the purpose of applying named entity recognition for de-identification of electronic medical records. Different language models were compared that achieved state-of-the-art results in the literature, based on deep neural architectures such as bidirectional long short-term memory with conditional random field in combination with different word representations, i.e., embeddings capable of capturing morphosyntactic variations and polysemy of words, and bidirectional encoder representations from transformers. In addition, the effectiveness of different transfer learning mechanisms to improve performance in the Italian language using English language data was tested. The examination allows for highlighting the advantages and disadvantages of the different approaches, showing how to push performance in low-resource scenarios such as Italian, and highlighting some key design points to consider in the analyzed scenarios.
Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary

Machine learning models are considered as the model owners’ intellectual property (IP). An attacker may steal and abuse others’ machine learning models such that it does not need to train its own model, which requires a large amount of resources. Therefore, it becomes an urgent problem how to distinguish such compromise of IP. Watermarking has been widely adopted as a solution in the literature. However, watermarking requires modification of the training process, which leads to utility loss and is not applicable to legacy models. In this chapter, we introduce another path toward protecting IP of machine learning models via fingerprinting the classification boundary. This is based on the observation that a machine learning model can be uniquely represented by its classification boundary. For instance, the model owner extracts some data points near the classification boundary of its model, which are used to fingerprint the model. Another model is likely to be a pirated version of the owner’s model if they have the same predictions for most fingerprinting data points. The key difference between fingerprinting and watermarking is that fingerprinting extracts fingerprint that characterizes the classification boundary of the model, while watermarking embeds watermarks into the model via modifying the training or fine-tuning process. In this chapter, we illustrate that we can robustly protect the model owners’ IP with the fingerprint of the model’s classification boundary.
Protecting Image Processing Networks via Model Watermarking

Deep learning has achieved tremendous success in low-level computer vision tasks such as image processing tasks. To protect the intellectual property (IP) of such valuable image processing networks, the model vendor can sell the service in the manner of the application program interface (API). However, even if the attacker can only query the API, he is still able to conduct model extraction attacks, which can steal the functionality of the target networks. In this chapter, we propose a new model watermarking framework for image processing networks. Under the framework, two strategies are further developed, namely, the model-agnostic strategy and the model-specific strategy. The proposed watermarking method performs well in terms of fidelity, capacity, and robustness.
Model Watermarking for Deep Neural Networks of Image Recovery

Recently, there is an increasing interest in applying deep learning to image recovery, an important problem in low-level vision. Publishing pre-trained DNN models of image recovery has become popular in the society. As a result, how to protect the intellectual property of the owners of those models has been a serious concern. To address it, this chapter introduces a framework developed in our recent work for watermarking the DNN models of image recovery. The DNNs of image recovery differ much from those of image classification in various aspects. Such differences pose additional challenges to the model watermarking, but meanwhile they also bring chances for improvement. Using image denoising and image super-resolution as case studies, we present a black-box watermarking approach for pre-trained models, which exploits the over-parameterization property of an image recovery DNN. Moreover, a watermark visualization method is introduced for additional subjective verification.
Protecting Recurrent Neural Network by Embedding Keys

Recent advancement in artificial intelligence (AI) has resulted in the emergence of Machine Learning as a Service (MLaaS) as a lucrative business model which utilizes deep neural networks (DNNs) to generate revenue. With the investment of huge amount of time, resources, and budgets into researching and developing successful DNN models, it is important for us to protect its intellectual property rights (IPRs) as these models can be easily replicated, shared, or redistributed without the consent of the legitimate owners. So far, a robust protection scheme designed for recurrent neural networks (RNNs) does not exist yet. Thus, this chapter proposes a complete protection framework that includes both white-box and black-box protection to enforce IPR on different variants of RNN. Within the framework, a key gate was introduced for the idea of embedding keys to protect IPR. It designates methods to train RNN models in a specific way such that when an invalid or forged key is presented, the performance of the embedded RNN models will be deteriorated. Having said that, the key gate was inspired by the nature of RNN model, to govern the flow of hidden state and designed in such a way that no additional weight parameters were introduced.
The Contribution of Digital Transformation Industry (DTI) in Micro- and Macro- Economy

Recently, national economies attempt to retard transformational changes of global economy and confront the economic recession by taking measures regarding protectionist measures and growing introduction of sanctions and trade restrictions partially caused by the Covid-19 pandemic. Besides, at a human resource side, the pursuing of high-quality and crisis-free economic growth is also attributed to an imperative need of following new styles of leadership, the adaptability towards digitalized economies, the adoption of Industry 4.0 forms of industrialization, as well as the introduction of a planning system based on new technological paradigms and novel patterns of production. In the relevant literature, the Digital Transformation in Industry (DTI) is extended to a wide range of applications, including the hospitality industry, single-industry “smart” cities, environmental concerns, Exploration and Production (E&P) industry. All these applications showed that DTI plays a decisive role in the contexts of micro- and macro-economy, especially in the sectors of entrepreneurship and manufacturing. Conclusively, the main DTI challenges and future prospects have been also signified.
THE EVOLUTION OF THE CENTRAL BANK’S POLICY ON CRYPTOCURRENCIES

In this article, we discuss the central banks’ attitude to cryptocurrencies and focus more on European Central Bank. First, based on the analysis of scientific literature, we show that cryptocurrency is money and performs all of the functions of money, such as the exchange medium, value storage, and accounts unit. We found a positive correlation between the level of economic development of a country and the level of regulation and the integration of crypto cryptocurrencies into the economic system. We discuss not only the approach of the ECB to cryptocurrencies, but also how it developed. According to the study data, the ECB only began to respond to cryptocurrency as an equivalent monetary instrument in 2021, established regulatory mechanisms, and developed the digital euro project.
Scribble as Music and Movement

In this chapter, scribble is explored as ‘a line being taken for a walk’, as something ‘on the move’, something doing, happening, effected and affecting. Ehrenzweig’s analogy of the apparently accidental glissando and vibrato inflexions, or the slight distortions of rhythm and intensity which defy musical notation and are left to the seemingly ‘arbitrary’ execution by the performer are linked to avant-garde practices of repetition-variation and scribbled graphic music scores, where composers, performers and audiences are given more room to improvise interpretation. Exemplified by various forms of artistic practice depicting music and movement and theoretical directions found in phenomenology, philosophy and physics, the idea of scribble movement is explored as emergent; scribble as movement, or forms of creating and thinking on the go, such as that of abstract expressionist and master-scribbler Cy Twombly.
Rural primary teachers’ perceived affordances of ICT training in Nepal

ICT training can be conceptualised based on affordance theory that offers a lens to observe how ICT training is perceived. This study examines teachers’ perceived value of ICT training and how they developed their awareness of affordances in the pedagogical practice of ICT. Since government teacher training does not cover ICT, ICT training along with computer lab is provided by NGOs. NGOs, thus, stepped in to complement the generic government teacher training. This study reports the findings of how goal-oriented ICT training offers teachers the capability of recognising the potential of training, and teachers progressively actualise knowledge of affordances into practices. ICT is still not a part of the key universities’ pre-service teacher training programmes. In-service training provided by NGOs that covers ICT is sometimes introductory and sometimes on-going. Following a one-week initial ICT training provided by NGOs, teachers were able to incorporate digital tools into lessons and use digital content to encourage students’ collaborative learning. When it first became apparent that teachers needed ICT training, the government directed them toward ICT training programmes at for-profit institutions. This study, thus, contributes to a practical understanding of affordance that can be utilised in government interventions of training and information system management.
Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users’ expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews’ classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users’ sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations.
Urdu paraphrase detection: A novel DNN-based implementation using a semi-automatically generated corpus

Abstract Automatic paraphrase detection is the task of measuring the semantic overlap between two given texts. A major hurdle in the development and evaluation of paraphrase detection approaches, particularly for South Asian languages like Urdu, is the inadequacy of standard evaluation resources. The very few available paraphrased corpora for these languages are manually created. As a result, they are constrained to smaller sizes and are not very feasible to evaluate mainstream data-driven and deep neural networks (DNNs)-based approaches. Consequently, there is a need to develop semi- or fully automated corpus generation approaches for the resource-scarce languages. There is currently no semi- or fully automatically generated sentence-level Urdu paraphrase corpus. Moreover, no study is available to localize and compare approaches for Urdu paraphrase detection that focus on various mainstream deep neural architectures and pretrained language models. This research study addresses this problem by presenting a semi-automatic pipeline for generating paraphrased corpora for Urdu. It also presents a corpus that is generated using the proposed approach. This corpus contains 3147 semi-automatically extracted Urdu sentence pairs that are manually tagged as paraphrased (854) and non-paraphrased (2293). Finally, this paper proposes two novel approaches based on DNNs for the task of paraphrase detection in Urdu text. These are Word Embeddings n-gram Overlap (henceforth called WENGO), and a modified approach, Deep Text Reuse and Paraphrase Plagiarism Detection (henceforth called D-TRAPPD). Both of these approaches have been evaluated on two related tasks: (i) paraphrase detection, and (ii) text reuse and plagiarism detection. The results from these evaluations revealed that D-TRAPPD ( $F_1 = 96.80$ for paraphrase detection and $F_1 = 88.90$ for text reuse and plagiarism detection) outperformed WENGO ( $F_1 = 81.64$ for paraphrase detection and $F_1 = 61.19$ for text reuse and plagiarism detection) as well as other state-of-the-art approaches for these two tasks. The corpus, models, and our implementations have been made available as free to download for the research community.
HYBRID PARTIAL TYPE THEORY

Abstract In this article we define a logical system called Hybrid Partial Type Theory ( $\mathcal {HPTT}$ ). The system is obtained by combining William Farmer’s partial type theory with a strong form of hybrid logic. William Farmer’s system is a version of Church’s theory of types which allows terms to be non-denoting; hybrid logic is a version of modal logic in which it is possible to name worlds and evaluate expressions with respect to particular worlds. We motivate this combination of ideas in the introduction, and devote the rest of the article to defining, axiomatising, and proving a completeness result for $\mathcal {HPTT}$ .
IPP: A Seismic P-Wave’s Detection Method Based on the Initial Power of the P-Wave

The detection of P-wave is essential for the earthquake early warning system. How to detect the P-wave under noise circumstance accurately and rapidly is a challenge work. Based on the relative power of the seismic signal, a new parameter, the initial power of the P-wave (IPP), is proposed to detect the P-wave. By using the relative power of the seismic signal, the interference of the background noise is eliminated, and the initial power of the P-wave and sigma criterion are both applied to detect the P-wave. Through simulation analysis and example tests whose magnitudes are varied from Ms4.0 to Ms8.0, both the misjudgment and the missing rate of the IPP method are 0, and the time deviation within 0.2 s can reach 78.95%. Comparing with the STA/LTA, STP/LTP and PhaseNet methods, the IPP method proposed in this paper has strong robustness and high accuracy, which represents the initial power characteristic of P-wave and can effectively eliminate the interference of background noise.
ARTIFICIAL INTELLIGENCE IN THE SECURITY SYSTEM OF ENTERPRISE

Artificial intelligence (AI) is transforming a number of business functions, including enterprise security systems. Recent years have seen an increase in the use of AI in security systems to detect and mitigate security threats. The purpose of this study is to investigate the use of AI in enterprise security systems. The study aims to identify the novelties in the application of AI to enterprise security systems, the research voids in the existing literature, and the benefits of AI to enterprise security systems. Existing research on the applicability of AI in enterprise security systems will be analyzed through a literature review methodology. The study will shed light on the use of artificial intelligence in enterprise security systems and its future implications.