Reviewerly/exHarmony huggingface dataset:
DeepPatch: Maintaining Deep Learning Model Programs to Retain Standard Accuracy with Substantial Robustness Improvement

Maintaining a deep learning (DL) model by making the model substantially more robust through retraining with plenty of adversarial examples of non-trivial perturbation strength often reduces the model’s standard accuracy. Many existing model repair or maintenance techniques sacrifice standard accuracy to produce a large gain in robustness or vice versa. This article proposes DeepPatch, a novel technique to maintain filter-intensive DL models. To the best of our knowledge, DeepPatch is the first work to address the challenge of standard accuracy retention while substantially improving the robustness of DL models with plenty of adversarial examples of non-trivial and diverse perturbation strengths. Rather than following the conventional wisdom to generalize all the components of a DL model over the union set of clean and adversarial samples, DeepPatch formulates a novel division of labor method to adaptively activate a subset of its inserted processing units to process individual samples. Its produced model can generate the original or replacement feature maps in each forward pass of the patched model, making the patched model carry an intrinsic property of behaving like the model under maintenance on demand. The overall experimental results show that DeepPatch successfully retains the standard accuracy of all pretrained models while improving the robustness accuracy substantially. However, the models produced by the peer techniques suffer from either large standard accuracy loss or small robustness improvement compared with the models under maintenance, rendering them unsuitable in general to replace the latter.
Bilateral hashing model of ECG signal encryption system using downhill peak follow (DPF)-based encryption technique

This research suggests a unique ECG security solution based on the data pattern-based encryption paradigm to address security issues in ECG signal transmission or storage environments. According to this concept, the encryption system controls the data security process by extracting key signatures from the ECG signal peaks to identify the characteristics or attributes of the data and encrypt it. To estimate the hashing qualities, the approach is to build a random key pattern based on the signal peaks and extract the signature. The signal was then encrypted and sent to a doctor or signal analyzer. Bilateral random hashing (BRH) is used to achieve this. This updates the hashing generation model by analyzing the parameters using probabilistic distributional characteristics. This crucial motif was applied. This key pattern was utilized to encrypt the data samples using the downhill peak follow (DPF) encryption technique, which follows the strength of the peak value in the ECG waveform. This suggested project is carried out using MATLAB scripting.
Scientometric Indicators of Professional Activity of a Modern Scientific and Pedagogical Worker

The article discusses the main scientometric indicators of the professional activity of a modern scientific and pedagogical worker. The historical roots of scientometry as an independent scientific discipline and its extrapolation into the field of professional activity of a scientific and pedagogical worker are revealed. The author analyzes the tools for monitoring the achievements of scientific activity of a lecturer at the university. The article also describes the key scientometric indicators used in world practice to assess the effectiveness of scientific and pedagogical worker’s scientific activity. The possible perspective guidelines for obtaining subjective conclusions about the scientific activity of a scientific and pedagogical worker in general in the conditions of digital service and the development of social networks are outlined. The research materials deepen the understanding of scientific and pedagogical worker’s research activities monitoring in the context of globalization, which expands the methodology of pedagogy.
Teaching FAIR in computational chemistry: managing and publishing data using the twin tools of compute portals and repositories

The history of the emerging area of tools for managing research resources and the data produced from them is summarised from the perspective of two decades of use in teaching and research at one institution. These tools are a portal or electronic laboratory notebook for computational chemistry interfaced in one direction to a high-performance computing resource and in the other direction to a modern research data repository. The essential features of both these tools are described over two generations of each, with examples of student work cited as examples using persistent identifiers or PIDs, better known as DOIs. Underpinning this is the metadata describing the data being processed. The article outlines the evolution of managing such metadata-rich data and its progress towards what can now be summarised by the acronym FAIR data, itself enabling future emerging areas such as knowledge graphs.
Formation of Professional Competencies Among Students of Theater Universities

The article touches upon the problems of formulating competencies in relation to creative areas of training students of higher educational institutions in educational standards. The author comes to the conclusion that the lack of clarity in the application of the competency-based approach makes the effectiveness of the entire educational process and the formation of criteria for evaluating the effectiveness of students' professionalism dependent on the teaching staff of a theater university.
Cognitive Tools for Abstract Thinking Autonomous Intelligent Mobile Systems

The actual problems of artificial intelligence related to the development of tools for abstract thinking of autonomous intelligent mobile systems are being solved, which allow planning purposeful behavior in hard-to-reach and aggressive environments for humans. Cognitive tools are proposed that provide intelligent systems with the ability to organize purposeful multi-stage activities related to solving complex problems, when a behavior plan is automatically built in some conditions of a problematic environment, and a given behavior goal is achieved in other operating conditions that are beyond the resolution of technical vision. An important feature of the proposed typical elements of knowledge representation and processing is that they allow intelligent systems to organize the output of solving complex problems, relying only on the data stored in the knowledge representation model and coming from the current operating conditions. In the general case, the developed knowledge model of intelligent systems for various purposes consists of declarative and procedural typical elements of their representation. For a formal description of typical elements of declarative knowledge representation, traditional semantic networks and various sets of restrictions are used, reflecting additional conditions for the future functioning of autonomous mobile intelligent systems. As for the formal description of the typical elements of the representation of procedural knowledge, regardless of a specific subject area, fuzzy semantic networks are used for this. This allows autonomous intelligent mobile systems to adapt to specific operating conditions in underdetermined problematic environments and perform complex tasks formulated by them on this basis. The practical significance of the results obtained lies in the effectiveness of their use for the development of intelligent problem solvers that provide autonomous intelligent mobile systems for various purposes with the ability to perform complex tasks in a priori underdetermined problematic environments by adapting the purposeful activity plan formed in general form to specific current operating conditions.
Data encryption technology application in enterprise cost operation management based on cloud computing

As a new type of calculation method, 5G cloud computing provides convenient and fast service items for enterprises and ordinary users with its characteristics of stability, reasonableness, and easy operation. Although private cloud storage guarantees the safe storage of its own business secrets and secret data, its storage operation scale, service capabilities, and openness may not be extended to cloud computing platforms. Therefore, hybrid cloud computing is the development trend of cloud computing technology in the future, but how to ensure the efficient and safe storage of data and the consistency of data information must rely on data encryption technology. With the increase in market demand, the change of business model and the development of technological progress, the company’s information management level has clearly put forward higher regulations. Only the application of a powerful information management system can quickly capture market information and fully grasp important customer and corporate information to improve business service management decisions which flexibly change the work process to integrate into the continuous changes of customers and ultimately win the market leadership. As a concept of scientific research enterprise accounting management method, ERP can create an efficient supply chain, reduce inventory, and reduce cost accounting for enterprises. This article aims to realize its practical significance by studying the data encryption technology of 5G cloud computing and applying it to enterprise accounting cost calculation.
The Current Status of Central Bank Digital Currencies and Their Development

Relying on the big data era, economic growth is also undergoing an accelerated transformation towards digitalisation and has triggered digital changes in finance due to the accelerated development of information technology and the emergence of technologies such as blockchain, artificial intelligence, and big data. This is why the BIS (Bank for International Settlements) received worldwide attention when it introduced the concept of a central bank digital currency (CBDC) in 2017. CBDC has many advantages, will inject new vitality into the global monetary and financial system, may become a new cornerstone of payment transactions and future de-velopment; But it also brings potential risks and complex policy issues. In this paper, through a review of the research literature on CBDC in recent years, with a specific example of China's central digital currency (DC/EP), this paper provides a concise demonstration of the current state of CBDC and its development.
Multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

Abstract The control of a wing-in-ground craft (WIG) usually allows for many needs, like cruising, speed, survival and stealth. Various degrees of emphasis on these requirements result in different trajectories, but there has not been a way of integrating and quantifying them yet. Moreover, most previous studies on other vehicles’ multi-objective trajectory is planned globally, lacking for local planning. For the multi-objective trajectory planning of WIGs, this paper proposes a multi-objective function in a polynomial form, in which each item represents an independent requirement and is adjusted by a linear or exponential weight. It uses the magnitude of weights to demonstrate how much attention is paid relatively to the corresponding demand. Trajectories of a virtual WIG model above the wave trough terrain are planned using reward shaping based on the introduced multi-objective function and deep reinforcement learning (DRL). Two conditions are considered globally and locally: a single scheme of weights is assigned to the whole environment, and two different schemes of weights are assigned to the two parts of the environment. Effectiveness of the multi-object reward function is analysed from the local and global perspectives. The reward function provides WIGs with a universal framework for adjusting the magnitude of weights, to meet different degrees of requirements on cruising, speed, stealth and survival, and helps WIGs guide an expected trajectory in engineering.
Lotfi A. Zadeh’s Life and His Scientific Legacy

The paper describes in detail the life and scientific heritage of Professor Lotfi A. Zadeh. The paper covers the period from the place of his birth, the city of Baku, to the end of his life. It also describes Lotfi’s information about education (doctoral studies), work, and family in the United States. Here, we also describes Zadeh's papers and the impact research results of his scientific contribution, as well as new knowledge discoveries (the history and features of the fuzzy theory, and how it originated). His scientific collaboration with other scientists (his mentors were Norbert Wiener, John R. Ragazzini. Rudolf Kalman, Charles A. Desoer and others) is described in detail and we also describe their scientific life by years up to the last stage of his life. The paper also provides information about his visit to his homeland, and participation in the international exhibition of telecommunications and information technologies BakuTel 2008. Following his will, Lotfi A. Zadeh was buried in his homeland.
Ideas of Lotfi Zadeh in Explainable Artificial Intelligence

Various aspects of fuzzy systems applications for explainable artificial intelligence are described. It is shown how the pioneering works of L. A. Zadeh can be used for the current explainable artificial intelligence challenges.
SIMULATION OF THE WASTE WATER PURIFICATION SYSTEM OF LIVESTOCK FARMS BASED ON PETRI NETS

The development of animal husbandry on an industrial basis causes a sharp increase in the amount of highly polluted wastewater, the purification and disinfection of which is one of the most important tasks when deciding on their use in agriculture as fertilizer or discharge into reservoirs or irrigation fields. The effectiveness of such systems is achieved with the help of new ways of processing information and mathematical characteristics of technology processes. Based on the study of the main methods of creating discrete chemical and technological systems (CTS), the expediency of using the design of Petri nets (SP) to simulate the process of wastewater treatment of livestock farms (OSWF) was developed. To characterize the system, we use N–schemes, which are based on the mathematical system of the joint venture, which can form a network structure both in analytical form for automating work during analysis, and in graphical form to create visibility of the created model. Analyzing technological and chemical processes, it is important to take into account the formality condition of N–circuits, which assumes that the circuits do not take the properties of simulated systems in time, since the timing of the transition is taken to be zero. We took into account these conditions and proposed updated Petri nets that are focused on the analysis and simulation of discrete CTS, by changing the time delay of labels on transitions and positions, as well as by enabling transitions that are in priority. We have created a model in the form of a modified updated Petri net (SME). With the help of SCADA TRACE MODE technologies, we have developed a whole software package for the control system in the process of OSWF. This system can perform dispatching monitoring of the main parts of the control apparatus, as well as stop and analyze and monitor its condition, this is feasible both in general and within the framework of forecasting the occurrence of emergency situations.
Solving Text-to-SQL Task Through Machine Translation

Technical people use structured Query Language (SQL) to retrieve information from relational databases, whereas novice users lack the expertise to use SQL. For these people, natural language interface to the database (NLIDB) systems is nowadays being developed to make them deal with data comfortably. These NLIDB systems convert a query in natural language (NL) to its corresponding structured query language (SQL). This task is famously known as text-to-SQL task. Most of the works in this area consider this task as a semantic parsing problem or a variation of it and though research in this area is gradually converging to satisfactory outcomes, till date no widely accepted commercial product is available. This paper considers Text-to-SQL task as a machine translation problem and describes a model that uses an open-source neural machine translation toolkit OpenNMT for training and translation and a parallel corpus developed from the Spider dataset. The training accuracy of the model is 99.97% which no existing NLIDB system have achieved so far. This result proves that a more organized dataset may lead to cent percent training accuracy and the model may be used in certain real-life applications to ease human work load. The Python code for parallel corpora generation, training and testing will be available at https://github.com/Ali-Das/Text_to_SQL_using_OpenNMT_with_Spider site.
Deep learning‐based image processing for financial audit risk quantification in healthcare

Abstract Recently, deep learning technology has gradually penetrated various fields. Today, the field of healthcare is also closely linked to deep learning technology. Image processing technology based on deep learning can accurately segment medical images, which is convenient for medical research and pathological analysis. Accurate distribution of images can effectively save medical resources. Therefore, image processing techniques can contribute to the quantification and assessment of economic audit risks. In recent years, medical image segmentation has achieved many research results. However, with the improvement of accuracy, the segmentation standards of medical images are also becoming more and more stringent. For medical images, they tend to have rough and fuzzy boundaries and noise disturbances of different shapes. The above problems pose challenges for accurate localization and segmentation of lesion regions. On the other hand, in the field of medical images, there are also problems such as unbalanced number of samples and scarcity of large medical image datasets. In response to these problems, this paper conducts research work and proposes an Attention Mechanism and Multi‐Scale spatial Pooling‐based conditional Adversarial Network (AM‐MSP‐cGAN) model to achieve automatic segmentation of medical images. AM‐MSP‐cGAN can learn more detailed features from fuzzy boundaries, and effectively solve the problem of data lack, thereby promoting economic audit risk quantification and assessment in the healthcare field.
Microarchitectural Analysis of Graph BI Queries on RDBMS

We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB overheads, and overall low (but short-term high) memory bandwidth utilization. Using huge pages increased query performance by up to 65% and workload performance by 23%.
Experimental Analysis of the Machine Learning Algorithms for Crime Web Page Classification

The evolution of web 3.0 results in storing data in plenty in the web which are huge as that of hard to search and its specific classification and this sort of issue attracts research attention. This task specifically addresses the problem of classifying web documents covering the crime data in it. Manual classification of crime related documents from the available ones is unimaginable and also observed that the existing models have not produced considerable results. It is obvious that machine learning approaches performs well in handling large amount of data. Hence, the problem of crime related web document classification is handled with an automated web page classification technique based on word similarity. The baseline is to identify the best performing feature selection approaches and combine them with machine learning models for isolating the best performing combination. The feature selection methods used are Jaccard Similarity, Information Gain, Gain Ratio and Chi Square Analysis and each of them is individually combined with four machine learning classifiers such as Support Vector Machine, K-Nearest Neighbor, J48 and Naïve Bayes. The models are tested with the data set containing Web documents pertaining to Crime, Politics, Sports and Terrorism collected from different sources. The models have proved better performance when added with feature selection approaches than without them. When it comes for comparing different combinations of feature selection approaches and machine learning models, Support vector machine combined with Jaccard Similarity is found to perform well than the other approaches with an accuracy of 98.74%.
Cryptocurrencies and portfolio diversification before and during COVID-19

Purpose This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19. Design/methodology/approach This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures. Findings The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off. Originality/value To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.