Reviewerly/exHarmony huggingface dataset:
Development of a modified UMAC algorithm based on crypto­code constructions

The development of computer technology has determined the vector for the expansion of services based on the Internet and “G” technologies. The main requirements for modern services in the banking sector are security and reliability. At the same time, security is considered not only as ensuring the confidentiality and integrity of transactions, but also their authenticity. However, in the post-quantum period, US NIST specialists question the durability of modern means of providing basic security services based on symmetric and asymmetric cryptography algorithms. The increase in computing resources allows attackers to use modern threats in combination. Thus, there is a need to search for new and/or modify known algorithms for generating MAC (message authentication codes). In addition, the growth of services increases the amount of information that needs to be authenticated. Among the well-known hash algorithms, the hash functions of universal hashing are distinguished, which allow initially determining the number of collisions and their uniform distribution over the entire set of hash codes. Possibilities of modifying the cascade hashing algorithm UMAC (message authentication code based on universal hashing, universal MAC) based on the use of McEliece crypto-code construction on algebrogeometric (elliptic codes (EC), modified elliptic codes (MEC) and damaged codes (DC). This approach allows preserving the uniqueness property, in contrast to the classical UMAC scheme based on a block symmetric cipher (AES). The presented algorithms for evaluating the properties of universality and strict universality of hash codes make it possible to evaluate the security of the proposed hashing constructs based on universal hash functions, taking into account the preservation of the universality property
Public Opinion Monitoring for Proactive Crime Detection Using Named Entity Recognition

Public opinion monitoring has been well studied in sociology and informatics. Considerable amounts of crime-related information are available on social media platforms every day. Current methods for monitoring public opinion are typically based on rule matching and manual searching instead of automated processing and analysis. However, the extraction of useful information from large volumes of social media data is a major challenge in public opinion monitoring. This chapter describes a methodology for extracting key information from a large volume of Chinese text using named entity recognition based on the LSTM-CRF model. Since traditional named entity recognition datasets are small and only contain a few types, a custom crime-related corpus was created for training. The results demonstrate that the methodology can automatically extract key attributes such as person, location, organization and crime type with a precision of 87.58%, recall of 83.22% and F1 score of 85.24%.
Blockchain Technology, Inter-Organizational Relationships and Management Accounting: A Synthesis and Research Agenda

From their origins in cryptocurrencies, blockchains are increasingly emerging as an important organizational phenomenon, especially for collaboration across firm boundaries. Over the past several decades, accounting scholars have shown significant interest in management accounting and control mechanisms that are used to sustain these inter-organizational relationships (IORs). This paper outlines fundamental technical features and limitations of permissioned blockchain technology and analytically proposes it as an empirical concept with implications for management accounting practices that underpin inter-organizational collaboration, trust, control, and information exchange. Particular focus of the analysis is on the interplay between the technical capabilities of blockchain and inter-organizational management control procedures. The analysis is distilled into a series of theoretical propositions that illustrate how these procedures affect the way in which blockchain is enacted in IORs, and how they are affected by blockchain themselves. The paper concludes with a research agenda for accounting scholars and offers directions for future research.
Curriculum Management and Graduate Outcomes in the Animation Game Study Program

The Animation Study Program is part of the Art and Design degree from the Faculty of Letters State University of Malan. It is designed to offer guidance to professional beginners in the animation world and applied media, allowing graduates to apply for roles such as a 2D/3D Animator,Character Designer, beginner Developer in Animation Industry, Game Designer/Multimedia, beginner Developer of Game Industry, Game Asset Designer. The graduates from Animation program study gain particular expertise in 3D animation games and using Indonesian local culture. The course also emphasizes education-based animation games in global context and insteal entrepreneurship for the graduates. The goal of this study is to analyze the curriculum and graduate achievement in the animation game study program. This research used a qualitative study case with a descriptive approach. Documentation and interviews were used to collect data. The analysis of data used a descriptive approach. Results of the study show that the curriculum used in Animation Game program is designed based on the graduates in diploma D1, D2, D3, and D4. They are in D1 (Asset Game Operator), D2 (Young Asset Game Designer), D3 (Young Game Designer), and D4 (Senior Game Designer). Meanwhile, the animation field is divided into D1 (Asset Animation Operator), D2 (2D/3D Young Animator), D3 (2D/3D Senior Animator), and D4 (2D/3D Lead Animator).&#x0D; Keywords: Curriculum, Graduates Outcome, Animation Game
Target Tracking in Boost Stage with Quantized Measurements of Space-based Infrared Cameras

A novel state estimation method is proposed for target tracking in the boost stage using space-based infrared cameras (SBIRC) whose measurements are essentially corrupted by both Gaussian noise and quantization noise. As the quantization noise has non-Gaussian properties, conventional extended Kalman filtering (EKF) suffers from poor performance. The quantization noise of SBIRC is modelled using the mid-riser quantizer, which is usually adopted in the digital signal processing field. A novel minimum square error (MMSE) state estimation algorithm with quantized measurements, named the quantized extended Kalman filtering (QEKF), is then proposed. The time update is given based on first-order linearization of the nonlinearities, and the measurement update is derived based on the conditional mean estimate given the quantized measurements. As the multidimensional integrals in the measurement update derived doesn't have analytical solutions, a numerical integration method is proposed by combining Genz's transformation and quasi-Monte Carlo (QMC) method, which can avoid the curse of dimensionality. To further improve the tracking accuracy, quantized high-degree cubature Kalman filtering (QHCKF) is developed by integrating the fifth-degree cubature rule into the framework of the QEKF. Numerical simulation results illustrate the superiority of the proposed QEKF and QHCKF methods.
Formation of the Leadership Position of Professionals in Higher Education Institutions

The article theoretically substantiates, develops and experimentally verifies pedagogical conditions for forming the leadership position of future engineers in higher education institutions of technical profile. The concept of the leadership position of a future engineer was first defined as a conscious willingness to fulfill the role of a leader and the totality of all relations of his personality to the reality formed in a certain system of group relationships generated by this awareness. The basic components of a leadership position are defined: motivational-value, cognitive, activity and personal. It is experimentally proved that pedagogical conditions of formation of the leadership position of the future engineer in the institution of higher technical education are: involvement of students in practical independent activity, which involves the application of organizational, communicative, cognitive, reflexive skills and causes the manifestation of their leadership; modeling of professionally oriented tasks, which require students to choose the best ways of leadership influence in situations of dialog and group interaction; nurturing a valuable attitude to leadership in out-of-class and classroom work. The model of realization of pedagogical conditions of formation of the leadership position of future engineers in institutions of higher technical education has been developed, which includes the purpose, methodological approaches, methodological support of the realization of pedagogical conditions, estimation of levels of formation of leadership position in future engineers. The special importance of the complex and consistent introduction of pedagogical conditions in work with future engineers is shown.
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning

DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs during the DNN computations to reduce data movement from DRAM to the chip. The reuse is captured by the accelerator's dataflow. While there has been significant prior work in exploring and comparing various dataflows, the strategy for assigning on-chip hardware resources (i.e., compute and memory) given a dataflow that can optimize for performance/energy while meeting platform constraints of area/power for DNN(s) of interest is still relatively unexplored. The design-space of choices for balancing compute and memory explodes combinatorially, as we show in this work (e.g., as large as O(10^(72)) choices for running \mobilenet), making it infeasible to do manual-tuning via exhaustive searches. It is also difficult to come up with a specific heuristic given that different DNNs and layer types exhibit different amounts of reuse. In this paper, we propose an autonomous strategy called ConfuciuX to find optimized HW resource assignments for a given model and dataflow style. ConfuciuX leverages a reinforcement learning method, REINFORCE, to guide the search process, leveraging a detailed HW performance cost model within the training loop to estimate rewards. We also augment the RL approach with a genetic algorithm for further fine-tuning. ConfuciuX demonstrates the highest sample-efficiency for training compared to other techniques such as Bayesian optimization, genetic algorithm, simulated annealing, and other RL methods. It converges to the optimized hardware configuration 4.7 to 24 times faster than alternate techniques.
Technical Report: The Policy Graph Improvement Algorithm

Optimizing a partially observable Markov decision process (POMDP) policy is challenging. The policy graph improvement (PGI) algorithm for POMDPs represents the policy as a fixed size policy graph and improves the policy monotonically. Due to the fixed policy size, computation time for each improvement iteration is known in advance. Moreover, the method allows for compact understandable policies. This report describes the technical details of the PGI [1] and particle based PGI [2] algorithms for POMDPs in a more accessible way than [1] or [2] allowing practitioners and students to understand and implement the algorithms.
Designing Digital Advertising with Scribble Animated Video Techniques

Digital Advertising using the scribble animation video technique is one of the promotional media used to provide information about Footwear Marks.C products. This video advert aims to attract consumers to buy local products. In order stand out from other promotional videos of similar products, in this video scribble animation effect is added to strengthen the message. The process of visualizing this video advertising starts from data searching, then the data is analyzed and the findings used in the concept drafting. Finally the title, synopsis, and storyboard are made. The next step begins with the selection of actors, the process of filming, video editing, and music composing. The vfx editing stage is where the scribble animation is added with adobe after-effect software. Then, the final stage of rendering occurs, followed by publishing on social media platforms such as Youtube and Instagram. This design produced an animation effect video entitled ”Digital advertising Marks. C Footwear with scribble animation” which has been uploaded on Youtube and Instagram with a video duration of 3 minutes 22 seconds.&#x0D; Keywords: digital advertising, scribble animation
Quantitative Detection of Plasmodium falciparum Using, LUNA-FL, A Fluorescent Cell Counter

The microscopic examination of Giemsa-stained thin and/or thick blood films (Giemsa microscopy) is the standard method of malaria diagnosis. However, the results of the diagnosis significantly depend on the skills of clinical technicians. Furthermore, sample preparation and analysis are laborious and time-consuming. Therefore, in this study, we investigated if a commercially available fluorescent cell counter, LUNA-FL, was useful for the detection of Plasmodium parasite and the estimation of parasitemia. Whole blood samples from uninfected persons, spiked with P. falciparum-infected erythrocytes, were analysed. Most of the leucocytes and platelets were removed from whole blood samples with SiO2-nanofiber filters set on spin columns. The filtered samples were stained with acridine orange, and automatic detection, as well as counting of erythrocytes and parasites, were performed using LUNA-FL. Whole blood, with various levels of parasites, was analysed by Giemsa microscopy or with LUNA-FL to estimate parasitemia, and a comparative analysis was performed. The coefficient determination value of the regression line was high (R2 = 0.98), indicating that accurate quantitative parasite detection could be performed using LUNA-FL. LUNA-FL has a low running cost; it is compact, fast, and easy to operate, and may therefore be useful for point-of-care testing in the endemic areas.
Quantum Computation for Big Information Processing

Big data is a hot buzzword that has impacted every field nowadays. Big data applies to applications that are beyond storage, processing and analyzing capabilities of traditional processes and tools. Big data is the gold mine of data piled up and to mine it for useful insights, brands it both opportunistic and challenging at one go. The healthcare sector is the best example of big data, where data is not only produced in huge quantities but is very complex and diverse because of the varied formats of the data. In such a scenario, quantum computing offers promises of rescue by providing new features in information processing systems, particularly in big data analytics. In this study, we have proposed a framework of how the power of quantum computing can be used for solving the big data problem particularly focusing it on healthcare data sector. A brief review of quantum computing techniques is also carried in the form of literature review. The challenges, promises, future directions, and techniques of quantum computing in big data analytics are also highlighted.
Sistem Pakar Dengan Metode Forward Chaining untuk Diagnosis Penyakit dan Hama Tanaman Semangka

Watermelon crops are in demand by farmers to cultivate. Diseases and Pests are not separated from watermelon crops experienced by farmers. To overcome this, farmers rely solely on rough living and getting information from others, this is ineffective because watermelon plant experts are very limited. An expert system is the solution to the limitations of an expert in their field. In this expert system use advanced chain methods and website-based applications. This expert system can be accessed online for users who need information, consultation of diseases and pests on watermelon plants. The expert system will diagnose the symptoms that will be answered by the user of the application and will produce conclusions along with solutions from diseases and pests of watermelon plants. With expert limitations online system is no longer an obstacle for watermelon plant farmers. Information and consultation of diseases and pests of watermelon plants biased online without having to meet with experts.
Management of the nursing processes of internal students in the confrontation of the COVID-19

Nursing plays a key role in the management of services and care to guarantee the safety of patients and their professionals. This includes a diversity of care practices and initiatives arising from the new pandemic. The objective of the research was to describe the determining role of comprehensive student training for the correct management of nursing processes in the community boarding school, based on health education in the face of the current pandemic. A qualitative study was carried out, which describes the perception of four nursing interns in their rotation through the Health Center. Individual and group interviews were developed. It was evidenced that the students, during their training, have acquired abilities, skills, knowledge, attitudes, and values, in terms of biosafety measures, nursing management, and nursing care process, which allowed them to carry out the management of community nursing processes and the confrontation and combat of the pandemic caused by the SARS-CoV2 coronavirus, based on health education for the population.
Research on Vehicle Tracking Algorithm Based on Deep Learning

Abstract Comparing with the advantages and disadvantages of the existing target tracking algorithms based on deep learning, a vehicle tracking algorithm based on Yolov2 and GOTURN algorithm is proposed, which is called YOLOv2-tracker vehicle tracking algorithm. The Algorithm is trained and tested by using the collected training set and test set. The results show that the YOLOv2-tracker vehicle tracking algorithm can achieve higher tracking accuracy and faster tracking speed, and can effectively overcome environmental interference. Further analysis of the test results, the algorithm found that there is “errof” phenomenon, the paper discusses and analyzes the causes of this phenomenon, and put forward a reasonable solution. In addition, a “dynamic save” method is proposed to solve the “lost track” problem.
Notice of Proposed Rulemaking: Classification of Cloud Transactions and Transactions Involving Digital Content

This submission makes comments on REG-130700-14, which includes proposed regulations on the classification of cloud transactions and transactions involving digital content.In addition to some suggested changes to reflect industry practice, the submission importantly suggests recognition and the inclusion of an example to deal with profit-shifting structures.
Efficiency assessment of seismological information, monitoring system and seismicity study for the Republic of Armenia

&amp;lt;p&amp;gt;To study the deep structure of the earth it is important to have an optimal monitoring network and reliable seismic baseline database for the investigated area. In addition, the territory of Armenia according to its geology and seismological conditions is a full-scale experimental polygon for seismic problems, in particular the study of Earth's deepest structure by seismic methods. For this purpose, the work aims to assess the effectiveness of the existing seismic monitoring system in Armenia, to offer optimal solutions for the station layout, to evaluate the accuracy of seismic information registered in the RA by performing hypocenter recalculation. Then, within of the work organized modern seismic stations in Armenia and Russia towns with seismic equipment made and produced in Armenia (IGES NAS RA). The work was supported by MESCS Science Committee of the Republic of Armenia (grant &amp;amp;#8470; 18SH-1E012).&amp;lt;/p&amp;gt;
Moleküler Potansiyel Enerji Fonksiyonu İçin Geliştirilmiş Sosyal Örümcek Algoritması

The social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors to solve continuous optimization problems. In this study, SSA is used in order to minimize a simplified model of the energy function of the molecule. The Molecular potential energy function problem is one of the most important real-life problems. The Molecular potential energy function problem attempts to predict the 3D structure of a protein. SSA is developed by various techniques (Crossover-mutation and Gbest convergence-silent spider techniques) and SSA is called Improved SSA (ISSA). By these techniques, the exploration and exploitation capabilities of SSA in the continuous search space are improved. The general performances of SSA and ISSA are tested on low-scaled and large-scaled thirteen benchmark functions and obtained results are compared with each other. Wilcoxon signed-rank test is applied to SSA and ISSA results. Then, the general performance of the SSA and ISSA is tested on a simplified model of the molecule for different dimensions. Also, the performance of the ISSA is compared to various state-of-art algorithms in the literature. The results showed the superiority of the performance of ISSA.
Q-Learning Induced Artificial Bee Colony for Noisy Optimization

The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding of the given solution. The fitness estimate and the fitness variance of a sub-population surrounding the given solution are jointly used to signify the degree of noise contamination in its local neighborhood (LN). The adaptation of sample size based on the characteristics of the fitness landscape in the LN of a solution is realized here with the temporal difference Q-learning (TDQL). The merit of the present work lies in utilizing the reward-penalty based reinforcement learning mechanism of TDQL for sample size adaptation. This sidesteps the prerequisite setting of any specific functional form of relationship between the sample size requirement of a solution and the noisy fitness profile in its LN. Experiments undertaken reveal that the proposed algorithms, realized with artificial bee colony, significantly outperform the existing counterparts and the state-of-the-art algorithms.