Reviewerly/exHarmony huggingface dataset:
Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Authors:
raw_author_name: Nasib Marbun
raw_affiliation_string: Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia
raw_author_name: Shahnoza Khaydaraliyevna Pozilova
raw_affiliation_string: Tashkent University of Information Tehgnologies Named after Mukhammad Al-Kharazmi, Tashkent, Uzbekistan
raw_author_name: Haider Ismael Shahadi
raw_affiliation_string: University of Kerbala, Karbala, Iraq
raw_author_name: Ankush Ghosh
raw_affiliation_string: University Center for Research & Development (UCRD), Chandigarh University, Mohali, Punjab, India

Abstract: Search Engine Optimization Specialist is one of the important factors that can help digital agencies improve services to all their customers. This research aims to develop a decision-making system in the recruitment process of Search Engine Optimization Specialists by applying the Preference Selection Index method. The Search Engine Optimization Specialist recruitment decision-making system in this study uses 5 (five) criteria, namely educational background, ability to use SEO tools, work experience as a Search Engine Optimization Specialist, digital marketing skills, and age. The research sample data collection technique related to the recruitment of Search Engine Optimization Specialists used in this research is Literature Study. After the author succeeds in collecting research sample data, at the next stage the author analyzes the application of the Preference Selection Index method in the recruitment of Search Engine Optimization Specialists. The results of this study show that alternative A02 (0.855641604) has the highest value and gets the first ranking position. Meanwhile, the alternatives that get the second to fifth ranking positions are A05 (0,834620739), A03 (0,741086734), A04 (0,716537597), and A01 (0,708920064). So that the most recommended alternative based on the calculation of the Preference Selection Index on the recruitment of Search Engine Optimization Specialists in this study is alternative A02 (0,855641604).
Interdisciplinary technical perspective: transformation of blockchain and opportunities for organizational training

Authors:
raw_author_name: Zhisheng Chen
raw_affiliation_string: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, People’s Republic of China

Abstract: This study explores how blockchain technology can be applied in the area of training in organizations, the benefits it brings, and the management challenges that blockchain technology raises. By exploring the decentralized, transparent, and intelligent contract nature of blockchain technology, we analyzed the application of blockchain technology to organizational training. The results show that organizations can achieve training resource sharing, supply and demand balancing, training certification, and intelligent contract enforcement based on blockchain technology. However, it also presents potential challenges, mainly regarding engagement across sectors, support from all members, global technology rollout, staff competency, data confidentiality, and return on investment. Case and industry analysis further validates the content of our research and demonstrates the potential and challenges of blockchain technology in the field of organizational training.
Identification of Panoramic Photographic Image Composition Using Fuzzy Rules

Authors:
raw_author_name: Tsorng-Lin Chia
raw_affiliation_string: Department of Applied Artificial Intelligence, Ming Chuan University, Taoyuan City 333, Taiwan
raw_author_name: Ping-Sheng Huang
raw_affiliation_string: Department of Electronic Engineering, Ming Chuan University, Taoyuan City 333, Taiwan

Abstract: Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future.
Multifractal signal generation by cascaded chaotic systems and their analog electronic realization

Authors:
raw_author_name: Stanisław Drożdż
raw_affiliation_string: Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31–342, Kraków, Poland
raw_author_name: Pedro A. Valdes-Sosa
raw_affiliation_string: School of Life Science and Technology, University of Electronic Science and Technology of China, 611731, Chengdu, China
raw_author_name: Mattia Frasca
raw_affiliation_string: Department of Electrical Electronic and Computer Engineering, University of Catania, 95131, Catania, Italy
raw_author_name: Ludovico Minati
raw_affiliation_string: School of Life Science and Technology, University of Electronic Science and Technology of China, 611731, Chengdu, China

Abstract: Multifractality, that is, self-similarity where in scaling follows a continuous spectrum of exponents, is a ubiquitous property of the morphology and dynamics of large-scale complex systems. However, to date, the prerequisites for the generation of multifractal time series by dynamical systems remain an open issue. This work demonstrates that multifractality can emerge in the time series spontaneously generated by a small ensemble of cascaded nonlinear oscillators, which are deterministic, autonomous and delayless. Namely, a chain of four Rössler systems with directed couplings is investigated numerically and realized experimentally in the form of analog electronic circuits. The observation of multifractality is established using the detrended fluctuation analysis and confirmed through surrogate tests, wavelet-based analyses and cascade structure visualization. Multifractality consistently arises when the oscillation frequencies of the coupled oscillators span a sufficient range along the chain, and the couplings have an intermediate strength which engenders partial entrainment between the adjacent nodes. These results affirm that neither external entropy injection nor driving are indispensable, and provide a blueprint for the design of self-contained generative circuits toward diverse applications across physiology modeling and unconventional computing.
Hardware and Software Solutions for the Generation of a Database of HSV-Color Characteristics for the Main Ores and Rocks of the Khibiny Massif

Authors:
raw_author_name: Alena Arkadievna Kompanchenko
raw_affiliation_string: Geological Institute, Kola Science Centre, Russian Academy of Sciences, 184209 Apatity, Russia
raw_author_name: Denis Olegovich Volkov
raw_affiliation_string: Department of Mining Earth Sciences and Environmental Engineering, Apatity Branch of Murmansk Arctic University, 184209 Apatity, Russia; Mining Institute, Kola Science Centre, Russian Academy of Sciences, 184209 Apatity, Russia

Abstract: This article presents developed hardware and software solutions based on the application of machine vision technology. The hardware and software solutions were created in order to generate a database of HSV-color value for the main ore types, host rocks, and minerals to define criteria for the in-process identification of the Khibiny apatite in testing the walls of blasting boreholes. The hardware ensures a multi-parametric assessment of the optical characteristics of samples and minerals located on their surface. The designed software solution allows the user to control the measurement process; systematize a description of the textural and structural features of the sample under study; and process images of the core surface. The resultant database of HSV-color value for the main ores and rocks of the Khibiny massif and their constituent minerals will provide an opportunity to search for criteria for the in-process identification of the Khibiny apatite in a mineral mixture.
Advancing Human Motion Recognition with SkeletonCLIP++: Weighted Video Feature Integration and Enhanced Contrastive Sample Discrimination

Authors:
raw_author_name: Lin Yuan
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Zhen He
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Qiang Wang
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Leiyang Xu
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Abstract: This paper introduces 'SkeletonCLIP++', an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, 'Weighted Frame Integration' (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, 'Contrastive Sample Identification' (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model's ability to distinguish between closely related actions. Incorporating the 'BERT Text Encoder Integration' (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model's performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. 'SkeletonCLIP++' thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.
Enhanced YOLOv5s-Based Algorithm for Industrial Part Detection

Authors:
raw_author_name: Qingxiao Wu
raw_affiliation_string: Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
raw_author_name: Sicong Li
raw_affiliation_string: Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
raw_author_name: Jian Guan
raw_affiliation_string: Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Yunge Cui
raw_affiliation_string: Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: In complex industrial environments, accurate recognition and localization of industrial targets are crucial. This study aims to improve the precision and accuracy of object detection in industrial scenarios by effectively fusing feature information at different scales and levels, and introducing edge detection head algorithms and attention mechanisms. We propose an improved YOLOv5-based algorithm for industrial object detection. Our improved algorithm incorporates the Crossing Bidirectional Feature Pyramid (CBiFPN), effectively addressing the information loss issue in multi-scale and multi-level feature fusion. Therefore, our method can enhance detection performance for objects of varying sizes. Concurrently, we have integrated the attention mechanism (C3_CA) into YOLOv5s to augment feature expression capabilities. Furthermore, we introduce the Edge Detection Head (EDH) method, which is adept at tackling detection challenges in scenes with occluded objects and cluttered backgrounds by merging edge information and amplifying it within the features. Experiments conducted on the modified ITODD dataset demonstrate that the original YOLOv5s algorithm achieves 82.11% and 60.98% on mAP@0.5 and mAP@0.5:0.95, respectively, with its precision and recall being 86.8% and 74.75%, respectively. The performance of the modified YOLOv5s algorithm on mAP@0.5 and mAP@0.5:0.95 has been improved by 1.23% and 1.44%, respectively, and the precision and recall have been enhanced by 3.68% and 1.06%, respectively. The results show that our method significantly boosts the accuracy and robustness of industrial target recognition and localization.
Experience in Measuring Productivity of Suffix Morphemes in Chinese Military News Discourse Based on a Universal Measure

Authors:
raw_author_name: D. S. Korshunov
raw_affiliation_string: Military University of Radio Electronics

Abstract: The article studies the issues of measuring derivational productivity of affixes. It considers the concept of productivity and its various aspects which can be quantified by corpus linguistics methods. In particular, it analyzes the measures of “expanding” and “potential” productivity by H. Baayen, and proposes a new formula for “universal” productivity on the basis of the analysis. These three productivity measures are applied to 80 Chinese suffix morphemes (suffixes and semi-suffixes) within a thematic collection of texts – Chinese media reports on military topics for 2019. The language material comprises 2066 texts (2.852.262 characters). The study resulted in three variants of the rating list for the most productive suffix morphemes of the Chinese military news discourse, which can be used for didactic and applied purposes. H. Baayen’s “expanding productivity” and the “universal productivity” proposed in the work showed a high correlation (0.85), while H. Baayen’s “potential productivity” correlates with them negatively. A meaningful analysis of the results also brings up a question of the appropriateness of using the “potential productivity” measure. Comparison of existing measures and the formula proposed in the work allow us to conclude that for an approximate assessment of the suffix morphemes productivity one can use the formula of “expanding productivity” by H. Baayen, which, despite relying only on the single word token (hapax legomenon) frequency, gives quite plausible results. To measure the suffix morphemes productivity more precisely, it is recommended to use the “universal productivity” formula proposed in this paper which takes into account a larger number of significant parameters (the number of hapax legomena with the estimated affix, the total type count with the given affix, and the average regular word token frequency of the estimated affix) and is more reasonable in this respect.
Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Authors:
raw_author_name: Kene Li
raw_affiliation_string: Guangxi Engineering Research Center for Mechanism and Control Technology of Mobile Robots, Liuzhou 545006, China; School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China
raw_author_name: Wanning Lu
raw_affiliation_string: School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China
raw_author_name: Xiangsuo Fan
raw_affiliation_string: School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China

Abstract: In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.
Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone

Authors:
raw_author_name: Sayed S. R. Moustafa
raw_affiliation_string: Egyptian National Seismic Network (ENSN), Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Cairo P.O. Box 11421, Egypt

Abstract: A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.
Hardware acceleration of DNA pattern matching using analog resistive CAMs

Authors:
raw_author_name: Mohammed E. Fouda
raw_affiliation_string: Rain Neuromorphics Inc., San Francisco, CA, United States
raw_author_name: Rouwaida Kanj
raw_affiliation_string: ECE Department, American University of Beirut, Beirut, Lebanon; Synopsys Inc., Austin, TX, United States
raw_author_name: Ahmed M. Eltawil
raw_affiliation_string: King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract: DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient pipelined hardware accelerator that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and accelerates the DNA pattern matching task by more than 100× compared to the approaches described in the literature.
Quantum neural network with privacy protection of input data and training parameters

Authors:
raw_author_name: Yan Chang

Abstract: Abstract It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for <?CDATA ${{\rm{R}}}_{{\rm{z}}},$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> </mml:math> <?CDATA ${{\rm{R}}}_{{\rm{x}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">x</mml:mi> </mml:mrow> </mml:msub> </mml:math> and <?CDATA ${{\rm{R}}}_{{\rm{y}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">y</mml:mi> </mml:mrow> </mml:msub> </mml:math> gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O (4n).