Reviewerly/exHarmony huggingface dataset:
Architecture to Distribute Deep Learning Models on Containers and Virtual Machines for Industry 4.0*

Deep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative to them is the use of distributed DL (DDL) which differs from Federated Deep Learning in that it focuses on accelerating calculations and does not address data privacy. DLL requires having several computing nodes. This is where cloud computing comes in. Cloud computing allows resources or virtual machines to be allocated on demand, which reduces costs. However, the allocation of GPU resources has a higher cost than CPU resources, which can be problematic for small businesses. This article proposes to exploit the DDL on CPUs via the on-demand allocation of virtual machines in order to reduce costs. In addition, a solution for deploying the software stack necessary for proper operation is proposed. This is achieved using a containerization which is only composed of the software suites needed to run the DDL to minimize the container transfer size and consequently minimize the container deployment time.
Control of the von Neumann Entropy for an Open Two-Qubit System Using Coherent and Incoherent Drives

This article is devoted to developing an approach for manipulating the von Neumann entropy S(ρ(t)) of an open two-qubit system with coherent control and incoherent control inducing time-dependent decoherence rates. The following goals are considered: (a) minimizing or maximizing the final entropy S(ρ(T)); (b) steering S(ρ(T)) to a given target value; (c) steering S(ρ(T)) to a target value and satisfying the pointwise state constraint S(ρ(t))≤S¯ for a given S¯; (d) keeping S(ρ(t)) constant at a given time interval. Under the Markovian dynamics determined by a Gorini-Kossakowski-Sudarshan-Lindblad type master equation, which contains coherent and incoherent controls, one- and two-step gradient projection methods and genetic algorithm have been adapted, taking into account the specifics of the objective functionals. The corresponding numerical results are provided and discussed.
A Novel Approach for Recognizing Occluded Objects Using Feature Pyramid Network Based on Occlusion Rate Analysis

In response to the rising adoption of smart video surveillance systems (SVS), this paper addresses a common challenge: occlusion in object detection. Existing object recognition methods often overlook the relative occlusion of neighboring objects, leading to real-world SVS systems encountering significant issues. Recent occlusion-handling approaches have limitations, such as data type inflexibility and difficulty distinguishing objects. In this study, we propose an end-to-end solution. Our approach utilizes the Feature Pyramid Network (FPN) for small and overlapping object detection, replaces Grey Level Co-Occurrence Matrices (GLCM) with point cloud density analysis for accurate occlusion rate determination, and integrates depth information with RGB data to improve occluded object separation. This holistic method aims to enhance object detection performance, particularly in challenging occlusion scenarios.
A Comprehensive Model for Enhancing Cybersecurity Resilience and IT Governance Through Red Teaming Exercises

This study explores the pressing need for more effective IT governance and cybersecurity resilience within enterprises by strategically integrating red teaming exercises. Our research approach involved a comprehensive investigation encompassing literature review, surveys, interviews, and robust data analysis. We leveraged established frameworks like ISO 27001:2022, NIST CSF, and COBIT 2019 for model development. The results demonstrate a significant correlation between the frequency of red teaming exercises and higher IT governance maturity, highlighting the positive impact of increased engagement. The study emphasizes the value of incorporating red teaming insights to enhance IT governance maturity and bolster cybersecurity resilience, accounting for organizational size and industry sector variables. It underscores the critical importance of seamlessly integrating red teaming outcomes into governance procedures to fortify cybersecurity defenses and enable organizations to adapt swiftly to evolving threats, thus enhancing their overall security posture. Our model provides a practical roadmap for organizations dedicated to strengthening cybersecurity resilience in today's fast-changing digital landscape.
The Influence of Temperature on the Electromagnetic Leakage in Side Channel Attack Based on Deep Learning

This paper investigates the impact of temperature on the quality of electromagnetic leakage signals in the context of deep learning-based attacks. The Simeck32/64 algorithm is employed as a case study to exemplify this phenomenon. The proposed methodology involves training a deep learning model to perform attacks on electromagnetic leakage signals collected at three distinct temperature levels: −20°C, 20°C, and 60°C. The experimental results demonstrate that the model achieves optimal attack performance under a low-temperature scenario, exhibiting a test accuracy of 100% and successfully compromising all 64-bit keys of the Simeck32/64 algorithm. However, as the temperature increases, the model's attack success rate diminishes significantly, yielding test accuracies of 94.5% and 74.5% in normal and high-temperature scenarios, respectively.
CLFM: Contrastive Learning and Filter-attention Mechanism for Joint Relation Extraction

Relation extraction is a fundamental task in natural language processing, which involves extracting structured information from textual data. Despite the success of joint methods in recent years, most of them still have the propagation of cascade errors. Specifically, the error in former step will be accumulated into the final combined triples. Meanwhile, these methods also encounter another challenges related to insufficient interaction between subtasks. To alleviate these issues, this paper proposes a novel joint relation extraction model that integrates a contrastive learning approach and a filter-attention mechanism. The proposed model incorporates a potential relation decoder that utilizes contrastive learning to reduce error propagation and enhance the accuracy of relation classification, particularly in scenarios involving multiple relationships. It also includes a relation-specific sequence tagging decoder that employs a filter-attention mechanism to highlight more informative features, alongside an auxiliary matrix that amalgamates information related to entity pairs. Extensive experiments are conducted on two public datasets and the results demonstrate that this approach outperforms other models with the same structure in recall and F1. Moreover, experiments show that both the contrastive learning strategy and the proposed filter-attention mechanism work well.
Analysis Of Biometric-Based Identification Algorithms In Electronic Trust Services Systems

The article is devoted to the usage of identification algorithms based on biometric personal data (biometrics) in the electronic trust services. This theme is of considerable interest due to the fact that it is becoming one of the most used tools in everyday life. It can include such things as fingerprint and facial scanners in modern cell phones as well as more official aspects such as a personal passport or signature. The main problem of electronic identification is that the most effective algorithms: based on abstract-minutiae cylindrical codes, where minutiae are unique to each fingerprint and determine the points of change in the structure of the capillary lines (ending, splitting, breaking, etc.), the orientation of capillary lines and coordinates at these points. Also, algorithms based on fuzzy extractors, where by fuzzy extractor we mean the system (object, algorithm) which transforms biometric data into random sequences, providing the opportunity to apply encryption methods for biometric security. Although they perform their role, they do not always work correctly and present a possible danger to a user. A comprehensive analysis of the advantages and disadvantages of such algorithms requires further investigation and combination of these algorithms to solve existing problems and improve overall response. Not only could that provide a higher level of protection, but also greatly simplify the mathematical complexity of data processing, as well as lead to an increase in the number of correct triggers and overall increase the efficiency of using biometric technology in electronic trust services.
Importance of the psychological nursing approach in pre-surgical adult patients

Introduction: Psychological preparation in the patient is very important before the surgical intervention regardless of the diagnosis and procedure to be performed, in this way it helps to reduce the degree of anxiety, fear and anguish presented before, during and after surgery. Providing the necessary information that the patient should know is important to clarify all types of doubts and concerns regarding the condition they present; It is necessary to inform the benefits and risks that surgical treatment entails, as well as to remain calm so as not to inhibit the administration of medication such as analgesics. Objective: Determine the importance of the psychological approach by nursing staff in pre-surgical adult patients to reduce anxiety and fear as well as reduce complications in the operating room. Methodology: A review of scientific articles from the databases PubMed, Scielo, Google Scholar was carried out, where the years of publication were considered to be from 5 years ago, in order to know the psychological preparation before surgery. Results: It was identified that psychological preparation is the fundamental phase to reduce anxiety before a surgical intervention, it helps to remain calm throughout the procedure, reducing complications and favoring the patient's prompt recovery. Conclusions: Psychological preparation towards the patient is of great importance to reduce fear, anxiety, worry, fear and stress, it must be done in a way that allows quality care to be provided with a more humanized treatment.
A Deep Dive into The World of NFT Market Place: Exploring Horizons

Abstract: In an era increasingly dominated by digital interactions, the secure management and exchange of digital assets have emerged as critical challenges. This research project aims to tackle these challenges through the development of a decentralized application (dApp) that harnesses the capabilities of blockchain technology, specifically focusing on the realm of Non-Fungible Tokens (NFTs). The dApp encompasses essential features, including secure wallet connections, NFT image generation, minting, marketplace functionality, and profile management. The back-end infrastructure of the dApp is implemented on the Goerli testnet, utilizing Solidity-based smart contracts. Decentralized storage is facilitated through IPFS, while front-end development is executed using nextJs/ReactJS/EtherJS.This project seeks to contribute to the broader discourse on secure digital asset management by presenting a comprehensive exploration of the methodologies, technologies, and outcomes involved in the creation of this decentralized application. The research delves into the theoretical foundations, design intricacies, and practical implications of the dApp, offering insights into its performance, security, and potential impact on the evolving landscape of digital asset management. Through the integration of decentralized technologies and innovative features, our dApp endeavors to provide a secure and efficient solution for the contemporary challenges associated with the trade and management of digital assets.
Comparison of the Packet Wavelet Transform Method for Medical Image Compression

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.
The Correlation Between Reading Comprehension and Vocabulary Mastery of Class XI Students at Madrasah Aliyah Al-Khairaat Ulatan

This study aims to investigate the relationship between the reading comprehension and vocabulary proficiency of students in the eleventh grade at Madrasah Aliyah Al-khairaat Ulatan. The research employs a quantitative approach with a correlational design, and the sampling method utilized is total sampling. The study includes 26 students from the eleventh-grade class at Madrasah Aliyah Al-khairaat Ulatan. The research instrument consists of a multiple-choice test, and data collection involves two types of tests: a reading comprehension test and a vocabulary mastery test, each comprising 10 questions. Data analysis was conducted manually using SPSS 25. The findings of the research reveal a significant correlation between reading comprehension and vocabulary mastery, with a two-tailed significance (Sig.) value of 0.007. The calculated correlation coefficient (r) is 0.518, indicating a strong correlation (greater than 0.5 but less than 0.75). The Pearson correlation coefficient of 1 signifies a positive correlation between the two variables. Therefore, the research concludes that the alternative hypothesis (Ha): "The value of r count 0.518 &gt; r table 0.388" is accepted, indicating a correlation between reading comprehension and vocabulary mastery among students in class XI at Madrasah Aliyah Al-khairaat Ulatan.
Re-ranking Image Retrieval in Challenging Geographical Iconographic Heritage Collections

As the number of digitized geographic iconographic heritage collections increases, their global use is under-exploited by their lack of structure at large scale, which does not facilitate their access nor their understanding. Using automatic image retrieval methods appears to be the solution to bring structure by building links between contents, within and between collections. This paper presents an overview of methods for image retrieval applied to geographic iconographic heritage collections, both from the perspectives of image content description and of post-processing re-ranking. The article evaluates features and methods to identify their efficiency when faced with a challenging dataset. Moreover, new re-ranking approaches exploiting structuring information (scene geometry, metadata) are proposed to improve retrieval without having to adapt image descriptors to the specific data (retraining, fine-tuning, etc.) for every new specific collection.
Multi-granularity Feature Fusion for Transformer-Based Single Object Tracking

The recently developed transformer has been largely explored in the research field of computer vision and especially improve the performance of single object tracking. However, the majority of current efforts concentrate on combining and enhancing convolutional neural network (CNN)-generated features and cannot fully excavating the potential of transformer. Motivated by this, we introduce multi-granularity theory into the pure transformer-based single object tracker and design a multi-granularity feature fusion module. With a view to fuse the feature of different granularity and enhance the feature representation, we design the double-branch transformer feature extractor and utilize cross-attention mechanism to fuse the feature. In our extensive experiments on multiple tracking benchmarks, including OTB2015, VOT2020, TrackingNet, GOT-10k, LaSOT, our proposed method named MGTT, the results could demonstrate that the proposed tracker achieves better performance than multiple state-of-the-art trackers.
Enhancing Students' Speaking Skills in Describing People through the Presentation-Practice-Production (PPP) Method at SMP Negeri 5 Sigi

This study addresses the students' need for enhanced speaking skills, particularly in terms of fluency. The inquiry posed in this thesis originates from the question: Can the application of the Presentation, Practice, and Production (PPP) method contribute to the improvement of students' speaking abilities when describing a person at SMP Negeri 5 Sigi? The objective is to ascertain whether the use of the PPP method has a positive impact on students' proficiency in describing individuals. The research adopts a quantitative approach through quasi-experimental methods. The study encompasses the eighth-grade students of SMP Negeri 5 Sigi, comprising three classes. The experimental class comprises 20 students, while the control class consists of 20 students. Purposive sampling is employed to select students from classes VIII A and VIII B. The research findings demonstrate that the utilization of the Presentation Practice Production (PPP) method significantly enhances students' speaking skills, specifically in terms of fluency. This is evident from the mean scores of the experimental and control classes, which are 17.65 and 8.9, respectively. The calculated t-value of 4.26, with a degree of freedom (df) of 38 (20+20-2) and a significance level of 0.05, surpasses the critical t-table value of 2.00. Consequently, the hypothesis of the research is accepted, affirming that the implementation of the Presentation Practice Production method is effective in improving the speaking skills of eighth-grade students at SMP Negeri 5 Sigi.
Application of Multivariate Statistical Methods for Determining Geochemical Trends of Elements on the Territory of Slovenia

The main objective of this study is to map multi-element geochemical anomalies in soil on a regional scale. We aimed to determine and evaluate the baseline geochemical values and main geochemical trends in soil that may serve as reference values against any future changes. A total of 817 topsoil samples (0–10 cm) were collected in a 5 × 5 km grid and analyzed for 35 elements using ICP-ES after multi-acid digestions (HClO4/HNO3/HCl/HF) and 53 elements using ICP-MS after modified aqua regia digestion (HCl/HNO3/H2O). The analytical results for the two different digestion methods (multi-acid digestion vs. aqua regia) were also compared for each chemical element. Multivariate statistical methods were applied to identify the geochemical trends and main sources of trace elements over the territory of Slovenia. Based on these results, seven natural and one mixed natural/anthropogenic geochemical association were established. The contents and trends of the determined factors are presented according to 8 natural units, 4 drainage areas, and geological units characteristic of Slovenia. The identified anthropogenic geochemical association combines toxic elements (Ag, Bi, Cd, Hg, P, Pb, S, Sn, and Zn). Increased values of these elements can be found in mining areas and metallurgic centers, in Quaternary sediments of the Sava River, and Adriatic Basin as the consequence of past mining activities and in the Julian Alps, where their origin could be connected to the atmospheric deposition.
PENGARUH MEDIA VIDEO ANIMASI GIZI SEIMBANG TERHADAP PENGETAHUAN DAN SIKAP GIZI SEIMBANG SISWA KELAS IV SD HANG TUAH 10 JUANDA

Introduction : Wrong eating habit in elementary school students make nutritional problems. Balanced nutrition education needed by learning media can attract elementary school students’s attention. This investigation seeks to determine the effect of delivering information on balanced nutrition through the use of animated videos on the knowledge and attitudes of fourth-grade students at SD Hang Tuah 10 Juanda, Sidoarjo Regency. Method : Quantitative Research with QuasyExperimental trough Pre Test Post Test Control Group. The sample wasfourth grade pupils at SDHang Tuah 10 Juanda with 96 students. Results:The paired sample T-test revealed that the mean scores for knowledgeand attitude differ before and after the animation video interventionwith sig value (0,00) &lt; 0,05. Independent T-Test shows sig value (0,00) &lt;0,05. Conclusion : Animation in video content influences the knowledgeand attitudes of fourth-grade pupils at SD Hang Tuah 10 Juanda.
Visual servoing for dual arm shaping of soft objects in 3D

We propose a real-time vision-based dual arm controller for manipulating soft objects in three dimensions. Our controller relies on a data-based approach to learn how the object deforms, without any prior knowledge of the shape nor material. We control the two arms with a cooperative task representation, which makes them move together in an absolute frame and/or relatively to one another. This allows object deformation in the relative frame and/or object motion in the absolute frame. Through a short initialization phase for extracting visual features with a Principal Component Analysis on the 3D contour points of the object, we estimate an interaction matrix. This matrix relates the visual features to the robot control inputs for the cooperative task. At each step of the deformation, the process is reiterated with the last data and the interaction matrix is updated along a sliding window. Experiments on a few different objects show the performance of the method in real-time.
Daily Assistive View Control Learning of Low-Cost Low-Rigidity Robot via Large-Scale Vision-Language Model

In this study, we develop a simple daily assistive robot that controls its own vision according to linguistic instructions. The robot performs several daily tasks such as recording a user's face, hands, or screen, and remotely capturing images of desired locations. To construct such a robot, we combine a pretrained large-scale vision-language model with a low-cost low-rigidity robot arm. The correlation between the robot's physical and visual information is learned probabilistically using a neural network, and changes in the probability distribution based on changes in time and environment are considered by parametric bias, which is a learnable network input variable. We demonstrate the effectiveness of this learning method by open-vocabulary view control experiments with an actual robot arm, MyCobot.