Reviewerly/exHarmony huggingface dataset:
Mathematical Modeling of Gait for Lower Limb Exoskeleton

This study presents the mathematical Modeling of gait. This model is for the lower limb exoskeleton. The exoskeleton has wide scope in hospitals, physical training, and army. The comfortable gait with exoskeleton requires a good mathematical model of gait. This Modeling of this study starts with a C3D file which is taken from a hospital. This Modeling uses MLS viewer, MOKKA, Any Body, and Matlab software. The basic information of the C3D file read with the MLS viewer software. This software tells the type of force plate, name of maker points, the number of force plate and marker points use in the gait analysis. The motion of marker points visualizes in MOKKA software. These visuals in MOKKA software give the position of all marker points. The MOKKA software also gives information about the starting frame number and last frame number for the gait on the force plates. The information collected from the MLS viewer and MOKKA software uses for modification of Any Script of the inbuilt model of Any Body software. The kinematic and inverse dynamic analysis of C3D file data performs in Any Body software after proper changes in Any Script of the model. The linear position and velocity data obtained from kinematic analysis use in Mat lab Modeling. Mat lab Modeling has two-step: curve fitting and curve plotting. The sum of sine equation fits on this linear position and velocity data using the curve fitting app of Mat lab. Another velocity equation is evaluated by the differentiation of the position equation. These equations of linear position and velocity simulate using Mat lab software. The plot of kinematic analysis data and plot of modeled equations compare in the Mat lab software. The goodness of the results of all these equations is decided based on root mean square error (RMSE). Keywords: Gait Analysis; Biomechanics; C3D files; Anybody Software; Root mean square error (RMSE); Electromyography(EMG); Open Sim; Mokka Analysis; Any Body Analysis; MLS viewer; Mat Lab Modeling.
Multilevel Hate Speech Classification Based on Multilingual Case-Law

This paper presents classification tools to detect hate speech topics using NLP tools in four different languages (Italian, Spanish, Germany, English) using the selected case-law from national and international jurisdiction. The research is conducted inside the FAST-LISA European project with the aim to classify the hate speech in online public debate.
Impact of IoT on Security and Data Protection

The Internet of Things (IoT) is revolutionizing the way we live and work, but it also brings new challenges to security and data protection. The vast number of interconnected devices, the diversity of their software and hardware, and the sensitivity of the data they collect and transmit create a complex security landscape.&#x0D; The IoT has the potential to bring about significant benefits, but it is important to address the security and data protection challenges before it can be fully realized. By implementing strong security measures, educating users, and developing data protection regulations, we can ensure that the IoT is a safe and secure environment for everyone.&#x0D; IoT devices collect and transmit vast amounts of data, including personal information, location data, and usage patterns. This data can be used for legitimate purposes, such as improving the performance of IoT devices or providing personalized services. However, it can also be misused for malicious purposes, such as identity theft, stalking, or discrimination. While IoT offers immense benefits in terms of efficiency, convenience, and automation, it also raises concerns about data security and privacy.
Tools for distance learning in mathematics

The article deals with the main tools designed to organize distance learning in mathematics. The characteristic features of each tool are analyzed. The necessity of introducing electronic tools for effective lessons in distance learning is revealed. The advantages and disadvantages of each tool when used in the educational process are highlighted. The list of tools that can provide a variety of teaching methods and arouse interest in mathematics through interactivity and visualization is given.
Personal Data Vulnerability in the Digital Era: Study of Modus Operandi and Mechanisms to Prevent Phishing Crimes

Rapid digital transformation has significantly increased the vulnerability of personal data, leading to an alarming increase in phising crimes. This scholarly article aims to investigate the modus operandi and prevention mechanisms of phising crimes in the era of digital transformation. Phising, a fraudulent practice that aims to obtain sensitive information through fraudulent means, has become a common cybercrime, targeting both individuals and organizations. This research utilizes a comprehensive literature review and analysis of various phising attack scenarios to identify common strategies used by cybercriminals. This analysis focuses on the techniques used to manipulate unsuspecting victims, such as social engineering, email forgery, and website forgery. In addition, this research explores the exploitative nature of data breaches and their implications for personal privacy. This article highlights the importance of collaboration between stakeholders, including government agencies, technology providers and individuals, in addressing the issue of personal data vulnerability. By sharing information, resources and best practices, stakeholders can work together to improve cybersecurity measures and effectively protect personal data. The findings from this study provide valuable insights into the modus operandi of phising crimes and the prevention mechanisms needed to protect personal data. By understanding the strategies used by cybercriminals and implementing proactive measures.
Openness to Change Scale in Higher Education: Adaptation and Validity of the Indonesia Version

As change is unavoidable, there is a need for reform in higher education systems across the world. One’s openness to change is one of the many elements contributing to an effective change. There is a need for a capable instrument to assess openness to change within the higher education sector. This study investigates the scale developed by Miller for use in Indonesia. The research matches its findings with the pioneering work, offering insight into the CFA analyses performed. The findings revealed results on scale adaptation, substantiating its strong validity, internal consistency, and reliability. The scale contributes considerably to the academic discourse around transformational dynamics within higher education institutions by providing a tangible method for assessing their openness to change.
COMPARATIVE PERFORMANCE ANALYSIS BETWEEN K-NEAREST NEIGHBORS AND FEED-FORWARD NEURAL NETWORKS IN CLASSIFYING DIMENSIONALITYREDUCED IMAGE DATA OF HAND-WRITTEN DIGITS.

Image classication is an essential tool used across various industries in our technologically enabled world. The process utilises the power of machine learning algorithms to transform industries and solve complex real-world problems. In machine learning, how does a streamlined and straightforward algorithm like the k-Nearest Neighbors algorithm perform, compared to a more intricate Backpropagation multilayer neural network like Feedforward Neural Network, especially in the case of image classication? This research aims to examine this comparative performance analysis, illuminating the intriguing complexities and efciencies inherent in these contrasting approaches. Drawing from Yuchun Lee's 1991 ndings, which revealed that both the backpropagation algorithm and the k-nearest neighbours algorithm had closely matched error rates of approximately 5.14% and 5.15%, respectively, on regular, non-dimensionally reduced data, this study hypothesises that a backpropagation algorithm, like the FeedForward Neural Network, and K-Nearest Neighbors might demonstrate comparable performance on such data. To perform this experimental analysis, however, various mediums are used. Python and essential libraries, including numpy, matplotlib, sklearn, and keras, are used to evaluate the research hypothesis. The experiment employs a Backpropagation Neural Network and K-Nearest Neighbors model, trained and tested on dimensionally reduced data. keras is employed for data acquisition, sklearn for creating the two separate models, and matplotlib for data and result visualization. This approach facilitates acquiring a more nuanced comparison of the two models' performance under varying data conditions. This research reveals distinctive/similar performance characteristics of both Backpropagation Neural Networks and K-Nearest Neighbors models when processing dimensionally reduced image data. The results are essential, for it enhances our comprehension of these models' behaviour in this particular context, thus providing a foundation for informed decisions in subsequent research and practical applications in image classication.
QUANTUM MARVELS: NAVIGATING THE QUANTUM MAZE FOR UNPRECEDENTED COMPUTATIONAL FEATS

In the dynamic landscape of modern ideas, the pursuit of groundbreaking discoveries is a force to be reckoned with. From the transformative impact of Articial Intelligence to the life-altering possibilities of Biotechnology, the world of innovation is ablaze. And now, we nd ourselves on the cusp of another historic shift – the advent of Quantum Computing. Our lives are deeply intertwined with traditional computers, from the ubiquitous smartphones in our pockets to the engines powering our vehicles. Yet, amid the familiar hum of processors and transistors, the emergence of quantum computers promises something truly extraordinary. It's the ultimate frontier in computing, where the atom – the most elemental building block of matter – takes center stage. Quantum computers are not just a leap forward; they are a leap into the very fabric of reality itself. This is where my own research comes into play, as we navigate the quantum labyrinth toward unprecedented computational feats. Join us on this thrilling journey into the heart of Quantum Marvels, where the world of quantum computing redenes the very essence of computation itself
The Application of Generative Adversarial Networks in Cultural Heritage Preservation

Generative Adversarial Networks (GANs) have shown great potential and flexibility in numerous fields such as image generation, image editing, and text generation. Applying GANs to the preservation and restoration of cultural heritage can effectively contribute to the protection and development of cultural heritage. This article provides an overview of the development of GANs and analyzes their main advantages and disadvantages. Additionally, it summarizes typical GAN architectures and focuses on introducing key application scenarios. Finally, it conducts a detailed investigation of the application scenarios of GANs in cultural heritage, such as artifact restoration, automatic generation, and image enhancement. In addition, we give the future development direction of GANs in cultural heritage preservation.
Message from the Program Chairs ACSOS 2023

Welcome to the 4th IEEE International Conference on Autonomic Computing and Self- Organizing Systems (ACSOS 2023) that was held September 25th-29th 2023 in Toronto, Canada.
PHOTOREALISTIC RENDERING AND MODIFICATION OF MOTION BLUR FOR THE SIMULATION OF VIDEO DATA FOR STRUCTURAL DYNAMICS

In recent years imager-based approaches to structural dynamics measurements have gained increasing interest. Imager-based approaches to measurement have a number of attractive properties including being able to monitor large areas at a relatively high spatial resolution with a relatively small number of imagers. The stand-off monitoring capabilities of imagers are also highly attractive for many structural dynamics and structural health monitoring applications, particularly those in dangerous, inaccessible, extreme, and high temperature environments. For their advantages though, a number of challenges must be addressed when using imager-based techniques for measuring the deformation and motion of structures. There are issues of lighting conditions that can potentially vary during the measurement period, due to movement of the sun or lights in a facility being turned on and off. In addition, video measurement of dynamic structures also requires making a number of choices associated with the measurement setup including location of the imagers, focal lengths of lenses, numberof- pixels, lens characteristics, framerates, shutter speeds, ISO, aperture settings, planes of focus, and depth-of-field. There can potentially be effects such as specular reflections or shadows which might complicate downstream data processing. The large number of parameters associated with imager measurements make them very flexible, but it also can mean that setting up imager measurements can take substantial time even under controlled laboratory conditions. The complication associated with imager measurements suggests that highfidelity, photorealistic rendering tools that are capable of capturing the interactions between structural dynamics, light transport, and the measurement process at the imaging plane are needed. To date, substantial work has been done by the computer graphics community to develop photorealistic rendering tools which are becoming increasingly accessible. However, in structural dynamics we often care about sub-pixel motion and it is not understood whether current techniques for modeling effects such as motion blur have sufficient fidelity for structural dynamics. Furthermore, photorealistic video renders can take a large amount of time to complete, and given current techniques do not allow for simple changes such as frame-rate/shutter speed in post processing without redoing the entire render. In this work we present an approach based on digital coded exposures for forming frames of dynamic scenes, that allows for the framerate and shutter speed to be changed in post-processing. The proposed approach is inspired by the physical operation of conventional cameras. In addition, the digital coded exposure proposed approach suggests theoretical alternatives to data capture that could potentially be used to control motion blur properties on a per-pixel basis.
NEXT GENERATION 3D-DIC TECHNIQUE WITH SENSOR-BASED EXTRINSIC PARAMETER CALIBRATION AND NATURAL PATTERN TRACKING

Three-dimensional Digital Image Correlation (3D-DIC) is a computer vision technique capable of extracting full-field displacements of structures by processing images acquired from synchronized stereo cameras and performing triangulation. To properly reconstruct the 3D points in space, the stereo cameras must be calibrated to compute the lens distortion (i.e., intrinsic parameters) and the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking pictures of a calibration target (e.g., a checkerboard). However, the calibration target must occupy most of the cameras’ field of view (FOV) and have a size comparable to that of the object being tested, which becomes impractical for large-scale FOVs. Additionally, 3D-DIC requires the application of a stochastic speckle pattern on the surface to identify and track unique points across multiple images. This can be a problem when structures on which the application of a speckle pattern is impractical have to be analyzed (e.g., wind turbine blades). This research proposes a multi-sensor system using three inertial measurement units and a laser distance sensor to compute the extrinsic parameters of a stereo vision system and accelerate the calibration procedure. In addition, a feature-based natural pattern tracking algorithm is proposed to exploit features that are naturally present on the structure instead of applying a speckle pattern to perform 3D-DIC. Laboratory tests show that cameras calibrated using the multi-sensor system reconstruct displacements with an accuracy above 95% compared to displacements measured using a traditional calibration method. At the same time, the proposed feature-tracking algorithm can identify and reconstruct the 3D positions and displacements of preexisting features without requiring an applied speckle pattern. The research shows that the proposed methods can simplify and streamline the use of 3DDIC and open the way to the use of this technique to perform condition monitoring of large-scale civil and mechanical engineering structures.
NOVEL APPROACH FOR IMAGING TIME SERIES FOR THE IMPROVEMENT OF CLASSIFICATION RESULTS “GRAYSCALE FINGERPRINT FEATURES FIELD IMAGING (G3FI)”

The remarkable successes achieved by Deep Learning (DL) in computer vision drew the attention of researchers to exploit this capability to apply it for the processing of time series and multivariate data. Thus, researcher began the development of suitable methods to transform time series signals into, images to enable the use of DL techniques to improve the classification of time series data. The most state-of-art techniques are Recurrence Plot (RP), Gramian Angular Field (GAF), or Markov Transition Field (MTF). These techniques transform each time series into RGB image. In this paper, a new transformation technique of time series and multivariate data to images technique is proposed. This technique is called &lt;Grayscale Fingerprint Features Field Imaging" (G3FI). The main differences between this technique and state-of-art techniques are: a) the resulted image is a grayscale; b) the size of the resulted image is much smaller than the size of resulted images using state-of-art techniques. These differences provide the proposed technique with several advantages over the prior art, as it (a) results in avoiding redundant information and (b) noise, and (c) leads to a significant reduction in the required computational power. For the proof of concept, a dataset "Sonar, Mines vs. Rocks" is investigated and individually transformed using RP, GAF, and MTF, and the new developed technique "G3FI". The resulted images from each transformer are individually used to train and test Convolutional Neural Networks (CNN). The proposed method leads to competitive results in comparison to RP, GAF, and MTF.
Progress in research on supply chain finance based on blockchain technology

Since the 2020, the financing environment for small and medium-sized enterprises (SMEs) has become even more severe, and the lack of financial support has made it difficult for them to make progress in future development.In such a macro environment, it is particularly urgent to help small and micro enterprises get out of financing difficulties. However, due to many drawbacks in the traditional supply chain financing model, such as information sharing, high financing costs for end enterprises, frequent default events, and low payment efficiency, it is no longer sufficient to meet the strong financing needs of numerous small and micro enterprises in the supply chain. The emergence of blockchain technology has provided a new approach to traditional supply chain finance. The automated processing mechanism brought by its smart contract, the consistency mechanism provided by the distributed bookkeeping, as well as the characteristics of non-tampering and traceability can well solve the traditional supply chain finance problems. In this case, through blockchain technology, efficient financing services can be provided to SMEs, and the financing cost of SMEs can also be reduced.
Some scientifically-methodical questions of research of complex systems

The contents of scientifically-methodical questions of research of complex systems (СS) is certain. Ways of quantitative estimations of characteristics СS are examined and examples of the scientifically-practical device of their realization are brought. The block diagram of a modelling complex as basic practical device of research СS is brought. Its primary goals, requirements to it and variants of its realization are analysed.
Analysis of approaches to the semantic processing of unstructured textual information

Scientific and methodological approaches to semantic processing of unstructured text information in computer systems based on the methodology of heuristic and neuromathematical data processing are considered. The results of a conceptual analysis of heuristic and neuromathematical methods for semantic processing of unstructured text information are presented. The basic provisions for the implementation of semantic processing of unstructured text information based on the integrated application of heuristic and neuromathematical approaches are outlined.
Implementation of PageRank Algorithm for Visualization and Weighting of Keyword Networks in Scientific Papers

Papers are written works that contain thoughts about a particular problem or topic that are written systematically accompanied by logical analysis. Scientific papers are often found on the internet or in libraries for various titles of scientific papers, citations or references can be found in every scientific paper and can be obtained easily, but to display all citations in scientific papers in the form of visualization cannot be done easily. Visualizing the citation network of scientific papers in the form of a graph, with nodes representing research papers and edges representing the relationship between researchers' scientific papers and other scientific papers based on scientific paper citations. This research uses the pagerank algorithm to create a keyword network that has a high relationship and potential relevance in a data library. This research is the first research in using the pagerank algorithm and testing its accuracy by comparing with KNN and linear clustering. The presentation displays the citation of scientific papers based on the size of the node by showing the number of citations of the scientific paper. It can be concluded that all processes in the system have run according to design, and functionally the visualization system and weighting of the scientific paper citation network are in accordance with the design. The results obtained from 51 articles, this algorithm produces a visual user interest of 81.60%, compared to the accuracy of the data suitability produced by the linear clustering and KNN algorithms in the form of 71.22% and 61.34%, helping to facilitate the search for scientific papers in large quantities.
Analysis and Utilization of the Base64 Algorithm for Image Encryption and Decryption Security in Web-Based Images

Data is often the target of irresponsible people for misuse. The theft is done is to benefit from the person who has the data. In addition to theft of work files, theft is also carried out on image files. The purpose of this file theft is to find out what the image contains. Someone has a private image that cannot be known by others. Misuse of image files will be fatal for the owner of the image. Cryptographic techniques are needed in securing images and one of the methods used to secure these images is using the base64 algorithm. The Caesar Cipher algorithm can help users secure the image file. The Base64 algorithm can be used to change the ASCII 256 format to Base64 so that it is easy to send or store on a storage medium. By applying the Base64 algorithm to image files, the security and confidentiality of these files will protect data from irresponsible people.
The impact of transformational leadership on work engagement: The mediating role of job crafting

The main purpose of this study is to examine the impact of transformational leadership on work engagement and the mediating effect of job crafting in the relationship between transformational leadership and work engagement among Saudi employees working in the private sector. An online structured questionnaire is used to gather data from 337 Saudi employees working in the private sector. The study findings reveal that transformational leadership is significantly related to work engagement. Job crafting significantly mediates the relationship between transformational leadership and work engagement. Theoretical and practical implications are discussed.