Reviewerly/exHarmony huggingface dataset:
Construction of a Matrix by an Elliptic Curve for Image Encryption

— So far, elliptic curves can give very efficient cryptographic systems thanks to the difficulty of the discrete logarithm problem on these curves. For this, in this article I will present a new method of cryptography based on the algebraic structure of such a curve defined on a finite field, where the problem of the discrete logarithm requires an exponential time for its resolution. Elliptic curves are applicable for key size because they allow smaller keys (256-bit) to provide security equivalent to the size of a 1024-bit RSA key that is based on several integer factoring algorithms.
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection

We present a framework for attention-based video object detection using a simple yet effective external memory management algorithm. An attention mechanism has been adopted in video object detection task to enrich the features of key frames using adjacent frames. Although several recent studies utilized frame-level first-in-first-out (FIFO) memory to collect global video information, such a memory structure suffers from collection inefficiency, which results in low attention performance and high computational cost. To address this issue, we developed a novel scheme called diversity-aware feature aggregation (DAFA). Whereas other methods do not store sufficient feature information without expanding memory capacity, DAFA efficiently collects diverse features while avoiding redundancy using a simple Euclidean distance-based metric. Experimental results on the ImageNet VID dataset demonstrate that our lightweight model with global attention achieves 83.5 mAP on the ResNet-101 backbone, which exceeds the accuracy levels of most existing methods with a minimum runtime. Our method with global and local attention stages obtains 84.5 and 85.9 mAP on ResNet-101 and ResNeXt-101, respectively, thus achieving state-of-the-art performance without requiring additional post-processing methods.
Gold-Based Financial Information System Design Using Blockchain Application

In the current era of monetary inflation involving several countries worldwide, between 3% and more than 25% of the value of currencies has declined in 2019. Inflation is the decrease in the value of a currency relative to a currency with a price. Increase on a large scale and take place over some time. This event occurs in currency (paper and metal). One reason is that the physical nature of silver has no intrinsic value, is easy to print and distribute, and today's silver no longer uses gold as the standard of the exchange rate. Gold has an intrinsic value against inflation. Its role as a medium of exchange can be reused in the computer age thanks to communication applications that run on top of the information system. An information system manages savings, payments, remittances, investments, and other financial activities. The current situation shows that gold is no longer a medium of exchange but a tradable commodity. This information system aims to make gold the basis of the currency circulating within the system. This information system uses blockchain technology as the transaction backbone to manage transactions. You cannot change the transaction data on the blockchain. All data affects the encryption sequence for the next block. In addition, the blockchain will be distributed to all users in the blockchain to protect transactions. The user must use a computer connected to the internet. This study aims to determine the potential of blockchain systems for use in gold-based financial information systems design. This information system is gold-based, resistant to inflation, and gives users confidence.
Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

&lt;div class="WordSection1"&gt;&lt;p&gt;The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the effectiveness of prompted te xt speaker verification. At a context-free, single mixed mono phony level, this study has been constructing a continuous speech system based on HMM. After that, suitable voice data is used to build the client and world models. In order to verify speakers, the text-dependent speaker ver-ification system uses sentence HMM that have been concatenated for the key text. Normalized log-likelihood is determined from client model forced by Viterbi algorithm and world model, in the verification step as the difference between the log-likelihood. At long last, a method for figuring out the verification results is revealed.&lt;/p&gt;&lt;/div&gt;
Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest. The feature selection method utilized has an impact on the dataset's correctness and learning and training duration. Learning speed, comprehension and execution ease, and properly chosen features influence all high-quality outcomes. The two feature selection methods, relief-F and chi-square, are compared in this research. Each technique assesses and ranks attributes based on distinct criteria. Six of the most important features with the highest ranking have been chosen. The six features are utilized to compare the performance accuracy ratios of the four classifiers: k-nearest neighbor (KNN), naive Bayes (NB), multilayer perceptron (MLP), and random forests (RF) in terms of expression recognition. The final goal of the proposed strategy is to employ the least number of features from both feature selection methods to distinguish the four classifiers' accuracy performance. The proposed approach was trained and tested using the CK+ facial expression recognition dataset. According to the findings of the experiment, RF is the best accurate classifier on chi-square feature selection, with an accuracy of 94.23 %. According to a dataset utilized in this study, the relief-F feature selection approach had the best classifier, KNN, with an accuracy of 94.93 %
Towards an approach based on particle swarm optimization for Arabic named entity recognition on social media

Named entity recognition is an essential task for various applications related to natural language processing (NLP). It aims to retrieve a variety of named entities (NEs) from text and categorize them according to predetermined target categories. In many cases, using the entire feature set can be time-consuming and negatively impact the performance. Moreover, it is challenging to find the relevant subsets of features for a particular task due to the high number. The feature selection technique is an unsupervised process for selecting informative features by creating a new subset of informative features. This technique is used to enhance the underlying algorithm's performance. This article implements an effective feature selection algorithm using particle swarm optimization (PSO) to identify and classify the Arabic NEs in the text from social media. PSO is a search algorithm that utilizes a population of particles in a multidimensional space. The proposed method is evaluated using two publicly available Arabic Dialect social media datasets. It is demonstrated through comparisons with both baselines and previous models that the new approach achieves significant accuracy with considerably reduced feature sets in all parameters.
Face morphing attacks and face image quality: The effect of morphing and the unsupervised attack detection by quality

Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous studies touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. The authors theorise that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. The authors’ study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. The authors’ study looks into intra- and inter-dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The authors’ final results point out that a set of quality measures, such as MagFace and CNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.
PAISE<sup>®</sup> – process model for AI systems engineering

Abstract The application of artificial-intelligence-(AI)-based methods within the context of complex systems poses new challenges within the product life cycle. The process model for AI systems engineering, PAISE ® , addresses these challenges by combining approaches from the disciplines of systems engineering, software development and data science. The general approach builds on a component-wise development of the overall system including an AI component. This allows domain specific development processes to be parallelized. At the same time, component dependencies are tested within interdisciplinary checkpoints, thus resulting in a refinement of component specifications.
Explaining and Evaluating Deep Tissue Classification by Visualizing Activations of Most Relevant Intermediate Layers

Abstract Deep Learning-based tissue classification may support pathologists in analyzing digitized whole slide images. However, in such critical tasks, only approaches that can be validated by medical experts in advance to deployment, are suitable. We present an approach that contributes to making automated tissue classification more transparent. We step beyond broadly used visualizations for last layers of a convolutional neural network by identifying most relevant intermediate layers applying Grad-CAM. A visual evaluation by a pathologist shows that these layers assign relevance, where important morphological structures are present in case of correct class decisions. We introduce a tool that can be easily used by medical experts for such validation purposes for any convolutional neural network and any layer. Visual explanations for intermediate layers provide insights into a neural network’s decision for histopathological tissue classification. In future research also the context of the input data must be considered.
The Design of an Avatar in a Multiplayer Serious Game

Abstract Avatars are often used to represent people in the virtual world. In the process, they can also have numerous psychological influences on the users. Therefore, this paper discusses a multiplayer application in virtual reality and how a appealing avatar could be designed in it. In a small study with six persons the application was tested competitively in teams of two, the users filled out a questionnaire and answered some additional questions. While an increase in motivation was observed in all participants, the choice of the preferred avatar was not uniform. Thus, this showed that an appealing avatar for everyone could not simply be determined, but personal preferences and experience must be taken into account.
Augmented Reality for 6-DoF Motion Recording, Preview, and Execution to Enable Intuitive Surgical Robot Control

Abstract In robot-assisted surgeries, the surgeon focuses on the surgical tool and its pose, and not on the complete robot’s shape. However, the joints of redundant robots (robots that have more degrees of freedom (DoF) than needed for the positioning of surgical tools) might move in unexpected/undesired ways. Joint motions that lead to patient or collisions are safety critical. We assume that the medical personnel in the operating room can best decide if a planned robot motions come too close to the patient or not. Therefore, we propose an augmented reality-based solution to interact with the robot during surgery planning, and intervention. The tool can be used to command a robot by drawing a trajectory in augmented reality (AR), visualizing the robot movement to check if it is safe before execution. The proposed solution allows surgeons to plan safe robot motion paths before-hand and adapt them when necessary in situ. As a proof-of-concept, we implemented and demonstrated the proposed solution on a 7-DoF redundant robot by commanding different trajectories. The control architecture to plan and execute motion for a surgical robot using AR is a key result of this work.
Suitability Testing of the LucidGloves Prototype 4 for Extended Reality Medical Teaching

Abstract The current information era provides the population with new innovative technologies, including extended reality (XR). These innovations enhance their scope with new opportunities. While extended reality found its way into medical teaching, its application is limmited. Causal is the lack of haptic feedback, which limits the immersive experience. While simulation devices exist, haptic gloves are oftentimes only applicable for enterprise use and not available for everyone, because of their high prices. Therefore Lucas VRTech developed the LucidGloves to allow a widespread use of cheap haptic gloves. In this paper, a suitability test is performed to determine whether the Prototype 4 of the LucidGloves is suitable for use in medical teaching, based on expert interviews with four physicians.
Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping

Abstract Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is burdensome and time-consuming. Since annotating images for semantic segmentation is particularly exhausting, methods to reduce the human effort are especially valuable. We propose a combined framework that utilizes unsupervised machine learning to automatically generate segmentation masks. Experiments on two biomedical datasets show that our approach generates noticeably better annotations than Otsu thresholding and k-means clustering without needing any additional manual effort. Using our framework, unannotated datasets can be amended with pre-annotations fully unsupervised thus reducing the human effort to a minimum.
Augmented Reality Application for Simulation of Mamma Palpation

Abstract The teaching of medicine is in a state of flux. New technologies enable students to learn faster and more efficiently than ever before. Especially the possibilities that augmented reality brings with it can be applied very well in this field. The possibility to project additional information directly into the user’s field of vision allows him to perceive additional knowledge without being distracted from the actual work. In this paper, a possibility of training Mamma Palpation is presented, which teaches students the course of action and the detection of possible tumors through virtual points. Through guideline-based interviews, added value for teaching could be highlighted.
A Classification of Al-hur Arabic Poetry and Classical Arabic Poetry by Using Support Vector Machine, Naïve Bayes, and Linear Support Vector Classification

Most of the world languages have made strides in analyzing and classifying texts electronically; hence, the use of electronic text has become a great alternative to manual classification as it reduces time, cost, and difficulty. However, in the Arabic language, electronic analysis has not progressed due to several limitations faced by researchers in this field, such as the complexity of the Arabic language, the lack of related research, as well as the use of the classical Arabic language. In addition, Arabic poetry has other limitations, such as the use of a system that uses a single activation function. In this research, a new method was developed for the classification of the classical Arabic poetry and Al-hur poetry. This new approach is based on features that indicate the type of poetry. Pre-processing of some data is important in this new approach as it helps increase the accuracy of classification
Parallel Hybrid 2-Opt Flower Pollination Algorithm for Real-Time UAV Trajectory Planning on GPU

Abstract. The development of autonomous Unmanned Aerial Vehicles (UAVs) is a priority to many civilian and military organizations. An essential aspect of UAV autonomy is the ability for automatic trajectory planning. In this paper, we use a parallel Flower Pollination Algorithm (FPA) to deal with the problem's complexity and compute feasible and quasi-optimal trajectories for fixed-wing UAVs in complex 3D environments, taking into account the vehicle's flight properties. The global optimization algorithm is improved with the addition of 2-opt local search providing a significant improvement. The proposed trajectory planner in implemented and parallelized on a multicore processor (CPU) using OpenMP and a Graphics Processing Unit (GPU) using CUDA resulting in a 9.6x and a 68.5x speedup respectively compared to the sequential implementation on CPU. Index Terms—Flower Pollination Algorithm, Graphics Processing Unit, Parallel Programming, Trajectory Planning, Unmanned Aerial Vehicle.
Performance Appraisal, Job Satisfaction and Organizational Commitment in Nepalese Commercial Banks

The purpose of this paper is to examine the impact of performance appraisal and job satisfaction on employee organizational commitment in Nepalese commercial banks. The primary data has been used to assess the opinions of the respondents through structured questionnaires constituting Likert items. The survey is based on 172 respondents, which represents 78 percent response of the total efforts made to collect samples from 16 commercial banks of Nepal. Descriptive and causal research designs have been employed in this study. The Pearson correlation coefficient has been used to analyze the relationship whereas the multiple linear regression models have been used for examining the significance and impacts of performance appraisal and job satisfaction on employee organizational commitment in the commercial banks of Nepal. The result reveals that performance appraisal and job satisfaction are positively related to employee organizational commitment. The study, therefore, concludes that job satisfaction and performance appraisal are the important factors affecting employees' organizational commitment. As this study is confined to commercial banks only, future studies can consider other banking institutions, and among employees‟ profile and human resource management factors as well.
Exploration of micro-video teaching mode of college students using deep learning and human–computer interaction

In order to improve the efficiency of teaching and learning in Colleges and Universities (CAUs), this work combines the Browser/Server (B/S) framework with Model View Presenter (MVP) technology to build a college student-oriented micro-video teaching system based on Deep Learning (DL) and Human-Computer Interaction (HCI) technology. Firstly, it makes an in-depth analysis of the problems in the classroom teaching of Chinese CAUs. Three functional modules are designed for the micro-video online teaching platform: video management, user learning, and system management. Then, it uses MVP technology to analyze the use-cases of these three functional modules in detail. Based on this, the micro-video online teaching platform is designed using the B/S framework. The teaching platform interface layer realizes the HCI between the platform and users. The business logic layer responds to the user requests submitted and returns the processing results to the interface layer. Finally, the function test and stress test of each module of the micro-video online teaching platform is carried out. The test results show that the response time of the proposed micro-video teaching platform increases with the number of users. Under the peak concurrent users, the system response time is 6 s, without abnormalities. Meanwhile, the proposed teaching platform has improved students' satisfaction with classroom teaching by nearly 15% and improved the compactness of the college classroom by nearly 12%. When the number of virtual users increases and the number of services increases linearly, the Random Access Memory and Central Processing Unit growth rate is significantly lower than that of the number of services. These outcomes indicate that many system resources are reused, and the system has good scalability, which can meet users' needs for the network video teaching system. The proposed teaching platform provides a new idea for applying DL and HCI technology in researching college students' micro-video teaching mode.
A Study on Protocols of Cross-Chain Data Synchronization for Permission Blockchain for Construction Management

The construction industry is encountering management issues, such as low efficiency, inadequate regulation and enforcement, absence of good coordination and knowledge sharing, and ineffective billing practices. Blockchain is also gaining momentum as a part of the digital transformation in the construction area and response to various challenges. Since various blockchain implementations have different specifications for distributed ledger functionality (e.g., high throughput, scalability), blockchains currently function as independent knowledge islands that cannot access external data or conduct transactions on their own. Such information silos must be bridged to create more stable blockchain applications. This research investigates data synchronization protocols for cross-chain technologies in construction blockchain. It screens the most suitable blockchain frameworks for use in the construction industry to establish a consistent approach to support their application in construction blockchain. Finally, a cross-chain data interaction protocol for blockchain-based supply chain and IFC chain construction is proposed.