Large-Scale Clustering on 100 M-Scale Datasets Using a Single T4 GPU via Recall KNN and Subgraph Segmentation

Abstract: Abstract Despite the promising progress that has been made, large-scale clustering tasks still face various challenges: (i) high time and space complexity in K-nearest neighbors (KNN), which is often overlooked by most methods, and (ii) low recall rate caused by simply splitting the dataset. In this paper, we propose a novel framework for large-scale clustering tasks named large-scale clustering via recall KNN and subgraph segmentation (LS-RKSS) to perform faster clustering with guaranteed clustering performance, which embraces the ability of handling large-scale data up to 100 million using a single T4 GPU with less than 10% of the running time. We propose recall KNN (RKNN) and subgraph segmentation (SS) to effectively address the primary challenges in large-scale clustering tasks. Firstly, the recall KNN is proposed to perform efficient similarity search among dense vectors with lower time and space complexity compared to traditional exact search methods of KNN. Then, the subgraph segmentation is proposed to split the whole dataset into multiple subgraphs based on the recall KNN. Given the recall rate of RKNN based on traditional exact search methods, it is theoretically proved that dividing the dataset into multiple subgraphs using recall KNN and subgraph segmentation is a more reasonable and effective approach. Finally, clusters are generated independently on each subgraph, and the final clustering result is obtained by combining the results of all subgraphs. Extensive experiments demonstrate that LS-RKSS outperforms previous large-scale clustering methods in both effectiveness and efficiency.


Phantom in the opera: adversarial music attack for robot dialogue system

Abstract: This study explores the vulnerability of robot dialogue systems' automatic speech recognition (ASR) module to adversarial music attacks. Specifically, we explore music as a natural camouflage for such attacks. We propose a novel method to hide ghost speech commands in a music clip by slightly perturbing its raw waveform. We apply our attack on an industry-popular ASR model, namely the time-delay neural network (TDNN), widely used for speech and speaker recognition. Our experiment demonstrates that adversarial music crafted by our attack can easily mislead industry-level TDNN models into picking up ghost commands with high success rates. However, it sounds no different from the original music to the human ear. This reveals a serious threat by adversarial music to robot dialogue systems, calling for effective defenses against such stealthy attacks.


Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

Abstract: The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study’s conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention.


Classification of fragmented pottery with the use of Kohonen self‐organising maps (case study from the Hlyboke Ozero‐2 settlement in Eastern Ukraine)

Abstract: Abstract The paper is devoted to testing Kohonen self‐organising maps, with elliptic Fourier coefficients as quantitative variables, for the task of morphological classification of fragmented and non‐standardised ceramics. The advantage of the methodology used is its ability to account for the systematic statistical relationships inherent in the dataset, build models of varying degrees of generalisation and visualise multivariate data. The approbation of the method was carried out on materials from the Hlyboke Ozero‐2 settlement in Eastern Ukraine. The results are compared with the results obtained using principal component analysis + k‐ means clustering.


Extracting informational cues between initial coin offering projects and the public

Abstract: Abstract An initial coin offering (ICO) seeks to raise initial investments for companies by selling crypto tokens to individuals interested in crowdfunding. As ICOs have gained popularity, a lack of information regarding startup companies' prior financial status and business health has led to information asymmetry between potential investors and ICO projects. This study explores the informational cues necessary to bridge this gap. Employing signaling theory, we elucidate the presence of information asymmetry and conduct two case analyses to identify relevant informational cues. Twitter data from two ICO projects (Stratis and NEO), which achieved the highest return on investment at the time of data collection, are extracted and analyzed to identify informational themes. Our findings reveal multiple and distinctive themes of informational cues for each ICO project, demonstrating that transmitted signals vary across different stages and are generated by both the coin publisher and the public. This research illuminates the flow of informational cues between ICO initiators and potential investors on social media. By analyzing online communication threads using qualitative methods, this study significantly contributes to the understanding of information asymmetry in the context of ICOs.


ggVennDiagram: Intuitive Venn diagram software extended

Abstract: Graphical Abstract Highlights of ggVennDiagram include: (1) Subset/Region filling Venn diagram up to seven sets; (2) Upset plot with unlimited sets; (3) Venn Calculator for two or more sets; (4) Provide as R package, Shiny App, and TBtools plugin.


Prediction of permeability in a tight sandstone reservoir using a gated network stacking model driven by data and physical models

Abstract: Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures. Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness. Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.


Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

Abstract: The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.


Staged cluster transformers for intracranial aneurysms segmentation from structure fused <scp>3D MRA</scp>

Abstract: Abstract Intracranial aneurysms (IAs) manifest as atypical dilatation within the intracranial arterial structures, the rupture of which accounts for high mortality and morbidity rates. Current clinical protocols require radiologists to manually annotate IAs on Magnetic Resonance Angiography (MRA) images, which is inherently subjective and time‐consuming. Given these limitations, there is an urgent need to explore methods for automated and accurate segmentation of IAs from MRA images. In particular, recent years have witnessed the proliferation of sophisticated computational techniques, with deep learning algorithms—especially the 3D U‐Net and its derivatives—gaining prominence in segmentation works. Nevertheless, convolutional neural network (CNN)‐based models have an inherent limitation in capturing long‐range spatial dependencies, which inadvertently compromises the retention of global features critical for segmentation. In response to this challenge, we introduce an avant‐garde architectural design, dubbed staged cluster transformers (SCTR), which incorporates cluster mechanism into vision transformers to perform volumetric MRA image segmentation. In addition to the MRA clustering branch, the spatially aligned brain Magnetic Resonance Imaging (MRI) representation branch is also combined to extract the structural features and assist the network in learning richer contextual and boundary information for accurate voxel prediction. For validation, we utilized both a publicly available challenge dataset and an internal clinical dataset in this study. Our proposed model achieves dice similarity coefficients (DSC) of 0.5587 and 0.8110 on these two datasets, respectively, outperforming other state‐of‐the‐art approaches. The results suggest that SCTR is a promising method for automatic segmentation of IAs. Our code is available at https://github.com/guolilin/SCTR .


Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix

Abstract: The classification method is part of data mining which is used to predict existing problems and also as predictions for the future. The form of dataset used in the classification method is supervised data. The random forest classification method is processed by forming several decision trees and then combining them to get better and more precise predictions. while a decision tree is the concept of changing a pile of data into a decision tree that presents the rules of a decision. From these two classification methods, researchers will compare the level of accuracy of predictions from both methods with the same dataset, namely the employee dataset in India, to predict the level of accuracy of employees who leave their jobs or still remain to work at their company. The number of records available is 4654 records. Of the existing data, 90% was used as training data and 10% was used as test data. From the results of testing this method, it was found that the accuracy level of the random forest method was 86.45%, while the decision tree method was 84.30% accuracy level. Then, by using the confusion matrix, you can see the magnitude of the distribution of experimental validity visually to calculate precision, recall and F1-Score. The random forest algorithm obtained precision of: 96.7%, sensitivity of: 84.7%, specificity of: 91.4%, and F1-Score of: 90.2%. Meanwhile, the decision tree algorithm obtained precision of: 95.7%, sensitivity of: 82.9%, specificity of: 88.4%, and F1-Score of: 88.8%.


OCR concerned with different treatment of students with disabilities

Abstract: Case name: Letter re: Central Community School District 301 , No. 05‐23‐1246 (08/17/23).


Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Abstract: The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.


Classification of Student Grade Data Using the K-Means Clustering Method

Abstract: The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: "High Achievers," "Average Performance," and "Needs Improvement." Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.


Stakeholder Cues, National Origin, and Public Opinion Towards Firms: Evidence in the Context of the First Bank in an American Indian Nation

Abstract: Abstract When and how does stakeholder credibility matter in shaping public opinion? We explore this question in a real-world setting: in order to fight its citizens’ financial exclusion—a key barrier to development in Indian Country—American Indian Nation “A” negotiated the first entry of the first bank to its reservation. The bank is owned by American Indian Nation “B.” To the Federal Reserve, the bank branch is a potential proof-of-concept for the capacity of tribe-to-tribe investment to improve capital access in underserved Native communities. The bank’s success ultimately depends on whether Nation A’s citizens use its services; in the months before its opening, all three stakeholders independently attempted to influence public opinion toward the bank. We collaborated to conduct a first-of-its-kind survey of Nation A’s tribal members, finding high baseline buy-in especially given the bank’s nationality, but weak and even counterproductive treatment effects of pro-banking cues provided by Nation A and the Federal Reserve. Our results make clear the practical benefits of theory-building around stakeholder credibility, and the crucial role of individual attitudes in the political economy of development.


Understanding OCR's Complaint and Resolution Process

Abstract: The Office for Civil Rights is the enforcement arm of the United States Department of Education. OCR enforces numerous federal civil rights laws, including Section 504 and Title II of the ADA, that prohibit discrimination. To this end, the agency investigates complaints filed by students or other persons against their educational institution. This article explores frequently asked questions about the OCR complaint process.


The influence of work environment and work motivation on employee performance

Abstract: This research aims to determine the influence of the work environment and work motivation on employee performance at PT XYZ Central Jakarta, both partially and simultaneously. The method used is quantitative. The sampling technique used saturated sampling, and the sample obtained in this study was 75 respondents. Data analysis uses validity tests, reliability tests, classical assumption tests, regression analysis, correlation coefficient analysis, coefficient of determination analysis, and hypothesis testing. The results of this research show that the work environment has a significant effect on employee performance. Work motivation has a substantial impact on employee performance. The work environment and work motivation simultaneously significantly affect employee performance. The correlation coefficient value is 0.823, meaning that the independent and dependent variables have an extreme level of relationship.


Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education

Abstract: The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.


The surface edge explorer (SEE): A measurement-direct approach to next best view planning

Abstract: High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.


On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Abstract: Abstract Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.


Performance Analysis of Five U-Nets on Cervical Cancer Datasets

Abstract: Image segmentation is crucial for precise analysis and classification of biomedical images, especially in the realm of cervical cancer detection. The accuracy of segmentation significantly influences the efficacy of subsequent image classification processes. While traditional algorithms exist for image segmentation, recent advancements in convolutional neural networks, particularly U-Nets have showcased exceptional effectiveness, especially in the realm of biomedical imaging. This research focuses on evaluating the accuracy of various U-Net architectures applied to three distinct cervical cancer datasets i.e., DSB containing 1340 images, SipakMed containing 1849 images and Intel Images for Screening containing 2000 images datasets taken from 2018 Data Science Bowl. The investigated U-Net architectures comprise the fundamental U-Net, Attention U-Net, Double U-Net, Spatial Attention U-Net, and Residual U-Net. The performance of the u-nets is judged on the metrics: Recall, Precision, F1, Jaccard and Accuracy. It is observed that Basic U-Net on the DSB dataset provides highest value on these metrices and accuracy obtained is 96%. The reason of high accuracy for DSB dataset can be attributed to the contrast of the images which by using co-occurrence matrix is calculated as 145.


A YOLOX Object Detection Algorithm Based on Bidirectional Cross-scale Path Aggregation

Abstract: Abstract To solve the problem of insufficient feature fusion between the deep and shallow feature layers of the original YOLOX algorithm, which resulting in a loss of object semantic information, this paper proposes a YOLOX object detection algorithm based on attention and bidirectional cross-scale path aggregation. First, an efficient channel attention module is embedded in the YOLOX backbone network to reinforce the key features in the object region by distinguishing between the importance of the different channels in the feature layer, thus enhancing the detection accuracy of the network. Second, a bidirectional cross-scale path aggregation network is designed to change the information fusion circulation path while increasing the cross-scale connections. Weighted feature fusion is used to learn the importance of the different path input features for differentiated fusion, thereby improving the feature information fusion capability between the deep and shallow layers. Finally, the SIOU loss function is introduced to improve the detection performance of the network. The experimental results show that on the PASCAL VOC2007 and MS COCO2017 datasets, the algorithm in this paper improves mAP by 2.32% and 1.53% compared with the original YOLOX algorithm, and has comprehensive performance advantages compared with other algorithms. The mAP reaches 99.44% on the self-built iron ore metal foreign matter dataset, with a recognition speed of 56.90 frames/s.


The improvement of block chain technology simulation in supply chain management (case study: pesticide company)

Abstract: Abstract This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs.


Rancang Bangun Sistem Informasi Laporan Keuangan Bulanan Pesantren di Kantor Kepesantrenan Putri

Abstract: This research is the design of a monthly financial reporting information system for Islamic boarding schools at the Girls' Islamic Boarding School office, which was built using the PHP programming language. This information system will henceforth be named SIM-KA (Financial and Budget Management Information System). SIM-KA is accessed using the local network of the women's boarding school office. The financial reporting and budgeting of female Islamic boarding schools is still less than optimal, and errors often occur in the amounts reported. It is hoped that the design of this information system will (1) help Islamic boarding school administrators in reporting and making Islamic boarding school budgets so that they are more optimal, (2) help speed up work in making monthly Islamic boarding school reports and budgets, and (3) reduce errors in calculating recapitulation reports and budgets.


Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos

Abstract: Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.


Modeling of Specialized Training Curricula

Abstract: The article focuses on the need to modernize approaches to organizing specialized education in high school in connection with the change in vectors of economic and technological development of the country, which are reflected in the latest decisions of the President and government of the Russian Federation. To achieve this task, an organizational and content model of specialized training is presented, which makes it possible to combine the goals of participants in educational relations, including the state, and the requirements of the regulatory framework of Russian education. The result of this combination is a curriculum for specialized training, examples of the compilation of which for a technological profile of an engineering orientation complete the article.


An exercise in experimental mathematics: calculation of the algebraic entropy of a map

Abstract: We illustrate the use of the notion of derived recurrences introduced earlier to evaluate the algebraic entropy of self-maps of projective spaces. We in particular give an example, where a complete proof is still awaited, but where different approaches are in such perfect agreement that we can trust we get to an exact result. This is an instructive example of experimental mathematics.


Improved honey badger algorithm based on elementary function density factors and mathematical spirals in polar coordinate systema

Abstract: Abstract The Honey Badger Algorithm (HBA) is a new swarm intelligence optimization algorithm by simulating the foraging behavior of honey badgers in nature. To further improve its convergence speed and convergence accuracy, an improved HBA based on the density factors with the elementary functions and the mathematical spirals in the polar coordinate system was proposed. The algorithm proposes six density factors for attenuation states based on elementary functions, and introduces mathematical expressions of the polar diameters and angles of seven mathematical spirals (Fibonacci spiral, Butterfly curve, Rose spiral, Cycloid, Archimedean spiral, Hypotrochoid and Cardioid) in the polar coordinate system based on the density factors with the best synthesized effect to replace the foraging strategy of honey badger digging pattern in HBA. By using 23 benchmark test functions, the above improvements are sequentially compared with the original HBA, and the optimization algorithm with the best improvement, α4CycρHBA, is selected to be compared with SOA, MVO, DOA, CDO, MFO, SCA, BA, GWO and FFA. Finally, four engineering design problems (pressure vessel design, three-bar truss design, cantilever beam design and slotted bulkhead design) were solved. The simulation experiments results show that the proposed improved HBA based on the density factors with the elementary functions and the mathematical spirals of the polar coordinate system has the characteristics of balanced exploration and expiration, fast convergence and high accuracy, and is able to solve the function optimization and engineering optimization problems in a better way.


Parasitic Reflection Eliminating for Planar Elements Based on Multi-Frequency Phase-Shifting in Phase Measuring Deflectometry

Abstract: Phase measuring deflectometry (PMD) stands as an extremely important technique for specular surface measurement. However, the parasitic reflection from the rear surface poses a challenge for PMD. To solve this problem, this paper proposes an effective method based on multi-frequency and phase-shifting to search for the correct phase. Firstly, the relationship between the phase error and fringe frequency is adequately investigated. Subsequently, an auxiliary function is established to find the special frequency at which the phase error is zero theoretically and the unwrapped phase is the phase of the top surface exactly. Then, the shape of the top surface can be reconstructed correctly. A standard plane element with a thickness of 40 mm and a flat glass with 19 mm were measured. The experimental results verify the feasibility of the proposed method. Considering the result of the interferometer as a reference, the RMSE of the error map is up to 20 nm for the standard plane element. The experimental results demonstrate that the proposed method can successfully untangle the superposed reflections and reliably reconstruct the top surface of the object under test.


Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

Abstract: This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The ability to interpret can be made more accurate by reducing uncertain learning data, applying data in different environments, and applying techniques that explain the reasoning behind the results. We designed a generalized model using three different datasets, and each speech was converted into a spectrogram image through STFT preprocessing. The spectrogram was divided into the time domain with overlapping to match the input size of the model. Each divided section is expressed as a Gaussian distribution, and the quality of the data is investigated by the correlation coefficient between distributions. As a result, the scale of the data is reduced, and uncertainty is minimized. VGGish and YAMNet are the most representative pretrained deep learning networks frequently used in conjunction with speech processing. In dealing with speech signal processing, it is frequently advantageous to use these pretrained models synergistically rather than exclusively, resulting in the construction of ensemble deep networks. And finally, various explainable models (Grad CAM, LIME, occlusion sensitivity) are used in analyzing classified results. The model exhibits adaptability to voices in various environments, yielding a classification accuracy of 87%, surpassing that of individual models. Additionally, output results are confirmed by an explainable model to extract essential emotional areas, converted into audio files for auditory analysis using Grad CAM in the time domain. Through this study, we enhance the uncertainty of activation areas that are generated by Grad CAM. We achieve this by applying the interpretable ability from previous studies, along with effective preprocessing and fusion models. We can analyze it from a more diverse perspective through other explainable techniques.


Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Abstract: The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.


Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction

Abstract: This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.


Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeleddata. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are transferable to downstream tasks. Barlow Twins (BTs) is an SSL technique inspired by theories of redundancy reduction in human perception. In downstream tasks, BTs representations accelerate learning and transfer this learning across applications. This study applies BTs to speech data and evaluates the obtained representations on several downstream tasks, showing the applicability of the approach. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone being insufficient for factorization of learned latents into modular, compact, and informative codes. Our ablation study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights presented in this paper pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BTs self-supervision framework.


Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI

Abstract: Abstract As Artificial Intelligence (AI) rapidly advances and integrates into various domains, cybersecurity emerges as a critical field grappling with both the benefits and pitfalls of AI technologies. This paper explores the multifaceted dimensions of AI-driven cyberattacks, offering insights into their implications, mitigation strategies, underlying motivations, and profound societal impacts. The research centres on developing and presenting the AI Cybersecurity Dimensions (AICD) Framework, a comprehensive, multidimensional schema designed to guide academics, policymakers, and industry professionals in understanding and combating the evolving challenges posed by AI-driven cyber threats. The research unveils the complex dynamics of offensive AI, stressing the need for adaptive defences and ethical considerations. Concurrently, the study highlights adversarial AI threats, calling for proactive measures to address their potential ramifications. Through rigorous textual analyses and extensive literature reviews, the paper underscores the urgency for interdisciplinary approaches to bridge the technology-humanity chasm traditionally observed in cybersecurity discussions. By synthesising these diverse elements, the AICD Framework emerges as an instrumental tool for holistic understanding and practical interventions in the AI-infused cybersecurity landscape. The paper concludes with an urgent call for collaborative efforts in research and practice to navigate the intricate challenges and capitalise on the opportunities borne from the convergence of AI and cybersecurity.


Formation of Educational and Cognitive Motives in Younger Schoolchildren in Lessons about the Surrounding World

Abstract: The article discusses a current pedagogical problem: the role of motivation in the formation and development of educational and cognitive activity of a primary school student. The problem is considered on the basis of lessons on the subject “The World Around Us”. The conditions for constructing the learning process are revealed, in which the student’s acceptance and retention of the motive for activity is successful. Examples of methods for presenting the motive and new structural elements of the lesson are given: a minute for the curious, emotional warm-up, etc.


Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Abstract: Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi


Deep Learning Video Prediction Based on Enhanced Skip Connection

Abstract: Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M).


Flirting and winking in Tinder chats

Abstract: Abstract Existing research across the diverse field(s) of ‘discourse studies’ has started to explore the communicative orders and sequential practices surrounding emoji use ( Skovholt, Grønning and Kankaanranta 2014 ; Herring and Dainas 2017 ; Gibson, Huang and Yu 2018 ; Sampietro 2019 ). However, researchers have not yet systematically analysed one of the demonstrable phenomena of emoji, which is their ambiguity as meaning-making devices ( Miller et al. 2016 ; Jaeger et al. 2017 ). This study draws on Conversation Analysis to explore the issue of ambiguity in the use of one particular type of emoji, the wink (e.g., 😉, 🥴, 😜). Drawing on a data corpus of text conversations in Danish and in Spanish by users of the dating app ‘Tinder’, the analysis explores the phenomena of ambiguity in relation to the practice of flirting. The paper highlights four possible sources of ambiguity: idiosyncratic use, semiotic references, sequential placement, and relationship to ambiguous textual actions. The paper ends with reflections on possible future areas of research in the study of emoji and communication.


Entanglement Detection with Trace Polynomials

Abstract: We provide a systematic method for nonlinear entanglement detection based on trace polynomial inequalities. In particular, this allows us to employ multipartite witnesses for the detection of bipartite states, and vice versa. We identify pairs of entangled states and witnesses for which linear detection fails, but for which nonlinear detection succeeds. With the trace polynomial formulation a great variety of witnesses arise from immanant inequalities, which can be implemented in the laboratory through the randomized measurements toolbox.


LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients

Abstract: Abstract The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.


Spacetime-Efficient Low-Depth Quantum State Preparation with Applications

Abstract: We propose a novel deterministic method for preparing arbitrary quantum states. When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-dimensional state in depth <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">spacetime allocation</mml:mtext></mml:mrow></mml:math> (a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>, which are both optimal. When compiled into the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo fence="false" stretchy="false">{</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">H</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">S</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">T</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">N</mml:mi><mml:mi mathvariant="normal">O</mml:mi><mml:mi mathvariant="normal">T</mml:mi></mml:mrow><mml:mo fence="false" stretchy="false">}</mml:mo></mml:math> gate set, we show that it requires asymptotically fewer quantum resources than previous methods. Specifically, it prepares an arbitrary state up to error <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&#x03F5;</mml:mi></mml:math> with optimal depth of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>+</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&#x03F5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math> and spacetime allocation <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&#x03F5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>, improving over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&#x03F5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&#x03F5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>, respectively. We illustrate how the reduced spacetime allocation of our protocol enables rapid preparation of many disjoint states with only constant-factor ancilla overhead – <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> ancilla qubits are reused efficiently to prepare a product state of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>w</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-dimensional states in depth <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>w</mml:mi><mml:mo>+</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math> rather than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>w</mml:mi><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>, achieving effectively constant depth per state. We highlight several applications where this ability would be useful, including quantum machine learning, Hamiltonian simulation, and solving linear systems of equations. We provide quantum circuit descriptions of our protocol, detailed pseudocode, and gate-level implementation examples using Braket.


Spectro Temporal Fusion with CLSTM-Autoencoder based approach for Anomalous Sound Detection

Abstract: Abstract Deep learning models are proved efficient for complex learning tasks. Anomalous sound detection is one such complex task for which self-supervised deep architectures are emerging in recent days. Self-supervised deep models efficiently capture the underlying structure of data. Self-supervised anomalous sound detection attempts to distinguish between normal sounds and unidentified anomalous sounds. With the use of appropriate autoencoders, reconstruction error based decision making is effective for anomaly detection in domains such as computer vision. Auditory image (Spectrogram) based representation of sound signals are commonly used in sound event detection. We propose convolutional long short-term memory (CLSTM) Auto Encoder based approach for anomalous sound detection. In this approach, we explore fusion of spectral and temporal features to model characteristics of normal sounds with noises. The proposed approach is evaluated using MIMII dataset and the DCASE Challenge (2020) Task 2—Anomalous sound detection dataset. Experiments on proposed approach reveal significant improvement over the state-of-the-art approaches.


Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance

Abstract: For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier.


Integrating Ai and Machine Learning Technology for Educational Enhancement and Career Guidance System

Abstract: Career and education guidance is undergoing a transformative phase, especially in the context of life-long learning. Modernizing career services to meet the diverse needs of students is imperative in the age of technology. This article navigates the considerations for career services professionals, exploring the characteristics and needs of today's students, available technologies, funding requirements, and confidentiality issues. The challenge lies in creating accessible services that seamlessly connect education with employment. This paper presents a pioneering study that explores the untapped potential of artificial intelligence (AI) in revolutionizing career and education guidance, not only for higher education but also for students above the 9th standard. Drawing insights from focus groups, scenario work, and practical trials, the research provides a comprehensive view of the requirements and possibilities of integrating AI into career guidance. Perspectives from students, guidance staff, and institutions shape the findings, highlighting potential values, functions, and the driving forces and obstacles in adopting AI for career guidance. The study introduces varied modes of agency and maturity levels, establishing a framework for AI involvement in guidance processes. Future research directions include a focus on agency in guidance interaction, developing a guidance data ecosystem, and addressing ethical concerns.


Democracy In The Dark: Internet Shutdowns In India As A Challenge to Freedom of Speech and Expression

Abstract: The right to access the internet is intrinsically linked to fundamental rights such as the right to education and the right to privacy under Articles 21A and 21 of the Indian Constitution, respectively. The internet not only enhances the quality of education but also serves as a powerful tool for the free expression of knowledge and ideas. It has been recognized globally as a human right, and adherence to this right is imperative for countries signatory to the Universal Declaration of Human Rights. Freedom of speech and expression, enshrined in Article 19(1)(a) of the Indian Constitution, extends to the internet, with reasonable restrictions outlined in Article 19(2). In recent years, internet shutdowns in India have become increasingly common, particularly during periods of civil unrest and protests. While the government has the authority to impose reasonable restrictions on freedom of speech and expression to maintain public order, the arbitrary use of internet shutdowns has become a concerning trend. This paper explores the reasons behind and the consequences of these shutdowns on the right to freedom of speech and expression. The internet is a crucial medium for citizens to communicate, express dissent, and seek support, especially during times of crisis and unrest. Its importance is evident in regions like Jammu and Kashmir and the North Eastern states of India, where prolonged internet shutdowns have isolated affected communities from the rest of the country. The imposition of internet shutdowns as a means to restore peace raises questions about its reasonableness and adherence to constitutional provisions. The paper cites landmark cases, such as Anuradha Bhasin v. Union of India, where the Supreme Court emphasized the importance of necessity and proportionality in internet shutdown orders. The lack of accountability mechanisms, combined with normalized shutdowns, has allowed the government to stifle dissent without justifiable cause. To protect fundamental rights and uphold the principles of democracy, judicial activism and a critical evaluation of government actions are essential. The paper concludes that internet shutdowns, as a means of suppressing free speech and expression, often fail to meet the criteria of reasonable restrictions and are, therefore, a violation of the fundamental rights guaranteed under the Indian Constitution.


Improving Performance of Massive Text Real-Time Classification for Document Confidentiality Management

Abstract: For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.


Dual view deep learning for enhanced breast cancer screening using mammography

Abstract: Abstract Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.


The effects of tones and time pressure on the user experience of voice input on mobile devices for Mandarin Chinese: an experimental study

Abstract: This study experimented with mobile devices to investigate the effects of tones and time pressure on the voice input experience for Mandarin Chinese. Voice input for Mandarin Chinese is gaining popularity, and its implementation on mobile devices has recently developed very rapidly. Previous studies have focused mainly on technical considerations for designing a voice input system and have not investigated the user experience of voice input. Two experiments were conducted to test single-character and two-character phrases. Four measurements, the number of errors, user satisfaction, mental workload, and skin conductance, were gathered from sixty-four participants for each experiment. The results indicated that Mandarin tones had a significant influence on the number of errors and user satisfaction, and time pressure significantly influenced all measurements. Specifically, the falling-rising tone produced the highest number of errors, and the rising tone led to the lowest satisfaction. A high level of time pressure resulted in low satisfaction, a high mental workload, and high skin conductance. The results indicated that (a) Mandarin tones significantly affected error rates and user satisfaction, and (b) time pressure had a significant effect on user experience. The findings have design implications for voice input systems in terms of user experience.


Disinformation and Hate Speech

Abstract: chapter Share on Disinformation and Hate Speech Authors: Ronald M. Baecker University of Toronto (Computer Science Department) University of Toronto (Computer Science Department)Search about this author , Jonathan Grudin University of Washington (Information School) University of Washington (Information School)Search about this author Authors Info & Claims Digital Dreams Have Become Nightmares: What We Must DoFebruary 2024https://doi.org/10.1145/3640479.3640486Published:15 February 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access


Strengths-based service solutions: mapping a way forward in marketplace vulnerabilities

Abstract: Definitions of vulnerability are increasingly contested, engaging scholars across many disciplines. The many definitions and debates point to the multifaceted nature of the service challenges associated with the multiple dimensions of experienced vulnerability. Importantly, the deficit discourse that underpinned much of the work on vulnerability in different domains is increasingly changing to a strengths-based perspective. A strengths-based approach seeks opportunities for risk reduction, adaptation, and coping solutions for actors in service delivery and value creation. Thus, service managers and researchers must address both assets and adaptive capabilities of actors within the service system (process, design, and delivery) and within actors’ own contexts. This chapter therefore explores contemporary thinking to support actors experiencing vulnerability in the marketplace. We seek to promote debate on ways to enhance equitable service sustainability, particularly in light of the United Nations Sustainable Development Goals (SDGs). A lack of focus on sustainable transformative service as per the SDGs, limited strengths-based thinking, together with potentially unmet customer needs that stem from experienced vulnerability (or related to it), exacerbate existing vulnerabilities. These complex factors threaten progress on the SDGs’ agenda to reduce inequalities triggered by service systems. Service scholars and practitioners are therefore urged to consider sustainability and strengths-based approaches in their research and service design. In reviewing contemporary thinking, the chapter concludes with a new research agenda for considering strengths-based approaches to service delivery; service sustainability; and enhancing service inclusion for any actor in the system/network experiencing vulnerabilities.


Regulating the Animal Testing Industry

Abstract: Animal testing has been a customary practice in numerous civilizations throughout human history. Humanity tends to want to understand the true nature of our physiology as a whole, and animals have been beneficial to help satisfy this curiosity. Yet ethically speaking, scientists argue for the protection of the rights of humans, while many fail to do so for our natural counterparts, animals. Current standards of the industry bring up concern for the ethical treatment of animals; additionally, many of the standards that exist are outdated and not systematized throughout the scientific world. Furthermore, new scientific developments can possibly allow for the phasing out entirely of animal testing, with new computer modeling software. It can render many of the existing tests obsolete as this new software allows scientists to bypass some of the animal testing in drug trials. Due to many of the shortcomings in the industry, this has caused an inefficiency. This inefficiency can be described as an overall loss of time and money, which could instead be put towards developing new drugs or other studies. Although it is unlikely that animal testing will be removed entirely, it is necessary that new standards be put in place for the ethical treatment of animals. Keywords: animal testing, ethics, regulation, drug trials, computer modeling


Automatic label curation from large-scale text corpus

Abstract: Abstract The topic modeling technique extracts themes based on their probabilistic measurements from any large-scale text collection. Even though topic modeling pulls out the most important phrases that describe latent themes in text collections, a suitable label has yet to be found. Interpreting the topics extracted from a text corpus and identifying a suitable label automatically reduces the cognitive load for the analyst. Extractive methods are used typically to select a label from a given candidate set, based on probability metrics for each candidate set. Some of the existing approaches use phrases, words, and images to generate labels using frequency counts of different words in the text. The paper proposes a method to generate labels automatically to represent each topic based on a labeling strategy to filter candidate labels and then apply sequence-to-sequence labelers. The objective of the method is to get a meaningful label for the result of the Latent Dirichlet Allocation algorithm. The BERTScore metric is used to evaluate the effectiveness of the proposed method. The proposed method generates good interpretative labels as compared to baseline models for topic words or terms automatically. The comparison with the label generated through ChatGPT API shows the quality of the generated label with the experiment performed on Four Datasets NIPS, Kindle, PUBMED, and CORD-19.


A Multi-Attention Feature Distillation Neural Network for Lightweight Single Image Super-Resolution

Abstract: In recent years, remarkable performance improvements have been produced by deep convolutional neural networks (CNN) for single image super-resolution (SISR). Nevertheless, a high proportion of CNN-based SISR models are with quite a few network parameters and high computational complexity for deep or wide architectures. How to more fully utilize deep features to make a balance between model complexity and reconstruction performance is one of the main challenges in this field. To address this problem, on the basis of the well-known information multi-distillation model, a multi-attention feature distillation network termed as MAFDN is developed for lightweight and accurate SISR. Specifically, an effective multi-attention feature distillation block (MAFDB) is designed and used as the basic feature extraction unit in MAFDN. With the help of multi-attention layers including pixel attention, spatial attention, and channel attention, MAFDB uses multiple information distillation branches to learn more discriminative and representative features. Furthermore, MAFDB introduces the depthwise over-parameterized convolutional layer (DO-Conv)-based residual block (OPCRB) to enhance its ability without incurring any parameter and computation increase in the inference stage. The results on commonly used datasets demonstrate that our MAFDN outperforms existing representative lightweight SISR models when taking both reconstruction performance and model complexity into consideration. For example, for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>×</mo></math> 4 SR on Set5, MAFDN (597K/33.79G) obtains 0.21 dB/0.0037 and 0.10 dB/0.0015 PSNR/SSIM gains over the attention-based SR model AFAN (692K/50.90G) and the feature distillation-based SR model DDistill-SR (675K/32.83G), respectively.


Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm

Abstract: The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.


Theory of infinite sequences and series by Ludmila Bourchtein and Andrei Bourchtein , pp. 377, £54.99, (paper), ISBN 978-3-03079-430-9, Springer Verlag (2022)

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An exploratory study on the readability of authentic English chapter books for Croatian primary school students

Abstract: The paper explores chapter books, explaining their nature and providing information on their usefulness and possible uses in the context of teaching English as a foreign language. It outlines their properties, layout and subject matter. The goal of the exploratory study was to investigate whether young Croatian pupils could read English chapter books written for native speakers and whether illustrations helped them to comprehend the narrative. Furthermore, it aimed to examine pupils’ response to the format of chapter books. The study about the suitability of chapter books was conducted among pupils in the third, fourth and fifth grade in two Croatian schools, and its results show that young learners of English as a foreign language can grasp the essence of a story and can reflect on what they read, identify points of difficulty and the strategies applied. The findings suggest that carefully chosen chapter books can be valuable authentic materials in aiding language acquisition, fostering discussions, and engaging higher-order thinking skills in young EFL learners in Croatia.


Hubungan Kepercayaan Diri dengan Interaksi Sosial pada Remaja Kecanduan Game Online di SMP Negeri 1 Asembagus

Abstract: Confidence is one of the things social interaction well.But for the students had been addicted to online games of course would be questionable, people were addicted to online games would be hard to interact with others because they already feel comfortable with their activities.This study bertujuhan identify the effects confidence on social interaction in adolescents are addicted to online games in junior high schools the sample of 1 asembagus 168 teenagers. Using a scale addicted to online games, confidence and social interaction that will impact analysis to know. Hence analysis techniques the data used was pearson correlation.The result of the research is 200 >0,05 which means there is a confidence with social interaction in adolescents addicted to online games.


On constrained Kaczmarz algorithm with momentum for image reconstruction

Abstract: Summary The Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction.


Analisis Kesulitan Mahasiswa Santri Fakultas Tarbiyah Universitas Ibrahimy Sukorejo Situbondo dalam Menyusun dan Menyelesaikan Tugas Akhir Skripsi (TAS) pada Tahun 2023

Abstract: This research reveals the difficulties Santri students face at the Tarbiyah Faculty of UNIB Situbondo in compiling and completing their final thesis assignment (TAS) in 2023. The research method used combines two approaches, descriptive quantitative and qualitative. The descriptive approach is the preparation and presentation of data into images or graphs. Meanwhile, the quantitative approach analyzes quantitative data obtained through questionnaire instruments regarding the average percentage of difficulties faced by Islamic boarding school students. Quantitative data was analyzed using descriptive and inferential statistics with the help of the Excel/SPSS 23 program. The qualitative approach was to carry out an analysis of qualitative data obtained through interview instruments. The population in this study was 256 Tarbiyah Faculty students who graduated in 2022-2023. Meanwhile, the samples taken as research objects/subjects were 64 students. The sample size used was 25% of the total population. The results of the research show that three indications are very dominant faced by students, namely: 1) students have difficulty in determining statistical formulas that suit the data (percentage 82.26%), 2) students have difficulty in using statistical program applications such as SPSS, Eviews, Smart PLS and others are often used in processing/analyzing research data (percentage 91.94%), and 3) students feel anxious while waiting for the thesis exam, worried or afraid when facing the thesis exam (percentage 87.10%).


Quantum tomography for arbitrary single-photon polarization-path states

Abstract: Quantum state tomography (QST), the process through which the density matrix of a quantum system is characterized from measurements of specific observables, is a fundamental pillar in the fields of quantum information and computation. In this work, we propose a simple QST method to reconstruct the density matrix of two qubits encoded in the polarization and path degrees of freedom of a single photon, which can be realized with a single linear-optical setup. We demonstrate that the density matrix can be fully described in terms of the Stokes parameters related to the two possible paths of the photon, together with a quantum version of the two-point Stokes parameters introduced here. Our findings put forward photonic circuits for the investigation of the dynamics of open quantum systems.


Factors influencing the adoption of robotic technologies in academic libraries: A systematic literature review (SLR)

Abstract: The study aimed to identify the factors influencing the adoption of robotic technologies in academic libraries, investigate the impact of robots on library services, and reveal challenges to adopt robotic applications in academic libraries. A systematic literature review was applied to address the study’s objectives. The 16 most relevant seminal studies published in peer-reviewed journals were selected to conduct the study. Findings displayed that the factors of innovation, advancements in artificial intelligence, library security, and personalized services influenced positively the adoption of robotic technologies in academic libraries. The study also illustrated that robots had a significant positive impact on academic libraries by performing a variety of roles, solving space issues, and providing entertainment to library patrons. Results also revealed that loss of critical thinking, technical issues, lack of organizational planning, financial hurdles, fear of job loss, shortage of skilled manpower, and ethical considerations caused barriers to the successful implementation of robotic applications in academic libraries. The study has added valuable literature to the existing body of knowledge. It is the first systematic literature review on the factors influencing the adoption of robotic technologies in academic libraries. It has provided practical solutions in light of evidence-based data for the effective adoption of robotic technologies in academic libraries.


Athena – The NSF AI Institute for Edge Computing

Abstract: Abstract The National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.


A secure and efficient electronic medical record data sharing scheme based on blockchain and proxy re-encryption

Abstract: Abstract With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs always raises significant concerns about potential security issues and breaches of privacy. Previous research has demonstrated that centralized cloud-based EMR systems are at high risk, e.g., single points of failure, denial of service (DoS) attacks, and insider attacks. With this motivation, we propose an EMR sharing scheme based on a consortium blockchain that is designed to prioritize both security and privacy. The interplanetary file system (IPFS) is used to store the encrypted EMR while the returned hash addresses are recorded on the blockchain. Then, the user can authorize other users to decrypt the EMR ciphertext via the proxy re-encryption algorithm, ensuring that only authorized personnel may access the files. Moreover, the scheme attains personalized access control and guarantees privacy protection by employing attribute-based access control. The safety analysis shows that the designed scheme meets the expected design goals. Security analysis and performance evaluation show that the scheme outperforms the comparison schemes in terms of computation and communication costs.


From Strings to Sensors: Movement Representation in AI Theatre

Abstract: The assimilation of AI technology into theatre practices has inaugurated an expansive frontier of possibilities for both thespians and spectators. In terms of movement, this involves the use of avatars, which inhabit a customary screen milieu (encompassing three-dimensional in-world scenography) that necessitates simultaneous consideration of a tridimensional theatrical space and coexisting performers, within a moment of real-time inception and interconnectedness. This complex confluence raises questions pertaining to the ‘avatarisation’ of corporeal embodiments on the theatrical stage and the consequent emergence of novel performative methodologies. Within AI-enabled performances, the use of motion capture technology, commonly known as ‘mocap’, entails the recording of skeletal data from physical actors, referred to as ‘mocaptors’, who wear a geo-spatial system for motion capture. This is then translated into digital data that can subsequently be used to animate digital characters or avatars.


Classification of Rice Growth Phases Using the K-Nearest Neighbor Algorithm in the Irrigation area of Seulimeum Sub District, Aceh, Indonesia

Abstract: Abstract Rice ( Oryza sativa L.) is the main food commodity for most of Indonesia’s population. The existence of irrigation areas dramatically affects the water demand in each phase of rice growth. Due to a lack of remote sensing knowledge, classifying the rice growth phase is done manually. This study aims to classify rice growth phases using the K-Nearest Neighbor Algorithm in the irrigation area of the Seulimuem Sub-District. This research uses Google Earth satellite imagery in November 2021 through three stages: multi-resolution segmentation, guided classification, and accuracy testing. The results showed that the paddy and non-paddy fields are 1,425.56 ha and 6,581.70 ha, respectively. The classification results of rice plant growth phases consist of 4 phases, namely the inundation phase, the vegetative phase, the generative phase, and the fallow phase. Based on the accuracy assessment results in November 2021, the overall accuracy ranged from 90.65% to 92.92%, with a kappa index value of 0.85 to 0.87, categorizing it as “almost perfect.”


Utilizing modern computer architectures to solve mathematical optimization problems: A survey

Abstract: Numerical algorithms to solve mathematical optimization problems efficiently are essential to applications in many areas of engineering and computational science. To solve optimization problems of ever-increasing scale and complexity, we need methods that exploit emerging hardware systems. However, the complexities of specific architectures and their impact on performance can be challenging. This article provides an overview of emerging hardware architectures and how they are used to solve mathematical optimization problems. We focus on parallel high-performance computing architectures, which are well-established yet challenging to employ for optimization, as well as digital quantum computing, which has recently gained attention due to its potential for transformative computational performance. Furthermore, we highlight several other emerging hardware architectures that may become relevant for mathematical optimization. We intend for this review to encourage the optimization and process engineering communities to increasingly consider both hardware and software developments in the pursuit of superior computational performance.


Fast random opposition-based learning aquila optimization algorithm

Abstract: Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t-test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.


Secure Medical Data on Cloud storage via DNA Homomorphic Encryption technique

Abstract: Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. However, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. The efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10%, 20%, and 35% than OHE, HE and ECC algorithms.


Radiation-training system with a custom survey-meter mock-up in a browser-based mixed reality environment

Abstract: Training for radiation protection and control requires a visual understanding of radiation, which cannot be perceived by the human senses. Trainees must also master the effective use of measuring instruments. Traditionally, such training has exposed trainees to radiation sources. Here, we present a novel e-training strategy that enables safe, exposure-free handling of a radiation measuring tool called a survey meter. Our mixed reality radiation-training system merges the physical world with a digital one. Collaborating with a mixed reality headset (HoloLens 2), this system constructs a mock-up of a survey meter in real-world space. The HoloLens 2 employs a browser-based application to visualize radiation and to simulate/share the use of the survey meter, including its physical movements. To provide a dynamic learning experience, the system adjusts the survey-meter mock-up readings according to the operator's movements, distance from the radiation source, the response time of survey meter, and shielding levels. Through this approach, we expect that trainees will acquire practical skills in interpreting survey-meter readings and gain a visual understanding of radiation in real-world situations.


Robust sequential fusion Kalman estimators with asymptotic equivalence and stability for networked uncertain sensor systems

Abstract: For networked uncertain sensor systems (NUSSs) with an improved unified sensor model with “hard” and “soft” sensors including five uncertainties plus two-step random measurement delays and colored measurement noises, applying the block matrix inversion formula, a new sequential inverse covariance fusion (SICF) approach is presented. It can sequentially compute the global inverse covariance according to the time order of the received data, but need not wait until all data are received. The corresponding minimax robust time-varying and steady-state sequential fusion (SF) Kalman estimators (predictor, filter, and smoother) are presented in the sense that their actual error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. They have lower computation complexities and higher accuracies equivalent to ones of the robust optimal batch-fusion (BF) Kalman estimators, and are suitable for real-time applications. Their asymptotic equivalence, absolute asymptotic stability, and accuracy relations are proved. Their strict complexity analysis is given. They constitute a novel universal robust SF estimation, convergence and stability theory for NUSSs. One simulation example applied to the uncertain ARMA signal processing verifies the effectiveness of the proposed approach and theory.