Sentiment Analysis on Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique

Abstract: Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which performs better for Roman Urdu Sentiment analysis. Our suggested model employs the BiLSTM network to maintain the context in both directions and eventually, results for ternary classification are obtained by using the final softmax output layer. A manually labeled data set was used to evaluate the model, data is collected from the HEIs of Pakistan. Model was empirically evaluated on two datasets of Roman Urdu, the newly developed student’s feedback dataset and RUSA-19 publically available data set of Roman Urdu. Our model performs effectively using the word embedding and BiLSTM layer. The proposed model is compared with the baseline models of CNN, RNN, GRU and classic LSTM. The experimental findings demonstrate the proposed model's efficacy with an F1score of 90%.


The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application

Abstract: When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluated based on its sparse-categorical loss during training and BLEU in the inference phase, utilizing a neural generative attention-based additive scoring function. Despite memory constraints that limited ADAMW to ten epochs, this optimizer showed promising results compared to configurations using early stopping techniques. SGD provided higher BLEU scores for generalization but was very time-consuming. The results highlight the importance of finding a balance between optimization performance and computational efficiency, positioning ADAMW as a promising alternative when training efficiency and generalization are primary concerns.


Updated exploration of the Tor network: advertising, availability and protocols of onion services

Abstract: Abstract The Tor network is known for its opaque characteristics and involvement in illicit activities, motivating to shed light on the exposure, lifetime, and functionalities of onion services. This study focuses on the appearance of Tor links in online advertising and monitors the connectivity status and protocols of the collected onion domains through the Tor network over 105 days. Out of 54,602 onion addresses gathered, it was found that 38% of Tor links were advertised only once, 43% between two and five times, and 19% more than five times. Furthermore, 50% of the addresses were exclusively advertised on the surface web, 6% on the dark web, and 44% on both portions. The temporal analysis revealed that 67% of the addresses were predominantly active, 7% were intermittent, and 26% were mostly inactive. The study examined fifteen protocols used by onion services, concluding that 94% employed a single protocol, while 6% utilized between two and eight protocols. Among active sites, HTTP was present in 99.75% of cases, followed by SSH (4.95%) and HTTPS (0.64%). Additionally, onion services without web services often deploy cryptocurrency or instant messaging servers. This study offers a comprehensive and current understanding of the dark web, surpassing previous research in its scope.


ViViD: Aplikasi Kencantikan sebagai Wadah Edukasi dan Identifikasi Personal Color<b></b>

Abstract: Makeup has been a part of human culture for more than 6000 years, continuing to evolve as new trends emerge in the beauty industry. Everyone has a unique color of skin, eyes, hair, makeup applications are created to determine personal color and shade. The application aims to increase user confidence by helping them find the best matching makeup color based on their individual characteristics. Using the SMART method in its development, the app focuses on specific features such as personal color identification and appropriate makeup color recommendations. Measurable aspects involve calculating the accuracy of personal color recommendations and makeup colors. The initiative is highly relevant, addressing the general concerns of women and in line with the growing market demand for makeup as a lifestyle


The Information‐Processing Perspective on Categorization

Abstract: Categorization behavior can be fruitfully analyzed in terms of the trade-off between as high as possible faithfulness in the transmission of information about samples of the classes to be categorized, and as low as possible transmission costs for that same information. The kinds of categorization behaviors we associate with conceptual atoms, prototypes, and exemplars emerge naturally as a result of this trade-off, in the presence of certain natural constraints on the probabilistic distribution of samples, and the ways in which we measure faithfulness. Beyond the general structure of categorization in these circumstances, the same information-centered perspective can shed light on other, more concrete properties of human categorization performance, such as the results of certain prominent experiments on supervised categorization.


Visitor Experience Map and NFC-Based Scoring for Data-Driven Exhibition Enhancement

Abstract: In the current exhibition industry, it is crucial for organizers and exhibitors to comprehend and enhance visitor experiences. The objective of this study is to improve the exhibition setting by utilizing Near Field Communication (NFC) technology to capture, monitor, and analyze visitor behavior, engagement, and satisfaction. The main approach entails combining NFC technology with the Visitor Experience Map to fully understand the complexities of the visitor experience. NFC-enabled smartphones facilitate seamless interaction with the system, as users simply need to bring their smartphones close to NFC tags. This enables data collection and triggers the activation of a visitor scoring form for ratings and feedback. The study's findings indicate a mean system usability score of 81.4, which demonstrates successful implementation and great usability. This confirms the effective and easy-to-use nature of the strategy, guaranteeing that visitors can effortlessly provide their ratings and feedback. The originality and enhancement reside in the successful integration of NFC technology with the Visitor Experience Map, providing a strong and user-focused approach for organizers and exhibitors to enhance the exhibition experience. This study creates a favorable situation for both visitors and stakeholders, demonstrating the potential of technological advancements to greatly influence the exhibition industry. Doi: 10.28991/ESJ-2024-08-01-015 Full Text: PDF


Optimized RMDL with Transfer Learning for Sentiment Classification in the MapReduce Framework

Abstract: A core task in sentiment analysis is sentiment categorization, and it is crucial to understand user feelings based on their remarks in social media or product evaluations. Due to ambiguous phrases, refusal words, and other factors, categorizing sentiment presents several challenging issues. The objective of this research is to develop a hybrid optimization-based deep learning model and MapReduce framework-based sentiment categorization approach. The review document is taken from a dataset and used in this case with the MapReduce methodology. MapReduce is a software framework and programming model for analyzing massive volumes of data that consists of two phases, mapper and reducer. BERT tokenization and aspect term extraction are executed in the mapper phase, whereas sentiment analysis is performed in the reducer stage utilizing random multimodal deep learning (RMDL) with transfer learning and AlexNet and ResNet 50 as pre-trained models. In addition, the exponential coot political algorithm (ECPA) is offered as an optimization algorithm for weight optimization in RMDL. The ECPA is obtained by combining the exponential weighted moving average model (EWMA) with the coot algorithm, as well as a political optimizer (PO). The proposed ECPA_RMDL model has acquired 90.9% precision, 89.7% recall, and 89.9% f-measure.


LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

Abstract: Ontology-based data access focuses on enabling query evaluation over heterogeneous relational databases according to the model represented by an ontology. The relationships between the ontology and the data sources are commonly defined with declarative mappings, which are used by systems to perform SPARQL-to-SQL query translation or to generate RDF dumps from the relational databases. Besides the potential homogenization of data because of using an ontology, some additional advantages of this paradigm are that it may allow applying reasoning thanks to the ontology, as well as querying for meta knowledge, which describes statements with information such as provenance or certainty. In this paper, (i) we adapt a widely used RDF graph store benchmark, namely LUBM, for ontology-based data access, (ii) extend the benchmark for the evaluation of queries that exploit meta knowledge, and (iii) apply it for performance evaluation of state-of-the-art declarative mapping systems. Our proposal, the LUBM4OBDA Benchmark, considers inference capabilities that are not covered by previous ontology-based data access benchmarks, and it is the first one for the evaluation of meta knowledge and the RDF-star data model. The experimental evaluation shows that current virtualization systems cannot handle some advanced inference tasks, and that optimizations are needed to scale RDF-star materialization.


A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Abstract: Gliomas are primary brain tumors caused by glial cells. These cancers' classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.


The United Nations and Blockchain Technology

Abstract: Abstract In this essay, I discuss the United Nations and its activities as they pertain to the use of blockchain technology. I argue that while this technology is rather new, the United Nations has been open to using such technological innovations to better solve human rights challenges that include fighting to end child trafficking, better distributing food aid, and reducing carbon emissions. Finally, the essay discusses the future prospects for blockchain technology and the UN.


Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

Abstract: Twitter is a popular social media platform that is widely used by individuals and businesses. However, it is vulnerable to bot attacks, which can have negative effects on society. Supervised machine learning techniques can detect bots but require labeled data to differentiate between human and bot users. Twitter generates a significant amount of unlabeled data, which can be expensive to label. Unsupervised machine learning techniques, specifically clustering algorithms, are crucial for managing this data and reducing computational complexity. Effective feature selection is necessary for clustering, as some features are more important than others. This study aims to enhance feature reliability, introduce new features, and reduce them to improve bot identification accuracy using clustering algorithms. The study achieved an accuracy rate of 0.99 in four clustering algorithms, including agglomerative hierarchy, k-medoids, DBSCAN, and K-means. This was accomplished by minimizing dataset dimensions and selecting essential features. By employing unsupervised machine learning techniques, Twitter can detect and mitigate bot attacks more efficiently, which can positively impact society


Monitoring Kesehatan Warga Desa Sidodadi Ramunia Melalui Pemberdayaan Kader Posyandu Berbasis Cloud Computing

Abstract: Empowering village residents requires support from network infrastructure and devices, software, digital literacy and assistance. Posyandu plays a role in helping the Village Government (Pemdes) improve basic health and social services. Problems that arise are that Posyandu cadres. IT skills need to be trained so that reporting from the field can be directly connected to village reports. The solutions provided through PKM activities are increasing the participation of mothers and toddlers, the elderly in posyandu and increasing the IT skills of Posyandu cadres. Community empowerment activities produce digital applications. during eight months service activity resulted in a health monitoring application system aimed at facilitating monitoring of posyandu activities in Ramunia Village.


Gesture Controlled Virtual Mouse and Voice Assistant

Abstract: The Al Virtual Mouse uses computer vision techniques to track hand movements and translates them into cursor movements on the screen. The system is designed user-friendly, allowing users to interact with their computer without the need for a physical mouse. The system is constructed using advanced Python packages like Mediapipe, OpenCV, etc. All i/o activities are physically controlled by a hand motion and a voice assistance. The research uses advanced technologies like machine learning and computer vision techniques, which operates well without the use of any additional computer resources, to recognize hand movements and spoken instruction. The developed system provides an alternative to conventional mouse devices, for individuals with disabilities or those who prefer a more natural way of interacting with their computers.


Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

Abstract: Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.


Incentive approaches for cloud computing: challenges and solutions

Abstract: Abstract Cloud computing enables highly configurable and reliable computing resources on a rentable per-use scheme, facilitating quick and cost-effective provisioning of large-scale applications. Thanks to the fast-paced evolution of cutting-edge technologies and the rapid spread of cloud-based solutions, the cloud computing ecosystem is now part of our everyday lives. Nevertheless, cloud computing relies on highly sophisticated data centers comprising energy-consuming servers and equipment that require much energy. Stimulating cloud services for active participation and network contributions presents several challenges. Strategies based on artificial intelligence (AI), game theory, and blockchain have great potential to create an economically sustainable cloud ecosystem. This paper explores strategies grounded in AI, game theory, and blockchain to foster an economically sustainable cloud ecosystem. Informed by a survey study, our research delves into incentive approaches within cloud computing. Theoretical foundations, motivations, and enabling techniques are comprehensively examined to provide valuable insights for a broad audience. The primary contributions of this work lie in elucidating the application of AI, game theory, and blockchain to address challenges in incentivizing cloud services, paving the way for a more sustainable and efficient cloud computing landscape.


Exploring and reviewing the potential of quantum computing in enhancing cybersecurity encryption methods

Abstract: As the landscape of cybersecurity continually evolves, traditional encryption methods face unprecedented challenges from the impending era of quantum computing. This paper undertakes a comprehensive exploration of the potential transformative impact that quantum computing could have on enhancing cybersecurity encryption methods. Commencing with an overview of quantum computing fundamentals, including the principles of quantum mechanics and key quantum properties, the paper delves into the disruptive power of Shor's algorithm. This algorithm, capable of exponentially faster factorization than classical counterparts, poses a significant threat to prevalent cryptographic techniques such as RSA and ECC. In response to the vulnerabilities exposed by quantum algorithms, the paper investigates the field of post-quantum cryptography, examining cryptographic algorithms designed to resist quantum attacks. Additionally, the study scrutinizes Quantum Key Distribution (QKD) as a potential solution for secure communication in a quantum environment, analyzing its strengths and limitations. The paper provides an updated survey of the current state of quantum computing, highlighting achievements, milestones, and a comparative analysis of existing quantum computing platforms. Subsequently, it assesses the potential impact of quantum computing on cybersecurity, addressing both its ability to fortify encryption and potential risks and vulnerabilities. Striking a balance between benefits and challenges, the research offers insights into the coexistence of quantum and classical cryptographic methods. Looking toward the future, the paper explores ongoing research and development in quantum computing, identifying challenges and ethical considerations. In conclusion, it synthesizes key findings, emphasizing the implications for the future of cybersecurity and advocating for continued research to ensure the development of resilient encryption methods in the quantum era.


Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature

Abstract: The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm.


A Hybrid Approach to Detect and Identify Text in Picture

Abstract: In order to create computer systems that can automatically read text from images or pictures, researchers focus on detecting and recognizing text in images. This issue is particularly difficult because images often have complicated backgrounds and a wide range of properties, including color, size, shape, orientation, and texture. Our proposed approach is based on morphology, which consists of a dilation and erosion process to extract text and recognize black-and-white text areas that contain document text or images. This suggested approach has been investigated for its ability to automatically identify text aligned with text pictures, such as store names, street names, banners, and posters. The design, application, and outcomes of the device's experiments are covered in this manuscript using Optical Character Recognition (OCR) Tesseract standards and the optimized OCR Tesseract. Our result shows that the optimized OCR Tesseract performs much better compared to the standard. Image preprocessing and text processing modules comprise this device's two modules. With an Arduino Uno and drawbot/flutter for text printing, this device was created using the Raspberry Pi and a 1.2GHz processor. Doi: 10.28991/ESJ-2024-08-01-016 Full Text: PDF


Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Abstract: Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation.


A small sample bearing fault diagnosis method based on ConvGRU relation network

Abstract: Abstract Considering that in the fault diagnosis of bearing based on relation network, using the sample mean value as the class prototype for each class is susceptible to outliers, resulting in inaccurate class prototypes, this paper proposes a ConvGRU relation network fault diagnosis model; Firstly, the model utilizes a embedding module to extract bearing fault features, and then uses the Convolutional Gate Recurrent Unit (ConvGRU) as a learnable class prototype generator to generate class prototypes for each class. Secondly, a relation module is utilized to measure the similarity between class prototypes and the sample features of the query set, obtaining relation scores, and ultimately achieving fault diagnosis. In order to test the validity and advantages of the model, experimental verification and analysis were conducted on the Case Western Storage rolling bearing dataset. The results of the experiment show that the class prototypes generated by the ConvGRU class prototype generation module have better discrimination and accuracy compared to the class prototypes generated by the relation network. In the 10-way 5-shot experiment, the accuracy of the model proposed in this paper reaches 99.60%, which increases by 6.63%, 5.10%, 4.80% , and 1.75% compared to KNN, CNN, Prototype Network , and Relation Network. The method proposed in this paper helps to generate more accurate class prototypes and has a certain effect on improving the accuracy of model fault diagnosis.


Eﬃcient Light Propagation Algorithm using Quantum Computers

Abstract: Abstract Quantum algorithms can potentially overcome the boundary of computationally hard problems. One of the cornerstones in modern optics is the beam propagation algorithm, facilitating the calculation of how waves with a particular dispersion relation propagate in time and space. This algorithm solves the wave propagation equation by Fourier transformation, multiplication with a transfer function, and subsequent back transformation. This transfer function is determined from the respective dispersion relation, which can often be expanded as a polynomial. In the case of paraxial wave propagation in free space or picosecond pulse propagation, this expansion can be truncated after the quadratic term. The classical solution to the wave propagation requires $\mathcal{O}(N log N)$ computation steps, where $N$ is the number of points into which the wave function is discretized. Here, we show that the propagation can be performed as a quantum algorithm with $\mathcal{O}((log{}N)^2)$ single-controlled phase gates, indicating exponentially reduced computational complexity. We herein demonstrate this quantum beam propagation method (QBPM) and perform such propagation in both one- and two-dimensional systems for the double-slit experiment and Gaussian beam propagation. We highlight the importance of the selection of suitable observables to retain the quantum advantage in the face of the statistical nature of the quantum measurement process, which leads to sampling errors that do not exist in classical solutions.


The emergence of self-awareness: insights from robotics

Abstract: The ability for self-related thought is historically considered to be a uniquely human characteristic. Nonetheless, as technological knowledge advances, it comes as no surprise that the plausibility of humanoid self-awareness is not only theoretically explored but also engineered. Could the emerging behavioural and cognitive capabilities in artificial agents be comparable to humans? By employing a cross-disciplinary approach, the present essay aims to address this question by providing a comparative overview on the emergence of self-awareness as demonstrated in early childhood and robotics. It argues that developmental psychologists can gain invaluable theoretical and methodological insights by considering the relevance of artificial agents in better understanding the behavioural manifestations of human self-consciousness.


Recovering coefficients in a system of semilinear Helmholtz equations from internal data

Abstract: Abstract We study an inverse problem for a coupled system of semilinear Helmholtz equations where we are interested in reconstructing multiple coefficients in the system from internal data measured in applications such as thermoacoustic imaging. We derive results on the uniqueness and stability of the inverse problem in the case of small boundary data based on the technique of first- and higher-order linearization. Numerical simulations are provided to illustrate the quality of reconstructions that can be expected from noisy data.


A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Abstract: Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.


Opportunities for Bicycle Tourism in Zomin, Bulungur and Bakhmal Districts

Abstract: This article discusses the creation of a web page for tourists to travel in the Zomin, Bulungur, and Bakhmaldistricts, as well as the location of service assistance, bicycle rental, guest houses, restaurants, and, most importantly,determining the months.


Optimal Rates of Approximation by Shallow ReLU$$^k$$ Neural Networks and Applications to Nonparametric Regression

Abstract: Abstract We study the approximation capacity of some variation spaces corresponding to shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established. Using these results, we are able to prove the optimal approximation rates in terms of the number of neurons for shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is also shown how these results can be used to derive approximation bounds for deep neural networks and convolutional neural networks (CNNs). As applications, we study convergence rates for nonparametric regression using three ReLU neural network models: shallow neural network, over-parameterized neural network, and CNN. In particular, we show that shallow neural networks can achieve the minimax optimal rates for learning Hölder functions, which complements recent results for deep neural networks. It is also proven that over-parameterized (deep or shallow) neural networks can achieve nearly optimal rates for nonparametric regression.


Artificial Intelligence and future of higher education

Abstract: Many engaged in the development of Artificial Intelligence (AI) see education as a potential market for sales. They predict that AI will transform teaching, learning and assessment. In describing just how this will occur, AI advocates suggest a combination of individualized learning, adaptive and engaged assessment, and personal support for the learners' journey will be the hallmarks of the new reality for education. Harsh reality suggests something different. Using a simple framework of the potential impact of AI tools and the burden they require; a more incremental adoption is likely. This paper presents both the transformation framework and the more likely incremental approach as the choice for instructors, administrators, and policymakers.


Constructing an Interpretive Structural Model to Unravel the Interconnected Drivers of Teaching Quality in Higher Education

Abstract: Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived from previous research and recognizes the inherent complexity within their relationships. Emphasizing the necessity for a structured model, this work employs an interpretive structural modelling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis for constructing a hierarchical model that delineates the interrelationships among the factors influencing teaching quality. The findings indicate the substantial impact of intrinsic factors, particularly teachers' individual and psychological characteristics, on other factors. Additionally, our analysis highlights the critical role of student composition in enhancing overall teaching quality. These insights significantly contribute to the literature by offering valuable guidance to decision makers for maintaining teaching quality within higher education institutions.


Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Abstract: The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to their abilities in classifying requirements written in Arabic as decision tree. The findings reveal that random forest outperformed all stemmers, achieving an accuracy of 0.95 without employing a stemmer, 0.99 with the ISRI stemmer, and 0.97 with the Arabic light stemmer. These results highlight the robustness and practicality of the random forest algorithm.


Underwater image enhancement using multi-task fusion

Abstract: Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.


Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework

Abstract: Abstract This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding frameworks. The Ritual Dialog Framework (RDF) is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges. RDF focuses on building trust and a user-centered approach in XAI. By undertaking such an initiative, we aim to foster a thorough Understanding of XAI, capable of resolving the current issues of acceptance and recognition.


A Systematic Review of the Design of Serious Games for Innovative Learning: Augmented Reality, Virtual Reality, or Mixed Reality?

Abstract: The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.


Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Abstract: Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm’s effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm.


Amphibole Group Minerals in the Ozren Massif Ophiolites of Bosnia and Herzegovina as Petrogenetic Indicators

Abstract: The Ozren ophiolite complex (OOC) of the Dinaridic Ophiolite Belt is one of the six ophiolite complexes in Bosnia and Herzegovina. This paper deals with the mineral chemistry of amphiboles determined by electron probe micro-analysis and micro-Raman spectroscopy. The detected amphibole generations and types in mafic, ultramafic, and metamorphic rocks suggest a polystage evolution and are therefore useful petrogenetic indicators of the investigated OOC. Most gabbroic rocks and dolerites contain primary magmatic amphibole1 (magnesio-hornblende to pargasite, occasionally hastingsite) and prismatic to needle-like aggregates of late magmatic amphibole2 (magnesio-hornblende), while plagiogranite contains ferri-winchite and ferro-ferri-winchite as primary magmatic amphibole. Post-magmatic amphiboles were detected in dolerites, troctolites, and lesser in peridotites. The Na-(Ti)-rich amphibole3 (ferri-winchite and ferro-ferri-winchite to katophorite and ferri-katophorite) with amphibole4 (grunerite) rim formed along the grain boundaries of clinopyroxene, amphibole1, and plagioclase in dolerites. A part of these amphiboles grows into amphibole1, 2. Kaersutite to ferri-kaersutite, associated with phlogopite, occur in troctolites and dunites, while Mhbl was detected in harzburgite. The ultramafic rocks (lherzolites, harzburgites, and dunites) and the gabbroic layer are crosscut by clinopyroxene–plagioclase gabbroic and clinopyroxene–plagioclase–amphibole gabbro–dolerite dykes, suggesting ‘dry’ and ‘hydrated’ percolating melts generated in inferred subridge and supra-subduction settings, respectively. The amphibole3 and 4 in gabbros and dolerites and similar amphibole types in ultramafic rocks could be related to inferred arc-type basaltic and plagiogranitic percolating melts and fluids. Low-Al amphibole5 (tremolite and actinolite) and associated chlorite, albite, and clinozoisite represent the ocean-floor alterations in mafic rocks. Amphibole6 (magnesio-hornblende to pargasite) was identified in metamorphic sole amphibolites. Micro-Raman spectroscopy provided typical Raman spectra for the studied amphiboles, highlighting distinct features such as bands related to CMg content, CFe3+ presence, TO4 ring-breathing mode, TiO6 stretching mode, presence > 0.3 apfu of CTi, and TO4 stretching indicating CFe2+ in the structure. Applied amphibole geothermobarometry revealed the formation P–T conditions of amphibole (Amp)1 (avg. 863 °C at 0.23 GPa), Amp2 (avg. 747 °C at 0.17 GPa), Amp in the mantle rocks (avg. 853 °C at 0.64 GPa), Amp5 (avg. 349 °C at 0.03 GPa), and Amp6 (avg. 694 °C at 0.46 GPa).


Mapping of American English vocabulary by grade levels

Abstract: Abstract We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and L2 English.


Towards a graded lexical inventory of multi-word combinations

Abstract: Abstract Collocations have become increasingly important in our understanding of foreign language learning. When it comes to setting vocabulary learning goals, concerns about how to address collocations still arise today. This article explores the distribution of collocations in L1 and L2 Spanish production with the ultimate goal of informing the design of graded lexical inventories of multi-word combinations. To do so, we explore three defining properties of collocations in L1 and L2 production data, and across different levels of L2 proficiency: syntactic structure, semantic transparency, and the strength of association. Results indicate that there is an increase of collocational density and diversity, but that isolated features of collocations fail to predict L2 proficiency. Findings suggest the need to evaluate collocation use at a high level of granularity.


Swedish word family resource

Abstract: Abstract The article introduces a novel lexical resource for Swedish based on word family principles. The development of the Swedish Word Family (SweWF) resource is set into the context of linguistic complexity in second language acquisition. The SweWF is particularly appropriate for that, given that it contains lexical items used in second language corpora, namely, in a corpus of coursebook texts, and in a corpus of learner essays. The main focus of the article is on the construction of the resource with its user interface and on its applicability for research, although it also opens vast possibilities for practical applications for language learning, testing and assessment. We demonstrate the value of the resource through several case studies.


A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm

Abstract: An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.


A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Abstract: In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.


Training Object Detectors from Scratch: An Empirical Study in the Era of Vision Transformer

Abstract: Abstract Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performance of self-attention mechanism in the language field, transformers tailored for visual data have drawn significant attention and triumphed over CNNs in various vision tasks. These vision transformers heavily rely on large-scale pre-training to achieve competitive accuracy, which not only hinders the freedom of architectural design in downstream tasks like object detection, but also causes learning bias and domain mismatch in the fine-tuning stages. To this end, we aim to get rid of the “pre-train and fine-tune” paradigm of vision transformer and train transformer based object detector from scratch. Some earlier works in the CNNs era have successfully trained CNNs based detectors without pre-training, unfortunately, their findings do not generalize well when the backbone is switched from CNNs to a vision transformer. Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch. In particular, we expect those insights to help other researchers and practitioners, and inspire more interesting research in other fields, such as remote sensing, visual-linguistic pre-training, etc. One of the key findings is that both architectural changes and more epochs play critical roles in training vision transformer based detectors from scratch. Experiments on the MS COCO dataset demonstrate that vision transformer based detectors trained from scratch can also achieve similar performance to their counterparts with ImageNet pre-training.


Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

Abstract: Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.


Applying human-centered design to the construction of a cirrhosis management clinical decision support system

Abstract: Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx.We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts.Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists).This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.


Improved RRT* Algorithm for Disinfecting Robot Path Planning

Abstract: In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.


Enhancing Prediction Precision and Reliability in Cervical, Lung, and Breast Cancer Diagnosis

Abstract: This study covers a wide range of cancers, focusing on cervical, lung, and breast cancer. Developing fast, accurate, and interpretable machine learning models for early diagnosis is critical to reducing the multifactorial mortality associated with these cancer types. Using a two-stage hybrid feature selection method, this study evaluates classification models using specific cervical, lung, and breast cancer data obtained from the UCI Machine Learning Repository. The cervical cancer dataset contains 36 features, the lung cancer dataset contains 16 features, and the breast cancer dataset contains 31 features. In the first stage, a random forest architecture is used for feature selection to identify features 5,7, and 7 that show a strong correlation with their cancer while reducing the difference between them. In Stage 2. Logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), and decision making (DT) were used to identify cervical cancer, lung, and breast cancer patients for five selections.


Managing and Monitoring Patient's Healthcare Using AI and IoT Technologies

Abstract: This book chapter explores the transformative integration of AI and IoT technologies in the realm of healthcare, focusing on their collective impact on patient monitoring. The chapter delves into the emerging trends and challenges associated with the utilization of AI and IoT in the digital healthcare landscape, emphasizing their role in real-time patient monitoring, data analytics, and personalized healthcare solutions. Through an examination of case studies and current applications, the chapter highlights the potential of these technologies to revolutionize patient care by enabling continuous and remote monitoring, early detection of health issues, and data-driven decision-making for healthcare professionals. Furthermore, the chapter addresses the ethical considerations and challenges that accompany the implementation of AI and IoT in healthcare settings. Overall, the chapter provides a comprehensive overview of the innovative ways AI and IoT technologies are reshaping patient monitoring practices, offering insights into the future of digital healthcare.


An Integrated Approach to Next-Generation Telemedicine and Health Advice Systems Through AI Applications in Disease Diagnosis

Abstract: This chapter explores the transformative potential of integrating knowledge engineering and artificial intelligence in healthcare, focusing on constructing a knowledge-based clinical decision support system (KBCDS). The primary objective is to design an AI-enabled health portal to enhance accessibility to medical advice globally. The study investigates the application of AI in diagnosing COVID-19 and pneumonia, aiming to improve diagnostic accuracy and speed, reducing the burden on healthcare systems, and saving lives. The research is based on extensive literature study, delving into the depths of knowledge engineering, AI applications in healthcare, and medical ontology. The findings underscore the transformative potential of the integrated approach, highlighting its impact on healthcare disparities globally. The AI-enabled health portal proves to be a reliable source of medical advice, demonstrating that the fusion of knowledge engineering and AI technologies empowers medical professionals and significantly enhances healthcare accessibility and diagnostic capabilities.


Fuzzy Thresholding-Based Brain Image Segmentation Using Multi-Threshold Level Set Model

Abstract: Region of interest with reference to medical image is a challenging task. Clustering or grouping data objects can be used to isolate certain area of interest called image segmentation from human brain MRI scans is considered here. Together with the combination of Multilevel Otsu's thresholding and Level set approach, the most widely used fuzzy-based clustering like fuzzy C means (FCM) thresholding are taken into consideration. Here, the proper thresholding is determined using FCM thresholding. This threshold value can also be used to modify the Multilevel Otsu' method's threshold. The level set technique is then used to this segmented output image, yielding a more precise boundary level estimation. To improve brain MRI image segmentation, the proposed FTMLS system integrates the three aforementioned methods.


The Digital Competence of Students Preparing to become Primary School Teachers - Perspectives for Development

Abstract: The analysis of the European Digital Competence Framework for Citizens, outlined the need to study: the digital competence of students - future primary teachers; their attitudes towards the study of required, elective and optional subjects developing digital competence. If students preparing to become primary teachers are offered sufficient and varied required, elective and optional subjects to develop these competences, they will be able to successfully apply digital competences in their future teaching practice. In May 2023, a survey was conducted with 200 students preparing to become primary teachers at the Pedagogical Faculty of Plovdiv University "Paisii Hilendarski", which would establish their attitudes on the issues raised. The results of this study showed: the students' high assessment of the usefulness of the studied compulsory disciplines for the development of digital competences, as well as an increased interest in the offered elective and optional disciplines with the same focus; the importance of primary teacher candidates having a good level of digital competences. The obtained results support the process of updating the current curricula in the disciplines developing digital competences and developing new training courses adapted to the needs of students for an even more complete development of their digital competences.


An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network

Abstract: Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.


Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department

Abstract: This paper proposed two models of data warehouse schema for the fire department of DKI Jakarta, where the 1st model contains six tables consisting of 3 fact and 3-dimensional tables, and the 2nd model only contains three fact tables. The 2nd model denormalises the 1st model, where the number of tables is less than the 1st model, where at the end of the day, the 2nd model will reduce the join table process, which increases the SQL performances. These two models have been recognised as fact constellation schema with more than one fact table and sharing dimension and sub-dimension tables. The database resources were collected from http://data.jakarta.go.id under the Fire and Rescue Service Agency. Those two data warehouse schema models were developed based on a report sector list, a report on Hydrants list, and vehicle register reports. This paper proposes to support Automatic Identification Systems (AIS) research, particularly implementing the data warehouse concept.


The Barriers and Readiness to Deal With Digital Transformation in Higher Education

Abstract: During the recovery from the COVID-19 pandemic, stakeholders in higher education institutions have been encouraged to adapt to the changes caused by digital transformation. This study employs a case study approach to investigate the barriers and readiness of students, educators, and higher education institutions for digital transformation. Data were collected through questionnaires and interviews with stakeholders in a higher education institution in Indonesia. The data from the questionnaire were analysed using qualitative descriptive analysis, while the transcripts of the interview data were run into the ATLAS.ti Qualitative Data Analysis application and analysed thematically. Codes developed from digital transformation barriers were suggested by Volgesang and later interpolated during data analysis into readiness and strategy codes. The emerging themes related to students and lecturers, and universities obstacles, readiness and strategies in dealing with digital transformation could then be identified. The findings are crucial for higher education institutions to thrive in the digitally evolving environment. Understanding how the university's stakeholders perceive the transformation would help anticipate a more knowledgeable and organized method to handle the intricacies and difficulties associated with such a profound change.


Survey Analysis of University Teachers in Ukraine Regarding the Use of Google Workspace for Education

Abstract: The article highlights the results of a survey of teachers at higher education institutions of Ukraine regarding the possibilities provided by Google Workspace for Education tools in the organization of the educational process. A questionnaire was used to gather data for this purpose, in which 167 respondents from different regions of the country took part. The questionnaire focused on the frequency of using specific educational applications, such as learning management systems, collaborative tools, and educational games. Most often, Google applications are used in blended learning (72% of respondents). To organize and conduct lectures/practical classes, teachers quite actively use Google Slides (79.4%/73.8% of respondents), Google Meet (69.2%/65.4% of respondents), YouTube (60.7%/56.1% of respondents). It was found that the vast majority of teachers (91%) positively evaluate the impact of Google Workspace for education tools on the effectiveness of teaching methods. In the future, it is planned to research students’ opinions regarding the use of Google Workspace for education while studying at university.


Quantum Computing in The Cloud - A Systematic Literature Review

Abstract: Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.


Measuring digital skills in community adult learning settings – implications for Australian policy development

Abstract: Social and economic participation is increasingly dependent on the proficient use of digital technologies in everyday life. Digital skills, alongside literacy and numeracy, are now foundational skills that all Australians should have. Yet, how policy makers, scholars, and adult educators can effectively identify people’s current and desired digital skills is not well established. Existing frameworks tend to be instrumentalist, listing generic abilities that all citizens need for success. Digital abilities, however, are context-specific and attempts to generalise need to be responsive to local circumstances. We present a theory-driven Digital Ability Self-Assessment Tool, developed for a government agency and piloted in a community organisation. The Tool takes a distinctive socio-material approach to overcome shortcomings of other digital ability assessment tools, which often focus on normative measures of skills attainment. Overall, we argue that authentic digital skills policy and programme development must account for the contexts in which digital technologies are used.


A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation

Abstract: Abstract For semantic segmentation tasks, it is expensive to get pixel-level annotations on real images. Domain adaptation eliminates this process by transferring networks trained on synthetic images to real-world images. As one of the mainstream approaches to domain adaptation, most of the self-training based domain adaptive methods focus on how to select high confidence pseudo-labels, i.e., to obtain domain invariant knowledge indirectly. A more direct means to explicitly align the data of the source and target domains globally and locally is lacking. Meanwhile, the target features obtained by traditional self-training methods are relatively scattered and cannot be aggregated in a relatively compact space. We offer an approach that utilizes data augmentation and contrastive learning in this paper to perform more effective knowledge migration with the basis of self-training. Specifically, the style migration and image mixing modules are first introduced for data augmentation to cope with the problem of large domain gaps in the source and target domains. To assure the aggregation of features from the same class and the discriminability of features from other classes during the training process, we propose a multi-scale pixel-level contrastive learning module. What’s more, a cross-scale contrastive learning module is proposed to help each level of the model gain the capability to obtain more information on the basis of its own original task. Experiments show that our final trained model can effectively classify the images from target domain.


Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Abstract: Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.


Preventing Double Spending Attacks through Crow Search Algorithm to Enhance E-Voting System Security

Abstract: Electronic voting system is the process of polling votes and counting votes. In most of the countries voting may now be done electronically, there are still several difficulties involved, including the expense of paper, how ballots are organized, the possibility of varying results when tallying the votes, and others. Duplicate votes pose a significant concern as they can be fraudulently cast by individuals. To focus on this issue, Distributed Ledger Technology (DLT) is employed to enhance the voting procedure in a secured manner. A directed acyclic graph is used by the Internet of Things Application (IOTA), a promising distributed ledger system. Faster transaction confirmation, high scalability and zero transaction fees are achieved via the Directed Acyclic Graph structure. In both IOTA tangle and blockchain technology, the public cast duplicate votes. The unauthorized user can create duplicate votes in the blockchain as well as IOTA tangle. This can be focused in this proposed method. The double spending problem can be solved by using Crow Search Algorithm (CSA). This Optimization problem produces an improved result for resolving double spending in e-voting systems.


A fast and efficient algorithm for time synchronization in satellite quantum key distribution

Abstract: Abstract Time synchronization is one of the most crucial issues that must be addressed in developing quantum key distribution (QKD) systems. It not only lets the transmitter and the receiver to assign a sequence number to each event and then do correct basis reconciliation, but also allows to increase signal-to-noise ratio. Time synchronization in satellite communications is especially complicated due to such factors as high loss, signal fading and Doppler effect. In this work, a simple, efficient and robust algorithm for time synchronization is proposed. It was tested during experiments on QKD between Micius, the world’s first quantum communications satellite, and an optical ground station located in Russia. The obtained synchronization precision lies in the range from 467 to 497 ps. The authors compare their algorithm for time synchronization with the previously used methods. The proposed approach can also be applied to terrestrial QKD systems.


Quantum Key Distribution Shared Protocol Using Teleportation and Delayed Measurement

Abstract: Abstract With the rise of quantum computers, cryptographic protocols are being required to set secure communications. In the current study, a 3-party Quantum Key Distribution protocol is proposed to ensure safe communication considering ideas from BB84 and GR10 protocols against eavesdropping by using a parameterized partially entangled resource. The protocol is built so that the sender is the only one knowing the code key and the message encryption is made ongoing. Delayed measurements are applied just at the end of the protocol to decode the message effectively. The parameter of the entangled resource is used as a double control for the exchange rate of classical communications involved in the procedure. Finally, a security analysis is made, where multiple coordinated attacks could be a threat to this protocol.


KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Abstract: Recognition of handwritten digits has garnered significant interest among researchers in the domain of recognizing pattern. This interest stems from the recognition's relevance in various real-life applications, including reading financial checks and official documents, which has remained a persistent obstacle. To address this challenge, researchers have developed numerous algorithms focusing on recognizing handwritten digits across different human languages. This paper presents a new Kurdish Handwritten dataset, consisting of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants, encompassing a broad and varied group. It serves as the primary dataset for training and evaluating algorithms in Kurdish digit recognition. We used Kurdish dataset named (KurdSet) and Arabic dataset for handwritten recognition, which holds 70,000 images of Arabic digits that were written by 700 various participants. Additionally, various models are utilized in the study, including ResNet50, DenseNet121, MobileNet, and a custom CNN (convolutional neural network). Additionally, the models' effectiveness was assessed through the examination of test accuracy, which measures the percentage of correctly classified digits in the evaluation phase. ResNet50 also performs exceptionally well that achieved test accuracy 99.67%, indicating its All models exhibit good performance, DenseNet121 and the Custom CNN Model demonstrate the highest test accuracy of 99.73%, highlighting their superior performance. capabilities in capturing relevant features. Despite its accuracy, MobileNet still exhibits good recognition capability with a test accuracy 99.54%.


Deep learning-based segmentation of breast masses using convolutional neural networks

Abstract: Abstract Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.


Text Recognition for Library Collection in Different Light Conditions

Abstract: Book arrangement problems in Indonesia mainly utilize traditional systems. However, it contributes to user frustration and dissatisfaction, leading to difficulties in the retrieval process and hindering book monitoring. Therefore, an efficient arrangement is needed to make it easier for librarians to organize collections at the library. Hence, this study proposes and evaluates Optical Character Recognition (OCR) approaches such as Tesseract-OCR and Efficient and Accurate Scene Text (EAST) by considering various light factors and device applications. An evaluation was performed to determine the effectiveness of the methods for detecting classification numbers using the Character Error Rate (CER) and Word Error Rate (WER). The results obtained can be seen from the lighting and the use of image capture tools that can affect the text detection results. The higher the lighting, the better the text detection results obtained. However, in the extraction tool using a security camera yields a better output than the others. In conclusion, the modernization of book arrangement systems by implementing OCR through security cameras could enhance user experience in the retrieval process.


Overcoming challenges in deep inspect of vpn and proxy by deep learning

Abstract: Abstract The rapid growth of proxying and VPN techniques has presented formidable challenges for internet operators in classifying network traffic. These methods not only hinder effective traffic policing and resource restriction but also introduce complexities by allowing VPNs to masquerade under application protocols. Furthermore, the increasing popularity of applications like Voice over IP (VoIP) and peer-to-peer (P2P) technologies further exacerbates the difficulty in controlling and classifying such traffic. Conventional techniques like Server Name Indication (SNI) analysis are witnessing diminishing effectiveness over time. Network providers now face the critical task of acquiring detailed knowledge about the specific applications and protocols utilised by their customers, enabling them to accurately allocate resources and ensure robust traffic management in this challenging landscape.


Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Abstract: In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.


A Model to Assess the Impact of Computer Simulations and Photonics Demonstrations in Quantum Information Learning

Abstract: Abstract Quantum information Education is a growing area where professionals and students of diverse disciplines converge to develop proper solutions. The interest of those students is materialized seeking introductory approaches to this knowledge. Still, learning design for diverse backgrounds requires multiple approximations for theoretical contents, applications, calculation scaffolding, etc. Innovation oriented towards this effort becomes valuable due to students’ limited time for such an experience. This work presents a proposed assessment for three technical innovation initiatives in Quantum information Education based on a mathematical model proposed to measure their impact on the research products through eight dimensions. Outcomes show an explicit sustained improvement in the quality of those products, while the model lets to identify the fingerprint of each innovation through a set of eight dimensions.


Perception Study on Kinetic Recycled Compact Disc Facade in Tourism Hall, Jarak - Wonosalam

Abstract: Abstract Indonesia is a country, rich with culture and natural resources that brings high tourism potential. Jarak village in Wonosalam is one of the villages in East Java that has these tourism potential, besides the religious harmony between the dwellers that initiates the construction of a tourism hall with the concept of sustainability. The hall reflects the harmony between man and nature. The tourism hall uses the kinetic panels from recycled compact discs as the facade, which is rotatable when blown by the wind. This research is meant to study people’s perception of the installed kinetic facade panel. Here, we divide the respondents into two groups, professionals (academician and architects), and non professionals (Jarak dwellers, visitors and general). The aspects observed are psychology, aesthetics, flexibility, impact to the environment and economics. The result is that there is a difference in perception regarding the flexibility, and also, the colour aspect of the installed kinetic facade is the most memorable aspect of the installed kinetic facade.


Assessment of sand dunes in Missan Governorate, South-eastern Iraq: Implications for Extraction High Purity Silica

Abstract: Abstract Sand dunes are widely spread in Missan Governorate, South-eastern Iraq in many areas including; Manziliyah, Khazina, Zubadiate, and Chailat. X-ray diffraction (XRD) results of sand samples of sizes 0.063-2 mm, 0.125-0.250mm, and 0.250-0.500mm, revealed that quartz is the main mineral phase, followed by calcite, feldspar and trace of gypsum. Chemically SiO 2 is the main oxide in sand of an average 61.36 % while the rest oxides; CaO (14.08%), Al 2 O 3 (6. 76%), Fe 2 O 3 (3.05%), K 2 O+Na 2 O (2.30 %) are considered as impurities that could affect the properties of sand. XRD tests indicated the formation of crystalline phase of synthetic silica. XRF showed an increases in SiO 2 to 85.37%, while all other oxides (impurities) were reduced to their minimum rate, particularly CaO (1.06 %). Field emission scanning electron microscope (FESEM) and Brunauer-Emmett-Teller (BET) tests indicated the existence of well compiled spherical particles of synthetic silica within the size of 8.55 nm and surface area of 66.51 m 2 \g. The medium sand size (0.250-0.500mm) is the appropriate size to produce high purity crystalline silica as it contains the greatest percentage of silica and the low percent of impurities compared to other sizes of sand.


Interferensi Bahasa Ibu Terhadap Penguasaan Bahasa Indonesia Menulis Cerita Rakyak

Abstract: The aim of this research is to describe the form of mother tongue that often interferes with mastery of Indonesian, as well as to find out the factors that cause mother tongue interference with mastery of Indonesian in writing the Mamasa folklore by class IX students of SMPN 2 Sesenapadang Mamasa. The type of research used is qualitative research. The research method used is direct observation (observation). The analysis techniques used are data collection, data redaction, data presentation and conclusions and withdrawal/verification. The results of the research show that it is true that there is interference in Mamasa's mother tongue with the mastery of Indonesian in writing Mamasa folk tales by class IX students of SMPN 2 Sesenapadang Mamasa, due to the students/speakers' daily habit of using their mother tongue.


Dampak Penggunaan Disfemisme Para Gamers Pada Media Sosial Youtube

Abstract: The aim of this research is to explain the form and meaning of dysphemism expressions in content created by gamers on YouTube social media channels and to explain the impact of the use of dysphemism on the implications of character education. The type of research used is a qualitative descriptive method. Data collection procedures were carried out using tapping, note-taking and documentation techniques. The data analysis method used in this research uses descriptive analysis techniques with the triangulation method. The research results found in this study concluded that the use of dysphemism by game content creators on their YouTube channels contained five types of dysphemism expressions including; (a) the use of taboo terms, such as words that cannot be said, taboos, or prohibitions; (b) obscene cursing and swearing; (c) comparison of humans with animals; (d) nicknames or greetings related to human physical characteristics; and (e) terms of ridicule and disrespect towards someone. Meanwhile, the impact of dysphemism on character education in this research is in the form of; (1) Violation of religious values; (2) violation of the value of love of peace; (3) violation of the value of responsibility.


Pengaruh Penggunaan Metode Diskusi Terhadap Peningkatan Hasil Belajar Bahasa Indonesia

Abstract: The aim of this research is to find out how students' communication skills during discussions can have an influence on student learning outcomes, as well as knowing student learning outcomes, and to find out the significant influence between teacher communication skills on the learning outcomes of class XI students at SMKN 1 Tana Toraja. This research is a quantitative descriptive research with the type of research using a field survey. Data collection techniques in this research used observation, questionnaires, interviews and documentation. The data analysis technique in this research is Product moment correlation. The results of this research can be concluded that students' communication skills in the discussion method have a good influence with a percentage of 65% and student learning outcomes are sufficient with a percentage of 35%. There is a significant influence between students' communication skills on learning outcomes in the Indonesian language subject class XI SMKN 1 Tana Toraja. Hypothesis testing is as follows: where ro = 0.005 is greater than r table at a significance level of 5% = 0.396, thus Ha is accepted and HO is rejected. This means that the better the students' ability to communicate, the better the grades obtained by students at SMKN 1 Tana Toraja.


Clay minerals of Shiranish Formation in, Erbil, Duhok northern Iraq

Abstract: Abstract In the Upper Cretaceous of northern Iraq (upper Campanian-Maastrichtian), the Shiranish Formation is examined for clay minerals using X-ray diffraction in the Duhok (Sandor section) and Erbil cities (Lailuk section). In the marl section of the Shiranish Formation, the layers of chlorite, illite, kaolinite, palygorskite, and a mixed layer of montmorillonite-chlorite strata were discovered to have formed antigenically. The variation in minerals indicates the Cretaceous’ changing climate. Palygorskite is a result of leaching processes that are more successful due to variations in the parent rocks. The transition of palygorskite from montmorillonite and illite can occasionally be impacted by post-depositional diagenetic processes. Because of the warm conditions that supply the eroded clastic materials from earlier successions of Cretaceous in the area, chlorite, mixed-layers, and illite may have predominated over kaolinite. The variation in source rocks and drainage conditions is reflected in the variation of clay minerals in the investigated sequence, which could improve leaching rate processes.


Cavity-Enhanced Single-Photon Emission from a Single Impurity-Bound Exciton in ZnSe

Abstract: Impurity-bound excitons in ZnSe quantum wells are bright single-photon emitters─a crucial element in photonics-based quantum technology. However, to achieve the efficiencies required for practical applications, these emitters must be integrated into optical cavities that enhance their radiative properties and far-field emission patterns. In this work, we demonstrate cavity-enhanced emission from a single impurity-bound exciton in a ZnSe quantum well. We utilize a bullseye cavity structure optimized to feature a small mode volume and a nearly Gaussian far-field transverse mode that can efficiently couple to an optical fiber. The fabricated device displays emission that is more than an order of magnitude brighter than bulk impurity-bound exciton emitters in the ZnSe quantum well, as well as clear antibunching, which verifies the single-photon emission from the source. Time-resolved photoluminescence spectroscopy reveals a Purcell-enhanced radiative decay process with a Purcell factor of 1.43. This work paves the way toward high-efficiency spin-photon interfaces using an impurity-doped II–VI semiconductor coupled to nanophotonics.


Experimental Evaluation: Can Humans Recognise Social Media Bots?

Abstract: This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the VKontakte social network. We then compared the “human” answers with the “ground-truth” bot labels (‘a bot’/‘not a bot’). Based on the experiment, we evaluated the bot detection efficiency of annotators in three scenarios typical for cybersecurity but differing in their detection difficulty as follows: (1) detection among random accounts, (2) detection among accounts of a social network ‘community’, and (3) detection among verified accounts. The study showed that humans could only detect simple bots in all three scenarios but could not detect more sophisticated ones (p-value = 0.05). The study also evaluates the limits of hypothetical and existing bot detection systems that leverage non-expert-labelled datasets as follows: the balanced accuracy of such systems can drop to 0.5 and lower, depending on bot complexity and detection scenario. The paper also describes the experiment design, collected datasets, statistical evaluation, and machine learning accuracy measures applied to support the results. In the discussion, we raise the question of using human labelling in bot detection systems and its potential cybersecurity issues. We also provide open access to the datasets used, experiment results, and software code for evaluating statistical and machine learning accuracy metrics used in this paper on GitHub.


Grateful and productive tandem (in blessed memory of E. Ya. Patsia and B. I. Koshechkin)

Abstract: The article is dedicated to Boris Ivanovich Koshchechkin and Evgenia Yakovlevna Patsia, with whom the author has met, worked and befriended for over 30 years and is founded exclusively on personal memories of joint events, without any embellishment and fiction, in order to honor the memory of these wonderful people who have already left us and remind the living of the deeds they have done. The role of B. I. Koshechkin and E. Ya. Patsia in the development of humanitarian research at the Kola Scientific Center of the Russian Academy of Sciences is underlined.