Implementasi Peraturan Kepala Perpustakaan Nasional RI Nomor 13 Tahun 2017 tentang Standar Nasional Perpustakaan Perguruan Tinggi di UPT Perpustakaan Universitas Bung Hatta

Abstract: This study aims to describe the following. (1) implementation of collection standards at Bung Hatta University Library (2) implementation of facilities and infrastructure standards at Bung Hatta University Library (3) implementation of service standards at Bung Hatta University Library (4) implementation of library promotion standards at Bung Hatta University Library (5) implementation of library staff standards at Bung Hatta University Library, and (6) implementation of information and communication technology standards at Bung Hatta University Library. The research methodology used is qualitative research with the type of Field Research research and uses a descriptive approach. Data collection techniques through observation, interviews, and documentation. The informants in this study were one library officer and two people from the library users at the Bung Hatta University Library. Based on the results of the study, it can be concluded as follows. First, the implementation of collection standards, consisting of nine sub-indicators, of the nine sub-indicators, there are six sub-indicators that meet standard implementation and three sub-indicators that do not meet standard implementation. Second, the implementation of facilities and infrastructure standards, consisting of five sub-indicators, of the five sub-indicators, there are two sub-indicators that meet the implementation of standards and three sub-indicators that have not met the implementation of standards. Third, the implementation of service standards, consisting of two sub-indicators, of the nine sub-indicators, there is one sub-indicator that meets the implementation of standards and one sub-indicator has not met the implementation of standards. Fourth, the implementation of library promotion standards, this indicator has met the standard implementation. Fifth, the implementation of library staff standards, consisting of three sub-indicators, of the three sub-indicators, there is one sub-indicator that meets the standard implementation and two sub-indicators have not met the standard implementation. Sixth, the implementation of information and communication technology standards, this indicator has not met the implementation of standards. Thus, of the twenty-one sub-indicators assessing the implementation of the Regulation of the Head of the National Library of the Republic of Indonesia Number 13 of 2017 concerning National Library Standards at the UPT Library of Bung Hatta University, those that meet the National Standards for Higher Education Libraries based on the Regulation of the Head of the National Library of the Republic of Indonesia Number 13 of 2017 concerning National Standards for Higher Education Libraries are 11 sub-indicators and those that have not met the standards are 10 sub-indicators.


Penerimaan Teknologi ChatGPT Di Kalangan Mahasiswa: Studi Deskriptif Model TAM Pada Mahasiswa Program Studi Teknik Informatika Universitas Negeri Padang

Abstract: This research examines the acceptance of ChatGPT technology among students of the 2020 Informatics Engineering study program, Faculty of Engineering, Padang State University using the TAM model. This research aims to describe the acceptance of ChatGPT among students. The type of research used is quantitative research with a case study approach. The method used is descriptive research method. Data collection techniques through observation, interviews and questionnaires. Sampling in this research was by using saturated samples. Research data was obtained from the results of a questionnaire distributed to 89 samples who were informatics engineering students class of 2020. Based on the research results, it can be concluded that there is acceptance of ChatGPT technology among students of the Informatics Engineering Study Program Class of 2020, Faculty of Engineering, Padang State University, with five indicators which are obtained an average value of 2.61. The indicators include (1) Perception of ease of use, obtaining an average value of 2.64 whose interval scale is positive or categorized as good, meaning that the ease of use of ChatGPT so that it does not require special skills can be accepted by students of the Informatics Engineering Study Program Class of 2020. ( 2) Perception of user usefulness, obtained an average value of 2.65 whose interval scale is positive or categorized as good, meaning that ChatGPT provides benefits in helping user performance so that the indicator can be accepted by students of the 2020 Informatics Engineering Study Program. (3) Intention to use , obtained an average score of 2.57, which is a positive interval scale or categorized as good, meaning that the use of ChatGPT which will be reused over a longer period of time can be accepted by students of the 2020 Informatics Engineering Study Program. (4) Real conditions for using the system obtained an average score of 2.60 on a positive interval scale or categorized as good, meaning that users use ChatGPT truly to help user performance so that it can be accepted by students of the Informatics Engineering Study Program Class of 2020. (5) Attitude towards use, gain The average value is 2.61, the interval scale is positive or can be categorized as good, meaning that based on the evaluation of ChatGPT users, the impact of using ChatGPT can be felt so that this indicator can be accepted by students of the 2020 Informatics Engineering Study Program. Even though it is categorized as good, the use of ChatGPT can has a negative impact on students, such as dependence on ChatGPT which will affect students' critical thinking abilities in doing assignments and a lack of social interaction such as discussions because they rely too much on ChatGPT when doing assignments.


[Call attention to the overall benefit of digital pathology and promote its development].

Abstract: The successful operation of the complete digital pathology(CDP) in foreign countries indicates that the full digital pathology process has entered the full implementation stage. Digital pathology in China started late and progressed slowly, so far there has not been a truly meaningful CDP. The pathologist's understanding of digital pathology is not comprehensive enough, and there are still doubts about the time efficiency and cost effectiveness of digital pathology processes. Therefore,a comprehensive analysis of the process, overall advantages and cost-effectiveness of CDP was made by drawing on international successful experience and hands-on practice experience, in order to promote the construction and development of the CDP in our country.国外全数字化病理科的成功运行，标志着全数字病理流程已进入全面实施阶段。国内数字病理起步晚、进展慢，至今尚未有真正意义的全数字病理科出现。业界对数字病理的认识还不够全面深入，对数字病理流程的时间效率和成本效益还存有疑虑。因此，作者借鉴国际上的成功经验和实践感受对全数字化病理的流程、整体优势和成本效益等进行了全面的解析，以期促进我国的数字化病理科建设和发展。.


21st Century Learning Trends : What Educators Need to Know

Abstract: In the 21st century, a paramount emphasis lies on the education of the 21st century and the cultivation of multifaceted skills that bolster students in acclimating to dynamic transformations. A pivotal trajectory in educational paradigms manifests through the amalgamation of technology and the adoption of digital pedagogical methodologies. The transition towards this technological integration is propelled by the imperative to assimilate 21st-century proficiencies, fortifying students for an ever-evolving technological milieu. Concurrently, there is an accentuation on honing students' faculties of critical reasoning and amplifying scientific acumen. Character education assumes a centrality, underscoring its resonance with the perspectives of students navigating the complexities of the 21st century. Moreover, 21st-century education encompasses the advocacy for innovative aptitudes and entrepreneurial prowess. Global educational trends steer towards STEM-oriented frameworks, augmenting students' adeptness in science, technology, engineering, and mathematics. In summation, education in the 21st century is intricately entwined with technology, globalization, and the imperative for students to foster competencies germane to an expeditiously evolving world.


Impact of Corporate Social Responsibility Initiatives on Women’s Empowerment in India: A Comprehensive Analysis

Abstract: Corporate Social Responsibility (CSR) initiatives have gained prominence globally as a means to address social issues, including gender equality and women’s empowerment. In India, where gender disparities persist across various socio-economic indicators, CSR initiatives hold significant potential to contribute to the empowerment of women. This research article presents a comprehensive analysis of the impact of CSR initiatives on women’s empowerment in India. Through a systematic review of existing literature, case studies, and empirical data, this study evaluates the effectiveness of CSR programs in promoting gender equality, enhancing economic opportunities, and advancing social inclusion for women. The article also explores the challenges and opportunities encountered in the implementation of CSR initiatives aimed at women’s empowerment, considering diverse sectors and geographical contexts within India. Furthermore, the article discusses the role of government policies, institutional frameworks, and stakeholder collaborations in facilitating meaningful CSR interventions for women’s empowerment. By critically assessing the outcomes and limitations of CSR efforts, this research contributes to a nuanced understanding of the potential of corporate engagement in advancing gender equality and women’s empowerment in India.


Engaging Community College Students in Computer Engineering Research through Design and Implementation of a Versatile Gesture Control Interface

Abstract: Abstract Given the important role of community colleges in undergraduate education, in order to increase the recruitment of STEM students and improve undergraduate STEM education, it is crucial to develop strategies to inspire community college students' interest in STEM. With support from the Department of Education Minority Science and Engineering Improvement Program (MSEIP), a cooperative internship program between a community college and a public comprehensive university was developed to engage community college students in leading-edge engineering research. In summer 2016, five sophomore students from the community college participated in a ten-week computer engineering research internship project in a research lab at the four-year university. This internship project aimed to develop an intelligent electromyography (EMG)-based gesture control interface which deciphers EMG signals collected from forearm muscles to identify users' intended hand and arm movements. The gesture control interface has great potential to provide natural human-machine interaction in a variety of applications, from assistive devices through rehabilitation therapy to virtual reality (VR). The developed interface provides easy connection with a commercial EMG-based armband Myo and a modular software engine for customizable gesture recognition as well as a special pipeline for converting recognition decisions into control commands for external applications. The students also conducted usability testing of the gesture control interface on human subjects by using it to control a first-person shooter (FPS) virtual reality game which was developed by another community college student intern. The project provided a great opportunity for the student interns to gain research experience and learn valuable knowledge in human-machine interfaces, EMG signal processing, and gesture recognition. It also helped them improve their skills in experimental design, data analysis, scientific writing and presentation, as well as teamwork and time management. The outcome of this project indicated that the internship program was an effective method for inspiring community college students' interest in computer engineering research and strengthening their confidence and capability in pursuing an engineering profession.


What do we know about cryptocurrency investment? An empirical study of its adoption among Indian retail investors

Abstract: Purpose This study aims to examine the cryptocurrency adoption (CA) level among Indian retail investors who use cryptocurrency as an investment and mode of transaction. Design/methodology/approach Through self-administered survey questionnaires, data is collected from 397 retail investors of Haryana (India). This study adopted a quantitative method using partial least squares structural equation modeling (PLS-SEM). Findings This paper offered a robust model with a high explanatory value for CA in which four of the five proposed factors of diffusion of innovation theory (trialability, compatibility, complexity and observability) and one of the two proposed factors of consumer behavioral theory (perceived value) significantly influences CA. More specifically, the absence of regulatory support is a barrier to the broad adoption of cryptocurrencies, as its regulations are necessary to mitigate or minimize uncertain outcomes. Research limitations/implications This research primarily focuses on CA in India. Thus, it can be extended to cover diverse other countries for more precise results. Practical implications The results provide insights to the government to design the policies, better regulate and make investment strategies that can ultimately enhance CA. In addition, the study’s results also inform financial educators, policymakers, employers and academicians about the significance of several variables affecting CA in India. Social implications From a social standpoint, this study is an advance that directs central banks and governments to develop, regulate and manage digital currencies and implement a digital currency ecosystem. Moreover, the results assist in understanding investors’ perceptions and decision-making perspectives toward cryptocurrencies through the country’s digitalization. Originality/value This paper fills the study gap to assist policymakers and cryptocurrency experts in broadening their knowledge base and recognizing prioritized intentions. Additionally, this study provides a theoretical model with the latent variable for a present and pertinent matter.


Transformation of Crimes (Cybercrimes) in Digital Age

Abstract: The cybercrime thrives, understanding its evolution is critical for security and policy interventions. This research profiles cyber threat trends since the 1950s alongside legal developments. Background examines early hacking cultures, antivirus innovations, and seminal cases that set precedents. The objectives encompass analyzing major technical and legal inflection points over recent decades regarding cybercrimes. The study's aim is validating escalating cyber risks that demand urgent attention. Doctrinal methodology leverages scholarly journals, case law, and industry data. Key findings reveal sophisticated social engineering tactics, gaps in legal deterrence frameworks, and infrastructure vulnerabilities that underscore needs for global cooperation. Recommendations center on proactive reforms to cybersecurity statutes, public awareness, and cross-border collaboration to mitigate emerging threats. Tracing cybercrime's timeline provides vital context to catalyze preemptive actions against potential attacks in an increasingly interconnected online ecosystem.


Alteration Mapping for Porphyry Cu Targeting in the Western Chagai Belt, Pakistan, Using ZY1-02D Spaceborne Hyperspectral Data

Abstract: Abstract The Chagai porphyry Cu belt in Pakistan is an important metallogenic terrain extending approximately 400 km in an east-west direction. Most of the known porphyry Cu deposits, such as the world-class Reko Diq deposit and Saindak deposit, are located in the western Chagai belt. In this study, the ZY1-02D hyperspectral data acquired by a recently launched spaceborne imaging spectrometer with 166 bands within a 0.4- to 2.5-μm spectral region were used to map mineral information over 8,000 km2 for exploring potential targets of porphyry Cu mineralization in the western Chagai belt. False color composite, spectral angle mapper, and wavelength position mapping methods were used in this research leading to the identification of a series of alteration minerals (including muscovite [sericite], kaolinite, alunite, epidote, chlorite, and calcite) from ZY1-02D data. The alteration mineral maps derived from ZY1-02D data match well with the known deposits and field inspections. Twenty-three new targets were identified as potential porphyry Cu mineralization targets for further exploration in the study area. Three targets, north of Saindak, Koh-i-Sultan, and Durban Chah, and six alteration sites in the southwest of Durban Chah, were inspected in the field, and Cu-Au mineralization was confirmed in all these inspected areas. As the ZY1-02D hyperspectral data covers most of the land area of the earth, this study provides new insights for mineral exploration and lithologic mapping in remote regions.


Cross-scale multi-instance learning for pathological image diagnosis

Abstract: Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.


exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies

Abstract: Background Pathology reports contain key information about the patient’s diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. Objective To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) “What kind of rejection does the patient show?”; (2) “What is the grade of interstitial fibrosis and tubular atrophy (IFTA)?” Methods Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERT’s tokenizer with six technical keywords and repeating the pre-training procedure. This extended the model’s vocabulary. All three models were fine-tuned with information retrieval heads. Results The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. Conclusion ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizer’s vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.


Human-AI collaboration patterns in AI-assisted academic writing

Abstract: Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.


Mine-first association rule mining: An integration of independent frequent patterns in distributed environments

Abstract: Association rule mining is a widely used data mining technique in various domains. It enables the identification of trends, frequent patterns, and relationships among the data. This study introduced a new method for mining association rules independently from multiple data sources. It combined the frequent patterns obtained from each data source to discover frequent patterns applicable across the distributed environment. The model can also be extended to generate the rules with the specified target. The proposed method’s performance is compared to that of the traditional association rule mining method. The experimental results demonstrate that while the generated rules may not be identical to those produced by the traditional method, the proposed model offers better transparency and memory utilization in association rule generation. In addition, the model uncovers meaningful relationships, allowing decision-makers to access the frequent patterns for the individual data sources and the entire data across the environment.


Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Abstract: Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .


AI-Based Sports Highlight Generation for Social Media

Abstract: Social media plays a significant role for sports organizations with millions of active fans, but publishing highlights is often a tedious manual operation. With the development of AI, new tools are available for content generation and personalization to engage audiences. We propose an AI-based multimedia production framework for the automatic publishing of soccer and ice hockey highlights on social media, and disseminate our experiences with developing dedicated pipelines for event detection and classification, player detection and tracking, highlight clipping, cropping, thumbnail generation, game summarization, caption generation, and social media sharing.


Video Quality Assessment with Texture Information Fusion for Streaming Applications

Abstract: The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. We propose a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF) model for video streaming applications that determines the visual quality of the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the frames from the original and reconstructed videos and fuses them using a long short-term memory (LSTM)-based model to estimate the visual quality. Experimental results show that VQ-TIF estimates the visual quality with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy consumption, assuming an Ultra HD (2160p) display resolution.


Unsupervised clustering-based domain adaptation for estimating occupancy and recognizing activities in smart buildings

Abstract: Smart buildings rely on estimating the number of occupants and recognizing their behaviors to provide several benefits such as energy efficiency, HVAC (Heating, Ventilation, and Air Conditioning) systems optimization, and health safety. Examining the behavior and the counts of residents in different building areas can provide efficient management of resources (heating, cooling, lighting), and energy distribution across the building. Artificial intelligence (AI) and machine learning (ML) can exploit Internet of Things (IoT) data, such as power consumption data, to provide occupancy estimation (OE) and activity recognition (AR). Obtaining labeled smart building data can be very challenging because it is costly, sensitive, and time-consuming. Data scarcity is a serious problem for research development since few labeled data samples may lead to weak classifiers that do not satisfy real-world applications. Researchers have examined domain adaptation (DA) approaches to share knowledge from source domains where labeled data is abundant to target domains where labeled is scarce. Furthermore, researchers have considered a particular case of DA which is the unsupervised method that solves the case of unavailability of labeled data in target domains. Most previous research in domain adaptation has focused on minimizing the discrepancy between source and target feature distributions. In this work, we adapt and evaluate deep unsupervised domain adaptation (UDA) methods based on clustering (UCDA) to estimate the number of occupants and recognize their activities. We have adapted three methods called Structurally Regularized Deep Clustering (SRDC), Hybrid Structurally Regularized Deep Clustering (H-SRDC), and Distilled Discriminative Clustering for Domain Adaptation (DisClusterDA). We have used OE and AR datasets to evaluate the efficiency of the adapted techniques. The findings, with accuracy up to 99%, make these methods open for potential real-world applications. To further enhance the current research, we have provided the code in the following repository: https://github.com/JawDri/Unsupervised-Clustering-based-DA-for-OE-and-AR.git


Augmented reality in medical education: Exploring ethical considerations

Abstract: • AR and VR can improve medical practice and education, they also present ethical questions. • Perception of AR devices and patient consent are essential for utilizing AR in healthcare environments. • Learners' autonomy and privacy are at risk when using and collecting VR data. • Users and developers of AR and VR technologies should abide by moral standards and laws.


Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift

Abstract: Recently, federated fault diagnosis has garnered growing attention due to its promising capabilities in information fusion with privacy preservation. However, most of the existing approaches are based on the assumptions of no domain shift between multiple factories and no unseen domains for online applications. In actual industry, these assumptions are generally unsatisfied due to prominent environmental noises, mechanical wear, and changes in working conditions. Federated models that ignore domain shifts would face the negative aggregation problem and are not robust to unseen domains. To solve the domain shift problem, a federated domain generalization method is proposed for privacy-preserving fault diagnosis in this article. The key idea is to construct a sharable reference domain in cloud, which can convert the privacy-risky centralized alignment problem into a privacy-preserving pairwise alignment problem. Based on the recognition that any fault category in a discriminative feature space can be characterized by a particular position and volatility, we design a shareable domain generator to provide a reference for pairwise alignment. Then, the non-deterministic sampling and non-parametric alignment criterion are introduced to realize local domain alignment, which facilitates the domain-invariant feature extraction. Finally, by the alternation of local domain alignment and global reference synchronization, the alignment of multi-source domains is achieved implicitly. We give convergence guarantees for the proposed method and derive the generalization error bound of federated DG, which illustrates the positive effect of the proposed method on improving generalization. Experiments on two cases demonstrate the consistent superior generalization performance of our method without the risk of data leakage.


Multi-task few-shot text steganalysis based on context-attentive prototypes

Abstract: The current mainstream text steganalysis methods are based on supervised training, requiring a sufficient number of training samples, and necessitating that the training and testing distributions meet the prerequisite of being independently and identically distributed (i.i.d.). However, in practical applications, the distribution of stego text may vary with factors such as text corpora, steganographic algorithms, and embedding rates. This issue, known as domain mismatch, will significantly impact the models’ detection performance. To address this issue, we propose a multi-task few-shot text steganalysis model based on Context-sensitive Prototypes, namely CP-Stega. CP-Stega first extracts generic and task-specific text features from the input sentences and constructs a prototype for each task by averaging the sentence representations of the support set. Next, CP-Stega measures the similarity between the query sample and each task by calculating the distance between the sentence representations and each prototype for any given query sample. To improve the model's adaptability to diverse tasks, we design a loss function consisting of multi-margin loss and KL divergence loss, which expands inter-task prototype distance and shortens intra-task distance, enabling the model to update its meta-parameters accordingly. Experiments reveal that our model can effectively adapt to diverse tasks under different meta-learning scenarios based on different embedding algorithms and embedding payloads, significantly improving text steganalysis performance compared to current foremost steganalysis models and other meta-learning algorithms. By calculating the distance distribution between the sentence representations and each prototype for any given query sample, CP-Stega effectively measures the similarity between the query sample and each task. To enhance the model's adaptability to diverse tasks, we design a loss function comprising multi-margin loss and KL divergence loss. The experiments demonstrate that CP-Stega can effectively perform various steganalysis detection tasks on three self-built meta datasets with different embedding algorithms and embedding capacities.


Digital education: new paradigms for teaching

Abstract: From the advent of the internet to the latest innovations in artificial intelligence and virtual reality, it is essential to identify and understand the technological changes shaping this educational landscape. Thus, the general objective of the research is to analyze how the adoption of Digital Education as a new paradigm in teaching impacts the effectiveness of the teaching-learning process, considering the specific challenges faced by educators in the integration of these innovative practices. Regarding the methods, a literature review with a qualitative approach was used. It is concluded, therefore, that Digital Education emerges as a catalyzing tool for a more effective education, preparing students for the challenges of the 21st century.


Multi-view representation learning for tabular data integration using inter-feature relationships

Abstract: An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data.We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset.Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts.In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.


Measuring large tensile deformation of polymers using fluorescent 3D-digital image correlation with adaptive incremental calculation strategy

Abstract: Digital image correlation (DIC) techniques have been widely used for experimentally characterizing the mechanical behavior of polymer materials. However, practical applications of DIC for tensile testing of polymers often face serious image decorrelation caused by crazing and excessive deformation. In this work, a fluorescent three-dimensional digital image correlation (3D-DIC) with an adaptive incremental calculation strategy is proposed to realize reliable large deformation measurement of polymers by eliminating or mitigating the image decorrelation caused by these issues. The proposed technique first utilizes fluorescent 3D-DIC to eliminate image decorrelation caused by the crazing effect. Then, an adaptive incremental calculation strategy that can automatically update reference images is adopted to mitigate image decorrelation due to excessive deformation. The effectiveness and practicality of the proposed technique were demonstrated by measuring the full-field deformation in chloroprene rubber samples subjected to large tensile deformation. Also, the proposed method was applied to investigate the mechanical behavior of a specific waterproof coating material. Experimental results indicate that the combination of fluorescent 3D-DIC and adaptive incremental calculation strategy can effectively address image decorrelation problems in large deformations of polymers, yielding high-accuracy displacement and strain measurements. This technique holds potential for broader applications in studying the mechanical behavior of other materials or structures undergoing large or super-large deformations.


Monotonicity conjecture for multi-party entanglement. Part I

Abstract: A bstract In this paper, we conjecture a monotonicity property that we call monotonicity under coarse-graining for a class of multi-partite entanglement measures. We check these properties by computing the measures for various types of states using different methods.


Management of cyber risks in the library: analysis of information security awareness of Estonian library employees

Abstract: Purpose This article surveys why libraries are vulnerable to social engineering attacks and how to manage risks of human-caused cyber threats on organizational level; investigates Estonian library staff awareness of information security and shares recommendations concerning focus areas that should be given more attention in the future. Design/methodology/approach The data used in this paper is based on an overview of relevant literature highlighting the theoretical points and giving the reasons why human factor is considered the weakest link in information security and cyber security and studying how to mitigate the related risks in the organisation. To perform the survey, a web questionnaire was designed which included 63 sentences and was developed based on the knowledge-attitude-behaviour (KAB) model supported by Kruger and Kearney and Human Aspects of Information Security Questionnaire (HAIS-Q) designed by Parsons et al . Findings The research results show that the information security awareness of library employees is at a good level; however, awareness in two focus areas needs special attention and should be improved. The output of this study is the mapping of seven focus areas of information security policy in libraries based on the HAIS-Q framework and the KAB model. Originality/value The cyber awareness of library employees has not been studied in the world using HAIS-Q and KAB model, and to the best of the authors’ knowledge, no research has been previously carried out in the Estonian library context into cyber security awareness.


IGXSS: XSS payload detection model based on inductive GCN

Abstract: Abstract To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross‐site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high‐quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two‐layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.


Young children’s conceptions of computing in an African setting

Abstract: Background and Context Recognizing that digital technologies can enable economic transformation in Africa, computing education has been considered a subject relevant for all within the compulsory level of education. The implementation of the subject in many schools is, however, characterized by a myriad of challenges, including pedagogical affordances, especially within early stages of basic education. There is a need, therefore, to understand how children within primary education can be supported to learn computing.


A Deep Convolutional Encoder–Decoder–Restorer Architecture for Image Deblurring

Abstract: Abstract The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network cannot capture multi-scale information well. To tackle the problem, a novel convolutional encoder–decoder–restorer architecture is proposed. In this architecture, a multi-scale input structure is used in the encoder. Improved supervised attention module is inserted into the encoder for enhanced feature acquisition. In decoder, information supplement block is proposed to fuse multi-scale features. Finally, the fused features are used for image recovery in the restorer. In order to optimise the model in multiple domains, the loss function is calculated separately in the spatial and frequency domains. Our method is compared with existing methods on the GOPRO dataset. In addition, to verify the applications of our proposed method, we conduct experiments on the Real image dataset, the VOC2007 dataset and the LFW dataset. Experimental results show that our proposed method outperforms state-of-the-art deblurring methods and improves the accuracy of different vision tasks.


Exploring Transformer Model in Longitudinal Pharmacokinetic/Pharmacodynamic Analyses and Comparing with Alternative Natural Language Processing Models

Abstract: There remains a substantial need for a comprehensive assessment of various natural language processing (NLP) algorithms in longitudinal pharmacokinetic/pharmacodynamic (PK/PD) modeling despite recent advances in machine learning in the space of quantitative pharmacology. We herein investigated the application of the transformer model and further compared the performance among several different NLP models, including long short-term memory (LSTM) and neural-ODE (Ordinary Differential Equation) in analyzing longitudinal PK/PD data using virtual data containing three different regimens. Results suggested that LSTM and neural-ODE, along with their respective variants provide a strong performance when predicting from training-included (seen) regimens, albeit with slight information loss for training-excluded (unseen) regimens. Similarly, as with neural-ODE, the transformer exhibited superior performance in describing time-series PK/PD data. Nonetheless, when extrapolating to unseen regimens, while outlining the general data trends, it encountered difficulties in precisely capturing data fluctuations. Remarkably, a small integration of unseen data into the training dataset significantly bolsters predictive performance for both seen and unseen regimens. Our study marks a pioneering effort in deploying the transformer model for time-series PK/PD analysis and provides a systematic exploration of the currently available NLP models in this field.


Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation

Abstract: Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3–5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations.


An AK-iDNS framework to solve the condensation-driven aggregation with realistic kernels

Abstract: In this study, a concise and fast framework based on the average kernel and iterative direct numerical simulation is proposed to solve the generalized Smoluchowski coagulation equation with a physically realistic kernel. Taking advantage of the symmetry of the homogeneous collision kernel, the realistic kernel function can be represented as a simple power function through Laplace transformation under the average kernel method, and the corresponding moment model and self-preserving model can then be constructed and solved analytically. Compared with the classical Taylor-series expansion method of moments, the present moment model has the same asymptotic growth rate, but the form is more concise and the particle number density is decoupled from the other moments. To obtain a better agreement with the experimental data, the iterative direct numerical simulation can be employed to correct the similarity solution using the analytical similarity solution as the initial condition. The corrected similarity solution overcomes the inherent contradiction between the analytical solution and experimental data discussed in the literature. The results reveal that the shape of self-preserving distribution is independent of the initial distribution and that it does depend on the mechanism of coagulation. The results also show the universality, reliability, and strong robustness of the iterative direct numerical simulation algorithm.


INFORMATION SECURITY RISKS MONITORING AND MANAGEMENT SYSTEM

Abstract: In this research, the information phenomenon is examined from a wide perspective, the concepts of data, information and inventory are explained, the impact and dimensions of information technologies on information are revealed, the difficulty of obtaining information security arising from the need to protect information is investigated, and the main elements of information security are highlighted. This work summarizes the security processes needed to create effective information security in an environment where information security attacks are increasing in both number and variety. As a result, the issues explained, investigated and summarized were widely evaluated, and an important step was taken to create a new technology. Keywords: inventory, security, information security, information


The Internet is a scary place: How does evidence source and examinee race or ethnicity influence determinations of threat?

Abstract: Violent rhetoric online is becoming increasingly relevant to the practice of forensic mental health assessment as examinee's virtual lives may transform into real-world acts of violence. With the rise of a diverse subculture of violent online communities, the aim of the present study was to inform how concerns with online sources of collateral data and racial/ethnic biases may influence determinations of violence potential. Using an experimental design, jury-eligible participants (N = 278) and forensic mental health experts (N = 78) were presented with mock Twitter (now referred to as X) posts that varied by data source (i.e., how information was accessed) and the examinee's race/ethnicity. Results showed no differences in participants' ratings of data credibility, how much weight they would place on the posts in a threat assessment, or how likely the examinee was to act violently against his intended target. Implications regarding the interpretation of social media evidence, relevant limitations, and future research are discussed.


INTELLIGENT CONTROL SYSTEM OF MULTI-MOTION MOBILE ROBOT

Abstract: Technical devices with irregular motion are described by complex non-linear differential equations, as they have an uncertain environment. Due to interactions and simplifications in these types of control objects, their mathematical models may have certain errors. For this reason, by known methods, for example, the linear matrix inequality method, Lyapunov's quadratic function, etc. The synthesized automatic control systems (ACS) for dynamic objects written by nonlinear mathematical models based on the quadratic matrix determined by The synthesis of regulators for the management of dynamic objects described in nonlinear and uncertain conditions is based on fuzzy logic theory. Since the controllers synthesized by fuzzy logic are based on knowledge, their application to other objects (robots) is limited. In such systems, it is difficult or not at all to determine the dependencies between ACS quality indicators, stability and object parameters. Taking into account the above, a method of analytically parametric synthesis of regulators that ensures the degree of stability and accuracy in ACS’s designed for fuzzy TS-type dynamic objects is proposed. In the article, the solution to the problem of the synthesis of the control system of the mobile robot was considered, and it was successfully applied in the multi-motion mobile robot system. Modeling and experimental results confirm that the operation and stability of the obtained system fully meet the technical requirements. The proposed method has led to an increase in both tracking and control accuracy during high-speed movements. Keywords: multi-motion mobile robot, mathematical model, mechatronic modules, fuzzy TS model, robust controller, fuzzy logic.


A virtual reality data visualization tool for dimensionality reduction methods

Abstract: Abstract In this paper, we present a virtual reality interactive tool for generating and manipulating visualizations for high-dimensional data in a natural and intuitive stereoscopic way. Our tool offers support for a diverse range of dimensionality reduction (DR) algorithms, enabling the transformation of complex data into insightful 2D or 3D representations within an immersive VR environment. The tool also allows users to include annotations with a virtual pen using hand tracking, to assign class labels to the data observations, and to perform simultaneous visualization with other users within the 3D environment to facilitate collaboration.


Servant leadership and knowledge employee performance: the mediating role of employee innovative behavior in knowledge-based firms

Abstract: Purpose The purpose of this paper is to examine the relationship between servant leadership, employee innovative behavior and knowledge employee performance in knowledge-based firms. Design/methodology/approach A quantitative approach was used to conduct the present study. The respondents were sampled from knowledge-based firms in Iran. Overall, 726 knowledge employees in 121 firms were selected using convenience sampling. A confirmatory factor analysis was conducted to ascertain the validity and reliability of the observed items, and a structural equation model was employed for testing the hypotheses. Findings In the studied firms, servant leadership has a significant effect on employee innovative behavior. Moreover, the findings of this study show that firms that enhance their employees’ innovative behavior have higher knowledge employee performance. Research limitations/implications The study was conducted in knowledge-based firms in Iran. Therefore, our conclusions may not be applicable to other countries. Future studies should be carried out with samples from other contexts. Practical implications We found that servant leadership is conducive to employee innovative behaviors, and this effect leads to high knowledge employee performance. Accordingly, knowledge-based firms’ leaders should encourage employees’ innovative behavior through stimulating employee thriving at work, supporting employees’ development and empowering them with decision-making discretion. Originality/value This study contributes to advance research on servant leadership literature by linking servant leadership to knowledge employee performance in knowledge-based firms through employee innovative behavior as a mediator.


Banff scoring of kidney allograft biopsies: “Manual” application vs software-assisted sign-out

Abstract: Abstract Objectives Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement. Methods We constructed a web-based “smart template” (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees. Results Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence–assisted scoring of biopsies. Conclusions Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms.


Prediction of Agricultural Commodity Prices using Big Data Framework

Abstract: The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.


A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Abstract: Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.


RIOD:Reinforced Image-based Object Detection for Unruly Weather Conditions

Abstract: Deep Neural Network (DNN) object detectors have proved their efficiency in the detection and classification of objects in normal weather. However, these models suffer a lot during bad weather conditions (foggy, rain, haze, night, etc.). This study presents a new scheme to reduce the aforementioned issue by attenuating the noise in the input image before feeding it to any kind of neural network-based object detector. In this study, the image optimization function transforms subpar-quality images due to bad weather into pictures with the optimal possible quality by estimating the proper illumination and transmission function. These optimized images showed improved object detection rates in the YOLOv4 and YOLOv5 models. This improvement in object detection was also noticed in the case of video input. This scheme was tested with images/videos from various weather conditions, and the results showed an encouraging improvement in detection rates.


Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation

Abstract: Word Sense Disambiguation (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications. A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naïve Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars. Furthermore, the Assamese-Hindi transliteration system was merged with the baseline MT system for the translation of proper nouns. This study marks a remarkable contribution to Assamese NLP, which is a low computationally aware Indian language.


The Innovative Role of Process Mining in building Face Re-identification Trajectory

Abstract: Face recognition and tracking technology have witnessed significant advancements during the recent decades. These advancements include improved accuracy and speed in identifying and tracking individuals, as well as the ability to recognize faces in various scale and lighting conditions. Leveraging the potential of face recognition and tracking, this article explores the integration of process mining techniques to discover and visualize face’s appearing trajectories in crowd scenarios, aiming to enhance crowd security, surveillance, and personal identification. Notably, existing face recognition tools typically focus on bounding box localization, neglecting the utilization of face coordinates to construct trajectory models upon face re-identification. In this paper, full system architecture for building a trajectory model of re-identified faces in a crowd is proposed. This approach significantly helped in building a large database of visitor faces, and the proposed trajectory model resulted in a high rate of true positive face re-identification.


Making Different Topographic Maps with the Surfer Software Package

Abstract: The main objective of this study is to describe the preparation of topographic maps using the Surfer software. A total of 159 regularly distributed Ground Control Points (GCPs) were collected with the use of the Differential Global Positioning System (DGPS). Seven methods (Contour Map, Post Map, 3D Surface Map, 3D Wireframe Maps, Grid Vector-1 Map, Color Relief Map, and Shaded Relief Maps) at the Surfer environment were used to prepare the topographic maps at the Mukhtar Village near the Al-Fallujah City. Contour lines with other features were superimposed on the DEM layer, which refers to the topography of the terrain inside this study area. The accuracy of the database's results was estimated, essential maps were given, and the results were efficient and effective. The most appropriate method to represent topographic maps was proposed, each of these techniques has been enough to provide us with a general understanding of the subject area.


Presenting the Secure Collapsible Makerspace with Biometric Authentication

Abstract: Traditional product design learning is inefficient, costly, and limited by safety concerns in physical and smaller-scale school workshops. The Secure Collapsible Makerspace is a mobile makerspace with smart sensors and an expandable work area facility. This study aims to develop a smart mobile makerspace platform using edge computing architecture and the biometric security model to compare real-time learners’ engagement time and design performance. The developed platform consists of four blocks that provide service as a makerspace for the learners and monitor their utilization in a secure environment. The edge computing architecture allows real-time analysis of the users’ makerspace utilization. A total of 750 learners were accessed for their product design outcome which presented a positive correlation of 0.72 between the engagement time and the design assessment with a corresponding p-value less than 0.001. This study concludes that makerspaces can be a platform that improves students’ performance in hands-on activity-related learning.


Weber's Law-based Regularization for Blind Image Deblurring

Abstract: Blind image deblurring aims to recover an output latent image and a blur kernel from a given blurred image. Kernel estimation is a significant step in blind image deblurring and requires a regularization technique to minimize the cost function and the edges of objects to generate a sharp image in a better way. This study proposes a new image regularization technique called Weber's Law Regularization (WLR) based on the Weber law phenomenon. The Weber ratio was used to preserve the edges of small salient objects and to minimize the cost function to obtain a sharp image while minimizing the ringing effect. To validate the WLR, experiments were conducted on benchmark synthetic and real word images and compared with existing state-of-the-art methods. The experimental results showed that WLR can effectively and efficiently deblur images even in the absence of prior knowledge.


A T-S Fuzzy Approach with Extended LMI Conditions for Inverted Pendulum on a Cart

Abstract: The Inverted Pendulum On a Cart (IPOC) system poses a challenge in control engineering due to its inherent instability, nonlinearity, and underactuation. This addresses the fundamental issues arising from its underactuated nature and introduces an approach that combines Takagi-Sugeno (T-S) fuzzy control with an awareness of real-world constraints to create a control system ensuring both stability and practicality. By aligning theoretical insights with extended considerations, the Linear Matrix Inequality (LMI)-based control design is demonstrated in a comprehensive framework. Theorems are introduced and validated, leading to the derivation of LMI conditions. The simulation results are assessed with accompanying comments to demonstrate the effectiveness of the theorems. Through this integration of T-S fuzzy control with additional considerations, the paper aims to bridge the gap between theory and practical applications, advancing the field of control engineering.


An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments

Abstract: Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts’ data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments.


Advancing Cloud Image Security via AES Algorithm Enhancement Techniques

Abstract: Communication system and internet dominance in our society, has made image security a matter of paramount concern. Cryptography involves encrypting data to protect information exchange between senders and receivers, establishing a foundation for secure communication. The Advanced Encryption Standard (AES) is an exceptional algorithm that plays a pivotal role in this area because of its ability to consistently transform plain data into cipher data using the same encryption key. This algorithm engages intricate encryption techniques, harnessing a variety of algorithms and transformations to ensure robust data security. This study introduces an image encryption technique to comprehensively address security requirements. The proposed method uses the AES algorithm to provide high reliability and unparalleled safety, effectively protecting precious images against any form of malicious attack. The sender's image is intelligently fragmented into multiple pieces to maximize data confidentiality, employing a sophisticated algorithm. Upon decryption by the intended recipient, these fragments seamlessly merge to reconstruct the original image. The suggested technique allows users to securely store their data in the cloud, mitigating concerns related to potential data leakage, damage, or corruption. Thus, by integrating a cloud-based storage system, and utilizing digital signatures through OpenSSL, this method guarantees airtight security and reliability for sensitive data.


Using genetic algorithm for drawing path planning in a robotic arm pencil sketching system

Abstract: The purpose of this study is to implement a robotic arm pencil sketching system through a novel drawing path planning method. Firstly, to enhance the craftsmanship and uniqueness of the artwork, the original image is transformed into an animated style using AnimeGANv2. After image processing, the image is divided into boundary and background layers for drawing path planning. In the realm of path planning, traditional methods often involve manual path design by artists, and only recently have automated path planning methods been proposed. However, these recent approaches still have some limitations. For instance, a pursuit of efficiency can lead to overly uniform strokes, making the artwork resemble a print rather than a genuine piece of art. Therefore, this study introduces a new path-planning approach based on the traveling salesman problem (TSP), utilising various algorithms for path-planning experiments. Different stroke effects generated by distinct algorithms are applied to appropriate areas. The aim is to let the generated artworks exhibit the technological appeal of algorithms while retaining the distinctive characteristics of pencil sketches. For robotic arm control, a force sensor is integrated onto the arm, and a proportional-integral-derivative (PID) controller is employed to monitor and precisely control the drawing force. Experimental results demonstrate that artworks created using genetic algorithms to generate drawing paths exhibit distinct colour-layering effects, emphasising the characteristics of pencil strokes. In comparison with previous methods, this approach effectively addresses the issue of overly uniform and rigid strokes in past techniques.


The added value of temporal data and the best way to handle it: A use-case for atrial fibrillation using general practitioner data

Abstract: Temporal data has numerous challenges for deep learning such as irregularity of sampling. New algorithms are being developed that can handle these temporal challenges better. However, it is unclear how the performance ranges from classical non-temporal models to newly developed algorithms. Therefore, this study compares different non-temporal and temporal algorithms for a relevant use case, the prediction of atrial fibrillation (AF) using general practitioner (GP) data. Three datasets with a 365-day observation window and prediction windows of 14, 180 and 360 days were used. Data consisted of medication, lab, symptom, and chronic diseases codings registered by the GP. The benchmark discarded temporality and used logistic regression, XGBoost models and neural networks on the presence of codings over the whole year. Pattern data extracted common patterns of GP codings and tested using the same algorithms. LSTM and CKConv models were trained as models incorporating temporality. Algorithms which incorporated temporality (LSTM and CKConv, (max AUC 0.734 at 360 days prediction window) outperformed both benchmark and pattern algorithms (max AUC 0.723, with a significant improvement using the 360 days prediction window (p = 0.04). The difference between the benchmark and the LSTM or CKConv algorithm decreased with smaller prediction windows, indicating temporal importance for longer prediction windows. The CKConv and LSTM algorithm performed similarly, possibly due to limited sequence length. Temporal models outperformed non-temporal models for the prediction of AF. For temporal models, CKConv is a promising algorithm to handle temporal data using GP data as it can handle irregular data.


Syntactic and typological properties of constrained language: A study based on dependency treebanks of translated English and non-native English

Abstract: Abstract To explore if translation-intrinsic features are apparent in other types of bilingualism-influenced constrained language use such as non-native production, this study approaches syntactic and typological properties of constrained English translated from Chinese and written by native Chinese speakers via two cognitively-motivated dependency metrics, viz. mean dependency distance (MDD) and dependency direction (DDir). Results of this study show that translated English (both L1 and L2) and non-native English differ from the non-constrained native English in a similar way yet to a slightly different extent, but not from each other in both indicators. Syntactically, bilingually-constrained varieties exhibit reduced syntactic complexity with shorter MDDs, suggesting a simplification tendency. Typologically, cross-linguistic influences are detected in constrained varieties for being more head-final in word-order primed by the source or native language Chinese. Surprisingly, it seems that language directionality affects, albeit marginally, the affinity between constrained varieties, with non-native English being more syntactically and typologically similar to translated English from L1 than from L2.


The Navigation of Home Service Robot Based on Deep Learning and Machine Learning

Abstract: This paper discusses how to improve the accuracy of navigation for home service robot based on the deep learning and machine learning. First, the crawling programing is applied to collect enough images of fridge and washing machine on the web; a deep learning framework is proposed that can distinguish fridge and washing machine more accurately. Following, the data come from the robot operating system topics are collected and cleaned, the linear regression, decision tree, and linear SVR algorithms are applied and compared to predict the power consumption of the robot, and a conclusion is obtained that liner movement will consume more power, which provides a reference for the path planning of the robot. Lastly, the conclusions are proposed that a novel methodology is applied to distinguish different home appliances, which is useful for the accurate navigation of the robot; the liner movement will consume more power compared to turning left or right, which supplies a reference for the optimized path planning for the robot.


Understanding security behaviour among healthcare professionals by comparing results from technology threat avoidance theory and protection motivation theory

Abstract: We investigate security behaviour among healthcare professionals while comparing upon two prevalent theories in information security (IS) research: Protection Motivation Theory (PMT) and Technology Threat Avoidance Theory (TTAT). Although IS research often uses these models, very few studies compare the two models using a diverse sample of 245 healthcare professionals. The data were analysed using partial least squares regression - structural equation modelling (PLS-SEM). Results indicated that both models were similarly robust at predicting security behaviour. Perceived severity and response efficacy positively influenced avoidance motivation (AM) for PMT. All factors of the threat appraisal arm of TTAT and safeguard efficacy played an influencing role on AM in TTAT. Response cost and self-efficacy were not supported in either model, providing us with potentially new insights into this specific population's security intentions. With the addition of Perceived Threat, TTAT appears to be slightly more robust model with 64% of the variance explained by the given model while PMT explains only 60% of the variance. Given the ever-increasing risk of data breaches in healthcare, practical applications and implications of the study's findings to health information technology (HIT) policy and infrastructure are also discussed.


eyeScrollR: A software method for reproducible mapping of eye-tracking data from scrollable web pages

Abstract: The Internet has become an important part of our lives and an increasing number of researchers use eye-tracking technology to examine attention and behavior in online environments. Researchers, however, face a significant challenge in mapping eye-tracking data from scrollable web pages. We describe the R package eyeScrollR for mapping eye-tracking data from scrollable content such as web pages. The package re-maps eye-tracking gaze coordinates to full-page coordinates with a deterministic algorithm based on mouse scroll data. The package includes options for handling common situations, such as sticky menus or ads that remain visible when the user scrolls. We test the package's validity in different hardware and software settings and on different web pages and show that it is highly accurate when tested against manual coding. Compared to current methods, eyeScrollR provides a more reproducible and reliable approach for mapping eye-tracking data from scrollable web pages. With its open code and free availability, we recommend eyeScrollR as an essential tool for eye-tracking researchers, particularly those who adhere to open-science principles. The eyeScrollR package offers a valuable contribution to the field of eye-tracking research, facilitating accurate and standardized analysis of eye-tracking data in web scrolling contexts.


Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations

Abstract: Abstract This article's main objective is to maximize solar radiations (SRs) through the use of the gorilla troop algorithm (GTA) for identifying the optimal tilt angle (OTA) for photovoltaic (PV) panels. This is done in conjunction with an experimental work that consists of three 100 W PV panels tilted at three different tilt angles (TAs). The 28°, 30°, and 50° are the three TAs. The experimental data are collected every day for 181-day and revealed that the TA of 28° is superior to those of 50° and 30°. The GTA calculated the OTA to be 28.445°, which agrees with the experimental results, which show a TA of 28°. The SR of the 28o TA is 59.3% greater than that of the 50° TA and 4.5% higher than that of the 30° TA. Recent methods are used to compare the GTA with the other nine metaheuristics (MHTs)—the genetic algorithm, particle swarm, harmony search, ant colony, cuckoo search, bee colony, fire fly, grey wolf, and coronavirus disease optimizers—in order to figure out the optimal OTA. The OTA is calculated by the majority of the nine MHTs to be 28.445°, which is the same as the GTA and confirms the experimental effort. In only 181-day, the by experimentation it may be documented SR difference between the TAs of 28° and 50° TA is 159.3%. Numerous performance metrics are used to demonstrate the GTA's viability, and it is contrasted with other recent optimizers that are in competition.


Rapid trajectory generation for quadrotor with limited motor speed

Abstract: Planning a safe and dynamic flyable trajectory for unmanned aerial vehicle (UAV) is the precondition for autonomous flight. Considering environmental complexity and flight safety under limited motor speed, we proposed a new time-segmented trajectory planning method, which can ensure motor speed within the allowable range and achieve safe flight. Firstly, the candidate trajectory for quadrotor UAV is expressed by high-order polynomials. Herein, polynomial is used as the expression of trajectory, and nonlinear programming (NLP) is used to calculate polynomial coefficient. Then, two strategies and an initial value calculation method are proposed to improve the efficiency of NLP. Furthermore, aiming at the problem that the initial trajectory of UAV motor cannot meet the current speed limit constraint after the motor speed drops, an adjustment strategy based on feasible trajectory is designed. The method presented in this paper not only enhances flexibility of the flight process, improves solving efficiency of the algorithm but also enables UAV to continue flying when the motor speed is limited. Finally, simulation analysis and comparison illustrate the effectiveness and superiority of the proposed method.


Constructing immersive toy trial experience in mobile augmented reality

Abstract: When consumers purchase toys from retail stores, the majority of toys are packaged, making it difficult for them to observe the toys comprehensively. This limitation may hinder their ability to make informed purchase decisions. To address this challenge, this paper introduces an immersive toy experience program utilizing augmented reality (AR) technology. The program utilizes the camera on mobile devices to scan and identify the toy's cover image, subsequently showcasing corresponding virtual toy models in a simulated environment. Additionally, interactive controls enable users to manipulate the viewing angles. In terms of methodology, we have specifically designed an expandable collection of toy images, allowing the recognition of recently introduced toys by adding them to the database, enhancing the scalability of our application. In comparison to previous research, our work transcends the constraints of traditional toy shopping, providing a more intuitive, interactive, and personalized experience through AR technology.


Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Abstract: Action recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions.


Numerically Stable Locality-Preserving Partial Least Squares Discriminant Analysis for Efficient Dimensionality Reduction and Classification of High-dimensional Data

Abstract: Dimensionality reduction plays a pivotal role in preparing high-dimensional data for classification and discrimination tasks by eliminating redundant features and enhancing the efficiency of classifiers. The effectiveness of a dimensionality reduction algorithm hinges on its numerical stability. When data projections are numerically stable, they lead to enhanced class separability in the lower-dimensional embedding, consequently yielding higher classification accuracy. This paper investigates the numerical attributes of dimensionality reduction and discriminant subspace learning, with a specific focus on Locality-Preserving Partial Least Squares Discriminant Analysis (LPPLS-DA). High-dimensional data frequently introduce singularity in the scatter matrices, posing a significant challenge. To tackle this issue, the paper explores two robust implementations of LPPLS-DA. These approaches not only optimize data projections but also capture more discriminative features, resulting in a marked improvement in classification accuracy. Empirical evidence supports these findings through numerical experiments conducted on synthetic and spectral datasets. The results demonstrate the superior performance of the proposed methods when compared to several state-of-the-art dimensionality reduction techniques in terms of both classification accuracy and dimension reduction.


A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

Abstract: Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient, safe and smooth across their wide range of application scenarios. In this study, a novel UAV path planning method is proposed. Firstly, the UAV path planning under numerous obstacles is modelled as a continuous constrained optimization problem. The cost function is formulated as a linear combination of length, height variation, and smoothness, while the constraints include obstacle avoidance, height limitation, and the maneuverability of UAV. Subsequently, a novel constrained state transition algorithm with adaptive fuzzy penalty (AFSTA) is proposed to solve the optimization problem. In AFSTA, a novel adaptive fuzzy penalty function is designed to leverage expert knowledge to establish a reasonable mapping relationship from the fitness value and the degree of constraint violation to the penalty factor for a candidate solution. Meanwhile, the state transition algorithm (STA) is used as the search engine for both global and local search. Experimental results illustrate that the proposed method can find energy-efficient, safe, and maneuverable flight paths successfully with the superiority over other state-of-the-art metaheuristic algorithms.


Efficient learning of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math>-doped stabilizer states with single-copy measurements

Abstract: One of the primary objectives in the field of quantum state learning is to develop algorithms that are time-efficient for learning states generated from quantum circuits. Earlier investigations have demonstrated time-efficient algorithms for states generated from Clifford circuits with at most <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> non-Clifford gates. However, these algorithms necessitate multi-copy measurements, posing implementation challenges in the near term due to the requisite quantum memory. On the contrary, using solely single-qubit measurements in the computational basis is insufficient in learning even the output distribution of a Clifford circuit with one additional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gate under reasonable post-quantum cryptographic assumptions. In this work, we introduce an efficient quantum algorithm that employs only nonadaptive single-copy measurement to learn states produced by Clifford circuits with a maximum of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&#x2061;</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> non-Clifford gates, filling a gap between the previous positive and negative results.


A Survey about Post Quantum Cryptography Methods

Abstract: Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.


Effective prime factorization via quantum annealing by modular locally-structured embedding

Abstract: This paper investigates novel techniques to solve prime factorization by quantum annealing (QA). First, we present a very-compact modular encoding of a multiplier circuit into the architecture of current D-Wave QA devices. The key contribution is a compact encoding of a controlled full-adder into an 8-qubit module in the Pegasus topology, which we synthesized using Optimization Modulo Theories. This allows us to encode up to a 21 × 12-bit multiplier (and a 22 × 8-bit one) into the Pegasus 5760-qubit topology of current annealers. To the best of our knowledge, these are the largest factorization problems ever encoded into a quantum annealer. Second, we investigated the problem of actually solving encoded PF problems by running an extensive experimental evaluation on a D-Wave Advantage 4.1 quantum annealer. In the experiments we introduced different approaches to initialize the multiplier qubits and adopted several performance enhancement techniques. Overall, 8,219,999 = 32,749 × 251 was the highest prime product we were able to factorize within the limits of our QPU resources. To the best of our knowledge, this is the largest number which was ever factorized by means of a quantum annealer; also, this is the largest number which was ever factorized by means of any quantum device without relying on external search or preprocessing procedures run on classical computers.


Stabilizing two-qubit entanglement with dynamically decoupled active feedback

Abstract: We propose and analyze a protocol for stabilizing a maximally entangled state of two noninteracting qubits using active state-dependent feedback from a continuous two-qubit half-parity measurement in coordination with a concurrent, noncommuting dynamical decoupling drive. We demonstrate that such a drive can be simultaneous with the measurement and feedback, while also playing a key part in the feedback protocol itself. We show that robust stabilization with near-unit fidelity can be achieved even in the presence of realistic nonidealities, such as time delay in the feedback loop, imperfect state-tracking, inefficient measurements, dephasing from $1/f$-distributed qubit-frequency noise, and relaxation. We mitigate feedback-delay error by introducing a forward-state-estimation strategy in the feedback controller that tracks the effects of control signals already in transit. More generally, the steady state is globally attractive without the need for ancillas, regardless of the error state, in contrast to most known feedback and error-correction schemes.


Qibolab: an open-source hybrid quantum operating system

Abstract: We present <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math>, an open-source software library for quantum hardware control integrated with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibo</mml:mtext></mml:mrow></mml:math> quantum computing middleware framework. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math> provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">Qibolab</mml:mtext></mml:mrow></mml:math> enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.


Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources

Abstract: Abstract Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high quality, closely mimicking human speech. Nevertheless, given the wide array of applications now employing TTS models, mere high-quality speech generation is no longer sufficient. Present-day TTS models must also excel at producing expressive speech that can convey various speaking styles and emotions, akin to human speech. Consequently, researchers have concentrated their efforts on developing more efficient models for expressive speech synthesis in recent years. This paper presents a systematic review of the literature on expressive speech synthesis models published within the last 5 years, with a particular emphasis on approaches based on deep learning. We offer a comprehensive classification scheme for these models and provide concise descriptions of models falling into each category. Additionally, we summarize the principal challenges encountered in this research domain and outline the strategies employed to tackle these challenges as documented in the literature. In the Section 8, we pinpoint some research gaps in this field that necessitate further exploration. Our objective with this work is to give an all-encompassing overview of this hot research area to offer guidance to interested researchers and future endeavors in this field.