Information

62 pages, 1897 KiB

Open AccessReview

Construction of Knowledge Graphs: Current State and Challenges

by Marvin Hofer, Daniel Obraczka, Alieh Saeedi, Hanna Köpcke and Erhard Rahm

Information 2024, 15(8), 509; https://doi.org/10.3390/info15080509 - 22 Aug 2024

Viewed by 599

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources [...] Read more.

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirements for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction with respect to the introduced requirements for specific popular KGs, as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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25 pages, 5483 KiB

Open AccessArticle

Automated Negotiation Agents for Modeling Single-Peaked Bidders: An Experimental Comparison

by Fatemeh Hassanvand, Faria Nassiri-Mofakham and Katsuhide Fujita

Information 2024, 15(8), 508; https://doi.org/10.3390/info15080508 - 22 Aug 2024

Viewed by 406

Abstract

During automated negotiations, intelligent software agents act based on the preferences of their proprietors, interdicting direct preference exposure. The agent can be armed with a component of an opponent’s modeling features to reduce the uncertainty in the negotiation, but how negotiating agents with [...] Read more.

During automated negotiations, intelligent software agents act based on the preferences of their proprietors, interdicting direct preference exposure. The agent can be armed with a component of an opponent’s modeling features to reduce the uncertainty in the negotiation, but how negotiating agents with a single-peaked preference direct our attention has not been considered. Here, we first investigate the proper representation of single-peaked preferences and implementation of single-peaked agents within bidder agents using different instances of general single-peaked functions. We evaluate the modeling of single-peaked preferences and bidders in automated negotiating agents. Through experiments, we reveal that most of the opponent models can model our benchmark single-peaked agents with similar efficiencies. However, the accuracies differ among the models and in different rival batches. The perceptron-based P1 model obtained the highest accuracy, and the frequency-based model Randomdance outperformed the other competitors in most other performance measures. Full article

(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)

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33 pages, 6672 KiB

Open AccessReview

Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review

by Meng Wang, Xinyan Guo, Yanling She, Yang Zhou, Maohan Liang and Zhong Shuo Chen

Information 2024, 15(8), 507; https://doi.org/10.3390/info15080507 - 21 Aug 2024

Viewed by 1291

Abstract

The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, [...] Read more.

The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions. Full article

(This article belongs to the Special Issue Deep Learning Approach for Time Series Forecasting)

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16 pages, 2069 KiB

Open AccessArticle

Trading Cloud Computing Stocks Using SMA

by Xianrong Zheng and Lingyu Li

Information 2024, 15(8), 506; https://doi.org/10.3390/info15080506 - 21 Aug 2024

Viewed by 411

Abstract

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, [...] Read more.

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show that SMA can achieve satisfying performance in terms of several measures, including MAE, RMSE, and R-squared. Full article

(This article belongs to the Special Issue Blockchain Applications for Business Process Management)

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29 pages, 1854 KiB

Open AccessArticle

Information Security Awareness in the Insurance Sector: Cognitive and Internal Factors and Combined Recommendations

by Morgan Djotaroeno and Erik Beulen

Information 2024, 15(8), 505; https://doi.org/10.3390/info15080505 - 21 Aug 2024

Viewed by 651

Abstract

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates [...] Read more.

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of “How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?” has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). Full article

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26 pages, 10462 KiB

Open AccessArticle

The Optimal Choice of the Encoder–Decoder Model Components for Image Captioning

by Mateusz Bartosiewicz and Marcin Iwanowski

Information 2024, 15(8), 504; https://doi.org/10.3390/info15080504 - 21 Aug 2024

Viewed by 489

Abstract

Image captioning aims at generating meaningful verbal descriptions of a digital image. This domain is rapidly growing due to the enormous increase in available computational resources. The most advanced methods are, however, resource-demanding. In our paper, we return to the encoder–decoder deep-learning model [...] Read more.

Image captioning aims at generating meaningful verbal descriptions of a digital image. This domain is rapidly growing due to the enormous increase in available computational resources. The most advanced methods are, however, resource-demanding. In our paper, we return to the encoder–decoder deep-learning model and investigate how replacing its components with newer equivalents improves overall effectiveness. The primary motivation of our study is to obtain the highest possible level of improvement of classic methods, which are applicable in less computational environments where most advanced models are too heavy to be efficiently applied. We investigate image feature extractors, recurrent neural networks, word embedding models, and word generation layers and discuss how each component influences the captioning model’s overall performance. Our experiments are performed on the MS COCO 2014 dataset. As a result of our research, replacing components improves the quality of generating image captions. The results will help design efficient models with optimal combinations of their components. Full article

(This article belongs to the Special Issue Information Processing in Multimedia Applications)

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17 pages, 368 KiB

Open AccessArticle

An Approach for Maximizing Computation Bits in UAV-Assisted Wireless Powered Mobile Edge Computing Networks

by Zhenbo Liu, Yunge Duan and Shuang Fu

Information 2024, 15(8), 503; https://doi.org/10.3390/info15080503 - 21 Aug 2024

Viewed by 440

Abstract

With the development of the Internet of Things (IoT), IoT nodes with limited energy and computing capability are no longer able to address increasingly complex computational tasks. To address this issue, an Unmanned Aerial Vehicle (UAV)-assisted Wireless Power Transfer (WPT) Mobile Edge Computing [...] Read more.

With the development of the Internet of Things (IoT), IoT nodes with limited energy and computing capability are no longer able to address increasingly complex computational tasks. To address this issue, an Unmanned Aerial Vehicle (UAV)-assisted Wireless Power Transfer (WPT) Mobile Edge Computing (MEC) system is proposed in this study. By jointly optimizing variables such as energy harvesting time, user transmission power, user offloading time, CPU frequency, and UAV deployment location, the system aims to maximize the number of computation bits by the users. This optimization yields a challenging non-convex optimization problem. To address these issues, a two-stage alternating method based on the Lagrangian dual method and the Successive Convex Approximation (SCA) method is proposed to decompose the initial problem into two sub-problems. Firstly, the UAV position is fixed to obtain the optimal values of other variables, and then the UAV position is optimized based on the solved variables. Finally, this iterative process continues until the algorithm convergences, and the optimal solution for the given problem is obtained. The simulation results indicate that the proposed algorithm exhibits good convergence. Compared to other benchmark solutions, the proposed approach performs optimally in maximizing computation bits. Full article

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15 pages, 2185 KiB

Open AccessArticle

Cost Estimation and Prediction for Residential Projects Based on Grey Relational Analysis–Lasso Regression–Backpropagation Neural Network

by Lijun Chen and Dejiang Wang

Information 2024, 15(8), 502; https://doi.org/10.3390/info15080502 - 21 Aug 2024

Viewed by 460

Abstract

In the early stages of residential project investment, accurately estimating the engineering costs of residential projects is crucial for cost control and management of the project. However, the current cost estimation of residential engineering in China is primarily carried out by cost personnel [...] Read more.

In the early stages of residential project investment, accurately estimating the engineering costs of residential projects is crucial for cost control and management of the project. However, the current cost estimation of residential engineering in China is primarily carried out by cost personnel based on their own experience. This process is time-consuming and labour-intensive, and it involves subjective judgement, which can lead to significant estimation errors and fail to meet the rapidly developing market demands. Data collection for residential construction projects is challenging, with small sample sizes, numerous attributes, and complexity. This paper adopts a hybrid method combining a grey relational analysis, Lasso regression, and Backpropagation Neural Network (GAR-LASSO-BPNN). This method has significant advantages in handling high-dimensional small samples and multiple correlated variables. The grey relational analysis (GRA) is used to quantitatively identify cost-driving factors, and 14 highly correlated factors are selected as input variables. Then, regularization through Lasso regression (LASSO) is used to filter the final input variables, which are subsequently input into the Backpropagation Neural Network (BPNN) to establish the relationship between the unit cost of residential projects and 12 input variables. Compared to using LASSO and BPNN methods individually, the GAR-LASSO-BPNN hybrid prediction method performs better in terms of error evaluation metrics. The research findings can provide quantitative decision support for cost estimators in the early estimation stages of residential project investment decision-making. Full article

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12 pages, 244 KiB

Open AccessPerspective

Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery

by Khalida Akbar, Anna Passaro, Mariacarla Di Gioia, Elvira Martini, Mirella Dragone, Antonio Zullo and Fabrizio Stasolla

Information 2024, 15(8), 501; https://doi.org/10.3390/info15080501 - 21 Aug 2024

Viewed by 612

Abstract

People with disorders of consciousness, either as a consequence of an acquired brain injury or a traumatic brain injury, may pose serious challenges to medical and/or rehabilitative centers with an increased burden on caregivers and families. The objectives of this study were as [...] Read more.

People with disorders of consciousness, either as a consequence of an acquired brain injury or a traumatic brain injury, may pose serious challenges to medical and/or rehabilitative centers with an increased burden on caregivers and families. The objectives of this study were as follows: to explore the use of extended reality as a critical means of rehabilitative support in people with disorders of consciousness and brain injuries; to evaluate its impact on recovery processes; to assess the improvements in the participants’ quality of life, and to reduce the burden on families and caregivers by using extended reality and artificial-intelligence-based programs. A selective review of the newest empirical studies on the use of extended reality and artificial-intelligence-based interventions in patients with brain injuries and disorders of consciousness was conducted over the last decade. The potential for bias in this selective review is acknowledged. A conceptual framework was detailed. The data showed that extended reality and artificial-intelligence-based programs successfully enhanced the adaptive responding of the participants involved, and improved their quality of life. The burden on caregivers and families was reduced accordingly. Extended reality and artificial intelligence may be viewed as crucial means of recovery in people with disorders of consciousness and brain injuries. Full article

(This article belongs to the Special Issue Extended Reality and Cybersecurity)

25 pages, 514 KiB

Open AccessArticle

Bridging Linguistic Gaps: Developing a Greek Text Simplification Dataset

by Leonidas Agathos, Andreas Avgoustis, Xristiana Kryelesi, Aikaterini Makridou, Ilias Tzanis, Despoina Mouratidis, Katia Lida Kermanidis and Andreas Kanavos

Information 2024, 15(8), 500; https://doi.org/10.3390/info15080500 - 20 Aug 2024

Viewed by 434

Abstract

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique [...] Read more.

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique challenges that hinder comprehension for native speakers, learners, tourists, and international students. This paper introduces a comprehensive dataset for Greek text simplification, containing over 7500 sentences across diverse topics such as history, science, and culture, tailored to address these challenges. We outline the methodology for compiling this dataset, including a collection of texts from Greek Wikipedia, their annotation with simplified versions, and the establishment of robust evaluation metrics. Additionally, the paper details the implementation of quality control measures and the application of machine learning techniques to analyze text complexity. Our experimental results demonstrate the dataset’s initial effectiveness and potential in reducing linguistic barriers and enhancing communication, with initial machine learning models showing promising directions for future improvements in classifying text complexity. The development of this dataset marks a significant step toward improving accessibility and comprehension for a broad audience of Greek speakers and learners, fostering a more inclusive society. Full article

(This article belongs to the Special Issue Information Extraction and Language Discourse Processing)

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20 pages, 2982 KiB

Open AccessArticle

Exploring Tourist Experience through Online Reviews Using Aspect-Based Sentiment Analysis with Zero-Shot Learning for Hospitality Service Enhancement

by Ibrahim Nawawi, Kurnia Fahmy Ilmawan, Muhammad Rifqi Maarif and Muhammad Syafrudin

Information 2024, 15(8), 499; https://doi.org/10.3390/info15080499 - 20 Aug 2024

Viewed by 474

Abstract

Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich [...] Read more.

Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich source of user-generated content, but the sheer volume of reviews makes manual analysis impractical. This study proposes integrating aspect-based sentiment analysis with zero-shot learning to analyze online tourist reviews effectively without requiring extensive annotated datasets. Using pretrained models like RoBERTa, the research framework involves keyword extraction, sentence segment detection, aspect construction, and sentiment polarity measurement. The dataset, sourced from TripAdvisor reviews of attractions, hotels, and restaurants in Central Java, Indonesia, underwent preprocessing to ensure suitability for analysis. The results highlight the importance of aspects such as food, accommodation, and cultural experiences in tourist satisfaction. The findings indicate a need for continuous service improvement to meet evolving tourist expectations, demonstrating the potential of advanced natural language processing techniques in enhancing hospitality services and customer satisfaction. Full article

(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)

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17 pages, 809 KiB

Open AccessArticle

Intelligent Risk Evaluation for Investment Banking IPO Business Based on Text Analysis

by Lei Zhang, Chao Wang and Xiaoxing Liu

Information 2024, 15(8), 498; https://doi.org/10.3390/info15080498 - 20 Aug 2024

Viewed by 384

Abstract

By constructing a text quality analysis system and company quality analysis system based on a prospectus, the intelligent analysis method of investment banking IPO business risk is proposed based on the machine learning method and text analysis technology. Taking the Sci-Tech Innovation Board [...] Read more.

By constructing a text quality analysis system and company quality analysis system based on a prospectus, the intelligent analysis method of investment banking IPO business risk is proposed based on the machine learning method and text analysis technology. Taking the Sci-Tech Innovation Board in China as a sample, the empirical analysis results show that the text quality and the company quality disclosed in the prospectus can affect the withdrawal rate of investment banking IPO business. By carrying out a text analysis and machine learning on the text quality and company quality, the risk of investment banking IPO business can be predicted intelligently and effectively. The research results can not only improve the business efficiency of investment banking IPO, and save resource cost, but also improve the standardization and authenticity of investment banking IPO business. Full article

(This article belongs to the Section Information Applications)

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14 pages, 291 KiB

Open AccessArticle

Minimum Mean Squared Error Estimation and Mutual Information Gain

by Jerry Gibson

Information 2024, 15(8), 497; https://doi.org/10.3390/info15080497 - 20 Aug 2024

Viewed by 453

Abstract

Information theoretic quantities such as entropy, entropy rate, information gain, and relative entropy are often used to understand the performance of intelligent agents in learning applications. Mean squared error has not played a role in these analyses, primarily because it is not felt [...] Read more.

Information theoretic quantities such as entropy, entropy rate, information gain, and relative entropy are often used to understand the performance of intelligent agents in learning applications. Mean squared error has not played a role in these analyses, primarily because it is not felt to be a viable performance indicator in these scenarios. We build on a new quantity, the log ratio of entropy powers, to establish that minimum mean squared error (MMSE) estimation, prediction, and smoothing are directly connected to mutual information gain or loss in an agent learning system modeled by a Markov chain for many probability distributions of interest. Expressions for mutual information gain or loss are developed for MMSE estimation, prediction, and smoothing, and an example for fixed lag smoothing is presented. Full article

(This article belongs to the Special Issue Fundamental Problems of Information Studies)

26 pages, 3537 KiB

Open AccessArticle

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

by Giannis Tzimas, Nikos Zotos, Evangelos Mourelatos, Konstantinos C. Giotopoulos and Panagiotis Zervas

Information 2024, 15(8), 496; https://doi.org/10.3390/info15080496 - 20 Aug 2024

Viewed by 624

Abstract

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This [...] Read more.

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers. Full article

(This article belongs to the Special Issue Second Edition of Predictive Analytics and Data Science)

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27 pages, 19187 KiB

Open AccessArticle

Analyzing Tor Browser Artifacts for Enhanced Web Forensics, Anonymity, Cybersecurity, and Privacy in Windows-Based Systems

by Muhammad Shanawar Javed, Syed Muhammad Sajjad, Danish Mehmood, Khawaja Mansoor, Zafar Iqbal, Muhammad Kazim and Zia Muhammad

Information 2024, 15(8), 495; https://doi.org/10.3390/info15080495 - 19 Aug 2024

Viewed by 639

Abstract

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics [...] Read more.

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics to identify its use in unlawful activities and explore its consequences. This research analyzes artifacts generated by Tor on Windows-based systems. The methodology integrates forensic techniques into incident responses per NIST SP (800-86), exploring areas such as registry, storage, network, and memory using tools like bulk-extractor, autopsy, and regshot. We propose an automated PowerShell script that detects Tor usage and retrieves artifacts with minimal user interaction. Finally, this research performs timeline analysis and artifact correlation for a contextual understanding of event sequences in memory and network domains, ultimately contributing to improved incident response and accountability. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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19 pages, 1050 KiB

Open AccessArticle

Enhancing Biomedical Question Answering with Large Language Models

by Hua Yang, Shilong Li and Teresa Gonçalves

Information 2024, 15(8), 494; https://doi.org/10.3390/info15080494 - 19 Aug 2024

Viewed by 619

Abstract

In the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal is to provide accurate and relevant answers to the posed queries related to medical conditions, treatments, procedures, [...] Read more.

In the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal is to provide accurate and relevant answers to the posed queries related to medical conditions, treatments, procedures, medications, and other healthcare-related topics. Well-designed models should efficiently retrieve relevant passages. Early retrieval models can quickly retrieve passages but often with low precision. In contrast, recently developed Large Language Models can retrieve documents with high precision but at a slower pace. To tackle this issue, we propose a two-stage retrieval approach that initially utilizes BM25 for a preliminary search to identify potential candidate documents; subsequently, a Large Language Model is fine-tuned to evaluate the relevance of query–document pairs. Experimental results indicate that our approach achieves comparative performances on the BioASQ and the TREC-COVID datasets. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

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14 pages, 1516 KiB

Open AccessArticle

Early Recurrence Prediction of Hepatocellular Carcinoma Using Deep Learning Frameworks with Multi-Task Pre-Training

by Jian Song, Haohua Dong, Youwen Chen, Xianru Zhang, Gan Zhan, Rahul Kumar Jain and Yen-Wei Chen

Information 2024, 15(8), 493; https://doi.org/10.3390/info15080493 - 17 Aug 2024

Viewed by 530

Abstract

Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due [...] Read more.

Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due to limited annotated data. We propose a multi-task pre-training method using self-supervised learning with medical images for predicting the ER of HCC. This method involves two pretext tasks: phase shuffle, focusing on intra-image feature representation, and case discrimination, focusing on inter-image feature representation. The effectiveness and generalization of the proposed method are validated through two different experiments. In addition to predicting early recurrence, we also apply the proposed method to the classification of focal liver lesions. Both experiments show that the multi-task pre-training model outperforms existing pre-training (transfer learning) methods with natural images, single-task self-supervised pre-training, and DINOv2. Full article

(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)

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14 pages, 4112 KiB

Open AccessArticle

A Feasibility Study of a Respiratory Rate Measurement System Using Wearable MOx Sensors

by Mitsuhiro Fukuda, Jaakko Hyry, Ryosuke Omoto, Takunori Shimazaki, Takumi Kobayashi and Daisuke Anzai

Information 2024, 15(8), 492; https://doi.org/10.3390/info15080492 - 16 Aug 2024

Viewed by 392

Abstract

Accurately obtaining a patient’s respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from [...] Read more.

Accurately obtaining a patient’s respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from body movements. Conversely, a capnometer coupled with a ventilator offers a method of measuring the respiratory rate that is unaffected by body movements. However, capnometers are mainly used to evaluate respiration when using a ventilator or an Ambu bag by measuring the CO₂ concentration at the breathing circuit, and they are not used only to measure the respiratory rate. Furthermore, capnometers are not suitable as wearable devices because they require intubation or a mask that covers the nose and mouth to prevent air leaks during the measurement. In this study, we developed a reliable system for measuring the respiratory rate utilizing a small wearable MOx sensor that is unaffected by body movements and not connected to the breathing circuit. Subsequently, we conducted experimental assessments to gauge the accuracy of the rate estimation achieved by the system. In order to avoid the effects of abnormal states on the estimation accuracy, we also evaluated the classification performance for distinguishing between normal and abnormal respiration using a one-class SVM-based approach. The developed system achieved 80% for both true positive and true negative rates. Our experimental findings reveal that the respiratory rate can be precisely determined without being influenced by body movements. Full article

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18 pages, 1873 KiB

Open AccessArticle

Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems

by Hamed Taherdoost

Information 2024, 15(8), 491; https://doi.org/10.3390/info15080491 - 16 Aug 2024

Viewed by 678

Abstract

Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a [...] Read more.

Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL’s capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. Full article

(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)

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23 pages, 1044 KiB

Open AccessArticle

Optimized Early Prediction of Business Processes with Hyperdimensional Computing

by Fatemeh Asgarinejad, Anthony Thomas, Ryan Hildebrant, Zhenyu Zhang, Shangping Ren, Tajana Rosing and Baris Aksanli

Information 2024, 15(8), 490; https://doi.org/10.3390/info15080490 - 16 Aug 2024

Viewed by 426

Abstract

There is a growing interest in the early prediction of outcomes in ongoing business processes. Predictive process monitoring distills knowledge from the sequence of event data generated and stored during the execution of processes and trains models on this knowledge to predict outcomes [...] Read more.

There is a growing interest in the early prediction of outcomes in ongoing business processes. Predictive process monitoring distills knowledge from the sequence of event data generated and stored during the execution of processes and trains models on this knowledge to predict outcomes of ongoing processes. However, most state-of-the-art methods require the training of complex and inefficient machine learning models and hyper-parameter optimization as well as numerous input data to achieve high performance. In this paper, we present a novel approach based on Hyperdimensional Computing (HDC) for predicting the outcome of ongoing processes before their completion. We highlight its simplicity, efficiency, and high performance while utilizing only a subset of the input data, which helps in achieving a lower memory demand and faster and more effective corrective measures. We evaluate our proposed method on four publicly available datasets with a total of 12 binary prediction tasks. Our proposed method achieves an average 6% higher area under the ROC curve (AUC) and up to a 14% higher F1-score, while yielding a 20× earlier prediction than state-of-the-art conventional machine learning- and neural network-based models. Full article

(This article belongs to the Special Issue Second Edition of Predictive Analytics and Data Science)

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15 pages, 956 KiB

Open AccessArticle

Enhancing Brain Tumour Multi-Classification Using Efficient-Net B0-Based Intelligent Diagnosis for Internet of Medical Things (IoMT) Applications

by Amna Iqbal, Muhammad Arfan Jaffar and Rashid Jahangir

Information 2024, 15(8), 489; https://doi.org/10.3390/info15080489 - 16 Aug 2024

Viewed by 422

Abstract

Brain tumour disease develops due to abnormal cell proliferation. The early identification of brain tumours is vital for their effective treatment. Most currently available examination methods are laborious, require extensive manual instructions, and produce subpar findings. The EfficientNet-B0 architecture was used to diagnose [...] Read more.

Brain tumour disease develops due to abnormal cell proliferation. The early identification of brain tumours is vital for their effective treatment. Most currently available examination methods are laborious, require extensive manual instructions, and produce subpar findings. The EfficientNet-B0 architecture was used to diagnose brain tumours using magnetic resonance imaging (MRI). The fine-tuned EffeceintNet B0 model was proposed for the Internet of Medical Things (IoMT) environment. The fine-tuned EfficientNet-B0 architecture was employed to classify four different stages of brain tumours from the MRI images. The fine-tuned model showed 99% accuracy in the detection of four different classes of brain tumour detection (glioma, no tumour, meningioma, and pituitary). The proposed model performed very well in the detection of the pituitary class with a precision of 0.95, recall of 0.98, and F1 score of 0.96. The proposed model also performed very well in the detection of the no-tumour class with values of 0.99, 0.90, and 0.94 for precision, recall, and the F1 score, respectively. The precision, recall, and F1 scores for Glioma and Meningioma classes were also high. The proposed solution has several implications for enhancing clinical investigations of brain tumours. Full article

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12 pages, 1035 KiB

Open AccessArticle

A Methodology to Distribute On-Chip Voltage Regulators to Improve the Security of Hardware Masking

by Soner Seçkiner and Selçuk Köse

Information 2024, 15(8), 488; https://doi.org/10.3390/info15080488 - 16 Aug 2024

Viewed by 438

Abstract

Hardware masking is used to protect against side-channel attacks by splitting sensitive information into different parts, called hardware masking shares. Ideally, a side-channel attack would only work if all these parts were completely independent. But in real-world VLSI implementations, things are not perfect. [...] Read more.

Hardware masking is used to protect against side-channel attacks by splitting sensitive information into different parts, called hardware masking shares. Ideally, a side-channel attack would only work if all these parts were completely independent. But in real-world VLSI implementations, things are not perfect. Information from a hardware masking share can leak to another, making it possible for side-channel attacks to succeed without needing data from every hardware masking share. The theoretically supposed independence of these shares often does not hold up in practice. The effectiveness of hardware masking is reduced because of the parasitic impedance that stems from power delivery networks or the internal structure of the integrated circuit. When the coupling effect and noise spread among the hardware masking shares powered by the same power delivery network, side-channel attacks can be carried out with fewer measurements. To address this, we propose a new method of distributing on-chip voltage regulators to improve hardware masking security. The benefits of distributed on-chip voltage regulators are evident. Placing the regulators close to the load minimizes power loss due to resistive losses in the power delivery network. Localized regulation allows for more efficient adjustments to the varying power demands of different chip sections, improving overall power efficiency. Additionally, distributed regulators can quickly respond to power demand changes, maintaining stable voltage levels for high-performance circuits, leading to improved control over noise. We introduce a new DLDO voltage regulator that uses random clocking and randomizing limit cycle oscillations to enhance security. Our simulations show that with these distributed DLDO regulators, the t-test value can be as low as 2.019, and typically, a circuit with a t-test value below 4.5 is considered secure. Full article

(This article belongs to the Special Issue Hardware Security and Trust)

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35 pages, 8757 KiB

Open AccessReview

From Information to Knowledge: A Role for Knowledge Networks in Decision Making and Action Selection

by Jagmeet S. Kanwal

Information 2024, 15(8), 487; https://doi.org/10.3390/info15080487 - 15 Aug 2024

Viewed by 464

Abstract

The brain receives information via sensory inputs through the peripheral nervous system and stores a small subset as memories within the central nervous system. Short-term, working memory is present in the hippocampus whereas long-term memories are distributed within neural networks throughout the brain. [...] Read more.

The brain receives information via sensory inputs through the peripheral nervous system and stores a small subset as memories within the central nervous system. Short-term, working memory is present in the hippocampus whereas long-term memories are distributed within neural networks throughout the brain. Elegant studies on the mechanisms for memory storage and the neuroeconomic formulation of human decision making have been recognized with Nobel Prizes in Physiology or Medicine and in Economics, respectively. There is a wide gap, however, in our understanding of how memories of disparate bits of information translate into “knowledge”, and the neural mechanisms by which knowledge is used to make decisions. I propose that the conceptualization of a “knowledge network” for the creation, storage and recall of knowledge is critical to start bridging this gap. Knowledge creation involves value-driven contextualization of memories through cross-validation via certainty-seeking behaviors, including rumination or reflection. Knowledge recall, like memory, may occur via oscillatory activity that dynamically links multiple networks. These networks may show correlated activity and interactivity despite their presence within widely separated regions of the nervous system, including the brainstem, spinal cord and gut. The hippocampal–amygdala complex together with the entorhinal and prefrontal cortices are likely components of multiple knowledge networks since they participate in the contextual recall of memories and action selection. Sleep and reflection processes and attentional mechanisms mediated by the habenula are expected to play a key role in knowledge creation and consolidation. Unlike a straightforward test of memory, determining the loci and mechanisms for the storage and recall of knowledge requires the implementation of a naturalistic decision-making paradigm. By formalizing a neuroscientific concept of knowledge networks, we can experimentally test their functionality by recording large-scale neural activity during decision making in awake, naturally behaving animals. These types of studies are difficult but important also for advancing knowledge-driven as opposed to big data-driven models of artificial intelligence. A knowledge network-driven understanding of brain function may have practical implications in other spheres, such as education and the treatment of mental disorders. Full article

(This article belongs to the Special Issue The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg)

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16 pages, 1563 KiB

Open AccessArticle

Assessment in the Age of Education 4.0: Unveiling Primitive and Hidden Parameters for Evaluation

by Anil Verma, Parampreet Kaur and Aman Singh

Information 2024, 15(8), 486; https://doi.org/10.3390/info15080486 - 15 Aug 2024

Viewed by 466

Abstract

This study delves into the nuanced aspects that influence the quality of education within the Education 4.0 framework. Education 4.0 epitomizes a contemporary educational paradigm leveraging IoT devices, sensors, and actuators to facilitate real-time and continuous assessment, thereby enhancing student evaluation methodologies. Within [...] Read more.

This study delves into the nuanced aspects that influence the quality of education within the Education 4.0 framework. Education 4.0 epitomizes a contemporary educational paradigm leveraging IoT devices, sensors, and actuators to facilitate real-time and continuous assessment, thereby enhancing student evaluation methodologies. Within this context, the study scrutinizes the pivotal role of infrastructure, learning environment, and faculty, acknowledged as fundamental determinants of educational excellence. Identifying five discrete yet crucial hidden parameters, awareness, accessibility, participation, satisfaction, and academic loafing, this paper meticulously examines their ramifications within the Education 4.0 landscape. Employing a comparative analysis encompassing pre- and post-implementation scenarios, the research assesses the transformative impact of Education 4.0 on the educational sector while dissecting the influence of these hidden parameters across these temporal contexts. The findings underscore the substantial enhancements introduced by Education 4.0, including the provision of real-time and continuous assessment mechanisms, heightened accessibility to educational resources, and amplified student engagement levels. Notably, the study advocates for bolstering stakeholders’ accountability as a strategic measure to mitigate academic loafing within an ambient educational milieu. In essence, this paper offers invaluable insights into the intricate interplay between hidden parameters and educational quality, elucidating the pivotal role of Education 4.0 in catalyzing advancements within the education industry. Full article

(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)

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25 pages, 4636 KiB

Open AccessArticle

Application of Multi-Source Remote Sensing Data and Machine Learning for Surface Soil Moisture Mapping in Temperate Forests of Central Japan

by Kyaw Win, Tamotsu Sato and Satoshi Tsuyuki

Information 2024, 15(8), 485; https://doi.org/10.3390/info15080485 - 15 Aug 2024

Viewed by 1006

Abstract

Surface soil moisture (SSM) is a key parameter for land surface hydrological processes. In recent years, satellite remote sensing images have been widely used for SSM estimation, and many methods based on satellite-derived spectral indices have also been used to estimate the SSM [...] Read more.

Surface soil moisture (SSM) is a key parameter for land surface hydrological processes. In recent years, satellite remote sensing images have been widely used for SSM estimation, and many methods based on satellite-derived spectral indices have also been used to estimate the SSM content in various climatic conditions and geographic locations. However, achieving an accurate estimation of SSM content at a high spatial resolution remains a challenge. Therefore, improving the precision of SSM estimation through the synergies of multi-source remote sensing data has become imperative, particularly for informing forest management practices. In this study, the integration of multi-source remote sensing data with random forest and support vector machine models was conducted using Google Earth Engine in order to estimate the SSM content and develop SSM maps for temperate forests in central Japan. The synergy of Sentinel-2 and terrain factors, such as elevation, slope, aspect, slope steepness, and valley depth, with the random forest model provided the most suitable approach for SSM estimation, yielding the highest accuracy values (overall accuracy for testing = 91.80%, Kappa = 87.18%, r = 0.98) for the temperate forests of central Japan. This finding provides more valuable information for SSM mapping, which shows promise for precision forestry applications. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

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48 pages, 894 KiB

Open AccessReview

Earlier Decision on Detection of Ransomware Identification: A Comprehensive Systematic Literature Review

by Latifa Albshaier, Seetah Almarri and M. M. Hafizur Rahman

Information 2024, 15(8), 484; https://doi.org/10.3390/info15080484 - 14 Aug 2024

Viewed by 991

Abstract

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware [...] Read more.

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware that aims to steal user’s money. Ransomware is a form of malware that encrypts a victim’s files. The attacker then demands a ransom from the victim to restore access to the data upon a large payment. Ransomware is a way of stealing money in which a user’s files are encrypted and the decrypted key is held by the attacker until a ransom amount is paid by the victim. This systematic literature review (SLR) highlights recent papers published between 2020 and 2024. This paper examines existing research on early ransomware detection methods, focusing on the signs, frameworks, and techniques used to identify and detect ransomware before it causes harm. By analyzing a wide range of academic papers, industry reports, and case studies, this review categorizes and assesses the effectiveness of different detection methods, including those based on signatures, behavior patterns, and machine learning (ML). It also looks at new trends and innovative strategies in ransomware detection, offering a classification of detection techniques and pointing out the gaps in current research. The findings provide useful insights for cybersecurity professionals and researchers, helping guide future efforts to develop strong and proactive ransomware detection systems. This review emphasizes the need for ongoing improvements in detection technologies to keep up with the constantly changing ransomware threat landscape. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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15 pages, 5521 KiB

Open AccessArticle

A Historical Handwritten French Manuscripts Text Detection Method in Full Pages

by Rui Sang, Shili Zhao, Yan Meng, Mingxian Zhang, Xuefei Li, Huijie Xia and Ran Zhao

Information 2024, 15(8), 483; https://doi.org/10.3390/info15080483 - 14 Aug 2024

Viewed by 388

Abstract

Historical handwritten manuscripts pose challenges to automated recognition techniques due to their unique handwriting styles and cultural backgrounds. In order to solve the problems of complex text word misdetection, omission, and insufficient detection of wide-pitch curved text, this study proposes a high-precision text [...] Read more.

Historical handwritten manuscripts pose challenges to automated recognition techniques due to their unique handwriting styles and cultural backgrounds. In order to solve the problems of complex text word misdetection, omission, and insufficient detection of wide-pitch curved text, this study proposes a high-precision text detection method based on improved YOLOv8s. Firstly, the Swin Transformer is used to replace C2f at the end of the backbone network to solve the shortcomings of fine-grained information loss and insufficient learning features in text word detection. Secondly, the Dysample (Dynamic Upsampling Operator) method is used to retain more detailed features of the target and overcome the shortcomings of information loss in traditional upsampling to realize the text detection task for dense targets. Then, the LSK (Large Selective Kernel) module is added to the detection head to dynamically adjust the feature extraction receptive field, which solves the cases of extreme aspect ratio words, unfocused small text, and complex shape text in text detection. Finally, in order to overcome the CIOU (Complete Intersection Over Union) loss in target box regression with unclear aspect ratio, insensitive to size change, and insufficient correlation between target coordinates, Gaussian Wasserstein Distance (GWD) is introduced to modify the regression loss to measure the similarity between the two bounding boxes in order to obtain high-quality bounding boxes. Compared with the State-of-the-Art methods, the proposed method achieves optimal performance in text detection, with the precision and [email protected] reaching 86.3% and 82.4%, which are 8.1% and 6.7% higher than the original method, respectively. The advancement of each module is verified by ablation experiments. The experimental results show that the method proposed in this study can effectively realize complex text detection and provide a powerful technical means for historical manuscript reproduction. Full article

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14 pages, 1356 KiB

Open AccessArticle

Combined-Step-Size Affine Projection Andrew’s Sine Estimate for Robust Adaptive Filtering

by Yuhao Wan and Wenyuan Wang

Information 2024, 15(8), 482; https://doi.org/10.3390/info15080482 - 14 Aug 2024

Viewed by 355

Abstract

Recently, an affine-projection-like M-estimate (APLM) algorithm has gained popularity for its ability to effectively handle impulsive background disturbances. Nevertheless, the APLM algorithm’s performance is negatively affected by steady-state misalignment. To address this issue while maintaining equivalent computational complexity, a robust cost function based [...] Read more.

Recently, an affine-projection-like M-estimate (APLM) algorithm has gained popularity for its ability to effectively handle impulsive background disturbances. Nevertheless, the APLM algorithm’s performance is negatively affected by steady-state misalignment. To address this issue while maintaining equivalent computational complexity, a robust cost function based on the Andrew’s sine estimator (ASE) is introduced and a corresponding affine-projection Andrew’s sine estimator (APASE) algorithm is proposed in this paper. To further enhance the tracking capability and accelerate the convergence rate, we develop the combined-step-size APASE (CSS-APASE) algorithm using a combination of two different step sizes. A series of simulation studies are conducted in system identification and echo cancellation scenarios, which confirms that the proposed algorithms can attain reduced misalignment compared to other currently available algorithms in cases of impulsive noise. Meanwhile, we also establish a bound on the learning rate to ensure the stability of the proposed algorithms. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning, 2nd Edition)

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10 pages, 594 KiB

Open AccessArticle

Performance Management Decision-Making Model: Case Study on Foreign Language Learning Curriculums

by Kuen-Suan Chen, Chun-Min Yu, Chun-Hung Yu and Yen-Po Chen

Information 2024, 15(8), 481; https://doi.org/10.3390/info15080481 - 14 Aug 2024

Viewed by 489

Abstract

Foreign language learning courses can be regarded as a service operation system, and a complete foreign language learning course performance evaluation model can help improve the effectiveness of student learning. The performance evaluation matrix (PEM) is an excellent tool for evaluation and resource [...] Read more.

Foreign language learning courses can be regarded as a service operation system, and a complete foreign language learning course performance evaluation model can help improve the effectiveness of student learning. The performance evaluation matrix (PEM) is an excellent tool for evaluation and resource management decision making, and the administrator uses the satisfaction and the importance indices to establish evaluation coordinate points based on the rules of statistical testing. The coordinate points of all service items are plotted into the PEM to grasp the full picture and to make decisions on what to improve or to consider resource transfers so as to elevate the overall satisfaction of the entire service. However, plotting all the coordinate points on the PEM can only be performed by programming, which will lead to limitations in practice. Therefore, instead of the above evaluation rules, this article uses the confidence intervals of decision-making indicators to form a validity evaluation table, to decide which teaching service items should be improved, maintained, or transferred to improve the satisfaction of the entire service system. This form of performance evaluation can be completed using any commonly used word-processing software, so it is easy to apply and promote. Finally, this article provides an applied example to illustrate the proposed method. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis II)

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19 pages, 1196 KiB

Open AccessArticle

AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge

by Dimitrios Giagkos, Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis, Francky Catthoor and Dimitrios Soudris

Information 2024, 15(8), 480; https://doi.org/10.3390/info15080480 - 13 Aug 2024

Viewed by 632

Abstract

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams in IoT environments. To avoid data communication overheads, a common practice is to have computation close to [...] Read more.

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams in IoT environments. To avoid data communication overheads, a common practice is to have computation close to the data source rather than Cloud offloading. Typically, video analytics are organized as separate tasks, each with different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a promising approach for mapping such types of applications, enabling fine-grained deployment and management in a per-function, and per-device manner. However, there is a tradeoff between QoS adherence and resource efficiency. Performance variability due to function co-location and prevalent resource heterogeneity make maintaining QoS challenging. At the same time, resource efficiency is essential to avoid waste, such as unnecessary power consumption and CPU reservation. In this paper, we present Darly, a QoS-, interference- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments on top of distributed Edge nodes. The proposed framework incorporates a DRL agent that exploits performance counters to identify the levels of interference and the degree of heterogeneity in the underlying Edge infrastructure. It combines this information along with user-defined QoS requirements to improve resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. We evaluate Darly on a typical Edge cluster with a real-world workflow composed of commonly used serverless video analytics functions and show that our approach achieves efficient scheduling of the deployed functions by satisfying multiple QoS requirements for up to 91.6% (Profile-based) of the total requests under dynamic conditions. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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Information, Volume 15, Issue 8 (August 2024) – 87 articles

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