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21n\\n"],[9],[0,"\\n \\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/ICJEC 2024-a7ae5011458f516551a8679d71e4b312.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://ieeexplore.ieee.org/abstract/document/10705901"],[10,"rel","noopener"],[8],[0,"\\nA Secure Lightweight Wireless M-Bus Protocol for IoT: Leveraging the Noise Protocol Framework "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n ICJECE 2024\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstractâThe escalating demand for secure communication in the Internet of Things (IoT), particularly in energy-sensitive devices \\nsuch as smart meters, highlights a critical challenge: achieving robust security without excessive energy consumption. \\nWhile various solutions have been proposed to minimize energy use, many fail to address the unique constraints of the IoT \\ndevices effectively. This article introduces an innovative approach by proposing a secure, lightweight wireless meter-bus \\n(wM-Bus) protocol, specifically designed for the stringent resource constraints of the IoT environments. \\n\\n\\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"By incorporating the noise protocol framework (NPF), our protocol significantly reduces computational and power requirements without compromising security integrity. Through a methodical implementation that spanned five distinct phases, including a comparative analysis with the conventional transport layer security (TLS), our findings are compelling. The NPF, particularly with its NX and XX patterns, dramatically surpasses TLS in performance, extending operational lifetimes to approximately 9 and 7.88 years, respectively, in contrast to the 3.81 years offered by TLS. These results not only demonstrate the superior efficiency of the NPF in the IoT settings but also highlight its potential in striking an optimal balance between security and operational longevity."],[9],[0,"\\n"],[6,"p"],[8],[0,"\\n Index TermsâAdvanced metering infrastructure (AMI), cryptography, Internet of Things (IoT), lightweight, noise protocol framework (NPF), protocol, security, smart metering, wireless meter-bus (wM-Bus).\\n"],[9],[0," \\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n"],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/Computers2024-ed81723a8c9a1d75927e12cea44d5b4b.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/2073-431X/13/7/159"],[10,"rel","noopener"],[8],[0,"\\n Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Computers, Volume 13, Issue 7 (July 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. \\nSmartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing \\nan additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric \\nbiometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder \\nhas been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. \\n\\n\\
21n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphoneâs accelerometer sensor, along with advanced statistical methods, to detect the userâs gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived featureâs probability distribution function (PDF) is computed and compared to the known userâs PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold. \\n"],[9],[0,"\\n"],[6,"p"],[8],[0,"Keywords: access control systems; identity verification; gait cycle analysis; smartphone sensors."],[9],[0," \\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n"],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/big_cover-computers-v13-i4-088c2cb21893172c641690d57261c465.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/2073-431X/13/4/103"],[10,"rel","noopener"],[8],[0,"\\n Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Computers, Volume 13, Issue 4 (April 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: This research article delves into the development of a reinforcement learning (RL)-based continuous authentication system \\nutilizing behavioral biometrics for user identification on computing devices. Keystroke dynamics are employed to capture unique behavioral \\nbiometric signatures, while a reward-driven RL model is deployed to authenticate users throughout their sessions. The proposed system augments \\nconventional authentication mechanisms, fortifying them with an additional layer of security to create a robust continuous authentication \\nframework compatible with static authentication systems. \\n\\n\\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"The methodology entails training an RL model to discern atypical user typing patterns and identify potentially suspicious activities. Each userâs historical data are utilized to train an agent, which undergoes preprocessing to generate episodes for learning purposes. The environment involves the retrieval of observations, which are intentionally perturbed to facilitate learning of nonlinear behaviors. The observation vector encompasses both ongoing and summarized features. A binary and minimalist reward function is employed, with principal component analysis (PCA) utilized for encoding ongoing features, and the double deep Q-network (DDQN) algorithm implemented through a fully connected neural network serving as the policy net. Evaluation results showcase training accuracy and equal error rate (EER) ranging from 94.7% to 100% and 0 to 0.0126, respectively, while test accuracy and EER fall within the range of approximately 81.06% to 93.5% and 0.0323 to 0.11, respectively, for all users as encoder features increase in number. These outcomes are achieved through RLâs iterative refinement of rewards via trial and error, leading to enhanced accuracy over time as more data are processed and incorporated into the system. \\n\\n"],[9],[0,"\\n"],[6,"p"],[8],[0,"Keywords: behavioral biometrics; continuous authentication; keystroke dynamics;
21 Markov decision process (MDP); Q-learning; reinforcement learning (RL); static authentication; user authentication; identification.\\n\\n"],[9],[0," \\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n\\n "],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/Electronics2022-992a93595215ae0577af38bb0b7633a2.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/2079-9292/11/23/4016"],[10,"rel","noopener"],[8],[0,"\\n Smart Chatbot for User Authentication\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Electronics, Volume 11, Issue 23 (December-1 2022)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed \\nor brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple \\ndifferent authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new \\nauthentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up \\nto date information on the user from which authentication questions can be dynamically generated. \\n\\n\\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a userâs recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the userâs recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the userâs recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA. \\n"],[9],[0," \\n"],[6,"p"],[8],[0,"Keywords: bmachine learning; authentication; natural language understanding; big data; chatbots.\\n"],[9],[0," \\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/big_cover-information-v15-i3-1ff151b0b560c29d78c146ba73179ae7.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/2078-2489/15/3/146"],[10,"rel","noopener"],[8],[0,"\\n Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Information, Volume 15, Issue 3 (March 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, \\nit is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information \\nor are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are \\ntoo sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to \\nfacilitate further analysis. \\n\\n\\
21n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task. \\n\\n"],[9],[0," \\n"],[6,"p"],[8],[0,"\\n Keywords: data generation; anomaly data; user behavior generation; data analytics.\\n"],[9],[0," \\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n\\n"],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/big_cover-futureinternet-v16-i5-67d0629934d03b67d45f78262f02dbc0.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/1999-5903/16/5/148"],[10,"rel","noopener"],[8],[0,"\\nHSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Future Internet, Volume 16, Issue 5 (May 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are \\nof critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security \\napproaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, \\nlacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about \\nthe safety and integrity of intra-domain environments, where all communication occurs within a single domain. \\n\\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"As a result, this paper introduces HSM4SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted comparing HSM4SSL with a benchmark tool for cryptographic operations. The results indicate that HSM4SSL achieved 33% higher requests per second (RPS) compared to OpenSSL, along with a 13% lower latency rate. Additionally, HSM4SSL efficie
21ntly utilizes CPU and network resources, outperforming OpenSSL in various aspects. These findings highlight the effectiveness and reliability of HSM4SSL in providing secure communication within intra-domain environments, thus addressing the pressing need for enhanced security mechanisms.\\n\\n\\n"],[9],[0," \\n "],[6,"p"],[8],[0,"\\n Keywords: intra-domain; secure socket layer; transport layer security; public-key certificates; hardware security modules.\\n "],[9],[0,"\\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n\\n "],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/big_cover-futureinternet-v16-i10-cd4282536e2b1f9ec84ba76c2436b100.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.westerncalendar.uwo.ca/Courses.cfm?CourseAcadCalendarID=MAIN_017647_1&SelectedCalendar=Live&ArchiveID="],[10,"rel","noopener"],[8],[0,"\\nA Performance Benchmark for the PostgreSQL and MySQL Databases\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Future Internet, Volume 16, Issue 10 (October 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale \\nbehavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, \\nminimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert \\nqueries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQLâs superior performance \\nin select operations. \\n\\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"In primary tests, PostgreSQLâs execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQLâs ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQLâs 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQLâs performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQLâs suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems. \\n"],[9],[0," \\n"],[6,"p"],[8],[0,"\\n Keywords: benchmark; postgreSQL; mySQL; database; biometrics.\\n"],[9],[0,"\\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n\\n "],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/big_cover-applsci-v13-i12-a9d873300916adc0e1e1a22d2d13fabe.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.westerncalendar.uwo.ca/Courses.cfm?CourseAcadCalendarID=MAIN_015926_2&SelectedCalendar=Live&ArchiveID="],[10,"rel","noopener"],[8],[0,"\\nAn Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Appl. Sci., Volume 13, Issue 12 (June-2 2023)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses \\nunique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities \\ninherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific \\ndomain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and \\nlinguistic analysis. \\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies. \\n\\n"],[9],[0," \\n"],[6,"p"],[8],[0,"\\n Keywords: machine translation; technical patents; natural language processing; translation quality; cross-lingual information retrieval; corpus-based translation; domain adaptation; language model fine-tuning; neural machine translation.\\n"],[9],[0,"\\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\
21n\\n "],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","item"],[8],[0,"\\n "],[6,"div"],[10,"class","image"],[8],[0,"\\n "],[6,"img"],[11,"src",[27,[[20,"rootURL"],"assets/images/Computers2024-ed81723a8c9a1d75927e12cea44d5b4b.png"]]],[8],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"a"],[10,"class","header"],[10,"target","_blank"],[10,"href","https://www.mdpi.com/2073-431X/13/7/183"],[10,"rel","noopener"],[8],[0,"\\nData Lakes: A Survey of Concepts and Architectures\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","meta"],[8],[0,"\\n "],[6,"span"],[8],[0,"\\n Computers, Volume 13, Issue 7 (July 2024)\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","description"],[8],[0,"\\n "],[6,"p"],[8],[0,"\\nAbstract: This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake \\narchitectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures \\nthat have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, \\nidentifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this \\nrapidly evolving field. \\n"],[6,"div"],[10,"class","ui accordion"],[8],[0,"\\n "],[6,"div"],[10,"class","title"],[8],[0,"\\n "],[6,"i"],[10,"class","dropdown icon"],[8],[9],[0,"\\n Show more ...\\n "],[9],[0,"\\n "],[6,"div"],[10,"class","content"],[8],[0,"\\n "],[6,"p"],[10,"class","transition hidden"],[8],[0,"We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further enhance the comparative analysis of the different architectural components. We also explore the differences between data warehouses and data lakes. Our findings provide valuable insights for researchers and practitioners seeking to understand the current state of data-lake technology and its potential future trajectory. \\n\\n\\n"],[9],[0," \\n "],[6,"p"],[8],[0,"\\n Keywords: big data; hadoop; data lake; data warehouse; data lakehouse; data management; schema; internet of things.\\n "],[9],[0,"\\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n\\n\\n "],[9],[0,"\\n\\n "],[9],[0,"\\n "],[9],[0,"\\n "],[9],[0,"\\n"],[9],[0,"\\n"],[6,"div"],[10,"class","ui center aligned basic segment"],[8],[0,"\\n "],[6,"div"],[10,"class","ui horizontal divider"],[8],[0,"\\n Selected Conference Papers\\n "],[9],[0,"\\n"],[9],[0,"\\n\\n\\n"],[6,"ol"],[8],[0,"\\n"],[6,"li"],[8],[0,"Iman IM Abu Sulayman and Abdelkader Ouda. Data analytics methods for anomaly detection: Evolution and\\n recommendations.\\n In 2018 International Conference on Signal Processing and Information Security (ICSPIS), pages 1â4. IEEE, 2018."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Wafaa Anani, Abdelkader Ouda, and Ali Hamou. A survey of wireless communications for IoT echo-systems. In 2019 IEEE\\n Canadian\\n Conference of Electrical and Computer Engineering (CCECE), pages 1â6. IEEE, 2019."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Osama Ali-Ozkan and Abdelkader Ouda. Key-based reversible data masking for business intelligence healthcare\\n analytics platforms.\\n In 2019 International Symposium on Networks, Computers and Communications (ISNCC), pages 1â6. IEEE, 2019."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Iman IM Abu Sulayman and Abdelkader Ouda. User modeling via anomaly detection techniques for user authentication. 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Comparison of machine learning techniques\\n for activities of daily living classification with electromyographic data. In 2022 International Conference on\\n Rehabilitation Robotics (ICORR), pages 1â6. IEEE, 2022."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Yazan Aref and Abdelkader Ouda. Autonomous vehicle cyber-attacks classification framework. In 2023 15th I\\n nternational Conference on COMmunication Systems & NETworkS (COMSNETS), pages 373â377. IEEE, 2023."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Sanket Vilas Salunke and Abdelkader Ouda. Ensemble learning to enhance continuous user authentication\\n for real world environments. In 2023 IEEE International Black Sea Conference on Communications and Networking\\n (BlackSeaCom), pages 102â108. IEEE, 2023."],[9],[0,"\\n "],[6,"br"],[8],[9],[0,"\\n"],[6,"li"],[8],[0,"Iman I. M. Abu Sulayman, Peter Voege, and Abdelkader Ouda. 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