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Articles 6421 - 6450 of 63282
Full-Text Articles in Entire DC Network
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Application Of The Neutrosophic Poisson Distribution Series On The Harmonic Subclass Of Analytic Functions Using The Salagean Derivative Operator, Adeniyi M. Gbolagade, Ibrahim T. Awolere, Olusola Adeyemo, Abiodun T. Oladipo
Neutrosophic Systems with Applications
The authors explore the innovative application of the Neutrosophic series, particularly the Neutrosophic Poisson Distribution Series (NPDS), to investigate various indeterminacy or uncertainties inherent in the classical univalent harmonic function class. The Neutrosophic Poisson Distribution Series is equipped with a Salangean derivative operator and convoluted with analytic univalent harmonic function class to derive new properties, such as inclusion relation, and coefficient inequalities for star-likeness. The results obtained demonstrate the effectiveness of this approach in capturing the inherent uncertainties and complexities associated with harmonic functions. There are several other areas of importance of our results that can be unlocked by computer …
Optimizing Hidden Neurons In Multilayer Perceptrons With A Hybrid Feature Selection Approach For Classification Tasks, Susmitha Boyidapu
Optimizing Hidden Neurons In Multilayer Perceptrons With A Hybrid Feature Selection Approach For Classification Tasks, Susmitha Boyidapu
USF Tampa Graduate Theses and Dissertations
Identifying the ideal number of hidden neurons in multilayer perceptron (MLP) networks remains an ongoing challenge in the field of machine learning. Our research presents an innovative strategy to address this issue by combining data preprocessing, feature selection by a hybrid method, and a methodical approach to MLP architecture design. The extracted features through feature selection help to construct MLP architecture and implement k-fold cross-validation along with early stopping mechanism for finding optimal number of hidden neurons. Our method's key innovation is its capacity to dynamically adjust the hidden neuron count while optimizing model performance. Through iterative refinement of the …
Human Capital Development : Bridging The Skills Gap In The Maritime Administration Of Namibia, Agnes Matheus
Human Capital Development : Bridging The Skills Gap In The Maritime Administration Of Namibia, Agnes Matheus
World Maritime University Dissertations
No abstract provided.
An Evaluation Of The Legal Framework For Seizure And Detention Of Ships For Maritime Law Enforcement In Nigeria, Adetayo Yusuf Adesokan
An Evaluation Of The Legal Framework For Seizure And Detention Of Ships For Maritime Law Enforcement In Nigeria, Adetayo Yusuf Adesokan
World Maritime University Dissertations
No abstract provided.
Side-Channel Analysis Platform For A Hardware Implementation Of Fips 203 (Crystals-Kyber), Mohamed Mossad
Side-Channel Analysis Platform For A Hardware Implementation Of Fips 203 (Crystals-Kyber), Mohamed Mossad
USF Tampa Graduate Theses and Dissertations
In 2024, NIST selected the Post-Quantum Cryptography algorithm CRYSTALS-Kyber for standardization as a public-key encryption, key establishment scheme. CRYSTALS-Kyber was standardized under the Federal Information Processing Standard (FIPS), specifically FIPS 203. FIPS standards represent a set of guidelines, developed by NIST, for secure data handling in federal information systems, mandating cryptographic algorithms that protect sensitive information. This highlights the importance of identifying potential vulnerabilities in the algorithm and assessing how CRYSTALS-Kyber implementations react to hardware side channel attacks. Previous research identified several vulnerabilities in implementations of CRYSTALS-Kyber in software, which were addressed in subsequent releases. This thesis focuses on expanding …
A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis
A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis
Department of Medicine Faculty Papers
This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade …
Can Large-Language Models Help Us Better Understand And Teach The Development Of Energy-Efficient Software?, Ryan Hasler, Konstantin Laufer, George K. Thiruvathukal, Huiyun Peng, Kyle Robinson, Kirsten Davis, Yung-Hisang Lu, James C. Davis
Can Large-Language Models Help Us Better Understand And Teach The Development Of Energy-Efficient Software?, Ryan Hasler, Konstantin Laufer, George K. Thiruvathukal, Huiyun Peng, Kyle Robinson, Kirsten Davis, Yung-Hisang Lu, James C. Davis
Computer Science: Faculty Publications and Other Works
Computing systems are consuming an increasing and unsustainable fraction of society's energy footprint, notably in data centers. Meanwhile, energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class. There is one major problem with such an endeavor: undergraduate curricula have limited space for mastering energy-related systems programming aspects. To address this problem, we propose to leverage the domain expertise afforded by large language models (LLMs). In our preliminary studies, we observe that LLMs can generate energy-efficient variations of basic linear algebra …
Unveiling Scientic Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha
Unveiling Scientic Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha
Computer Science: Faculty Publications and Other Works
The increasing prevalence of fake publications created by paper mills poses a significant challenge to maintaining scientific integrity. While integrity analysts typically rely on textual and visual clues to identify fake articles, determining which papers merit further investigation can be akin to searching for a needle in a haystack, as these fake publications have non-related authors and are published on non-related venues. To address this challenge, we developed a new methodology for provenance analysis, which automatically tracks and groups suspicious figures and documents. Our approach groups manuscripts from the same paper mill by analyzing their figures and identifying duplicated and …
Unveiling Scientific Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha
Unveiling Scientific Articles From Paper Mills With Provenance Analysis, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha
Computer Science: Faculty Publications and Other Works
The increasing prevalence of fake publications created by paper mills poses a significant challenge to maintaining scientific integrity. While integrity analysts typically rely on textual and visual clues to identify fake articles, determining which papers merit further investigation can be akin to searching for a needle in a haystack, as these fake publications have non-related authors and are published on non-related venues. To address this challenge, we developed a new methodology for provenance analysis, which automatically tracks and groups suspicious figures and documents. Our approach groups manuscripts from the same paper mill by analyzing their figures and identifying duplicated and …
Bridging The Protection Gap: Innovative Approaches To Shield Older Adults From Ai-Enhanced Scams, Ld Herrera, London Van Sickle, Ashley L. Podhradsky
Bridging The Protection Gap: Innovative Approaches To Shield Older Adults From Ai-Enhanced Scams, Ld Herrera, London Van Sickle, Ashley L. Podhradsky
Research & Publications
Artificial Intelligence (AI) is rapidly gaining popularity as individuals, groups, and organizations discover and apply its expanding capabilities. Generative AI creates or alters various content types including text, image, audio, and video that are realistic and challenging to identify as AI-generated constructs. However, guardrails preventing malicious use of AI are easily bypassed. Numerous indications suggest that scammers are already using AI to enhance already successful scams, improving scam effectiveness, speed and credibility, while reducing detectability of scams that target older adults, who are known to be slow to adopt new technologies. Through hypothetical cases analysis of two leading scams, the …
Transitioning Our Website To Libguides Cms, Samantha Duncan, Eric Resnis
Transitioning Our Website To Libguides Cms, Samantha Duncan, Eric Resnis
Library Faculty Presentations
In this presentation, we describe how we used data from rapid and in-depth student usability testing to assist with the redesign of the library’s website as we finally transitioned to LibGuides CMS. Using the Springy tools; LibGuides, LibGuides CMS, LibCal, and LibWizard we outlined how we were able to create and carry out this highly effective testing, resulting in a better understanding of how our students navigate our site and how to improve it. During this journey, attendees were provided with the detailed and some might say lengthy process that was undertaken to achieve our goals. We did this by …
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
USF Tampa Graduate Theses and Dissertations
Suicide prevention through early detection using social media data has been widely studied. However, the critical role of peer support interactions among individuals with similar mental disorders has not been deeply investigated or explored. In this study, we explore peer interactions in online communities for individuals with bipolar disorder and leverage this information to predict suicide risk levels. We propose a model that uses contextualized posts and comments along with their sentiment features. By embedding these features into a peer support network, our model captures peer interactions and predicts suicide risk levels using the bidirectional LSTM Graph Neural Networks (Bi-LSTM …
Vulnerability Discovery On Broadcom Devices With Qemu, Christian Shane Douglas Little
Vulnerability Discovery On Broadcom Devices With Qemu, Christian Shane Douglas Little
USF Tampa Graduate Theses and Dissertations
System-on-chip (SoC) devices in the form of routers, access points, and smart appliances have become an increasingly prevalent aspect of our infrastructure. Performing vulnerability research on these devices may be exceptionally difficult given the on-hardware limitations, inability to guarantee certain aspects of the system, and lack of information when the appliance’s source code is not available.In this thesis, we present Broadfeather, a QEMU-based emulation target, and framework for dynamic analysis and instrumentation. While Broadfeather supports data types shared across many chips under the Broadcom umbrella, it is primarily geared towards the BCM6755 Wi-Fi 6E capable chipset. Broadfeather also features the …
Photoluminescence Intensity Enhancement And Stability In Cdte/Sio2 Quantum Dots Through Water Molecule Adsorption And Trap Passivation, Daniil S. Daibagya, Ivan A. Zakharchuk, Sergei A. Ambrozevich, Mikhail S. Smirnov, Anna V. Osadchenko, Oleg V. Ovchinnikov, Alexandr S. Selyukov
Photoluminescence Intensity Enhancement And Stability In Cdte/Sio2 Quantum Dots Through Water Molecule Adsorption And Trap Passivation, Daniil S. Daibagya, Ivan A. Zakharchuk, Sergei A. Ambrozevich, Mikhail S. Smirnov, Anna V. Osadchenko, Oleg V. Ovchinnikov, Alexandr S. Selyukov
Karbala International Journal of Modern Science
The study of the luminescence photostability for colloidal nanocrystals is an important task since the understanding of the corresponding physical processes advances new electronic devices based on semiconductor nanoparticles as well as other important applications such as biomarkers. In this paper, we provide the first study and comprehensive analysis of the photostability of the luminescent properties for colloidal CdTe/SiO2 core/shell quantum dots prepared by an aqueous-based method. The quantum dots were exposed to continuous laser radiation during two time intervals with prolonged break in between. The photoluminescence intensity of the quantum dots increased over time under continuous laser irradiation. …
Removal Of Selenium Ions From Contaminated Aqueous Solutions By Adsorption Using Lemon Peels As A Non-Conventional Medium, Alanood A. Alsarayreh, Suha Anwer Ibrahim, Salem Jawad Alhamd, Thekra Atta Ibrahim, Mohammed Nsaif Abbas
Removal Of Selenium Ions From Contaminated Aqueous Solutions By Adsorption Using Lemon Peels As A Non-Conventional Medium, Alanood A. Alsarayreh, Suha Anwer Ibrahim, Salem Jawad Alhamd, Thekra Atta Ibrahim, Mohammed Nsaif Abbas
Karbala International Journal of Modern Science
The disposal of heavy metals from various activities has become a pervasive issue. The investigation of sustain-able, low-cost adsorbents for the remediation of these hazardous pollutants has been widely disregarded. This research emphasizes the recovery of selenium ions from contaminated water using lemon peels as a cost-effective adsorbent. The adsorption was explored in a batch unit under various operational parameters, including initial selenium concentration (1-90 ppm), pH (1-11), agitation speed (100-500 rpm), adsorbent dose (0.4-5.5 g), contact time (5-180 minutes), and temperature (25-55 °C). The BET surface area of the lemon peels was found to be 27.86 m²/g before adsorption …
Assessing The Impact Of Ai Assisted Software Development And User Experience Of A College Football Simulation Game: A Study Of Player And Industry Professional Perspectives, Augustus J. Scarlato Iii
Assessing The Impact Of Ai Assisted Software Development And User Experience Of A College Football Simulation Game: A Study Of Player And Industry Professional Perspectives, Augustus J. Scarlato Iii
USF Tampa Graduate Theses and Dissertations
This research examines the use of Artificial Intelligence (AI) in the design of video games, specifically the development of a college football simulation game. This study documents the creation of an alpha version college simulation game assisted by Open AI’s Chat GPT 4.0 API, to potentially improve game development, gameplay realism, and user interaction. The study then assesses how both student players and industry professionals perceive AI-enhanced gaming, emphasizing the usability, gameplay experience, and overall quality of the game using a Likert scale survey. The analysis also highlights differences in perceptions between students and industry professionals, with the latter group …
Radiomics-Based Machine Learning With Natural Gradient Boosting For Continuous Survival Prediction In Glioblastoma, Mert Karabacak, Shiv Patil, Zachary C. Gersey, Ricardo J. Komotar, Konstantinos Margetis
Radiomics-Based Machine Learning With Natural Gradient Boosting For Continuous Survival Prediction In Glioblastoma, Mert Karabacak, Shiv Patil, Zachary C. Gersey, Ricardo J. Komotar, Konstantinos Margetis
SKMC Student Presentations and Publications
(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences …
Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill
Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill
University of Cincinnati Law Review
Federal agencies have caught the artificial intelligence (AI) bug. A December 2023 report by the Government Accountability Office found that twenty of twenty-three federal agencies surveyed reported using some form of AI, with about two hundred current use cases for algorithms and about one thousand more in the planning phase. These agencies are using algorithms in all aspects of administration, including rulemaking, adjudication, and enforcement. The risks of AI are well-documented. Previous work has shown that algorithms can be, among other things, biased and prone to error. However, perhaps no problem poses a more serious threat to the use of …
Leveraging Large Language Models For Enhancing Literature-Based Discovery, Ikbal Taleb, Alramzana Nujum Navaz, Mohamed Adel Serhani
Leveraging Large Language Models For Enhancing Literature-Based Discovery, Ikbal Taleb, Alramzana Nujum Navaz, Mohamed Adel Serhani
All Works
The exponential growth of biomedical literature necessitates advanced methods for Literature-Based Discovery (LBD) to uncover hidden, meaningful relationships and generate novel hypotheses. This research integrates Large Language Models (LLMs), particularly transformer-based models, to enhance LBD processes. Leveraging LLMs’ capabilities in natural language understanding, information extraction, and hypothesis generation, we propose a framework that improves the scalability and precision of traditional LBD methods. Our approach integrates LLMs with semantic enhancement tools, continuous learning, domain-specific fine-tuning, and robust data cleansing processes, enabling automated analysis of vast text and identification of subtle patterns. Empirical validations, including scenarios on the effects of garlic on …
A Decentralized Digital Watermarking Framework For Secure And Auditable Video Data In Smart Vehicular Networks, Xinyun Liu, Ronghua Xu, Yu Chen
A Decentralized Digital Watermarking Framework For Secure And Auditable Video Data In Smart Vehicular Networks, Xinyun Liu, Ronghua Xu, Yu Chen
Michigan Tech Publications
Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular …
Early Detection And Categorization Of Cervical Cancer Cells Using Smoothing Cross Entropy-Based Multi-Deep Transfer Learning, Rania Ahmed, Nadia Dahmani, Ghada Dahy, Ashraf Darwish, Aboul Ella Hassanien
Early Detection And Categorization Of Cervical Cancer Cells Using Smoothing Cross Entropy-Based Multi-Deep Transfer Learning, Rania Ahmed, Nadia Dahmani, Ghada Dahy, Ashraf Darwish, Aboul Ella Hassanien
All Works
Cervical cancer is one of the leading causes of death in women worldwide. Prompt and accurate diagnosis is imperative for the treatment of cervical cancer through the utilization of pap smear slides, albeit it is a multifaceted and time-intensive process. An automatic diagnosis model based on deep learning models, particularly a convolutional neural network (CNN), can enhance cervical cancer's accuracy and rapid identification. This paper proposes a cross entropy-based multi-deep transfer learning model for the early detection and categorization of cervical cancer cells. The proposed model consists of four phases: the pre-processing phase, the feature extraction and fusion phase, the …
What Do We Know About Hugging Face? A Systematic Literature Review And Quantitative Validation Of Qualitative Claims, Jason Jones, Wenxin Jiang, Nicholas Synovic, George K. Thiruvathukal, James C. Davis
What Do We Know About Hugging Face? A Systematic Literature Review And Quantitative Validation Of Qualitative Claims, Jason Jones, Wenxin Jiang, Nicholas Synovic, George K. Thiruvathukal, James C. Davis
Computer Science: Faculty Publications and Other Works
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Prior research has examined SPRs for traditional software, such as NPM (JavaScript) and PyPI (Python). Pre-Trained Model (PTM) Registries are an emerging class of SPR of increasing importance, because they support the deep learning supply chain.
Aims: Recent empirical research has examined PTM registries in ways such as vulnerabilities, reuse processes, and evolution. However, no existing research synthesizes them to provide a systematic understanding of the current knowledge. Some of the existing research includes qualitative …
Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan
Interpreting The Biological Effects Of Protons As A Function Of Physical Quantity: Linear Energy Transfer Or Microdosimetric Lineal Energy Spectrum?, Fada Guan, Lawrence Bronk, Matthew Kerr, Yuting Li, Leslie A Braby, Mary Sobieski, Xiaochun Wang, Xiaodong Zhang, Clifford Stephan, David R Grosshans, Radhe Mohan
Faculty, Staff and Student Publications
The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The …
Gas Chromatography-Mass Spectrometry (Gc-Ms), Computational Analysis, And In Vitro Effect Of Essential Oils From Two Aromatic Plants, Bubonium Graveolens And Launaea Arborescens Growing In Southwest Algeria Against Potato Cyst Nematodes, Souad Ziane, Chaouki Selles, Khaldun M. Al Azzam, Bounoua Nadia, Belal O. Al-Najjar, Ali Al-Samydai, Obada A. Sibai, El-Sayed Negim
Gas Chromatography-Mass Spectrometry (Gc-Ms), Computational Analysis, And In Vitro Effect Of Essential Oils From Two Aromatic Plants, Bubonium Graveolens And Launaea Arborescens Growing In Southwest Algeria Against Potato Cyst Nematodes, Souad Ziane, Chaouki Selles, Khaldun M. Al Azzam, Bounoua Nadia, Belal O. Al-Najjar, Ali Al-Samydai, Obada A. Sibai, El-Sayed Negim
Karbala International Journal of Modern Science
The study tested the nematicidal effects of essential oils from Bubonium graveolens and Launaea arborescens on the potato cyst nematode Globodera rostochiens. The chemical composition of the essential oils was analyzed using GC-MS. To determine the concentration that killed 50% of the nematode population (LC50), five concentrations of the essential oils were applied to the tested organisms. The effects of essential oils on the hatching of cyst nematode (Globodera rostochiensis sp.) eggs in vitro demonstrated a wide variety of effects ranging from no impact to mild, moderate, and strong effects, which increased dramatically with exposure duration and concentration. All the …
Broken Su(3) Description Of Energy Levels And Decay Properties In Gadolinium Isotopes (A=156-160), Fahmi Sh. Radhi, Amir Abdul Ameer Mohammed Ali Dr., Ali H. Al-Musawi
Broken Su(3) Description Of Energy Levels And Decay Properties In Gadolinium Isotopes (A=156-160), Fahmi Sh. Radhi, Amir Abdul Ameer Mohammed Ali Dr., Ali H. Al-Musawi
Karbala International Journal of Modern Science
This study presents an in-depth examination of the energy levels and decay properties of Gadolinium (Gd) isotopes with mass numbers (A=156-160), utilizing the Interacting Boson Model-1 (IBM-1) within a broken SU(3) symmetry framework. Through this approach, we systematically calculated and analyzed the energy spectra, B(E2) transition probabilities, quadrupole moments, and potential energy surface (PES) which provided valuable insights into the shape and collective behavior of nuclei, as well as the decay properties of the selected Gd isotopes. The broken SU(3) symmetries provide a good description to the isotopes under study. This comprehensive analysis enhances the understanding of the nuclear structure …
Skin Microbiome: Current Target For Cosmeceuticals, Priyanka Kakkar, Neeraj Wadhwa
Skin Microbiome: Current Target For Cosmeceuticals, Priyanka Kakkar, Neeraj Wadhwa
Karbala International Journal of Modern Science
Skin acts as a barrier to the external environment and perform various functions like maintaining internal homeostasis, sensations to touch based stimuli, vitamin D production and defence against foreign pathogens, prevent dehydration. Skin has its own diverse microbiota like bacteria, virus, fungi that is collectively called as skin microbiome. Skin microbiome balance is disturbed (condition called dysbiosis) by both internal and external factors which lead to skin problems like acne, psoriasis, dandruff. It is important to maintain the healthy skin ecosystem. Cosmeceuticals i.e., combination of cosmetic and pharmaceuticals, is a recent trend in the skin care industry where we add …
Next-Generation Block Ciphers: Achieving Superior Memory Efficiency And Cryptographic Robustness For Iot Devices, Saadia Aziz, Ijaz Ali Shoukat, Mohsin Iftikhar, Mohsin Murtaza, Abdulmajeed M. Alenezi, Cheng-Chi Lee, Imran Taj
Next-Generation Block Ciphers: Achieving Superior Memory Efficiency And Cryptographic Robustness For Iot Devices, Saadia Aziz, Ijaz Ali Shoukat, Mohsin Iftikhar, Mohsin Murtaza, Abdulmajeed M. Alenezi, Cheng-Chi Lee, Imran Taj
All Works
Traditional cryptographic methods often need complex designs that require substantial memory and battery power, rendering them unsuitable for small handheld devices. As the prevalence of these devices continues to rise, there is a pressing need to develop smart, memory-efficient cryptographic protocols that provide both high speed and robust security. Current solutions, primarily dependent on dynamic permutations, fall short in terms of encryption and decryption speeds, the cryptographic strength, and the memory efficiency. Consequently, the evolution of lightweight cryptographic algorithms incorporating randomised substitution properties is imperative to meet the stringent security demands of handheld devices effectively. In this paper, we present …
Key Technology Foresight And Policy Recommendation For Information Technology Enabling Smart Justice, Yirong Wu, Shuo Liu
Key Technology Foresight And Policy Recommendation For Information Technology Enabling Smart Justice, Yirong Wu, Shuo Liu
Bulletin of Chinese Academy of Sciences (Chinese Version)
Fair justice is the last line of defense for maintaining social fairness and justice, and promoting the construction of smart justice is an important means to improve the efficiency and fairness of judicial work. Currently, China’s smart judiciary is in a critical stage of upgrading its intelligence in areas such as investigation, trial, supervision, and judicial execution, empowered by information technology. The application of information technology in national governance faces order dilemmas and security risks, with the establishment of corresponding laws and regulations demonstrating noticeable lag. Designating to meet the demand of information technology supporting the construction of smart justice …
Enhancing Scenario-Oriented Technology Foresight On Information Technology For Public Governance, Kaihua Chen, Shuo Wang, Chao Zhang, Jie Yang, Xiaoguang Yang, Lei Guo, Yue Hao
Enhancing Scenario-Oriented Technology Foresight On Information Technology For Public Governance, Kaihua Chen, Shuo Wang, Chao Zhang, Jie Yang, Xiaoguang Yang, Lei Guo, Yue Hao
Bulletin of Chinese Academy of Sciences (Chinese Version)
To effectively adapt to the reshaping of public governance concepts, models, and tools driven by the new generation of information technologies represented by artificial intelligence, it is essential to take practical application scenarios as the entry point. Implementing periodic technology foresight on public governance information technologies can promote the integrated development of governance and digital technologies. This is an urgent task for both the public governance and information technology domains. Foresight for information technologies supporting public governance must be oriented toward the demands of complex data-driven and intelligent governance scenarios. It should focus on guiding technological directions with social value, …
Challenges, Countermeasures, And Forward-Looking Technologies For Cyber Society Governance From System Security Perspective, Hongbin Pei, Jingxin Hai, Pinghui Wang, Xiaohong Guan
Challenges, Countermeasures, And Forward-Looking Technologies For Cyber Society Governance From System Security Perspective, Hongbin Pei, Jingxin Hai, Pinghui Wang, Xiaohong Guan
Bulletin of Chinese Academy of Sciences (Chinese Version)
With the rapid development and widespread application of artificial intelligence and information technologies, the cyber society has evolved into a new form featuring human-machine integration, mass interaction, and intelligent interconnection. While transforming human life and production, it has also brought security risks such as cyberattacks, privacy breaches, online rumors, and information cocoons, posing serious challenges to social order. Cyber society governance has thus become a key component of national governance modernization. This study proposes the STC governance paradigm from a system security perspective, covering three key dimensions: security (cyber infrastructure security), trustworthiness (credibility of cyber entity identities), and controllability (content …