Hasil untuk "Information technology"

I. Vessey, D. Galletta

T. Berners-Lee, R. Cailliau, J.-F. Groff et al.

F. Niederman, J. C. Brancheau, James C. Wetherbe

A. Jaffe, Robert N. Stavins

David W. Farrell, B. Rimer, Laura R. Olevitch et al.

Yi-Shun Wang, Hsin‐Hui Lin, P. Luarn

J. I. Criado, Rodrigo Sandoval-Almazán, J. Ramon Gil-Garcia

D. Avison, Jill Jones, P. Powell et al.

Hipolito Guzman-Miranda, Marcos Lopez Garcia, Alberto Urbon Aguado

With the increasing complexity of digital designs, functional verification is becoming unmanageable. Bugs that survive verification cause a number of issues with functional, performance, security, safety and economic impact, and are unfortunately prevalent in current FPGA and ASIC designs, manifesting in later stages of development or even after the design has been deployed or manufactured. In this context, Formal Verification poses itself as a powerful complement to verification by simulation, which is currently the most extended verification method. By mathematically proving properties of the designs, Formal Verification allows to verify them with high confidence, but also requires designers to have deep expertise of the methods, techniques and tools. Thus, adoption of formal methods for verification is not as extended as their usefulness may suggest, and even less in the case of VHDL teams. To lower the adoption barriers for formal verification of digital designs, the present article proposes a Formal Verification Methodology, which is complemented by a build and test framework and a repository of examples. Results of applying the Formal Verification Methodology to the repository of examples show compelling results both in manageable design complexity and verification productivity.

Tiancai Huang, Shiwang Zhang, Hao Luo et al.

Outlier detection is pivotal in data mining and machine learning, as it focuses on discovering unusual behaviors that deviate substantially from the majority of data samples. Conventional approaches, however, often falter when dealing with complex data that are multimodal or sparse or that exhibit strong nonlinearity. To address these challenges, this paper introduces a novel outlier detection framework named Multimodal Granular Distance-based Outlier Detection (MGDOD), which leverages granular computing principles in conjunction with multimodal granulation techniques. Specifically, similarity measures and granulation methods are employed to generate granules from single-modal data, thereby reducing inconsistencies arising from different data modalities. These granules are then combined to form multimodal granular vectors, whose size, measurement, and operational rules are carefully defined. Building on this conceptual foundation, we propose two multimodal granular distance measures, which are formally axiomatized, and develop an associated outlier detection algorithm. Experimental evaluations on benchmark datasets from UCI, ODDS, and multimodal sources compare the proposed MGDOD method against established outlier detection techniques under various granulation parameters, distance metrics, and outlier conditions. The results confirm the effectiveness and robustness of MGDOD, demonstrating its superior performance in identifying anomalies across diverse and challenging data scenarios.

Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor et al.

Fake news is false or misleading information that looks like real news and spreads through traditional and social media. It has a big impact on our social lives, especially in politics. In Pakistan, where Urdu is the main language, finding fake news in Urdu is difficult because there are not many effective systems for this. This study aims to solve this problem by creating a detailed process and training models using machine learning, deep learning, and large language models (LLMs). The research uses methods that look at the features of documents and classes to detect fake news in Urdu. Different models were tested, including machine learning models like Naïve Bayes and Support Vector Machine (SVM), as well as deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), which used embedding techniques. The study also used advanced models like BERT and GPT to improve the detection process. These models were first evaluated on the Bend-the-Truth dataset, where CNN achieved an F1 score of 72%, Naïve Bayes scored 78%, and the BERT Transformer achieved the highest F1 score of 79% on Bend the Truth dataset. To further validate the approach, the models were tested on a more diverse dataset, Ax-to-Grind, where both SVM and LSTM achieved an F1 score of 89%, while BERT outperformed them with an F1 score of 93%.

Maria Waqas, Shehzad Hasan, Ammar Farid Ghori et al.

Objective: To overcome the scarcity of annotated dental X-ray datasets, this study presents a novel pipeline for generating high-resolution synthetic orthopantomography (OPG) images using customized generative adversarial networks (GANs). Methods: A total of 4777 real OPG images were collected from clinical centres in Pakistan, Thailand, and the U.S., covering diverse anatomical features. Twelve GAN models were initially trained, with four top-performing variants selected for further training on both combined and region-specific datasets. Synthetic images were generated at 2048 × 1024 pixels, maintaining fine anatomical detail. The evaluation was conducted using (1) a YOLO-based object detection model trained on real OPGs to assess feature representation via mean average precision, and (2) expert dentist scoring for anatomical and diagnostic realism. Results: All selected models produced realistic synthetic OPGs. The YOLO detector achieved strong performance on these images, indicating accurate structural representation. Expert evaluations confirmed high anatomical plausibility, with models M1 and M3 achieving over 50% of the reference scores assigned to real OPGs. Conclusion: The developed GAN-based pipeline enables the ethical and scalable creation of synthetic OPG images, suitable for augmenting datasets used in artificial intelligence-driven dental diagnostics. Clinical Significance: This method provides a practical solution to data limitations in dental artificial intelligence, supporting model development in privacy-sensitive or low-resource environments.

Scott Humr, Mustafa Canan

Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial intelligence applications and their impact on society demands the field of Information Science acknowledge the sociotechnical nature of these technologies. Previous definitions of Information Science over the last six decades have inadequately addressed the environmental, human, and social aspects of these technologies. This perspective piece advocates for an expanded definition of Information Science that fully includes the sociotechnical impacts information has on the conduct of research in this field. Proposing an expanded definition of Information Science that includes the sociotechnical aspects of this field should stimulate both conversation and widen the interdisciplinary lens necessary to address how intelligent technologies may be incorporated into society and our lives more fairly.

T. Coltman, Paul P. Tallon, Rajeev Sharma et al.

M. Limayem, Christy M. K. Cheung

Richard Heeks

Xiaolin Xing, Xiaolin Xing, Tianhua Hu et al.

Radish (Raphanus sativus L.) is a vegetable crop with economic value and ecological significance in the genus Radish, family Brassicaceae. In recent years, developed countries have attached great importance to the collection and conservation of radish germplasm resources and their research and utilization, but the lack of population genetic information and molecular markers has hindered the development of the genetic breeding of radish. In this study, we integrated the radish genomic data published in databases for the development of single-nucleotide polymorphism (SNP) markers, and obtained a dataset of 308 high-quality SNPs under strict selection criteria. With the support of Kompetitive Allele-Specific PCR (KASP) technology, we screened a set of 32 candidate core SNP marker sets to analyse the genetic diversity of the collected 356 radish varieties. The results showed that the mean values of polymorphism information content (PIC), minor allele frequency (MAF), gene diversity and heterozygosity of the 32 candidate core SNP markers were 0.32, 0.30, 0.40 and 0.25, respectively. Population structural analysis, principal component analysis and genetic evolutionary tree analysis indicated that the 356 radish materials were best classified into two taxa, and that the two taxa of the material were closely genetically exchanged. Finally, on the basis of 32 candidate core SNP markers we calculated 15 core markers using a computer algorithm to construct a fingerprint map of 356 radish varieties. Furthermore, we constructed a core germplasm population consisting of 71 radish materials using 32 candidate core markers. In this study, we developed SNP markers for radish cultivar identification and genetic diversity analysis, and constructed DNA fingerprints, providing a basis for the identification of radish germplasm resources and molecular marker-assisted breeding as well as genetic research.

Kosuke Tomimatsu, Takeru Fujii, Ryoma Bise et al.

Abstract Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

Zuo Yanhong, Xia Shilong, Zhou Chao et al.

Automobiles have become the main means of transportation for human beings, and their failures in the process of operation are directly related to the life and property safety of drivers. Therefore, real-time operational status evaluation technology have become urgent problems in the current academic world. The premise of real-time operational status evaluation technology of automobiles is to obtain high-quality information data in real time, but the automobile operating environment is complex and changeable, resulting in the measured information data under the influence of multiple factors, such as equipment performance and signal interference. There is an unpredictable measurement error, which greatly affects the reliability of operational status evaluation systems. In this paper, on the basis of studying the structure and operation characteristics of automobiles, we design a method that can be used for real-time operational status evaluation of automobiles; through the study of fractional-order calculus theory, we establish a mathematical model of information data fusion based on fractional-order differential operators. By providing high-quality information data to automobile real-time operational status evaluation systems, real-time operational status evaluation technology for automobile faults can be realized. The feasibility and effectiveness of the method were verified through an experiment applying the technology in automobile real-time operational status evaluation systems. The research results are of great significance for promoting the development of the automobile industry and ensuring the safety of drivers' lives and property.

Zeshui Xu