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Student Conference on Research and Development (SCOReD)

作者： Novi Yanti Sarjon Defit Okfalisa Department of Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Riau Indonesia Department of Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Sumatera Barat Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The assessment of an academic’s performance is not solely based on the tangible results achieved. Nonetheless, the formation aspect of the Knowledge Management (KM) process, which arises in the pursuit of outcomes, must be acknowledged. This emphasizes the significance of KM process development for academics in achieving their essential responsibilities including teaching and learning, research and publishing, and community service. Consequently, it is essential to develop a KM model for assessing the accomplishments of academics in higher education. An evaluation of academic performance in higher education was conducted to identify the success factors of knowledge management, by considering five key elements viz, knowledge creation, knowledge acquisition, knowledge storage, knowledge sharing, and knowledge application. Accordingly, sixty-five sub-variables were identified as success model factors for evaluating academic knowledge management performance. A K-Means clustering method was utilized to assess the KM qualifications of academics at an Islamic public institution in Indonesia (University X) as the primary study. Herein, a 5-point Likert scale questionnaire was administered to 500 academics and subsequently categorized into three performance levels such as low, medium, and high performance. As a result, 140 academicians were categorized as low KM performance, 139 were clustered as medium qualification, and 221 academicians were positioned in high performance. Furthermore, a recommendation analysis is presented to elucidate the accomplishments of academic credentials for each cluster and propose corrective actions for enhancement. Therefore, the scholars at University X might enhance their knowledge management practices to get a superior quality university.

关键词： Measurement Technological innovation Publishing Knowledge acquisition Education Knowledge management Filling Space exploration Research and development Qualifications

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Exploring the Semantic Complexity of Adjective-Noun Collocations between Uzbek and English for Improved Machine Translation

Exploring the Semantic Complexity of Adjective-Noun Collocat...

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International Conference on computer Science and engineering (UBMK)

作者： Nilufar Abdurakhmonova Nargiza Shamieva Eşref Adali Dept. of Computational and applied linguistics National University of Uzbekistan Tashkent Uzbekistan Dept. of Foreign language and Literature National Univ. of Uzbekistan Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The bilingual thesaurus of Uzbek-English adjectives is a field that has not been studied well explored yet for machine translation. Several websites and electronic dictionaries created nearby show that not enough semantically close adjectives are included in them. Despite extensive research on the meaning the adjectives in both languages, there remains a lack of understanding about the relationship between the adjective itself and the noun, which has an absolutely new connotation in collaboration. Our result shows that a recent bilingual thesaurus demonstrates how one adjective could acquire numerous new synonyms while combining with different nouns. The meanings of words, in both languages have been extensively studied, Little is understood about the dynamic connection between these words and the nouns they describe. The complexity of how adjectives pair with nouns is evident in how this relationship leads to meanings and connotations. Our study aims to bridge this gap by demonstrating how a bilingual thesaurus can capture the meanings and variations of adjectives when combined with nouns. The findings indicate that depending on the noun it accompanies a contemporary bilingual thesaurus can generate synonyms, for a single adjective.

关键词： Vocabulary Dictionaries Sensitivity Semantics Linguistics Thesauri Complexity theory Machine translation Cultural differences Probes

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A computational test of quantum contextuality, and even simpler proofs of quantumness

arXiv

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arXiv 2024年

作者： Arora, Atul Singh Bharti, Kishor Cojocaru, Alexandru Coladangelo, Andrea University of Maryland IQIM Caltech United States Singapore Centre for Quantum Engineering Research and Education TCG CREST India School of Informatics University of Edinburgh United Kingdom QuICS University of Maryland United States Paul G. Allen School of Computer Science and Engineering University of Washington United States

Bell non-locality is a fundamental feature of quantum mechanics whereby measurements performed on "spatially separated" quantum systems can exhibit correlations that cannot be understood as revealing predetermined values. This is a special case of the more general phenomenon of "quantum contextuality", which says that such correlations can occur even when the measurements are not necessarily on separate quantum systems, but are merely "compatible" (i.e. commuting). Crucially, while any non-local game yields an experiment that demonstrates quantum advantage by leveraging the "spatial separation" of two or more devices (and in fact several such demonstrations have been conducted successfully in recent years), the same is not true for quantum contextuality: finding the contextuality analogue of such an experiment is arguably one of the central open questions in the foundations of quantum mechanics. In this work, we show that an arbitrary contextuality game can be compiled into an operational "test of contextuality" involving a single quantum device, by only making the assumption that the device is computationally bounded. Our work is inspired by the recent work of Kalai et al. (STOC’23) that converts any non-local game into a classical test of quantum advantage with a single device. The central idea in their work is to use cryptography to enforce spatial separation within subsystems of a single quantum device. Our work can be seen as using cryptography to enforce "temporal separation", i.e. to restrict communication between sequential measurements. Beyond contextuality, we employ our ideas to design a "proof of quantumness" that, to the best of our knowledge, is arguably even simpler than the ones proposed in the literature so far. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

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Multi-VentNet: Multimodal Fusion Framework for Predicting Need for Mechanical Ventilation

Multi-VentNet: Multimodal Fusion Framework for Predicting Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ye Sel Lee Xiaolei Lu Michael Miller Jonathan Y. Lam Supreeth Shashikumar Shamim Nemati Department of Electrical and Computer Engineering University of California San Diego La Jolla USA Department of Biomedical Informatics University of California San Diego La Jolla USA Division of Pulmonary and Critical Care University of California San Diego Health La Jolla USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Invasive Mechanical Ventilation (MV) is a common and critical intervention for patients with respiratory failure in intensive care units (ICUs). Early identification of patients who will require MV is crucial to improve patient outcomes. Prior studies using single-modality models face challenges in fully capturing complex interrelationships between clinical data sources. To address these challenges, we propose Multi-VentNet, an integrative multimodal fusion approach that combines Electronic Health Records (EHR) and Chest X-rays (CXR) to predict the need for MV up to 24 hours in advance. Our approach consists of three key components: (1) Single-Modality Encoders for extracting modality-specific features from EHR and CXR data, (2) Cross-Modality Encoder to capture inter- and intra-modality relationships, and (3) Risk Integration Module that generates a final risk score by combining predictions from each modality. Experimental results demonstrate that Multi-VentNet achieved an area under the curve (AUC) of 0.8362, a specificity (SPC) of 79.63%, and a positive predictive value (PPV) of 26.41%, outperforming single-modality models by 0.0478 in AUC, 2.01% in SPC, and 1.51% in PPV. These results highlight the effectiveness of our multimodal framework in combining EHR and CXR data for more accurate and timely predictions of MV needs.

关键词： Soft sensors Refining Predictive models Ventilation Feature extraction Data models Electronic medical records X-ray imaging Faces Biomedical imaging

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Systematic review of application of artificial intelligence tools in architectural, engineering and construction

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Frontiers in engineering and Built Environment 2021年第2期1卷 203-216页

作者： Momade, Mohammed Hamza Durdyev, Serdar Estrella, Dave Ismail, Syuhaida School of Civil Engineering Universiti Teknologi Malaysia Skudai Malaysia Engineering and Architectural Studies Ara Institute of Canterbury Christchurch New Zealand Faculty of Business AMA Computer University Quezon City Philippines Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Skudai Malaysia

Purpose: This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry. Design/methodology/approach: A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study. Findings: The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario. Practical implications: The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field. Originality/value: There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from. © 2021, Mohammed Hamza Momade, Serdar Du

关键词： AI tools Architectural engineering and construction industry Forecasting Prediction

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Cypher by Example: A Visual Query Language for Graph Databases

SSRN

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SSRN 2023年

作者： Rabuzin, Kornelije Šestak, Martina Faculty of Organization and Informatics University of Zagreb Pavlinska ulica 2 Varaždin42000 Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

Considering the constantly increasing connectivity in terms of data exchanged between applications, devices etc., modern IT systems are in need of a data storage solution able to capture and handle data about connections and patterns formed between system participants (entities). As a potential solution for this challenge, graph database emerged in the previous decade. Since the standardization of a graph database query language is still an undergoing effort, any interested parties can interact with a graph database and explore graph data by using query languages such as Cypher or Gremlin, which are supported by modern Graph Database Management Systems (GDBMSs). Even though the syntax of these languages is well developed and documented, potential end-users with little knowledge of graph databases and the graph data model can still face a steep learning curve before being able to write queries on their own data stored in a form of a property graph. Consequently, this brings to a limited interest among the users to implement graph databases in their domains, as there is obviously a missing tool for maintaining the graph database in a visual manner. To address this gap, this paper introduces Cypher by Example, a visual graph query language consisting of a visual interface and a set of operations for interacting with the underlying graph database. The paper presents the basic elements of the proposed query language and demonstrates its effectiveness and completeness on a series of Cypher queries executed for a sample education domain. © 2023, The Authors. All rights reserved.

关键词： Query languages

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Enhancing Robotic Arm Activity Recognition with Vision Transformers and Wavelet-Transformed Channel State Information

arXiv

引用

arXiv 2024年

作者： Zandi, Rojin Behzad, Kian Motamedi, Elaheh Salehinejad, Hojjat Siami, Milad Department of Electrical and Computer Engineering Northeastern University BostonMA United States Kern Center for the Science of Healthcare Delivery Mayo Clinic RochesterMN United States Department of Artificial Intelligence and Informatics Mayo Clinic RochesterMN United States

Vision-based methods are commonly used in robotic arm activity recognition. These approaches typically rely on line-of-sight (LoS) and raise privacy concerns, particularly in smart home applications. Passive Wi-Fi sensing represents a new paradigm for recognizing human and robotic arm activities, utilizing channel state information (CSI) measurements to identify activities in indoor environments. In this paper, a novel machine learning approach based on discrete wavelet transform and vision transformers for robotic arm activity recognition from CSI measurements in indoor settings is proposed. This method outperforms convolutional neural network (CNN) and long shortterm memory (LSTM) models in robotic arm activity recognition, particularly when LoS is obstructed by barriers, without relying on external or internal sensors or visual aids. Experiments are conducted using four different data collection scenarios and four different robotic arm activities. Performance results demonstrate that wavelet transform can significantly enhance the accuracy of visual transformer networks in robotic arms activity recognition. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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XRadNet: A Radiomics-Guided Breast Cancer Molecular Subtype Prediction Network with a Radiomics Explanation

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Liang, Yinhao Tang, Wenjie Zhang, Jianjun Wang, Ting Ng, Wing W. Y. Chen, Siyi Jiang, Kuiming Wei, Xinhua Jiang, Xinqing Guo, Yuan South China University of Technology School of Computer Science and Engineering China South China University of Technology Department of Radiology Guangzhou First People's Hospital School of Medicine Guangzhou China South China Agricultural University College of Mathematics and Informatics Guangzhou510642 China Guangdong Women and Children Hospital Department of Radiology Guangdong Guangzhou510010 China

In this work, we propose a radiomics-guided neural network, XRadNet, for breast cancer molecular subtype prediction. XRadNet is a two-head neural network, with one for predicting molecular subtypes and the other for approximating radiomic features. In addition, a training scheme with radiomics guidance is proposed to improve performance. First, we conduct a series of experiments to test the radiomic feature learning capacity of different neural networks, which determines the backbone of XRadNet. Moreover, significant radiomic features are also determined according to radiomics and prior knowledge. XRadNet is subsequently pretrained in a self-supervised manner. The pretraining uses synthetic samples to train the backbone and radiomic feature regression head. This mitigates the impact of an insufficient number of samples. Finally, XRadNet is fine-tuned with a downstream real-world dataset by enabling all heads. Furthermore, a logistic regression is built with radiomic features and learned features, which provides a new way to interpreting the trained model with concepts familiar to radiologists. The experimental results show that XRadNet effectively predicts the four molecular subtypes of breast cancer. These results also demonstrate that the proposed training scheme yields better or competitive performance than those models pretrained on ImageNet or medical datasets. © 2013 IEEE.

关键词： Logistic regression

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A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks

A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G...

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IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Giuseppe Caso Mohammad Rajiullah Anna Brunstrom Luca De Nardis Özgü Alay Marco Neri Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Informatics University of Oslo Oslo Norway Rohde & Schwarz Mobile Network Testing Rome Italy

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

5th Generation (5G) mobile systems are being deployed to address the Quality of Service and Experience (QoS/QoE) requirements of several use cases, including enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). While eMBB performance testing inherits well-established methodologies and Key Performance Indicators (KPIs), beyond-eMBB services (i.e., URLLC and eMBBURLLC real-time applications) are often tested by adopting simplistic or in-house methodologies, which do not help towards accurate assessment and comparison. In this paper, we fill this gap by providing a detailed analysis of a methodology, recently standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), that targets systematic performance evaluations of beyond-eMBB services. The methodology relies on the definition of a QoE KPI, i.e., the interactivity score (i-score), on top of three QoS KPIs measuring service latency, stability, and continuity. To this aim, we perform a multi-service measurement campaign on two 5G networks across two cities in Sweden, during which we run a large number of tests compliant with the ITU-T methodology, and analyze the collected data. Our results empirically validate the methodology, showcasing its ability of capturing heterogeneous service characteristics and requirements, as well as the interdependencies between $\boldsymbol{i}$-score and QoS KPIs, and the impact of different factors on QoS/QoE performance, including user mobility, connection capability, and server location.

关键词： Accuracy Key performance indicator Urban areas Quality of service Ultra reliable low latency communication Traffic control Servers Quality of experience Vehicle-to-everything Testing

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Conditional Cascaded Networks (Ccn) Approach for Diagnosis of Covid-19 in Chest X-Ray and Ct Images Using Transfer Learning

SSRN

引用

SSRN 2023年

作者： Rashed, Amr Ez Eldin Bahgat, Waleed M. Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif Saudi Arabia Information Technology Department Faculty of Computer and Information Sciences Mansoura University 35516 Egypt Department of Computer Science and Informatics Taibah University Al Medina Al Munawara Medina Saudi Arabia

The COVID-19 pandemic has caused significant global health and economic damage, with over five million confirmed cases worldwide. The importance of rapid and accurate diagnosis of infected patients has been underscored. However, due to the shortage of testing kits and the time-consuming and troublesome nature of the manual RT-PCR test, an automated and efficient diagnosis system using chest medical images for early screening of COVID-19 is crucial. In this paper, we present an automated approach for rapid and accurate diagnosis of COVID-19 in chest X-ray and CT images using transfer learning. The proposed technique leverages the Conditional Cascaded Networks (CCN) approach, which employs multiple levels of networks to analyze images with high confidence. Transfer learning is employed with seven commonly used existing CNN architectures and four datasets for X-ray and CT images. Various regularization, optimization, dropout, and data augmentation techniques are examined through a series of experiments with three optimizers. Our technique is compared with other state-of-the-art techniques, and the achieved results demonstrate highly promising performance metrics. Additionally, occlusion specificity and Grad-CAM techniques are employed to better understand the network output. The proposed technique is highly adaptable and scalable and does not require manual hyperparameter tuning. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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