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arXiv 2024年

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Computer Science University of Haifa Israel

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms—it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n×n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permutation

关键词： Optimization

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Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis

Oxygen: A Distributed Health Care Framework for Patient Heal...

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Advancements in computing (ICAC), International Conference on

作者： Maleesha Wickramarathna Kithmini De Silva Vihanga Lekamalage Janith Senanayake Jeewaka Perera Laneesha Ruggahakotuwa Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide.

关键词： Oxygen Pandemics Image processing Optical character recognition Sociology Medical services Statistics

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Existence of strongly nonlocal sets of three states in any N-partite system

arXiv

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arXiv 2024年

作者： Xiong, Zong-Xing Li, Mao-Sheng Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Mathematics South China University of Technology Guangzhou510641 China

The notion of strong nonlocality, which refers to local irreducibility of a set of orthogonal multipartite quantum states across each bipartition of the subsystems, was put forward by Halder et al. in [Phys. Rev. Lett. 122, 040403 (2019)]. Here, we show the existence of three orthogonal quantum states in (C2)⊗N that cannot be perfectly distinguished locally across any bipartition of the subsystems. Specifically, all these three states are genuinely entangled, among which two are the N-qubit GHZ pairs. Since any three locally indistinguishable states are always locally irreducible, the three N-partite orthogonal states we present are strongly nonlocal. Thus, the caridnality of strongly nonlocal sets here is dramatically smaller than all known ones. © 2024, CC BY.

关键词： Quantum entanglement

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Examining the Effects of Layout and Working Memory on UML Class Diagram Defect Identification

Examining the Effects of Layout and Working Memory on UML Cl...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Bonita Sharif Kang-il Park Michael P. DeJournett Isaac Baysinger Mohammed Aly Jonathan I. Maletic School of Computing University of Nebraska - Lincoln Lincoln Nebraska USA Software Engineering Department Assiut University Assiut Egypt Department of Computer Science Kent State University Kent Ohio USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

A controlled experiment investigating the effect layout has on how students find defects in UML class diagrams with respect to requirements is presented. Two layout schemes from prior literature namely, multi-cluster and orthogonal layouts, are compared with respect to two open source systems, Doxygen and Qt. The experiment is conducted with 89 students from two universities in a classroom lab setting. Each participant is placed in one of two groups where each group are given 2 defect detection tasks (with five sub-parts) with each task using one of the two layouts in each subject system. The only difference between groups is that the layouts were flipped between the two tasks. Feedback is collected after each task. A mental rotation and object memory task is conducted at the end of the two tasks to correlate their spatial and working memory skills to the task performance. Results indicate that the multi-cluster layout performed better in terms of accuracy of finding defects, but not significantly. There is also not much difference in time to find them. Furthermore, it is found that the object memory skills are sometimes correlated with the performance of the defect detection tasks. These results can be used to help improve the teaching of UML class diagram defect detection skills by incorporating clustered layouts and object memory tasks. In addition, they can help identify people who are best suited for finding critical defects in design.

关键词： Visualization Accuracy Layout Unified modeling language Education software Defect detection

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Predicting Lung Cancer Risk Using Artificial Intelligence

Predicting Lung Cancer Risk Using Artificial Intelligence

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computing and Data Analytics (ICCDA), International Conference on

作者： Syed Mehr Ali Shah Danish Jamil Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed Al-Jarwani College of Computing & Information Sciences University of Technology and Applied Sciences shinas Sultanat of Oman Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection

关键词： Surveys Analytical models Ethics Accuracy Linear regression Lung cancer Convolutional neural networks Medical diagnosis Artificial intelligence X-ray imaging

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis

Empowering Precision Machine: Deep Learning Technique for Lu...

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computing and Data Analytics (ICCDA), International Conference on

作者： Danish Jamil Syed Mehr Ali Shah Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed AlJarwani Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Sultanat of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics.

关键词： Deep learning Technological innovation Computed tomography Computational modeling Precision medicine Lungs Lung cancer Medical services Data models Medical diagnostic imaging

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DYNAMIC DATA SAMPLER FOR CROSS-LANGUAGE TRANSFER LEARNING IN LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Li, Yudong Feng, Yuhao Zhou, Wen Zhao, Zhe Shen, Linlin Hou, Cheng Hou, Xianxu School of Computer Science and Software Engineering Shenzhen University China Tencent AI Lab China LIESMARS Wuhan University China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool China

Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose ChatFlow, a cross-language transfer-based LLM, to address these challenges and train large Chinese language models in a cost-effective manner. We employ a mix of Chinese, English, and parallel corpus to continuously train the LLaMA2 model, aiming to align cross-language representations and facilitate the knowledge transfer specifically to the Chinese language model. In addition, we use a dynamic data sampler to progressively transition the model from unsupervised pre-training to supervised fine-tuning. Experimental results demonstrate that our approach accelerates model convergence and achieves superior performance. We evaluate ChatFlow on popular Chinese and English benchmarks, the results indicate that it outperforms other Chinese models post-trained on LLaMA-2-7B. © 2024, CC BY.

关键词： Cost effectiveness

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The Applications of Personalized Micro-videos in Computer Network Course Teaching 11

The Applications of Personalized Micro-videos in Computer Ne...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Fu, Wenxiang Yu, Xiaomei Gong, Zhaokun Che, Xueyu Mao, Qian Li, Yan Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

ISBN: (纸本)9781665406796

Nowadays, the computer network course becomes one of the core compulsory courses for computer related majors. This paper analyzes the problems existing in computer network course teaching. And then, aiming at the problems of low utilization of curriculum resources and poor classroom learning effect, this paper presents the method of improving teaching mode based on personalized micro-videos from the perspective of educational equity. Specially, a personalized micro-videos teaching model based on autonomous learning and collaborative learning is constructed. Taking 'home network construction' in the course of computer network in vocational education as an example, the course is designed and the teaching practice is carried out. The results of teaching practice have proved that the novel teaching model improves the teaching effect and raises the learning enthusiasm of students greatly. © 2021 IEEE.

关键词： Teaching

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Personalized Federated Learning with Multiple Classifier Aggregation

SSRN

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SSRN 2024年

作者： Zheng, Shaifeng Zhu, Qingling Lin, Qiuzhen Liu, Songbai Wong, Ka-Chun Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science City University of Hong Kong Hong Kong

Personalized federated learning (PFL) has garnered attention due to its capability to address statistical heterogeneity among clients. Typically, prevailing PFL methods aggregate a single global model for personalization, which may be inadequate for clients with diverse data distributions. Furthermore, in the local update, each private dataset is used to optimize the model independently, which increases the risk of overfitting the current data distribution and losing previously acquired knowledge, resulting in knowledge forgetting. In this study, personalized federated learning with multiple classifier aggregation is proposed. The method involves splitting the client model into its head and base, then optimizing them respectively using an alternating optimization strategy that first optimizes the head and then the base. Initially, to address the suboptimal model problem, the proposed approach aggregates multiple classifiers using data distribution and employs knowledge distillation to impart positive and negative classifier knowledge for learning the most suitable personalized model head. Additionally, to mitigate knowledge forgetting, a learnable personalization layer is introduced, and hidden loss is utilized to learn the knowledge of the global base and prevent overfitting of the model base. Empirical results demonstrate that the proposed method achieves competitive performance across various benchmarks, outperforming the majority of state-of-the-art PFL algorithms. The source code is publicly available at \href{https://***/xiaye-maker/FedMCA}{https://***/xiaye-maker/FedMCA} © 2024, The Authors. All rights reserved.

关键词： Aggregates

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability Design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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