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arXiv 2024年

作者： Zahid, Anwar Hossain Laguna, Ignacio Le, Wei Department of Computer Science Iowa State University AmesIA United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs;then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD’s HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY. © 2024, CC BY.

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Human-centered Software Platform for Social Network of Self-Help Groups

Human-Centered Software Platform for Social Network of Self-...

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International Conference on computing and Networking Technology (ICCNT)

作者： Abhiram Bulusu Gudla Dinesh Reddy Vuppala Naga Sushanth Kumar Sruthy Anand Ramesh Guntha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Designing user interfaces (UI) for marginalized rural women presents unique challenges that require a good understanding of Human-computer Interaction (HCI) principles to ensure usability. This research paper focuses on developing a contemporary UI for a social network of Self-Help Groups (SHGs) in India, catering to the diverse age groups and educational backgrounds within these communities. The goal is to blend HCI principles into the design to develop an interface that is attractive, user-friendly, and easy to navigate for the users of these communities. This study tries to employ a user-centered design thinking approach to guide the development of the UI prototype. The study details the use of financial module of the application and its implementation. The software utilizes a multi-level client-server architecture using Flutter frameworks for UI design.

关键词： Social networking (online) Navigation User centered design Prototypes Organizations Manuals Human factors Software User experience Usability

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User Feedback Module for Women Self Help Group Chatbot for Increased Usability

User Feedback Module for Women Self Help Group Chatbot for I...

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Emerging Technology (INCET), International Conference for

作者： Moturi Karthikeya Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The recent advancement in AI and deep learning is revolutionizing various sectors, such as healthcare, education, and entertainment, to name a few. In this work, we explore the use of AI chatbots in rural digital empowerment and how AI chatbots can be utilized for supporting the small-scale businesses of rural self-help group members. The work explores the need for a user feedback module for a multi-lingual chatbot designed for a rural women's self-help group (SHGs) in India. Integration of the user feedback module will further enhance the usability and usefulness of the multi-lingual SHG chatbot. The work also examines the need for increased usefulness of the multi-lingual chatbot for improved adoption in SHGs. The work also emphasizes the need to design software based on the context of use considering the needs and objectives of the user group.

关键词： User-generated content Entertainment industry Oral communication Medical services Chatbots Software User experience

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Verifying Quantum Phase Estimation (QPE) using Prove-It

arXiv

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arXiv 2023年

作者： Witzel, Wayne M. Craft, Warren D. Carr, Robert Kapur, Deepak Center for Computing Research Quantum Computer Science Sandia National Laboratories AlbuquerqueNM87123 United States Department of Computer Science The University of New Mexico AlbuquerqueNM87131 United States

The general-purpose interactive theorem-proving assistant called Prove-It was used to verify the Quantum Phase Estimation (QPE) algorithm, specifically claims about its outcome probabilities. Prove-It is unique in its ability to express sophisticated mathematical statements, including statements about quantum circuits, integrated firmly within its formal theorem-proving framework. We demonstrate our ability to follow a textbook proof to produce a formally certified proof, highlighting useful automation features to fill in obvious steps and make formal proving nearly as straightforward as informal theorem proving. Finally, we make comparisons with formal theorem-proving in other systems where similar claims about QPE have been proven. Copyright © 2023, The Authors. All rights reserved.

关键词： Theorem proving

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Advancing Tourism Chatbots: Understanding Irony, Sarcasm, and Negative Emotions of Users

Advancing Tourism Chatbots: Understanding Irony, Sarcasm, an...

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International Conference on computing and Networking Technology (ICCNT)

作者： Ommi Balamurali A.M. Abhishek Sai Sruthy Anand Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The recent trend in chatbots for several application areas shows their potential to act as a support system to assist users with their needs. However, there is a need to direct efforts to build emotional intelligence into this software to enhance its usability and user experience. Building emotionally intelligent chatbots helps to interpret and respond to users according to their emotions, resulting in rich and healthy user experiences. Understanding irony, sarcasm and Negative emotions of the user are important aspects of sentimental analysis as they largely affect the emotional tone. This research utilises Long Short-Term Memory (LSTM) networks, to enhance chatbot sentiment analysis. In our experimentation, we leverage a dataset comprising over a million comments from Reddit, distinguishing between sarcastic and non-sarcastic expressions, to train and test our LSTM model. Along with the utilization of LSTMs Networks for sentiment analysis in the chatbot, the experiment incorporates BERT, a Transformer model architecture, to further enhance the chatbot’s capacity to recognize and react to sarcasm and satire. Using BERT’s capacity for contextual comprehension and pre-trained languages, the conversational agent can resonate with the user’s input tone and intent, whether they are sarcastic or not.

关键词： Sentiment analysis Social networking (online) Chatbots Transformers Data models User experience Software Artificial intelligence Usability Long short term memory

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Implementation of a Mixed Triple Logistic Map and the BB84 Quantum Key Distribution for Secure Image Communication

Implementation of a Mixed Triple Logistic Map and the BB84 Q...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Virnika Shefira Gina Morissa De Rosal Ignatius Moses Setiadi Informatics Engineering Department Universitas Dian Nuswantoro Semarang Indonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks.

关键词： Seminars Resistance Quantum computing Correlation Image communication Entropy Encryption Quantum key distribution Logistics Resilience

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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∂H: Differentiable Holography

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Laser and Photonics Reviews 2023年第9期17卷

作者： Chen, Ni Wang, Congli Heidrich, Wolfgang Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Visual Computing Center King Abdullah University of Science and Technology Jeddah Thuwal23955 Saudi Arabia

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. Differentiable holography ((Formula presented.)), a novel framework for automatically self-calibrating experimental imperfections in inverse holographic imaging, is presented here. The technique is demonstrated on auto-focused complex field imaging from a single intensity-only inline hologram. © 2023 Wiley-VCH GmbH.

关键词： Inverse problems

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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AI IoT Congress (AIIoT), World

作者： Syed Tangim Pasha Ashraful Islam Mohammed Masudur Rahman Eshtiak Ahmed Md. Ferdouse Ahmed Foysal Md Zahangir Alam Department of Computing and Information System Daffodil International University Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Department of Computer Science and Engineering International Standard University Bangladesh Department of Computer Science and Engineering Daffodil International University Bangladesh

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.

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