Reviewerly/exHarmony huggingface dataset:
A Patch Distribution-based Active Learning Method for Multiple Instance Alzheimer's Disease Diagnosis

Authors:
raw_author_name: Tianxiang Wang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
raw_author_name: Qun Dai
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Abstract: Medical data, particularly the complex brain imaging structures, acquisition presents significant difficulties and high diagnostic expenses, resulting in a scarcity of the trainable samples in the real-world scenarios. To overcome this limitation, we present an active learning-based sampling strategy that selects the most informative samples from the unlabeled candidate sample pool for expert annotation, leading to high classification performance with a reduced number of training samples. This study adopts a patch-level perspective and introduces a multi-instance learning framework for Alzheimer's Disease diagnosis. Initially, a patch pre-selection module is designed to identify pathology-prone regions while excluding background areas and irrelevant information. Subsequently, an inner-patch local attention mechanism block and an outer-patch global attention mechanism block are developed to enhance the extraction of discriminative local and global information by the network model. Finally, an active learning sampling strategy is devised to minimize the costs associated with data acquisition and expert annotation in medical domain. The effectiveness of the proposed network framework and active learning strategy was validated through four sets of control experiments on the ADNI dataset.
Enhancing User Experience With a Generative AI Chatbot

Authors:
raw_author_name: Jeong Soo Kim
raw_affiliation_string: Department of Media and Communication, Sungkyunkwan University, Seoul, Republic of Korea
raw_author_name: Minseong Kim
raw_affiliation_string: Department of Management &amp; Marketing, College of Business, Louisiana State University Shreveport, Shreveport, LA, USA
raw_author_name: Tae Hyun Baek
raw_affiliation_string: Department of Media and Communication, Sungkyunkwan University, Seoul, Republic of Korea

Abstract: With the rapid evolution of artificial intelligence (AI), this study aims to examine the interplay among the perceived usability, perceived enjoyment, perceived responsiveness, and intention to continue using ChatGPT. Structural equation modeling (SEM) was used to investigate our proposed model. We recruited 446 ChatGPT users through an online survey conducted on the Connect platform, powered by CloudResearch. Perceived usability (β = 0.254) and enjoyment (β = 0.438) significantly influence satisfaction with ChatGPT. However, perceived responsiveness is not significantly related to perceived attachment or satisfaction. Furthermore, we established that perceived attachment (β = 0.405) and satisfaction (β = 0.447) are pivotal in shaping users' intentions to continue using ChatGPT, providing insights into human–AI interactions. The practical implications of our findings suggest that generative AI chatbots should be crafted with a focus on enjoyable, user-centered interfaces that foster long-term user satisfaction and engagement. AI developers should design AI conversation flows and chatbot personas to optimize the user experience.
Precise path planning and trajectory tracking based on improved A-star algorithm

Authors:
raw_author_name: Boyang Xu
raw_affiliation_string: College of Design and Engineering, National University of Singapore, Singapore, Singapore

Abstract: Path planning and trajectory tracking are very meaningful for the field of autonomous driving, but currently path planning still has problems such as non-optimal paths and insufficiently accurate paths. This paper addresses the issue of path planning by proposing a improved A-star algorithm and locally zooming on the map technique to achieve precise path planning. Compared with the conventional method, this method reduces the time by 23% and the path length by 21% in the scenarios shown in the paper, respectively, and provides a reference for related research. Moreover, trajectory tracking was achieved using the improved LQR control. Compared with the conventional method, the improved LQR control algorithm reduces the average error by 80% in the scenario shown in the paper. Firstly, the A-star algorithm is enhanced by incorporating an unknown path cost estimation function, thereby improving the effect of its path planning in complex environments. Additionally, the method of locally zooming on the map is incorporated, effectively enhancing the accuracy and safety of path planning. Building upon the path planning, further improvements are made to the LQR control algorithm, enabling autonomous deceleration in complex sections, which facilitates better trajectory tracking and enhances the motion control performance of the robot during practical operations.
Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Authors:
raw_author_name: Shaofu Xu
raw_affiliation_string: State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Binshuo Liu
raw_affiliation_string: State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Sicheng Yi
raw_affiliation_string: State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Jing Wang
raw_affiliation_string: State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Weiwen Zou
raw_affiliation_string: State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.
Physics-Constrained Hardware-Efficient Ansatz on Quantum Computers That Is Universal, Systematically Improvable, and Size-Consistent

Authors:
raw_author_name: Xiaoxiao Xiao
raw_affiliation_string: Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
raw_author_name: Hewang Zhao
raw_affiliation_string: Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
raw_author_name: Jiajun Ren
raw_affiliation_string: Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
raw_author_name: Wei-Hai Fang
raw_affiliation_string: Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
raw_author_name: Zhendong Li
raw_affiliation_string: Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China

Abstract: Variational wave function ansätze are at the heart of solving quantum many-body problems in physics and chemistry. Previous designs of hardware-efficient ansatz (HEA) on quantum computers are largely based on heuristics and lack rigorous theoretical foundations. In this work, we introduce a physics-constrained approach for designing HEA with rigorous theoretical guarantees by imposing a few fundamental constraints. Specifically, we require that the target HEA to be universal, systematically improvable, and size-consistent, which is an important concept in quantum many-body theories for scalability but has been overlooked in previous designs of HEA. We extend the notion of size-consistency to HEA and present a concrete realization of HEA that satisfies all these fundamental constraints while only requiring linear qubit connectivity. The developed physics-constrained HEA is superior to other heuristically designed HEA in terms of both accuracy and scalability, as demonstrated numerically for the Heisenberg model and some typical molecules. In particular, we find that restoring size-consistency can significantly reduce the number of layers needed to reach a certain accuracy. In contrast, the failure of other HEA to satisfy these constraints severely limits their scalability to larger systems with more than 10 qubits. Our work highlights the importance of incorporating physical constraints into the design of HEA for efficiently solving many-body problems on quantum computers.
Adoption of distributed ledger technology for construction projects: a study of the challenges in a developing country

Authors:
raw_author_name: Ayodeji Emmanuel Oke
raw_author_name: John Aliu
raw_author_name: Ahmed Farouk Kineber
raw_author_name: Seyi Segun Stephen

Abstract: Purpose The construction industry in Nigeria, like many others worldwide, grapples with increasing demands for enhanced efficiency, transparency and security. While distributed ledger technology (DLT) offers substantial potential in tackling these issues, its implementation faces numerous obstacles. This study aims to investigate the challenges hindering the adoption of DLT within the Nigerian construction sector, capitalizing on a recognized research gap in this field. Design/methodology/approach A comprehensive literature review identified 20 challenges to DLT adoption. A structured questionnaire was developed and distributed to 98 construction professionals selected from a pool of 5,330 members using purposive and snowball sampling. Background information was assessed using frequency and percentage, data normality with the Shapiro–Wilk test and nonparametric data compared using the Kruskal–Wallis H -test. Factor analysis was then used to reduce the challenges into a more coherent set. Findings Findings revealed the top five challenges: power supply issues, data privacy and compliance, an uncertain legal framework, insufficient training and education and a lack of skilled workforce. Further analysis revealed four clusters of challenges, namely, technical and infrastructure barriers, organizational and cultural challenges, financial and economic challenges and regulatory and legal challenges. Practical implications Through the evaluation of challenges and potential strategies to overcome them, this study aims to lay a foundation that assists construction entities in effectively harnessing the benefits of DLT while navigating the obstacles inherent in its adoption. Originality/value This study contributes to the existing body of knowledge by offering insights into the challenges surrounding the adoption of DLT, specifically within the Nigerian construction sector. By juxtaposing the findings from this study with existing research conducted in other countries such as India, South Africa and the USA, a more comprehensive understanding of the challenges unique to the Nigerian context can be gained.
Is deep learning a useful tool for the pure mathematician?

Authors:
raw_author_name: Geordie Williamson
raw_affiliation_string: Sydney Mathematical Research Institute, University of Sydney, A14 - Quadrangle, NSW 2006, Sydney, Australia

Abstract: A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.
Investigation on storage level data integrity strategies in cloud computing: classification, security obstructions, challenges and vulnerability

Authors:
raw_author_name: Paromita Goswami
raw_affiliation_string: Department of Computer Engineering, Mizoram University, Aizawl, MZ, 796004, India
raw_author_name: Neetu Faujdar
raw_affiliation_string: Department of Computer Engineering and Application, GLA University, Mathura, UP, 281406, India
raw_author_name: Somen Debnath
raw_affiliation_string: Department of Computer science and Enginering, Tripura University, Agartala, Tripura, 796022, India
raw_author_name: Ajoy Kumar Khan
raw_affiliation_string: Department of Computer Engineering, Mizoram University, Aizawl, MZ, 796004, India
raw_author_name: Ghanshyam Singh
raw_affiliation_string: Centre for Smart Information and Communication Systems Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park Campus, PO Box. 524, Johannesburg, 2006, South Africa

Abstract: Abstract Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup, some organizations still prefer traditional data storage over cloud storage due to concerns about data correctness and security. Data integrity is a critical issue in cloud computing, as data owners need to rely on third-party cloud storage providers to handle their data. To address this, researchers have been developing new algorithms for data integrity strategies in cloud storage to enhance security and ensure the accuracy of outsourced data. This article aims to highlight the security issues and possible attacks on cloud storage, as well as discussing the phases, characteristics, and classification of data integrity strategies. A comparative analysis of these strategies in the context of cloud storage is also presented. Furthermore, the overhead parameters of auditing system models in cloud computing are examined, considering the desired design goals. By understanding and addressing these factors, organizations can make informed decisions about their cloud storage solutions, taking into account both security and performance considerations.
Joint streaming model for backchannel prediction and automatic speech recognition

Authors:
raw_author_name: Yong‐Seok Choi
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute  Daejeon Republic of Korea
raw_author_name: Jeong‐Uk Bang
raw_affiliation_string: Integrated Intelligence Research Section Electronics and Telecommunications Research Institute  Daejeon Republic of Korea

Abstract: Abstract In human conversations, listeners often utilize brief backchannels such as “uh‐huh” or “yeah.” Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human–machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single‐task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single‐task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.
Framework for evaluating code generation ability of large language models

Authors:
raw_author_name: Sangyeop Yeo
raw_affiliation_string: Division of Artificial Intelligence University of Science and Technology Daejeon Republic of Korea
raw_author_name: Yu‐Seung Ma
raw_affiliation_string: Artificial Intelligence Computing Research Laboratory Electronics and Telecommunications Research Institute Daejeon Republic of Korea; Division of Artificial Intelligence University of Science and Technology Daejeon Republic of Korea
raw_author_name: Taeho Kim
raw_affiliation_string: Artificial Intelligence Computing Research Laboratory Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: Abstract Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, , which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the metric.
Named entity recognition using transfer learning and small human‐ and meta‐pseudo‐labeled datasets

Authors:
raw_author_name: Kyoungman Bae
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Joon‐Ho Lim
raw_affiliation_string: Language Intelligence Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: Abstract We introduce a high‐performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta‐pseudo‐label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human‐ and pseudo‐labeled data. Second, the influence of noisy pseudo‐labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human‐labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.
Real-time sign language recognition based on YOLO algorithm

Authors:
raw_author_name: Ishak Pacal
raw_affiliation_string: Department of Computer Science, Engineering Faculty, Igdir University, 76000, Igdir, Turkey

Abstract: Abstract This study focuses on real-time hand gesture recognition in the Turkish sign language detection system. YOLOv4-CSP based on convolutional neural network (CNN), a state-of-the-art object detection algorithm, is used to provide real-time and high-performance detection. The YOLOv4-CSP algorithm is created by adding CSPNet to the neck of the original YOLOv4 to improve network performance. A new object detection model has been proposed by optimizing the YOLOv4-CSP algorithm in order to provide more efficient detection in Turkish sign language. The model uses CSPNet throughout the network to increase the learning ability of the network. However, Proposed YOLOv4-CSP has a learning model with Mish activation function, complete intersection of union (CIoU) loss function and transformer block added. The Proposed YOLOv4-CSP algorithm has faster learning with transfer learning than previous versions. This allows the proposed YOLOv4-CSP algorithm to perform a faster restriction and recognition of static hand signals simultaneously. To evaluate the speed and detection performance of the proposed YOLOv4-CSP model, it is compared with previous YOLO series, which offers real-time detection, as well. YOLOv3, YOLOv3-SPP, YOLOv4-CSP and proposed YOLOv4-CSP models are trained with a labeled dataset consisting of numbers in Turkish Sign language, and their performances on the hand signals recognitions are compared. With the proposed method, 98.95% precision, 98.15% recall, 98.55 F1 score and 99.49% mAP results are obtained in 9.8 ms. The proposed method for detecting numbers in Turkish sign language outperforms other algorithms with both real-time performance and accurate hand sign prediction, regardless of background.