Reviewerly/exHarmony huggingface dataset:
Modernizing and Revolutionizing Land Record Management with Blockchain

Authors:
raw_author_name: None Dr. Pramod Patil
raw_affiliation_string: Dr. D. Y. Patil Institute of Technology, Pune, India

Abstract: In the modern era, access to land records has become essential, enabling individuals to verify land ownership for both personal and business purposes. In India, managing land records has proven to be a formidable challenge given the country's vast population. The current system has suffered from incomplete registries, which, in turn, has facilitated fraudulent activities. Moreover, the existing government system is plagued by inconsistencies and involves numerous intermediaries with access to the records, raising concerns about unauthorized alterations. With the increasing acceptance of blockchain technology, the government is actively exploring its implementation to address these issues. In a world moving towards Web 3.0, integrating land records into a blockchain offers significant advantages, primarily through enhanced transparency. This approach ensures that citizens have unhindered access to land records, complete with visual representations on maps, promoting greater inclusivity and accountability
A Blockchain-based federated learning framework for secure aggregation and fair incentives

Authors:
raw_author_name: XiaoHui Yang
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, People’s Republic of China

Abstract: Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.
Transformation of Elliptic Curve Discrete Logarithm Problem to QUBO Using Direct Method in Quantum Annealing Applications

Authors:
raw_author_name: Michał Wroński
raw_affiliation_string: NASK National Research Institute, Warsaw, Poland
raw_author_name: Elżbieta Burek
raw_affiliation_string: Military University of Technology, Warsaw, Poland
raw_author_name: Łukasz Dzierzkowski
raw_affiliation_string: Military University of Technology, Warsaw, Poland
raw_author_name: Olgierd Żołnierczyk
raw_affiliation_string: Military University of Technology, Warsaw, Poland

Abstract: This paper investigates how to reduce the elliptic curve discrete logarithm problem over prime fields to the quadratic unconstrained binary optimization (QUBO) problem in order to obtain as few logical qubits as possible. In the best case scenario, if n is the bitlength of a characteristic of prime field Fp, approximately 3n³ logical qubits are required for such a reduction in the Edwards curve case. We present a practical attack on an elliptic curve discrete logarithm problem over the 3-bit prime field F7 for an elliptic curve with the subgroup of order 8. We solved this problem using the D-Wave Advantage QPU. To the best of the authors' knowledge, no one has made, so far, a practical attack on the elliptic curve discrete logarithm over a prime field using the direct quantum method.
Flipover outperforms dropout in deep learning

Authors:
raw_author_name: Yuxuan Liang
raw_affiliation_string: Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
raw_author_name: Chuang Niu
raw_affiliation_string: Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
raw_author_name: Pingkun Yan
raw_affiliation_string: Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
raw_author_name: Ge Wang
raw_affiliation_string: Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

Abstract: Abstract Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.
Self-attention Guidance Based Crowd Localization and Counting

Authors:
raw_author_name: Guanghua Gu
raw_affiliation_string: School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066000, China

Abstract: Most existing studies on crowd analysis are limited to the level of counting, which cannot provide the exact location of individuals. This paper proposes a self-attention guidance based crowd localization and counting network (SA-CLCN), which can simultaneously locate and count crowds. We take the form of object detection, using the original point annotations of crowd datasets as supervision to train the network. Ultimately, the center point coordinate of each head as well as the number of crowds are predicted. Specifically, to cope with the spatial and positional variations of the crowd, the proposed method introduces transformer to construct a globallocal feature extractor (GLFE) together with the convolutional structure. It establishes the near-to-far dependency between elements so that the global context and local detail features of the crowd image can be extracted simultaneously. Then, this paper designs a pyramid feature fusion module (PFFM) to fuse the global and local information from high level to low level to obtain a multiscale feature representation. In downstream tasks, this paper predicts candidate point offsets and confidence scores by a simple regression header and classification header. In addition, the Hungarian algorithm is used to match the predicted point set and the labelled point set to facilitate the calculation of losses. The proposed network avoids the errors or higher costs associated with using traditional density maps or bounding box annotations. Importantly, we have conducted extensive experiments on several crowd datasets, and the proposed method has produced competitive results in both counting and localization.
Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics

Authors:
raw_author_name: Jen-Yu Han
raw_affiliation_string: Department of Civil Engineering, National Taiwan University, Taipei City, Taiwan;
raw_author_name: Sin-Yi Li
raw_affiliation_string: Department of Civil and Industrial Engineering, Uppsala University, Uppsala, Sweden

Abstract: Solar irradiance and temperature are two primary factors that affect the energy generation efficiency of solar photovoltaic (PV) systems, meaning that climate change may significantly impact the production of solar energy in the future.In this study, a two-way sensitivity analysis is carried out to determine the energy generation potential under future climate change conditions, and conditions of shadow covering are also considered.The simulation results indicate that the impact of solar irradiance causes the most variation in energy generation, and that shadow covering has a weak impact on the output.In the one-way sensitivity analysis, solar irradiance makes a ± 18% difference in energy generation, whereas shadow covering represents about ± 2%.In the future trend of climate change in the world and Taiwan, the energy generation potential can decrease up to 21% and increase 8%, respectively, mainly corresponded by solar irradiance.We suggest that compared with the issue of temperature, the efficiency of solar irradiance usage is more important, especially in view of the global trend toward solar irradiance loss.Taiwan also suffers the issue, but the different trend of climate change impact may make the challenge different.
Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Authors:
raw_author_name: Guorong Cai
raw_affiliation_string: Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China
raw_author_name: Hang Xu
raw_affiliation_string: Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China

Abstract: Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies. In this paper, the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles: uncertainty probability, text similarity difference degree and active learning variant prediction divergence degree. By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs, the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples. As the optimal samples are continuously added into the initial training set, the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved. Therefore, compared with other active learning strategies, a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples. Finally, the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%, the weight-recall by 2.45%, and the weight-precision by 2.64% in comparison with other strategies, saving approximately 46% of the labelled data compared with the machine learning approach.
The AI empathy effect: a mechanism of emotional contagion

Authors:
raw_author_name: Tingting Zhang
raw_affiliation_string: College of International Tourism and Public Administration, Hainan University, Haikou, China
raw_author_name: Wei Han
raw_affiliation_string: College of International Tourism and Public Administration, Hainan University, Haikou, China
raw_author_name: Yupeng Zhu
raw_affiliation_string: DonSys Technology (Shandong) Co. Ltd, China

Abstract: While advances in technology have led to the widespread use of artificial intelligence (AI) in frontline services, the AI empathic effect is often overlooked. Based on emotional contagion theory, this research explores the impacts of AI emotional mimicry and empathic concern in service success and failure via experimental designs, respectively. Results indicate that AI with high emotional mimicry and empathic concern resulted in higher arousal and pleasure, increasing tourists’ continuous usage intention. Arousal and pleasure played significant mediating roles in service success. However, while empathic concern significantly affected continuous usage intention through pleasure, arousal did not have a similar effect. The joint effects of extrinsic and intrinsic anthropomorphism (emotional mimicry) were significant in a successful service context, but not in service failure. These results have important implications for the development and promotion of AI and enriches theoretical applications of emotional contagion theory and empathy.
Mask-Pyramid Network: A Novel Panoptic Segmentation Method

Authors:
raw_author_name: Peng-Fei Xian
raw_affiliation_string: Department of Electronic Engineering, City University of Hong Kong, Hong Kong
raw_author_name: Lai-Man Po
raw_affiliation_string: Department of Electronic Engineering, City University of Hong Kong, Hong Kong
raw_author_name: Yu-Zhi Zhao
raw_affiliation_string: Department of Electronic Engineering, City University of Hong Kong, Hong Kong
raw_author_name: Wing-Yin Yu
raw_affiliation_string: Department of Electronic Engineering, City University of Hong Kong, Hong Kong
raw_author_name: Kwok-Wai Cheung
raw_affiliation_string: School of Communication, The Hang Seng University of Hong Kong, Hong Kong

Abstract: In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most of the box proposals are suppressed and discarded in the Non-Maximum Suppression process. Additionally, for panoptic segmentation, it is a problem to properly fuse the semantic segmentation results with the Mask RCNN-produced instance segmentation results. To address these issues, we propose a new mask pyramid mechanism to distinguish objects and generate much fewer proposals by referring to existing segmented masks, so as to reduce computing resource consumption. The Mask-Pyramid Network generates object proposals and predicts masks from larger to smaller sizes. It records the pixel area occupied by the larger object masks, and then only generates proposals on the unoccupied areas. Each object mask is represented as a H × W × 1 logit, which fits well in format with the semantic segmentation logits. By applying SoftMax to the concatenated semantic and instance segmentation logits, it is easy and natural to fuse both segmentation results. We empirically demonstrate that the proposed Mask-Pyramid Network achieves comparable accuracy performance on the Cityscapes and COCO datasets. Furthermore, we demonstrate the computational efficiency of the proposed method and obtain competitive results.
Dynamic educational recommender system based on Improved LSTM neural network

Authors:
raw_author_name: Hadis Ahmadian Yazdi
raw_affiliation_string: Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

Abstract: Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human-computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.
Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations

Authors:
raw_author_name: Lichao Peng
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
raw_author_name: Longfei Li
raw_affiliation_string: National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
raw_author_name: Qinyong Dai
raw_affiliation_string: National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
raw_author_name: Mengjiao Pei
raw_affiliation_string: National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
raw_author_name: Jian Su
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
raw_author_name: Han Zhang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
raw_author_name: Yun Li
raw_affiliation_string: National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China

Abstract: The brain's function can be dynamically reconfigured through a unified neuron–synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric–electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.
Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

Authors:
raw_author_name: Hossam Magdy Balaha
raw_affiliation_string: Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
raw_author_name: Eman M. El-Gendy
raw_affiliation_string: Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
raw_author_name: Mahmoud M. Saafan
raw_affiliation_string: Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

Abstract: Abstract Prostate cancer is the one of the most dominant cancer among males. It represents one of the leading cancer death causes worldwide. Due to the current evolution of artificial intelligence in medical imaging, deep learning has been successfully applied in diseases diagnosis. However, most of the recent studies in prostate cancer classification suffers from either low accuracy or lack of data. Therefore, the present work introduces a hybrid framework for early and accurate classification and segmentation of prostate cancer using deep learning. The proposed framework consists of two stages, namely classification stage and segmentation stage. In the classification stage, 8 pretrained convolutional neural networks were fine-tuned using Aquila optimizer and used to classify patients of prostate cancer from normal ones. If the patient is diagnosed with prostate cancer, segmenting the cancerous spot from the overall image using U-Net can help in accurate diagnosis, and here comes the importance of the segmentation stage. The proposed framework is trained on 3 different datasets in order to generalize the framework. The best reported classification accuracies of the proposed framework are 88.91% using MobileNet for the “ISUP Grade-wise Prostate Cancer” dataset and 100% using MobileNet and ResNet152 for the “Transverse Plane Prostate Dataset” dataset with precisions 89.22% and 100%, respectively. U-Net model gives an average segmentation accuracy and AUC of 98.46% and 0.9778, respectively, using the “PANDA: Resized Train Data (512 × 512)” dataset. The results give an indicator of the acceptable performance of the proposed framework.