Reviewerly/exHarmony huggingface dataset:
The (Limited) Power of Blockchain Networks for Information Provision

Authors:
raw_author_name: Benedikt Franke
raw_affiliation_string: University of Würzburg, 97070 Würzburg, Germany;
raw_author_name: André Stenzel
raw_affiliation_string: Bank of Canada, Ottawa, Ontario K1A 0G9, Canada

Abstract: We investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We provide a theoretical model in which heterogeneous firms choose between adopting a blockchain application or relying on traditional third-party intermediaries to inform the capital market. The blockchain’s ability to generate information depends on each firm’s data profile and all firms’ endogenous adoption decisions. We show that blockchain technology can improve the information environment and outperform traditional institutions with firms’ adoption decisions serving as a credible value signal and the application uncovering firm values by analyzing all participating firms’ data. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms, but also in aggregate. The equilibrium is a warning sign that has broad implications for policymakers’ regulatory effort and investors’ assessment of corporate blockchain applications. This paper was accepted by Suraj Srinivasan, accounting. Funding: B. Franke and Q. Gao Fritz gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 403041268–TRR 266 Accounting for Transparency. A. Stenzel gratefully acknowledges financial support from the DFG through CRC TR 224 (Project C03) during prior employment at the University of Mannheim. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4718 .
Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems

Authors:
raw_author_name: Adit Goyal
raw_affiliation_string: Department of Computer Science, Northwestern University, Evanston, IL, USA
raw_author_name: Murari Mandal
raw_affiliation_string: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India
raw_author_name: Moayad Aloqaily
raw_affiliation_string: Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Masdar City, Abu Dhabi, United Arab Emirates
raw_author_name: Vinay Chamola
raw_affiliation_string: Department of Electrical and Electronics Engineering and APPCAIR, BITS-Pilani, Pilani, India

Abstract: Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset: https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.
Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Authors:
raw_author_name: Dazhi Yang
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang, China
raw_author_name: Sebastian Zainali
raw_affiliation_string: Mälardalen University, Department of Sustainable Energy Systems, Västerås, Sweden
raw_author_name: Bengt Stridh
raw_affiliation_string: Mälardalen University, Department of Sustainable Energy Systems, Västerås, Sweden
raw_author_name: Anders Avelin
raw_affiliation_string: Mälardalen University, Department of Sustainable Energy Systems, Västerås, Sweden
raw_author_name: Pietro Campana
raw_affiliation_string: Mälardalen University, Department of Sustainable Energy Systems, Västerås, Sweden

Abstract: Photosynthetically active radiation is a key parameter for modelling the photosynthetic behaviour of plants in response to sunlight and, subsequently, for determining crop yield. Separating photosynthetically active radiation into direct and diffuse components is of significance to agrivoltaic systems, which combine solar energy conversion and agricultural farming on the same portion of land. Placing photovoltaic on agricultural land results in varying shading conditions throughout the day and seasons, producing a higher contribution of incident diffuse radiation to the crops beneath the system in these shaded regions. Additionally, photosynthesis is more efficient under conditions of diffuse radiation than direct radiation per unit of total photosynthetically active radiation. This study introduces a new separation model capable of accurately estimating the diffuse component from the global photosynthetically active radiation and conveniently retrievable meteorological parameters. The model builds upon one of the currently most performing separation models for decomposing global solar radiation into diffuse radiation, the YANG2 model. Four new predictors, found relevant in the literature to influence diffuse photosynthetically active radiation, are added: optical thickness, vapour pressure deficit, aerosol optical depth, and surface albedo. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58° N, outperforming four other models in all examined locations, with R2 values greater than 0.90. To alleviate the scarcity of photosynthetically active radiation studies in high-latitude regions, the seasonal trends and variation of the various radiation components are analysed. To demonstrate the applicability of the developed model, the location of Sweden's first agrivoltaic system is used to assess a variety of cases that take into account the various data availability that is representative of current and future agrivoltaic systems. In cases where in-situ measurements of diffuse photosynthetically active radiation are not available, the results indicate that the model can be utilised and adjusted for adjacent stations. Utilising predictor values derived from satellite data is an alternative method, but the spatial resolution must be considered with caution.
Generalize Deep Neural Networks With Adaptive Regularization for Classifying

Authors:
raw_author_name: Kehua Guo
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Ze Tao
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Lingyan Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Bin Hu
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Xiaoyan Kui
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China

Abstract: Regularization is a crucial technology to improve the generalization of deep neural networks. However, traditional regularization method approaches are all scenario-specific, because they are generally with ingeniously designed feature representations from input layer, hidden layer, and output layer, which increase the difficulty of model development and interpretation. To this end, a novel practical and flexible regularization method is presented to obtain higher generalization and interpretability. Specifically, the feature maps are decoupled by global suppression and partial suppression from various scales and locate the salient feature with strong low-resolution semantic information. Moreover, the guided discarding specification for feature decoupling by measuring the feature contributions to network decisions, leads to the logics with better interpretability. Subsequently, the max values of the feature map are suppressed by discarding the corresponding salient features. Comprehensive experiments demonstrate that the proposed adaptive regularization outperforms the state-of-the-art performance in image classification accuracy, generalization, and interpretability on several widely used datasets. And adaptive regularization helps the network to mine the connection between salient features, nonsalient features, and ground truth, encouraging the network to construct multiple layers of feature associations.
Attacking Distance-aware Attack: Semi-targeted Model Poisoning on Federated Learning

Authors:
raw_author_name: Yuwei Sun
raw_affiliation_string: University of Tokyo, Tokyo, Japan
raw_author_name: Hideya Ochiai
raw_affiliation_string: University of Tokyo, Tokyo, Japan
raw_author_name: Jun Sakuma
raw_affiliation_string: University of Tsukuba, Tsukuba, Japan

Abstract: Existing model poisoning attacks on federated learning (FL) assume that an adversary has access to the full data distribution. In reality, an adversary usually has limited prior knowledge about clients' data. A poorly chosen target class renders an attack less effective. This work considers a semi-targeted situation where the source class is predetermined but the target class is not. The goal is to cause the misclassification of the global classifier on data from the source class. Approaches such as label flipping have been used to inject malicious parameters into FL. Nevertheless, it has shown that their performances are usually class-sensitive, varying with different target classes. Typically, an attack becomes less effective when shifting to a different target class. To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) that enhances model poisoning in FL by finding the optimized target class in the feature space. ADA deduces pair-wise class attacking distances using a Fast LAyer gradient MEthod (FLAME). Extensive evaluations were performed on five benchmark image classification tasks and three model architectures using varying attacking frequencies. Furthermore, ADA's robustness to conventional defenses of Byzantine-robust aggregation and differential privacy was validated. The results showed that ADA succeeded in increasing attack performance to 2.8 times in the most challenging case with an attacking frequency of 0.01 and bypassed existing defenses, where differential privacy that was the most effective defense still could not reduce the attack performance to below 50%.
Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure

Authors:
raw_author_name: Xiao-Yu Zhang
raw_affiliation_string: School of Artificial Intelligence, Anhui University, Hefei, China
raw_author_name: Jose-Rodrigo Cordoba-Pachon
raw_affiliation_string: School of Business and Management, Royal Holloway, University of London, Egham, U.K
raw_author_name: Chris Watkins
raw_affiliation_string: Department of Computer Science, Royal Holloway, University of London, Egham, U.K
raw_author_name: Stefanie Kuenzel
raw_affiliation_string: Department of Electronic Engineering, Royal Holloway, University of London, Egham, U.K

Abstract: Advanced metering infrastructure (AMI) is the backbone of the next generation smart city and smart grid; it not only provides near real-time two-way communication between the consumers and the energy systems, but also enables the third parties (TPs) to provide relevant value-added services to the consumers to improve user satisfaction. However, the existing services are implemented in a centralized manner, which has potential and associated security and privacy risks also increased with Internet-of-Things (IoT) devices. To better balance the quality of the services and ensure users’ privacy, a TP AMI service model based on differentially private federated learning (FL) is proposed in this article. Instead of sending the private energy data to the cloud server, the proposed service model trains the neural network models locally, and only model parameters are shared with the central server. Moreover, the identity of individuals is eliminated by adding random Gaussian noise during the secure aggregation. Furthermore, an attention-based bidirectional long short-term memory (ATT-BLSTM) neural network model is adopted to solve the long-range dependency problem of conventional neural networks. In the case study, a residential short-term load forecasting (STLF) task is implemented to evaluate the performance of the proposed model. Compared with other state-of-the-art energy service models, the proposed one can achieve similar accuracy as the typical centralized model and balances the trade-off between privacy loss and prediction accuracy flexibly.
Efficiency Improvement to Neural-Network-Driven Optimal Path Planning via Region and Guideline Prediction

Authors:
raw_author_name: Yuan Huang
raw_affiliation_string: Graduate School of Information, Production and Systems, University of Waseda, Kitakyushu, Japan
raw_author_name: Cheng-Tien Tsao
raw_affiliation_string: Graduate School of Information, Production and Systems, University of Waseda, Kitakyushu, Japan
raw_author_name: Hee-Hyol Lee
raw_affiliation_string: Graduate School of Information, Production and Systems, University of Waseda, Kitakyushu, Japan

Abstract: Traditional sampling-based algorithms rely on random samples to explore a whole configuration space of robots for optimal path planning, while a uniform sampler impedes the exploration with randomly generated samples, leading to a long calculation time, especially in complex environments. Recently, neural-network-driven methods have attracted wide interest in developing non-uniform sampling to improve the sampling efficiency and reduce the calculation time. A region that contains an optimal path is predicted by neural networks and employed subsequently to biasedly generate samples. This work aims at enhancing the sampling efficiency and reducing the calculation time of the optimal path planning by a novel region and guideline prediction (denoted as RGP) model. We innovatively propose the RGP model with a guideline prediction module to estimate the guideline distributions, which are characterized by the central line of the predicted region. The predicted region and guideline are integrated into a sampling-based algorithm, namely RGP-RRT*, with an adaptively biased sampling strategy to select a proper domain for sampling. Simulations demonstrate the RGP model outperforms other region prediction models in accuracy and robustness. Besides, the RGP-RRT* reliably achieves a 7.2-80.1% reduction in calculation time and a 2.0-58.1% reduction in sample number compared with other neural-network-driven methods. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RTPWDSDM/RGP-RRTstar</uri> .
Rethinking of BERT Sentence Embedding for Text Classification

Authors:
raw_author_name: Omar Galal
raw_affiliation_string: Cairo University
raw_author_name: Ahmed H. Abdel-Gawad
raw_affiliation_string: Cairo University
raw_author_name: Mona Farouk
raw_affiliation_string: Cairo University

Abstract: Abstract Text classification is a fundamental task in NLP that is used in several real-life tasks and applications. Large pre-trained language models such as BERT achieve state-of-the-art performance in several NLP tasks including text classification tasks. Although BERT boosts text classification performance, the common way of using it for classification lacks many aspects of its advantages. This work rethinks the way of using BERT final layer and hidden layers embeddings by proposing different aggregation architectures for text classification tasks such as sentiment analysis and sarcasm detection. This research also proposes different approaches for using BERT as a feature extractor without fine-tuning whose performance surpasses its fine-tuning counterpart. It also proposes promising multi-task learning aggregation architectures to improve the performance of the related classification problems. The experiments of the different architectures show that freezing BERT can outperform fine-tuning it for sentiment analysis. The experiments also show that multi-task learning while freezing BERT boosts the performance of yet hard tasks such as sarcasm detection. The best-performing models achieved new state-of-the-art performance on the ArSarcasm-v2 dataset for Arabic sarcasm detection and sentiment analysis. For multi-task learning and freezing BERT, a new SOTA F1-score of 64.41 was achieved for the sarcasm detection with a 3.47% improvement and near SOTA FPN of 75.78 for the sentiment classification. For single-task learning, a new SOTA FPN of 75.26 was achieved for the sentiment with a 1.81% improvement.
Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme

Authors:
raw_author_name: Lianhai Wang
raw_affiliation_string: The Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China; The Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan 250014, China

Abstract: To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.
Adaptive variational simulation for open quantum systems

Authors:
raw_author_name: Huo Chen
raw_affiliation_string: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
raw_author_name: Niladri Gomes
raw_affiliation_string: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
raw_author_name: Siyuan Niu
raw_affiliation_string: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
raw_author_name: Wibe Albert de Jong
raw_affiliation_string: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

Abstract: Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop quantum algorithms that can effectively simulate open quantum systems. Here we present an adaptive variational quantum algorithm for simulating open quantum system dynamics described by the Lindblad equation. The algorithm is designed to build resource-efficient ansatze through the dynamical addition of operators by maintaining the simulation accuracy. We validate the effectiveness of our algorithm on both noiseless simulators and IBM quantum processors and observe good quantitative and qualitative agreement with the exact solution. We also investigate the scaling of the required resources with system size and accuracy and find polynomial behavior. Our results demonstrate that near-future quantum processors are capable of simulating open quantum systems.
Are we ready for education in Metaverse? PLS-SEM analysis

Authors:
raw_author_name: Tri-Quan Dang
raw_affiliation_string: Ho Chi Minh City University of Foreign Languages-Information Technology, HUFLIT, Vietnam
raw_author_name: Luan-Thanh Nguyen
raw_affiliation_string: Ho Chi Minh City University of Foreign Languages-Information Technology, HUFLIT, Vietnam
raw_author_name: Thuy-Thanh Thi Tran
raw_affiliation_string: Vinh University, Vinh City, Vietnam

Abstract: The objective of this study is to investigate the adoption of met-averse education in Ho Chi Minh City, Vietnam, by employing the Theory of Planned Behavior (TPB) as the foundational theoretical framework. Metaverse education, utilizing immersive and interactive virtual environments for educational objectives, is an innovative and auspicious methodology in the contemporary digital realm. Data collection was conducted via judgmental sampling. Partial least squares structural equation modeling was used for the analysis. The results suggest a significant and positive impact of Consumer Intention (CI) on the practical use (AU) of the met-averse in the field of education. Furthermore, the factors of Perceived Behavioral Control (PBC), Subjective Norm (SN), and Perceived Value strongly influenced Consumer Intention (CI) in a positive manner. Moreover, Emotional Value (EV) and Social Value (SV) had a substantial impact on the Perceived Value (PV) of using the metaverse in educational contexts. Nevertheless, the lack of statistical significance in the relationship between Attitude towards Behavior (ATB) and Consumer Intention (CI) is recognized. This research not only addresses a significant gap in the theory of IS literature but also offers valuable insights for educators, policymakers, and developers. If you really understand the things that make people open to metaverse education, you can make big changes to plans and actions that are used to successfully add metaverse education to the regular school setting, especially in Ho Chi Minh City, Vietnam.