Reviewerly/exHarmony huggingface dataset:
Modeling to Achieve Area Business Continuity Management Implementation via a Fuzzy Cognitive Map

Area business continuity management (Area-BCM) is introduced to enhance sustainable economic growth by building public–private partnerships. It is implemented in pilot industrial zones in disaster-prone regions to tackle problems beyond a single organization’s capacity. The framework emphasizes multiple stakeholders in the decision-making process, but participation and implementation remain major challenges for many practitioners in the search for potential pathways. Therefore, this study presents a model of causal relationships between concepts to achieve the implementation of Area-BCM. To capture expert perceptions and visualize relationships, a fuzzy cognitive map (FCM) is deployed. The use of fuzzy logic facilitates the integration of diverse viewpoints and the representation of ambiguous and complex scenarios. Initially, 28 appropriate concepts were identified by reviewing the literature on practical Area-BCM cases, which were then scrutinized by experts, including eight driving causes, eleven required actions, and nine outcome variables. Subsequently, FCMs were constructed through individual interviews. Since the FCMs had been aggregated, a scenario analysis was performed under five different conditions to evaluate potential strategies. The simulation results present promising concepts that could improve Area-BCM implementation. The findings emphasize that these strategies will have a positive influence when top management is committed, government support is achieved, and workshops exist.
A Sea Fog Image Defogging Method Based on the Improved Convex Optimization Model

Due to the high fog concentration in sea fog images, serious loss of image details is an existing problem, which reduces the reliability of aerial visual-based sensing platforms such as unmanned aerial vehicles. Moreover, the reflection of water surface and spray can easily lead to overexposure of images, and the assumed prior conditions contained in the traditional fog removal method are not completely valid, which affects the restoration effectiveness. In this paper, we propose a sea fog removal method based on the improved convex optimization model, and realize the restoration of images by using fewer prior conditions than that in traditional methods. Compared with dark channel methods, the solution of atmospheric light estimation is simplified, and the value channel in hue–saturation–value space is used for fusion atmospheric light map estimation. We construct the atmospheric scattering model as an improved convex optimization model so that the relationship between the transmittance and a clear image is deduced without any prior conditions. In addition, an improved split-Bregman iterative method is designed to obtain the transmittance and a clear image. Our experiments demonstrate that the proposed method can effectively defog sea fog images. Compared with similar methods in the literature, our proposed method can actively extract image details more effectively, enrich image color and restore image maritime targets more clearly. At the same time, objective metric indicators such as information entropy, average gradient, and the fog-aware density evaluator are significantly improved.
Tight lower bounds on the time it takes to generate a geometric phase

Abstract Geometric phase is a concept of central importance in virtually every branch of physics. In this paper, we show that the evolution time of a cyclically evolving quantum system is restricted by the system’s energy resources and the geometric phase acquired by the state. Specifically, we derive and examine three tight lower bounds on the time required to generate any prescribed Aharonov-Anandan geometric phase. The derivations are based on recent results on the geometric character of the Mandelstam-Tamm and Margolus-Levitin quantum speed limits.
Large Language Models as Recommendation Systems in Museums

This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.
Self-Supervised Interactive Embedding for One-shot Organ Segmentation

One-shot organ segmentation (OS2) aims at segmenting the desired organ regions from the input medical imaging data with only one pre-annotated example as the reference. By using the minimal annotation data to facilitate organ segmentation, OS2 receives great attention in the medical image analysis community due to its weak requirement on human annotation. In OS2, one core issue is to explore the mutual information between the support (reference slice) and the query (test slice). Existing methods rely heavily on the similarity between slices, and additional slice allocation mechanisms need to be designed to reduce the impact of the similarity between slices on the segmentation performance. To address this issue, we build a novel support-query interactive embedding (SQIE) module, which is equipped with the channel-wise co-attention, spatial-wise co-attention, and spatial bias transformation blocks to identify "what to look", "where to look", and "how to look" in the input test slice. By combining the three mechanisms, we can mine the interactive information of the intersection area and the disputed area between slices, and establish the feature connection between the target in slices with low similarity. We also propose a self-supervised contrastive learning framework, which transforms knowledge from the physical position to the embedding space to facilitate the self-supervised interactive embedding of the query and support slices. Comprehensive experiments on two large benchmarks demonstrate the superior capacity of the proposed approach when compared with the current alternatives and baseline models.
Unveiling the nexus and promoting integration of diverse factors: Prospects of big data-driven artificial intelligence technology in achieving carbon neutrality in Chongming District

Climate change is one of the most pressing challenges facing the world today. The large amount of greenhouse gas emissions produced by human activities, especially the emission of carbon dioxide, is an important driving factor behind climate issues. Under the background of China’s “3060” decarbonization goal”, Chongming District in Shanghai is actively promoting the construction of a world-class ecological island and is committed to creating a carbon–neutral demonstration zone with global influence. However, Chongming District faces challenges as the mechanism of carbon-neutrality transition path remains unclear. The data related to evaluating carbon neutrality status are heterogeneous from multiple sources. It is difficult to effectively implement relevant evaluation and response measures, impeding the progress of its low-carbon transformation. In response to the aforementioned challenges, this paper will propose and discuss the potential methods based on the new generation of information technology, represented by big data and artificial intelligence. These technologies aim to facilitate the integration of diverse factors, including carbon, and explore the nexus among them, thus exploring pathways for low-carbon transformation, and ultimately achieving decarbonization goal in Chongming District. Hopefully, the research conducted in this paper will contribute to the efforts of China and the global community in addressing carbon-related challenges and advancing towards a more sustainable and low-carbon future.
HFedSNN: Efficient Hierarchical Federated Learning using Spiking Neural Networks

Federated Learning (FL) has emerged in edge computing to address privacy concerns in mobile networks. It allows the mobile devices to collaboratively train a model while keeping training data where they were generated. However, in practice, it suffers from several issues such as (i) robustness, due to a single point of failure, (ii) latency, as it requires a significant amount of communication resources, and (iii) convergence, due to system and statistical heterogeneity. To cope with these issues, Hierarchical FL (HFL) has been proposed as a promising alternative. HFL adds the edge servers as an intermediate layer for sub-model aggregation, several iterations will be performed before the global aggregation at the cloud server takes place, thus making the overall process more efficient, especially with non-independent and identically distributed (non-IID) data. Moreover, using traditional Artificial Neural Networks (ANNs) with HFL consumes a significant amount of energy, further hindering the application of decentralized FL on energy-constrained mobile devices. Therefore, this paper presents HFedSNN: an energy-efficient and fast-convergence model by incorporating Spike Neural Networks (SNNs) within HFL. SNN is a generation of neural networks, which promises tremendous energy and computation efficiency improvements. Taking advantage of HFL and SNN, numerical results demonstrate that HFedSNN outperforms FL with SNN (FedSNN) in terms of accuracy and communication overhead by 4.48% and 26×, respectively. Furthermore, HFedSNN significantly reduces energy consumption by 4.3× compared to FL with ANN (FedANN).
Enhancing Branch-and-Bound for Multiobjective 0-1 Programming

In the biobjective branch-and-bound literature, a key ingredient is objective branching, that is, to create smaller and disjoint subproblems in the objective space, obtained from the partial dominance of the lower bound set by the upper bound set. When considering three or more objective functions, however, applying objective branching becomes more complex, and its benefit has so far been unclear. In this paper, we investigate several ingredients that allow us to better exploit objective branching in a multiobjective setting. We extend the idea of probing to multiple objectives for the 0-1 case, enhance it in several ways, and show that, when coupled with objective branching, it results in significant speedups in terms of CPU times. We also show how to adapt it to the general integer case. Furthermore, we investigate cut generation based on the objective branching constraints. Besides, we generalize the best bound idea for node selection to multiple objectives, and we show that the proposed rules outperform, in the multiobjective literature, the commonly employed depth-first and breadth-first strategies. We also analyze problem-specific branching rules. We test the proposed ideas on available benchmark instances for three problem classes with three and four objectives, namely, the capacitated facility location problem, the uncapacitated facility location problem, and the knapsack problem. Our enhanced multiobjective branch-and-bound algorithm outperforms the best existing branch-and-bound–based approach and is the first to obtain competitive and even slightly better results than a state-of-the-art objective space search method on a subset of the problem classes. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms – Discrete. Funding: This work was supported in whole, or in part, by the Austrian Science Fund [Grant P 31366]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.0299 .
Augmented reality in orthodontics for bracket placement using conventional mobile devices: Technical note

Improving bracket placement accuracy through computer-aided design and a bracket navigation set supported by augmented reality (AR).A technical workflow was developed for implementing AR-assisted orthodontic bracket positioning through a smartphone application. This innovative approach eliminates the need for three-dimensional radiation imaging or physical guides, making it a safe and convenient option for clinical use by overlapping the digitally planned bracket position over the patient clinical crown for a precise recommendation of bracket positioning.It was found that it is achievable and can be easily recognized from all view angles, and this proves that new techniques with new opportunities could be considered.AR smartphone applications can potentially be used for the accurate placement of dental brackets; thus, such applications show promise for use in the field of orthodontics.
Multicamera edge-computing system for persons indoor location and tracking

This paper presents an indoor person localization and tracking system that uses multiple smart cameras equipped with artificial intelligence (AI) accelerators serving as edge-computing nodes. Our main contributions are as follows: (a) the development of a new multicamera tracking system for indoor scenarios; (b) the release of a multitarget multicamera tracking dataset; and (c) the development of an annotation mechanism based on waypoints. The system can simultaneously track several individuals while preserving their privacy and anonymity, because no images or sensitive data are transmitted outside the edge nodes. Only the position and appearance of each person were transmitted to the central server. In addition, a multitarget multicamera tracking dataset was released. The dataset contains recordings from five cameras in an indoor scenario and is annotated with the real-world coordinates of individuals. Ground-truth annotations were semiautomatically generated using a mechanism in which people equipped with mobile phones followed specific paths with predefined waypoints. Software related to the ground-truth annotation mechanism has also been released as open source.
Progress analysis of a multi-recombinative evolution strategy on the highly multimodal Rastrigin function

A first and second order progress rate analysis was conducted for the intermediate multi-recombinative Evolution Strategy (μ/μI,λ)-ES with isotropic scale-invariant mutations on the highly multimodal Rastrigin test function. Closed-form analytic solutions for the progress rates are obtained in the limit of large dimensionality and large populations. The first order results are able to model the one-generation progress including local attraction phenomena. Furthermore, a second order progress rate is derived yielding additional correction terms and further improving the progress model. The obtained results are compared to simulations and show good agreement, even for moderately large populations and dimensionality. The progress rates are applied within a dynamical systems approach, which models the evolution using difference equations. The obtained dynamics are compared to real averaged optimizations runs and yield good agreement. The results improve further when dimensionality and population size are increased. Local and global convergence is investigated within given model showing that large mutations are needed to maximize the probability of global convergence, which comes at the expense of efficiency. An outlook regarding future research goals is provided.
Implementation of Private Blockchain Extension on Open Source IoT Platform using Hyperledger Besu

The Internet of Things has been rapidly developed in many applications. Currently, a lot of IoT applications are using a conventional database as their data accumulation process. A conventional database is centralized and not immutable. So, data security will be a challenging task in IoT applications, especially for confidential data and reliable data. In this research, we would like to build a comprehensive security system to increase IoT data security in the IoT platform. The proposed system is an implementation of blockchain extension in the IoT platform as a connector to the blockchain network. Blockchain extension will be contained of WebSocket, Kafka, and smart contracts. WebSocket act as a data collector from the IoT platform, Kafka act as data queue processing, and the smart contract will act as data models and contract that will be stored and executed as a transaction of data in the blockchain. Kafka makes the blockchain extension more accessible from the outside platform, so users are able to build their smart contract by subscribing to Kafka’s topic. We have tested our system on several bandwidths in several scenarios (Blockchain API execution time, Blockchain API speed, Blockchain, retention performance, and Smart Contract Vulnerability Testing). In Kafka, the optimal Transaction Per Second value is on 8Mbps with 0.76TPS and the smallest TPS value is 0.46TPS at 2Mbps. We also tested the smart contract vulnerability, the results are high severity, and the estimated gas will be 29898 – 136483 gas fees.
Short-Term Photovoltaic Power Prediction Based on Bayesian Regularized BP Neural Networks

In view of the problem that the randomness and volatility of photovoltaic power generation lead to low prediction accuracy, this paper proposes a short-term prediction method for photovoltaic power generation power based on Bayesian regularization algorithm to optimize BP neural network, and analyzes the factors affecting photovoltaic power generation power prediction model for photovoltaic power generation was established, which was trained in neural networks by Bayesian regularization algorithm optimization, L-M optimization algorithm and traditional gradient drop method, and the prediction results of the three methods were compared with the study, and the experimental results showed that the method proposed in this paper was effective Improved photovoltaic power prediction accuracy, which can be used for short-term accurate prediction of photovoltaic power.
Underwater shipwreck target detection based on audio-visual heterogeneous data

As marine exploration continues to grow, the frequency of maritime emergency contingencies increases year by year. It is crucial to enhance marine surveillance to effectively detect underwater maritime shipwreck targets. Based on the excellent target detection performance of the YOLO5 model, we constructed the pure visual image dataset, pure sonar image dataset, and audio-visual heterogeneous dataset. We also trained the YOLOv5m model with a personal computer using the visual image dataset, sonar image dataset, and the hybrid audio-visual image dataset of maritime shipwrecks respectively. The test results show that the detection accuracy of the model trained with the hybrid maritime wrecks audio-visual image dataset is higher than that of the model trained with pure visual and sonar image datasets. This method may have an inspiration to future research.
Anglo-Bangla Language-Based AI Chatbot for Bangladeshi University Admission System

Many Bangladeshi students face significant challenges during university admission because of a lack of communication. However, some of the usual chatbots have been implemented in English/Bangla. Most students are comfortable communicating in a slang mixture of Bengali and English (Anglo-Bangla). This article presents a language detection model for Anglo-Bangla using machine learning algorithms, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Multinomial Naive Bias (MNB). The model is designed to assist the university admission process in Bangladesh through an automated chatbot system. The chatbot system utilizes the language detection model to understand and respond to the queries of potential students in English, Bengali, and Anglo-Bangla. When the chatbot is unable to provide a proper answer in rare cases, the system logs the query along with user contact information. University officials will then update the appropriate response and the system will send the response to the user via email or SMS. The detector continuously detects the language and creates separate datasets to improve the chatbot’s performance. Our model was trained on a dataset of 0.15 million conversations collected from Socian Ltd., a leading NLP company. The dataset consisted of unlabeled Bangla and Anglo-Bangla languages and an English dataset collected from Kaggle, which was a mixed-language dataset. The model’s performance shows that the Multinomial Naive Bayes algorithm yields the highest accuracy in detecting the following languages: 99.64% for the Mixed, 99.13% for English, 100% for Bangla, and 99.51% for Anglo-Bangla. The proposed model can be used to improve the performance of other multilingual chatbot systems and assist with tasks such as customer service, medical assistance, and data collection in polyglot societies.
Scaling W State Circuits in the qudit Clifford Hierarchy

We identify a novel qudit gate which we refer to as the gate. This is an alternate generalization of the qutrit T gate to any odd prime dimension d, in the dth level of the Clifford hierarchy. Using this gate which is efficiently realizable fault-tolerantly should a certain conjecture hold, we deterministically construct in the Clifford+ gate set, d-qubit W states in the qudit {|0⟩, |1⟩} subspace. For qutrits, we show deterministic and fault-tolerant constructions for the three-qubit W state with T count three, for the six-qubit W state, and for any power-of-three-qubit W state.
A Blockchain-based Co-Simulation Platform for Transparent and Fair Energy Trading and Management

With the rapid adoption of renewable energy and smart meters, more distributed energy consumers are becoming capable of generating energy and participating in energy trading. However, it raises great challenges to establish trust among these distributed energy sources. A more transparent and fair energy trading market is required. With its unique advantages in supporting fair and transparent transactions, blockchain is recognized as an effective solution to facilitate distributed energy transactions. However, existing studies often propose blockchain-based energy trading schemes without considering the management of energy generation, consumption, and transmission. In addition, the sensitive nature of the power grid may make the grid operators hesitate to adopt anyone to directly access the energy trading market. Therefore, in this study, we adopt a permission-based blockchain, Hyper-ledger Fabric, to establish a fair and transparent distributed energy trading market, due to its strong access control and efficient consensus mechanism. Furthermore, the proposed blockchain-based energy trading market is integrated with the Packetized Energy Management and Trading Co-Simulation platform (PEMT-CoSim), developed by our prior work, so that a holistic co-simulation platform is established to facilitate further studies by closely coordinating energy trading and management. The demonstration results based on the proposed blockchain-based co-simulation platform are discussed in detail, which validate the effectiveness of the proposed architecture.