Reviewerly/exHarmony huggingface dataset:
FedMBC: Personalized federated learning via mutually beneficial collaboration

Data heterogeneity is a challenge of federated learning. Traditional federated learning aims to obtain a global model, but a single global model cannot meet the needs of all clients when the clients' local data are distributed differently. To alleviate this problem, we propose a mutually beneficial collaboration method for personalized federated learning (FedMBC), which provides each client with a personalized model by enhancing collaboration among similar clients. First, we use the task layer outputs and soft outputs of the client model to measure the similarity of the clients. Then, for each client, we adopt a dynamic aggregation method based on the similarity of clients on the server in each communication round to aggregate a model suitable for its local data distribution. That is, the aggregated model is a personalized model of the client. Furthermore, since the data heterogeneity and the different clients selected for each communication round may lead to slow convergence of the aggregated model, we adopt the aggregated model from the previous round in the local update stage of the client to accelerate the convergence of the model. Finally, we compare our method with different federated learning algorithms on various datasets in a variety of settings, and the results show that our method is superior to them in terms of test performance and communication efficiency. In particular, when the distributions of data among clients are diverse, FedMBC can improve the test accuracy by approximately 2.3% and reduce the number of communication rounds required by up to 35% compared with FedAvg on the CIFAR-10 dataset.
A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.
A Brain Storm and Chaotic Accelerated Particle Swarm Optimization Hybridization

Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploration with a random initialization, while PSO is more capable at local exploitation if given a predetermined initialization. The two algorithms have also been examined as a hybrid. In this work, the BSO algorithm was hybridized with the chaotic accelerated particle swarm optimization (CAPSO) algorithm in order to investigate how such an approach could serve as an improvement to the stand-alone algorithms. CAPSO is an advantageous variant of APSO, an accelerated, exploitative and minimalistic PSO algorithm. We initialized CAPSO with BSO in order to study the potential benefits from BSO’s initial exploration as well as CAPSO’s exploitation and speed. Seven benchmarking functions were used to compare the algorithms’ behavior. The chosen functions included both unimodal and multimodal benchmarking functions of various complexities and sizes of search areas. The functions were tested for different numbers of dimensions. The results showed that a properly tuned BSO–CAPSO hybrid could be significantly more beneficial over stand-alone BSO, especially with respect to computational time, while it heavily outperformed stand-alone CAPSO in the vast majority of cases.
An efficient and secure data collection scheme for predictive maintenance of vehicles

Through the real-time evaluation of vehicle operating status, the predictive maintenance operated by cloud servers can detect the abnormal condition of vehicles, and prompt early warning information before vehicle failure, so as to guide vehicle owners to carry out relevant vehicle repair and maintenance to achieve the goal of safe driving. However, vehicles still face data security and privacy preservation issues in the data collection stage of predictive maintenance. To solve this problem, in this paper, we propose an efficient and secure data collection scheme, named ESDC, for predictive maintenance of vehicles. Specifically, ESDC exploits the superincreasing sequence and lightweight homomorphic encryption technique to protect the vehicle's data content, and adds geometric noise to resist differential attack. In addition, ESDC employs shared secret key and lightweight authentication technology to ensure data integrity and authentication. Security analysis shows that ESDC can reach security requirements, including privacy preservation, differential privacy, data integrity, authentication, and filtering false data. Furthermore, the performance evaluations also show that ESDC is efficient and outperforms the existing approaches.
TT-Net: Tensorized Transformer Network for 3D medical image segmentation

Accurate segmentation of organs, tissues and lesions is essential for computer-assisted diagnosis. Previous works have achieved success in the field of automatic segmentation. However, there exists two limitations. (1) They are remain challenged by complex conditions, such as segmentation target is variable in location, size and shape, especially for different imaging modalities. (2) Existing transformer-based networks suffer from a high parametric complexity. To solve these limitations, we propose a new Tensorized Transformer Network (TT-Net). In this paper, (1) Multi-scale transformer with layers-fusion is proposed to faithfully capture context interaction information. (2) Cross Shared Attention (CSA) module that based on pHash similarity fusion (pSF) is well-designed to extract the global multi-variate dependency features. (3) Tensorized Self-Attention (TSA) module is proposed to deal with the large number of parameters, which can also be easily embedded into other models. In addition, TT-Net gains a good explainability through visualizing the transformer layers. The proposed method is evaluated on three widely accepted public datasets and one clinical dataset, which contains different imaging modalities. Comprehensive results show that TT-Net outperforms other state-of-the-art methods for the four different segmentation tasks. Besides, the compression module which can be easily embedded into other transformer-based methods achieves lower computation with comparable segmentation performance.
Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques

Educational models currently integrate a variety of technologies and computer applications that seek to improve learning environments. With this objective, information technologies have increasingly adapted to assume the role of educational assistants that support the teacher, the students, and the areas enrolled in educational quality. One of the technologies that are gaining strength in the academic field is computer vision, which is used to monitor and identify the state of mind of students during the teaching of a subject. To do this, machine learning algorithms monitor student gestures and classify them to identify the emotions they convey in a teaching environment. These systems allow the evaluation of emotional aspects, based on two main elements, the first is the generation of an image database with the emotions generated in a learning environment such as interest, commitment, boredom, concentration, relaxation, and enthusiasm. The second is an emotion recognition system, through the recognition of facial gestures using non-invasive techniques. This work applies techniques for the recognition and processing of facial gestures and the classification of emotions focused on learning. This system helps the tutor in a modality of face-to-face education and allows him to evaluate emotional aspects and not only cognitive ones. This arises from the need to create a base of images focused on the spontaneous learning of emotions since most of the works reviewed focus on these acted-out emotions.
Semi-Supervised Anomaly Detection Via Neural Process

Many deep (semi-) supervised neural network-based methods have been proposed for anomaly detection, tackling the issue of limited labeled data. They have shown good performance but still face two major challenges. First, insufficient labeled data limits their flexibility. Second, measuring the uncertainty of the prediction, especially when dealing with objects deviating largely from training data, has not been well studied. Another common reason preventing them from prevailing is that they learn a determined function to make predictions from the input. This usually makes the predicted results uncertain and lacks robustness. To address these problems, we propose a novel framework, incorporating the neural process into the semi-supervised anomaly detection paradigm and efficiently using unlabeled data and a handful of labeled data in training. Different from other methods, ours is equivalent to modeling the distribution of functions representing anomalous patterns according to the labeled data rather than learning a single determined function for anomaly detection. Our approach improves the flexibility and robustness under the condition of insufficient training data, and can measure the uncertainty of prediction results. Extensive experiments under real-world datasets demonstrate that our proposed method can significantly improve anomaly detection performance compared to several cutting-edge benchmarks.
Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations

Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges.The optimisation of the shape and size of large-scale truss structures is difficult due to the nonlinear interplay between the cross-sectional and nodal coordinate pressures of structures.Recently, it was demonstrated that the newly proposed Marine Predator Algorithm (MPA) performs very well on mathematical challenges.The MPA is a meta-heuristic that simulates the essential hunting habits of natural marine predators.However, this algorithm has some disadvantages, such as becoming locked in locally optimal solutions and not exhibiting a high level of exploratory behaviour.This paper proposes two hybrid marine predator algorithms, Nonlinear Marine Predator (HNMPA) and Nonlinear-Chaotic Marine Predator Algorithm (HNCMPA), as improved variations of the marine predator algorithm paired with a hill-climbing (HC) technique for truss optimisation on form and size.The major advantage of these techniques are that they seek to overcome the MPA's disadvantages by using nonlinear values and prolonging the exploration phase with chaotic values; also, the HC algorithm has been used to avoid locally optimum solutions.In terms of truss optimisation performance, the proposed algorithm is compared to fourteen well-known meta-heuristics, including the Dragonfly Algorithm (DA), Henry Gas Solubility optimisation (HGSO), Arithmetic optimisation Algorithm (AOA), Generalized Normal Distribution Optimisation (GNDO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), Neural Network Algorithm (NNA), Water Cycle Algorithm (WCA), Artificial Gorilla Troops Optimiser (GTO), Gray Wolf Optimiser (GWO), Moth Flame Optimiser (MFO), Multi-Verse Optimiser (MVO), Equilibrium Optimiser (EO), and Cheetah Optimiser (CO).Furthermore, seven algorithms were chosen to test HNCMPA performance on benchmark optimisation sets, including MPA, MVO, PSO, MFO, SSA, GWO, and WOA.The results of the experiment demonstrate that the optimisation techniques put forth surpass previously established meta-heuristics in the field of optimisation, encompassing both traditional and CEC problems, by a margin of over 95% in terms of attaining a superior ultimate solution.Additionally, with regards to solving truss optimisation difficulties as a large-scale real-world engineering challenge, the outcomes indicate a performance boost of over 65% in obtaining significantly better solutions for problems involving 260-bar and 314-bar; conversely, in the case of 340-bar issues, the improvement rate is slightly lower at almost 25%.
Encrypting Hashing Against Localization

Hashing for localization (HfL) is an effective method for fast localizing specific scenes in a large-scale remote sensing image. Key to its efficiency arises from a comprehensive deep hashing network that generates representational binary hash codes for image patches cropped from the remote sensing image. On the other hand, this paper will investigate the problem of encrypting the remote sensing image against the HfL task. We refer to the new task as encrypting hashing against localization (EHaL). We characterize the EHaL task in term of two cues: (I) An encrypted image patch is supposed to appear as visually similar to its original image patch as possible; (II) The hash code generated by the deep hashing network for the encrypted image patch is supposed to be not close to its original class but close to a different class. Following the two cues, we develop an encrypted patch generator, which is trained in an adversarial fashion. Based on the encrypted patch generator, we propose two remote sensing image encryption frameworks that can cause non-localization and mis-localization to the HfL task separately. Experiments validates the effectiveness of our method. Reproducible executions are given at https://github.com/JingpengHan/EHaL.
Capsule-inferenced Object Detection for Remote Sensing Images

Frequent and accurate object detection based on remote sensing images can effectively monitor dynamic objects on the Earth's surface. While the Detection Transformer (DETR) offers a simple encoder-decoder structure and a direct set prediction approach to object detection, it falls short in complex remote sensing scenes where entity information and relative positions between objects are critical to target reasoning. Notably, the DETR model's feedforward neural network (FFN) relies on weighted summation for target reasoning, disregarding interactive feature information, which is a major factor affecting detection effectiveness. To address these shortcomings, we propose a DETR-based detection model called (CI_DETR), which uses capsule inference to improve remote sensing object detection. Our approach adds a Multi-level Feature Fusion module to the DETR network, allowing the network to learn how to spatially alter features at different levels, preserving only beneficial information for combination. In addition, we introduce a capsule reasoning module to mine entity information during inference and more effectively model the hierarchical correlation of internal knowledge representation in the neural network, consistent with the thinking model of the human brain. Lastly, we employ a sausage model to measure the similarities and differences of capsules, projecting them onto a curved surface for nonlinear function approximation and maximum preservation of the local responsiveness of capsule entities. Our experiments on public datasets confirm the superior detection performance of our proposed algorithm relative to many current detectors.
Multi-UAV Coverage Path Planning: A Distributed Online Cooperation Method

Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) plays a significant role in intelligent distributed surveillance systems. However, due to poor cooperation, most existing CPP methods may cause strongly overlapped trajectories, missing areas, or even collisions in uncertain and complex environments, leading to long task completion time and low coverage efficiency. To this end, in this paper we propose a novel multi-UAV distributed online cooperation (MDOC) CPP method that aims to minimize task completion time. Moreover, this method allows UAVs to quickly respond to unknown obstacles and complex emergencies, such as UAV breakdown or communication interruption. To establish close cooperation between UAVs, we propose an efficient environmental information map (EI-map) fusion technique that enables them to obtain global exploration in real-time in a cooperative manner. Then we innovatively develop a distributed cooperative deep Q-learning (DCDQN) algorithm to obtain UAVs' coverage paths online that are determined by minimizing task time and avoiding overlaps, missing areas, and collisions. Specifically, attributing to the fused EI-map, we expand the state space of DCDQN to collect sufficient observations and design a novel cooperative learning pattern to efficiently plan the path for global optimization. Simulation results show that our method outperforms the state-of-the-art in task completion time and coverage efficiency, especially in uncertain and complex environments. In addition, we validate that our method can efficiently complete full coverage even in emergencies.
A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding.
InstantChain: Enhancing Order-Execute Blockchain Systems for Latency-Sensitive Applications

As an emerging replicated transactional processing system, blockchain is being adopted by more and more industries. Some blockchain-based applications require low latency and high throughput. However, the ordering phase is being a bottleneck of the order-execute blockchain. Poor ordering will result in low throughput due to transaction conflicts, and poor quality of service for latency-sensitive applications because their transactions are not processed within their expected times. Therefore, this paper enhances order-execute blockchain systems to minimize latency and maximize parallelism by considering transaction latency requirements, conflicts and varied execution times. First, we propose a parallelism-oriented and latency-sensitive transaction ordering method. Then, we propose a deep reinforcement learning-based method to determine optimal block parameters. Finally, we propose an adaptive packing method based on space occupancy that enables packed transactions to fully exploit parallelism. Experimental results show that our implemented system (InstantChain) outperforms 3 related systems under both real and simulated datasets.
Fintech Key-Phrase: A New Chinese Financial High-Tech Dataset Accelerating Expression-Level Information Retrieval

Expression-Level Information Extraction is a challenging Natural Language Processing (NLP) task that aims to retrieve important information from the linguistic documents. However, there still lacks the up-to-date data sources for accelerating the Expression-Level Information Extraction, especially in the field of Chinese financial high technology. To fill this gap, we present Fintech Key-Phrase: a human-annotated Chinese financial high technology field related key-phrase dataset, which contains more than 12K paragraphs together with the annotated domain-specific key-phrases. We extract the publicly released reports on Chinese Management's Discussion and Analysis (CMD &A) from the well-known Chinese Research Data Services Platform (CNRDS) and then filter the Financial High-Tech related reports. The Financial High-Tech key-phrases are annotated through pre-defined philosophy guidelines to control the annotation quality. To demonstrate that our released Fintech Key-Phrase helps retrieve valuable information in the field of Chinese financial high technology, we adopt several superior Information Retrieval systems as representative baselines to validate its significance and report the performance statistics correspondingly. We hope this dataset can facilitate the scientific research and further exploration in the Chinese Financial High-Tech domain. We have made our Fintech Key-Phrase dataset and experimental code of the adopted baselines accessible at Github ( https://github.com/albert-jin/Fintech-Key-Phrase/ ). To motivate newcomers to get involved in the Information Retrieval of the Chinese financial high technology field, we have built an open website ( https://albert-jin.github.io/FintechKP-frontend/ ) and a real-time information retrieval API tool ( https://31863ew564.zicp.fun/information_retrieval/ ).
Spartan and Bulletproofs are Simulation-Extractable (for Free!)

Increasing deployment of advanced zero-knowledge proof systems, especially zkSNARKs, has raised critical questions about their security against real-world attacks. Two classes of attacks of concern in practice are adaptive soundness attacks, where an attacker can prove false statements by choosing its public input after generating a proof, and malleability attacks, where an attacker can use a valid proof to create another valid proof it could not have created itself. Prior work has shown that simulation-extractability ( $$\textsf{SIM}\text {-}\textsf{EXT} $$ ), a strong notion of security for proof systems, rules out these attacks. In this paper, we prove that two transparent, discrete-log-based zkSNARKs, Spartan and Bulletproofs, are simulation-extractable ( $$\textsf{SIM}\text {-}\textsf{EXT} $$ ) in the random oracle model if the discrete logarithm assumption holds in the underlying group. Since these assumptions are required to prove standard security properties for Spartan and Bulletproofs, our results show that $$\textsf{SIM}\text {-}\textsf{EXT} $$ is, surprisingly, "for free" with these schemes. Our result is the first $$\textsf{SIM}\text {-}\textsf{EXT} $$ proof for Spartan and encompasses both linear- and sublinear-verifier variants. Our result for Bulletproofs encompasses both the aggregate range proof and arithmetic circuit variants, and is the first to not rely on the algebraic group model (AGM), resolving an open question posed by Ganesh et al. (EUROCRYPT'22). As part of our analysis, we develop a generalization of the tree-builder extraction theorem of Attema et al. (TCC'22), which may be of independent interest.