Reviewerly/exHarmony huggingface dataset:
Two-Phase Deep Reinforcement Learning of Dynamic Resource Allocation and Client Selection for Hierarchical Federated Learning

This paper presents a new two-phase Deep Deter-ministic Policy Gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of a federated learning (FL) process in an energy harvesting hierarchical FL (HFL) system. The key idea is to design a DDPG-based approach to learn the selection of participating clients, the CPU configuration, and the transmission powers of the clients, while the other decisions are efficiently optimized by a new straggler-aware client association and bandwidth allocation algorithm. The algorithm evaluates the reward of the DDPG, and substantially improves its convergence rate and stability. Experimental results demonstrate that the proposed TP-DDPG can substantially reduce the training time while achieving a higher test accuracy over the existing benchmarks.
Deep learning based RFF recognition with differential constellation trace figure towards closed and open set

Although radio frequency fingerprint (RFF) has been widely adopted in the field of wireless communication equipment identification, most researches are focused on closed set recognition. Considering the open set recognition challenge faced by RFF recognition with the rapid increase of wireless devices, this paper proposes a deep learning based RFF recognition framework towards closed and open set. RFF caused by the analog parts of the transmitter is extracted based on differential constellation trace figure (DCTF) algorithm, and then preprocessed to enhance features. By utilizing SoftMax and OpenMax algorithms, a deep learning based RFF recognition network with ResNet framework towards closed and open set is built to recognize the extracted RFF features. Simulation results show that the proposed deep learning based RFF recognition framework achieves an accuracy of more than 99% in closed set when SNR reaches 20dB. In open set simulation experiment, the recognition accuracy of the proposed RFF recognition method with OpenMax is superior to that of the traditional closed set framework by more than 13% when the openness is greater than 0.074.
SPDT: Secure and Privacy-Preserving Scheme for Digital Twin-based Traffic Control

With the increasing complexity of the driving environment, more and more attention has been paid to the research on improving the intelligentization of traffic control. Among them, the digital twin-based internet of vehicle can establish a mirror system on the cloud to improve the efficiency of communication between vehicles, provide warning and safety instructions for drivers, avoid driving potential dangers. To ensure the security and effectiveness of data sharing in traffic control, this paper proposes a secure and privacy-preserving scheme for digital twin-based traffic control. Specifically, in the data uploading phase, we employ a group signature with a time-bound keys technique to realize data source authentication with efficient members revocation and privacy protection, which can ensure that data can be securely stored on cloud service providers after it synchronizes to its twin. In the data sharing stage, we employ the secure and efficient attribute-based access control technique to provide flexible and efficient data sharing, in which the parameters of a specific sub-policy can be stored during the first decryption and reused in subsequent data access containing the same sub-policy, thus reducing the computing complexity. Finally, we analyze the security and efficiency of the scheme theoretically.
In Silico Simulations and Analysis of Human Phonological Working Memory Maintenance and Learning Mechanisms with Behavior and Reasoning Description Language (BRDL)

Human memory systems are commonly divided into different types of store, the most basic distinction being between short-term memory (STM) and long-term memory (LTM). Phonological STM, as proposed in the working memory model is closely linked to semantic LTM. Nevertheless, the mechanisms of maintenance with STM, and transfer of information with LTM are poorly understood. Candidate mechanisms within phonological STM are rehearsal (either articulatory or elaborative), and refreshing. There is also evidence of long-term learning within STM. In this paper we use the Behavior and Reasoning Descriptive Language (BRDL) to model human memory contents as well as the perceptions that allow humans to input information into STM. By using the Maude rewrite system to provide semantics to BRDL and dynamics to BRDL models, we can explore various cognitive theories about phonological STM maintenance and transfer of information for long-term retention, such as articulatory rehearsal, elaborative rehearsal, and refreshing. This approach has been implemented in a tool that allows cognitive scientists to carry out in silico the simulation of learning processes as well as the replication of experiments conducted with human beings in order to contrast alternative cognitive theories.
Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments

For cloud environments, task scheduling focusing on the optimal completion time (makespan) is vital. Metaheuristic approaches can be used to produce efficient solutions that will provide important cost savings to both the cloud service provider and the clients. On the other hand, since there is a high probability of getting stuck in local minima in metaheuristic solutions due to the type of problem, it may not always be possible to quickly reach the optimal solution. This study, using a metaheuristic approach, proposes a solution based on the Cloneable Jellyfish Algorithm for optimal task distribution in cloud environments. The unique feature of the proposed algorithm is that it allows dynamic population growth to be carried out in a controlled manner in order not to get stuck in local minima during the exploration phase. In addition, this algorithm, which uses a different cloning mechanism so that similar candidates are not generated in the population growth, has made it possible to achieve the optimal solution in a shorter time. To observe the solution performance, cloud environment simulations created in the Cloudsim simulator have been used. In experiments, the success of the proposed solution compared to classical scheduling algorithms has been proven.
Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications (e.g., social recommendation, emergency event detection, and cyberattack event detection). This paper proposes temporal graph association rules (TGARs) that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints. We introduce quality measures (e.g., support, confidence, and diversification) to characterize meaningful TGARs that are useful and diversified. In addition, the proposed support metric is an upper bound for alternative metrics, allowing us to guarantee a superset of patterns. We extend conventional confidence measures in terms of maximal occurrences of TGARs. The diversification score strikes a balance between interestingness and diversity. Although the problem is NP-hard, we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph. We propose pruning strategies to filter TGARs that have low support or cannot make top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> as early as possible. Moreover, we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space. We experimentally verify the effectiveness, efficiency, and scalability of our algorithms in discovering diversified top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> TGARs from temporal graphs in real-life applications.
Towards comprehensive expert finding with a hierarchical matching network

In Community Question Answering (CQA) websites, expert finding aims to seek relevant experts for answering questions. The core of expert finding is to match candidate experts and target questions precisely. Most existing methods usually learn a single feature vector for the expert from the historically answered questions, and then match the target question, which would lose fine-grained and low-level semantic matching information. In this paper, instead of matching with a unified expert embedding, we propose an expert finding method with a multi-grained hierarchical matching framework, named EFHM. Specifically, we design a word-level and question-level match encoder to learn the fine-grained semantic matching between each historical answered question and target question, and then propose an expert-level match encoder to learn an overall expert feature for matching the target question. Through the hierarchical matching mechanism, our model has the potential to capture the comprehensive relevance between candidate experts and target questions. Experimental results on six real-world CQA datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.
Deep Reinforcement Learning for Analog Circuit Sizing with an Electrical Design Space and Sparse Rewards

There is still a great reliance on human expert knowledge during the analog integrated circuit sizing design phase due to its complexity and scale, with the result that there is a very low level of automation associated with it. Current research shows that reinforcement learning is a promising approach for addressing this issue. Similarly, it has been shown that the convergence of conventional optimization approaches can be improved by transforming the design space from the geometrical domain into the electrical domain. Here, this design space transformation is employed as an alternative action space for deep reinforcement learning agents. The presented approach is based entirely on reinforcement learning, whereby agents are trained in the craft of analog circuit sizing without explicit expert guidance. After training and evaluating agents on circuits of varying complexity, their behavior when confronted with a different technology, is examined, showing the applicability, feasibility as well as transferability of this approach.
Towards Blockchain-based Hierarchical Federated Learning for Cyber-Physical Systems

Cyber-physical systems (CPS) have evolved over the years and are now integrated into intelligent manufactory. The Internet of Things (IoT) has played a significant role in the advancement of such systems. CPS have become more intelligent and self-automated with the aid of advances in Artificial Intelligence (AI). Automating the process of CPS management requires that AI and secure transaction processing be integrated within all stakeholders, including the cloud, fog, edge, network, storage, and industrial devices. This integration necessitates the distribution and decentralization of the self-configuring, self-managing, self-healing, and self-governing process in CPS. This paper presents a blockchain-based hierarchical federated learning (HFL) solution that maintains quick, secure, and accurate decision-making for industrial machines. A two-stage federated learning (FL) algorithm, where during the first stage, industrial devices are grouped into clusters and perform local ML training. Local models are shared with network edge devices and a set of global models are created using FL averaging. During the second stage, a main global model is created from the distributed first-stage global models using a FL aggregating algorithm. Blockchain is used to verify and validate the trained models on the edge. System evaluations are performed to compare the proposed HFL solution against traditional FL in terms of training accuracy and network overhead.
Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application

Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.
Single‐Site Forecasts for 130 Photovoltaic Systems at Distribution System Operator Level, Using a Hybrid‐Physical Approach, to Improve Grid‐Integration and Enable Future Smart‐Grid Operation

With rising shares of photovoltaic (PV) generated electricity in the power grids, PV power forecasting is rapidly gaining importance for energy suppliers and grid operators. But specifically, the distribution system operators (DSOs) require detailed forecasts at higher spatial resolutions, to manage grid congestions and avoid voltage band violations, which are local phenomena. In view of future smart grid operation, the authors propose a forecasting scheme using numerical weather prediction (NWP) data to feed a PV performance model and compute single‐site power forecasts. The intraday outcomes of this rather conventional forecasting scheme are combined with a supervised‐learning model, based on recurrent neural networks (type: long short‐term memory) which is fed by the 15 min measurement data of the two previous days for each PV system. This approach accounts for different local effects, not covered by the NWP‐based physical model, such as partial shading or snow cover. Furthermore, it mitigates the impacts of “imperfect” data on side of the DSOs, used to parametrize the PV performance model. This hybrid‐physical approach has been applied to about 130 PV systems, validated over a 2 year period, and yielded a normalized root‐mean‐square error of 8.3% and 8.7% in 2020 and 2021.
Weak-keys and key-recovery attack for $$\text{ TinyJAMBU }$$

In this paper, we study NIST lightweight 3rd round candidate [Formula: see text]. The core component of [Formula: see text] is the keyed permutation [Formula: see text], which is based on a non-linear feedback shift register. By analysing this permutation carefully, we are able to find good cubes that are used to build distinguishers in the weak-key setting. In particular, we show that there are at least [Formula: see text] keys for which TinyJAMBU can be distinguished from a random source for up to 476 rounds. These distinguishers outperform the best-known distinguishers, which were proposed in 'Scientific Reports - Nature' by Teng et al. We are the first to study the exact degree of the feedback polynomial [Formula: see text] in the nonce variables. This helped us in concluding that [Formula: see text] with more than 445 rounds is secure against distinguishers using 32 sized cubes in the normal setting. Finally, we give new key-recovery attacks against [Formula: see text] using the concepts of monomial trail presented by Hu et al. at ASIACRYPT 2020. Our attacks are unlikely to jeopardise the security of the entire 640 rounds [Formula: see text], but we strongly anticipate that they will shed new lights on the cipher's security.
On Modality Bias Recognition and Reduction

Making each modality in multi-modal data contribute is of vital importance to learning a versatile multi-modal model. Existing methods, however, are often dominated by one or few of modalities during model training, resulting in sub-optimal performance. In this article, we refer to this problem as modality bias and attempt to study it in the context of multi-modal classification systematically and comprehensively. After stepping into several empirical analyses, we recognize that one modality affects the model prediction more just because this modality has a spurious correlation with instance labels. To primarily facilitate the evaluation on the modality bias problem, we construct two datasets, respectively, for the colored digit recognition and video action recognition tasks in line with the Out-of-Distribution (OoD) protocol. Collaborating with the benchmarks in the visual question answering task, we empirically justify the performance degradation of the existing methods on these OoD datasets, which serves as evidence to justify the modality bias learning. In addition, to overcome this problem, we propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned according to the training set statistics. Thereafter, we apply this method on 10 baselines in total to test its effectiveness. From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
Automating Safety and Security Co-design through Semantically Rich Architecture Patterns

During the design of safety-critical systems, safety and security engineers make use of architecture patterns, such as Watchdog and Firewall, to address identified failures and threats. Often, however, the deployment of safety architecture patterns has consequences on security; e.g., the deployment of a safety architecture pattern may lead to new threats. The other way around may also be possible; i.e., the deployment of a security architecture pattern may lead to new failures. Safety and security co-design is, therefore, required to understand such consequences and tradeoffs in order to reach appropriate system designs. Currently, architecture pattern descriptions, including their consequences, are described using natural language. Therefore, their deployment in system design is carried out manually by experts and thus is time-consuming and prone to human error, especially given the high system complexity. We propose the use of semantically rich architecture patterns to enable automated support for safety and security co-design by using Knowledge Representation and Reasoning (KRR) methods. Based on our domain-specific language, we specify reasoning principles as logic specifications written as answer-set programs. KRR engines enable the automation of safety and security co-engineering activities, including the automated recommendation of which architecture patterns can address failures or threats, and consequences of deploying such patterns. We demonstrate our approach on an example taken from the ISO 21434 standard.
Wavelet transform of multiplicators and convolutors for ultradistributions

In this paper, we discuss multiplicators and convolutors for ultradistributions. A Calderóns reproducing formula for convolutors is derived. Also, we investigate the wavelet transform on multiplicators and convolutors of ultradistribution space. Further, an inversion formula is established for the wavelet transform and a tabular representation of the spaces along with the operations is also given.
Parameters Tuning of Fractional-Order Proportional Integral Derivative in Water Turbine Governing System Using an Effective SDO with Enhanced Fitness-Distance Balance and Adaptive Local Search

Supply-demand-based optimization (SDO) is a swarm-based optimizer. However, it suffers from several drawbacks, such as lack of solution diversity and low convergence accuracy and search efficiency. To overcome them, an effective supply-demand-based optimization (ESDO) is proposed in this study. First, an enhanced fitness-distance balance (EFDB) and the Levy flight are introduced into the original version to avoid premature convergence and improve solution diversity; second, a mutation mechanism is integrated into the algorithm to improve search efficiency; finally, an adaptive local search strategy (ALS) is incorporated into the algorithm to enhance the convergence accuracy. The effect of the proposed method is verified based on the comparison of ESDO with several well-regarded algorithms using 23 benchmark functions. In addition, the ESDO algorithm is applied to tune the parameters of the fractional-order proportional integral derivative (FOPID) controller of the water turbine governor system. The comparative results reveal that ESDO is competitive and superior for solving real-world problems.
Design of Graph Neural Network Social Recommendation Algorithm Based on Coupling Influence

With the explosively growing amount of online information, recommender system becomes an important tool to help users efficiently find their desired information. In this paper, we propose a Graph Neural Network Social Recommendation Based on Coupled Influence by analyzing the social influence of 2-level friends (CI-GNNSR). First, we mine the user’s historical rating information and second-degree social information. Then, to learn the feature representation of users and movies, multiple Graph Attention Networks (GAT) are used to model the user-movie Graph and social network Graph. Our algorithm uses an attention-based memory network to learn the interest influence representation between users and their collaborative friends, which can distinguish the related factors among different users’ friends. The experiment results show that CI-GNNSR enhances the accuracy of recommendation by considering users’ social influence factors from multiple perspectives.
Adaptive context- and scale-aware aggregation with feature alignment for one-shot object detection

Given a query image of a novel object category at the inference stage, One-Shot Object Detection (OSOD) aims to target the detection towards reference category through the guidance of query image without fine-tuning. It can be widely applied to many realistic applications but remains challenging so far. Existing attention-based models mainly utilize query features to modulate the target branch to finish features retrieval and information propagation, which generally cannot comprehensively exploit context extracted from the only template to mine out co-occurrent object features, also neglect the cross-scale and feature spatial misalignment problems, leading to imprecise results. Observing these problems, we propose an adaptive context- and scale-aware feature aggregation module (ACS), that harnesses global–local context enrichment to fully preserve contextual features, and performs conditioned multi-scale interaction to learn scale-invariant representations. To tackle the spatial misalignment issue between the query image and generated proposals, we leverage the spatial transformer network (STN) to align features, which facilitates the subtask of classification. Extensive experiments on multiple OSOD benchmarks show that our proposed approach significantly outperforms the baseline by a large margin and achieves state-of-the-art results, demonstrating its effectiveness. Meanwhile, the visualization results of geometric semantic matching between query-target image pairs also verify the robustness of our proposed algorithm.