Reviewerly/exHarmony huggingface dataset:
A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain

Traditional video conference systems depend largely on computational complexity to ensure system security, but with the development of high-performance computers, the existing encryption system will be seriously threatened. To solve this problem, a hybrid encryption scheme for quantum secure video conferencing combined with blockchain is proposed in this study. In the system solution architecture, first, the quantum key distribution network is embedded in the classic network; then, the “classical + quantum” hybrid encryption scheme is designed according to the secret level required for the video conference content. Besides, the real-time monitoring module of the quantum key distribution network is designed to ensure that users can check the running state of the network at any time. Meeting minutes can be shared by combining with blockchain. In order to quickly query meeting minutes, a cache-efficient query method based on B+ tree is proposed. The experimental results show that compared with the traditional video conference system, the quantum secure video conference system sufficiently integrates the technical advantages of the quantum key distribution to resist the security threats such as channel eavesdropping and high-performance computational attacks while ensuring the stable operation of the classic system, thus providing a video conference system with a higher security level. Meanwhile, the query time cost of blockchain with different lengths is tested, and the query efficiency of the proposed method is 3.15-times higher than the original query efficiency of blockchain.
Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges.However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution.In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to overcome some inherent limitations of the original SSA.SSALEO is a novel search technique that accounts for population diversity, the imbalance between exploitation and exploration, and the SSA algorithm's premature convergence.By implementing LEO in SSALEO, the search slowdown in SSA is eliminated, and the local search efficiency of swarm agents is improved.The proposed SSALEO method is tested using the CEC 2017 benchmark with 50 and 100 decision variables, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to other metaheuristic algorithms (MAs) and advanced algorithms, including seven Salp swarm variants.The comparisons show that SSA greatly benefits from LEO by enhancing the quality and accelerating its solutions' convergence rate.The SSALEO was then assessed using a benchmark set of seven well-known constrained design challenges in various engineering domains defined in the CEC 2020 conference benchmark.Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results.ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, the SSALEO algorithm is very competitive and often superior to the algorithms used in the studies.Further, the proposed approach can be viewed as a special LSGO optimizer whose performance exceeds that of specialized state-of-the-art algorithms like CMA-ES and SHADE.
Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment

Resistance to differential cryptanalysis is a fundamental security requirement for symmetric block ciphers, and recently, deep learning has attracted the interest of cryptography experts, particularly in the field of block cipher cryptanalysis, where the bulk of these studies are differential distinguisher based black-box attacks. This paper provides a deep learning-based decryptor technique for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing cipher text pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plain text from the ciphered text without any P-box expertise. The evaluation of testing methods has been conceptualized as a regression task in which neural networks are supervised using a variety of parameters such as variations between input and output, number of iterations, and P-box generation patterns. On the other hand, the transfer learning skills demonstrated in this study indicate that discovering suitable testing models from the ground is also achievable using our model with optimum prior cryptographic expertise, where we contribute the results of deep learning in the field of deep learning based differential cryptanalysis development.Various experiments were performed on discrete and continuous chaotic and non-chaotic permutation patterns, and the best-performing model had an MSE of 1.8217 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-04</sup> and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 1, demonstrating the practicality of the suggested technique.
PipeFL: Hardware/Software co-Design of an FPGA Accelerator for Federated Learning

Federated learning has solved the problems of data silos and data fragmentation on the premise of satisfying privacy.However, cryptographic algorithms in federated learning brought significant increase in computational complexity, which limited the speed of model training.In this paper, we propose a hardware/software (HW/SW) co-designed field programmable gate array (FPGA) accelerator for federated learning.Firstly, we analyzed the time consumption of each stage in federated learning and the involved cryptographic algorithms, and found the performance bottleneck.Secondly, a HW/SW co-designed architecture is introduced, which can speed up encryption, decryption and ciphertext-space computation at the same time without reconfiguring FPGA circuit.In the HW part, we proposed a Hardware-aware Montgomery Algorithm (HWMA) which utilized data parallelism and pipeline, and designed an FPGA architecture to decouple data access and computation.In the SW part, an Operator Scheduling Engine (OSE) is designed, which can flexibly resolve the target algorithm into multiple HWMA calls, and complete other non-computation-intensive calculations.Finally, evaluations for both specific algorithms and practical applications are implemented.Experimental results show that when deployed on Intel Stratix 10 FPGA, our accelerator can increase the throughput of 2048-bit modular multiplication, modular exponentiation and Paillier algorithm to more than 3x of the CPU.When integrated into a industrial grade federated learning open source framework, the end-to-end training time of linear regression and logistic regression can be shortened by 2.28x and 3.30x respectively, which is more than 2x faster than the reported best results of FPGA accelerator.
Connected Blockchain Federations for Sharing Electronic Health Records

With the growing utility of blockchain technology, the desire for reciprocal interactions among different blockchains is growing. However, most operational blockchain networks currently operate in a standalone setting. This fragmentation in the form of isolated blockchains creates interoperability difficulties, inhibiting the adoption of blockchains in various ecosystems. Interoperability is a key factor in the healthcare domain for sharing EHRs of patients registered in independent blockchain networks. Each blockchain network could have its own rules and regulations, obstructing the exchange of EHRs for improving diagnosis and treatments. Examples include patients being treated by healthcare providers in different countries or regions, or within one country but with a different set of rules per state or emirate. By contrast, a federation of blockchain networks can provide better communication and service to stakeholders in healthcare. Thus, solutions for facilitating inter-blockchain communication in such a blockchain federation are needed. However, this possibility has not been fully explored, and further investigations are still being conducted. Hence, the present study proposes a transaction-based smart contract triggering system for inter-blockchain communication, enabling EHR sharing among independent blockchains. We use local and global smart contracts that will be executed once a transaction is created in the blockchain. Local smart contracts are used for EHR sharing within the blockchain, whereas global smart contracts are used for EHR sharing among independent blockchains. The experimental setup is conducted using the Hyperledger Fabric blockchain platform. Inter-blockchain communication between two independent fabric networks is conducted through a global smart contract using Hyperledger Cactus for EHR sharing in a health federation setup. To the best of our knowledge, our study is the first to implement an inter-blockchain communication model in the healthcare domain.
Visualization Design Practices in a Crisis: Behind the Scenes With COVID-19 Dashboard Creators

During the COVID-19 pandemic, a number of data visualizations were created to inform the public about the rapidly evolving crisis. Data dashboards, a form of information dissemination used during the pandemic, have facilitated this process by visualizing statistics regarding the number of COVID-19 cases over time. Prior work on COVID-19 visualizations has primarily focused on the design and evaluation of specific visualization systems from technology-centered perspectives. However, little is known about what occurs behind the scenes during the visualization creation processes, given the complex sociotechnical contexts in which they are embedded. Yet, such ecological knowledge is necessary to help characterize the nuances and trajectories of visualization design practices in the wild, as well as generate insights into how creators come to understand and approach visualization design on their own terms and for their own situated purposes. In this research, we conducted a qualitative interview study among dashboard creators from federal agencies, state health departments, mainstream news media outlets, and other organizations that created (often widely-used) COVID-19 dashboards to answer the following questions: how did visualization creators engage in COVID-19 dashboard design, and what tensions, conflicts, and challenges arose during this process? Our findings detail the trajectory of design practices—from creation to expansion, maintenance, and termination—that are shaped by the complex interplay between design goals, tools and technologies, labor, emerging crisis contexts, and public engagement. We particularly examined the tensions between designers and the general public involved in these processes. These conflicts, which often materialized due to a divergence between public demands and standing policies, centered around the type and amount of information to be visualized, how public perceptions shape and are shaped by visualization design, and the strategies utilized to deal with (potential) misinterpretations and misuse of visualizations. Our findings and lessons learned shed light on new ways of thinking in visualization design, focusing on the bundled activities that are invariably involved in human and nonhuman participation throughout the entire trajectory of design practice.
A dynamical scan-path model for task-dependence during scene viewing.

In real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan-path statistics across individual observers. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Deep Fake Video Detection Using Transfer Learning Approach

The usage of the internet as a fast medium for spreading fake news reinforces the requirement of computational utensils in order to fight for it. Fake videos also called deep fakes that create great intimidation in society in an assortment of social and political behaviour. It can also be utilized for malevolent intentions. Owing to the availability of deep fake generation algorithms at cheap computation power in cloud platforms, realistic fake videos or images are created. However, it is more critical to detect fake content because of the increased complexity of leveraging various approaches to smudge the tampering. Therefore, this work proposes a novel framework to detect fake videos through the utilization of transfer learning in autoencoders and a hybrid model of convolutional neural networks (CNN) and Recurrent neural networks (RNN). Unseen test input data are investigated to check the generalizability of the model. Also, the effect of residual image input on accuracy of the model is analyzed. Results are presented for both, with and without transfer learning to validate the effectiveness of transfer learning.
BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing

Image dehazing aims to remove haze in images to improve their image quality. However, most image dehazing methods heavily depend on strict prior knowledge and paired training strategy, which would hinder generalization and performance when dealing with unseen scenes. In this paper, to address the above problem, we propose Bidirectional Normalizing Flow (BiN-Flow), which exploits no prior knowledge and constructs a neural network through weakly-paired training with better generalization for image dehazing. Specifically, BiN-Flow designs 1) Feature Frequency Decoupling (FFD) for mining the various texture details through multi-scale residual blocks and 2) Bidirectional Propagation Flow (BPF) for exploiting the one-to-many relationships between hazy and haze-free images using a sequence of invertible Flow. In addition, BiN-Flow constructs a reference mechanism (RM) that uses a small number of paired hazy and haze-free images and a large number of haze-free reference images for weakly-paired training. Essentially, the mutual relationships between hazy and haze-free images could be effectively learned to further improve the generalization and performance for image dehazing. We conduct extensive experiments on five commonly-used datasets to validate the BiN-Flow. The experimental results that BiN-Flow outperforms all state-of-the-art competitors demonstrate the capability and generalization of our BiN-Flow. Besides, our BiN-Flow could produce diverse dehazing images for the same image by considering restoration diversity.
Orthogonal product sets with strong quantum nonlocality on a plane structure

In this paper, we consider the orthogonal product set (OPS) with strong quantum nonlocality in multipartite quantum systems. Based on the decomposition of plane geometry, we present a sufficient condition for the triviality of orthogonality-preserving positive operator-valued measures on fixed subsystem and partially answer an open question given by Yuan et al. [Phys. Rev. A 102, 042228 (2020)]. The connection between the nonlocality and the plane structure of OPSs is established. We successfully construct a strongly nonlocal OPS in ${\mathcal{C}}^{{d}_{A}}\ensuremath{\bigotimes}{\mathcal{C}}^{{d}_{B}}\ensuremath{\bigotimes}{\mathcal{C}}^{{d}_{C}}\phantom{\rule{4pt}{0ex}}({d}_{A},{d}_{B},{d}_{C}\ensuremath{\ge}4)$, which contains fewer quantum states, and generalize the structures of known OPSs to any possible three and four-partite systems. In addition, we propose several entanglement-assisted protocols for perfectly local discrimination of the sets. It is shown that the protocols without teleportation use less entanglement resources that on average and these sets can always be discriminated locally with multiple copies of two-qubit maximally entangled states. These results also exhibit nontrivial signification of maximally entangled states in the local discrimination of quantum states.
Sine Cosine Algorithm for Reducing Communication Costs of Federated Learning

Federated Learning (FL) is a Machine Learning (ML) setting in which several clients (e.g., mobile devices) train a model cooperatively under the direction of a central server (e.g., cloud server), while training data is decentralized. Due to the fact that FL clients frequently have restricted transmission capacity, communication among clients and servers needs to be reduced to enhance presentation. FL clients frequently employ Wi-Fi and must interact in Unstable Network Environments (UNE). Existing FL aggregation techniques send and receive a huge number of weights, which dramatically reduces the accuracy of the UNE. In this paper, we propose a Federated Sine Cosine Algorithm (FedSCA) to reduce data communication by transferring score principles rather than all client models' weights and utilizing the Sine Cosine Algorithm (SCA) mechanism as a weight updating technique to improve the clients' models. This paper reveals that using FedSCA significantly decreases the quantity of data utilized in network communication and increases the global model's accuracy by an average of 9.87% over FedAvg and 2.29% over Federated Particle Swarm Optimization (FedPSO). Moreover, in studies conducted on an unstable network, it demonstrated a 4.3% improvement in comparison to accuracy loss in existing algorithms.
Gradient-Based Uncertainty for Monocular Depth Estimation

In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous predictions. For that reason, uncertainty estimates for each pixel are necessary, in particular for safety-critical applications such as automated driving. We propose a post hoc uncertainty estimation approach for an already trained and thus fixed depth estimation model, represented by a deep neural network. The uncertainty is estimated with the gradients which are extracted with an auxiliary loss function. To avoid relying on ground-truth information for the loss definition, we present an auxiliary loss function based on the correspondence of the depth prediction for an image and its horizontally flipped counterpart. Our approach achieves state-of-the-art uncertainty estimation results on the KITTI and NYU Depth V2 benchmarks without the need to retrain the neural network. Models and code are publicly available at https://github.com/jhornauer/GrUMoDepth .
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.
Stagnation detection meets fast mutation

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution (“fast mutation”, Doerr et al. (2017) [2]) and increasing the mutation strength based on a stagnation detection mechanism (Rajabi and Witt (2020) [3]). Whereas the latter can obtain the asymptotically best probability of finding a single desired solution in a given distance, the former is more robust and performs much better when many improving solutions in some distance exist. In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution. However, when several improving solutions exist, it can outperform both the stagnation-detection approach and fast mutation. The new operator is more than an interleaving of the two previous mechanisms and it outperforms any such interleaving.
Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: evidence from developed and emerging markets

Purpose This paper aims to quantify the volatility spillover impact and the directional predictability from stock market indexes to Bitcoin. Design/methodology/approach Daily data of 15 developed and 15 emerging stock markets are used for the period March 2017–December 2021.; The author uses vector autoregressive (VAR) model, Granger causality test and impulse response function (IRF) to estimate the results of the study. Findings Empirical results show a significant unidirectional volatility spillover impact from emerging markets to Bitcoin and only six stock markets are powerful predictors of Bitcoin return in the short term. Additionally, there is no a difference between developed and developing markets regarding the directional predictability however there is difference in the reaction of Bitcoin return to shocks in the emerging markets compared to developed ones. Originality/value The paper proposes different econometric techniques from prior research and presents a comparative analysis between developed and emerging markets.