Reviewerly/exHarmony huggingface dataset:
On the Image Encryption Algorithm Based on the Chaotic System, DNA Encoding, and Castle

Hundreds of image encryption algorithms have been developed for the security and integrity of images through the combination of DNA computing and chaotic maps. This combination of the two instruments is not sufficient enough to thwart the potential threats from the cryptanalysis community as the literature review suggests. To inject more robustness and security stuff, a novel image encryption scheme has been written in this research by fusing the chaotic system, DNA computing and Castle —a chess piece. As the plain image is input, its pixels are shifted to the scrambled image at the randomly chosen pixel addresses. This scrambling has been realized through the routine called Image Scrambler using Castle (ISUC). Castle randomly moves on the hypothetical large chessboard. Pixels taken from the plain image are shifted to the addresses of the scrambled image, where Castle lands in each iteration. After the plain image is scrambled, it is DNA encoded. Two mask images are also DNA encoded. Then to throw the diffusion effects in the cipher, DNA Addition and DNA XOR operations between the DNA encoded pixels data and the DNA encoded mask images have been conducted. Next, the pixels data are converted back into their decimal equivalents. Four dimensional chaotic system has been used to get the chaotic vectors. The hash codes given by the SHA-256 function have been used in the cipher to introduce the plaintext sensitivity in its design. We got an information entropy of 7.9974. Simulation carried out through the machine, and the thorough security analyses demonstrate the good security effects, defiance to the varied attacks from the cryptanalysis community, and the bright prospects for some real world application of the proposed image cipher.
Namib beetle optimization algorithm: A new meta‐heuristic method for feature selection and dimension reduction

Summary Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta‐heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta‐heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta‐heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting

In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc. Thus, for learning a general model, training with data from multiple different datasets might be a remedy and be of great value. In this paper, we resort to the multi-domain joint learning and propose a simple but effective Domain-specific Knowledge Propagating Network (DKPNet) for unbiasedly learning the knowledge from multiple diverse data domains at the same time. It is mainly achieved by proposing the novel Variational Attention(VA) technique for explicitly modeling the attention distributions for different domains. And as an extension to VA, Intrinsic Variational Attention(InVA) is proposed to handle the problems of over-lapped domains and sub-domains. Extensive experiments have been conducted to validate the superiority of our DKPNet over several popular datasets, including ShanghaiTech A/B, UCF-QNRF and NWPU.
Intense: In-Depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming

With the recent growth of multimedia traffic over the Internet and emerging multimedia streaming service providers, improving Quality of Experience (QoE) for HTTP Adaptive Streaming (HAS) becomes more important. Alongside other factors, such as the media quality, HAS relies on the performance of the media player’s Adaptive Bitrate (ABR) algorithm to optimize QoE in multimedia streaming sessions. QoE in HAS suffers from weak or unstable internet connections and suboptimal ABR decisions. As a result of imperfect adaptiveness to the characteristics and conditions of the internet connection, stall events and quality level switches could occur and with different durations that negatively affect the QoE. In this paper, we address various identified open issues related to the QoE for HAS, notably (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events. Therefore, we have studied these open issues from both objective and subjective evaluation perspectives and presented the correlation between the two types of evaluations. The findings documented in this paper can be used as a baseline for improving ABR algorithms and policies in HAS.
Enterprise Credential Spear-phishing attack detection

The latest report by Kaspersky on email Spam and targeted Phishing attacks, by percentage, highlights the need of an urgent solution. Attachment-driven Spear-phishing struggles to succeed against many email providers’ malware-filtration systems, which proactively check emails for malicious software. In this paper, we provided a solution that can detect targeted Spear-phishing attacks based on required similarities in the specific domain which it has been targeted. The strategy is to figure out whether the domain is genuine or a forgery, which is to be evaluated by multi novel grading algorithms. Therefore, this research addresses targeted attacks on specific organisations by presenting a new enterprise solution. This detection system focuses on domain names, which tend to be registered domain names trusted by the victims. The results from this investigation show that this detection system has proven its ability to reduce email phishing attacks significantly.
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation

Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time images, while their performance degrades for all-day images due to large domain shift and the variation of illumination between day and night images. To relieve these limitations, we propose a domain-separated network for self-supervised depth estimation of all-day images. Specifically, to relieve the negative influence of disturbing terms (illumination, etc.), we partition the information of day and night image pairs into two complementary sub-spaces: private and invariant domains, where the former contains the unique information (illumination, etc.) of day and night images and the latter contains essential shared information (texture, etc.). Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected. Meanwhile, the reconstruction and photometric losses are utilized to estimate complementary information and depth maps effectively. Experimental results demonstrate that our approach achieves state-of-the-art depth estimation results for all-day images on the challenging Oxford RobotCar dataset, proving the superiority of our proposed approach. Code and data split are available at https://github.com/LINA-lln/ADDS-DepthNet.
Performance of Domain-Wall Encoding for Quantum Annealing

In this paper we experimentally test the performance of the recently proposed domain-wall encoding of discrete variables from [Chancellor Quantum Sci. Technol. 4 045004] on Ising model flux qubit quantum annealers. We compare this encoding with the traditional one-hot methods and find that they outperform the one-hot encoding for three different problems at different sizes both of the problem and of the variables. From these results we conclude that the domain-wall encoding yields superior performance against a variety of metrics furthermore, we do not find a single metric by which one hot performs better. We even find that a 2000Q quantum annealer with a drastically less connected hardware graph but using the domain-wall encoding can outperform the next generation Advantage processor if that processor uses one-hot encoding.
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks where the mobile users may roam across edge access points (APs), leading to incompletion of inconsistent FL training. We provide the convergence analysis of conventional HFL with user mobility. Our analysis proves that the learning performance of conventional HFL deteriorates drastically with highly-mobile users. And such a decline in the learning performance will be exacerbated with small number of participants and large data distribution divergences among users’ local data. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm by redesigning the access mechanism, local update rule, and model aggregation scheme. We also conduct experiments to evaluate the learning performance of conventional HFL, a cluster federated learning (CFL) with simple averaging, and our proposed MACFL. The results show that our MACFL can enhance the learning performance, especially for three different cases: ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) the case of users with non-independent and identically distributed (non-IID) data, ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) the case of users with high mobility, and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) the case with a small number of users.
Spacecraft Time-Series Anomaly Detection Using Transfer Learning

Anomaly detection in telemetry channels is a high priority for spacecraft, especially when considering the harsh environment of space and the magnitude of launch and operation costs. Traditional spacecraft anomaly detection methods are limited in scope and rely on domain experts to correctly determine abnormal behavior. However, with thousands of distinct telemetry channels being transmitted, the amount of data is difficult to monitor manually. Deep learning models can be used to learn the normal behavior of the telemetry channels and flag or label any deviations. The problem is that we have to train a unique model for each channel to ensure best performance. With the large number of channels to monitor, this may not always be possible. We propose using principles of transfer learning to quickly adapt a general pre-trained model to any specific telemetry channel, greatly reducing the number of unique models needed and reducing the training time for each model. We present the results of our approach on the NASA SMAP/MSL dataset to show that we can achieve performance comparable to state-of-the-art anomaly detection methods.
Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation

Selecting a final machine learning (ML) model typically occurs after a process of hyperparameter optimization in which many candidate models with varying structural properties and algorithmic settings are evaluated and compared. Evaluating each candidate model commonly relies on k-fold cross validation, wherein the data are randomly subdivided into k folds, with each fold being iteratively used as a validation set for a model that has been trained using the remaining folds. While many research studies have sought to accelerate ML model selection by applying metaheuristic and other search methods to the hyperparameter space, no consideration has been given to the k-fold cross validation process itself as a means of rapidly identifying the best-performing model. The current study rectifies this oversight by introducing a greedy k-fold cross validation method and demonstrating that greedy k-fold cross validation can vastly reduce the average time required to identify the best-performing model when given a fixed computational budget and a set of candidate models. This improved search time is shown to hold across a variety of ML algorithms and real-world datasets. For scenarios without a computational budget, this paper also introduces an early stopping algorithm based on the greedy cross validation method. The greedy early stopping method is shown to outperform a competing, state-of-the-art early stopping method both in terms of search time and the quality of the ML models selected by the algorithm. Since hyperparameter optimization is among the most time-consuming, computationally intensive, and monetarily expensive tasks in the broader process of developing ML-based solutions, the ability to rapidly identify optimal machine learning models using greedy cross validation has obvious and substantial benefits to organizations and researchers alike.
Two-dimensional quantum walks of correlated photons

Quantum walks in an elaborately designed graph are a powerful tool for simulating physical and topological phenomena, constructing novel quantum algorithms, and realizing universal quantum computing. Integrated photonics technology has emerged as a versatile platform for implementing a variety of quantum information tasks and as a promising candidate for performing large-scale quantum walks. Both extending physical dimensions and involving more particles will increase the complexity of the evolving systems. Pioneering studies have demonstrated a single particle walking on two-dimensional lattices and multiple walkers interfering on a one-dimensional structure. However, multiple particles evolving in a genuine two-dimensional space in a scalable fashion has remained a vacancy for nearly 10 years. We present a genuine two-dimensional quantum walk with correlated photons on a triangular photonic lattice, which is mapped to a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>37</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mn>37</mml:mn> </mml:math> high-dimensional state space. The genuine two-dimensional quantum walk breaks through the physical restrictions of single-particle evolution, allowing for the encoding of information in large spaces and construction of high-dimensional graphs, which are beneficial for quantum information processing. Between the chip and the two-dimensional fanout interface, site-by-site addressing enables simultaneous detection of over 600 nonclassical interferences and observation of quantum correlations that violate a classical limit by 57 standard deviations. Our implementation provides a paradigm for multi-photon quantum walks in a two-dimensional configuration on a large scale, paving the way for practical quantum simulation and computation beyond the classical regime.
Internet of Things (IoT) Technology Research in Business and Management Literature: Results from a Co-Citation Analysis

In coherence with the progressive digitalization of all areas of life, the Internet of Things (IoT) is a flourishing concept in both research and practice. Due to the increasing scholarly attention, the literature landscape has become scattered and fragmented. With a focus on the commercial application of the IoT and corresponding research, we employ a co-citation analysis and literature review to structure the field. We find and describe 19 research themes. To consolidate the extant research, we propose a research framework, which is based on a theoretical implementation process of IoT as a concept, specific IoT applications, or architectures integrated in an adapted input–process–output model. The main variables of the model are an initial definition and conceptualization of an IoT concept (input), which goes through an evaluation process (process), before it is implemented and can have an impact in practice (output). The paper contributes to interdisciplinary research relating to a business and management perspective on IoT by providing a holistic overview of predominant research themes and an integrative research framework.
Supervised Compression for Resource-Constrained Edge Computing Systems

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural networks are often too resource-intensive in terms of energy and storage. As a result, the bulk part of the machine learning operation is therefore often carried out on an edge server, where the data is compressed and transmitted. However, compressing data (such as images) leads to transmitting information irrelevant to the supervised task. Another popular approach is to split the deep network between the device and the server while compressing intermediate features. To date, however, such split computing strategies have barely outperformed the aforementioned naive data compression baselines due to their inefficient approaches to feature compression. This paper adopts ideas from knowledge distillation and neural image compression to compress intermediate feature representations more efficiently. Our supervised compression approach uses a teacher model and a student model with a stochastic bottleneck and learnable prior for entropy coding (Entropic Student). We compare our approach to various neural image and feature compression baselines in three vision tasks and found that it achieves better supervised rate-distortion performance while maintaining smaller end-to-end latency. We furthermore show that the learned feature representations can be tuned to serve multiple downstream tasks.
Towards robust explanations for deep neural networks

Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible to manipulations. With this work, we aim to enhance the resilience of explanations. We develop a unified theoretical framework for deriving bounds on the maximal manipulability of a model. Based on these theoretical insights, we present three different techniques to boost robustness against manipulation: training with weight decay, smoothing activation functions, and minimizing the Hessian of the network. Our experimental results confirm the effectiveness of these approaches.
Conversational Image Search

Conversational image search, a revolutionary search mode, is able to interactively induce the user response to clarify their intents step by step. Several efforts have been dedicated to the conversation part, namely automatically asking the right question at the right time for user preference elicitation, while few studies focus on the image search part given the well-prepared conversational query. In this paper, we work towards conversational image search, which is much difficult compared to the traditional image search task, due to the following challenges: 1) understanding complex user intents from a multimodal conversational query; 2) utilizing multiform knowledge associated images from a memory network; and 3) enhancing the image representation with distilled knowledge. To address these problems, in this paper, we present a novel contextuaL imAge seaRch sCHeme (LARCH for short), consisting of three components. In the first component, we design a multimodal hierarchical graph-based neural network, which learns the conversational query embedding for better user intent understanding. As to the second one, we devise a multi-form knowledge embedding memory network to unify heterogeneous knowledge structures into a homogeneous base that greatly facilitates relevant knowledge retrieval. In the third component, we learn the knowledge-enhanced image representation via a novel gated neural network, which selects the useful knowledge from retrieved relevant one. Extensive experiments have shown that our LARCH yields significant performance over an extended benchmark dataset. As a side contribution, we have released the data, codes, and parameter settings to facilitate other researchers in the conversational image search community.
A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles

Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad.