Reviewerly/exHarmony huggingface dataset:
Image encryption algorithm based on improved Zigzag transformation and quaternary DNA coding

Image security becomes increasingly critical as the multimedia technology and Internet of things evolve rapidly. To ensure image security and improve the efficiency of image encryption, a novel image encryption algorithm is proposed in this paper. Firstly, a novel one-dimensional hybrid chaotic map is proposed and employed to obtain the chaotic sequences to be used in the permutation and diffusion processes. Secondly, an improved Zigzag transformation is employed to permute the plain image. Thirdly, the dynamic quaternary DNA coding and operations are used to diffuse the permuted image. In the diffusion process, the DNA encoded image is permuted using the index sequence to further enhance the permutation effect. Finally, a look-up table method is proposed to quickly obtain the DNA operation results. The experimental results and the security analyses illustrate that the proposed algorithm has an excellent performance in terms of encryption effect, key space, key sensitivity, statistical performance, differential attack, encryption quality, robustness, execution time and computational complexity. Therefore, the proposed algorithm can be applied to some fields with high confidentiality requirements, such as military, commercial and medical fields. • A novel 1D hybrid chaotic map is designed, and its chaos characteristics are proved to be excellent. • An improved Zigzag transformation is proposed to overcome the disadvantages of the standard Zigzag transformation. • A novel look-up table method based on quaternary DNA coding is proposed to shorten the time for DNA operations. • A novel image encryption algorithm is proposed, which has high security and efficiency.
An improved federated learning approach enhanced internet of health things framework for private decentralized distributed data

With the privacy protection increasingly being concerned, Data centralization often heavily causes a big risk of privacy protection, gradually, there is a prevailing trend to enhance the security performance by means of data decentralization, above all, for health care internet of things (IoT) data. Meanwhile, Federated learning has obvious privacy advantages compared to data center training on protecting privacy data. For this reason, a novel framework based on federated learning is presented in this paper, which is suitable for private and decentralized data sets, such as big data in healthy Internet of Things. Specifically, the main work of the puts forward framework includes: (1) Multi-center data collection of healthy Internet of Things. (2) healthy data analysis of Internet of Things. (3) privacy protection method for data of healthy Internet of Things. Finally, related experiments show that the proposed method is feasible, and compared with the traditional methods, it has significantly improved the performance in Quality of Service (QoS) and IoUs indicator.
Diversified sine–cosine algorithm based on differential evolution for multidimensional knapsack problem

The sine–cosine algorithm (SCA) is one of the simplest and efficient stochastic search algorithms in the field of metaheuristics. It has shown its efficacy in solving several real-life applications. However, in some cases, it shows stagnation at local optima and premature convergence issues due to low exploitation ability and insufficient diversity skills. To overcome these issues from the SCA, its enhanced version named ISCA is developed in this paper. The proposed ISCA is designed based on modifying the original search mechanism of the SCA and hybridizing it with a differential evolution (DE) algorithm. The search procedure in the ISCA switches between the modified search mechanism of the SCA and DE based on the evolutionary states of candidate solutions and a parameter called the switch parameter. The modified SCA enhances the exploitation ability and convergence speed, while the DE maintains the diversity of the population to avoid local optimal solutions. The parameters of the ISCA are tuned in such as way that they could balance the exploration and exploitation features. Validation of the ISCA is conducted on a benchmark set of 23 continuous optimization problems through different performance measures, which reveals its effectiveness as a better optimizer for continuous optimization problems. Furthermore, the proposed ISCA is extended to develop its efficient binary version named BISCA for solving multidimensional knapsack problems. A benchmark collection of 49 instances is used for the performance evaluation of the BISCA. Comparison of results produced by the BISCA with other algorithms and previous studies indicates its better search efficiency and verifies it as an effective alternative for solving the MKP.
Two-variable boosting bifurcation in a hyperchaotic map and its hardware implementation

There are few reports on the nondestructive adjustment of the oscillation amplitude of the chaotic sequence in the discrete map. To study the lossless regulation of the oscillation amplitude of chaotic sequences, this article proposes a new simple two-dimensional (2D) hyperchaotic map with trigonometric functions. It not only exhibits the offset boosting bifurcation and offset boosting coexistence attractors, but also shows the offset boosting of two state variables with respect to arbitrary parameters in the 2D map. The simulation results of bifurcation diagram, maximum Lyapunov exponent and attractor phase diagram show that the map can produce complex dynamical behaviors. In addition, the introduction of new control parameters into the 2D hyperchaotic map can also make the hyperchaotic map exhibit rich multi-stable phenomena. At the same time, the covariation of the initial state and control parameters can result in arbitrary switching and coexistence of attractors in the phase plane. The 2D hyperchaotic map was tested and verified by hardware experiment platform. Moreover, we design a pseudo-random number generator (PRNG) to test the hyperchaotic map. The results show that the pseudo-random numbers generated by the hyperchaotic map have high randomness.
Learning Hybrid Behavior Patterns for Multimedia Recommendation

Multimedia recommendation aims to predict user preferences where users interact with multimodal items. Collaborative filtering based on graph convolutional networks manifests impressive performance gains in multimedia recommendation. This is attributed to the capability of learning good user and item embeddings by aggregating the collaborative signals from high-order neighbors. However, previous researches [37,38] fail to explicitly mine different behavior patterns (i.e., item categories, common user interests) by exploiting user-item and item-item graphs simultaneously, which plays an important role in modeling user preferences. And it is the lack of different behavior pattern constraints and multimodal feature reconciliations that results in performance degradation. Towards this end, We propose a Hybrid Clustering Graph Convolutional Network (HCGCN) for multimedia recommendation. We perform high-order graph convolutions inside user-item clusters and item-item clusters to capture various user behavior patterns. Meanwhile, we design corresponding clustering losses to enhance user-item preference feedback and multimodal representation learning constraint to adjust the modality importance, making more accurate recommendations. Experimental results on three real-world multimedia datasets not only demonstrate the significant improvement of our model over the state-of-the-art methods, but also validate the effectiveness of integrating hybrid user behavior patterns for multimedia recommendation.
Source-Free Domain Adaptation for Real-World Image Dehazing

Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains. To address these issues, we present a novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available. Specifically, we devise the Domain Representation Normalization (DRN) module to make the representation of real hazy domain features match that of the synthetic domain to bridge the gaps. With our plug-and-play DRN module, unlabeled real hazy images can adapt existing well-trained source networks. Besides, the unsupervised losses are applied to guide the learning of the DRN module, which consists of frequency losses and physical prior losses. Frequency losses provide structure and style constraints, while the prior loss explores the inherent statistic property of haze-free images. Equipped with our DRN module and unsupervised loss, existing source dehazing models are able to dehaze unlabeled real hazy images. Extensive experiments on multiple baselines demonstrate the validity and superiority of our method visually and quantitatively.
Wav2vec2-based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering

With the rapid advancement in automatic speech recognition and natural language understanding, a complementary field (paralinguistics) emerged, focusing on the non-verbal content of speech. The ACM Multimedia 2022 Computational Paralinguistics Challenge introduced several exciting tasks of this field. In this work, we focus on tackling two Sub-Challenges using modern, pre-trained models called wav2vec2. Our experimental results demonstrated that wav2vec2 is an excellent tool for detecting the emotions behind vocalisations and recognising different types of stutterings. Albeit they achieve outstanding results on their own, our results demonstrated that wav2vec2-based systems could be further improved by ensembling them with other models. Our best systems outperformed the competition baselines by a considerable margin, achieving an unweighted average recall of 44.0 (absolute improvement of 6.6% over baseline) on the Vocalisation Sub-Challenge and 62.1 (absolute improvement of 21.7% over baseline) on the Stuttering Sub-Challenge.
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing

With various face presentation attacks emerging continually, face anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains. However, they neglect individual source domains' discriminative characteristics and diverse domain-specific information of the unseen domains, and the trained model is not sufficient to be adapted to various unseen domains. To address this issue, we propose an Adaptive Mixture of Experts Learning (AMEL) framework, which exploits the domain-specific information to adaptively establish the link among the seen source domains and unseen target domains to further improve the generalization. Concretely, Domain-Specific Experts (DSE) are designed to investigate discriminative and unique domain-specific features as a complement to common domain-invariant features. Moreover, Dynamic Expert Aggregation (DEA) is proposed to adaptively aggregate the complementary information of each source expert based on the domain relevance to the unseen target domain. And combined with meta-learning, these modules work collaboratively to adaptively aggregate meaningful domain-specific information for the various unseen target domains. Extensive experiments and visualizations demonstrate the effectiveness of our method against the state-of-the-art competitors.
Modality-aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection

Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early or intermediate manner, yet overlooking the modality heterogeneousness over the weakly-supervised setting. In this paper, we analyze the modality asynchrony and undifferentiated instances phenomena of the multiple instance learning (MIL) procedure, and further investigate its negative impact on weakly-supervised audio-visual learning. To address these issues, we propose a modality-aware contrastive instance learning with self-distillation (MACIL-SD) strategy . Specifically, we leverage a lightweight two-stream network to generate audio and visual bags, in which unimodal background, violent, and normal instances are clustered into semi-bags in an unsupervised way. Then audio and visual violent semi-bag representations are assembled as positive pairs, and violent semi-bags are combined with background and normal instances in the opposite modality as contrastive negative pairs. Furthermore, a self-distillation module is applied to transfer unimodal visual knowledge to the audio-visual model, which alleviates noises and closes the semantic gap between unimodal and multimodal features. Experiments show that our framework outperforms previous methods with lower complexity on the large-scale XD-Violence dataset. Results also demonstrate that our proposed approach can be used as plug-in modules to enhance other networks. Codes are available at https://github.com/JustinYuu/MACIL_SD.
AdaMask: Enabling Machine-Centric Video Streaming with Adaptive Frame Masking for DNN Inference Offloading

This paper presents AdaMask, a machine-centric video streaming framework for remote deep neural network (DNN) inference. The objective is to optimize the accuracy of downstream DNNs, offloaded to a remote machine, by adaptively changing video compression control knobs at runtime. Our main contributions are twofold. First, we propose frame masking as an effective mechanism to reduce the bandwidth consumption of video stream, which only preserves regions that potentially contain objects of interest. Second, we design a new adaptation algorithm that achieves the Pareto-optimal tradeoff between accuracy and bandwidth by controlling the masked portions of frames together with conventional H.264 control knobs (eg. resolution). Through extensive evaluations on three sensing scenarios (dash camera, traffic surveillance, and drone), frame masking saves the bandwidth by up to 65% with < 1% accuracy degradation, and AdaMask improves the accuracy by up to 14% over the baselines against the network dynamics.
Folding lattice proteins with quantum annealing

Quantum annealing is a promising approach for obtaining good approximate solutions to difficult optimization problems. Folding a protein sequence into its minimum-energy structure represents such a problem. For testing new algorithms and technologies for this task, the minimal lattice-based HP model is well suited, as it represents a considerable challenge despite its simplicity. The HP model has favorable interactions between adjacent, not directly bound hydrophobic residues. Here, we develop a novel spin representation for lattice protein folding tailored for quantum annealing. With a distributed encoding onto the lattice, it differs from earlier attempts to fold lattice proteins on quantum annealers, which were based upon chain growth techniques. With our encoding, the Hamiltonian by design has the quadratic structure required for calculations on an Ising-type annealer, without having to introduce any auxiliary spin variables. This property greatly facilitates the study of long chains. The approach is robust to changes in the parameters required to constrain the spin system to chain-like configurations, and performs very well in terms of solution quality. The results are evaluated against existing exact results for HP chains with up to $N=30$ beads with 100% hit rate, thereby also outperforming classical simulated annealing. In addition, the method allows us to recover the lowest known energies for $N=48$ and $N=64$ HP chains, with similar hit rates. These results are obtained by the commonly used hybrid quantum-classical approach. For pure quantum annealing, our method successfully folds an $N=14$ HP chain. The calculations were performed on a D-Wave Advantage quantum annealer.
Quantum encryption with quantum permutation pad in IBMQ systems

Abstract Quantum permutation pad or QPP is a quantum-safe symmetric cryptographic algorithm proposed by Kuang and Bettenburg in 2020. The theoretical foundation of QPP leverages the linear algebraic representations of quantum gates which makes QPP realizable in both, quantum and classical systems. By applying the QPP with 64 of 8-bit permutation gates, holding respective entropy of over 100,000 bits, we accomplished quantum random number distributions digitally over today’s classical internet. The QPP has also been used to create pseudo quantum random numbers and served as a foundation for quantum-safe lightweight block and streaming ciphers. This paper continues to explore numerous applications of QPP, namely, we present an implementation of QPP as a quantum encryption circuit on today’s still noisy quantum computers. With the publicly available 5-qubit IBMQ devices, we demonstrate quantum secure encryption (256 bits of entropy) using 2-qubit QPP with 56 permutation gates, and 3-qubit QPP with 17 permutation gates respectively. Initial qubits of the encryption circuit correspond to the plaintext and after applying quantum encryption operations, cipher qubits are measured with probabilistic distributions, and the results with the highest probability are recorded as cipher bits. The cipher bits are then decrypted with an inverse QPP circuit. The output state plaintext qubits are measured and the most frequent count measurement results are recorded as plaintext bits. This quantum encryption and decryption process clearly demonstrates that QPP quantum implementations works exactly as symmetric encryption and decryption schemes should. The plaintext and ciphertext bits can also be encrypted and decrypted respectively by any classical computing device with the corresponding QPP algorithm as in quantum computers. This work reveals that it is possible to build quantum-secure communications between quantum-to-quantum and quantum-to-classical computers over today’s internet and the future quantum internet.
Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram

Audio copy-move-forgery created by copying one or more segments of an audio file and pasting it in a different position within the same audio is one of the most widely used methods in the field of audio forensics. This type of forgery is easy to apply but difficult to detect in the case of post-processing operations applied to forged speech to hide traces of forgeries. This paper proposes a robust method for the detection and localization of the audio copy-move forgery using a keypoint-based approach to the Mel spectrogram representation of audio. In the proposed method, first, the Mel spectrogram image is created from the input audio. Then, SIFT keypoints are obtained from each RGB color channel of this image. The obtained keypoints from each channel are matched via feature vectors to reveal the clues of the forgery regions, and the image sub-blocks whose keypoints are determined to be the center are labeled as forged blocks. Then the blocks in the neighborhood of the forged blocks are investigated whether forged or not. The proposed post-processing stage completes the determination of the forged regions. This stage eliminates the possible false positives and marks the forged areas in the spectrogram image. The forged segments are marked in the audio file by utilizing the positions of the forged regions in the spectrogram image. Experimental studies are carried out on two pitch-based datasets, using TIMIT and Arabic Speech Corpus. The paper presents the detailed performance results of popular referenced studies on these datasets. The performance results prove that the proposed method is more robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.
A new genetic algorithm based evolutionary neural architecture search for image classification

Deep Learning (DL) has achieved the great breakthrough in image classification. As DL structure is problem-dependent and it has the crucial impact on its performance, it is still necessary to re-design the structures of DL according to the actual needs, even there exists various benchmark DL structures. Neural Architecture Search (NAS) which can design the DL network automatically has been widely investigated. However, many NAS methods suffer from the huge computation time. To overcome this drawback, this research proposed a new Evolutionary Neural Architecture Search with RepVGG nodes (EvoNAS-Rep). Firstly, a new encoding strategy is developed, which can map the fixed-length encoding individual to DL structure with variable depth using RepVGG nodes. Secondly, Genetic Algorithm (GA) is adopted for searching the optimal individual and its corresponding DL model. Thirdly, the iterative training process is designed to train the DL model and to evolve the GA simultaneously. The proposed EvoNAS-Rep is validated on the famous CIFAR 10 and CIFAR 100. The results show that EvoNAS-Rep has obtained 96.35% and 79.82% with only near 0.2 GPU days, which is both effectiveness and efficiency. EvoNAS-Rep is also validated on two real-world applications, including the NEU-CLS and the Chest XRay 2017 datasets, and the results show that EvoNAS-Rep has achieved the competitive results.
Academic library services extension during the COVID-19 pandemic: considerations in higher education institutions in the Gauteng Province, South Africa

Purpose This paper aimed to determine the extent to which academic libraries and information services were extended due to the emergence of COVID-19 in the Gauteng Province, South Africa. Design/methodology/approach Founded on a pragmatism paradigm, the sequential explanatory research design was adopted to engage with participants and respondents on their experience of library services extensions to support users during the COVID-19 pandemic. Data were collected using online questionnaires and interviews. Cluster and purposive sampling were used and data for the quantitative part were analyzed using the Statistical Package for the Social Sciences (SPSS), whilst qualitative data were analyzed manually. Findings Findings revealed that academic libraries operating in a higher education environment provided extensive support to remote users during the COVID-19 pandemic. This was done through the utilization of a variety of technology utilization, ranging from traditional e-mail support to the use of technology related to Artificial Intelligence such as the BOTsa, which is a Chatbot aimed to assist users in receiving speedy responses to library-related inquiries. Originality/value This study is unique in that it focuses on academic libraries that operate in higher education environments where support for achieving academic endeavors becomes imperative to ensure the smooth execution of teaching and learning activities within the restrictions put in place due to the COVID-19 pandemic. Adaptions and improvements to academic library services during and post-COVID-19 era were successful in ensuring that remote users could obtain similar services and access to information as was the case before the outbreak of the COVID-19 pandemic.