Reviewerly/exHarmony huggingface dataset:
Parameter analysis of chirplet transform and high-resolution time-frequency representation via chirplets combination

The standard chirplet transform (CT) with the chirp-modulated Gaussian window provides a valuable tool for analyzing linear chirp signals. The parameters present in the window determine the performance of the CT and play a vital role in high-resolution time-frequency (TF) analysis. In this paper, we give the window shape analysis of the CT and compare it with the extension that employs a rotating Gaussian window by the fractional Fourier transform. The given parameters analysis provides theoretical guidance for developing high-resolution CT. We then propose a multi-resolution chirplet transform (MrCT) by combining multiple CTs with different parameter combinations. These are combined geometrically to obtain an improved TF resolution by overcoming the limitations of any single representation of the CT. We further develop a TF post-processing approach to enhance the readability of the MrCT by deriving a combined instantaneous frequency equation, which can characterize the mixture of the chirp-like and impulse-like signals. Numerical experiments on simulated and real signals verify the effectiveness of the proposed approaches.
Structured light-based underwater 3-D reconstruction techniques: A comparative study

This paper analyzes the influence of underwater scattering on structured light-based 3-D reconstruction techniques and improves the performance of the 3-D reconstruction under turbid water condition. Two typical structured light-based 3-D reconstruction techniques, fringe projection profilometry (FPP) and single-pixel imaging-based metrology (SIM), are selected to reconstruct the 3-D shape of underwater objects. First, we formulate the error model of underwater FPP, and introduce a Hilbert transform-based method (HT-based FPP) to compensate the measurement error. Second, we first introduce SIM for the underwater 3-D reconstruction, analyze the SNR of underwater image reconstructed by single-pixel imaging, and propose a HSI-based visible region location method to reduce the measurement time. The provided simulation and experiment verify the correctness of the theoretical analysis and the effectiveness of the proposed methods, and compare FPP, HT-based FPP and SIM under water with different turbidities.
Noise-free thumbnail-preserving image encryption based on MSB prediction

People are increasingly storing their images on the cloud as cloud storage grows in popularity, delivering a variety of benefits. However, the associated privacy concern may pose a serious challenge to data users. While traditional confusion-diffusion-based encryption is an effective way to resolve this concern by yielding a noise-like image without visual meaning, it sacrifices visual usability, i.e., it fails to make privacy and usability compatible. Thumbnail-preserving encryption (TPE) is proposed to balance privacy and usability, which requires that the thumbnail of the encrypted image is identical or approximate to that of the original. Although approximate-TPE has higher efficiency than ideal-TPE, the existing approximate-TPE methods cannot decrypt without loss. Motivated by this, we design a novel approximate-TPE method that is noise-free for the first time, which employs reversible data hiding based on the most significant bit prediction. Experimental results show that the method can effectively eliminate noise and finally perform decryption without sacrificing the quality of the decrypted image. Furthermore, image owners can recognize their images through the preserved coarse perceptual characteristics in the encrypted image, though it is difficult for an unauthorized third party to successfully discern with limited prior knowledge.
Lightweight Feature De-redundancy and Self-calibration Network for Efficient Image Super-resolution

In recent years, thanks to the inherent powerful feature representation and learning abilities of the convolutional neural network (CNN), deep CNN-steered single image super-resolution approaches have achieved remarkable performance improvements. However, these methods are often accompanied by large consumption of computing and memory resources, which is difficult to be adopted in real-world application scenes. To handle this issue, we design an efficient Feature De-redundancy and Self-calibration Super-resolution network (FDSCSR). In particular, a Feature De-redundancy and Self-calibration Block (FDSCB) is proposed to reduce the repetitive feature information extracted by the model and further enhance the efficiency of the model. Then, based on FDSCB, a Local Feature Fusion Module is presented to elaborately utilize and fuse the feature information extracted by each FDSCB. Abundant experiments on benchmarks have demonstrated that our FDSCSR achieves superior performance with relatively less computational consumption and storage resource than other state-of-the-art approaches. The code is available at https://github.com/IVIPLab/FDSCSR .
Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud–edge–client​ learning for image classification

The cloud–edge–client hierarchical computing architecture takes into account the dual advantages of cloud and edge computing. However, in realistic scenarios, the explosive increase of distributed data promotes the widespread application of the cloud–edge–client architecture, but also brings a variety of security threats. Relying on the advantages of federated learning technology, this has led to a growing interest in federated learning as the intersection of machine learning and cloud–edge–client fields. However, recent studies found that federated learning is potentially vulnerable to data poisoning attacks. Existing explorations of federated learning poisoning attacks and defense techniques in the cloud–edge–client environment are largely attack capabilities-specific and privacy protection-based: they are designed but fail to consider the characteristics of federated learning vulnerability and the difference in attack intensity, and lack flexibility. Yet few different attack capabilities and general defense strategies have been developed. To address these concerns, we introduce federated cloud–edge–client learning collaborative framework. Based on this architecture, a client-launched multi-label flipping attack method is discussed, which exhibits different attack capabilities. Further, we propose SPAD, a Server-based Parameter Adaptive Defense method in response to different attack intensities, to identify malicious attackers and benign participants, thereby improving the security of the federated learning image classification task. The results on image classification tasks demonstrate that in cloud–edge–client scenario, the multi-label flipping attack method presents different degrees of poisoning effect, and SPAD method has strong feasibility and effectiveness against poisoning attacks, which verify the significant advantages of the proposed method.
A Siamese Neural Network for Learning Semantically-Informed Sentence Embeddings

Semantic representation is a way of expressing the meaning of a text that can be processed by a machine to serve a particular natural language processing (NLP) task that usually requires meaning comprehension such as text summarisation, question answering or machine translation. In this paper, we present a semantic parsing model based on neural networks to obtain semantic representation of a given sentence. We utilise semantic representation of each sentence to generate semantically informed sentence embeddings for extrinsic evaluation of the proposed semantic parser, in particular for the semantic textual similarity task. Our neural parser utilises self-attention mechanism to learn semantic relations between words in a sentence to generate semantic representation of a sentence in UCCA (Universal Conceptual Cognitive Annotation) semantic annotation framework (Abend and Rappoport, 2013), which is a cross-linguistically applicable graph-based semantic representation. The UCCA representations are conveyed into a Siamese Neural Network built on top of two Recursive Neural Networks (Siamese-RvNN) to derive semantically informed sentence embeddings which are evaluated on semantic textual similarity task. We conduct both single-lingual and cross-lingual experiments with zero-shot and few-shot learning, which have shown superior performance even in low-resource scenario. The experimental results show that the proposed self-attentive neural parser outperforms the other parsers in the literature on English and German, and shows significant improvement in the cross-lingual setting for French which has comparatively low sources. Moreover, the results obtained from other downstream tasks such as sentiment analysis confirm that semantically informed sentence embeddings provide higher-quality embeddings compared to other pre-trained models such as SBERT (Reimers et al., 2019) or SimCSE (Gao et al., 2021), which do not utilise such structured information.
Phishing website prediction using base and ensemble classifier techniques with cross-validation

Abstract Internet or public internetwork has become a vulnerable place nowadays as there are so many threats available for the novice or careless users because there exist many types of tools and techniques being used by notorious people on it to victimize people somehow and gain access to their precious and personal data resulting in sometimes smaller. However, these victims suffer considerable losses in many instances due to their entrapment in such traps as hacking, cracking, data diddling, Trojan attacks, web jacking, salami attacks, and phishing. Therefore, despite the web users and the software and application developer's continuous effort to make and keep the IT infrastructure safe and secure using many techniques, including encryption, digital signatures, digital certificates, etc. this paper focuses on the problem of phishing to detect and predict phishing websites URLs, primary machine learning classifiers and new ensemble-based techniques are used on 2 distinct datasets. Again on a merged dataset, this study is conducted in 3 phases. First, they include classification using base classifiers, Ensemble classifiers, and then ensemble classifiers are tested with and without cross-validation. Finally, their performance is analyzed, and the results are presented at last to help others use this study for their upcoming research.
Blockchain concept to combat drug counterfeiting by increasing supply chain visibility

This paper explores problems of pharmaceutical supply chains, such as drug counterfeiting, and the opportunities of using digital technologies to overcome them. It also evaluates the risks associated with the use of digital technologies. Expert interviews were conducted with pharmaceutical supply chain stakeholders in the first step. The data obtained was assessed through qualitative content analysis. In addition to the lack of transparency, strong individual interests of the supply chain actors and different implementation levels of digitisation were revealed. It is also noticeable that the industry is still paper-oriented. Nevertheless, the chosen experts are optimistic about the increased use of digital technologies. Combining blockchain with other technologies, such as IoT and AI, can improve efficiency, traceability, and trust in pharmaceutical supply chains. A digitalised supply chain concept is developed based on these technologies, enhancing transparency and information availability in the supply chain. In addition, communication and processes along the entire pharmaceutical supply chain are optimised.
Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing

Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex systems. The brightest example is the brain neural network described by the electroencephalogram. Forecasting the behavior of these systems is a more challenging task but provides a potential for real-life application. Here, we trained reservoir computer to predict the macroscopic signal produced by the network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens' theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. We found that short-time prediction required a large number of features, while long-time prediction utilizes a limited number of features. These results refer to the bias-variance trade-off, an important concept in machine learning.
Dependency maximization forward feature selection algorithms based on normalized cross-covariance operator and its approximated form for high-dimensional data

Supervised feature selection (FS) for classification aims at finding a more discriminative subset from original features, to facilitate classifier training, improve classification performance and enhance model interpretability. Primary normalized cross-covariance operator (NOCCO) is a nonlinear kernel-based dependency measure between features and labels, including two inverse matrices, and its approximated version (ANOCCO) is simplified via exploiting linear kernel for features, delta kernel for labels and Moore–Penrose inverse. In this paper, we apply NOCCO and ANOCCO to FS task. According to sequential forward selection, a forward NOCCO-based FS algorithm (i.e., FoNOCCO) is directly implemented via various accelerating strategies, but its computational complexity is extremely high. To this end, we propose its fast version via maximizing ANOCCO (i.e., FoANOCCO), where each candidate feature is evaluated efficiently according to column recursive algorithm for Moore–Penrose inverse. Theoretical analysis shows that evaluating a candidate feature needs a time complexity of O(N2) in FoANOCCO and O(N3) in FoNOCCO, where N is the number of instances. On eight small-size high-dimensional benchmark data sets, our detailed experiments illustrate that our two forward FS algorithms are superior to six state-of-the-art FS methods with four baseline classifiers, and FoANOCCO runs 70 times faster than FoNOCCO with a 1.79 % accuracy drop.
ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks

With recent advancements, graph neural networks (GNNs) have shown considerable potential for various graph-related tasks, and their applications have gained considerable attention. However, adversarial attacks can significantly degrade the performance of GNNs, hindering their deployment in critical real-world tasks. GNNs must be robust against adversarial attacks, in which imperceptible adversarial perturbations are introduced to induce serious security issues. To achieve this goal, we propose a robust graph convolutional network, ERGCN, for node classification via data enhancement. ERGCN simultaneously utilizes properties from the "data domain" and "model space" as guidance. Based on the feature smoothness assumption, a graph structure enhancement (GSE) mechanism is proposed to improve the structural reliability of input graphs. Moreover, inspired by self-training methods that assign pseudo-labels to unlabeled training samples and use them to optimize the target model iteratively, a reliable node selection metric, model boundary distance (MBD), is defined based on the distance from training samples to model decision boundary. Finally, a self-training-based robust graph convolutional network is proposed for node classification. Extensive experiments on three public datasets demonstrate the superiority of our model over existing state-of-the-art methods. Our study provides a solution for trustworthy graph machine learning systems in adversarial environments. The code is available at https://github.com/star4455/ERGCN.
A novel image encryption mechanism based on umbrella map and Yang-Gu algorithm

A novel image encryption algorithm based on nonlinear discrete chaotic umbrella map and Yang-Gu mixture amplitude-phase retrieval algorithm in fractional Fourier domain is proposed in this paper. Here in the proposed mechanism the input image is encrypted with a deterministic phase mask and later subjected to fractional Fourier transform. Thereafter, for scrambling the pixels of transformed image the umbrella map is utilized. The umbrella map along with providing a broader range of control parameters also enhances the key space. Scrambled image is further encoded with additional deterministic phase mask before subjecting it to another level of fractional Fourier transform. The phase truncated portion of the resultant image is used as an input to the Yang-Gu mixture amplitude-phase retrieval algorithm in the Fourier domain. The encrypted image attained by one-way binary modulation is real-valued, which makes it more appropriate for storage and transmission. Comprehensive statistical and attack analysis of the proposed scheme has been carried out. Sensitivity analysis of the scheme to various keys and crucial parameters has also been done. The results demonstrate the robustness and efficacy of the proposed mechanism. The exceptional resistance to noise attacks makes it suitable for use in extremely noisy channels and real valued cipher image is more convenient to store and transmit.
User acceptance of smart home voice assistant: a comparison among younger, middle-aged, and older adults

Voice assistants are widely used in smart home environments. This study aimed to investigate user acceptance of a smart home voice assistant. A questionnaire was designed, and 471 Chinese adults were recruited to complete the questionnaire. The data were analyzed using exploratory factor analysis and regression analysis. The results revealed that user requirements of adults were composed of six factors: hedonic motivation and trust (β = .41, p < .001), social influence (β = .22, p < .001), performance expectancy (β = .15, p < .001), effort expectancy (β = .08, p = .018), product features (β = .15, p = .009), and facilitating conditions (β = .06, p = .049). Among these six factors, hedonic motivation and trust are considered the most important. Younger, middle-aged, and older adults differed significantly in their requirements and acceptance of a smart home voice assistant. These findings have implications for the design of smart home voice assistants so that they are more acceptable to younger and older adults.The online version contains supplementary material available at 10.1007/s10209-022-00936-1.
Modality-invariant temporal representation learning for multimodal sentiment classification

Multimodal sentiment classification is a notable research field that aims to refine sentimental information and classify the sentiment tendency from sequential multimodal data. Most existing sentimental recognition algorithms explore multimodal fusion schemes that achieve good performance. However, there are two key challenges to overcome. First, it is essential to effectively extract inter- and intra-modality features prior to fusion, while simultaneously reducing ambiguity. The second challenge is how to learn modality-invariant representations that capture the underlying similarities. In this paper, we present a modality-invariant temporal learning technique and a new gated inter-modality attention mechanism to overcome these issues. For the first challenge, our proposed gated inter-modality attention mechanism performs modality interactions and filters inconsistencies from multiple modalities in an adaptive manner. We also use parallel structures to learn more comprehensive sentimental information in pairs (i.e., acoustic and visual). In addition, to address the second problem, we treat each modality as a multivariate Gaussian distribution (considering each timestamp as a single Gaussian distribution) and use the KL divergence to capture the implicit temporal distribution-level similarities. These strategies are helpful in reducing domain shifts between different modalities and extracting effective sequential modality-invariant representations. We have conducted experiments on several public datasets (i.e., YouTube and MOUD) and the results show that our proposed method outperforms the state-of-the-art multimodal sentiment categorization methods.
Interactive Narrative and Story-telling

chapter Share on Interactive Narrative and Story-telling Author: Ruth Aylett View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 463–492https://doi.org/10.1145/3563659.3563674Published:03 November 2022Publication History 5citation9DownloadsMetricsTotal Citations5Total Downloads9Last 12 Months9Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars' attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted for various optimization problems in different domains in a short period. Therefore, this paper reviews and summarizes the recently published studies that utilized the BOA for optimization problems. Initially, introductory information about the BOA is presented to illustrate the essential foundation and its relevant optimization concepts. In addition, the BOA inspiration and its mathematical model are provided with an illustrative example to prove its high capabilities. Subsequently, all reviewed studies are classified into three main classes based on the adaptation form, including original, modified, and hybridized. The main BOA applications are also thoroughly explained. Furthermore, the BOA advantages and drawbacks in dealing with optimization problems are analyzed. Finally, the paper is summarized in conclusion with the future directions that can be investigated further.