Reviewerly/exHarmony huggingface dataset:
Holographic and speckle encryption using deep learning

Vulnerability analysis of optical encryption schemes using deep learning (DL) has recently become of interest to many researchers. However, very few works have paid attention to the design of optical encryption systems using DL. Here we report on the combination of the holographic method and DL technique for optical encryption, wherein a secret image is encrypted into a synthetic phase computer-generated hologram (CGH) by using a hybrid non-iterative procedure. In order to increase the level of security, the use of the steganographic technique is considered in our proposed method. A cover image can be directly diffracted by the synthetic CGH and be observed visually. The speckle pattern diffracted by the CGH, which is decrypted from the synthetic CGH, is the only input to a pre-trained network model. We experimentally build and test the encryption system. A dense convolutional neural network (DenseNet) was trained to estimate the relationship between the secret images and noise-like diffraction patterns that were recorded optically. The results demonstrate that the network can quickly output the primary secret images with high visual quality as expected, which is impossible to achieve with traditional decryption algorithms.
EMPIRICAL EXAMINATION OF MS TEAMS IN CONDUCTING WEBINAR: EVIDENCE FROM INTERNATIONAL ONLINE PROGRAM CONDUCTED IN OMAN

The quick adoption of digital platforms in the education sector will always give credit to covid-19. The imposition of lock downs and closure of border as remedial measures forced educational institutes to shift to online platforms for their routine teaching and learning activities. The higher education institutes (HEIs) were found to be more dynamic, as they utilised their online presence in interacting with external members by way of other online activities predominantly webinars. The researchers suggest that online learning will remain to be a vital part of higher education and is going to stay along with other online activities like webinar as it is flexible, accessible, and convenient for the larger section of the students. This study examines the MS team’s platform from the learner’s perspective through technology acceptance model. The 320 responses were gathered from the participants of 22 countries who participated in one-week international webinar organised by two leading HEIs in Oman an online feedback form was analysed through PLS-SEM in SmartPLS 3.3.3. The study aimed to test the degree to which the participants were able to accept MS teams, a popular online learning platform in Oman, as a suitable webinar platform. The results indicate that MS teams is found to be a sufficiently good platform to conduct webinars however, it lags in terms of ease of use or user-friendliness. The study also confirms that attitude towards e-learning influences behavioural intention that subsequently influences the actual use of the platform.
Research on Color Image Encryption Algorithm Based on Bit-Plane and Chen Chaotic System

In response to the problems of high complexity and the large amount of operations of existing color image encryption algorithms, a low-complexity, low-operation color image encryption algorithm based on a combination of bit-plane and chaotic systems is proposed that is interrelated with plaintext information. Firstly, three channels of an RGB image are extracted, and the gray value of each pixel channel can be expressed by an eight-bit binary number. The higher- and lower-four bits of the binary gray value of each pixel are exchanged, and the position of each four-bit binary number is scrambled by a logistic chaotic sequence, and all the four-bit binary numbers are converted into hexadecimal numbers to reduce the computational complexity. Next, the position of the transformed image is scrambled by a logistic chaotic sequence. Then, the Chen chaos sequence is used to permute the gray pixel values of the permuted image. Finally, the gray value of the encrypted image is converted into a decimal number to form a single-channel encrypted image, and the three-channel encrypted image is synthesized into an encrypted color image. Through MATLAB simulation experiments, a security analysis of encryption effects in terms of a histogram, correlation, a differential attack, and information entropy is performed. The results show that the algorithm has a better encryption effect and is resistant to differential attacks.
Private and Energy-Efficient Decision Tree-Based Disease Detection for Resource-Constrained Medical Users in Mobile Healthcare Network

In mobile healthcare networks (MHN), outsourced disease detection services demand the privacy preservation of medical users and health service providers (health clouds). This necessitates the use of a fully homomorphic encryption (FHE) while providing disease detection services, such as decision tree-based disease detection. However, the existing homomorphic encryption schemes utilized in decision tree-based disease detection that ensure the privacy of the medical user and health cloud are computationally-intensive and energy-hungry at the edge devices. Hence the medical user finds it difficult to exploit the existing private decision tree-based disease detection services due to restrictions on battery capacity and computing resources. Therefore, this work proposes a protocol for private decision tree classification with low resource consumption (PDTC-LRC) on edge devices of medical users by considering decision tree parameters as confidential to the health cloud. An energy-efficient, additively homomorphic, symmetric key-based FHE-compatible Rivest scheme (FCRS) is developed for implementing PDTC-LRC. FCRS can be decrypted homomorphically at the health cloud to support additive and multiplicative homomorphism. Also, an energy and bandwidth-efficient secure integer comparison protocol is developed for realizing PDTC-LRC. Experiments on the Raspberry Pi 3B+ board validate the improved energy efficiency and real-time applicability of the proposed secure integer comparison protocol and decision tree classifier compared with similar schemes available in the literature. Simulation and mathematical analysis ensure that user and health cloud privacy requirements are achieved by maintaining the classification accuracy same as that of decision tree classification in the plain domain.
Practical galaxy morphology tools from deep supervised representation learning

ABSTRACT Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.
Bodily ownership of an independent supernumerary limb: an exploratory study

Can our brain perceive a sense of ownership towards an independent supernumerary limb; one that can be moved independently of any other limb and provides its own independent movement feedback? Following the rubber-hand illusion experiment, a plethora of studies have shown that the human representation of "self" is very plastic. But previous studies have almost exclusively investigated ownership towards "substitute" artificial limbs, which are controlled by the movements of a real limb and/or limbs from which non-visual sensory feedback is provided on an existing limb. Here, to investigate whether the human brain can own an independent artificial limb, we first developed a novel independent robotic "sixth finger." We allowed participants to train using the finger and examined whether it induced changes in the body representation using behavioral as well as cognitive measures. Our results suggest that unlike a substitute artificial limb (like in the rubber hand experiment), it is more difficult for humans to perceive a sense of ownership towards an independent limb. However, ownership does seem possible, as we observed clear tendencies of changes in the body representation that correlated with the cognitive reports of the sense of ownership. Our results provide the first evidence to show that an independent supernumerary limb can be embodied by humans.
Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.
Robust text line detection in historical documents: learning and evaluation methods

Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years. In this paper, we address the limitations that currently prevent people from building line segmentation models with a high generalization capacity. We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages. This paper also highlights the importance of the annotations used during training: each existing dataset is annotated differently. We present a unification of the annotations and show its positive impact on the final text recognition results. In this end, we present a complete evaluation strategy using standard pixel-level metrics, object-level ones and introducing goal-oriented metrics.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese).Essentially, the CLS task is the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT&MS and CLS.Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective.However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize.To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder.The hierarchical model contains two kinds of latent variables at the local and global levels, respectively.At the local level, there are two latent variables, one for translation and the other for summarization.As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables.Experiments on two language directions (English⇔Chinese) verify the effectiveness and superiority of the proposed approach.In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the fewshot setting.
A metaheuristic approach for interval type-2 fuzzy fractional order fault-tolerant controller for a class of uncertain nonlinear system*

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Hybrid-Flash Butterfly Optimization Algorithm with Logistic Mapping for Solving the Engineering Constrained Optimization Problems

Only the smell perception rule is considered in the butterfly optimization algorithm (BOA), which is prone to falling into a local optimum. Compared with the original BOA, an extra operator, i.e., color perception rule, is incorporated into the proposed hybrid-flash butterfly optimization algorithm (HFBOA), which makes it more in line with the actual foraging characteristics of butterflies in nature. Besides, updating the strategy of the control parameters by the logistic mapping is used in the HFBOA for enhancing the global optimal ability. The performance of the proposed method was verified by twelve benchmark functions, where the comparison experiment results show that the HFBOA converges quicker and has better stability for numerical optimization problems, which are compared with six state-of-the-art optimization methods. Additionally, the proposed HFBOA is successfully applied to six engineering constrained optimization problems (i.e., tubular column design, tension/compression spring design, cantilever beam design, etc.). The simulation results reveal that the proposed approach demonstrates superior performance in solving complex real-world engineering constrained tasks.
What are Different Research Approaches? Comprehensive Review of Qualitative, Quantitative, and Mixed Method Research, Their Applications, Types, and Limitations

There are different ways to examine and explain a study and its findings based on using numbers as a measure, a descriptive style, or a mixture of both. These three research approaches are quantitative, qualitative, and mixed methods that are commonly used by researchers in various research studies. However, with many options regarding the research design, it seems challenging for researchers to select the most appropriate approach based on the study and realize differences. This study provides a comprehensive review of qualitative, quantitative, and mixed-method research methods. Each method is clearly defined and specifically discussed based on applications, types, advantages, and limitations to help researchers identify select the most relevant type based on each study and navigate accordingly.
A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems

This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distribution affects the dynamics and observations of state variables. Stochastic control is intended to determine the time course of control variables so that the control target is achievable even with minimal cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this paper, a type-3 fuzzy neural network is used to estimate the parameters of the backstepping control method. In the simulation, the proposed method is compared with the Type-1 fuzzy and RBFN methods. Results clearly show that the proposed method has a very good performance and can be used for any system in this class.
Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation

A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e., the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (UCD). In a nutshell, UCDis operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. In order to mitigate catastrophic forgetting, we contrast features of the new model with features extracted by a frozen model learned at the previous incremental step. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks for incremental semantic segmentation.
Towards Relatable Explainable AI with the Perceptual Process

Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current contrastive explanations are rudimentary comparisons between examples or raw features, which remain difficult to interpret, since they lack semantic meaning. We argue that explanations must be more relatable to other concepts, hypotheticals, and associations. Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations. We investigated the application of vocal emotion recognition, and implemented a modular multi-task deep neural network to predict and explain emotions from speech. From think-aloud and controlled studies, we found that counterfactual explanations were useful and further enhanced with semantic cues, but not saliency explanations. This work provides insights into providing and evaluating relatable contrastive explainable AI for perception applications.
Power Analysis on NTRU Prime

This paper applies a variety of power analysis techniques to several implementations of NTRU Prime, a Round 2 submission to the NIST PQC Standardization Project. The techniques include vertical correlation power analysis, horizontal indepth correlation power analysis, online template attacks, and chosen-input simple power analysis. The implementations include the reference one, the one optimized using smladx, and three protected ones. Adversaries in this study can fully recover private keys with one single trace of short observation span, with few template traces from a fully controlled device similar to the target and no a priori power model, or sometimes even with the naked eye. The techniques target the constant-time generic polynomial multiplications in the product scanning method. Though in this work they focus on the decapsulation, they also work on the key generation and encapsulation of NTRU Prime. Moreover, they apply to the ideal-lattice-based cryptosystems where each private-key coefficient comes from a small set of possibilities.
Aggression Detection in Social Media from Textual Data Using Deep Learning Models

It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, e.g., TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant margin.