Reviewerly/exHarmony huggingface dataset:
Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace, the digital communication signals identification plays an important role in cognitive radio networks, the communication monitoring and management systems. AI has become a promising solution to this problem due to its powerful modeling capability, which has become a consensus in academia and industry. However, because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space, the physical layer digital communication signals identification model is threatened by adversarial attacks. Adversarial examples pose a common threat to AI models, where well-designed and slight perturbations added to input data can cause wrong results. Therefore, the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications. In this paper, we first launch adversarial attacks on the end-to-end AI model for automatic modulation classification, and then we explain and present three defense mechanisms based on the adversarial principle. Next we present more detailed adversarial indicators to evaluate attack and defense behavior. Finally, a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model, which should be paid more attention in future research.
Immersive Virtual Reality for procedural training: Comparing traditional and learning by teaching approaches

Virtual Reality (VR) has been widely adopted for the creation of training experiences, since it appears to allow overcoming, especially for practical training, some of the limitations of real exercises. Many previous works focused on investigating the effectiveness of VR Training Systems (VRTS) in a variety of fields, but the efficacy of these systems is very task-dependent, and the best way to integrate them into existing learning paths has yet to be thoroughly investigated. The goal of the present paper is to explore the latter aspects considering a case study in the context of energy management in industry, and focusing on a measuring procedure that is part, e.g., of energy audit inspections. To this aim, a VRTS was developed and used to conduct two user studies: a first study designed to investigate the effectiveness of the devised system when used as an alternative or in combination with lecture and laboratory-based teaching experiences, and a second study aimed to compare two different approaches (traditional learning, TL, and learning by teaching, LBT) for exploiting the provided VR-based functionalities in a learning path. Experimental results showed that the use of the VRTS alone improved the participants’ performance compared to traditional experiences, and that LBT proved to be more effective that TL for practical learning purposes.
How virtual reality shopping experience enhances consumer creativity: The mediating role of perceptual curiosity

With the rise of the contact-free era, consumers enjoy shopping via virtual reality (VR) without any physical constraints. The expansion of a sensory shopping experience via VR can enrich consumers' positive emotions and stimulate their imagination and creative thinking. However, there is a lack of research exploring the role of VR in enhancing consumer creativity in the context of shopping. This study aims to investigate the mechanism between the VR shopping experience and consumer creativity. Specifically, two laboratory experiments demonstrate that the experience mode of VR (non-immersive vs immersive) increases consumers' creative ability through an increased perceptual curiosity toward the VR store. Furthermore, we find that the function of store fantasy (reality-based vs fantasy-based) can boost perceptual curiosity. This study contributes to existing literature and provides value to retailers who plan to create VR shopping content.
Deep hybrid learning for facial expression binary classifications and predictions

Image processing is a technique used for applying different operations to an image to produce an improved image or extract relevant information. Image processing has multiple applications in numerous fields, such as robotics, vision, pattern recognition, video processing, and the medical industry. One prominent application of facial recognition in image processing is identifying human expression. This research examines the accuracy of categorizing human facial expressions as happy or angry with deep learning and transfer learning methods such as CNN, LSTM, Inception, ResNet, VGG, Xception, and InceptionResnet. The proposed deep hybrid learning (DHL) approach classifies facial expressions using transfer learning and deep neural networks. This approach emphasizes the enhancement of prediction and classification by combining multiple deep learning models to perform better than a single model. The proposed model has a testing accuracy of 81.42% and a training accuracy of 95.93% with a multisource image dataset.
Aliasing black box adversarial attack with joint self-attention distribution and confidence probability

Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words selection problem for textual attack models. To this end, the generation of semantically adversarial examples in this model is adopted to mislead a text classification model. Then, this model integrates the self-attention mechanism and confidence probabilities for the selection of the important words. Moreover, an alternative model similar to the transfer attack is introduced to reflect the correlation degree of words inside the texts. Finally, adversarial training experimental results demonstrate the superiority of the proposed model.
A systematic review of research on technological, pedagogical, and content knowledge (TPACK) for online teaching in the humanities

In recent years, studies on technological, pedagogical, and content knowledge (TPACK) have been increasing; however, integrating TPACK into online teaching has generated concerns among teachers across various subject domains in the humanities. Since there have been few recent reviews of teachers’ online TPACK research in the humanities, the present study systematically reviews research in this area published between January 2012 and June 2022. This review aims to advance the knowledge on using TPACK in different subject disciplines while providing practical implications for technology integration in online teaching. The review of 24 articles revealed article frequency reached a peak in 2021 and 2022 covering various subject domains in the humanities. Most studies collected data through qualitative methods while focusing on in-service teachers. The articles were categorized into three research areas: exploring, measuring, and applying TPACK. Among the disciplines, the results showed that language teachers had various TPACK competencies in online teaching; journalism teachers’ collaborations during online course delivery were facilitated when integrating TPACK; and music teachers’ individual beliefs for online teaching were predicted by TPACK. The reviewed research used TPACK as the theoretical basis for scale development to assess teachers’ readiness for flipped teaching. The reviewed articles indicated that TPACK was helpful for language teachers’ technology integration as well as course and platform design during their online teaching.
The Geomorphological and Geological Structure of the Samaria Gorge, Crete, Greece—Geological Models Comprehensive Review and the Link with the Geomorphological Evolution

The Samaria Gorge is a dominant geomorphological and geological structure on Crete Island and it is one of the national parks established in Greece. Due to the complex tectonics and the stratigraphic ambiguities imprinted in the geological formations of the area, a comprehensive review of the geological models referring to the geological evolution of the area is essential in order to clarify its geomorphological evolution. In particular, the study area is geologically structured by the Gigilos formation, the Plattenkalk series and the Trypali unit. Regarding lithology, the Gigilos formation predominantly includes phyllites and slates, while the Plattenkalk series and the Trypali unit are mainly structured by metacarbonate rocks; the Plattenkalk series metacarbonate rocks include cherts, while the corresponding ones of the Trypali unit do not. Furthermore, the wider region was subjected to compressional tectonics, resulting in folding occurrences and intense faulting, accompanied by high dip angles of the formations, causing similar differentiations in the relief. Significant lithological differentiations are documented among them, which are further analyzed in relation to stratigraphy, the tectonics, and the erosion rate that changes, due to differentiations of the lithological composition. In addition, the existing hydrological conditions are decisive for further geomorphological evolution.
Security of Decoy-State Quantum Key Distribution with Correlated Intensity Fluctuations

One of the most prominent techniques to enhance the performance of practical quantum key distribution (QKD) systems with laser sources is the decoy-state method. Current decoy-state QKD setups operate at GHz repetition rates, a regime where memory effects in the modulators and electronics that control them create correlations between the intensities of the emitted pulses. This translates into information leakage about the selected intensities, which cripples a crucial premise of the decoy-state method, thus invalidating the use of standard security analyses. To overcome this problem, a novel security proof that exploits the Cauchy-Schwarz constraint has been introduced recently. Its main drawback is, however, that the achievable key rate is significantly lower than that of the ideal scenario without intensity correlations. Here, we improve this security proof technique by combining it with a fine-grained decoy-state analysis, which can deliver a tight estimation of the relevant parameters that determine the secret key rate. This results in a notable performance enhancement, being now the attainable distance double than that of previous analyses for certain parameter regimes. Also, we show that when the probability density function of the intensity fluctuations, conditioned on the current and previous intensity choices, is known, our approach provides a key rate very similar to the ideal scenario, which highlights the importance of an accurate experimental characterization of the correlations.
From Automation to Symmation: Ethnographic Perspectives on What Happens in Front of the Screen

Abstract The work of automation in education is not automatic but needs to be ‘done’. Grounded in an ethnographic study which followed a Grade 9/10 class through their daily activities in a ‘regular’ high school for a year, this paper asks how automation is enacted by students and teachers, and what these practices imply for forms of knowledge and relationality. Inspired by feminist technoscience, and drawing on recent work on everyday automation, the paper suggests that the ‘auto-’ of automation in practice is very often more of a ‘sym-’, a ‘with’, in which students and machines co-produce something that looks like automation. Rather than ‘automation’, observing practices in classrooms shows practices of ‘symmation’. The paper elaborates on symmation scenes of realigning, revising and reworking relations. Automation is, in these scenes, deeply embedded in social relations, involving the processing of ability, difference and hierarchy. Rather than the industry hype of automation, these sets of socio-technical practices alert us to the messy, non-linear, contested, warm realities of education (and not just learning) in schools today. The paper identifies specific aspects of how these socio-technical realities impact knowledge and teacher-student relations.
Impact on blockchain-based AI/ML-enabled big data analytics for Cognitive Internet of Things environment

Cognitive Internet of Things (CIoT) supports the organizations to learn from the information (data) arriving from various connected devices, sensors, machines and other sources, and at the same time it inspires intelligence into different business operations, products, customer experiences, and people. Data poising attacks are very serious concerns because they may play a significant factor for businesses and organizations for both financial terms and damaging their reputations, when the Big data analytics on the analyzed data is itself corrupted. To mitigate this issue, in this paper, we suggest a blockchain-based Artificial Intelligence(AI)/Machine Learning(ML)-enabled Big data analytics mechanism for CIoT environment. The comprehensive experimental results have been provided under two circumstances: (1) performance of the ML model under data poisoning attacks and (2) performance of the ML model without data poisoning attacks. In the first case, we show how the data poison attacks can effect the ML model when the data is on some cloud storage (i.e. not in the blockchains), whereas in the second case we show the effect when the data is in the blockchains (i.e., without data poisoning attacks). The experimental results demonstrate that we have significant gains in performance in terms of accuracy, recall, precision and F1 score when there are no data poisoning attacks on the data. Moreover, a detailed blockchain simulation has carried out to demonstrate the practical aspects of the proposed security framework.
Virtual Reality Induced Symptoms and Effects: Concerns, Causes, Assessment &amp; Mitigation

The utilization of commercially available virtual reality (VR) environments has increased over the last decade. Motion sickness that is commonly reported while using VR devices is still prevalent and reported at a higher than acceptable rate. The virtual reality induced symptoms and effects (VRISE) are considered the largest barrier to widespread usage. Current measurement methods have uniform use across studies but are subjective and are not designed for VR. VRISE and other motion sickness symptom profiles are similar but not exactly the same. Common objective physiological and biomechanical as well as subjective perception measures correlated with VRISE should be used instead. Many physiological biomechanical and subjective changes evoked by VRISE have been identified. There is a great difficulty in claiming that these changes are directly caused by VRISE due to numerous other factors that are known to alter these variables resting states. Several theories exist regarding the causation of VRISE. Among these is the sensory conflict theory resulting from differences in expected and actual sensory input. Reducing these conflicts has been shown to decrease VRISE. User characteristics contributing to VRISE severity have shown inconsistent results. Guidelines of field of view (FOV), resolution, and frame rate have been developed to prevent VRISE. Motion-to-photons latency movement also contributes to these symptoms and effects. Intensity of content is positively correlated to VRISE, as is the speed of navigation and oscillatory displays. Duration of immersion shows greater VRISE, though adaptation has been shown to occur from multiple immersions. The duration of post immersion VRISE is related to user history of motion sickness and speed of onset. Cognitive changes from VRISE include decreased reaction time and eye hand coordination. Methods to lower VRISE have shown some success. Postural control presents a potential objective variable for predicting and monitoring VRISE intensity. Further research is needed to lower the rate of VRISE symptom occurrence as a limitation of use.
A feature selection model for software defect prediction using binary Rao optimization algorithm

In this digital world, using software has become an important part of daily life and business. The software must be rigorously tested in order to avert a financial crisis. The defect-free software enhances the functionality of the business. Predicting software defects in advance is a crucial task in the software industry. The aim of Software Defect Prediction (SDP) is to locate the possible software bugs. This paper proposes a hybrid feature selection (filter–wrapper) approach based on the multi-criteria decision making (MCDM) method and the Rao optimization method for selecting the more informative features to improve the software defect prediction rate. The proposed work measures the fitness of the candidate solution by using the defect prediction rate and the feature selection ratio. The performance of the proposed method is evaluated using three popular benchmark NASA datasets (PC5, JM1, and KC1) and compared with the state-of-the-art methods. The proposed feature subset selection scheme identifies the most significant feature subset for defect prediction with an average accuracy of 95% on the benchmark datasets. According to the experimental results, the proposed hybrid approach outperforms the standard strategy in terms of defect prediction rate.
A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing.
Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images

Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.
A novel particle swarm optimization algorithm with Lévy flight and orthogonal learning

• In LOPSO, the there major highlights are described below. • First, a novel PSO based on Lévy flight and orthogonal learning with enhanced exploitation ability and faster search efficiency is proposed. The jumping ability of Lévy flights is utilized to enhance the exploration, while the orthogonal learning process aims to strengthen the exploitation;. • Second, an orthogonal learning process for particle swarms is developed. In the orthogonal learning process, the inclusion of the Lévy flights term in the levels of the Taguchi method is determined based on the flight probability value. Besides, parallel computation is easier to perform during orthogonal learning, which greatly improves efficiency. • Third, each particle in the proposed LOPSO learns from the only best experience generated by a new search strategy instead of the history best experience of each particle and the global best experience that used in the classical PSO variants, thus avoiding the conflict between above two guides. Taguchi method This paper presents a novel particle swarm optimization algorithm (PSO) variant to tackle single-objective numerical optimization, named “Lévy flight orthogonal learning particle swarm optimization” (LOPSO). There are three contributions mentioned in the paper: First, a novel PSO based on Lévy flight and orthogonal learning with enhanced exploitation ability and faster search efficiency is proposed. The jumping ability of Lévy flight is utilized to enhance the exploration, while the orthogonal learning process aims to strengthen the exploitation; Second, an orthogonal learning process for particle swarms is developed. In the orthogonal learning process, the Lévy flight term's inclusion in the Taguchi method levels is determined based on the flight probability value. Besides, parallel computation is easier to perform during orthogonal learning, which greatly improves efficiency. Third, each particle in the proposed LOPSO learns from the only best experience generated by a new search strategy instead of the historical best experience of each particle and the global best experience that is used in the classical PSO variants, thus avoiding the conflict between the above two guides. A large test suite containing benchmarks from the CEC2013 and the CEC2015 test suites on real-parameter single-objective optimization is employed in the algorithm validation, and extensive experiments demonstrate that the comprehensive performance of LOPSO ranks first in terms of accuracy and Wilcoxon signed-rank test in most cases with the same population size and iteration steps and similar time consumption, when compared with the canonical PSO and 13 state-of-the-art PSO variants.