Reviewerly/exHarmony huggingface dataset:
Interactive Extended Reality Techniques in Information Visualization

Immersive techniques, such as virtual reality, augmented reality, and mixed reality, take immersive displays as carriers to provide immersive experience. A large number of approaches focus on the visualization of scientific data in immersive environments while just a few methods concentrate on interactive information visualization (InfoVis) in an immersive environment, although InfoVis has been extended to the 3-D space for a long time. In the era of data explosion, the traditional 2-D space is unable to convey large amounts of abstract information in an intuitive way. Meanwhile, desktop-based 3-D InfoVis generally leads to visual conflict and confusion owing to limited display size and field of vision. In this survey, we search for the interactive techniques in immersive InfoVis and summarize their commonalities and discuss their differences and potential trends. The data types of abstract information in InfoVis can be categorized into graph/network data, high-dimensional and multivariate data, time-varying data, and text and document data. Besides, the visual presentation of information in immersive environments is also summarized, especially for charts, plots, and diagrams, which are some basic components of InfoVis techniques. We also described the immersive applications of InfoVis techniques, including the tools or frameworks on immersive analytics and infographics. The discussion about the traditional nonimmersive and the immersive methods in data visualizations show that the latter one has the potential to become an alternative to explore massive information in the future.
Adversarial training with distribution normalization and margin balance

Adversarial training is the most effective method to improve adversarial robustness. However, it does not explicitly regularize the feature space during training. Adversarial attacks usually move a sample iteratively along the direction which causes the steepest ascent of classification loss by crossing decision boundary. To alleviate this problem, we propose to regularize the distributions of different classes to increase the difficulty of finding an attacking direction. Specifically, we propose two strategies named Distribution Normalization (DN) and Margin Balance (MB) for adversarial training. The purpose of DN is to normalize the features of each class to have identical variance in every direction, in order to eliminate easy-to-attack intra-class directions. The purpose of MB is to balance the margins between different classes, making it harder to find confusing class directions (i.e., those with smaller margins) to attack. When integrated with adversarial training, our method can significantly improve adversarial robustness. Extensive experiments under white-box, black-box, and adaptive attacks demonstrate the effectiveness of our method over other state-of-the-art methods.
Network-Level Adversaries in Federated Learning

Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy. Prior work identified a range of security threats on federated learning protocols that poison the data or the model. However, federated learning is a networked system where the communication between clients and server plays a critical role for the learning task performance. We highlight how communication introduces another vulnerability surface in federated learning and study the impact of network-level adversaries on training federated learning models. We show that attackers dropping the network traffic from carefully selected clients can significantly decrease model accuracy on a target population. Moreover, we show that a coordinated poisoning campaign from a few clients can amplify the dropping attacks. Finally, we develop a server-side defense which mitigates the impact of our attacks by identifying and up-sampling clients likely to positively contribute towards target accuracy. We comprehensively evaluate our attacks and defenses on three datasets, assuming encrypted communication channels and attackers with partial visibility of the network.
RadioNet: Robust Deep-Learning Based Radio Fingerprinting

Radio fingerprinting identifies wireless devices by leveraging hardware imperfections embedded in radio frequency (RF) signals. While neural networks have been applied to radio fingerprinting to improve accuracy, existing studies are not robust due to two major reasons. First, there is a lack of informative parameter selections in pre-processing over RF signals. Second, deep-learning-based radio fingerprinting derives poor performance against temporal variations in the cross-day scenario. In this paper, we enhance the robustness of deep-learning-based radio fingerprinting from three aspects, including parameter selection in pre-processing, learning methods, and evaluation metrics. First, we conduct extensive experiments to demonstrate that careless selections of parameters in pre-processing can lead to over-optimistic conclusions regarding the performance of radio fingerprinting. Second, we leverage adversarial domain adaptation to improve the performance of radio fingerprinting in the cross-day scenario. Our results show that adversarial domain adaptation can improve the performance of radio fingerprinting in the cross-day scenario without the need of recollecting large-scale RF signals across days. Third, we introduce device rank as an additional metric to measure the performance of radio fingerprinting compared to using accuracy alone. Our results show that pursuing extremely high accuracy is not always necessary in radio fingerprinting. An accuracy that is reasonably greater than random guess could lead to successful authentication within a second when we measure with device rank.
Extracting information and inferences from a large text corpus

The usage of various software applications has grown tremendously due to the onset of Industry 4.0, giving rise to the accumulation of all forms of data. The scientific, biological, and social media text collections demand efficient machine learning methods for data interpretability, which organizations need in decision-making of all sorts. The topic models can be applied in text mining of biomedical articles, scientific articles, Twitter data, and blog posts. This paper analyzes and provides a comparison of the performance of Latent Dirichlet Allocation (LDA), Dynamic Topic Model (DTM), and Embedded Topic Model (ETM) techniques. An incremental topic model with word embedding (ITMWE) is proposed that processes large text data in an incremental environment and extracts latent topics that best describe the document collections. Experiments in both offline and online settings on large real-world document collections such as CORD-19, NIPS papers, and Tweet datasets show that, while LDA and DTM is a good model for discovering word-level topics, ITMWE discovers better document-level topic groups more efficiently in a dynamic environment, which is crucial in text mining applications.
Mean-preserving interpolation with splines for solar radiation modeling

Interpolation is a fundamental process in solar resource assessment that glues consecutive components of the modeling chain. Most interpolation techniques assume that the interpolating function must go through the interpolation points. However, this assumption does not fit with averaged datasets or variables that must be conserved across interpolation. Here I present a mean-preserving splines method for interpolating one-dimensional data that conserves the interpolated field and is appropriate for averaged datasets. It uses second-order polynomial splines to minimize the fluctuations of the interpolated field, restricts the interpolation results to user-provided limits to prevent unphysical values, deals with periodic boundary conditions in the interpolated field, and can work with non-uniform averaging grids. The validity and performance of the method are illustrated against regular second- and third-order splines using relevant case examples in the solar resource assessment realm.
A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system

Real-time task scheduling in multiprocessor (MP) systems has become more critical. As quantum computing advances, scholars have proposed different Quantum-Inspired (QI) Optimization Algorithms to optimize real-time task scheduling problems. In this research, we first take advantage of the Simplified Swarm Optimization Algorithm (SSO) and then propose a Quantum-Inspired Simplified Swarm Optimization (QISSO) to not only overcome the early convergence problem but also enhance the exploration of solution space without premature. Furthermore, we propose the Mixed-Heuristic QISSO for real-time task scheduling in the MP system to improve the scheduling. The proposed MHQISSO achieves the best average percentage of success within the shortest running time in five different size task sets according to two scenarios, Earliest Deadline First (EDF) and Shortest Computational Time First (SCTF).
Fusion of Complementary 2D and 3D Mesostructural Datasets Using Generative Adversarial Networks

Abstract Modelling the impact of a material's mesostructure on device level performance typically requires access to 3D image data containing all the relevant information to define the geometry of the simulation domain. This image data must include sufficient contrast between phases, be of high enough resolution to capture the key details, but also have a large enough 3D field‐of‐view to be representative of the material in general. It is rarely possible to obtain data with all of these properties from a single imaging technique. In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi‐phase, high‐resolution, representative, 3D images. Specifically, the deep convolutional generative adversarial networks to implement super‐resolution, style‐transfer and dimensionality expansion. It is believed that this data‐driven approach is superior to previously reported statistical material reconstruction methods, both in terms of its fidelity and ease of use. Furthermore, much of the data required to train this algorithm already exists in the literature, waiting to be combined. As such, our open‐source code could precipitate a step change in the materials sciences by generating the desired high quality image volumes necessary to simulate behaviour at the mesoscale.
Fast colored video encryption using block scrambling and multi-key generation

Abstract Multimedia information usage is increasing with new technologies such as the Internet of things (IoT), cloud computing, and big data processing. Video is one of the most widely used types of multimedia. Videos are played and transmitted over different networks in many IoT applications. Consequently, securing videos during transmission over various networks is necessary to prevent unauthorized access to the video's content. The existing securing schemes have limitations in terms of high resource consumption and high processing time, which are not liable to IoT devices with limited resources in terms of processor size, memory, time, and power consumption. This paper proposed a new encryption scheme for securing the colored videos. The video frames are extracted, and then, the frame components (red, green, and blue) are separated and padded by zero. Then, every frame component (channel) is split into blocks of different sizes. Then, the scrambled blocks of a component are obtained by applying a zigzag scan, rotating the blocks, and randomly changing the blocks' arrangements. Finally, a secret key produced from a chaotic logistic map is used to encrypt the scrambled frame component. Security analysis and time complexity are used to evaluate the efficiency of the proposed scheme in encrypting the colored videos. The results reveal that the proposed scheme has high-level security and encryption efficiency. Finally, a comparison between the proposed scheme and existing schemes is performed. The results confirmed that the proposed scheme has additional encryption efficiency.
PatrIoT: practical and agile threat research for IoT

Abstract The Internet of things (IoT) products, which have been widely adopted, still pose challenges in the modern cybersecurity landscape. Many IoT devices are resource-constrained and almost constantly online. Furthermore, the security features of these devices are less often of concern, and fewer methods, standards, and guidelines are available for testing them. Although a few approaches are available to assess the security posture of IoT products, the ones in use are mostly based on traditional non-IoT-focused techniques and generally lack the attackers’ perspective. This study provides a four-stage IoT vulnerability research methodology built on top of four key elements: logical attack surface decomposition, compilation of top 100 weaknesses, lightweight risk scoring, and step-by-step penetration testing guidelines. Our proposed methodology is evaluated with multiple IoT products. The results indicate that PatrIoT allows cyber security practitioners without much experience to advance vulnerability research activities quickly and reduces the risk of critical IoT penetration testing steps being overlooked.
Improving adversarial transferability by temporal and spatial momentum in urban speaker recognition systems

DNN-based speaker recognition systems (SRSs) in smart cities suffer from adversarial attacks, which have caused widespread concern. An attacker can fool the SRSs by adding imperceptible perturbations to benign audio. Recent studies have shown that adversarial attacks could achieve almost 100% attack success rate in the white-box but perform poorly in the black-box. Existing attacks do not effectively use the gradient information of the available white-box models, which is easy to over-fit the target model. To tackle the problem, we propose a temporal and spatial momentum-based iteration gradient sign method (TSMI-FGSM). Specifically, we introduce the sample neighborhood and interior space, and accumulate the gradient information of the randomly sampled points in these two spaces to correct and update direction by tuning strategies during each iteration. The experiment results with 9 SRSs demonstrate that our method significantly enhances the transferability of the adversarial examples compared to state-of-the-art attacks. • We propose an idea of using spatial momentum to enhance the transferability of adversarial examples, which includes sample neighborhood spatial momentum and sample internal spatial momentum. The neighborhood space is usually the Gaussian space or Uniform space of the neighborhood of the sample, while interior space is the set of the signal sampling points. • We propose a temporal and spatial momentum-based iterative fast gradient sign method (TSMI-FGSM), which combines the previous temporal gradient information and the gradient information of the NS and IS, and integrates these gradient momentum into the iterative steps to further correct the gradient direction. In nine normal models with two speaker recognition tasks in the single-model setting, we can achieve a maximum attack success rate of 79.2%, which is a 16.5% improvement compared to the state-of-the-art attacks in SRSs. • The adversarial examples crafted from TSMI-FGSM have strong imperceptibility. In the case of attacking advanced SRSs with seven input construction-based defense methods, our method could achieve a 39.2% improvement in attack success rate compared to state-of-the-art methods in SRSs. The average SNR and PESQ are 31.76 dB and 3.15, respectively, which are also better than the advanced attack methods.
Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.
FlowSRNet: A multi-scale integration network for super-resolution reconstruction of fluid flows

A wide range of research problems in physics and engineering involve the acquisition of high-resolution data. Recently, deep learning has proved to be a prospective technique for super-resolution (SR) reconstruction of fluid flows. General deep learning methods develop temporal multi-branch networks to improve SR accuracy while ignoring computational efficiency. Further, the generalization ability of the deep learning model in different fluid flow scenarios is still an unstudied issue. In this article, we propose an efficient multi-scale integration network called FlowSRNet to reconstruct the high-resolution flow fields. Specifically, we elaborately design a lightweight multi-scale aggregation block (LMAB) to capture multi-scale features of fluid data, which contains a parallel cascading architecture and feature aggregation module. The residual backbone of FlowSRNet is built by cascading the LMABs (cascaded blocks number N = 8) in a serial manner. Also, we present a small architecture LiteFlowSRNet (cascaded blocks number N = 2) for comparison. In addition, a corresponding SR dataset is constructed to train and test the proposed model, which contains different kinds of fluid flows. Finally, extensive experiments are performed on different fluid data to evaluate the performance of the proposed model. The results demonstrate that our approach achieves state-of-the-art SR performance on various fluid flow fields. Notably, our method enjoys merit of lightweight, which facilitates the development of the complicated calculation in computational fluid dynamics.
Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine

Crude oil price has a large influence on global environment and economy of the country. Similarly, gold price has also impact on economy and it reflects the economy strength of a country. So, the prediction of nonlinear oil and gold prices is the most prominent and arduous factor. In this work, Cluster-based Quasi oppositional based Crow Search Algorithm (CQCSA) optimized Extreme Learning Machine-Fuzzy Inference System (ELMFIS) is proposed for oil and gold price prediction. The consolidation of FIS with ELM is established with benefits of better capability to handle nonlinear data and improved adaptation. The relevant parameters of ELMFIS are elected by CQCSA technique. The CSA algorithm is modified to CQCSA by converting the drawbacks of CSA into the capability to solve a complex problem with high dimension efficiently. The proposed learning algorithm performance is compared with CSA ELMFIS, ELMFIS, ANFIS and ELM models by conceding the performance measures and hypotheses tests. The result suggests that the proposed predictive model can be a promising technique to predict and analysis of oil and gold price.
Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents

Automatic text summarization plays a vital role in text retrieval. However, very little interest, as well as attention to Hindi text summarization and the wide use of hybridization of an evolutionary and swarm-based method for solving optimization problems, motivates us to develop a technique for achieving better results. We have used a combination of the Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Proper Noun and Term frequency & Inverse Sentence Frequency to find the results. The proposed BPSO-IGA comprising Binary Particle Swarm Optimization (BPSO) and an improved genetic algorithm finds a well-formatted summary from multiple documents. The method for calculating the score of the sentence is proposed initially. A combination of BPSO and IGA finds the features' optimal scores and then is used to generate the final summary. The proposed method is compared with six well-known techniques, SummaRuNNer, ChunkRank, TGraph, UniRank, FF-SE, and MDS-ACS, on FIRE 2011 Hindi dataset. To strengthen the hypothesis that all seven features are required in the summary generation, the summary is also generated using only ONE and a combination of more than one feature. It is found that the best summary is generated when all seven features are considered. The BPSO-IGA performs best among all the methods for precision, recall, f-measure, cohesion, and non-redundancy. This research aims to design a multi-document extractive text summarizer for Hindi documents using a swarm intelligence and evolutionary algorithm-based hybrid approach.
Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece

Abstract Nowadays, the improvement of digital learning with Artificial Intelligence has attracted a lot of research, as it provides solutions for individualized education styles which are independent of place and time. This is particularly the case for computer science, as a tutoring domain, which is rapidly growing and changing and as such, learners need frequent update courses. In this paper, we present a thorough evaluation of a fuzzy-based intelligent tutoring system (ITS), that teaches computer programming. The evaluation concerns multiple aspects of the ITS. The evaluation criteria are: (i) context, (ii) effectiveness, (iii) efficiency, (iv) accuracy, (v) usability and satisfaction, and (vi) engagement and motivation. In the evaluation process students of an undergraduate program in Informatics of the University of Piraeus in Greece participated. The evaluation method that was used included questionnaires, analysis of log files and experiments. Also, t-tests were conducted to certify the validity of the evaluation results. Indeed, the evaluation results are very positive and show that the incorporated fuzzy mechanism to the presented ITS enhances the system with Artificial Intelligence and through this, it increases the learners’ satisfaction and new knowledge learning and mastering, improves the recommendation accuracy of the system, the efficacy of interactions, and contributes positively to the learners’ engagement in the learning process.
Effectual Gesture Controlled Smart Wheelchair for the Incapacitated

The work proposed here is the usage of MEMS accelerometers and flex sensors to create a wheelchair control that is accessible to individuals with disabilities. This control can be used by someone who can’t move their hands to move them or recognize hand signals. In a short period of time, wheelchair technology has advanced significantly. Despite all of these advancements, quadriplegics still require assistance from a second person to navigate obstacles in their wheelchairs. This wheelchair is controlled by simple hand movements. The sensors detect these movements, which the PIC microcontroller converts to digital language, which the system recognizes as a required movement. There’s also a plug-and-play unit and a four-button keypad for persons of various physical abilities. Proteus, a package of simulation tools, is utilized throughout the design process to improve quality and flexibility.