Reviewerly/exHarmony huggingface dataset:
A Single-Pass and One-Round Message Authentication Encryption for Limited IoT Devices

In this work, we propose three efficient variants of a message authentication encryption (MAE) algorithm, which is based on the dynamic key-dependent concept and dynamic operation mode to reach a high level of security. These variants consist of a single pass and a single round, in addition to the use of common operations for the encryption and authentication processes to reduce the required execution time and resources. Accordingly, the proposed scheme outperforms the existing solutions that are based on the static approach with multiple rounds. Furthermore, to reduce the overhead associated with the regeneration of the dynamic key and the corresponding cryptographic primitives, we propose a simple, yet effective update process. In such a scheme, even when the same plaintext is processed, it will be encrypted and authenticated using different cryptographic primitives (substitution and permutation tables in addition to round keys), which guards against the existing cryptanalysis techniques. The experimental results show that the proposed MAE variants are more efficient than the counter with cipher block chaining message authentication code (CCM), Galois message authentication code (GMAC), offset codebook mode (OCB), and the Chacha20-poly1305. The best performance is achieved with the third MAE variant that presents a high throughput with an enhancement of at least 373% compared to CCM, 90% compared to GCM, 23% compared to OCB, and 22% compared to Chacha20-poly1305.
Spatial Impressions Monitoring during COVID-19 Pandemic Using Machine Learning Techniques

During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. Based on this study, various public service messages can be disseminated, and necessary steps can be taken to manage the scourge. There has already been a lot of work conducted in several languages, but little has been conducted on Arabic tweets. The primary goal of this study is to analyze Arabic tweets about COVID-19 and extract people’s impressions of different locations. This analysis will provide some insights into understanding public mood variation on Twitter, which could be useful for governments to identify the effect of COVID-19 over space and make decisions based on that understanding. To achieve that, two strategies are used to analyze people’s impressions from Twitter: machine learning approach and the deep learning approach. To conduct this study, we scraped Arabic tweets up with 12,000 tweets that were manually labeled and classify them as positive, neutral or negative feelings. Specialising in Saudi Arabia, the collected dataset consists of 2174 positive tweets and 2879 negative tweets. First, TF-IDF feature vectors are used for feature representation. Then, several models are implemented to identify people’s impression over time using Twitter Geo-tag information. Finally, Geographic Information Systems (GIS) are used to map the spatial distribution of people’s emotions and impressions. Experimental results show that SVC outperforms other methods in terms of performance and accuracy.
Effects of viewing distance and age on the performance and symptoms in a visual search task in augmented reality

In augmented reality (AR), virtual information is optically combined with the physical environment. In the most frequently used combination technique, optical settings in AR depart from the settings in natural viewing. Depending on the combination of viewing distances of the virtual task and its physical background, this deviation may lower visual performance and cause visual disturbance symptoms. The so-called vergence-accommodation conflict (VAC) has been identified as a cause for the visual disturbance symptoms in AR. In this study, for various distance combinations, the performance and symptoms when performing a search task displayed in a see-through head-mounted display (AR HMD, HoloLens 1st generation, Microsoft, USA) was investigated. The search task was displayed at a virtual distance of either 200 cm or 30 cm, and the real background was viewed either at a distance of 200 cm or 30 cm. Three combinations of viewing distances for the background and the virtual task were studied: 200 cm/200 cm, 200 cm/30 cm, and 30 cm/30 cm. Results revealed that both performance and visual disturbance symptoms depend on the combination of the viewing distances of the physical background and the virtual task. When the physical background was viewed at a distance of 200 cm, younger participants showed a significantly better search performance and reported stronger symptoms compared with older participants, no matter whether the virtual task was performed at 30 cm or at 200 cm. However, with the physical background at a distance of 30 cm, the performance of the younger group dropped to the level of the performance of the older group, and younger participants tended to report a stronger increase in visual disturbance symptoms compared with the older participants. From the AR HMD technology used in this study, it can be concluded that a near viewing distance of the virtual task does not cause a negative impact on performance and visual disturbance symptoms, provided any physical background seen through the AR HMD is not at a near viewing distance. The findings indicate that the VAC, which persists in augmented and virtual reality, depends, in addition to the physical component evaluating the optical distance, on a cognitive component evaluating the perceived distance. AR settings should therefore also be evaluated in terms of possible effects on perceived distance.
A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.
Dynamic Time Warping as an Alternative to Windowed Cross Correlation in Seismological Applications

Abstract We investigate the feasibility of using the dynamic time warping (DTW) technique as an alternative to windowed cross correlation (WCC) for an indirect measure to quantify both the similarity and relationship between two seismic time series. We first examine the sensitivity and performance of the DTW technique by analyzing both synthetic and real seismic time series in geophysical applications. Results show that DTW efficiently retrieves useful information from seismic data and has a high sensitivity to minor variations in time series that WCC fails to detect. We further propose four potential applications of DTW to routine seismic data interpretation—earthquake detection, template matching, clustering of waveforms, and full-waveform inversion for 1D velocity models. The earthquake detection scheme employing DTW is proposed in this study as an alternative to traditional methods. The accuracy of DTW in estimating similarity is explored for template matching and clustering of seismic traces. Finally, we discuss a realistic example of 1D Earth velocity model inversion using DTW and explore its feasibility in full-waveform inversion.
Digital self-tracking, habits and the myth of discontinuance: It doesn’t just ‘stop’

Digital self-tracking devices increasingly inhabit everyday landscapes, yet many people abandon self-trackers not long after acquisition. Although research has examined why people discontinue these devices, less explores what actually happens when people unplug. This article addresses this gap by considering the embodied and habitual dimensions of self-tracking and discontinuance. We consider the potential for digital data – and their unanticipated affects – to linger within habitual practices even after the device is abandoned. We draw on the philosophies of Felix Ravaisson and Gilles Deleuze to understand habit as a capacity for change, rather than a performance of sameness. We trace how self-tracking prompts new embodiments that continue to unfold even after people disengage. In decentring the device as our object of attention, we trouble the logic that self-tracking simply ‘stops’ in its absence. This holds implications for theorizing human–digital relations and for how self-tracking health interventions are implemented and evaluated.
Securing Low-Power Blockchain-enabled IoT Devices against Energy Depletion Attack

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.
Efficient profile-guided size optimization for native mobile applications

Positive user experience of mobile apps demands they not only launch fast and run fluidly, but are also small in order to reduce network bandwidth from regular updates. Conventional optimizations often trade off size regressions for performance wins, making them impractical in the mobile space. Indeed, profile-guided optimization (PGO) is successful in server workloads, but is not effective at reducing size and page faults for mobile apps. Also, profiles must be collected from instrumenting builds that are up to 2X larger, so they cannot run normally on real mobile devices.
A novel two-dimensional reversible data hiding scheme based on high-efficiency histogram shifting for JPEG images

In recent years, reversible data hiding technology has been widely used in JPEG images for special purposes such as file management and image authentication. Histogram shifting is one of the most popular techniques for achieving reversible data hiding technology. However, invalid shifting in histogram shifting limits the performance of existing reversible data hiding schemes. Therefore, we propose a two-dimensional histogram shifting-based reversible data hiding scheme in this article to improve the performance of marked JPEG images in terms of visual quality and file size. In the proposed histogram shifting method, only the coefficient pairs containing two non-zero quantized discrete cosine transform coefficients are changed for embedding data. Specifically, the coefficient pairs with at least one quantized discrete cosine transform coefficient valued −1 or +1 are shifted and the rests leave room for embedding data. With our proposed reversible data hiding scheme, the number of invalid shifting pixels is reduced so that it improves the performance of marked JPEG images. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio and has a lower increase in file size than state-of-art methods.
Reducing “COVID-19 Misinformation” While Preserving Free Speech

This Viewpoint discusses actions that health care leaders and policy makers can take to better assess the likely constitutionality of reducing “COVID misinformation” through government prohibitions or sanctions.
Recombination and Novelty in Neuroevolution: A Visual Analysis

Abstract Neuroevolution has re-emerged as an active topic in the last few years. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of neuroevolution systems. A variety of search strategies have been proposed such as Novelty search and Quality-Diversity search, but their impact on the evolutionary dynamics is not well understood. We propose using a data-driven, graph-based model, search trajectory networks (STNs) to analyse, visualise and directly contrast the behaviour of different neuroevolution search methods. Our analysis uses NEAT for solving maze problems with two search strategies: novelty-based and fitness-based, and including and excluding the crossover operator. We model and visualise the trajectories, contrasting and illuminating the behaviour of the studied neuroevolution variants. Our results confirm the advantages of novelty search in this setting, but challenge the usefulness of recombination.
We Know Where They Are Looking at From the RGB-D Camera: Gaze Following in 3D

Inferring gaze target or gaze following is an effective way to understand human actions and intentions, which makes quite a challenge. Some existing studies on gaze estimation cannot accurately locate the gaze target in a 3D scene by gaze direction alone, while other studies on gaze following have failed to exploit the contexts in the 3D scene. In this article, we make full use of the information obtained by the RGB-D camera and innovatively expand the gaze target estimation from 2D image to 3D space through the predicted 3D gaze vector. Specifically, we rebuild a new 3D gaze-following dataset, RGB-D Attention dataset, which contains 3D real-world gaze behaviors. In addition, we extend the depth information for the GazeFollow dataset to utilize its diverse scene information in the training process of 3D gaze following. Then, considering the gaze direction as a crucial clue, we propose a novel gaze vector space containing 3D information and a 3D gaze pathway for learning the gaze behavior in the 3D scene. After two-stage training, the entire model can output the predicted 3D gaze vector and the predicted gaze heatmap, which are used to estimate the 3D gaze target in the inference algorithm. Experiments in the 3D scenes show that our method can reduce the predicted average distance error to 0.307 m and the predicted average angle error to 19.8°. Compared with the state-of-the-art gaze inference method, our proposed method has reduced the prediction error by more than 45%. Our web page is at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/3dgazefollow</uri> .
Cost-Sensitive Broad Learning System for Imbalanced Classification and Its Medical Application

As an effective and efficient discriminative learning method, the broad learning system (BLS) has received increasing attention due to its outstanding performance without large computational resources. The standard BLS is derived under the minimum mean square error (MMSE) criterion, while MMSE is with poor performance when dealing with imbalanced data. However, imbalanced data are widely encountered in real-world applications. To address this issue, a novel cost-sensitive BLS algorithm (CS-BLS) is proposed. In the CS-BLS, many variations can be adopted, and CS-BLS with weighted cross-entropy is analyzed in this paper. Weighted penalty factors are used in CS-BLS to constrain the contribution of each sample in different classes. The samples in minor classes are allocated higher weights to increase their contributions. Four different weight calculation methods are adopted to the CS-BLS, and thus, four CS-BLS methods are proposed: Log-CS-BLS, Lin-CS-BLS, Sqr-CS-BLS, and EN-CS-BLS. Experiments based on artificially imbalanced datasets of MNIST and small NORB are firstly conducted and compared with the standard BLS. The results show that the proposed CS-BLS methods have better generalization and robustness than the standard BLS. Then, experiments on a real ultrasound breast image dataset are conducted, and the results demonstrate that the proposed CS-BLS methods are effective in actual medical diagnosis.
eXtended Reality (XR) Experiences in Museums for Cultural Heritage: A Systematic Review

The incorporation of different types of media promotes a multimodal approach to the dissemination, communication and exploitation of Cultural Heritage in Museums. By utilizing digital technologies allows new forms of interactions with cultural content. Cultural spaces such as museums have been integrating new digital tools, presenting their audiences immersive, interactive, and multisensory experience that is not possible in traditional exhibitions. Withing these approaches a new term of Extended Reality (XR) is emerging and increasing its role in these cultural spaces. XR is the umbrella that englobes all forms of immersion and interaction such as Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR). This article aims to provide a comprehensive state of the art of XR experiences for Cultural Heritage in Museums. To support this goal, a systematic review of the peer-reviewed articles was gathered from the Scopus and Web of Science databases. Results are analyzed and the case studies are presented in this paper.
Markov Chain Monte Carlo-Based Machine Unlearning

As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model maintenance. One such challenge is to 'undo' the effect of a specific subset of dataset used for training a model. This specific subset may contain malicious or adversarial data injected by an attacker, which affects the model performance. Another reason may be the need for a service provider to remove data pertaining to a specific user to respect the user's privacy. In both cases, the problem is to 'unlearn' a specific subset of the training data from a trained model without incurring the costly procedure of retraining the whole model from scratch. Towards this goal, this paper presents a Markov chain Monte Carlo-based machine unlearning (MCU) algorithm. MCU helps to effectively and efficiently unlearn a trained model from subsets of training dataset. Furthermore, we show that with MCU, we are able to explain the effect of a subset of a training dataset on the model prediction. Thus, MCU is useful for examining subsets of data to identify the adversarial data to be removed. Similarly, MCU can be used to erase the lineage of a user's personal data from trained ML models, thus upholding a user's "right to be forgotten". We empirically evaluate the performance of our proposed MCU algorithm on real-world phishing and diabetes datasets. Results show that MCU can achieve a desirable performance by efficiently removing the effect of a subset of training dataset and outperform an existing algorithm that utilizes the remaining dataset.
V-FIRST: A Flexible Interactive Retrieval System for Video at VBS 2022

Video retrieval systems have a wide range of applications across multiple domains, therefore the development of user-friendly and efficient systems is necessary. For VBS 2022, we develop a flexible interactive system for video retrieval, namely V-FIRST, that supports two scenarios of usage: query with text descriptions and query with visual examples. We take advantage of both visual and temporal information from videos to extract concepts related to entities, events, scenes, activities, and motion trajectories for video indexing. Our system supports queries with keywords and sentence descriptions as V-FIRST can evaluate the semantic similarities between visual and textual embedding vectors. V-FIRST also allows users to express queries with visual impressions, such as sketches and 2D spatial maps of dominant colors. We use query expansion, elastic temporal video navigation, and intellisense for hints to further boost the performance of our system.
Automatic fine-grained glomerular lesion recognition in kidney pathology

Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task. In this paper, we introduce a scheme to recognize fine-grained glomeruli lesions from whole slide images. First, a focal instance structural similarity loss is proposed to drive the model to locate all types of glomeruli precisely. Then an Uncertainty Aided Apportionment Network is designed to carry out the fine-grained visual classification without bounding-box annotations. This double branch-shaped structure extracts common features of the child class from the parent class and produces the uncertainty factor for reconstituting the training dataset. Results of slide-wise evaluation illustrate the effectiveness of the entire scheme, with an 8–22% improvement of the mean Average Precision compared with remarkable detection methods. The comprehensive results clearly demonstrate the effectiveness of the proposed method.
EGM: An Efficient Generative Model for Unrestricted Adversarial Examples

Unrestricted adversarial examples allow the attacker to start attacks without given clean samples, which are quite aggressive and threatening. However, existing works for generating unrestricted adversary examples are quite inefficient and cannot achieve a high success rate. In this article, we explore an end-to-end and effective solution for unrestricted adversary example generation. To stabilize the training process and make our generative model converge to satisfactory results, we design a novel decoupled two-step efficient generative model (EGM), which contains a conditional reference generator and a conditional adversarial transformer. The former is responsible for generating reference samples from noises and source classes. The latter is responsible for converting the reference sample into adversarial examples corresponding to target classes. To improve the success rate, we design a new strategy, augmentation of adversarial labels to produce dynamic target labels and enhance the exploration ability of EGM. Such a strategy can be also applied to existing attacks to improve their attack success rates, which is of independent interest. We conduct extensive experiments to evaluate our proposed model and demonstrate the necessity of decoupling the generation process in EGM. Experimental results show our EGM is much faster and achieves a higher success rate than the state-of-the-art attacks.