Reviewerly/exHarmony huggingface dataset:
Image Denoising Based on Quantum Calculus of Local Fractional Entropy

Images are frequently disrupted by noise of all kinds, making image restoration very challenging. There have been many different image denoising models proposed over the last few decades. Some models preserve the image’s smooth region, while others preserve the texture margin. One of these methods is by using quantum calculus. Quantum calculus is a branch of mathematics that deals with the manipulation of functions and operators in a quantum mechanical setting. It has been used in image processing to improve the speed and accuracy of image-processing algorithms. In quantum computing, entropy can be defined as a measure of the disorder or randomness of a quantum state. The concept of local fractional entropy has been used to study a wide range of quantum systems. In this study, an image denoising model is proposed based on the quantum calculus of local fractional entropy (QC-LFE) to remove a Gaussian noise. The local fractional entropy is used to estimate the image pixel probability, while the quantum calculus is used to estimate the convolution window mask for image denoising. A processing fractional mask with n x n elements was used in the suggested denoising algorithm. The proposed image denoising algorithm uses mask convolution to process each corrupted pixel one at a time. The proposed denoising algorithm’s effectiveness is assessed using peak signal-to-noise ratio and visual perception (PSNR). The experimental findings show that, compared to other similar fractional operators, the proposed method can better preserve texture details when denoising.
Lexicon-based sentiment analysis in texts using Formal Concept Analysis

In this paper, we present a novel approach for sentiment analysis that uses Formal Concept Analysis (FCA) to create dictionaries for classification. Unlike other methods that rely on pre-defined lexicons, our approach allows for the creation of customised dictionaries that are tailored to the specific data and tasks. By using a dataset of tweets categorised into positive and negative polarity, we show that our approach achieves a better performance than other standard dictionaries.
TFS-ABS: Traceable and Forward-Secure Attribute-Based Signature Scheme With Constant-Size

Attribute-based signature (ABS) is a versatile and useful cryptogrammic technology. In an ABS scheme, every signer is distributed a signing secret key in term of her/his attributes, and endorses a message in relation to some signing policy fulfilled by the signer's attributes. The verifier checks that the signature is indeed endorsed by the signer whose attributes match with the signing policy. However, existing ABS schemes suffer from the issue of abusing signature and key exposure. To address the above issues, we provide a traceable and forward-secure attribute-based signature (TFS-ABS) scheme with constant-size supporting flexible threshold predicates. Furthermore, we prove that the presented TFS-ABS scheme is existential unforgeability against selective predicate attack under the standard model. We reduce the security for the provided scheme to <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula> -Diffie-Hellman exponentiation assumption. The designed scheme can be used to alleviate the damage induced by key exposure and traces the real identity of signer by attribute authority (AA) when the signer occurs abusing behavior. Furthermore, the signature size in presented scheme keeps constant and is independent of the number of attributes. Experimental evaluations exhibit that the presented TFS-ABS scheme is efficient in term of the communication and computation overhead.
An Active Contour Model Based on Local Pre-Piecewise Fitting Bias Corrections for Fast and Accurate Segmentation

The lack of grasp of the image information and the unstable fluctuation of the model energy may cause segmentation failure of the active contour model (ACM). Minimizing the impact of these two factors is critical. A local pre-piecewise fitting (LPPF) bias correction (BC) model for fast and accurate segmentation is proposed in this article. It defines a prefitting function of local regions and an energy function. The grayscale information of small areas in the image is fully extracted, so that the contour accurately locates the target. Then, the optimal solution to the estimated value of the bias field is obtained. The real image information is described by the bias field, and the energy function of the model is constructed. The optimized distance regularized term and neighborhood average filtering method are utilized to achieve level set function regularization and contour smoothing. This optimization process reduces the amount of calculation and improves the robustness of LPPF model. Experiments are performed to verify that LPPF model has strong robustness to initial contours and has ability to segment blurry images while satisfactory segmentation efficiency and accuracy are obtained.
Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.
Image Encryption Using Dynamic Image as a Key Based on Multilayers of Chaotic Permutation

With the rapid expansion of electronic data transmission, unauthorized data access is growing. Information security against unwanted access is becoming increasingly essential in data storage and data transfer. Images are a common type of information that is used in almost every aspect of life, so the security of image data against unwanted access is critical. In this article, we propose an encryption technique that uses a symmetric encryption method based on XOR operation between the plain image and another image that will be used as a key agreed upon by both the sender and recipient. To ensure the security of the plain images, the scheme performs pixel permutation procedures dynamically using random numbers on the key image and plain image in each encryption process, which renders the encryption algorithm strong and brute-force resistant. Simulation results on several standard images showed a random distribution of pixel values and a higher pass rate correlated with entropy and ideal values for analysis parameters. Thus, through the use of randomization, the proposed algorithm is resistant to various attacks and offers promising security measurements while maintaining efficient and fast execution.
Security and Privacy in Connected Vehicle Cyber Physical System Using Zero Knowledge Succinct Non Interactive Argument of Knowledge over Blockchain

Security has been the most widely researched topic, particularly within IoT, and has been considered as the major hurdle in the adoption of different applications of IoT. When it comes to IoV, security is considered as the most inevitable component to ensure a safe and smooth driving experience. CAV is the new era of transportation, integrating intelligence and self-driving capabilities within vehicles and that requires strong security measures to ensure safety. Security alone is not enough. Instead, a complete package including privacy of the vehicles and passengers needs to be added in addition to secure communication. This is because CAVs are under continuous cyber threats and attacks and the most important among them is the DDoS, where a remote attacker can hijack/launch attacks on vehicles remotely. Single point of failure attacks target the centralized trusted body in order to mislead the connected vehicles for personal gains. In this paper, the authors have proposed a secure communication system for CAVs using blockchain, which also ensures the privacy of the vehicle/people. The paper highlights the major components of the proposed system, and its performance is evaluated to check its efficiency against DDoS and Eclipse attacks. The unlinkability and anonymity of the vehicles have been ensured using the zk-SNAKR protocol over Blockchain.
Hyperparameter Optimization of Ensemble Models for Spam Email Detection

Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats to cybersecurity globally. More than half of the emails sent in 2021 were spam, resulting in huge financial losses. The tenacity and perpetual presence of the adversary, the spammer, has necessitated the need for improved efforts at filtering spam. This study, therefore, developed baseline models of random forest and extreme gradient boost (XGBoost) ensemble algorithms for the detection and classification of spam emails using the Enron1 dataset. The developed ensemble models were then optimized using the grid-search cross-validation technique to search the hyperparameter space for optimal hyperparameter values. The performance of the baseline (un-tuned) and the tuned models of both algorithms were evaluated and compared. The impact of hyperparameter tuning on both models was also examined. The findings of the experimental study revealed that the hyperparameter tuning improved the performance of both models when compared with the baseline models. The tuned RF and XGBoost models achieved an accuracy of 97.78% and 98.09%, a sensitivity of 98.44% and 98.84%, and an F1 score of 97.85% and 98.16%, respectively. The XGBoost model outperformed the random forest model. The developed XGBoost model is effective and efficient for spam email detection.
Non-functional requirements for machine learning

Systems that rely on Machine Learning (ML systems) have differing demands on quality---non-functional requirements (NFRs)---compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope---and of the extent of existing research---is lacking compared to our understanding in traditional domains.
From anomaly detection to open set recognition: Bridging the gap

The classifiers that return compact acceptance regions are crucial for the success in anomaly detection and open set recognition settings since we have to determine and reject the anomalies and samples coming from the unknown classes. This paper introduces novel methods that approximate the class acceptance regions with compact hypersphere models for anomaly detection and open set recognition. As opposed to the other deep hypersphere classifiers, we treat the hypersphere centers as learnable parameters and update them based on the changing deep feature representations. In addition, we propose novel loss terms that are more robust to the noisy labels within the outlier exposure and background datasets. The proposed methods bear similarity to the deep distance metric learning classifiers using the triplet loss function with the exception that the anchors are set to the hypersphere centers which are updated dynamically. The experimental results show that the proposed methods achieve the state-of-the-art accuracies on the majority of the tested datasets in the context of anomaly detection and open set recognition.
Multiple View Stereo with quadtree-guided priors

Multi-View Stereo (MVS) algorithms rely on common photometric consistency measures and, therefore, in cases of low-textured surfaces tend to generate unreliable depth estimates or lack completeness due to matching ambiguities. Such textureless areas often imply dominant planar structures, typically occurring in man-made scenes. To support depth estimation in scenarios where challenging surfaces are present, we propose an extended PatchMatch pipeline using an adaptive accumulated matching cost calculation based on estimated prior plane hypotheses and the local textureness. Plane priors are detected in the object space and guided by quadtree structures in order to generate depth and normal hypothesis for every pixel, supporting, in this way, the propagation of more reliable depth estimates across the image. Experiments on the ETH3D high-resolution dataset and on custom real-world scenes demonstrate that our approach can favor the reconstruction of problematic regions by adding small complexity while preserving fine details in rich textured regions, achieving thus competitive results compared to state-of-the-art methods. The source code of the developed method is available at https://github.com/3DOM-FBK/openMVS.
Frequency Domain Filtered Residual Network for Deepfake Detection

As deepfake becomes more sophisticated, the demand for fake facial image detection is increasing. Although great progress has been made in deepfake detection, the performance of most existing deepfake detection methods degrade significantly when these methods are applied to detect low-quality images for the disappearance of key clues during the compression process. In this work, we mine frequency domain and RGB domain information to specifically improve the detection of low-quality compressed deepfake images. Our method consists of two modules: (1) a preprocessing module and (2) a classification module. In the preprocessing module, we utilize the Haar wavelet transform and residual calculation to obtain the mid-high frequency joint information and fuse the frequency map with the RGB input. In the classification module, the image obtained by concatenation is fed to the convolutional neural network for classification. Because of the combination of RGB and frequency domain, the robustness of the model has been greatly improved. Our extensive experimental results demonstrate that our approach can not only achieve excellent performance when detecting low-quality compressed deepfake images, but also maintain great performance with high-quality images.
Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion

Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training. However, the need for paired CT volume and X-ray images with ground truth registration limits the applicability in interventional scenarios. An alternative is to use simulated X-ray projections from CT volumes, thus removing the need for paired annotated datasets. Deep Neural Networks trained exclusively on simulated X-ray projections can perform significantly worse on real X-ray images due to the domain gap. We propose a self-supervised 2D/3D registration framework combining simulated training with unsupervised feature and pixel space domain adaptation to overcome the domain gap and eliminate the need for paired annotated datasets. Our framework achieves a registration accuracy of 1.83 ± 1.16 mm with a high success ratio of 90.1% on real X-ray images showing a 23.9% increase in success ratio compared to reference annotation-free algorithms.
DeepPrivacy2: Towards Realistic Full-Body Anonymization

Generative Adversarial Networks (GANs) are widely adopted for anonymization of human figures. However, current state-of-the-art limits anonymization to the task of face anonymization. In this paper, we propose a novel anonymization framework (DeepPrivacy2) for realistic anonymization of human figures and faces. We introduce a new large and diverse dataset for full-body synthesis, which significantly improves image quality and diversity of generated images. Furthermore, we propose a style-based GAN that produces high-quality, diverse, and editable anonymizations. We demonstrate that our full-body anonymization framework provides stronger privacy guarantees than previously proposed methods. Source code and appendix is available at: github.com/hukkelas/deep_privacy2.
Searching Efficient Neural Architecture with Multi-resolution Fusion Transformer for Appearance-based Gaze Estimation

For aiming at a more accurate appearance-based gaze estimation, a series of recent works propose to use transformers or high-resolution networks in several ways which achieve state-of-the-art, but such works lack efficiency for real-time applications on edge computing devices. In this paper, we propose a compact model to precisely and efficiently solve gaze estimation. The proposed model includes 1) a Neural Architecture Search(NAS)-based multi-resolution feature extractor for extracting feature maps with global and local information which are essential for this task and 2) a novel multi-resolution fusion transformer as the gaze estimation head for efficiently estimating gaze values by fusing the extracted feature maps. We search our proposed model, called GazeNAS-ETH, on the ETH-XGaze dataset. We confirmed through experiments that GazeNAS-ETH achieved state-of-the-art on Gaze360, MPIIFaceGaze, RTGENE, and EYEDIAP datasets, while having only about 1M parameters and using only 0.28 GFLOPs, which is significantly less compared to previous state-of-the-art models, making it easier to deploy for real-time applications.
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images. UDA adapts models trained on normal conditions to the target adverse-condition domains. Meanwhile, multiple datasets with driving scenes provide corresponding images of the same scenes across multiple conditions, which can serve as a form of weak supervision for domain adaptation. We propose Refign, a generic extension to self-training-based UDA methods which leverages these cross-domain correspondences. Refign consists of two steps: (1) aligning the normal-condition image to the corresponding adverse-condition image using an uncertainty-aware dense matching network, and (2) refining the adverse prediction with the normal prediction using an adaptive label correction mechanism. We design custom modules to streamline both steps and set the new state of the art for domain-adaptive semantic segmentation on several adverse-condition benchmarks, including ACDC and Dark Zurich. The approach introduces no extra training parameters, minimal computational overhead—during training only—and can be used as a drop-in extension to improve any given self-training-based UDA method. Code is available at https://github.com/brdav/refign.
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the existing methods by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.
Fully Immersive Virtual Reality Using 360° Videos to Manage Well-Being in Older Adults: A Scoping Review

The development of negative behavioral and psychosocial factors (depression, anxiety, apathy, etc) is associated with poor well-being, which can contribute to health issues in ageing, especially in the context of COVID-19. Despite its relative novelty, fully immersive virtual reality (VR) interventions through 360° immersive videos are becoming more accessible and flexible and constitute an emerging method to potentially enhance well-being. The aim of this scoping review is to assess the effectiveness of 360° interventions on well-being in older adults with or without cognitive impairment, as well as cybersickness and attitudes toward this technology.Scoping review.Older adults with or without cognitive impairment.The PRISMA-SR guideline was followed. Four databases were used, and we selected articles published until April 2022. We have analyzed the effect of 360° videos on the well-being of older adults with respect to the study design, the population, the contents, the duration of intervention, and the outcomes.A total of 2262 articles were screened, of which 10 articles were finally included in this review. Most of them are pilot studies and used mixed methods including scales and interviews. The material and content of VR are diversified. Many behavioral and psychological outcomes were assessed, including anxiety, apathy, loneliness, depression, social engagement, quality of life, and emotions. The results were positive or mixed, according to the outcomes. We recorded few adverse events, and the interviews show contrasting results concerning the participants' feelings (ie, degree of immersion, familiarity with technology, and VR content).The use of VR 360° videos seems feasible in community-dwelling older adults or residential aged care facilities, as they are safe and provide enjoyment. It constitutes an emerging and promising therapeutic tool to manage psychosocial disorders. This review provides key considerations for the design and implementation of interventions using VR 360° video in clinical practice.