Reviewerly/exHarmony huggingface dataset:
Bayesian Multispectral Videos Super Resolution

Due to hardware limitations, multispectral videos often exhibit significantly lower resolution compared to standard color videos. These videos capture images in multiple bands of the electromagnetic spectrum, providing valuable additional information that is not available in traditional RGB images. This paper proposes a Bayesian approach to estimate super resolved images from low-resolution spectral videos. We consider adjacent frames from a video sequence to provide a super-resolution image at a time. We include in our proposal the motion between adjacent frames and unlikely to the literature, we estimate the blur and noise while reconstructing the higher resolution image. Experimental results on spectral videos demonstrate the effectiveness of our approach in producing high-quality super resolved images.
Semi-Supervised Anomaly Detection in Electronic-Exam Proctoring Based on Skeleton Similarity

Anomaly detection in surveillance videos, particularly related to human behavior, is crucial for various applications. In electronic exams (e-exams), cheating can be detected using surveillance videos, but previous research mainly focused on predefined patterns, with less attention given to unsupervised methods. This study proposes a semi-supervised skeleton-based approach for abnormal behavior detection in e-exam proctoring videos. The proposed method segments the skeleton-based feature vectors of consecutive frames based on their similarity. The similarity is calculated based on the Euclidean distance between the mean feature vector and the standard deviation of segments. Similar segments of the training set, which is anomaly-free, are then combined to form distinct frame segments used to recognize the normal samples. Then, in the testing phase, a frame is recognized anomaly if it is not similar to any training segment, otherwise, it is considered normal. In addition, to get a better ranking of anomalous frames, we examine assigning a soft anomaly score considering the segment size and the maximum distance between the comparative features. In label assignment, the algorithm correctly categorizes the testing frame based on their similarity to the training segments. In comparison to the state-of-the-art reconstruction-based anomaly detection algorithms, the proposed method outperforms using the area under the ROC curve (AUC) metric. In addition to correctly detecting similar frames and segmenting videos, the advantage of the presented method is to determine the data labels without needing to process all inputs, making it possible for use in online applications.
Analyses of Photovoltaic Systems Performance with Fuzzy Inference Systems and Bayesian Networks

Renewable energy sources have been increasingly targeted as an alternative for reducing fossil fuel emissions. This scenario requires a better understanding of energy systems' performance and limitations, notably using photovoltaic setups under diverse meteorological conditions and determining device measurement uncertainties. Accordingly, this study conducts a performance evaluation of a photovoltaic system of copper, indium, and gallium (di)selenide technology. It compares the results provided by a reference normalized equation with two interpretable artificial intelligence techniques, Fuzzy Inference System and Bayesian Networks, when temperature is also considered. In the experiments, the study found significant percentage differences between the normalization procedure and the fuzzy system on days with lower irradiation values. On the other hand, the results provided by Bayesian networks presented lower difference percentages in relation to the normalization equation values.
A Hitchhiker’s Guide to White-Box Neural Network Watermarking Robustness

The present study deals with white-box Neural Network (NN) watermarking and focuses on the robustness property. The first contribution consists of formalizing neuron permutation as a geometric attack, thus demonstrating the very existence of this class of attacks for NN watermarking. The second contribution consists in devising and demonstrating the effectiveness of the corresponding counter-attack. As a side result, the possibility of extending NN white-box watermarking scope beyond image classification is brought to light. The experimental study considers three state-of-the-art methods, four NN models, three tasks (image classification, segmentation, and video coding), and five types of attacks. We underline that none of the existing methods is robust against the geometric attack, and using the counter-attack advanced in this paper effectively ensures the robustness.
Comparative study on Ecological Cognition in real space and AR environment

The purposes of this research are to investigate the information needed to cognize the AR environment using Reaching and Obstacle Striding to identify the differences in Ecological Cognition between the real and the AR environments. Two experiments were conducted to verify the Ecological Cognition acquired in real and Augmented Reality (AR) environments. The first experiment was compared Reaching in each environment using head-mounted displays (HMD) of optical see-through and video see-through. There were no significant differences on Reaching in each environment, and the results indicate that comparable cognition would have been obtained. However, it should be noted that the user's cognition when wearing the HMD which is video-see-through, is as if the user's view of the real is forward from the actual position by the amount of the space. Another experiment investigated how much the foot is lifted up when getting over an obstacle. The higher the obstacle, the lower the foot height to obstacle ratio. A comparison of the same height between the real and the AR environments showed that in the case of the AR environment, lifted up the foot was higher for only lower obstacles. Possible factors include the lack of sufficient information for cognitive judgment and the viewing angle of the device.
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression

In recent years, neural image compression has garnered considerable attention from both research and industry. It has shown great promise in surpassing traditional methods in terms of rate-distortion performance through the development of end-to-end deep neural codecs. Despite these advancements, there is still room for improvement, particularly in reducing the coding rate while maintaining high reconstruction fidelity, especially in non-homogeneous textured image areas. Current models, including attention-based transform coding, also tend to have a higher number of parameters and longer decoding times. To address these challenges, we propose ConvNeXt-ChARM, an efficient ConvNeXt-based transform coding framework. It is coupled with a compute-efficient channel-wise auto-regressive prior that captures both global and local contexts from the hyper and quantized latent representations. Our architecture can be optimized end-to-end, fully leveraging context information to extract compact latent representations and achieve higher-quality image reconstructions. Experimental results conducted on four widely-used datasets demonstrate the effectiveness of ConvNeXt-ChARM. It consistently delivers significant BD-rate (PSNR) reductions, averaging 5.24% over the VVC reference encoder (VTM-18.0) and 1.22% over the state-of-the-art learned image compression method SwinT-ChARM. Additionally, we conduct model scaling studies to verify the computational efficiency of our approach. Furthermore, we perform objective and subjective analyses to highlight the performance gap between ConvNeXt, the next-generation ConvNet, and the Swin Transformer. Overall, our proposed ConvNeXt-ChARM framework showcases improved compression efficiency and reconstruction quality, establishing itself as a promising solution in the field of neural image compression.
Deep Learning Convolutional Neural Networks (CNNs) on Recognition and Classification of White Blood Cells (WBCs)

The rapid evolution of Artificial Intelligence has brought significant advancements in Deep Learning, a widely applied subfield across various industries, including healthcare. This study focuses on leveraging Deep Learning and image processing techniques to classify white blood cells (WBCs). By comparing and evaluating multiple convolution neural network (CNN) models, such as GoogLeNet, ResNet50, VGG16, and Squeezenet, including YOLO (You Only Look Once) algorithm known for its segmentation capabilities, the objective is to improve the accuracy and efficiency of WBC recognition and classification. During training, both VGG16 and ResNet50 models achieved the highest accuracies, with VGG16 at 97.70% and ResNet50 at 97.38%. However, ResNet50 was chosen as the preferred model to be utilized alongside YOLO for improved classification and segmentation advancements. The testing phase of ResNet50 utilized 10% of the dataset from the initial data and demonstrated over 90% accuracy in each class. The process showed higher efficiency and lower capital costs, making it suitable for medical applications aided by Deep Learning Convolutional Neural Networks, addressing limitations in white blood cell classification.
Smart Wheelchiar for Disabled Person

Nowadays, people suffer from a variety of illnesses related to the body, including those that affect the lower body and make it difficult or impossible for patients to walk. The main goal of a wheelchair is to transport patients who cannot walk to their desired destination, either by themselves or with the help of a caregiver. The project's objective was to develop an effective solution for patients who are unable to operate the joystick on electronic wheelchairs. The smart wheelchair would be beneficial for patients who primarily reside in their rooms. This smart wheelchair will create a map of the room, allowing the patient to navigate it easily using only one finger to control it by simply clicking on the pre-created map.This project developed an electronic wheelchair that could be controlled and driven autonomously using a ROS (Robot Operating System) platform. Additionally, it utilized the SLAM (Simultaneous Localization and Mapping) method, which enables the construction of a map of an unknown environment while simultaneously localizing the wheelchair for navigation purposes. To assess the accuracy of map creation in an unknown environment, calibration was performed with and without loading weight to measure the distance and angle of the wheelchair's movement.The results of this project show that the developed electronic wheelchair successfully builds a map of an unknown environment and utilizes this map for navigation. Users can simply click on their desired destination on the map, and the wheelchair will autonomously navigate towards it. Moreover, the wheelchair can avoid obstacles while navigating. Although the smart wheelchair can perform well, there are some limitations due to the RPLidar. The RPLidar detects everything on the ground in parallel when creating the map and while navigating. Therefore, if any object is located below the RPLidar, it may not be able to detect it.
Blind Video Stabilization Quality Assessment based on convolutional LSTM

Assessing the performance of video stabilization algorithms is still in its infancy compared with the state-of-the-art in video quality assessment, where the focus is on classic distortions such as noise, blur, lighting problems, and coding artifacts. In this study, we present a new blind video stabilization quality assessment metric based on learning sensor outputs via a novel deep neural network. We build an architecture based on a convolutional LSTM to estimate accelerations given by an inertial measurement unit sensor with no additional information than video frames. The experimental study was carried out on a new dedicated dataset, which showed the effectiveness of our metric in characterizing the level of shakiness of videos, in addition, we demonstrate the efficiency of our system to learn acceleration data provided from sensors, which opens ambitious perspectives in retrieving sensors data from any video.
Medication Recommendation using Word Embedding and Recurrent Neural Network

Medication recommendation systems provide intelligent decision support by suggesting appropriate medications tailored to each patient’s health profile and diagnosis. This reduces or eliminates the need to manually search drug databases when prescribing medications. In our study, we present an approach that involves word embedding to transform unstructured outpatient records into dense numerical vectors and a hierarchical recurrent neural network to make hierarchical predictions at four different levels of Anatomical Therapeutic Chemical (ATC) medication codes. Our algorithm was developed on 191,496 anonymized outpatient records. We compared the Word2Vec and FastText word embedding techniques. FastText emerged as a superior choice, possibly due to its ability to capture sub-word information. Our model achieved F1 scores of 0.7128, 0.6475, 0.3934, and 0.3156 at the 1st, 2nd, 3rd, and 4th ATC levels, respectively. Our work offers a promising approach to extract meaningful medical information from unstructured medical records to optimise the drug recommendation process.
Medical Point Clouds Enhancement at the Network Edge

The paper proposes a method for the enhancement of medical point clouds suitable for implementation at the edge of a next generation network. This method exploits the 2D point clouds projection employed in compression algorithms and enhances the point cloud by applying a diffusion sampling model in the flattened domain. The proposed approach, to which we refer to as Projection Sampling based Point Cloud Enhancement, perfectly fits the e-health service architecture in next generation network since it can be implemented at the network edge or within the network, at an intermediate transcoding stage. The experimental findings with medical point clouds demonstrate the method’s efficacy in mitigating noise and preserving texture information, making it a valuable tool for incorporating point cloud enhancement into an Extended Reality transmission system.
Constructing high-dynamic-range spectral images for natural scenes

The cutting-edge research in high-dynamic-range (HDR) display, such as inverse tone-mapping operators for HDR TV, HDR image quality (IQ) evaluation, and HDR image enhancement, suffer from a lack of reliable training and testing images. To address this problem, we combined the HDR imaging technique with high-accuracy spectral images to construct a first-of-its-kind HDR spectral radiance image. A framework was presented to capture multi-exposure hyperspectral images of HDR outdoor scenes and establish the HDR spectral image. By employing spectrophotometric calibration, the spectral radiance-based HDR images were created. The metadata of the spectral images provide crucial colorimetry information that was absent in the existing datasets. To validate the superiority of the proposed HDR spectral images in typical applications, the spectral sensitivity functions of a camera equipped with a multispectral-filter-array sensor was optimized, and the IQ evaluation results indicate a better adaptability over existing ones. Furthermore, the HDR image augmentation suggests that it is possible to provide a large number of RGB training images for various research objectives based on the proposed images.
Soccer Player Detection and Soccer Team Classification using GoogLeNet and Histogram Analysis

Machine learning-based system are widely used in sport engineering including offside events in football. A challenge of technology applied into soccer sport is to improve performance of the Video Assistant Referee (VAR) technology. In this paper, we proposed a method combining player detection and team classification for soccer images. We used the googLeNet deep learning and histogram analysis technique for player detection and classification from a single image. The experiment is done to measure the performance of the player detection and team classification. 710 images and 457 images are used as input for player detection and team classification, respectively. For player detection performance, the accuracy rate is 72.68%, and sensitivity rate is 74.68%. For team classification performance, the accuracy rate is 90.43%, sensitivity rate is 76.15%, and specificity rate is 96.03%. Our method demonstrates satisfactory performance.
Post-Quantum, Order-Preserving Encryption for the Confidential Inference in Decision Trees: FPGA Design and Implementation

One main objective of this paper is to show how to adapt the well-known, lattice-based NTRU post-quantum encryption to the confidential inference in decision trees. Another objective is to describe a resource-efficient FPGA implementation of the adapted NTRU. The typical use case of such encryption is that of two parties where one party has proprietary ownership of the decision tree model while the other party has proprietary ownership of the data. Confidential inference in decision trees can be insured using order-preserving cryptography, which has much weaker requirements and is therefore easier to implement than fully homomorphic cryptography. Post-quantum NTRU is not order-preserving, but interestingly, it can be modified to obey the order-preserving property. We call the resulting cipher OP-NTRU. Lossless compression can be applied to the ciphertext produced by OP-NTRU to facilitate its hardware acceleration. OP-NTRU has been implemented on an FPGA with the HDL code automatically compiled from the machine learning framework. Confidential inference experiments report more than 96% compression without degrading inference accuracy in FPGA for the MNIST dataset.
Transformer-Based Question Answering Model for the Biomedical Domain

Motivation: Question Answering (QA) is a highly focused topic in the field of Natural Language Processing (NLP). Recent progress in neural network models and the availability of large datasets like SQuAD have played a significant role in improving performance in open domains. However, there remains a need to further effectively implement these systems in more specific domains, especially in the biomedical field, to help medical practitioners provide accurate solutions for inquiries related to medicine and healthcare, including specific subjects such as the COVID-19 disease. Fortunately, recent models, such as transformers, have opened up avenues and modern techniques for developing accurate systems.Aims: In this work, we aim to leverage transformer models and Transfer Learning to effectively train models in the biomedical domain. By taking a pre-trained model for Question Answering tasks and further fine-tuning it on specific domains, we enhance the system’s performance in the biomedical domain. Our ultimate goal is to develop a QA model specifically tailored for COVID-19 QA.Results: We have trained BERT and RoBERTa models on the COVID-QA dataset and achieved competitive results on COVID-19 QA. Our RoBERTa model achieved an Exact Match (EM)/F1 score of 0.38/0.64, respectively, on COVID-QA, indicating successful performance in COVID-19 QA.
Stream Reasoning approach on Human Behavior for Medication Risks Detection using LARS Framework

Human activity detection and analysis is essential in many areas, including healthcare, smart settings, and surveillance. Traditional techniques fail to efficiently evaluate streaming data, as the need for real-time analysis rises. In this study, we introduce a novel approach to identify potential medical risks by examining the behaviors of an elderly individual. Our approach analyzes human activities using rule-based common sense reasoning over data streams. We utilize LARS (Logic-Based Framework for Analyzing Reasoning over Streams), a system that permits the use of specific time operators, to perform the reasoning. This enables us to more effectively handle and reason about real-time data. It also provides incremental processing of streaming data, which improves results in terms of risk detection. We evaluate the effectiveness of our approach by doing comprehensive testing on synthetic data, which acts as a simulation of real-time recorded human actions, to assess the efficacy of our reasoning model. Our suggested approach provides real-time monitoring of human behavior, demonstrating its usefulness and need and giving an invaluable option for the precise detection of potential medical dangers among elderly people.
Task Scheduling Optimization in Cloud Computing: A Comparative Study Between Flower Pollination and Butterfly Optimization Algorithms

The No Free Lunch theorem states that no single algorithm can universally serve as the best solution for all optimization purposes. Consequently, it becomes imperative to adapt or combine the fundamental structures of metaheuristic optimization algorithms to fit with specific problem-solving requirements. In this paper, we conduct a comparative analysis of two widely recognized metaheuristic algorithms, namely the Butterfly Optimization Algorithm (BOA) and the Flower Pollination Algorithm (FPA) to deal with the task scheduling problem in cloud computing systems. Our objective is to orchestrate the most effective assignment of tasks across the available virtual machines in the system. Performance evaluation encompasses standard and synthetic workloads, focusing on conflicting quality of service metrics, namely time makespan or schedule length and resource utilization. The experiments conducted through the CloudSim framework demonstrate compelling outcomes, highlighting the importance of this study as a basis for future optimization research.
Embedding the Physics in Black-box Inverse Dynamics Identification: a Comparison Between Gaussian Processes and Neural Networks

In recent years, black-box estimators for robot inverse dynamics have drawn the attention of the robotics community. This paper compares two recent black-box approaches that try to improve generalization and data efficiency by embedding the physical laws governing the system dynamics in two different ways. The so-called Deep Lagrangian Networks (DeLaNs) impose the structure of the Lagrangian equations but do not constrain the basis functions used to model the dynamics. Instead, the Gaussian process model based on the recently introduced Geometrically Inspired Polynomial (GIP) kernel constrains the basis functions of the regression problem to a physically inspired finite-dimensional space but does not force structural properties to be guaranteed. We carried out extensive experiments both on simulated and real manipulators with increasing degrees of freedom (DOF). Our results show that: (i) the accuracy of the DeLaNs model deteriorates much more rapidly than the one of the GIP kernel model with the DOF increase. (ii) the GIP kernel model better estimates the different components of the dynamics, namely, the inertial, Coriolis, and gravitational torques, despite not directly imposing structural properties.
Kinodynamic path switching for robotic manipulators

We extend trajectory planning methods for robotic manipulators to the more general and complex path planning problem in both the space of joints and their velocities. We consider a set of prespecified feasible paths, together with their associated, precomputed safe sets in the projected space of the dynamics restricted to the path. We ask a natural question, namely, whether we can state geometric conditions, together with their algebraic equivalents, that allow switching between paths, providing thus greater flexibility, enabling path planning capabilities. We answer affirmatively, and pose conditions whose consistency can be checked by solving a set of algebraic conditions and by computing intersections between safe sets, precisely, reach-avoid sets. Controllers can also be extracted for closed-loop switched paths. Our proof-of-concept analysis suggests that under the developed framework, composition between paths is possible, thus, potentially significantly extending the applicability of the approach and complementing existing planning methods.