Reviewerly/exHarmony huggingface dataset:
Multi-Camera Face Detection and Recognition in Unconstrained Environment

Multi-camera face detection and recognition is an Artificial Intelligence (AI) based technology that leverages multiple cameras placed at different locations to detect and recognize human faces in real-world conditions accurately. While face detection and recognition technologies have exhibited high accuracy rates in controlled conditions, recognizing individuals in open environments remains challenging due to factors such as changes in illumination, movement, and occlusions. In this paper, we propose a multi-camera face detection and recognition (MCFDR) pipeline, which consists of three main parts - face detection, face recognition, and tracking. A series of model training is done with the open-source dataset to build a robust pipeline, and finally, the pipeline adopted trained YOLOv5n for face detection model with mAP 0.495, precision value of 0.868, and recall value of 0.781. The system also adopted the SphereFace SFNet20 model with an accuracy of 82.05% and a higher inference rate than SFNet64 for face recognition. These models are then fed into DeepSORT for multi-camera tracking. Our dataset has been applied to the pipeline and shows ideal outcomes with objectives achieved.
Point Anywhere: Directed Object Estimation from Omnidirectional Images

One of the intuitive instruction methods in robot navigation is a pointing gesture. In this study, we propose a method using an omnidirectional camera to eliminate the user/object position constraint and the left/right constraint of the pointing arm. Although the accuracy of skeleton and object detection is low due to the high distortion of equirectangular images, the proposed method enables highly accurate estimation by repeatedly extracting regions of interest from the equirectangular image and projecting them onto perspective images. Furthermore, we found that training the likelihood of the target object in machine learning further improves the estimation accuracy.
An Analysis of Behaviour-Driven Requirement Specification for Robotic Competitions

Recent scientific and technological advances have enabled robotic applications in various challenging domains, which motivates means to better represent and manage the subsequent increase in number and complexity of requirements. We look into rulebooks of robotic competitions and benchmarks as one publicly available source of requirements and acceptance criteria for evaluating robots’ performance. From our analysis, we derive a Feature Model containing common elements that recur in descriptions of different robotic competitions. We argue how these features can be used to express requirements and acceptance criteria for robotic applications, within the context of the Behaviour-Driven Development (BDD) paradigm. This can serve as a mean not only to analyse and manage requirements, but also to introduce automation into verifying and validating requirements in robotics.
Sentiment analysis and computational intelligence

The internet boom of this millennium and especially the explosion of social-networking platforms allow digital data to glide all around the world in real-time and evoke multi-faceted content data analysis as a challenging proposition to scientists as well as researchers. While much of the content originates from various social-media sources on different topics and in different languages, it fuels more challenges to analyze sentiments accurately while processing opinions accumulated from different channels. Emotion and polarity prediction from various social media such as Facebook, Twitter, websites, etc., is becoming an emerging field of predictive modeling. Thus Computational Intelligence (CI) techniques play important roles here to solve such inherent problems of sentiment-analysis applications by serving the key roles in development of recent technologies from academia, even in educational psychology, to industrial arenas such as remote sensing, interfacing technologies, retrieval, and even recommendation systems.
Efficient Workload Consolidation for Composable/Disaggregated Data Centers Considering Migration Cost

Resource disaggregation significantly improves resource flexibility, efficiency, and availability in Data Centers (DCs) by decoupling different resource modules in servers and interconnecting them with high-speed networking technologies, e.g., optical networking, leading to Composable/Disaggregated DCs (CDCs or DDCs). CDC also brings benefits to workload migration, a technique widely used to consolidate workloads to fewer nodes to save energy. CDC may reduce migration costs as a workload can be partly migrated instead of entirely as in a server-based DC (SDC). We consider the problem of workload consolidation in a CDC with objectives to minimize both the number of active nodes and the number of migrated elements. An integer linear programming formulation that uses a weighted sum approach is provided to address this two-objective problem. Results show that higher resource utilization and lower migration costs can simultaneously be achieved in a CDC as compared to an SDC.
Practical Verifiable &amp; Privacy-Preserving Double Auctions

Double auctions are procedures to trade commodities such as electricity or parts of the wireless spectrum at optimal prices. Buyers and sellers inform the auctioneer what quantity they want to buy or sell at specific prices. The auctioneer aggregates these offers into demand and supply curves and finds the intersection representing the optimal price. In this way, commodities exchange owners in an economically-efficient manner. Ideally, the auctioneer is a trusted third party that does not abuse the information they gain. However, the offers reveal sensitive information about the traders, which the auctioneer may use for economic gain as insider information. These concerns are not theoretical; investigations against auctioneers in electricity and advertisement auctions for manipulating auctions are ongoing. These concerns call for solutions that conduct double auctions in a privacy-preserving and verifiable way. However, current solutions are impractical: To the best of our knowledge, the only solutions satisfying these properties require full interaction of all participants. In this work, we design a more practical solution. We propose the first privacy-preserving and verifiable double auction scheme that does not require traders to interact actively, tailored to electricity trading on (inter)national exchanges. Our solution relies on homomorphic encryption, commitments, and zero-knowledge proofs. In a simulated auction with 256 traders, we observe that traders take up to 10 seconds to generate their order, the auctioneer takes 10 seconds to verify an order, and the auction result is computed and verified in 30 seconds. We extrapolate these results to larger auctions to show the practical potential.
Delivering Improved Care through Remote Health Monitoring Systems by Using Microservices. Case Study: Fall Detection Encompassed into Connected Care

Based on today’s modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring Systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors’ health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside “Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)” research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved.
EnsExam: A Dataset for Handwritten Text Erasure on Examination Papers

Handwritten text erasure on examination papers is an important new research topic with high practical value due to its ability to restore examination papers and collect questions that are answered incorrectly for review, thereby improving educational efficiency. However, to the best of our knowledge, there is no publicly available dataset for handwritten text erasure on examination papers. To facilitate the development of this field, we build a real-world dataset called SCUT-EnsExam (short for EnsExam). The dataset consists of 545 examination paper images, each of which has been carefully annotated to provide a visually reasonable erasure target. With EnsExam, we propose an end-to-end model, which introduces a soft stroke mask to erase the handwritten text precisely. Furthermore, we propose a simple yet effective loss called stroke normalization (SN) loss to alleviate the imbalance between text and non-text regions. Extensive numerical experiments shows that our proposed method outperforms previous state-of-the-art methods on EnsExam. In addition, quantitative experiments on scene text removal benchmark, SCUT-EnsText, demonstrate the generalizability of our method. The EnsExam will be made available at https://github.com/SCUT-DLVCLab/SCUT-EnsExam .
Software effort estimation using stacked ensembled techniques and proposed stacking ensemble using principal component regression as super learner

As the size and complexity of projects grows, estimates are increasingly used, especially in the agile community. Software development cannot begin without first conducting thorough planning and estimation. Estimating how much work a project will take is a common first step in the software development life cycle. By employing ensemble techniques, we integrate multiple learning algorithms to build a more accurate predictive model. The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model’s performance by incorporating a blend of these foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process.
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning

Few-shot learning addresses the issue of classifying images using limited labeled data. Exploiting unlabeled data through the use of transductive inference methods such as label propagation has been shown to improve the performance of few-shot learning significantly. Label propagation infers pseudo-labels for unlabeled data by utilizing a constructed graph that exploits the underlying manifold structure of the data. However, a limitation of the existing label propagation approaches is that the positions of all data points are fixed and might be sub-optimal so that the algorithm is not as effective as possible. In this work, we propose a novel algorithm that adapts the feature embeddings of the labeled data by minimizing a differentiable loss function optimizing their positions in the manifold in the process. Our novel algorithm, Adaptive Anchor Label Propagation, outperforms the standard label propagation algorithm by as much as 7% and 2% in the 1-shot and 5-shot settings respectively. We provide experimental results highlighting the merits of our algorithm on four widely used few-shot benchmark datasets, namely miniImageNet, tieredImageNet, CUB and CIFAR-FS and two commonly used backbones, ResNet12 and WideResNet-28-10. The source code can be found at https://github.com/MichalisLazarou/A2LP.
Revisiting the Issue of Modelling Living Matter

The modelling of living matter is an important issue for the scientific community, and we may soon see unexpected results in this field. This concerns us to a certain extent, and we would like to raise the issue of modelling living matter, and specifically our opinion on its possible model. We are not presenting the results of research and experiments. This is our view of this process and we would like to share our vision with the scientific community. For us, a living organism is a tool created to perform particular functions. We propose to treat the system of the living human body as a physical system using the existing scientific and technological developments of the IT industry. We lay out the opportunities for applying scientific and technical developments of physical systems in related medical systems. We discuss the opportunities for using the proposed model of a living human body to identify some diseases.
Masked Scale-Recurrent Network for Incomplete Blurred Image Restoration

Muilti-scale learning has been demonstrated to be an excellent deblurring approach in image restoration according to recent studies. It makes the optimization of the function easier to achieve the global optimum. In order to restore an image that is both incomplete and blurry, we propose a Masked Scale-Recurrent Network (MSRN) in this paper, a restoration method based on multi-scale learning and an asymmetric autoencoder. It implements restoration in an end-to-end manner without any prior knowledge or other given conditions. Firstly, we process the GoPro dataset and obtain a dataset of incomplete images. And then, we perform a self-supervised reconstruction pre-training on the autoencoder, with a series of resblocks that increase the quality of the input image and improve the representation learning in the latent space. Finally, on the processed data, we train the model and finish the adjustment of the entire network. Compared with classical multi-scale learning, we introduce masks to help the model train more efficiently by focusing on essential regions of the image. It is also shown that MSRN has successful image restoration capability as well as robustness, as demonstrated in our experiments.
Research on the immersive multimedia virtual interactive experience technology of the Great Wall World Heritage based on VR vision system

Abstract This paper constructs an immersive multimedia virtual interactive experience technology with the Great Wall World Heritage Site as the experimental object. By analyzing the VR vision system and algorithm, the process of stereo vision perception and the general algorithm of somatic image production are explained. The distance equation and depth calculation in stereoscopic projection are studied, and the virtual interactive experience technology is constructed and applied to the World Heritage Site of the Great Wall. The result data show that the immersive multimedia virtual interactive experience technology highlights the real feeling of users more, in which the model has 50% of the influence on the real feeling, the mapping has 15%, the lighting has 15%, and the rendering has 10%. The youth population has a higher percentage of interest in the virtual field, accounting for 50%, 65%, and 60%, respectively, while the middle-aged stage has a more average degree of interest in the virtual field, with 50% of the average interest, 30% of happy to experience, 25% of not interested and 50% of interested. This thesis is conducive to enriching the project of the Great Wall World Heritage Site and providing an effective reference for technological innovation of immersive multimedia virtual interactive experience.
Quantitation of Body Movement in a Motor Physical Therapy for Parkinson’s Disease

The present work shows the quantitation of body movement in a motor physical therapy for Parkinson’s Disease (PD). In recent years, many activities of therapy were carried out remotely using common RGB cameras to capture the body movements. We analyze the body movements of 8 subjects with clinical diagnosis of PD, and compare them with a control group of 11 healthy volunteers, processing their respective RGB video recordings with a software that identifies 17 specific body keypoints while subjects perform two motor rehabilitation therapies (cervical and lumbar spine). All videos were analyzed by OpenPose algorithm and angles from keypoints detected were computed to infer the rotation, rate and amplitude of movement of head, shoulder, back and pelvis. The results show that OpenPose algorithm could be used in a home environment specially in follow-up and management of the motor rehabilitation therapy for Parkinson’s disease.
Enhancing user experience and usability in engineering education: A comparative analysis of multiple functions and tangible user interfaces

A study was conducted at University Tenaga Nasional to compare engineering students’ preferences in using augmented reality technology user interface (UI) applications to solve selected engineering problems. The study utilized a cross-sectional design and the Wilcoxon signed-rank test approach within a controlled environment to analyse the difference in rankings of engineering applications. The study also measured learnability and assessed the software quality using questionnaire for user interaction satisfaction. The multiple function UI was found to be statistically significant more effective than the tangible user interface (TUI) for engineering students. The study provides reliable evidence that multiple function UI is a better option in engineering education than TUI.
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression

Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it improves decoding consistency for interoperability and reduces space-time complexity for implementation. Existing solutions often have to retrain the network for model quantization, which is time-consuming and impractical to some extent. This work suggests using Post-Training Quantization (PTQ) to process pretrained, off-the-shelf LIC models. We theoretically prove that minimizing quantization-induced mean square error (MSE) of model parameters (e.g., weight, bias, and activation) in PTQ is sub-optimal for compression tasks and thus develop a novel Rate-Distortion (R-D) Optimized PTQ (RDO-PTQ) to best retain the compression performance. Given a LIC model, RDO-PTQ layer-wisely determines the quantization parameters to transform the original floating-point parameters in 32-bit precision (FP32) to fixed-point ones at 8-bit precision (INT8), for which a tiny calibration image set is compressed in optimization to minimize R-D loss. Experiments reveal the outstanding efficiency of the proposed method on different LICs, showing the closest coding performance to their floating-point counterparts. Our method is a lightweight and plug-and-play approach without retraining model parameters but just adjusting quantization parameters, which is attractive to practitioners. Such an RDO-PTQ is a task-oriented PTQ scheme, which is then extended to quantize popular super-resolution and image classification models with negligible performance loss, further evidencing the generalization of our methodology. Related materials will be released at https://njuvision.github.io/RDO-PTQ.
The Emergence of Reproducibility and Consistency in Diffusion Models

Recently, diffusion models have emerged as powerful deep generative models, showcasing cutting-edge performance across various applications such as image generation, solving inverse problems, and text-to-image synthesis. These models generate new data (e.g., images) by transforming random noise inputs through a reverse diffusion process. In this work, we uncover a distinct and prevalent phenomenon within diffusion models in contrast to most other generative models, which we refer to as ``consistent model reproducibility''. To elaborate, our extensive experiments have consistently shown that when starting with the same initial noise input and sampling with a deterministic solver, diffusion models tend to produce nearly identical output content. This consistency holds true regardless of the choices of model architectures and training procedures. Additionally, our research has unveiled that this exceptional model reproducibility manifests in two distinct training regimes: (i) ``memorization regime,'' characterized by a significantly overparameterized model which attains reproducibility mainly by memorizing the training data; (ii) ``generalization regime,'' in which the model is trained on an extensive dataset, and its reproducibility emerges with the model's generalization capabilities. Our analysis provides theoretical justification for the model reproducibility in ``memorization regime''. Moreover, our research reveals that this valuable property generalizes to many variants of diffusion models, including conditional diffusion models, diffusion models for solving inverse problems, and fine-tuned diffusion models. A deeper understanding of this phenomenon has the potential to yield more interpretable and controllable data generative processes based on diffusion models.
Progressive Recurrent Neural Network for Multispectral Remote Sensing Image Destriping

An unstable imaging system often introduces additional stripe noise in multispectral remote sensing images during the data acquisition process given a variety of factors. The complicated stripe distributions lead to the residual stripe in the results of existing methods, thus increasing the difficulty of destriping in practice. Mainstream deep learning-based methods show the encouraging destriping performance on multispectral remote sensing images. However, they often require the model to handle the varying degrees of stripe noise in a single shot for each image, which results in the poor destriping performance when facing practical cases with diverse stripe distributions. To address the above issue, we propose a Progressive Recurrent Neural Network (PRNet) to remove the stripe noise for each degraded image in an iterative manner. More specifically, a progressive destriping strategy is designed to gradually restore the clean image, in which the Main Recurrent Module (MRM) is introduced to iteratively process the stripe removal results generated from previous timesteps until the clean image is obtained. Furthermore, since the uniformity of the entire image is supposed to be significantly enhanced after the destriping, it is necessary to take the local spatial correlation into account during the destriping. Therefore, we present the Patch-based Sequence Module (PSM) to leverage the local spatial correlation by splitting the image into multi-scale patch sequences and capturing the relationship among different patches. Extensive experimental results on different datasets demonstrate that the proposed model yields superior destriping performance compared to other methods, especially for removing the stripe noise with complex distributions.