Reviewerly/exHarmony huggingface dataset:
Expiratory Flow and Volume Estimation Through Thermal-CO$ {}_{\text{2}} $ Imaging

In this work, we introduce a quantitative non-contact respiratory evaluation method for fine-grain exhale flow and volume estimation through Thermal- CO2 imaging. This provides a form of respiratory analysis that is driven by visual analytics of exhale behaviors, creating quantitative metrics for exhale flow and volume modeled as open-air turbulent flows. This approach introduces a novel form of effort-independent pulmonary evaluation enabling behavioral analysis of natural exhale behaviors.CO2 filtered infrared visualizations of exhale behaviors are used to obtain breathing rate, volumetric flow estimations (L/s) and per-exhale volume (L) estimations. We conduct experiments validating visual flow analysis to formulate two behavioral Long-Short-Term-Memory (LSTM) estimation models generated from visualized exhale flows targeting per-subject and cross-subject training datasets.Experimental model data generated for training on our per-individual recurrent estimation model provide an overall flow correlation estimate correlation of R2=0.912 and volume in-the-wild accuracy of 75.65-94.44%. Our cross-patient model extends generality to unseen exhale behaviors, obtaining an overall correlation of R2=0.804 and in-the-wild volume accuracy of 62.32-94.22%.This method provides non-contact flow and volume estimation through filtered CO2 imaging, enabling effort-independent analysis of natural breathing behaviors.Effort-independent evaluation of exhale flow and volume broadens capabilities in pulmonological assessment and long-term non-contact respiratory analysis.
Compression scenarios for Federated Learning in Smart Manufacturing

Recent advances in Industrial Internet of Things (IIoT) and communication technologies have provided new concepts of smart manufacturing and paved the way for the new era of Industry 4.0. The huge amount of generated data from machinery, connected systems, and edge devices can be exploited to extract actionable insights that can enhance the decision-making process in smart manufacturing, targeting to improve the return of investment. Conventionally, smart decisions are obtained from centralised data using predefined machine/deep learning models. To achieve better accuracy, these models require training based on diverse, unbiased, and comprehensive datasets, which may not be feasible in distributed and remote facilities due to the high scalability of smart manufacturing and data privacy concerns. Federated learning stands as a primary solution to these issues by bringing collaborative smart manufacturing without requiring centralised data training rather than model sharing. However, these models with many parameters result in big size, which put pressure on the communication channels due to frequent transfers from server to clients and vice versa and may face communication delays and model corruption. In addition, bigger models create issues for edge devices with limited memory and processing capacity which needs consideration. Therefore, effective compression methods are mandatory to ensure Communication-Efficient Federated Learning. In this paper, first, we discuss some possible application opportunities of FL in smart manufacturing, especially for the automotive industry but that can be extended for others. Secondly, we highlight FL communication challenge in smart manufacturing for which we present the compression concept and objectives in FL. It is followed by discussing the state-of-the-art compression techniques in FL. Thirdly, a special focus is given to NNR and DeepCABAC for their demonstrated high compression gains with a wide variety of deep models. Finally, we gave some directions for compressing the FL models in the future.
Toward Label-Efficient Neural Network Training: Diversity-Based Sampling in Semi-Supervised Active Learning

Collecting large-labeled data is an expensive and challenging issue for training deep neural networks. To address this issue, active learning is recently studied where the active learner provides informative samples for labeling. Diversity-based sampling algorithms are commonly used for representation-based active learning. In this paper, a new diversity-based sampling is introduced for semi-supervised active learning. To select more informative data at the initial stage, we devise a diversity-based initial dataset selection method by using self-supervised representation. We further propose a new active learning query strategy, which exploits both consistency and diversity. Comparative experiments show that the proposed method can outperform other active learning approaches on two public datasets.
LiDAR Vehicle Point Cloud Reconstruction Framework for Axle-Based Classification

As light detection and ranging (LiDAR) technology rapidly advances, it is becoming increasingly viable as a solution to collect vehicle classification data. The main challenge of LiDAR compared with video in vehicle classification lies in its resolution, which limits the ability of LiDAR-based models to classify vehicles in detail from a single captured frame. This article proposes a novel framework for reconstructing the vehicle point clouds generated by a roadside LiDAR sensor, by considering ground plane constraints and developing a bootstrap aggregating deep neural network (bagging DNN) model to classify the reconstructed vehicle point clouds, according to the U.S. Federal Highway Administration (FHWA) axle-based vehicle classification scheme. First, a probabilistic-based registration algorithm is used to estimate the transformation matrix between consecutive frames of each vehicle point cloud. Then, a multiway registration is conducted to fine-tune the estimated transformation matrices to rebuild the 3-D model of each moving vehicle. Second, key features are extracted from the reconstructed vehicle models and fed into a bagging DNN model to provide classifications based on the FHWA scheme. The classification model with the reconstruction framework outperforms the latest developed LiDAR-based FHWA classification model in terms of both accuracy and robustness. The model has an 83% average correct classification rate (CCR) on the test set. Remarkably, the proposed model can accurately distinguish Class 5 and 8 trucks, which have overlapping axle configurations, with a 97% and a 90% CCR, respectively.
IndustrialEdgeML - End-to-end edge-based computer vision systemfor Industry 5.0

State-of-the-art object detection methods gain increasing attention in various industrial applications. However, current vision systems typically rely on cloud services for data storage and training of AI models. In this work, we present an end-to-end vision system for bin-picking applications at the edge of industrial premises via industrial computers, where bin-picking refers to the dedicated selection and displacement of objects into designated areas. Our system is designed for human-robot collaboration in a range of distinct challenging industrial environments with a focus on automatically learning new objects. To this end, we propose a new approach for automated oriented object detection consisting of three stages: (i) automatic training data labeling for the new object based on autoencoders, (ii) object detection using the YOLOR-S architecture, and (iii) inference of the object's orientation, more precisely, rotated bounding box detection based on attention maps using the TS-CAM algorithm. Our method achieves state-of-the-art performance, without the need for annotations of rotated bounding boxes, when compared to the single-stage architecture S2A-Net for rotated bounding box detection. In particular, the system maintains high-quality performance not only for learning all objects at once but also for incrementally learning one object after another.
Post-Quantum Cryptography

Cryptography is used broadly in the digital age, making our communications secure, ensuring our data is safe, and enabling secure transactions on which we rely daily. Our reality is connected, we send an email without thinking about all the underlying protocols, we buy online, and we check the weather on our fridge. Utilizations are countless and so is our exposure. Cryptographic systems keep us safe, a shield for our privacy and our fundamental rights. However, we have arrived at the dawn of a new age, the quantum computing era. Seen for a long as a theoretical emanation of quantum mechanics it gives the first baby steps in the real world, making the world as we know it less safe and more dangerous. Post-quantum cryptography is the paladin that is coming to the rescue but will it be up to the challenge of keeping our world safe.
Modified Iterative Quantum Amplitude Estimation is Asymptotically Optimal

In this work, we provide the first QFT-free algorithm for Quantum Amplitude Estimation (QAE) that is asymptotically optimal while maintaining the leading numerical performance. QAE algorithms appear as a subroutine in many applications for quantum computers. The optimal query complexity achievable by a quantum algorithm for QAE is $O\left(\frac{1}{\epsilon}\log \frac{1}{\alpha}\right)$ queries, providing a speedup of a factor of $1/\epsilon$ over any other classical algorithm for the same problem. The original algorithm for QAE utilizes the quantum Fourier transform (QFT) which is expected to be a challenge for near-term quantum hardware. To solve this problem, there has been interest in designing a QAE algorithm that avoids using QFT. Recently, the iterative QAE algorithm (IQAE) was introduced by Grinko et al. with a near-optimal $O\left(\frac{1}{\epsilon}\log \left(\frac{1}{\alpha} \log \frac{1}{\epsilon}\right)\right)$ query complexity and small constant factors. In this work, we combine ideas from the preceding line of work to introduce a QFT-free QAE algorithm that achieves a query complexity upper bound of $\frac{62}{\epsilon}\ln\frac{6}{\alpha}$ which matches the optimal $O\left(\frac{1}{\epsilon}\log \frac{1}{\alpha}\right)$ query complexity. We supplement our analysis with numerical experiments comparing our performance with IQAE where we find that our modifications retain the high performance, and in some cases even improve the numerical results.
An Overview of Opponent Modeling for Multi-agent Competition

Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Opponent modeling is generally used in competitive multi-agent systems, in which an agent models the actions, behaviors, and strategies of other agents (adversaries) to get better rewards and train stronger strategies for playing against each other. In this survey, we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, and agent modeling.
Increase Channel Attention Based on Unet++ Architecture for Medical Images

At present, image segmentation technology has become increasingly mature. The application of image segmentation technology in the field of medical images is also more and more extensive. However, the growing demand is not enough for current medical image segmentation models. Unet++ is a good basic model in the field of medical image segmentation. On this basis, we add a channel attention module for long skip connections. Solve the problem of serious loss of eigenvalues in the process of long skip connections. This can get better accuracy and better image segmentation. At the same time, our added channel attention module can effectively increase the robustness of the network model. Through our experimental analysis and result judgment, our designed C-Unet++ can play a better role in medical image segmentation.
A Novel Security Scheme for Mobile Healthcare in Digital Twin

The rapid development of mobile devices and 5G networks has promoted the widespread use of mobile healthcare, allowing us to check our health status at any time and from any location, as well as facilitating doctors’ analysis of the condition through timely analysis of these data. However, the convenience provided by mobile healthcare has also exposed data security issues. Medical data, for example, is easily leaked, and the collection of patient treatment data lacks standardized management. To address these issues, we designed a mobile medical data security scheme based on Federated Learning and Digital Twin to guarantee medical data security. The scheme consists of a state-aware module and a digital twin module. The state-aware module collects medical data and stores it encrypted to the blockchain, ensuring the security, trustworthiness, and traceability of medical data. The digital twin module uses horizontal federated learning and digital twin to combine data from multiple medical institutions to build a federated model and a patient digital twin model in an iterative process, which solves the problem of “data silos” while ensuring privacy and security, and aids in the improvement of medical solutions and helps improve medical care. We have conducted experiments and evaluations of the techniques adopted in this scheme, verifying the effectiveness and feasibility.
Generating Programs for Polynomial Multiplication with Correctness Assurance

Program-generation techniques prevail in domains that need high performance, such as linear algebra, image processing, and database. Yet, it is hard to generate high-performance programs with correctness assurance, and cryptography needs both. Masuda and Kameyama proposed a DSL-based framework for implementing a program generator, an analyzer, and a formula generator, and obtained an efficient and correct implementation of Number-Theoretic Transform (NTT) that is necessary for many cryptographic algorithms.
D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images

Mixed (random and stripe) noise will cause serious degradation of optical remotely sensed image quality, making it hard to analyze their contents. In order to remove such noise, various inverse problems are usually constructed with different priors, which can be solved by either model-based optimization methods or discriminative learning methods. However, they have their own drawbacks, such as the former methods are flexible but are time-consuming for the pursuit of good performance; while the later methods are fast but are limited for extensive applications due to their specialized tasks. To fast obtain pleasing results with combination of their merits, in this paper, we propose a novel denoising strategy, namely, Dual Denoiser Driven Convolutional Neural Networks (D3CNNs), to remove both random and stripe noise. The D3CNNs includes the following two key parts: one is that two auxiliary variables respective for the denoised image and the stripe noise are introduced to reformulate the inverse problem as a constrained optimization problem, which can be iteratively solved by employing the alternating direction method of multipliers (ADMM). The other is that the U-shape network is used for the denoised auxiliary variable while the residual CNN (RCNN) for the stripe auxiliary variable. The subjectively and objectively comparable results of experiments on both synthetic and real-world remotely sensed images verify that the proposed method is effective and is even better than the state-of-the-arts.
Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation

Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNeT produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments.
Future Pose Prediction from 3D Human Skeleton Sequence with Surrounding Situation

Human pose prediction is vital for robot applications such as human-robot interaction and autonomous control of robots. Recent prediction methods often use deep learning and are based on a 3D human skeleton sequence to predict future poses. Even if the starting motions of 3D human skeleton sequences are very similar, their future poses will have variety. It makes it difficult to predict future poses only from a given human skeleton sequence. Meanwhile, when carefully observing human motions, we can find that human motions are often affected by objects or other people around the target person. We consider that the presence of surrounding objects is an important clue for the prediction. This paper proposes a method for predicting the future skeleton sequence by incorporating the surrounding situation into the prediction model. The proposed method uses a feature of an image around the target person as the surrounding information. We confirmed the performance improvement of the proposed method through evaluations on publicly available datasets. As a result, the prediction accuracy was improved for object-related and human-related motions.
Parents’ ontological beliefs regarding the use of conversational agents at home: resisting the neoliberal discourse

This paper develops a critical perspective on the use of conversational agents (CAs) with children at home. Drawing on interviews with eleven parents of pre-school children living in Norway, we illustrate the ways in which parents resisted the values epitomised by CAs. We problematise CAs’ attributes in light of parents’ ontological perceptions of what it means to be human and outline how their attitudes correspond to Bourdieu’s [1998a. Acts of Resistance. New York: New Press] concept of acts of resistance. For example, parents saw artificial conversation designed for profit as a potential threat to users’ autonomy and the instant gratification of CAs as a threat to children’s development. Parents’ antecedent beliefs map onto the ontological tensions between human and non-human attributes and challenge the neoliberal discourse by demanding freedom and equality for users rather than productivity and economic gain. Parents’ comments reflect the belief that artificial conversation with a machine inappropriately and ineffectively mimics a nuanced and intimate human-to-human experience in service of profit motives.
HFCVO-DMN: Henry Fuzzy Competitive Verse Optimizer-Integrated Deep Maxout Network for Incremental Text Classification

One of the effectual text classification approaches for learning extensive information is incremental learning. The big issue that occurs is enhancing the accuracy, as the text is comprised of a large number of terms. In order to address this issue, a new incremental text classification approach is designed using the proposed hybrid optimization algorithm named the Henry Fuzzy Competitive Multi-verse Optimizer (HFCVO)-based Deep Maxout Network (DMN). Here, the optimal features are selected using Invasive Weed Tunicate Swarm Optimization (IWTSO), which is devised by integrating Invasive Weed Optimization (IWO) and the Tunicate Swarm Algorithm (TSA), respectively. The incremental text classification is effectively performed using the DMN, where the classifier is trained utilizing the HFCVO. Nevertheless, the developed HFCVO is derived by incorporating the features of Henry Gas Solubility Optimization (HGSO) and the Competitive Multi-verse Optimizer (CMVO) with fuzzy theory. The proposed HFCVO-based DNM achieved a maximum TPR of 0.968, a maximum TNR of 0.941, a low FNR of 0.032, a high precision of 0.954, and a high accuracy of 0.955.
What Makes Wearable Technologies Socially Acceptable?

Novel wearable computing devices challenge our perception of what is socially acceptable. Research in human-computer interaction asks, "What makes a wearable socially acceptable?" and provides insights that may help designers and developers design for social acceptability.
MIFD-Net : a hand gesture recognition model based on feature fusion of MLP and CNN

Gesture recognition can play a crucial role in addressing the issue of Human-computer interaction. In this paper, we proposed a vision-based Multi-input fusion deep network (MIFD-Net), which consists of Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN). MIFD-Net first processes hand keypoint data and gesture images using Euclidean distance normalization (ED-Normalization) and image segmentation technologies, respectively. Then, two kinds of data are simultaneously used as input to MIFD-Net. The experimental results show that the MIFD-Net achieves an average accuracy of 99.65% on the self-built dataset in this paper and 99.10% on the NUS hand posture datasets II (NUS-II). The MIFD-Net significantly decreases its FLOPs and the number of parameters and reduces the complexity of the model while maintaining a high recognition rate compared with other gesture recognition models. The MIFD-Net can obtain high accuracy and strong robustness in different environments, lighting, and angles.
Derivative Descendants and Ascendants of Binary Cyclic Codes, and Derivative Decoding

This paper defines derivative descendants and ascendants of extended cyclic codes from the derivative of the Mattson-Solomon polynomials. It proves that the derivative descendants of an extended cyclic code in different directions are the same. It allows us to perform soft-decision decoding on extended cyclic codes based on the soft-decision decoding of their descendants. Simulation results show that its performance over certain extended cyclic codes, including some extended BCH codes, can be close to that of the maximum likelihood decoding.