Reviewerly/exHarmony huggingface dataset:
3D Ball Localization From A Single Calibrated Image

Ball 3D localization in team sports has various applications including automatic offside detection in soccer, or shot release localization in basketball. Today, this task is either resolved by using expensive multi-views setups, or by restricting the analysis to ballistic trajectories. In this work, we propose to address the task on a single image from a calibrated monocular camera by estimating ball diameter in pixels and use the knowledge of real ball diameter in meters. This approach is suitable for any game situation where the ball is (even partly) visible. To achieve this, we use a small neural network trained on image patches around candidates generated by a conventional ball detector. Besides predicting ball diameter, our network outputs the confidence of having a ball in the image patch. Validations on 3 basketball datasets reveals that our model gives remarkable predictions on ball 3D localization. In addition, through its confidence output, our model improves the detection rate by filtering the candidates produced by the detector. The contributions of this work are (i) the first model to address 3D ball localization on a single image, (ii) an effective method for ball 3D annotation from single calibrated images, (iii) a high quality 3D ball evaluation dataset annotated from a single viewpoint. In addition, the code to reproduce this research will be made freely available at https://github.com/gabriel-vanzandycke/deepsport
Bidirectional Motion Estimation with Cyclic Cost Volume for High Dynamic Range Imaging

We propose a high dynamic range (HDR) imaging algorithm based on bidirectional motion estimation. First, we develop a motion estimation network with the cyclic cost volume and spatial attention maps to estimate accurate optical flows between input low dynamic range (LDR) images. Then, we develop the dynamic local fusion network that combines the warped and reference inputs to generate a synthesized image by exploiting local information. Finally, to further improve the synthesis performance, we develop the global refinement network that generates a residual image by exploiting global information. Experimental results on the dataset from the NTIRE 2022 HDR Challenge Track 1 (Low-complexity constrain) demonstrate the effectiveness of the proposed HDR image synthesis algorithm.
A transformer-based low-resolution face recognition method via on-and-offline knowledge distillation

It has been widely noticed the performance of algorithms for high-resolution face recognition (HRFR) degrades significantly for low-resolution face recognition (LRFR). In this paper, we discover the main source of this performance degradation comes from the human-defined inductive bias of CNN, which constrains the model to absorb effective information and leads to overfitting. To overcome the shortcoming, for the first time, we adopt a transformer-based DNN algorithm named DeiT to accomplish LRFR tasks. On the other hand, we further borrow the form of knowledge distillation. Traditional knowledge distillation network for LRFR sets the student model to be simpler than or the same as the teacher model while using the teacher model off-the-shelf, leading to the model capacity gap. Instead, we fuse an online network into the original parameter-fixed teacher model to learn how to transfer knowledge. The final "knowledge" is the sum fusion of the outputs of both the teacher model and the student model. Experiments show that even without training on LR face images, the performance of our model can be comparable to recent baselines delicately designed for LRFR tasks on certain LR face datasets. After the entire training process, in either real-world LR datasets, artificially down-sampled datasets, or generated LR face datasets, our method performs favorably against the state-of-the-art (SOTA) algorithms.
Hybrid deep learning diagonal recurrent neural network controller for nonlinear systems

Abstract In the present paper, a hybrid deep learning diagonal recurrent neural network controller (HDL-DRNNC) is proposed for nonlinear systems. The proposed HDL-DRNNC structure consists of a diagonal recurrent neural network (DRNN), whose initial values can be obtained through deep learning (DL). The DL algorithm, which is used in this study, is a hybrid algorithm that is based on a self-organizing map of the Kohonen procedure and restricted Boltzmann machine. The updating weights of the DRNN of the proposed algorithm are developed using the Lyapunov stability criterion. In this concern, simulation tasks such as disturbance signals and parameter variations are performed on mathematical and physical systems to improve the performance and the robustness of the proposed controller. It is clear from the results that the performance of the proposed controller is better than other existent controllers.
Zero-shot face recognition: Improving the discriminability of visual face features using a Semantic-Guided Attention Model

Zero-shot learning enables the recognition of classes not seen during training through the use of semantic information comprising a visual description of the class either in textual or attribute form. Despite the advances in the performance of zero-shot learning methods, most of the works do not explicitly exploit the correlation between the visual attributes of the image and their corresponding semantic attributes for learning discriminative visual features. In this paper, we introduce an attention-based strategy for deriving features from the image regions regarding the most prominent attributes of the image class. In particular, we train a Convolutional Neural Network (CNN) for image attribute prediction and use a gradient-weighted method for deriving the attention activation maps of the most salient image attributes. These maps are then incorporated into the feature extraction process of Zero-Shot Learning (ZSL) approaches for improving the discriminability of the features produced through the implicit inclusion of semantic information. For experimental validation, the performance of state-of-the-art ZSL methods was determined using features with and without the proposed attention model. Surprisingly, we discover that the proposed strategy degrades the performance of ZSL methods in classical ZSL datasets (AWA2), but it can significantly improve performance when using face datasets. Our experiments show that these results are a consequence of the interpretability of the dataset attributes, suggesting that existing ZSL datasets attributes are, in most cases, difficult to be identifiable in the image. Source code is available at https://github.com/CristianoPatricio/SGAM.
Evaluation of heavy metal contamination and human health risk using geo-statistical techniques in selected shallow hard rock aquifers of southwest India

Groundwater is a vital natural resource for drinking and irrigation purposes in India. Heavy metal contamination in groundwater is a global concern and leads to potential health risks to humans and the ecosystem. The aim of this study is to evaluate heavy metal contamination and human health risk in hard rock aquifers of southwest India. Groundwater samples were collected from open wells during the pre-monsoon and monsoon seasons. Heavy metal analysis was conducted using ICP-MS. Geostatistical interpolation technique was applied for spatial analysis. The results revealed that north-west region of the study area have a significant concentration of heavy metals in both the seasons due to intensive agricultural activity. Human health risk assessment in the study area revealed that children are more prone to health risks than adults and infants. Further, this study suspects moderate risk to the groundwater dependent ecosystem. Factor analysis indicates that heavy metals present in groundwater were derived from both geogenic and anthropogenic factors during both seasons. The study concludes that regular monitoring of groundwater is essential to control heavy metal contamination from anthropogenic activities. The heavy metal data obtained from this study can be a valuable database for decision makers to choose the areas of concern, for future water management plans.
Assessing Activities of Daily Living by Measuring Residential Water Use with Low Cost Thermistors

The ongoing assessment of well-being can enable aging adults to remain independent and continue to age in place in their own home. This provides them with a better quality of life and reduces demand on the limited supply of supportive living spaces. The assessment of changes in activities of daily living (ADL) including hygiene and food preparation such as reduced frequency or absence is important to support aging in place and independence. The measurement of when water is used, the use duration and where it is used within the home are assessments of importance within many ADLs. It can provide an indication of the ongoing capability for the resident(s) for daily hygiene (washroom) and nutrition (kitchen). This paper presents the use of low-cost thermistors applied to the outside of the hot and cold water pipes at point of use. This is an example of sensor substitution to measure water use where this substitution is important as the installation of typical flow meters can be complex especially if they are added to the pipes at multiple points of use. When water flows through the pipe, the pipe's temperature deviates from a steady-state room temperature and the paper shows how this deviation is detected by the system to indicate water use, distinguish between hot and cold water use and lastly measure the duration of water use. The sensor and presented method allow for the independent detection of hot and cold water. This provides an additional assessment that can be used in supportive smart home well-being and ADL assessment systems.
Improved PRM Path Planning in Narrow Passages Based on PSO

Probabilistic roadmap (PRM) has been successfully applied in mobile robot path planning. However, the task of path planning is still a challenging job when narrow passages exist in the configuration space. In this paper, we propose a new approach that combines the technique of particle swarm optimization (PSO) with PRM. By sharing the information of free space with the sampling points which were originally deployed in the area obstacles, these sampling points can explore the free area in the later move and thus increase connectivity of the undirected graph without increasing the total sampling time. Based on the simulation results, the proposed method improves both the utilization rate of sampling points and the success rate of narrow passages path planning.
Multimodal Fusion-based Swin Transformer for Facial Recognition Micro-Expression Recognition

Micro-expression recognition is the domain of vigorous computational vision research, which up against significant challenges stems from micro-expressions being spontaneous, brief and faint facial muscle movements. The paper presents a very novel method of Multimodal fusion micro-expression recognition using a visual transformer, which is not commonly used for micro-expression recognition. As compared to convolutional neural networks, transformers are widely thought to require more data. Then, we choose similar expression datasets to pre-training the model, while increasing the number of datasets. The results of the validation and evaluation of the model conducted with the CASME II, MMEW and SMIC datasets yielded state-of-the-art performance in terms of average accuracy of 81.50%, 82.97%, and 79.99%, respectively. When using Score-CAM to obtain the facial expression activation heat map, it is obvious that our model matches well with the expression action units. The proposed model obtains more promising recognition results than many other recognition methods.
The role of optimization in some recent advances in data-driven decision-making

Abstract Data-driven decision-making has garnered growing interest as a result of the increasing availability of data in recent years. With that growth many opportunities and challenges have sprung up in the areas of predictive and prescriptive analytics. Often, optimization can play an important role in tackling these issues. In this paper, we review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions that aim to advance both predictive and prescriptive models. First, we describe how we can optimally estimate clustered models that result in improved predictions. Next, we consider how we can optimize over objective functions that arise from tree ensemble models in order to obtain better prescriptions. Finally, we discuss how we can learn optimal solutions directly from the data allowing for prescriptions without the need for predictions. For all these new methods, we stress the need for good performance but also the scalability to large heterogeneous datasets.
Interactive Application of Data Glove Based on Emotion Recognition and Judgment System

In this paper, the interactive application of data gloves based on emotion recognition and judgment system is investigated. A system of emotion recognition and judgment is established based on the set of optimal features of physiological signals, and then a data glove with multi-channel data transmission based on the recognition of hand posture and emotion is constructed. Finally, the system of virtual hand control and a manipulator driven by emotion is built. Five subjects were selected for the test of the above systems. The test results show that the virtual hand and manipulator can be simultaneously controlled by the data glove. In the case that the subjects do not make any hand gesture change, the system can directly control the gesture of the virtual hand by reading the physiological signal of the subject, at which point the gesture control and emotion control can be carried out at the same time. In the test of the manipulator driven by emotion, only the results driven by two emotional trends achieve the desired purpose.
Reinforcement Learning Based Multipath QUIC Scheduler for Multimedia Streaming

With the recent advances in computing devices such as smartphones and laptops, most devices are equipped with multiple network interfaces such as cellular, Wi-Fi, and Ethernet. Multipath TCP (MPTCP) has been the de facto standard for utilizing multipaths, and Multipath QUIC (MPQUIC), which is an extension of the Quick UDP Internet Connections (QUIC) protocol, has become a promising replacement due to its various advantages. The multipath scheduler, which determines the path to which each packet should be transmitted, is a key function that affects the multipath transport performance. For example, the default minRTT scheduler typically achieves good throughput, while the redundant scheduler gains low latency. While the legacy schedulers may generally give a desirable performance in some environments, however, each application renders different requirements. For example, Web applications target low latency, while video streaming applications require low jitter and high video quality. In this paper, we propose a novel MPQUIC scheduler based on deep reinforcement learning using the Deep Q-Network (DQN) that enhances the quality of multimedia streaming. Our proposal first takes into account both delay and throughput as a reward for reinforcement learning to achieve a low video chunk download time. Second, we propose a chunk manager that informs the scheduler of the video chunk information, and we also tune the learning parameters to explore new random actions adequately. Finally, we implement our new scheduler on the Linux kernel and give results using the Mininet experiments. The evaluation results show that our proposal outperforms legacy schedulers by at least 20%.
Energy-Efficient Active Federated Learning on Non-IID Data

Distributed learning has gained much interest recently due to its ability to exploit the distributed resources, at end users and network edges, to cooperatively train a global model. However, the performance of such schemes is undermined by practical constraints, such as non-uniformly distributed data across participating users. This challenge is especially prevalent in several practical systems, where the acquired data is collected from diverse locations/users with various conditions. Thus, this paper proposes a novel active federated learning framework that aims at reducing the effects of non-Independent and Identically Distributed (non-IID) data by integrating the idea of Active learning within Federated Learning (FL) framework. Indeed, our proposed solution could significantly improve the performance of FL in terms of the obtained accuracy and convergence time. The proposed solution is tested using two real-world datasets, where it could reduce the convergence time by 61% and increase the classification accuracy by 4% using MNIST dataset, while improving the accuracy by 19.4% for Heartbeat dataset.
Path Planning of Mobile Robot Based on Improved TD3 Algorithm

An improved TD3 algorithm is studied for the low success rate and slow learning speed of TD3(Twin Delayed Deep Deterministic Policy Gradients) algorithm in mobile robot path planning. Prioritized experience replay is added and dynamic delay update strategy is designed which can reduce the impact of value estimation errors to improve the success rate of path planning, and reduce training time. Based on the ROS melodic operating system and the Gazebo simulation software, the Turtlebot3 robot model and simulation experimental environment are established. The effectiveness of the improved TD3 algorithm is verified by comparative experiments. For the path planning task of the mobile robot, the simulation results show that compared with TD3 algorithm, the success rate of the improved TD3 algorithm is increased by 15.6%, and the training navigation time is shortened by 20%. It indicates that the improved TD3 algorithm achieves better performance in the path planning of mobile robot with continuous action space.
Unifying multi-associations through hypergraph for bundle recommendation

Bundle recommendation, which seeks to recommend a group of items to users, is widely used in real-world applications. Despite the success of current bundle recommendation approaches, there are still significant challenges: (1) Multiple associations (e.g., user-bundle interactions, bundle-item affiliations, etc.) in bundle recommendation. (2) People's interactions with a single item and a bundle follow different patterns, e.g., users can quickly decide whether to purchase an item, but hard to determine with a bundle regardless of what items are included. (3) The data sparsity of user-bundle historical interactions. This paper proposes a Unified Hypergraph framework for Bundle Recommendation (UHBR) to tackle the aforementioned challenges. Specifically, UHBR first unifies multiple associations among users, bundles, and items into hypergraph, a more flexible and scalable data graph structure. Second, this hypergraph architecture allows both direct and indirect user-bundle relationships through items to be efficiently and comprehensively represented as hyperedges. Third, we leverage the potential of indirect association to diminish the impact of user-bundle interaction scarcity. Experimental results on two real-world datasets show that UHBR outperforms the state-of-the-art baselines by 15.9% on Recall and 19.8% on NDCG. Experiments further indicate that UHBR can alleviate data sparsity dilemma and has the highest efficiency.
Fully Trainable Gaussian Derivative Convolutional Layer

The Gaussian kernel and its derivatives have already been employed for Convolutional Neural Networks in several previous works. Most of these papers proposed to compute filters by linearly combining one or several bases of fixed or slightly trainable Gaussian kernels with or without their derivatives. In this article, we propose a high-level configurable layer based on anisotropic, oriented and shifted Gaussian derivative kernels which generalize notions encountered in previous related works while keeping their main advantage. The results show that the proposed layer has competitive performance compared to previous works and that it can be successfully included in common deep architectures such as VGG16 for image classification and U-net for image segmentation.
Scene Text Detection Based on Two-Branch Feature Extraction

Scene text detection refers to locating text regions in a scene image and marking them out with text boxes. With the rapid development of the mobile Internet and the increasing popularity of mobile terminal devices such as smartphones, the research on scene text detection technology has been highly valued and widely applied. In recent years, with the rise of deep learning represented by convolutional neural networks, research on scene text detection has made new developments. However, scene text detection is still a very challenging task due to the following two factors. Firstly, images in natural scenes often have complex backgrounds, which can easily interfere with the detection process. Secondly, the text in natural scenes is very diverse, with horizontal, skewed, straight, and curved text, all of which may be present in the same scene. As convolutional neural networks extract features, the convolutional layer with limited perceptual field cannot model the global semantic information well. Therefore, this paper further proposes a scene text detection algorithm based on dual-branch feature extraction. This paper enlarges the receptive field by means of a residual correction branch (RCB), to obtain contextual information with a larger receptive field. At the same time, in order to improve the efficiency of using the features, a two-branch attentional feature fusion (TB-AFF) module is proposed based on FPN, to combine global and local attention to pinpoint text regions, enhance the sensitivity of the network to text regions, and accurately detect the text location in natural scenes. In this paper, several sets of comparative experiments were conducted and compared with the current mainstream text detection methods, all of which achieved better results, thus verifying the effectiveness of the improved proposed method.
A novel content-selected image encryption algorithm based on the LS chaotic model

Many holistic image encryption schemes have been proposed in recent decades that achieve a certain security level. However, considering the actual application environment, sometimes it is not necessary to encrypt the whole image, but rather a selected specific content of the image. Thus, this paper proposes a chaos-based image encryption algorithm for specific contents with non-regular size. First, we construct a novel LS chaos model that combines the Sine map and Logistic map and then improve its chaotic complexity by perturbing control parameters and introducing delayed states. Second, we obtain the coordinates of each pixel of specific image content using the PSPNet semantic segmentation model trained on the Cityscapes dataset and the Faster-RCNN target detection model trained on the MS-COCO dataset. Finally, the selected pixels are encrypted using a proposed non-regular size encryption algorithm. Several simulation experimental results prove that this new LS chaos model exhibits superior chaotic properties and generates complex pseudo-random sequences. Furthermore, our chaotic encryption algorithm effectively implements image encryption for specific content and shows excellent encryption performance, indicating that our encryption algorithm is highly competitive in terms of security capability.