Reviewerly/exHarmony huggingface dataset:
Yoga Pose Detection Using Posenet and k-NN

Yoga offers a wide range of asanas, and the angle between body parts plays an important role here. This project carries a non-profit system that strives to develop core muscles using yoga-like poses. While practicing yoga asanas virtually, the proposed technique perfectly detects the human position. To contemplate the dissension of the angle formed with original values, the cosine similarity technique is applied. Multiple dimensions must be addressed since crucial angles are made up of a critical combination of angles. This system detects the difference between the actual and target positions and corrects the user by delivering real-time image output and necessary instructions to correct the identified pose. Human poses estimation is utilized in this research to estimate an individual&#x0027;s Yoga position using computer vision techniques and Open pose (open-source library). In most circumstances, the suggested method retains high accuracy while achieving real-time speed. The proposed model was trained with 90&#x0025; of data and tested with 10<sup>&#x0025;</sup> of same with real-time testing, resulting 94 &#x0025; of accuracy.
Next-Generation Capabilities in Trusted Research Environments: Interview Study

A Trusted Research Environment (TRE; also known as a Safe Haven) is an environment supported by trained staff and agreed processes (principles and standards), providing access to data for research while protecting patient confidentiality. Accessing sensitive data without compromising the privacy and security of the data is a complex process.This paper presents the security measures, administrative procedures, and technical approaches adopted by TREs.We contacted 73 TRE operators, 22 (30%) of whom, in the United Kingdom and internationally, agreed to be interviewed remotely under a nondisclosure agreement and to complete a questionnaire about their TRE.We observed many similar processes and standards that TREs follow to adhere to the Seven Safes principles. The security processes and TRE capabilities for supporting observational studies using classical statistical methods were mature, and the requirements were well understood. However, we identified limitations in the security measures and capabilities of TREs to support "next-generation" requirements such as wide ranges of data types, ability to develop artificial intelligence algorithms and software within the environment, handling of big data, and timely import and export of data.We found a lack of software or other automation tools to support the community and limited knowledge of how to meet the next-generation requirements from the research community. Disclosure control for exporting artificial intelligence algorithms and software was found to be particularly challenging, and there is a clear need for additional controls to support this capability within TREs.
A superconducting adiabatic neuron in a quantum regime

We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations. The obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function.
Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People's Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs. Dataset: https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.
YOLOX on Embedded Device With CCTV &amp; TensorRT for Intelligent Multicategories Garbage Identification and Classification

Today’s garbage classification is not fully automated and mostly relies on manual labor. In order to efficiently classify garbage and increase resource re-usability, society needs a modern solution. The development of artificial intelligence, especially in object detection, provides an excellent opportunity to develop real-time accurate garbage detection. This project is about real-time garbage detection using YOLOX and is used to detect seventeen categories of garbage. After the camera detects the garbage, it will automatically identify and analyze the type and specific the position of the garbage, which will be automatically picked up by a mechanical jaws. The datasets used in this paper consists of 5000 images with label. With the officially supported TensorRT optimization and acceleration technology, the model can be used in low-cost embedded devices and can detect garbage in near real-time without the need for a stable Internet connection. Our evaluation and comparison of these models includes some key metrics such as mean accuracy (mAP), inference time per frame, floating point operations(FLOPs), and the number of parameters of the model. The test results show that YOLOX’s mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5:0.95</sub> can exceed 97% and the inference speed can exceed 32 fps. The performance far exceeds that of other YOLO series, with high accuracy and speed in complex environments, making it highly applicable to today’s garbage classification needs.
Improved 3D Object Detector Under Snowfall Weather Condition Based on LiDAR Point Cloud

LiDAR sensors are now used to supplement structure information and depth information for 3D object detection in automated driving. In adverse weathers, however, LiDAR tends to collect many noisy points in rainy or snowy days, which may disturb the results of object detection. In order to enhance the performance of the detector, we improve existing LiDAR-only 3D object detectors from two aspects under real snow weather condition. Firstly, double-attention block including point-wise attention and channel attention is applied to reweight the input feature of stacked pillars for crucial information extraction. Secondly, a lightweight and effective global context based pillar feature refinement extraction block is employed to capture long-range contextual information. It aims to filter local noisy information in the feature map, especially for the data collected in adverse weather conditions. Moreover, most of the previous works tend to focus on dataset under normal weather condition, so driving scenarios in adverse weather will bring challenges to the generalization of the model. Hence, to adapt our network to diverse domains better, we design a maximum mean discrepancy (MMD) block to get the distribution of domain feature representations as well as calculate the MMD loss in training process. Accordingly, the distribution discrepancy of two domains is narrowed. The performance evaluated on Canadian Adverse Driving Condition (CADC) Dataset collected in snowfall weather condition and KITTI dataset verifies the improvement of our approach. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jiajia0408/i3detector_snowfall</uri> .
A Node-Level PathGraph-Based Bearing Remaining Useful Life Prediction Method

Graph deep learning-based prognostic methods have been successfully applied in bearing remaining useful life (RUL) prediction, as graph represents spatial and temporal dependencies of signals. However, graph data-driven prediction methods using single-sensor data are still insufficiently studied. And the graph construction is not interpretable, where the physical meaning of edges is unclear. To overcome these limitations, a node-level PathGraph-based bearing RUL prediction method is proposed, where a Chebyshev graph convolutional network (ChebCGN) with bi-directional long short-term memory network (BiLSTM) is designed. The node-level PathGraph is constructed to represent the relationships among the time-discrete signals, where edges denote the chronological order and nodes represent signals. After that, graph feature learning ability of ChebGCN-LSTM is enhanced by inputting different chronological PathGraphs related to bearings&#x2019; states. In ChebGCN-LSTM, the BiLSTM captures the temporal information, overcoming the limitation of ChebGCN that ignored global temporal dependencies of signals. The constructed PathGraphs are input to ChebGCN-LSTM simultaneously to realize RUL prediction. Experimental results on case studies verify the effectiveness of the proposed bearing RUL prediction method.
RBDF: Reciprocal Bidirectional Framework for Visible Infrared Person Reidentification

Visible infrared person reidentification (VI-REID) plays a critical role in night-time surveillance applications. Most methods attempt to reduce the cross-modality gap by extracting the modality-shared features. However, they neglect the distinct image-level discrepancies among heterogeneous pedestrian images. In this article, we propose a reciprocal bidirectional framework (RBDF) to achieve modality unification before discriminative feature learning. The bidirectional image translation subnetworks can learn two opposite mappings between visible and infrared modality. Particularly, we investigate the characteristics of the latent space and design a novel associated loss to pull close the distribution between the intermediate representations of two mappings. Mutual interaction between two opposite mappings helps the network generate heterogeneous images that have high similarity with the real images. Hence, the concatenation of original and generated images can eliminate the modality gap. During the feature learning procedure, the attention mechanism-based feature embedding network can learn more discriminative representations with the identity classification and feature metric learning. Experimental results indicate that our method achieves state-of-the-art performance. For instance, we achieve 54.41% mAP and 57.66% rank-1 accuracy on SYSU-MM01 dataset, outperforming the existing works by a large margin.
Encrypted dynamic control with unlimited operating time via FIR filters

Encrypted control enables confidential controller evaluations in cloud-based or networked control systems. From a technical point of view, an encrypted controller is a modified control algorithm that is capable of computing encrypted control actions based on encrypted system outputs. Unsurprisingly, encrypted implementations of controllers using, e.g., homomorphic cryptosystems entail new design challenges. For instance, in order to avoid overflow or high computational loads, only a finite number of operations should be carried out on encrypted data. Clearly, this guideline is hard to satisfy for dynamic controllers due to their recursive nature. To enable an unlimited operating time, existing implementations thus rely on external "refreshments" of the controller state, internal refreshments using bootstrapping, or recurring controller resets.We show in this paper that simple FIR filter-based controllers allow to overcome many drawbacks of the existing approaches. In fact, since FIR filters consider only a finite amount of the most recent input data, the recursion issue is immediately solved and controller refreshments or resets are no longer required. Moreover, well-designed FIR filters are often less complex than and equally effective as IIR controllers.
Digital Lean Operations: Smart Automation and Artificial Intelligence in Financial Services

The financial services industry covers banks, insurance companies and investment managers, as well as transaction or message processing companies. To streamline billions of daily transactions, lean principles and operational excellence programs have found their way to financial service firms. Today, the resulting cost and risk reductions are further enhanced by embracing new digital technologies–such as those falling under the umbrella of Industry 4.0. These digital technologies stimulate a new wave of operational efficiency improvements by making processes more automated, autonomous, and smart. We provide a framework to evaluate the transition towards digital, autonomous and smart operations in financial services. We report our findings from the digital operations journey of Euroclear, a service provider of settlements for securities transactions, on their quest for increased automation and autonomy. We also shed light on the potential of artificial intelligence in financial services. Data-driven solutions may support financial service firms from purely descriptive models and methods with strong predictive power, towards prescriptive decision-making algorithms.
An Overview of Blockchain and 5G Networks

The 5G wireless networks are potentially revolutionizing future technologies. The 5G technologies are expected to foresee demands of diverse vertical applications with diverse requirements including high traffic volume, massive connectivity, high quality of service, and low latency. To fulfill such requirements in 5G and beyond, new emerging technologies such as SDN, NFV, MEC, and CC are being deployed. However, these technologies raise several issues regarding transparency, decentralization, and reliability. Furthermore, 5G networks are expected to connect many heterogeneous devices and machines that will raise several security concerns regarding users’ confidentiality, data privacy, and trustworthiness. To work seamlessly and securely in such scenarios, future 5G networks need to deploy smarter and more efficient security functions. Motivated by the aforementioned issues, blockchain was proposed by researchers to overcome 5G issues because of its capacities to ensure transparency, data reliability, trustworthiness, immutability in a distributed environment. Indeed, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized technologies. In this chapter, we discuss the integration of the blockchain with 5G networks and beyond. We then present how blockchain applications in 5G networks and beyond could facilitate enabling various services at the edge and the core.
Unsupervised Segmentation of Smart Home Logs for Human Habit Discovery

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.
A Blockchain-Based Self-Tallying Voting Protocol With Maximum Voter Privacy

The development of secure and reliable voting mechanism has attracted more and more attention from academia and industry. Many researchers are trying to design a secure and reliable voting system. However, many existing blockchain-based proposals have more or less problems, such as limited number of participants, weak fault-tolerant mechanisms and inadequate privacy protection, which makes it difficult to apply them to the real world to solve the above problems. Therefore, in this paper, we proposed a blockchain-based self-tallying voting protocol to achieve high available, secure and anonymous voting. More specifically, we adopt threshold secret sharing to deal with abstention problem (this is a difficult problem to solve in self-tallying voting systems). We adopt homomorphic encryption and zero-knowledge proof to achieve anonymity and verifiability of encrypted data. Through the analysis and performance testing, and comparison with existing similar proposals, the results show that our scheme has advantages in security and robustness, and the performance test shows that our scheme has good practicability and scalability.
Continual Learning with Bayesian Model Based on a Fixed Pre-trained Feature Extractor

Current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g. with Gaussian mixture models, and naturally kept from forgetting in continual learning. Experiments on two skin image sets showed that the proposed approach outperforms state-of-the-art approaches which even keep some images of old classes during continual learning of new classes.
Autism and the making of emotion AI: Disability as resource for surveillance capitalism

This article uncovers how computers learned to recognize emotion from a neurological difference often conceived of as marked by a difficulty in recognizing it at all: autism. Developers of facial emotion recognition technologies have repeatedly mobilized autism to produce new kinds of knowledge at the interface of machine learning and human feeling, deploying it as a charismatic use-case, as a source for conceptual schemata for how emotion should be made computable, and as a testbed for development. The entanglement of emotion recognition and autism research shows how disability has been transformed into a rhetorical, conceptual, and material resource for the expansion of surveillance capitalism. This history has also underwritten a larger reconceptualization of emotional data, from a neglected variable in human–computer interaction to an exploitable corporate asset, a transformation that has allowed platform users’ emotional lives to be mined for new forms of knowledge, value, and power.
Hybrid CNN-SVM Inference Accelerator on FPGA Using HLS

Convolution neural networks (CNN), support vector machine (SVM) and hybrid CNN-SVM algorithms are widely applied in many fields, including image processing and fault diagnosis. Although many dedicated FPGA accelerators have been proposed for specific networks, such as CNN or SVM, few of them have focused on CNN-SVM. Furthermore, the existing accelerators do not support CNN-SVM, which limits their application scenarios. In this work, we propose a hybrid CNN-SVM accelerator on FPGA. This accelerator utilizes a novel hardware-reuse architecture and unique computation mapping strategy to implement different calculation modes in CNN-SVM so that it can realize resource-efficient acceleration of the hybrid algorithm. In addition, we propose a universal deployment methodology to automatically select accelerator design parameters according to the target platform and algorithm. The experimental results on ZYNQ-7020 show that our implementation can efficiently map CNN-SVM onto FPGA, and the performance is competitive with other state-of-the-art works.
LSR: Lightening super-resolution deep network for low-light image enhancement

In long-range shooting during low-light, we can only obtain a Low-Resolution (LR) image with poor visibility due to the limitation of physical devices. To get high-quality distant images, optical lenses are the optimal choice, but they are quite expensive and bulky. Low-light enhancement and Super-Resolution (SR) are the necessity of mobile phones and surveillance cameras and have wide applications in video surveillance, remote sensing, and night photography. Usually, the images that are taken from long-range in low-light suffer the loss of details not only due to low photon count but also due to low Signal-to-Noise Ratio (SNR), which makes it a highly ill-posed problem. Therefore, to resolve these two problems simultaneously, we propose a Lightening Super-Resolution (LSR) deep network. The proposed network uses the back-projection to learn the enhanced and dark features in low-resolution space iteratively and up-sample the enhanced features at the last stage of the network to get the final enhanced and high-resolution image. In particular, to train the network, the low-light images of the publicly available LOw Light (LOL) dataset are down-sampled using bicubic interpolation, and ground truth images are used as enhanced high-resolution images. For a fair comparison, a series of already available SR networks have been trained on the mentioned dataset, and their performances are compared. The promising results open up many opportunities for future work.
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis

Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions. Existing cross-domain sentiment classification methods need to distinguish pivots, i.e., the domain-shared sentiment words, and non-pivots, i.e., the domain-specific sentiment words, for excellent adaptation performance. In this paper, we first design a Category Attention Network (CAN), and then propose a model named CAN-CNN to integrate CAN and a Convolutional Neural Network (CNN). On the one hand, the model regards pivots and non-pivots as unified category attribute words and can automatically capture them to improve the domain adaptation performance; on the other hand, the model makes an attempt at interpretability to learn the transferred category attribute words. Specifically, the optimization objective of our model has three different components: 1) the supervised classification loss; 2) the distributions loss of category feature weights; 3) the domain invariance loss. Finally, the proposed model is evaluated on three public sentiment analysis datasets and the results demonstrate that CAN-CNN can outperform other various baseline methods.