Reviewerly/exHarmony huggingface dataset:
HiGCIN: Hierarchical Graph-Based Cross Inference Network for Group Activity Recognition

Group activity recognition (GAR) is a challenging task aimed at recognizing the behavior of a group of people. It is a complex inference process in which visual cues collected from individuals are integrated into the final prediction, being aware of the interaction between them. This paper goes one step further beyond the existing approaches by designing a Hierarchical Graph-based Cross Inference Network (HiGCIN), in which three levels of information, i.e., the body-region level, person level, and group-activity level, are constructed, learned, and inferred in an end-to-end manner. Primarily, we present a generic Cross Inference Block (CIB), which is able to concurrently capture the latent spatiotemporal dependencies among body regions and persons. Based on the CIB, two modules are designed to extract and refine features for group activities at each level. Experiments on two popular benchmarks verify the effectiveness of our approach, particularly in the ability to infer with multilevel visual cues. In addition, training our approach does not require individual action labels to be provided, which greatly reduces the amount of labor required in data annotation.
Flow-edge Guided Video Completion

We present a new flow-based video completion algorithm. Previous flow completion methods are often unable to retain the sharpness of motion boundaries. Our method first extracts and completes motion edges, and then uses them to guide piecewise-smooth flow completion with sharp edges. Existing methods propagate colors among local flow connections between adjacent frames. However, not all missing regions in a video can be reached in this way because the motion boundaries form impenetrable barriers. Our method alleviates this problem by introducing non-local flow connections to temporally distant frames, enabling propagating video content over motion boundaries. We validate our approach on the DAVIS dataset. Both visual and quantitative results show that our method compares favorably against the state-of-the-art algorithms.
A General Framework for Decentralized Optimization With First-Order Methods

Decentralized optimization to minimize a finite sum of functions, distributed over a network of nodes, has been a significant area within control and signal-processing research due to its natural relevance to optimal control and signal estimation problems. More recently, the emergence of sophisticated computing and large-scale data science needs have led to a resurgence of activity in this area. In this article, we discuss decentralized first-order gradient methods, which have found tremendous success in control, signal processing, and machine learning problems, where such methods, due to their simplicity, serve as the first method of choice for many complex inference and training tasks. In particular, we provide a general framework of decentralized first-order methods that is applicable to directed and undirected communication networks alike and show that much of the existing work on optimization and consensus can be related explicitly to this framework. We further extend the discussion to decentralized stochastic first-order methods that rely on stochastic gradients at each node and describe how local variance reduction schemes, previously shown to have promise in the centralized settings, are able to improve the performance of decentralized methods when combined with what is known as gradient tracking. We motivate and demonstrate the effectiveness of the corresponding methods in the context of machine learning and signal-processing problems that arise in decentralized environments.
Deep Partial Multi-View Learning

Although multi-view learning has made significant progress over the past few decades, it is still challenging due to the difficulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and flexibly take advantage of multiple partial views. We first provide a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the learned latent representations. For completeness, the task of learning latent multi-view representation is specifically translated to a degradation process by mimicking data transmission, such that the optimal tradeoff between consistency and complementarity across different views can be implicitly achieved. Equipped with adversarial strategy, our model stably imputes missing views, encoding information from all views for each sample to be encoded into latent representation to further enhance the completeness. Furthermore, a nonparametric classification loss is introduced to produce structured representations and prevent overfitting, which endows the algorithm with promising generalization under view-missing cases. Extensive experimental results validate the effectiveness of our algorithm over existing state of the arts for classification, representation learning and data imputation.
Autonomous skill discovery with quality-diversity and unsupervised descriptors

Quality-Diversity optimization is a new family of optimization algorithms that, instead of searching for a single optimal solution to solving a task, searches for a large collection of solutions that all solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as such a diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whether two solutions are different or similar. The choice of a behavioral descriptor is crucial, as it completely changes the solution types that the algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversity algorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the range of their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot can autonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to hand-crafted solutions that uses domain knowledge and significantly more diverse than when using existing unsupervised methods.
Quantum Blockchain Using Entanglement in Time

We propose a conceptual design for a quantum blockchain. Our method involves encoding the blockchain into a temporal GHZ (Greenberger–Horne–Zeilinger) state of photons that do not simultaneously coexist. It is shown that the entanglement in time, as opposed to an entanglement in space, provides the crucial quantum advantage. All the subcomponents of this system have already been shown to be experimentally realized. Furthermore, our encoding procedure can be interpreted as nonclassically influencing the past.
Social Distancing Detection with Deep Learning Model

The paper presents a methodology for social distancing detection using deep learning to evaluate the distance between people to mitigate the impact of this coronavirus pandemic.The detection tool was developed to alert people to maintain a safe distance with each other by evaluating a video feed.The video frame from the camera was used as input, and the open-source object detection pre-trained model based on the YOLOv3 algorithm was employed for pedestrian detection.Later, the video frame was transformed into top-down view for distance measurement from the 2D plane.The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red frame and red line.The proposed method was validated on a pre-recorded video of pedestrians walking on the street.The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video.The developed technique can be further developed as a detection tool in realtime application.
Laser-seeding Attack in Quantum Key Distribution

For effective quantum communication, the security of the photon source is particularly important in the era of measurement-device-independent quantum key distribution (MDI-QKD) and twin-field QKD (TF-QKD). In practice, the security of the source can still be cracked by an adversary. This study experimentally demonstrates that a practical source based on a semiconductor laser diode is vulnerable to a laser-seeding attack, in which light injected from the communication line into the laser yields increased intensities of the prepared states. Theory shows that the unnoticed intensity increase compromises the security of the prepare-and-measure decoy-state BB84 and MDI-QKD protocols.
Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods.
Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer

In industrial applications, anomaly detectors are trained to raise alarms when measured samples deviate from the training data distribution. The samples used to train the model should, therefore, be sufficient in quantity and representative of all healthy operating conditions. However, for systems subject to changing operating conditions, acquiring such comprehensive datasets requires a long collection period. To train more robust anomaly detectors, we propose a new framework to perform unsupervised transfer learning (UTL) for one-class classification problems. It differs, thereby, from other applications of UTL in the literature which usually aim at finding a common structure between the datasets to perform either clustering or dimensionality reduction. The task of transferring and combining complementary training data in a completely unsupervised way has not been studied yet. The proposed methodology detects anomalies in operating conditions only experienced by other units in a fleet. We propose the use of adversarial deep learning to ensure the alignment of the different units’ distributions and introduce a new loss, inspired by a dimensionality reduction tool, to enforce the conservation of the inherent variability of each dataset. We use a state-of-the-art once-class approach to detect the anomalies. We demonstrate the benefit of the proposed framework using three open source datasets.
Deep-Learning-Empowered Digital Forensics for Edge Consumer Electronics in 5G HetNets

The upcoming 5G heterogeneous networks (HetNets) have attracted much attention worldwide. Large amounts of high-velocity data can be transported by using the bandwidth spectrum of HetNets, yielding both great benefits and several concerning issues. In particular, great harm to our community could occur if the main visual information channels, such as images and videos, are maliciously attacked and uploaded to the Internet, where they can be spread quickly. Therefore, we propose a novel framework as a digital forensics tool to protect end users. It is built based on deep learning and can realize the detection of attacks via classification. Compared with the conventional methods and justified by our experiments, the data collection efficiency, robustness, and detection performance of the proposed model are all refined. In addition, assisted by 5G HetNets, our proposed framework makes it possible to provide high-quality real-time forensics services on edge consumer devices such as cell phone and laptops, which brings colossal practical value. Some discussions are also carried out to outline potential future threats.
A Procedure for Tracing Supply Chains for Perishable Food Based on Blockchain, Machine Learning and Fuzzy Logic

One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve the anti-counterfeiting area’s quality. Many food manufacturing systems have a low level of readability, scalability, and data accuracy. Similarly, this process is complicated in the supply chain and needs a lot of time for processing. The blockchain system creates a new ontology in the traceability system supply chain to deal with these issues. In this paper, a blockchain machine learning-based food traceability system (BMLFTS) is proposed in order to combine the new extension in blockchain, Machine Learning technology (ML), and fuzzy logic traceability system that is based on the shelf life management system for manipulating perishable food. The blockchain technology in the proposed system has been developed in order to address light-weight, evaporation, warehouse transactions, or shipping time. The blockchain data flow is designed to show the extension of ML at the level of food traceability. Finally, reliable and accurate data are used in a supply chain to improve shelf life.
Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Background Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. Objective This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. Methods We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. Results The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. Conclusions Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.
A Novel CNN-Based Detector for Ship Detection Based on Rotatable Bounding Box in SAR Images

Thanks to the excellent feature representation capabilities of neural networks, deep learning-based methods perform far better than traditional methods on target detection tasks such as ship detection. Although various network models have been proposed for SAR ship detection such as DRBox-v1, DRBox-v2, and MSR2N, there are still some problems such as mismatch of feature scale, contradictions between different learning tasks, and unbalanced distribution of positive samples, which have not been mentioned in these studies. In this article, an improved one-stage object detection framework based on RetinaNet and rotatable bounding box (RBox), which is referred as R-RetinaNet, is proposed to solve the above problems. The main improvements of R-RetinaNet as well as the contributions of this article are threefold. First, a scale calibration method is proposed to align the scale distribution of the output backbone feature map with the scale distribution of the targets. Second, a feature fusion network based on task-wise attention feature pyramid network is designed to decouple the feature optimization process of different tasks, which alleviates the conflict between different learning goals. Finally, an adaptive intersection over union (IoU) threshold training method is proposed for RBox-based model to correct the unbalanced distribution of positive samples caused by the fixed IoU threshold on RBox. Experimental results show that our method obtains 13.26%, 9.49%, 8.92%, and 4.55% gains in average precision under an IoU threshold of 0.5 on the public SAR ship detection dataset compared with four state-of-the-art RBox-based methods, respectively.
A chaotic image encryption algorithm based on random dynamic mixing

This paper proposes a new chaotic system, combined with the dynamic mixing of pixel values, pixel bits, and binary bits encryption algorithms, which are used together in image encryption. The whole process is as follows. First, the original plain text image is converted into an initial key through the SHA-512 algorithm and related operations, then the initial key is applied to the proposed delayed feedback dynamic mixed linear-nonlinear coupled mapping lattices (DFDMLNCML) to obtain the chaotic matrix required for encryption, next, perform the encryption operation of mixed pixel values, pixel bits and binary bits, scrambling and diffusion are done together to improve the security of the algorithm. Finally, the average NPCR, average UACI is 99.6075, 33.4795, respectively, experimental results and various security analyses show that the performance of the system and algorithm is good, and it can resist ordinary attacks, such as selecting plaintext attacks, cropping attacks, and noise attacks.
Pretraining boosts out-of-domain robustness for pose estimation

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe the generalization ability with three architecture classes (MobileNetV2s, ResNets, and EfficientNets) for pose estimation. We developed a dataset of 30 horses that allowed for both "within-domain" and "out-of-domain" (unseen horse) benchmarking-this is a crucial test for robustness that current human pose estimation benchmarks do not directly address. We show that better ImageNet-performing architectures perform better on both within- and out-of-domain data if they are first pretrained on ImageNet. We additionally show that better ImageNet models generalize better across animal species. Furthermore, we introduce Horse-C, a new benchmark for common corruptions for pose estimation, and confirm that pretraining increases performance in this domain shift context as well. Overall, our results demonstrate that transfer learning is beneficial for out-of-domain robustness.
Effectiveness of VR Head Mounted Displays in Professional Training: A Systematic Review

Abstract Over the past decade, virtual reality (VR) has re-emerged as a popular technology trend. This is mainly due to the recent investments from technology companies that are improving VR systems while increasing consumer access and interest. Amongst many applications of VR, one area that is particularly promising is for pedagogy. The immersive, experiential learning offered by VR provides new training and learning opportunities driven by the latest versions of affordable, highly immersive and easy to use head mounted display (HMD) systems. VR has been tested as a tool for training across diverse settings with varying levels of success in the past. However, there is a lack of recent review studies that investigates the effectiveness, advantages, limitations, and feasibility of using VR HMDs in training. This review aims to investigate the extent to which VR applications are useful in training, specifically for professional skill and safety training contexts. In this paper, we present the results from a systematic review of the effectiveness of VR-based simulation training from the past 30 years. As a secondary aim, the methodological trends of application and practical challenges of implementing VR in training curriculum were also assessed. The results suggest that there is generally high acceptance amongst trainees for VR-based training regardless of the technology limitations, usability challenges and cybersickness. There is evidence that VR is useful for training cognitive skills, such as spatial memory, learning and remembering procedures and psychomotor skills. VR is also found to be a good alternative where on the job training is either impossible or unsafe to implement. However, many training effectiveness studies reviewed lack experimental robustness due to limited study participants and questionable assessment methods. These results map out the current known strengths and weaknesses of VR HMDs and provide insight into required future research areas as the new era of VR HMD’s evolve.