Reviewerly/exHarmony huggingface dataset:
Hand Pose Estimation from RGB Images Based on Deep Learning: A Survey

With the development of computing technique, the emergence of computers and their derivatives as the carrier of modern artificial intelligence has penetrated into people's daily life from all aspects, and the status of human-computer interaction has become increasingly prominent. As hand is the main operating tool of human beings, its position and orientation in space are crucial for many potential applications, such as the occasion of interacting with VR devices. An important process of hand gesture recognition is hand pose estimation. Still, there are many difficulties in 3D hand pose estimation from a single RGB image. The rise of big data, the emergence of neural networks and the iteration of high computing power equipment have led to the emergence of deep learning in vision fields, and the application of hand pose estimation has made great breakthroughs. This paper briefly introduces the methods of hand pose estimation from RGB images based on deep learning, summarizes the existing research results and makes an outlook on the future development trend of the field.
Cascading Failure Dynamics against Intentional Attack for Interdependent Industrial Internet of Things

The emerging Industrial Internet of Things (IIoT) provides industries with an opportunity to collect, aggregate, and analyze data from sensors, including motion control, machine-to-machine communication, predictive maintenance, smart energy grid, big data analysis, and other smart connected medical systems. The physical systems and the cyber systems are organically integrated, forming an interdependent IIoT. This system provides us with enormous advantages, but at the same time, it also introduces the main safety challenges in the design and operation phase. To exploit the security threats of IIoT systems, in this paper, we propose a novel security-by-design approach for interdependent IIoT environments across two different levels, namely, theory modeling and runtime simulation. Our method theoretically analyzes the cascading failure dynamics of the intentional attack network. Simultaneously, we verified the theoretical results through simulations and gave the risk factors that affect the system’s security to mitigate potential security attack threats. Besides, we prove its applicability through comparative simulation experiments to study application environments that rely on IIoT, which shows that our method helps identify risk factors and mitigate IIoT attacks’ mechanism.
Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.
Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization

Obtaining multiple series of magnetic resonance (MR) images with different contrasts is useful for accurate diagnosis of human spinal conditions. However, this can be time consuming and a burden on both the patient and the hospital. We propose a Bloch equation-based autoencoder regularization generative adversarial network (BlochGAN) to generate a fat saturation T2-weighted (T2 FS) image from T1-weighted (T1-w) and T2-weighted (T2-w) images of human spine. To achieve this, our approach was to utilize the relationship between the contrasts using Bloch equation since it is a fundamental principle of MR physics and serves as a physical basis of each contrasts. BlochGAN properly generated the target-contrast images using the autoencoder regularization based on the Bloch equation to identify the physical basis of the contrasts. BlochGAN consists of four sub-networks: an encoder, a decoder, a generator, and a discriminator. The encoder extracts features from the multi-contrast input images, and the generator creates target T2 FS images using the features extracted from the encoder. The discriminator assists network learning by providing adversarial loss, and the decoder reconstructs the input multi-contrast images and regularizes the learning process by providing reconstruction loss. The discriminator and the decoder are only used in the training process. Our results demonstrate that BlochGAN achieved quantitatively and qualitatively superior performance compared to conventional medical image synthesis methods in generating spine T2 FS images from T1-w, and T2-w images.
Retina-like Imaging and Its Applications: A Brief Review

The properties of the human eye retina, including space-variant resolution and gaze characters, provide many advantages for numerous applications that simultaneously require a large field of view, high resolution, and real-time performance. Therefore, retina-like mechanisms and sensors have received considerable attention in recent years. This paper provides a review of state-of-the-art retina-like imaging techniques and applications. First, we introduce the principle and implementing methods, including software and hardware, and describe the comparisons between them. Then, we present typical applications combined with retina-like imaging, including three-dimensional acquisition and reconstruction, target tracking, deep learning, and ghost imaging. Finally, the challenges and outlook are discussed to further study for practical use. The results are beneficial for better understanding retina-like imaging.
Knowledge Graph-Based Image Recognition Transfer Learning Method for On-Orbit Service Manipulation

Visual perception provides state information of current manipulation scene for control system, which plays an important role in on-orbit service manipulation. With the development of deep learning, deep convolutional neural networks (CNNs) have achieved many successful applications in the field of visual perception. Deep CNNs are only effective for the application condition containing a large number of training data with the same distribution as the test data; however, real space images are difficult to obtain during large-scale training. Therefore, deep CNNs can not be directly adopted for image recognition in the task of on-orbit service manipulation. In order to solve the problem of few-shot learning mentioned above, this paper proposes a knowledge graph-based image recognition transfer learning method (KGTL), which learns from training dataset containing dense source domain data and sparse target domain data, and can be transferred to the test dataset containing large number of data collected from target domain. The average recognition precision of the proposed method is 80.5%, and the average recall is 83.5%, which is higher than that of ResNet50-FC; the average precision is 60.2%, and the average recall is 67.5%. The proposed method significantly improves the training efficiency of the network and the generalization performance of the model.
An intelligent approach for mining knowledge graphs of online news

Automatic extraction of background knowledge of news articles, published over the web is a complex task and less explored by knowledge-mining researchers. Suppose there is no source of background or related information available on editorial, in this case, the reader needs to search it, which is a time-consuming process of manually going over possibly an in-definite number of knowledge sources to check its authenticity. This paper aims to resolve this issue of fetching associated information regarding any text by identifying the entities in text and fetching the related entities. This work has proposed a model that can automate this process by generating a semantic network to build a Knowledge Graph on any chunk of textual content. It identifies the entities and their corresponding relations and further expands them to extract related entities from non-trivial knowledge sources. Extracted entities and relations are further converted into a node(s) and edge(s) in an environment where knowledge is represented in a graph format to generate a Knowledge Graph. The mining of such a Knowledge Graph is a complex task by acquiring and integrating different information from heterogeneous sources. The resulting Knowledge Graph would be significant to be used as a knowledge base for future news.
How Online Fraud Victims are Targeted in China: A Crime Script Analysis of Baidu Tieba C2C Fraud

E-commerce is the practice of purchasing, selling, transferring, and exchanging goods, services, or information through the Internet, a computer, and/or other devices. With the development of e-commerce markets, fraud is increasingly reported. This study uses a crime script analysis to examine how customer-to-customer (C2C) fraud occurs on China’s online platforms with a particular focus on the online marketplace Baidu Tieba and Tencent QQ, a tool that allows customers to make contact and negotiate deals and payment methods. The findings demonstrate that C2C fraud develops through pre-operation, operation, finalization, and exit stages, sharing commonalities with other online identity fraud processes. Implications for policy and interventions are also discussed in regard to e-commerce fraud in China and elsewhere.
Movie Review Prediction System by Real Time Analysis of Facial Expression

Movie review rating has become a huge demand for the current era due to the rapid growth of the movie industry. The current methods used for rating movie has failed to satisfy audience requirement due to fake reviews. As real-time facial expression recognition has encouraged more and more research. Therefore, a model has been developed by utilizing Machine Learning and Deep learning technologies to automatically review a movie or rate a movie based on the emotion of the spectators by initiating a real-time analysis.
BlockPerf: A Hybrid Blockchain Emulator/Simulator Framework

Blockchain is increasingly used for registering, authenticating and validating digital assets (financial assets, real estate, etc.) and transactions, governing interactions, recording data and managing identification among multiple parties in a trusted, decentralized, and secure manner.Today, a large variety of blockchain technologies is expanding in order to fulfill technical and non-technical needs and requirements.Within this context, determining and most importantly evaluating the characteristics/performance of a given blockchain platform is crucial for system designers before deploying it.A number of blockchain simulators have been proposed in the literature over the past few years, as reviewed in this paper, but are often limited in several respects (lack of extensibility, do not allow for evaluating all aspects of a blockchain. . .).This paper extends and improves a state-of-the-art simulator (BlockSim) into a new simulator called ''BlockPerf'' to overcome those limitations.Both simulators are compared based on a real-life (benchmarking) Bitcoin scenario, whose results show that BlockPerf provides more realistic results than BlockSim, improving by around ≈50% (in average) the outcomes.
A fusing framework of shortcut convolutional neural networks

Convolutional neural networks (CNNs) have proven to be very successful in learning task-specific computer vision features. To integrate features from different layers in standard CNNs, we present a fusing framework of shortcut convolutional neural networks (S-CNNs). This framework can fuse arbitrary scale features by adding weighted shortcut connections to the standard CNNs. Besides the framework, we propose a shortcut indicator (SI) of binary string to stand for a specific S-CNN shortcut style. Additionally, we design a learning algorithm for the proposed S-CNNs. Comprehensive experiments are conducted to compare its performances with standard CNNs on multiple benchmark datasets for different visual tasks. Empirical results show that if we choose an appropriate fusing style of shortcut connections with learnable weights, S-CNNs can perform better than standard CNNs regarding accuracy and stability in different activation functions and pooling schemes initializations, and occlusions. Moreover, S-CNNs are competitive with ResNets and can outperform GoogLeNet, DenseNets, Multi-scale CNN, and DeepID.
CompConv: A Compact Convolution Module for Efficient Feature Learning

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Further-more, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face representations susceptible to privacy leakage (b) it appears to contribute to bias in face recognition. However, existing bias mitigation approaches generally require end-to-end training and are unable to achieve high verification accuracy. Therefore, we present a descriptor-based adversarial de-biasing approach called ‘Protected Attribute Suppression System (PASS)’. PASS can be trained on top of descriptors obtained from any previously trained high-performing network to classify identities and simultaneously reduce encoding of sensitive attributes. This eliminates the need for end-to-end training. As a component of PASS, we present a novel discriminator training strategy that discourages a network from encoding protected attribute information. We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface. As a result, PASS descriptors outperform existing baselines in reducing gender and skintone bias on the IJB-C dataset, while maintaining a high verification accuracy.
Rapid Feedback Stabilization of Quantum Systems With Application to Preparation of Multiqubit Entangled States

For stochastic quantum systems with measurement feedback, this article proposes a rapid switching control scheme based on state space partition and realizes the rapid stabilization of an eigenstate of an observable operator. Meanwhile, we apply the proposed scheme to the preparation of typical entangled states in multiqubit systems. In view of the convergence obstacle caused by the symmetric structure of the state space, especially in the case with degenerate observable operators, we first partition the state space into a subset containing the target state and its complement to distinguish the target state from its antipodal points, and then design the corresponding control laws in these two subsets, respectively, by using different Lyapunov functions. The interaction Hamiltonians are also constructed to drive the system state to the desired subset first, and further to the target state. In particular, the control law designed in the undesired subset guarantees the strictly monotonic descent of the corresponding Lyapunov function, which makes the system trajectory switch between the two subsets at most twice and has the potential to speed up the convergence process. We also prove the stability of the closed-loop system with the proposed switching control law based on the stochastic Lyapunov stability theory. By applying the proposed switching control scheme to a three-qubit system, we achieve the preparation of a GHZ state and a W state.
Image Captioning Using Inception V3 Transfer Learning Model

As artificial intelligence has grown rapidly in recent years, picture captioning has attracted the interest of numerous experts, which has become a fascinating and challenging challenge. A critical component of scene analysis which combines machine vision and the natural languages of language processing capabilities is visual subtitles which automatically generate natural language interpretations based on image details. This paper utilizes different NLP strategies for perceiving and clarifying an image meaning in a natural language such as English. The proposed Inception V3 image caption generator model uses CNN (Coevolutionary Neural Networks) and LSTM (Long Short-Term Memory) units. The InceptionV3 model has been educated in 1000 different classes on an ImageNet dataset. The model was imported directly from the Keras module of applications. Remove from the InceptionV3model the last classification layer for the dimension (1343,) vector. The embedded matrix is used for vocabulary connections. A building matrix is the linear transformation of the original space with important relations into a real-life space. Image captions are commonly used and, for example, important in implementing interaction between humans and the computer.
An Efficient Video Steganography Method Based on HEVC

As one of the most popular communication media today, digital video is generally regarded as an ideal covert communication carrier. Therefore, video steganography technology has attracted the attention of researchers in the field of data hiding and has become one of the research hotspots in this field. This paper presents a HEVC video steganography method based on QDST coefficient. The secret message is embedded into the multi-coefficients of the selected 4 × 4 luminance QDST blocks to avert the intra-frame distortion drift. And the matrix encoding technology is used to reduce the modification of these embedded blocks. The experimental results show that the proposed algorithm can effectively increase the visual quality and get good embedding capacity.
Supervised-Learning-Based Intelligent Fault Diagnosis for Mechanical Equipment

Recently, anomaly detection for improving the productivity of machinery in industrial environments has drawn considerable attention.As large-scale data collection and processing are becoming easier owing to technological developments, data-based deep-learning technology is being developed to detect anomalies in mechanical equipment operation.This study proposes an ensemble model that combines stacked two-dimensional and one-dimensional convolutional neural networks (CNNs), residual long shortterm memory (LSTM), and LSTM based on supervised learning.The model, which is called the SCRLSTM model, can detect abnormal data generated by mechanical equipment.The proposed model can extract the spatial features of data using a CNN model and detect anomalous states in the time-series-based vibration datasets of machinery under various environments through residual LSTM.To verify this model, data augmentation was applied to the original time-series-based mechanical vibration dataset, which had unbalanced samples that lowered the performance of the abnormal anomaly detection model.In addition, an image-based analysis was performed by converting time-series-based raw-signal data to Mel-spectrogram images, thereby achieving better performance in the fault diagnosis system to which data augmentation was applied.The proposed SCRLSTM model shows better performance than other supervised-learning-based models on datasets having different lengths under various conditions.This indicates that the proposed anomaly detection model can be expected to improve the productivity of mechanical equipment in industrial settings.
Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges

With the widespread of blockchain technology, preserving the anonymity and confidentiality of transactions have become crucial. An enormous portion of blockchain research is dedicated to the design and development of privacy protocols but not much has been achieved for proper assessment of these solutions. To mitigate the gap, we have first comprehensively classified the existing solutions based on blockchain fundamental building blocks (i.e., smart contracts, cryptography, and hashing). Next, we investigated the evaluation criteria used for validating these techniques. The findings depict that the majority of privacy solutions are validated based on computing resources i.e., memory, time, storage, throughput, etc., only, which is not sufficient. Hence, we have additionally identified and presented various other factors that strengthen or weaken blockchain privacy. Based on those factors, we have formulated an evaluation framework to analyze the efficiency of blockchain privacy solutions. Further, we have introduced a concept of privacy precision that is a quantifiable measure to empirically assess privacy efficiency in blockchains. The calculation of privacy precision will be based on the effectiveness and strength of various privacy protecting attributes of a solution and the associated risks. Finally, we conclude the paper with some open research challenges and future directions. Our study can serve as a benchmark for empirical assessment of blockchain privacy.
Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation

Abstract We present computational modeling results based on a self‐paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k‐fold cross‐validation. We find that our data are better accounted for by an encoding‐based model of agreement attraction, compared to a retrieval‐based model. A novel methodological contribution of our study is the use of comprehension questions with open‐ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.