Reviewerly/exHarmony huggingface dataset:
LHDNN: Maintaining High Precision and Low Latency Inference of Deep Neural Networks on Encrypted Data

The advancement of deep neural networks (DNNs) has prompted many cloud service providers to offer deep learning as a service (DLaaS) to users across various application domains. However, in current DLaaS prediction systems, users’ data are at risk of leakage. Homomorphic encryption allows operations to be performed on ciphertext without decryption, which can be applied to DLaaS to ensure users’ data privacy. However, mainstream homomorphic encryption schemes only support homomorphic addition and multiplication, and do not support the ReLU activation function commonly used in the activation layers of DNNs. Previous work used approximate polynomials to replace the ReLU activation function, but the DNNs they implemented either had low inference accuracy or high inference latency. In order to achieve low inference latency of DNNs on encrypted data while ensuring inference accuracy, we propose a low-degree Hermite deep neural network framework (called LHDNN), which uses a set of low-degree trainable Hermite polynomials (called LotHps) as activation layers of DNNs. Additionally, LHDNN integrates a novel weight initialization and regularization module into the LotHps activation layer, which makes the training process of DNNs more stable and gives a stronger generalization ability. Additionally, to further improve the model accuracy, we propose a variable-weighted difference training (VDT) strategy that uses ReLU-based models to guide the training of LotHps-based models. Extensive experiments on multiple benchmark datasets validate the superiority of LHDNN in terms of inference speed and accuracy on encrypted data.
A Family of Automatic Modulation Classification Models Based on Domain Knowledge for Various Platforms

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models.
Face-based age estimation using improved Swin Transformer with attention-based convolution

Recently Transformer models is new direction in the computer vision field, which is based on self multihead attention mechanism. Compared with the convolutional neural network, this Transformer uses the self-attention mechanism to capture global contextual information and extract more strong features by learning the association relationship between different features, which has achieved good results in many vision tasks. In face-based age estimation, some facial patches that contain rich age-specific information are critical in the age estimation task. The present study proposed an attention-based convolution (ABC) age estimation framework, called improved Swin Transformer with ABC, in which two separate regions were implemented, namely ABC and Swin Transformer. ABC extracted facial patches containing rich age-specific information using a shallow convolutional network and a multiheaded attention mechanism. Subsequently, the features obtained by ABC were spliced with the flattened image in the Swin Transformer, which were then input to the Swin Transformer to predict the age of the image. The ABC framework spliced the important regions that contained rich age-specific information into the original image, which could fully mobilize the long-dependency of the Swin Transformer, that is, extracting stronger features by learning the dependency relationship between different features. ABC also introduced loss of diversity to guide the training of self-attention mechanism, reducing overlap between patches so that the diverse and important patches were discovered. Through extensive experiments, this study showed that the proposed framework outperformed several state-of-the-art methods on age estimation benchmark datasets.
Communication-Efficient Quantum Algorithm for Distributed Machine Learning

The growing demands of remote detection and an increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problems, in the scenario where the dataset is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of O(log_{2}(N)/ε), where N is the number of data points and ε is the bound on parameter errors. Compared to classical and other quantum methods that achieve the same goal, our methods provide a communication advantage in the scaling with data volume. The core of our methods, the quantum bipartite correlator algorithm that estimates the correlation or the Hamming distance of two bit strings distributed across two parties, may be further applied to other information processing tasks.
Atypical Salient Regions Enhancement Network for visual saliency prediction of individuals with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder and can result in atypical visual perception towards stimuli. Existing atypical saliency prediction models for individuals with ASD are highly inspired by typical saliency prediction models, while neglecting their special visual traits and preferences which are different from neurotypical individuals. In this paper, we propose an Atypical Salient Regions Enhancement Network (ASD-ASRENet) based on an encoder–decoder architecture to predict the atypical visual saliency of individuals with ASD. Concretely, the output of the encoder is treated as the initial prediction, then four Atypical Salient Regions Enhancement (ASRE) modules, which are specially designed for individuals with ASD, are deployed at different stages of the decoder to emphasize the atypical salient regions and further complete the prediction in a progressive manner. Besides, considering the problem that the semantic information from high levels is gradually diluted while the effect of noises contained in low levels is increasingly stronger during the top-down transmission in the decoder, we further design a Global Semantics Flow (GSF) module, which captures the global interdependencies from both spatial perspective and channel perspective, to guide each integration stage in the decoder. Extensive experiments demonstrate the effectiveness of the proposed modules and our ASD-ASRENet achieves superior performance compared with all state-of-the-art models on the Saliency4ASD benchmark.
Quantum private information retrieval over a collective noisy channel

Quantum Private Information Retrieval (QPIR) allows a user (Alice) to retrieve a database item from the database owned by the database holder (Bob) in such a way that Alice can query only the database item she wants, but cannot get other items, and Bob does not know which item Alice queries. However, the real quantum channel between Alice and Bob is noisy, and the noise may result in not only the chance that Alice obtains a false item, but also that both parties may cheat by camouflaging themselves with noise. In this paper, we use the decoherence-free subspace (DFS) for QPIR, which we call DFS-QPIR. The DFS-QPIR protocol removes the effect of the channel noise on the errors in the retrieved item so that two parties cannot cheat by replacing the noisy channel with a noiseless one. It can work over a collective noisy channel while retaining high reliability, database security and user privacy simultaneously. While only the collective unitary noise is taken into account, the proposed DFS-QPIR protocol can be straightforwardly extended to more general collective noise channel.
Scenario approach for training classroom management in virtual reality

Modern higher education faces serious challenge of teaching future teachers to provide a psychologically safe environment in their interactions with students. This ability involves addressing developing conflict situations, accurately recognizing issue’s root cause, and concentrating on emotional condition of participants. The goal of this study is to show possibility of utilizing scenario method while creating a virtual reality (VR) application for pre-service teachers to use in pedagogically complicated conflict scenarios. The proposed technology facilitates creation of scenarios based on actual teacher-student interactions. Different scenarios comprise dialogues that illustrate a variety of possible instructors’ responses to a disagreement, so demonstrating range of chosen behavior tactics in pedagogical interaction settings. The research was developed utilizing ADDIE model instructional design methodology. At this level, useful classroom management scenarios have been constructed. These scenarios have been examined by experts. According to scenario analysis, three scenarios received positive ratings for their relevance, realism, learning objectives, feedback, engagement, flexibility, and effectiveness. During level of implementation, participants are master’s degree candidates in “training in pedagogical conflict management.” Participants also studied use of text and video material in VR. Notwithstanding their assumption that they are more effective, a survey of participants showed that they feel learning and deploying VR technology is time-consuming. As VR development tools become more accessible and user-friendly, unfavorable opinions will change, since it will take user time to comprehend technology. On the other hand, while it is a great indicator that they feel they are competent in execution, it may be troublesome for institutions to believe they are incapable of reaching these goals.
Network Forensic Investigation of Collaborative Robots: A Case Study

Collaborative robots are currently facing significant security challenges and issues in the real world. It is well-established that representations of security issues may lead to human loss or property damage. This study aims to identify current robotics network forensic techniques to address the need for dedicated investigative capabilities. Specifically, it analyzes the ROS2-based communication network packets of collaborative robots. Network forensic investigation of collaborative robots helps in identifying and analyzing the important artifacts/evidence. The network forensic techniques discussed in this article can be used to acquire and analyze the artifacts related to tampered collaborative robots. Moreover, the information related to the environmental architecture of collaborative robots can be recovered using metadata of the network packets.
Analysis of the impact of the “double reduction” policy on the educational processes of Chinese students studying in Europe

The relevance of the stated topic of scientific research is determined by the presence of many problems associated with the need to study the impact of the “double reduction” policy on the educational processes of Chinese students who are educated in Europe. This research work is aimed at studying the features of the “double reduction” policy, its advantages and disadvantages, as well as its impact on the educational processes of Chinese students studying in European educational institutions. The basis of the methodological approach in this research work is a qualitative combination of various theoretical research methods. In particular, analysis, synthesis, interpretation and generalization were used to study the “double reduction” policy, and the descriptive method and the comparative method were used to analyse the impact of this policy on the education of Chinese students studying in Europe. The results of this scientific study, as well as the conclusions formulated on their basis, are of practical value for teachers, methodologists, government officials and many other scientists who are actively involved in the topical problems of modern education in China, the issues of improving the quality of education and the overall improvement of the educational process in educational institutions in China.
Transformer Sub-Patch Matching for High-Performance Visual Object Tracking

Visual tracking is a core component of intelligent transportation systems, especially for unmanned driving and road surveillance. Numerous convolutional neural network (CNN) trackers have achieved unprecedented performance. However, CNN features with regular spatial context relationships experience difficulty matching the rigid target templates when dramatic deformation and occlusion occur. In this paper, we propose a novel full Transformer Sub-patch Matching network for tracking (TSMtrack), which decomposes the tracked object into sub-patches, and interlaced matches the extracted sub-patches by leveraging the attention mechanism born with the Transformer. Roots in Transformer architecture, TSMtrack consists of image patch decomposition, sub-patch matching, and position prediction. Specifically, TSMtrack converts the whole frame into sub-patches and extracts the sub-patch features independently. By sub-patch matching and FFN-like prediction, TSMtrack enables independent similarity measurement between sub-patch features in an interlaced and iterative fashion. With a full Transformer pipeline implemented, we achieve a high-quality trade-off between tracking speed performance. Experiments on nine benchmarks demonstrate the effectiveness of our Transformer sub-patch matching framework. In particular, it realizes an AO of 75.6 on GOT-10K and SR of 57.9 on WebUAV-3M with 48 FPS on GPU RTX-2060s.
Exploring and Analyzing the Token Ecosystem: A Complex Network Analysis Perspective

The birth of Bitcoin created the era of cryptocurrency. At present, thousands of tokens are being published on blockchains, led by the popularity of Initial Coin Offerings (ICOs), launched by startups. Many have studied the token ecosystem from the perspective of economics. However, little is known about the characteristics of participants and tokens in the ecosystem. In order to fill this gap, and considering that more than 80% of ICOs are launched based on Ethereum, this article conducts a systematic survey of the Ethereum ERC20 and ERC721 token ecosystem to characterize the creators and holders of the tokens and transfer activities. By downloading the entire blockchain, and analyzing transaction records and event logs, we constructed three complex network graphs, a token creator graph, a token holder graph, and a token transfer graph. By analyzing these graphs, we are able to obtain many observations including token activeness distribution, token holder distribution, token relationship, and characteristics of token creation and decentralized exchanges. In addition, we propose an algorithm to reveal potential relationships between tokens and other addresses on Ethereum. The example shows that the proposed algorithm can effectively discover complex relationships between many accounts and find entities of addresses.
When Contactless Service Matters: The Use of Robotic Services in the Healthcare Sector

The Coronavirus disease 2019 (COVID-19) pandemic has led to a catastrophic public health emergency that impacted the global society's prosperity, health, and security. Concurrently, the swift technological development over the recent decades has enabled the rising implementation of robots in various industries. In particular, there is a growing demand for robotic technology in the healthcare sector as a precautionary measure since they significantly reduce the risk of cross-infection through interpersonal contact among medical professionals by shifting to computerized routine tasks. Therefore, this paper presents a comprehensive review of the use of robots in the healthcare sector during and post-COVID-19 pandemic. This paper highlights the increasing demand and adoption of robotic technology during and post-pandemic COVID-19 in healthcare sector. The benefits to the society and engineering managers, and challenges in implementing robotic technology in the healthcare sector are provided at the end of this paper before the paper ends with a concise conclusion.
Target Tracking Algorithm Based on Generalized Regression Neural Network for Passive Bistatic Radar

In passive bistatic radar, high-performance target tracking is of crucial importance to a variety of applications. However, most existing tracking algorithms cannot achieve satisfactory performance due to poor azimuth estimation and the nonlinear relationship between the target position and measurements. This article proposes a novel tracking algorithm based on improved generalized regression neural networks (GRNNs) and Kalman filtering (KF). First, the dependency between the measurements and the target position is modeled by GRNN regression. A cooperative co-evolutionary genetic algorithm (CCGA) is then introduced to optimize the sample size and smoothing factor of the GRNN model to improve the performance. Finally, this article applies KF to CCGA-GRNN localization results and forms the smoothing trajectories of the targets. Simulation and experimental results show that the performance of the proposed algorithm is better than that of the geometric localization (GL), GRNN, and extreme learning machine (ELM)-based tracking approaches. Furthermore, field experiment data verify the feasibility of the proposed algorithm.
Automated Quantum Circuit Design With Nested Monte Carlo Tree Search

Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years. Despite the adaptability and simplicity, their scalability and the selection of suitable ans\"atzs remain key challenges. In this work, we report an algorithmic framework based on nested Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed bandit (CMAB) model for the automated design of quantum circuits. Through numerical experiments, we demonstrated our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimisation on a graph, solving systems of linear equations, and finding encoding circuit for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimise quantum circuits for larger systems, showing both versatility and scalability.
Campus Abnormal Behavior Recognition with Temporal Segment Transformers (March 2023)

The intelligent campus surveillance system is beneficial to improve safety in school.Abnormal behavior recognition, a field of action recognition in computer vision, plays an essential role in intelligent surveillance systems.Computer vision has been actively applied to action recognition systems based on Convolutional Neural Networks (CNNs).However, capturing sufficient motion sequence features from videos remains a significant challenge in action recognition.This work explores the challenges of video-based abnormal behavior recognition on campus.In addition, a novel framework is established on long-range temporal video structure modeling and a global sparse uniform sampling strategy that divides a video into three segments of identical durations and uniformly samples each snippet.The proposed method incorporates a consensus of three temporal segment transformers (TST) that globally connects patches and computes self-attention with joint spatiotemporal factorization.The proposed model is developed on the newly created campus abnormal behavior recognition (CABR50) dataset, which contains 50 human abnormal action classes with an average of over 700 clips per class.Experiments show that it is feasible to implement abnormal behavior recognition on campus and that the proposed method is competitive with other peer video recognition in terms of Top-1 and Top-5 recognition accuracy.The results suggest that TST-L+ can improve campus abnormal behavior recognition, corresponding to Top-1 and Top-5 accuracy results of 83.57% and 97.16%, respectively.
Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.