Reviewerly/exHarmony huggingface dataset:
Application of computer virtual simulation technology in tourism industry

With the advent of the era of big data, China has made great achievements in artificial intelligence and computer virtual simulation technology. On the basis of introducing the characteristics and advantages of computer virtual simulation technology, this paper analyzes and studies the current situation of the application of virtual simulation technology in China's tourism industry under the platform of intelligent tourism, and the necessity of research on the integration development of VR and tourism. It also discusses the application of computer simulation technology in China's tourism industry, and puts forward how to further integrate VR virtual reality technology and tourism industry under the background of intelligent tourism, to realize virtual tourism and broaden the development path of "virtual + tourism" suggestions and prospects.
Accountable and Verifiable Secure Aggregation for Federated Learning in IoT Networks

In the Internet of things (IoT) networks, largescale IoT devices are connected to the Internet to collect users' data. As a distributed machine learning paradigm, federated learning (FL) collaboratively trains the global model by utilizing large-scale distributed devices, while protecting the privacy of the local data sets of each participant. Federated learning with secure aggregation employs an aggregation server (aggregator) to compute a multiparty sum of model parameter updates of each participants in a secure manner and further realizes the updates. However, existing schemes are usually based on semi-honest assumptions, which make them vulnerable to malicious clients. In addition, they address the random client dropouts problem by increasing the data size, which brings a large communication overhead. To solve these issues, we propose an accountable and verifiable secure aggregation for federated learning framework. Specifically, we employ an SMC protocol based on homomorphic proxy re-authenticators and homomorphic proxy re-encryption to execute secure aggregation, while integrating the blockchain to realize the function of penalty for malicious behavior. Our framework can guarantee the verifiability of data provenance and is accountable for malicious clients. To demonstrate the usability of our framework, we evaluate the specific cryptography schemes and develop a blockchain-based prototype system by using solidity language to test the performance of the framework.
Decode-and-Compare: An Efficient Verification Scheme for Coded Distributed Edge Computing

Recently, edge computing has demonstrated increasing potential to provide low-latency computing services. Coded edge computing can not only make full use of the resources of heterogeneous edge computing servers, but also significantly reduce the negative effects of slow computing devices on computing time. Nevertheless, since edge servers may be unreliable or untrustworthy, the user will decode and get incorrect computation results even if it uses one incorrect sub-computation result returned by faulty edge servers. In this paper, for the existing coded edge computing schemes, we focus on the distributed matrix-matrix multiplication and design a general and efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decode-and-Compare Verification</i> (DCV) scheme to verify the correctness of computation results and identify faulty edge servers by utilizing the properties of coded computing itself. The DCV scheme contains two components: (1) computation result verification, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , obtain the computation result and verify its correctness, and (2) faulty edge server identification, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , identify the faulty edge servers by verifying the correctness of returned sub-computation results. For both the independent and collusion faulty edge server models, we conduct solid theoretical analyses on the required decoding rounds, the coding redundancy and the successful verification probability to demonstrate that the correct computation result can be efficiently verified. We also conduct a lot of experiments on the DCV scheme from different aspects and the results show that it achieves much less computation time to get the correct computation result compared with other potential schemes, including homomorphic encryption and local computation.
An Asynchronous Entanglement Distribution Protocol for Quantum Networks

Entanglement-based quantum networks can provide unconditionally secure communication by distributing entangled pairs between distant end nodes. To achieve end-to-end entanglement distribution, multiple operations of entanglement swapping in a quantum repeater chain are always required. However, due to the non-determinism of entanglement swapping caused by imperfect physical devices, the execution pattern of swapping operations has a direct impact on the performance of entanglement distribution, which can be categorized into an entanglement access control (EAC) problem. In this article, we attribute the EAC problem to two aspects: the matching optimization within quantum nodes, and the swapping conflict avoidance between quantum nodes. Accordingly, we propose an asynchronous entanglement distribution protocol which contains a custom weighted maximum matching algorithm, and a reliable signaling interaction mechanism to avoid a swapping conflict, respectively. Based on the proposed protocol, quantum repeaters autonomously decide their behaviors and spontaneously construct the end-to-end entangled pairs asynchronously. Simulation results show that our protocol can significantly improve the entanglement distribution rate and fidelity of end-to-end entangled pairs while simplifying the deployment and management process of the quantum networks.
Extreme Underwater Image Compression Using Physical Priors

Underwater images (UWIs) require higher compression ratio than terrestrial images due to the limited bandwidth of underwater wireless acoustic channel. In many studies such as marine species, the foreground objects (FGOs) in UWIs need to be observed in detail, while the background only needs to be viewed in general. However, existing image compression methods achieve limited compression ratio and reconstruction quality, which cannot fulfill these practical applications since they do not consider the unique underwater physical priors. To overcome the limitation, we propose an underwater physical prior-based extreme compression network (PPECN) for UWIs compression, which includes an underwater physical prior-guided FGOs autoencoder (UPGAE) and a FGOs-assisted background generator (FG-BGGAN). Specifically, we design an underwater physical prior guidance structure that simulates the data flow in the underwater physical imaging process to adaptively adjust the distribution of received Gaussian features in the UPGAE to be more consistent with real UWIs. During the adjustment, some basic UWI properties can be reconstructed, which can improve the reconstruction quality and implicitly reduce bits through the end-to-end training. Furthermore, the background is generated from simple semantic map under the constraint of the perceptual consistency between background and FGOs, significantly saving coding bits and improving the perceptual quality of the generated background. Extensive experimental results on four underwater image datasets verify that, compared with state-of-the-art compression methods, our PPECN achieves both impressive improvement in the perceptual quality of the whole image and significant gain in the pixel fidelity of the FGOs at the similar low bitrate.
Lightweight Hybrid Deep Learning Scheme for Automatic Modulation Classification in Cognitive Radio Networks

Addressing the spectrum scarcity problem under the explosive growth of wireless network applications and data traffic, requires a decidedly global research focus on Cognitive Radio Networks (CRNs), which have been widely regarded as the front-runner to cope with spectrum inefficiency. In this paper, the emphasis is given to Automatic Modulation Classification (AMC) as a means to blindly identify the underlying modulation format of intercepted signals in Cognitive Radio Networks. Modulation recognition is a necessary task to accomplish a set of Cognitive Radio functions, such as dynamic spectrum access and interference detection. Recently, several Deep Learning (DL) schemes have been explored to address the existing limitations of conventional approaches in dealing with the AMC problem. DL techniques have delivered outstanding performance in terms of reliability, scalability, and applicability compared to traditional methods. However, when it comes to resource-constrained end-devices, most of these techniques are not supported due to their high processing and storage requirements. In this work, we propose a hybrid DL model with attention mechanism based on layers of bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN) architectures. Our model has a memory footprint of 126.6 kByte, slightly smaller than the memory footprint of the baseline model with 100 GRU cells (128 kByte), and achieves good performance on different scales of training data.
The DECENT Toolkit to Support Design of User Engagement of Mobile Health Technologies from a Practice Theory Perspective

User engagement is critical for the successful adoption and use of mobile health apps, but it is also influenced by a variety of factors that are not always easily accessible or quantified by designers in practice. The goal of this paper is to facilitate the application of activity theory and the communicative ecological framework to uncover various socio-cultural contexts for user engagement designs using the Design Process Engagement Enhancement System (DECENT) toolkit. The DECENT toolkit is an innovative theory-based design tool and associated method that includes socio-cultural filtration and a socio-cultural checklist that are augmented in the phases of the user-centered design model. This paper presents a set of consolidating concepts to guide future design practice which broaden designers’ knowledge of mobile health app designs for user engagement. Finally, to fully explain and design for user engagement in mobile health, an integrated approach incorporating a variety of technological and socio-cultural factors is presented.
Security Evaluation Model of Blockchain System Based on Combination Weighting and Grey Clustering

With the widespread application of blockchain technology in social and economic fields, various security challenges have become increasingly prominent and attracted many researchers to work in this domain. To address this issue, we propose a blockchain system security evaluation model based on game theory combination weighting and grey clustering. Considering the correlation between indexes, the security evaluation index system is constructed hierarchically from the technical system framework of blockchain. Then we use the combination weighting algorithm based on game theory to optimize the ratio of subjective and objective weights, and balance the evaluation error caused by the difference between subjective and objective weights to make the weight quantification more scientific and accurate. Finally, the security evaluation level of the blockchain system is determined by grey clustering evaluation method. The effectiveness of this method is verified by our experiments, which provides a useful reference for the security evaluation of blockchain systems.
IoT based application designing of Deep Fake Test for Face animation

Development of Deep Learning models of Internet of Things (IoT) enclosures with limited resources are difficult because Both Quality of Results are difficult to achieve - QoR as follows two models, DNN Model, and Inference Accuracy and Quality of Services such as power consumption, throughput, and latency. Currently, the development of DNN models is often separated from deploying them to IoT devices, which leads to the most effective solution. If there are many records that represent objects of substantially the same class (face, human body, etc.), you can apply frames to each object of this class. To achieve this, use an independent representation to distinguish between appearance and progress data. Deep fake detection is achieved by using a novel, lightweight Deep Learning method on the IoT platform that is memory-efficient and lightweight. It is carried out in two different stages. The first phase of the deep fake test aims to implement a method of extracting images from a video and using them in conjunction with a Deep Neural Network to implement a test for face animation. It has been reported that the impact of the background elimination has been reported before the background subtraction. Here the Trans GAN model is used for the image classification. In the second phase, the work can be recorded and executed by the IOT device that can record live video streams and then detect activity involved in live video. An activity detection prototype based on IoT devices with small processing power is presented. This prototype provides improvements to the system, extending its application in various ways to improve portability, networking, and other equipment capabilities. The proposed architecture will be evaluated against four highly competitive object detection benchmarking tasks CIFAR10, CIFAR100, SVHN, and ImageNet.
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples

Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples’ modeling needs, we propose two vertical federation learning algorithms for ridge regression via least-squares solution for two-party and multi-party scenarios, respectively. Compared with the state-of-the-art algorithms, our algorithms only need one round of calculation for the optimization instead of iteration. Furthermore, our algorithms can effectively handle large-scale samples due to the number of cryptographic operations in our algorithms being independent of the number of samples. Through our proposed the matrix secure agent computing theory and <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.
SHAPE: A Simultaneous Header and Payload Encoding Model for Encrypted Traffic Classification

Many end-to-end deep learning algorithms seeking to classify malicious traffic and encrypted traffic have been proposed in recent years. End-to-end deep learning algorithms require a large number of samples to train a model. However, it is hard for existing methods fully utilizing the heterogeneous multimodal input. To this end, we propose the SHAPE model (simultaneous header and payload encoding), which mainly consists of two autoencoders and a transformer layer, to improve model performance. The two auto encoders extract features from heterogeneous inputs—the statistical information of each packet and byte-form payloads—and convert them into a unified format; then, a lightweight Transformers layer further extracts the relationship hidden in simultaneous input. In particular, the autoencoder for payload feature extraction contains several depthwise separable residual convolution layers for efficient feature extraction and a token squeeze layer to reduce the computing overhead of the Transformers layer. Moreover, we train the SHAPE model using deep metric learning, which pulls samples with the same class label together and separates samples from different classes in the low-dimensional embedding space. Thus, the SHAPE model can naturally handle multitask classification, and its performance is approximately 5.43% better than the current SOTA on the traffic type classification of the ISCX-VPN2016 dataset, at the cost of 9.31 times the training time, and 1.45 times the inference time.
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation

Evaluating image generation models such as generative adversarial networks (GANs) is a challenging problem. A common approach is to compare the distributions of the set of ground truth images and the set of generated test images. The Frechét Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution. In this paper, we argue that this is an over-simplified assumption, which may lead to unreliable evaluation results, and more accurate density estimation can be achieved using a truncated generalized normal distribution. Based on this, we propose a novel metric for accurate evaluation of GANs, named TREND (TRuncated gEneralized Normal Density estimation of inception embeddings). We demonstrate that our approach significantly reduces errors of density estimation, which consequently eliminates the risk of faulty evaluation results. Furthermore, the proposed metric significantly improves robustness of evaluation results against variation of the number of image samples.
Dual-Branch Memory Network for Visual Object Tracking

In recent years, memory-based tracking has become the focus of visual object tracking due to its robustness toward objects with arbitrary forms. Although advanced memory networks have produced excellent results, balancing long-term and short-term memory remains a difficult problem in memory building. Therefore, we propose the dual-branch memory network (DualMN), which divided the memory-building task into long-term and short-term memory-building tasks, avoiding conflict between them skillfully. Specifically, the DualMN consists of a long-term memory branch and a short-term memory branch. The former is dedicated to learning the difference between the new target appearance and the surrounding environment. The latter focuses on learning the essential feature of the target to prevent the target drift. In the tracking process, the long-term memory branch and the short-term memory branch complement each other to achieve more accurate target positioning. Experiments results on OTB2015, NFS, UAV123, LaSOT, and TrackingNet benchmarks show that our DualMN achieves comparable performance to the advanced trackers.
Self-Adaptive Sampling Based Per-Flow Traffic Measurement

Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB’s on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.
Design and Implementation of a Secure and Distributed Electronic Voucher Management System

Dealing with vouchers, paper or electronic, for both in-store and online purchases is a common customer experience. Electronic vouchers are most popular in recent times as they can be distributed easily to a wide range of customers through the Internet. Maintaining the security and privacy of these electronic vouchers which are now ubiquitous in today's cyber marketspaces is a major challenge, as poor security measures can badly impact both business owners and customers. In this paper, we propose a secure and tamper-resistant electronic voucher management system using the eTRON cybersecurity architecture. Our system implements the full lifecycle of an electronic voucher from its creation to redemption.
Multi-dimensional Cybersecurity Education Design: A Case Study

Global economies depend on business continuity within the digital space, and cybersecurity is becoming a strategic capability among nations and continents. Accordingly, the need for the cyber workforce is increasing within all public and private sectors. International communities report that skilled specialists are lacking, and the building of cyber-resilience should consider individuals as humans are at the center of most attacks. Existing and emerging cybersecurity competence frameworks focus on the development of subject-specific skills. However, cybersecurity is an interdisciplinary subject requiring understanding human behavior and behaving in the digital space in security-conscious manner in daily routines. Therefore, several dimensions should be integrated into educational programs to support the development of critical competencies, including subject-specific skills and knowledge areas, general skills, and behavioral changes. The paper presents the intervention-mapping-based methodology supporting a multi-dimensional cybersecurity educational design. The methodology applies to different education paths (academic and professional). The pilot study supports the application of the methodology and provides a preliminary evaluation of the chosen approach. The pilot case incorporated continuing education (professional) and master-level (academic) student groups. The study considered critical thinking competence development linked to risk assessment based on cyber threats identifiable within the provided scenario. The results demonstrated the potential value of integrating non-technical topics into the development of role-specific competences.