Children with Autism Spectrum Disorder (ASD) often struggle with social communication and feel anxious in interactive situations. The Picture Exchange Communication System (PECS) is commonly used to enhance basic communication skills in children with ASD, but it falls short in reducing social anxiety during therapist interactions and in keeping children engaged. This paper proposes the use of virtual character technology alongside PECS training to address these issues. By integrating a virtual avatar, children's communication skills and ability to express needs can be gradually improved. This approach also reduces anxiety and enhances the interactivity and attractiveness of the training. After conducting a T-test, it was found that PECS assisted by a virtual avatar significantly improves children's focus on activities and enhances their behavioral responsiveness. To address the problem of poor accuracy of gaze estimation in unconstrained environments, this study further developed a visual feature-based gaze estimation algorithm, the three-channel gaze network (TCG-Net). It utilizes binocular images to refine the gaze direction and infer the primary focus from facial images. Our focus was on enhancing gaze tracking accuracy in natural environments, crucial for evaluating and improving Joint Attention (JA) in children during interactive processes.TCG-Net achieved an angular error of 4.0 on the MPIIGaze dataset, 5.0 on the EyeDiap dataset, and 6.8 on the RT-Gene dataset, confirming the effectiveness of our approach in improving gaze accuracy and the quality of social interactions.

Profiled side-channel analysis presents a significant risk to embedded devices in Internet of Things (IoT). Typically, a single trace is insufficient to successfully key recovery in practical scenarios. It still requires several traces based on Bayes’ posterior probability. In this article, we introduce a chosen-plaintext (CP) strategy into the deep learning-based profiled attacks to improve the attack efficiency. First, we present a general strategy to profile the leakage model by exploiting the sensitivity analysis and clustering analysis. The leakage model derived from deep neural network is to characterize the leakage of the target algorithm. Second, we propose an adaptive CP method in the deep learning-based attack, transforming the conditional probability distribution of the leakage into the entropy of the key candidates under the profiled leakage model. Finally, we evaluate the efficiency of the attack by practical measurements. The results demonstrate that the proposed method requires fewer traces to retrieve the key of AES on devices of different types, e.g., Smartcard, FPGA, and ARM. Moreover, our attack improves the attack efficiency on masked implementations.

The blockchain technology provides a revolutionary solution for information exchange through its decentralized, tamper-proof, and highly secure characteristics. It has wide application in many industries, with the potential to improve efficiency, reduce costs, and promote innovation. However, the full replication mechanism of blockchain results in the need for each device to store complete blockchain data, leading to inefficient storage. Additionally, as the scale of the blockchain network expands, the increasing data volume and frequent transactions can cause network congestion and latency, posing scalability issues for blockchain. Coded sharding blockchain has been proposed to address these issues. However, the current solutions face challenges such as dealing with malicious nodes and low computational efficiency, which hinder the enhancement of their scalability and computational performance. To resolve these problems, we propose AdaptiveShard by combining coded sharding blockchain with adaptive verifiable coded computing (AVCC). This solution is designed based on the Unspent Transaction Output (UTXO) model and is suitable for cryptocurrency transaction scenarios. Compared to traditional coded sharding blockchain solutions, AdaptiveShard can: 1) enhance the computational performance of coded sharding blockchain during block validation by combining AVCC with Gaussian variant of Freivalds algorithm (GVFA), reducing the decoding complexity toO(N2logN); 2) validate the computation results of each shard using GVFA and replace balance check verification functions with matrix multiplication, reducing the computational complexity of verification toO(√n); 3) reduce the additional number of nodes required to resolve malicious nodes from two to one using verifiable computation; 4) balance the system in the presence of straggler or malicious nodes through dynamic coding techniques, eliminating their impact and improving system reliability. Experiments demonstrate that at t=1000, the throughput is 25.6% higher compared to Polyshard. Compared to the solution without dynamic coding, the solution with dynamic coding can reduce the running time by 9.7% at t=50.

The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.

Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their daily function and rehabilitation progress, as well as providing a convenient and practical solution for human activity recognition (HAR). However, during the motor rehabilitation of stroke patients, sensors provide vast amounts of high-dimensional data that are large and complex. To enhance the accuracy of activity monitoring and identification, as well as address the limitations of real-time processing, data visualization, and tracking in conventional monitoring approaches, it is essential to perform valid data processing and analysis. This paper combines deep learning models to explore the potential relationships and patterns between data to build an intelligent post-stroke rehabilitation system. This paper proposes a novel framework aimed at accurately recognizing activities performed by stroke patients. Our approach leverages a data fusion mechanism based on multiple sensors to construct a fusion tensor and employs a bidirectional long and short-term memory (BiLSTM) network enhanced with an attention mechanism. This network effectively captures temporal patterns and long-term dependencies within the data, resulting in improved performance for wearable sensor-based activity classification. Furthermore, we introduce an enhanced loss function to optimize the learning process. To assess the performance of the proposed model algorithm, two benchmark datasets were employed. These datasets served as the basis for evaluating and comparing the baseline method as well as other proposed methods. The experimental results clearly demonstrated that the proposed model outperformed the compared methods, indicating its superior performance in activity recognition.

Graph learning is widely applied to process various complex data structures (e.g., time series) in different domains. Due to multidimensional observations and the requirement for accurate data representation, time series are usually represented in the form of multilabels. Accurately classifying multilabel time series can provide support for personalized predictions and risk assessments. It requires effectively capturing complex label relevance and overcoming imbalanced label distributions of multilabel time series. However, the existing methods are unable to model label relevance for multilabel time series or fail to fully exploit it. In addition, the existing multilabel classification balancing strategies suffer from limitations, such as disregarding label relevance, information loss, and sampling bias. This article proposes a dynamic graph attention autoencoder-based multitask (DGAAE-MT) learning framework for multilabel time series classification. It can fully and accurately model label relevance for each instance by using a dynamic graph attention-based graph autoencoder to improve multilabel classification accuracy. DGAAE-MT employs a dual-sampling strategy and cooperative training approach to improve the classification accuracy of low-frequency classes while maintaining the classification accuracy of high-frequency and mid-frequency classes. It avoids information loss and sampling bias. DGAAE-MT achieves a mean average precision (mAP) of 0.955 and an F1 score of 0.978 on a mixed medical time series dataset. It outperforms state-of-the-art works in the past two years.

As a new type of digital living space that blends virtual and reality, Metaverse combines many emerging technologies. It provides an immersive experience based on VR technology and stores and protects users’ digital content and digital assets through blockchain technology. However, different virtual environments are often highly heterogeneous in terms of underlying architecture and software implementation technology, which leads to many challenges in scalability and interoperability for blockchains serving the Metaverse. Cross-chain technology is an essential technology to realize the scalability and interoperability of blockchain. However, the current cross-chain technologies all have their own merits and demerits, and there is no cross-chain solution that can be fully applied to any scenario. To this end, in the blockchain-based Metaverse, this article proposes a cross-chain transaction scheme based on improved hash timelock, HCNCT. By combining the notary mechanism, this scheme uses a group of notaries to supervise and participate in cross-chain transactions, effectively solving the problem that malicious users create a large number of time-out transactions to block the transaction channel, which exists in the traditional hash timelock method. Besides, this article uses the verifiable secret sharing method in the notary group, which can effectively prevent the centralization problem of the notary mechanism. Moreover, this article discusses the process of key processing, cross-chain transaction and transaction verification of the scheme, and designs the user credibility evaluation mechanism, which can effectively reduce the occurrence of malicious default of users. Compared with existing solutions, our solution has the advantage of effectively addressing time-out transaction attacks and centralization issues while guaranteeing security. The experiments also verify the effectiveness of the proposed scheme.

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.

Obtaining timely and accurate traffic information is one of the most important problems in intelligent transportation system, which will make vehicles run smoothly, avoid road congestion, save road running time and reduce vehicle energy consumption. In the current Internet of Vehicles system, the traffic management center can learn from the feedback information of all vehicles to improve the ability of decision-making and traffic command. However, the existing feedback mechanism does not respond to the spatial-temporal characteristics of data in time, due to the lack of communication capability of the current equipment. So, it cannot meet the requirements of ultra-low delay, high reliability and high security in the Internet of Vehicles. To solve this problem, this paper proposes a blockchain-based proxy vote and revocation scheme for decision feedback in Internet of Vehicles, which allows the intelligent system to ignore the unevenness and heterogeneity in the 6G technology. In addition, blockchain technology notarizes the vote data of vehicles and outsources microservices. Secondly, we use the attributes of decision-related nodes instead of their identities to enable anonymous vote. Smart contracts can automatically expand the scalability of outsourced microservices. Finally, the security proof of the proposed scheme ensures the security and consistency of outsourced microservices. The simulation results also show that our scheme greatly improves the efficiency of voting feedback.

Intelligent transportation systems (ITS) have always been an important application of Internet of Things (IoT). Today, big data and cloud computing have further promoted the construction and development of ITS. At the same time, the development of blockchain has also brought new features and convenience to ITS. However, due to the endless emergence of increasingly advanced types of attacks, the security of blockchain-based ITS needs more attention from industry and academia. In this paper, we focus on exploring the primitives in cryptography to guarantee the security of blockchain-based ITS. In particular, the authentication, encryption, and key management schemes in cryptography are discussed. Furthermore, we propose two methods for achieving the threshold key management in blockchain-based ITS. The proposed threshold key management scheme (with threshold t ) enables various stakeholders to recover a secret if the number of participated stakeholders is at least t . It should be noted that the proposed threshold key management scheme is efficient and secure for multiple users in blockchain-based ITS, especially for the data-sharing scenario.

With the explosive growth in the number of IoT devices, ensuring the integrity of the massive data generated by these devices has become an important issue. Due to the limitation of hardware, most past data integrity verification schemes randomly select partial data blocks and then perform integrity validation on those blocks instead of examining the entire dataset. This will result in that unsampled data blocks cannot be detected even if they are tampered with. To solve this problem, we propose a new and effective integrity auditing mechanism of sensor data based on a bilinear map accumulator. Using the proposed approach will examine all the data blocks in the dataset, not just some of the data blocks, thus, eliminating the possibility of any cloud manipulation. Compared with other schemes, our proposed solution has been proved to be highly secure for all necessary security requirements, including tag forgery, data deletion, replacement, replay, and data leakage attacks. The solution reduces the computational and storage costs of cloud storage providers and verifiers, and also supports dynamic operations for data owners to insert, delete, and update data by using a tag index table (TIT). Compared with existing schemes based on RSA accumulator, our scheme has the advantages of fast verification and witness generation and no need to map data blocks to prime numbers. The new solution supports all the characteristics of a data integrity verification scheme.

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.

Internet of Things (IoT) is a research field that has been continuously developed and innovated in recent years and is also an important driving force for the improvement of people’s life in the future. There are lots of scenarios in IoT where we need to collaborate through devices to complete tasks; that is, a device sends data to other devices, and other devices operate on the aid of the data. These transmitted data are often users’ privacy data, such as medical data and grid data. We propose an instant encrypted transmission based security scheme for such scenarios in IoT. The analysis in this paper indicates that our scheme can guarantee the security of users’ data while ensuring rapid transmission and acquisition of instant IoT data.

A conditional proxy re-encryption (CPRE) scheme enables the proxy to convert a ciphertext from Alice to Bob, if the ciphertext satisfies one condition set by Alice. To improve the issue of more fine-grained on the condition set, Fang, Wang, Ge and Ren proposed a new primitive named Interactive conditional PRE with fine grain policy (ICPRE-FG) in 2011, and left an open problem on how to construct CCA-secure ICPRE-FG without random oracles. In this paper, we answer this open problem affirmatively by presenting a new construction of CCA-secure key-policy attribute-based PRE (KP-ABPRE) without random oracles. In this paper, we enhance the security model of Fang's ICPRE-FG scheme by allowing the adversary to make some extra queries, which do not help them win the game trivially. Finally, we present a CCA-secure KP-ABPRE without random oracles under the 3-weak decisional bilinear Diffie–Hellman inversion(3-wDBDHI) assumption.