Reviewerly/exHarmony huggingface dataset:
A User-centric Data Sharing Scheme with Hidden Strategies and Revocation

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to achieve fine-grained access control. However, the explicit access policies are prone to leaking its private information. Meanwhile, most existing schemes do not effectively support malicious user revocation. To solve the above issues, this paper proposes a user-centric data sharing scheme that supports policy hiding, fine-grained access control, and user revocation. The scheme uses CP-ABE to achieve fine-grained access control and hidden strategies. Binary tree structure is utilized to achieve malicious user revocation. The cloud server performs a search based on attribute matching to find users that match the access attributes of the data owner. From the experimental results, it can conclude that our protocol achieves user-centric security with performance advantages.
Darknet signatures

Recently, solutions such as distributed ledgers have been promoted in order to achieve undeniability of past transactions. This creates the situation where signed transactions of a given person are recorded in a distributed data structure and are publicly available. We show that behind such a fully legitimate and trustworthy ledger system one can create a perfect infrastructure for a covert ledger for presumably illegal activities. Namely, a signer may hide signatures behind purely innocent signatures of legitimate transactions. Such signatures, called darknet signatures within this paper, can be used by the signer to authenticate illegal operations, where signature delivery is by means of the distributed ledger, and thereby to some degree anonymous. The darknet signatures are undetectable apart from the designated recipient - the signatures posted in the ledger look exactly the same as in case of an honest signer. In particular no extra data need to appear, thereby there are no witnesses of malicious activities in the form, e.g. of suspicious random components. To demonstrate the reality of the threat, we present a simple example realization of darknet signatures hidden in Schnorr signatures. As the construction is quite elementary in this case, it should serve as a strong warning against using probabilistic signature schemes in a distributed ledger.
A Spatial-Temporal Feature Fusion Strategy for Skeleton-Based Action Recognition

Human action recognition (HAR) has attracted the attention of researcher because of its widespread applicability. With the rapid development of deep learning technology, HAR has been greatly improved by deep features. However, many challenges remain, including a shortage of training samples and the effects of view variation the ineffective spatial-temporal information features. To address these problems and further improve the accuracy of HAR, we proposed a novel HAR method based on human skeletons and joints. First, a coordinate transformation method was performed on the raw skeleton data to eliminate the influence of the camera position. Then, a data augmentation strategy was proposed to address the overfitting problem caused by an insufficient number of training samples. In our proposed method, a motion data structure named APoM, which is composed of the cross-frame distance vector, the specific angle and the position vector, connects the movement of joints and the skeleton in the spatial and temporal dimensions and captures skeleton motion details. To evaluate the effectiveness of our method, experiments were conducted on two small-scale public datasets: Florence3D, UTKinectAction3D. The experimental results show that the proposed method achieved competitive performance with accuracies of 98.51%, 98.33%.
GanNoise: Defending against black-box membership inference attacks by countering noise generation

In recent years, data privacy in deep learning has seen a notable surge of interest. Pretrained large-scale data-driven models are potential to be attacked risky with membership inference attacks. However, the current corresponding defenses to prevent the leak of data may reduce the performance of pre-trained models. In this paper, we propose a novel training framework called GanNoise that preserves privacy by maintaining the accuracy of classification tasks. Through utilizing adversarial regularization to train a noise generation model, we generate noise that adds randomness to private data during model training, effectively preventing excessive memorization of the actual training data. Our experimental results illustrate the efficacy of the framework against existing attack schemes on various datasets while outperforming advanced MIA defense solutions in terms of efficiency.
Decentralized Distributed Federated Learning Based on Multi-Key Homomorphic Encryption

Federated learning is a distributed learning paradigm for machine learning that has been widely studied and applied to a variety of scenarios. Since federated learning relies on only one central server to receive model updates from all clients, it has extremely high network bandwidth requirements and risks of single point of failure and privacy leakage. In order to prevent data leakage, this paper proposes a local data aggregation scheme based on xMK-CKKS. To realize decentralized services, this paper proposes a global model aggregation scheme based on RingAllreduce. Further, a decentralized distributed federated learning scheme based on multi-key homomorphic encryption is proposed to realize decentralized hierarchical federated learning with privacy protection. The security analysis and performance analysis show that the scheme in this paper is more scalable to support larger scale federated learning scenarios while ensuring data security, and is more robust to $k\lt N -1$ collusion between clients and distributed servers.
Accountable Ring Signatures from Symmetric-Key Primitives*

We propose an accountable ring signature scheme (ARS) that is constructed from only symmetric-key primitives; pseudorandom functions, hash functions and commitments. Introducing interaction between a user and an opener in the key-issuing phase, our ARS is a composition of the previous two schemes of the ring and group signatures that were proposed by Katz et al. at ACM-CCS 2018. It attains two non-trivial features. One is effective use of the Merkle trees to keep the length of a signature logarithmic to the size of a ring. The other is post-quantum security due to the security properties of the building blocks.
Issuer-Revocable Issuer-Hiding Attribute-Based Credentials Using an Accumulator

Attribute-based credential (ABC) system allows a user to anonymously prove his/her attributes to a verifier, using a credential issued by an issuer. In the conventional ABC systems, since the verifier knows who is the issuer from the attribute proof, the information on the issuer can reveal some attributes of the user. Thus, an issuer-hiding $ABC$ was recently proposed, where the issuer's ID (and the issuer's public key) is hidden. However, in the previous system, the verifier decides the accepted issuers and issues the signatures on the issuers' public keys to each user in advance, and thus the computational and communication costs depending on the number of issuers are required whenever an issuer is revoked. In this paper, we propose an issuer-revocable issuer-hiding ABC system that is extended from the previous system. In the proposed system, the verifier generates and issues the signatures of the issuers' public keys once. Then, whenever an issuer is revoked, the verifier sends only a short revocation list using a pairing-based accumulator to each user.
Shorter Post-quantum Secret Handshakes from Isogenies

Secret handshake schemes allow members from the same organization to authenticate each other anonymously. After its proposal, various schemes have been introduced to achieve advanced privacy protection. Regrettably, all the schemes based on number theoretic assumptions are insecure under quantum computers, and the known post-quantum designs are impractical because of the overhead cost ($\gg$ 100 MB). To fill the gap, we present the first isogeny-based secret handshake scheme with a friendly communication cost (132 KB). In particular, we apply the CSI-FiSh signature scheme to generate group keys and credentials. For each zero-knowledge transcript in the credential, we modify the CSI-FiSh signature by embedding the CSIDH ephemeral private key into the challenge space. After verifying the signature, two users recover the right ephemeral private key if they are in the same group, then they can authenticate each other and negotiate a session key. The security of our scheme relies on the multi-target group action inverse problems.
Reducing the Keysize of McEliece Cryptosystem with Improved Patterson’s Algorithm

McEliece cryptosystem has the advantages of security, fast encryption and decryption, but its key size is too large. Increasing the number of errors can enhance the security level without introducing any additional vulnerable structures. In this paper, we reduce the public key size of McEliece Cryptosystem in a way that increases errors. For the McEliece cryptosystem based on Goppa codes, we prove that directly traversing Patterson’s algorithm is faster than the list decoding algorithms. We also improve the Patterson’s algorithm to reduce the time of each traversal greatly, which makes the new given secure parameter sets not only reduce the public key size well, but also make the decoding time acceptable.
SEM: A Simple Yet Efficient Model-agnostic Local Training Mechanism to Tackle Data Sparsity and Scarcity in Federated Learning

Recent years have witnessed the emergence of Feder-ated Learning (FL) as a viable learning paradigm that permits the training of models without revealing sensitive data. FL systems typically consist of numerous clients that utilize their data to train models locally and an orchestration server responsible for combining local updates from the clients to produce a global model. Thus, the performance of a Federated learning system is highly dependent on the client data and the local model trained on these data. In this study, we present an early attempt at addressing the sparsity and scarcity of client data, which may lead to the overfitting phenomenon of local models and substantially reduce the overall accuracy of the global model. Specifically, we propose a novel local training strategy that explores transfer learning and allows each local model to be trained using the data of two randomly paired clients. The proposed method is orthogonal to other Federated Learning algorithms and can be integrated into most Federated Learning systems. Extensive experiments in various settings on MNIST, CIFAR-10, and CIFAR-100 datasets showed that using our proposed method can relatively enhance the accuracy of the global model by up to 12.48%. Our work, for the first time, offers a simple yet effective solution that reduces the undesired effects of data sparsity and scarcity in FL.
DoSChecker: An Efficient and Dedicated Tool for Detecting DoS Vulnerability in Smart Contracts

Smart contracts have recently gained popularity due to the ability to run on their own without third-party intervention. Unfortunately, Smart contracts are susceptible to various vulnerabilities, such as reentrancy, integer overflow and denial-of-service (DoS). Among these vulnerabilities, DoS is a particularly serious one, consuming many contract resources and rendering the contract unresponsive or even terminated abnormally. Therefore, it is significantly crucial to develop detection tools for DoS vulnerability in smart contracts. However, we find that the existing approaches only identify a few DoS patterns and are with high false positives. In this paper, we develop a dedicated automated detection tool, DoSChecker, which intends to detect DoS vulnerability in smart contracts in an efficient way. Specifically, we define four patterns of DoS vulnerability and analyze their function features. According to these features, we design automated detection schemes via symbolic execution technology. Experiments demonstrate that DoSChecker can detect more DoS vulnerability with lower false positives and higher efficiency, and consumes far fewer memory resources than the state-of-the-art tools. In addition, we use DoSChecker to evaluate 155,721 contracts on Ethereum and discover that 12% of them match at least one DoS pattern and might suffer from DoS attacks.
Accelerate Inference of CNN Models on CPU via Column Combining Based on Simulated Annealing

Convolutional Neural Networks (CNNs) have been successful in various computer vision tasks. However, the size of state-of-the-art CNN models tends to be tremendous, which results in very long inference times and high memory usage. Model compression technology such as unstructured pruning can prune a significant proportion of parameters without affecting accuracy, but efficient utilization of sparsity remains a challenge. Column combining compress unstructurally-pruned CNN models by combining multiple sparse columns in a convolutional filter matrix into a single dense column. In addition, pruning all but the largest magnitude weight in each row of the combined column further compresses the matrix effectively. However, previous work did not address the details of partitioning sparse columns to minimize the negative impact of additional pruning on the performance of the model. In this work, we first prove that the column partition problem is an NP-Complete problem. Next, we propose a column combining scheme based on simulated annealing and global unstructured pruning to minimize the adverse effects of additional pruning on model performance. We implement the acceleration of column-combined CNN models using the TVM AI compiler without special hardware support. The proposed scheme achieves more efficient model compression, leading to a 0.65% improvement in accuracy and a $1.24\times$ faster inference time on VGG19 under 88% sparsity with the TinyImageNet dataset.
Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model’s lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model’s comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology.
Sign Language Predictor System Using Human Activity Recognition

Sign language detection using LSTM is a rapidly evolving research area that focuses on developing intelligent systems capable of interpreting sign language gestures. This study proposes a deep learning approach to detect sign language gestures by using Long Short- Term Memory (LSTM) networks. The proposed system first extracts features from sign language videos and then feeds them to an LSTM network for classification. The LSTM model learns the temporal dependencies in the data and accurately identifies sign language gestures. Experimental results indicate that the proposed method achieved an accuracy rate of 94 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on the American Sign Language (ASL) dataset and 90% on the Turkish Sign Language (TSL) dataset, outperforming other state-of-the-art methods. The proposed approach also shows robustness against varying lighting conditions and camera viewpoints. We will be employing Media pipe framework to recognize faces and landmarks which will then enable us to recognize and predict sign language symbols effectively. This study presents a promising approach for sign language detection using LSTM, which has the potential to significantly enhance communication between hearing-impaired individuals and the rest of the population. The proposed approach can be further improved by incorporating additional data augmentation techniques and other deep learning architectures.
Approach form of regional cluster to support renewable energy development in Vietnam

The article is a brief for the analysis of current situation and reviews of the mechanisms and policies to support the development of the energy system in general and renewable energy particularly in Vietnam. The support is considered in the position of the trend green economic, sustainable development, emission reduction and environmental protection all over the world. Based on analysis and assessment, the method of support for the development of renewable energy sources in the locality is formed in the direction of establishment regional economic clusters for local renewable energy sources. Since, the structure of components for a regional renewable energy cluster is proposed as a method to approach to more effective support for the development of regional renewable energy sources. The expected benefits and efficience from clusterization are also considered in the cluster levels.
Consensus Mechanisms Analysis: A Remedy for the Byzantine Generals Problem

A The foundational technology behind bitcoin is blockchain. Blockchain is gaining more and more attention across numerous industries as a result of the growth in value and reliable functioning of bitcoin. Blockchain has the qualities of decentralization, consistency, security, and immutability. This might lead to changes in the network architecture. The blockchain's efficiency and safety are maintained in large part by the consensus algorithm. The performance of a blockchain application may be significantly improved by the use of the prop-er algorithm. The principles and traits of the consensus algorithms, as well as the functionality and use of a variety of consensus mechanisms, are discussed in this article. In addition, we provided an overview of current and potential future developments in blockchain technology as well as technical advice on choosing an appropriate consensus method.
A Short-Term Prediction Method for PV Power Generation Based on SVM Weather Classification and PSO-BP Neural Network

Photovoltaic (PV) power generation has strong uncertainty and volatility due to weather conditions, which reduces the accuracy of short-term PV power generation forecasting and hinders the efficient operation of the power grid. To reduce the impact of weather on the accuracy of short-term PV power generation forecasting, this paper proposes a method of combining support vector machine (SVM) and particle swarm optimization (PSO)-optimized backpropagation (BP) neural network for short-term PV power generation forecasting. Firstly, we use the SVM kernel function classification method to classify weather into three categories: sunny, cloudy, and rainy, and trains the neural network model after classification with PV power generation data under the three weather types. Then, in order to further improve the prediction accuracy, the PSO algorithm is used to optimize the initial parameters and learning rate of the BP neural network, and optimized PSO-BP neural network is applied to perform short-term forecasting on the SVM classified PV power generation data. Simulation results show that the proposed method (SVM-PSO-BP) can significantly improve the accuracy of short-term PV power generation forecasting.
An Autonomous Emotion Recognition Strategy Employing Deep Learning for Self-Learning

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression Recognition Challenge (FERC-2013) Dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process”s conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learning., which might have far-reaching applications across many different fields. Our method allows for more accurate and autonomous expression decoding., which opens the door to more nuanced interactions and promotes development and better communication across fields from finance to education.
A Tool for Monitoring and Forecasting Rooftop Photovoltaic Generation in Vietnamese Power System Utilizing Satellite Imagery Data

In recent years, distributed energy resources have grown rapidly in Vietnamese Power System, especially the rooftop Photovoltaic sources. The operational characteristics of these sources such as lack of real-time measuring data and controllable capability have caused significant difficulties in operation and management for power system operator at both transmission and distribution level, to maintain the stability and security of the power system. To cope with this situation, the development of an applications aimed to calculate and estimation the potential rooftop PV generation in near real-time, is crucial for operational monitoring and scheduling power systems for Vietnamese power system operator. In this study, an application is implemented for estimating and forecasting active power of rooftop PV systems based on satellite imagery data source. In addition, to increase the forecast accuracy, a feedback mechanism is proposed utilizing Linear Least Squares algorithm. The predicted outcomes indicate the superiority of the proposed procedure compared to the original forecast.