Search Results (23)

Large language models (LLMs) have driven transformative progress in artificial intelligence, yet critical challenges persist in data management and privacy protection during model deployment and training. The approximate nearest neighbor (ANN) search, a core operation in LLMs, faces inherent trade-offs between efficiency and security when implemented through conventional locality-sensitive hashing (LSH)-based secure ANN (SANN) methods, which often compromise either query accuracy due to false positives. To address these limitations, this paper proposes a novel secure ANN scheme based on nearest neighbor graph (NNG-SANN), which is designed to ensure the security of approximate k-nearest neighbor queries for vector data commonly used in LLMs. Specifically, a secure indexing structure and subset partitioning method are proposed based on LSH and NNG. The approach utilizes neighborhood information stored in the NNG to supplement subset data, significantly reducing the impact of false positive points generated by LSH on query results, thereby effectively improving query accuracy. To ensure data privacy, we incorporate a symmetric encryption algorithm that encrypts the data subsets obtained through greedy partitioning before storing them on the server, providing robust security guarantees. Furthermore, we construct a secure index table that enables complete candidate set retrieval through a single query, ensuring our solution completes the search process in one interaction while minimizing communication costs. Comprehensive experiments conducted on two datasets of different scales demonstrate that our proposed method outperforms existing state-of-the-art algorithms in terms of both query accuracy and security, effectively meeting the precision and security requirements for nearest neighbor queries in LLMs. Full article

With the continuous expansion of users, businesses, and services in electricity retail trading systems, the demand for personalized recommendations has grown significantly. To address the issue of reduced recommendation accuracy caused by insufficient data in standalone recommendation systems, the academic community has conducted in-depth research on distributed recommendation systems. However, this collaborative recommendation environment faces two critical challenges: first, how to effectively protect the privacy of data providers and power users during the recommendation process; second, how to handle the potential presence of malicious data providers who may supply false recommendation data, thereby compromising the system’s reliability. To tackle these challenges, a blockchain-based lightweight reputation-aware electricity retail trading service recommendation (BLR-ERTS) system is proposed, tailored for electricity retail trading scenarios. The system innovatively introduces a recommendation method based on Locality-Sensitive Hashing (LSH) to enhance user privacy protection. Additionally, a reputation management mechanism is designed to identify and mitigate malicious data providers, ensuring the quality and trustworthiness of the recommendations. Through theoretical analysis, the security characteristics and privacy-preserving capabilities of the proposed system are explored. Experimental results show that BLR-ERTS achieves an MAE of 0.52, MSE of 0.275, and RMSE of 0.52 in recommendation accuracy. Compared with existing baseline methods, BLR-ERTS improves MAE, MSE, and RMSE by approximately 13%, 14%, and 13%, respectively. Moreover, the system exhibits 94% efficiency, outperforming comparable approaches by 4–24%, and maintains robustness with only a 30% attack success rate under adversarial conditions. The findings demonstrate that BLR-ERTS not only meets privacy protection requirements but also significantly improves recommendation accuracy and system robustness, making it a highly effective solution in a multi-party collaborative environment. Full article

Similarity-based decision support systems have become essential tools for providing tailored and adaptive guidance across various domains. In agriculture, where managing extensive land areas poses significant challenges, the primary objective is often to maximize harvest yields while reducing costs, preserving crop health, and minimizing the use of chemical adjuvants. The application of similarity-based analysis enables the development of personalized farming recommendations, refined through shared data and insights, which contribute to improved plant growth and enhanced annual harvest outcomes. This study employs two algorithms, K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour (ANN) using Locality Sensitive Hashing (LSH) to evaluate their effectiveness in agricultural decision-making. The results demonstrate that, under comparable farming conditions, KNN yields more accurate recommendations due to its reliance on exact matches, whereas ANN provides a more scalable solution well-suited for large datasets. Both approaches support improved agricultural decisions and promote more sustainable farming strategies. While KNN is more effective for smaller datasets, ANN proves advantageous in real-time applications that demand fast response times. The implementation of these algorithms represents a significant advancement toward data-driven and efficient agricultural practices. Full article

When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the robust fitting mechanism of RANSAC. With proper hash functions designed, similar data points are mapped to the same hash bucket, thereby enabling the efficient identification and removal of outliers. RANSAC is then used to fit the model parameters of the processed dataset. The optimal parameters for the linear model are obtained after multiple iterative processes. This algorithm significantly reduces the influence of outliers on the dataset, resulting in improved fitting accuracy and enhanced robustness. Experimental results demonstrate that the proposed improved RANSAC linear fitting algorithm outperforms the Weighted Least Squares, traditional RANSAC, and Maximum Likelihood Estimation methods, achieving a reduction in the sum of squared residuals by 29%, 16%, and 8%, respectively. Full article

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children without imposing any distributional assumptions. In the searching stage of BN structure learning, the causal relationships between variables are achieved by using the conditional entropy scoring function and hill-climbing strategy. To further enhance the computational efficiency of our method, we incorporate a locality sensitive hashing (LSH) function into the KDE process. The method speeds up the calculations of KDE while maintaining the precision of the estimates, leading to a notable decrease in the time required for computing mutual information, conditional mutual information, and conditional entropy. A BN classifier (BNC) is established by using the computationally efficient BN learning method. Our experiments demonstrated that KDE using LSH has greatly improved the speed compared to traditional KDE without losing fitting accuracy. This achievement underscores the effectiveness of our method in balancing speed and accuracy. By giving the benchmark networks, the network structure learning accuracy with the proposed method is superior to other traditional structure learning methods. The BNC also demonstrates better accuracy with stronger interpretability compared to conventional classifiers on public datasets. Full article

Loop closure detection plays an important role in the construction of reliable maps for intelligent agricultural machinery equipment. With the combination of convolutional neural networks (CNN), its accuracy and real-time performance are better than those based on traditional manual features. However, due to the use of small embedded devices in agricultural machinery and the need to handle multiple tasks simultaneously, achieving optimal response speeds becomes challenging, especially when operating on large networks. This emphasizes the need to study in depth the kind of lightweight CNN loop closure detection algorithm more suitable for intelligent agricultural machinery. This paper compares a variety of loop closure detection based on lightweight CNN features. Specifically, we prove that GhostNet with feature reuse can extract image features with both high-dimensional semantic information and low-dimensional geometric information, which can significantly improve the loop closure detection accuracy and real-time performance. To further enhance the speed of detection, we implement Multi-Probe Random Hyperplane Local Sensitive Hashing (LSH) algorithms. We evaluate our approach using both a public dataset and a proprietary greenhouse dataset, employing an incremental data processing method. The results demonstrate that GhostNet and the Linear Scanning Multi-Probe LSH algorithm synergize to meet the precision and real-time requirements of agricultural closed-loop detection. Full article

Given the challenges in recommendation effectiveness, communication costs, and privacy issues associated with federated learning, the current algorithm amalgamates locality sensitive hash (LSH) with three federated recommendation models: Generalized Matrix Factorization, Multilayer Perceptions, and Neural Matrix Factorization. First, the participation weights of the model are determined based on the participation degree of the federated learning clients to improve the efficiency of joint learning. Second, the local parameters of the federated aggregation model are divided into two groups to protect user embedding. Finally, rapid mapping and similarity retrieval of the upload parameters are performed using LSH to protect user privacy and shorten training time. We conducted experiments to compare the performance differences between LSH-based and Laplace noise-based differential privacy methods in terms of recommendation effectiveness, communication costs, and privacy preservation. Experimental results demonstrate that LSH models achieved a favorable balance between recommendation effectiveness and privacy protection, with improved time performance. Full article

With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices. Full article

Cultural tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for peak visitor time in Riyadh, a city in Saudi Arabia. The main objective of the framework is to improve the cultural tourism of Riyadh by considering various factors to help in improving CT based on recommendation system (RS). Primarily, the map data and cultural event dataset were processed for location, such as grouping with Kriging interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes were processed with word embedding. Meanwhile, the social network sites (SNS) data like reviews and news were extracted with an external application programming interface (API). The review data were processed with keyword extraction and word embedding, whereas the news data were processed with score value estimation. Lastly, the data were fused, corresponding to a historical site, and given to the Multi-Quadratic-Long Short-Term Memory (MQ-LSTM) recommendation system (RS); also, the recommended result with the map was stored in a database. Lastly, the database security was maintained with locality sensitive hashing (LSH). From the experimental evaluation with multiple databases including the Riyadh Restaurants 20K dataset, the proposed recommendation model achieved a recommendation rate (RR) of 97.22%, precision of 97.7%, recall of 98.27%, and mean absolute error (MAE) of 0.0521. This result states that the proposed RS provides higher RR and reduced error compared to existing related RSs. Thus, by attaining higher performance values, the proposed model is experimentally verified. Full article

In recent years, federated GBDTs have gradually replaced traditional GBDTs, and become the focus of academic research. They are used to solve the task of structured data mining. Aiming at the problems of information leakage, insufficient model accuracy and high communication cost in the existing schemes of horizontal federated GBDTs, this paper proposes an algorithm of gradient boosting decision trees based on horizontal federated learning, that is, secure and efficient FL for GBDTs (SeFB). The algorithm uses locality sensitive hashing (LSH) to build a tree by collecting similar information of instances without exposing the original data of participants. In the stage of updating the tree, the algorithm aggregates the local gradients of all data participants and calculates the global leaf weights, so as to improve the accuracy of the model and reduce the communication cost. Finally, the experimental analysis shows that the algorithm can protect the privacy of the original data, and the communication cost is low. At the same time, the performance of the unbalanced binary data set is evaluated. The results show that SeFB algorithm compared with the existing schemes of horizontal federated GBDTs, the accuracy is improved by 2.53% on average. Full article

As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash). Full article

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods. Full article

We propose a novel method using Locality-Sensitive Hashing (LSH) for solving the optimization problem that arises in the training stage of support vector machines for large data sets, possibly in high dimensions. LSH was introduced as an efficient way to look for neighbors in high dimensional spaces. Random projections-based LSH functions create bins so that when great probability points belonging to the same bin are close, the points that are far will not be in the same bin. Based on these bins, it is not necessary to consider the whole original set but representatives in each one of them, thus reducing the effective size of the data set. A key of our proposal is that we work with the feature space and use only the projections to search for closeness in this space. Moreover, instead of choosing the projection directions at random, we sample a small subset and solve the associated SVM problem. Projections in this direction allows for a more precise sample in many cases and an approximation of the solution of the large problem is found in a fraction of the running time with small degradation of the classification error. We present two algorithms, theoretical support, and numerical experiments showing their performances on real life problems taken from the LIBSVM data base. Full article

Indexing images by content is one of the most used computer vision methods, where various techniques are used to extract visual characteristics from images. The deluge of data surrounding us, due the high use of social and diverse media acquisition systems, has created a major challenge for classical multimedia processing systems. This problem is referred to as the ‘curse of dimensionality’. In the literature, several methods have been used to decrease the high dimension of features, including principal component analysis (PCA) and locality sensitive hashing (LSH). Some methods, such as VA-File or binary tree, can be used to accelerate the search phase. In this paper, we propose an efficient approach that exploits three particular methods, those being PCA and LSH for dimensionality reduction, and the VA-File method to accelerate the search phase. This combined approach is fast and can be used for high dimensionality features. Indeed, our method consists of three phases: (1) image indexing within SIFT and SURF algorithms, (2) compressing the data using LSH and PCA, and (3) finally launching the image retrieval process, which is accelerated by using a VA-File approach. Full article

The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes. Full article