The rapid functional growth of mobile devices and increasing computing power have created new opportunities for the application of deep learning algorithms on mobile platforms. Deep learning is an important subdiscipline in the field of artificial intelligence and has made great progress in recent years. With these advances, deep learning methods have been used to increase data volumes, providing solutions for complex calculations with high levels of abstraction and pattern recognition capabilities. Since these capabilities provide great advantages in many application areas, the coordination of different systems that will work online with mobile devices has led to the emergence of a very important field of work. In recent years, the increasing popularity of users on mobile devices and the communication of online systems with each other internet of things have made the use of deep learning methods on mobile devices very attractive. In the study, a photo obtained by using the photo-taking feature of the Android device with the developed Android software or an image selected from the Android device's gallery was improved with image processing techniques. After this process, the image is analyzed using deep learning methods with a different device that will work online over the internet of things, aiming to scientifically determine which face type the person has. In addition, it is aimed to show the list of glasses types most suitable for the determined face type to the person with the android software developed within the scope of the study. In this way, this software not only recommends glasses to the person, but also saves users both space and time.
Existing security solutions often suffer from many practical issues in a blockchain environment such as generalization and actionability. This paper presents BCFW, a blockchain consensus firewall that couples uncertainty-aware intrusion detection with on-chain governance for Proof-of-Authority networks. We design an FT-Transformer tailored to tabular flow data and augment it with an aleatoric uncertainty head to make predictions actionable under operational risk. Validated on the CIC-IDS 2017 and the CSE-CIC-IDS 2018 dataset, our BCFW achieves 98.20
High-performance distributed systems (HPDS) are increasingly challenged by dynamic workloads, heterogeneous resources, and susceptibility to faults, which degrade scheduling efficiency and resilience. Traditional heuristic and learning-based scheduling approaches, on the other hand, tend to be poorly adaptable and have high overhead in large-scale deployments. To solve these issues, this paper proposes an Adaptive Quantum Neuro-Symbolic Scheduling and Federated Sparse Transformer (AQNSS-FST) framework that is augmented with Harris Hawks Optimization (HHO). The AQNSS component employs quantum-inspired symbolic reasoning for adaptive task-to-resource mapping, in which quantum operators boost convergence in high-dimensional scheduling spaces as compared to classical heuristics. The FST module facilitates collaborative, lightweight, and fault-resilient learning across distributed nodes, and HHO dynamically balances global exploration and local exploitation to avoid premature convergence under varying workloads. The experimental results on the Google Cluster Workload Traces and FEMNIST dataset show that AQNSS-FST achieves a significantly improved performance compared to the current baselines. The structure saves up to 25
In cognitive Internet of Things (CIoT) systems, data incompleteness is a recurring issue caused by sensor malfunctions, hardware limitations, and unreliable network conditions. Traditional matrix completion methods provide a solution but often fail when faced with randomly missing entries or long consecutive gaps, resulting in distorted recovery outcomes. To address this, we present a context-aware (situational) matrix completion framework that integrates anomaly detection, probabilistic clustering, and predictive reasoning. The method begins by identifying and removing abnormal sensor values to prevent their propagation during reconstruction. The incomplete matrix is then clustered using a probabilistic approach, with each cluster assigned a plausibility score. The most plausible cluster forms the contextual foundation for recovery. A regression model based on relational pair extraction from contextual data predicts missing values. For entirely missing rows or columns, regression outputs are directly employed, while randomly scattered missing values are refined using maximum a posteriori (MAP) estimation. We evaluate the proposed method on a 21.25-year multi-sensor environmental dataset. Experimental results and cross-validation analysis demonstrate significant improvements in recovery accuracy (> 99.30
Public-key encryption with equality test ( ) is an important cryptographic primitive for cloud computing, as it enables equality testing over ciphertexts without revealing the underlying plaintexts. In multi-replica cloud environments, it is often desirable that equality-test results be publicly verifiable. To support this functionality, publicly verifiable ( ) has been introduced. However, existing schemes achieve only chosen-plaintext attack (CPA) security, which is insufficient for security-critical cloud applications. In this paper, we propose the first chosen-ciphertext secure scheme. In the proposed construction, authorized cloud servers collaboratively perform equality testing, while any party can publicly verify the correctness of the test result. Moreover, only the authorized cloud servers can generate a valid proof for the equality-test outcome. In the random oracle model, we prove that the proposed scheme achieves one-wayness under chosen-ciphertext attacks against authorized internal adversaries based on the Bilinear Diffie-Hellman assumption and the preimage resistance of the hash function, while simultaneously achieving indistinguishability under chosen-ciphertext attacks against unauthorized external adversaries based on the decisional Bilinear Diffie-Hellman assumption. We further prove that the correctness proof is existentially unforgeable under chosen-message attacks based on the Computational Diffie-Hellman assumption. Experimental results show that, compared with the latest scheme of Li et al. (TDSC 2024), our scheme strengthens the security level from CPA to CCA and reduces the computational cost of public verification to 0.76× that of Li et al.’s scheme, with only moderate additional overhead in the overall workflow.
