As an important and widely studied research topic, the detection of fresh milk plays a crucial role in protecting consumer health. However, due to the diversity of milk brands and environmental conditions in real-world application scenarios, the accuracy of existing detection models often drops significantly when applied to new domains. A significant amount of new data needs to be gathered in order to retrain the model for new domain, which greatly increases both time and labor costs. To tackle this problem, this paper introduces a transfer learning-based cross-domain milk freshness detection method. The method uses transfer learning to generate data for unknown categories within the target domain. Specifically, the transfer generation model trains on data from both the target and source domain categories. With the proposed model, data from other categories in the source domain can be utilized to generate corresponding target domain data. This method comprehensively considers data generation from multiple perspectives of time, frequency, and spatial, to enhance the authenticity of the generated data. It uses a transfer generation architecture, consisting of a transfer network and a decoder, to learn the mapping relationship between the source and target domain, helping to narrow the gap between domains. Additionally, a feature subspace decomposition method is introduced into the decoder, and a cross-domain consistency loss function is formulated to strengthen the model’s learning capability. The proposed method is shown to generate high-quality data for unknown target-domain categories in multiple cross-domain settings, leading to a performance improvement ranging from 17.64% to 32.44% in the detection of cross-domain milk freshness.

In medical image analysis, computed tomography (CT) is essential for organ segmentation and computer-aided diagnosis. However, existing methods relying on single modalities (e.g., CT or MRI) face two issues: (1) similar grayscale characteristics among organs cause ambiguous boundaries, and (2) lacking semantic guidance limits organ discrimination. To overcome these challenges, this study proposes a CLIP-based semantic fusion method. The CLIP text encoder converts label text into semantic vectors, while SwinUNETR extracts image features. A DOKAN fusion network then achieves orthogonal fusion and cross-modal semantic alignment. Experiments on BTCV and AMOS datasets show Dice score gains of 1.81 % and 2.28 %, with over 4 % improvement in adrenal gland segmentation. The proposed method effectively bridges the semantic gap and advances multimodal fusion for intelligent diagnosis systems.

Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them.Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed.However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust.Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code.This leads to a large number of similar contracts with potential security risks.Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum.In this paper, we design a learning-effective and costefficient model, called SmartSD, for Ethereum smart contract similarity detection.Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks.The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers.

The autonomous driving industry has mushroomed over the past decade. Although autonomous driving has undoubtedly become one of the most promising technologies of this century, its development faces multiple challenges, of which security is the major concern. In this article, we present a thorough analysis of autonomous driving security. First, the attack surface of autonomous driving is presented. After an analysis of the operation of autonomous driving in terms of key components and technologies, the security of autonomous driving is elaborated in four dimensions: 1) sensors; 2) operating system; 3) control system; and 4) vehicle-to-everything (V2X) communication. Sensor security is examined from five components, which are mainly responsible for self-positioning and environmental perception. The analysis of operating system security, the second dimension, is concentrated on the robot operating system. Concerning the control system security, the controller area network is approached mainly from vulnerabilities and protection measures. The fourth dimension, V2X communication security, is probed from four categories of attacks: 1) authenticity/identification; 2) availability; 3) data integrity; and 4) confidentiality with corresponding solutions. Moreover, the drawbacks of existing methods adopted in the four dimensions are also provided. Finally, a conceptual multilayer defense framework is proposed to secure the information flow from external communication to the physical autonomous vehicle.

With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based on deep neural network can avoid studying electrochemical phenomena and manual extracting the features in battery. But single neural network has the different prediction accuracy and features extraction on different dataset. In this study, an ensemble method for the heterogeneous neural network is proposed, which integrates the prediction results of multiple heterogeneous neural networks with the adaptive weight. The weight of the neural network is higher with the closer correlation to the majority prediction results, vice versa. Furthermore, the weight of the neural network is adjusted via the predicting results for neural network on the different dataset, so that the computed weight of the neural network is adapted to the various dataset, and the effects of poor predictions of certain neural networks can be reduced sufficiently. The effectiveness of the ensemble method is verified on MIT-Stanford LIB degradation dataset, and the results show that the proposed method has higher accuracy than the existing ensemble methods for neural network.

Abstract Wireless sensor networks (WSNs) are extensively deployed to collect various data. Due to harsh environments and limitation of computing and communication capabilities of sensor nodes, the quality and reliability of sensor data are compromised by outliers. With the advent of 5G, sensors tend to generate increasingly more complex data. When faced with big data, traditional outlier detection methods relied on sensor nodes and remote cloud are unable to accord satisfactory performance in terms of delay and energy consumption. To address this problem, we propose a mobile edge–cloud collaboration outlier detection framework. Outlier detection is performed by edge nodes between the remote cloud and the underlying WSNs, while the training and updating of detection model are conducted on the cloud. A fast angle‐based outlier detection method is developed to obtain training data. The detection model is constructed based on support vector data description. An on‐line learning‐based iterative optimization scheme is devised to update the detection model. Besides, a fuzzy concept is incorporated into the detection model to alleviate the problem of loose decision boundary. Extensive experiments are conducted on real‐world data set. Simulation results show that our model is superior to three popular methods in terms of delay and energy consumption. In addition, when the percentage of operational nodes is 60%, our proposal prolongs the network lifetime by 14.2% to 69.8% compared to the three methods.

When a small portion of the decision makers hold the correct information and the majority hold the opposite, the correct ranking of the alternatives for the group decision-making cannot be obtained with the current methods. A novel method is thus developed to tackle this challenge in this article. The priori probabilities of each alternative can be calculated via the opinions of the group decision makers, which are presented as the pairwise comparisons of the alternatives in the form of the linguistic preference relation. Based on the aggregated probabilities of the alternatives in the group of the decision makers, the normalized-prediction selection rate (NPSR) is defined and calculated accordingly. The alternative with maximal NPSR is selected as the correct answer, whereas the accuracy of the correct alternative selection (CAS) is guaranteed by two propositions. The iterative algorithm is first devised to determine the ranking of the alternatives depending on the CAS. For the proposed method, the decision makers require no modification of the opinions as can avoid the consensus problem, and the CAS can be obtained under the circumstances that the correct information is held by the minority of the group. Finally, the experiment has been conducted to demonstrate the efficacy of the proposed method to obtain the CAS, and the main limitations of proposed method are carefully addressed as well.

Pythagorean Fuzzy Preference Relations (PFPRs) have been considered in recent literature more powerful and flexible than the popular intuitionistic fuzzy preference relation in dealing with the linguistic imprecision for decision makers in the large scale group decision making. Following on this promising trend, a novel approach based on the PFPRs is proposed for decision support. In particular, the proposed work starts with the acquisition of the optimal comparison matrices, which essentially record the pairwise comparison of the alternatives from the positive and negative opinions. The proposed consensus reaching process is then utilised to guide the decision makers to revise the provided information in order to reach the overall group consensus, before the derivation of rankings of the alternatives. Experimental studies are provided to demonstrate the workings and effectiveness of the proposed approach in comparison with two state-of-the-art methods.

As the volume of data grows exponentially, big data brings an unprecedented burden to the current computing infrastructure. How to deal with big data efficiently and concisely and reduce the burden of computing infrastructure has always been a big challenge. Therefore, this paper proposes a high-quality core data extraction method in edge computing nodes. Firstly, heterogeneous data are fused into a unified model, the data characteristics of the original data are retained. Then, a Lanzcos-based incremental tensor decomposition method is proposed to extracted the high quality core tensor dynamically. Finally, the model algorithm is verified using real data. The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy. At the same time, IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error.

The methods with consensus reaching process can obtain a collective solution which is supported by most of decision makers in larger-scale group decision making. However, in case decision makers who could give correct opinions are from the minority, the conventional methods with consensus reaching process can not obtain the correct answer. In this paper, a novel method is developed to tackle this challenge. The decision makers give the opinions utilizing pairwise comparisons of the alternatives from positive and negative views based on intuitionistic fuzzy preference relation. The obtained opinions are translated into intuitionistic fuzzy numbers, and are further grouped and aggregated according to the alternatives. Based on the aggregated intuitionistic fuzzy numbers, the prediction normalized rate is defined and calculated for each alternative, the alternative with the minimal prediction normalized rate is selected as correct one. The experimental results show that the proposed method can obtain the correct answer even when the actual correct opinions are reflected by a small number of decision makers.

Location-aware communication is one of the enabling techniques for future 5G networks. It requires accurate temporal and spatial channel estimation from multidimensional data. Most of the existing channel estimation techniques assume that the measurements are complete and noise is Gaussian. While these approaches are brittle to corrupted or outlying measurements, which are ubiquitous in real applications. To address these issues, we develop a lp-norm minimization based iteratively reweighted higher-order singular value decomposition algorithm. It is robust to Gaussian as well as the impulsive noise even when the measurement data is incomplete. Compared with the state-of-the-art techniques, accurate estimation results are achieved for the proposed approach.

Crowd intelligence opens up new ways for decision making in open environments, traditional decision making is unable to effectively make correct decisions in open environments. In this paper, positive and negative comparing method using linguistic scale is proposed to make decisions in the open environments with crowd intelligence. Firstly, the crowd participants compare the alternative with the corresponding positive and negative assessment points, and give their evaluations using linguistic scales form positive and negative views. The crowd participants' evaluations can be translated into Intuitionistic Fuzzy Numbers (IFNs). In the proposed methods, the evaluations given by the crowd participants do not depend on the pairwise comparisons of the alternatives, the consistent problem can be avoided. Secondly, the consensus measures between aggregating results and IFNs are proposed. Based on these concepts, the aggregating methods that without discarding any IFNs are proposed and studied. The studying results show that the proposed methods can improve the consensus measures between the aggregating result and evaluations given by crowd participants.

This article did research on multi-target tracking system based on the classic kinetics in Wireless Sensor Networks (WSN), a distributed Cell tracking model is brought up. The whole WSN was layered by Virtual Grid Architecture (VGA)[1][2]. When a target appeared in a grid, local aggregators (LA) aroused all nodes in the eight adjacent grids to compose a Cell to track target. A Cell election rule is proposed: when a target is escaping from the current Cell, based on the classical laws of kinetics and the historical track information, the position and velocity of the target can be estimated when it crossed the edge of Cell and the next Cell can be elected. Multi-Cell sequence can track multitarget concurrently. The concepts of main target and subtarget are introduced. When multi-target were space-time overlapped, a single Cell may have several main targets and sub-targets. The tracking algorithms are designed for them separately. This model can manage the problems like wrong association and missing target. The simulation showed the approach is effective.