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To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

To protect the privacy of power data, we usually encrypt data before outsourcing it to the cloud servers. However, it is challenging to search over the encrypted data. In addition, we need to ensure that only authorized users can retrieve the power data. The attribute-based searchable encryption is an advanced technology to solve these problems. However, many existing schemes do not support large universe, expressive access policies, and hidden access policies. In this paper, we propose an attribute-based keyword search encryption scheme for power data protection. Firstly, our proposed scheme can support encrypted data retrieval and achieve fine-grained access control. Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data. Secondly, to satisfy the requirement in the power grid environment, the proposed scheme can support large attribute universe and hidden access policies. The access policy in this scheme does not leak private information about users. Thirdly, the security analysis and performance analysis indicate that our scheme is efficient and practical. Furthermore, the comparisons with other schemes demonstrate the advantages of our proposed scheme.

With the development of blind bolts that can be installed from the outer side of a section, the application of steel pipe hollow sections in prefabricated structures has become possible. The novel one-side bolt with a flip-top collapsible washer (FTW-OSB) is proposed, which can fold the washer at the end of the bolt to achieve a simple and efficient installation. Firstly, the components and installation process of the FTW-OSB are introduced. Additionally, axial tensile tests were carried out on three types of new bolts, and the failure mode, load–displacement curve, and ultimate capacity were analyzed. Based on this, the finite element model was verified, and through a series of finite element parameter analyses, the influence of washer size and construction, the friction coefficient, and the contact surface size on the new bolt were analyzed and optimized. The results show that the L-shaped washer can prevent washer rotation and has better anchoring performance than the I-shaped washer. The washer thickness, height, and upper contact surface size are the key factors affecting the anchoring performance of the FTW-OSB bolt. Among these factors, when the washer thickness increases from 0.1 d0 to 0.3 d0, the ultimate capacity and initial stiffness of the FTW-OSB bolt increase by 136.5% and 100.3%, respectively. The lower contact surface size and friction coefficient can be ignored. The failure model of the FTW-OSB is mainly washer shear failure and bolt tension failure. By effectively adjusting the washer thickness, side wall height, and upper contact surface size, shear failure of the washer can be avoided and an anchoring performance can be achieved that is the same as that of the same type of 10.9-grade ordinary high-strength bolt.

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes.

The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.

This paper presents consensus based multi-person decision making (MPDM) using consistency graphs (additive consistent and order consistent) in a fuzzy environment. At the first level, consistency analysis is put forward after defining consistent fuzzy preference graph (CFPG) with the help of additive transitivity. This analysis further leads us to determine priority weights vector of the decision-makers (DMRs) after evaluation consistency weights. At the second level, the consensus analysis helps us to determine whether the selection process should be initiated or not. If the consensus degree amongst DMRs does not reach a minimum acceptable level, then the enhancement mechanism plays a central role to improve the consensus level by giving suitable suggestions to DMRs. In the end, the weighted sum operator (WSO) is used to get aggregated consistency fuzzy preference graph (AgCFPG) and the order consistency property provides us sufficient information to rank the alternatives.

Many state-of-the-art citation recommendation methods have been proposed for finding a list of reference papers for a given manuscript, among which the graph-based method has gained particular attention, due to its flexibility for incorporating various information that embodies user's preferences. To achieve a more synthetic, accurate, and personalized recommendation result than the previous graph-based methods, this paper proposes a new graph-based recommendation framework that exploiting diversified link information in a bibliographic network and the concise query information that embodies the specific requirement of user comprehensively. The proposed framework not only performs mutual reinforcement rules on all available multiple types of relations in a multi-layered graph but also incorporates the query information into the multi-layered mutual reinforcement schema to construct a multi-layered mutually reinforced query-focused (MMRQ) citation recommendation approach. Extensive experiments have been conducted on a subset of anthology network data set. Experimental results of Recall measures, normalized discounted cumulative gain measures, and case study all demonstrate that our MMRQ method obtains a superior citation recommendation.

Computing hardware has become an attractive attack surface due to the globalization of semi-conductor design and supply chain, and the wide integration of third-party intellectual property cores. Recently, gate-level information flow tracking (GLIFT) has been proposed to monitor the flow of information in secure hardware design by associating data objects with sensitivity labels and tracking the flow of labeled data. GLIFT can be used to model and verify security-related properties, such as confidentiality and integrity. However, existing work in this realm only considers binary labels. These are inadequate for understanding simultaneous information flow behaviors and the root source information flows. In this paper, we propose a precise multi-bit GLIFT method to perform simultaneous multi-bit flow tracking for understanding exactly which bits are affecting a data object at the same time. The proposed method provides more detailed insights into simultaneous information flow behaviors and thus allows proof of quantitative information flow data properties. We compare the complexity and verification performance for different information flow models using primitive gates, IWLS benchmarks, several cryptographic cores, and trustHUB benchmarks. Experimental results have demonstrated that our method can reason about multi-bit information flow behaviors and identify potential security flaws.

Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex networks of trade and financial transactions, which makes it difficult to detect the fraud entities and discover the features of fraud. Fortunately, trading/transaction network and features of entities in the network can be constructed from the complex networks of the trade and financial transactions. The trading/transaction network reveals the interaction between entities, and thus anomaly detection on trading networks can reveal the entities involved in the fraud activity; while features of entities are the description of entities, and anomaly detection on features can reflect details of the fraud activities. Thus, network and features provide complementary information for fraud detection, which has potential to improve fraud detection performance. However, the majority of existing methods focus on networks or features information separately, which does not utilize both information. In this paper, we propose a novel fraud detection framework, CoDetect, which can leverage both network information and feature information for financial fraud detection. In addition, the CoDetect can simultaneously detecting financial fraud activities and the feature patterns associated with the fraud activities. Extensive experiments on both synthetic data and real-world data demonstrate the efficiency and the effectiveness of the proposed framework in combating financial fraud, especially for money laundering.

Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just one label set in the training phase. In this paper, we focus on dealing with this kind of weak label learning task, which is commonly met in old legacy information system, and it is also called “Hercules Learning.”We propose a label-group-optimization-based Hercules learning algorithm, which divides the entire label set into multiple groups according to the classifier's capability to distinguish them, so for each group, we can train a classifier which can predict instance's label within the group with high accuracy. The experimental results show that our algorithm is obviously superior to the existing weak label learning algorithm.

The rapid growth of scientific papers makes it difficult to find relevant and appropriate citations. Context-aware citation recommendation aims to overcome this problem by providing a list of scientific papers given a short passage of text. In this paper, we propose a long-short-term memory (LSTM)based model for context-aware citation recommendation, which first learns the distributed representations of the citation contexts and the scientific papers separately based on LSTM, and then measures the relevance based on the learned distributed representation of citation contexts and the scientific papers. Finally, the scientific papers with high relevance scores are selected as the recommendation list. In particular, we try to incorporate author information, venue information, and content information in scientific paper distributed vector representation. Furthermore, we integrate author information of the given context in citation context distributed vector representation. Thus, the proposed model makes personalized context-aware citation recommendation possible, which is a new issue that few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP data sets, the results demonstrate the proposed LSTM-based model for context-aware citation recommendation is able to achieve considerable improvement over previous context-aware citation recommendation approaches. The personalized recommendation approach is also competitive with the non-personalized recommendation approach.

Next-app prediction is the task of predicting the next app that a user will choose to use on the smartphone. It helps to establish a variety of intelligent personalized services, such as fast-launch UI app, intelligent user-phone interactions, and so on. Since app names only provide limited semantic information, the intrinsic relation among apps cannot be fully exploited. Meanwhile, next-app to be used is largely determined by a sequence of apps that a user used recently. To address these challenging problems, this paper first enriches the semantic information of apps by extracting descriptive text of each app from the app store and thus proposes a topic model to transform apps as well as user preferences into latent vectors. Then, a set of nearest neighbors can be constructed based on the similarity of latent vectors and it is employed for training the prediction model. Furthermore, our prediction scheme is built on the temporal sequential data and is modeled by using the chain-augmented Naive Bayes model. Experimental results with a real smartphone application log data have demonstrated that our method achieves higher recall and DCG values compared with several baseline next-app prediction methods.

In the era of big scholarly data, citation recommendation is playing an increasingly significant role as it solves information overload issues by automatically suggesting relevant references that align with researchers' interests. Many state-of-the-art models have been utilized for citation recommendation, among which graph-based models have garnered significant attention, due to their flexibility in integrating rich information that influences users' preferences. Co-authorship is one of the key relations in citation recommendation, but it is usually regarded as a binary relation in current graph-based models. This binary modeling of co-authorship is likely to result in information loss, such as the loss of strong or weak relationships between specific research topics. To address this issue, we present a fine-grained method for co-authorship modeling that incorporates the co-author network structure and the topics of their published articles. Then, we design a three-layered graph-based recommendation model that integrates fine-grained co-authorship as well as author-paper, paper-citation, and paper-keyword relations. Our model effectively generates query-oriented recommendations using a simple random walk algorithm. Extensive experiments conducted on a subset of the anthology network data set for performance evaluation demonstrate that our method outperforms other models in terms of both Recall and NDCG.

This article proposes a family of spatial three translational and one rotational parallel mechanisms (PMs) for pick-and-place operation. Their features are one independent rotation of the mechanism with four identical limbs, which are provided by the four revolute joints on the moving platform. The rotational capability of the PMs has a range of at least 180°. This article focuses on the synthesis of the PMs and kinematics analysis of the 4- P (2-SS)R parallel mechanism. First, based on the Lie group theory, three parallelograms are used in designing the PMs. The limbs are listed and two types of three translational and one rotational PMs are synthesized. Then, a typical 4- P (2-SS)R PM is selected, the 6 × 6 Jacobian matrix and the 6 × 6 × 6 Hessian matrix of the mechanism are derived for solving the displacement, velocity, and acceleration of the mechanism. Finally, singularity configurations are disclosed from the 6 × 6 Jacobian matrix, and the workspace of the mechanism is provided to illustrate the high rotational capability.

This article proposes a new spatial 2-(PUU) 2 R hybrid mechanism that can perform the three degrees of freedom translation and one degree of freedom rotation and presents an analysis of the dynamics of the mechanism. An adaptive fuzzy terminal sliding mode controller with nonlinear observer for the hybrid mechanism is proposed to achieve a precise trajectory tracking, which could be utilized in solving the problems of the hybrid mechanism caused by model uncertainties, varying payloads, and external disturbances. Firstly, through the interrelation between the constraints, the 6 × 6 Jacobian matrix and 6 × 6 × 6 Hessian matrix for the mechanism are derived. Furthermore, dynamic modeling is established based on the virtual work principle, through which the characteristics of dynamic modeling can be proved. To achieve high-precision position tracking, a nonlinear observer was introduced to feed into the terminal sliding mode control which had improved the mechanism’s ability to resist the external disturbances. In addition, the chattering caused by the terminal sliding mode control was eliminated by approximating the switching gain with the usage of adaptive fuzzy logic in a finite time. Finally, a series of numerical simulations are carried out to prove the validity of the proposed approach, and the results verify better robustness and higher precision for the trajectory tracking than proportional–integral–derivative and sliding mode control.

The communication links of multi-agent systems (MASs) generate random time delays, which significantly affect accuracy and real-time control. A Markov chain is established at the input end and output end of a communication link to express this time delay and is introduced to asynchronous data fusion at an intelligent location. The influence of random time delays and data fusion on the positioning accuracy and real-time performance is investigated. A distributed fusion simulation system is established based on MASs and employed to simulate the random delay in the fusion of multi-sensor data. Different fusion algorithms are tested by changing the upper boundary of the time delay. The test results indicate the existence of a boundary between the data fusion and the time delay. The fusion accuracies on both sides of the boundary significantly differ. This difference is helpful for selecting sensors and fusion algorithms for different sampling frequencies in multi-agent collaborative control systems, improving the positioning accuracy of the system and effectively controlling computation costs.

Due to the disturbance of wind field, it is difficult to achieve precise airship positioning and navigation in the stratosphere. This paper presents a new constrained unscented particle filter (UPF) for SINS/GNSS/ADS (inertial navigation system/global navigation satellite system/atmosphere data system) integrated airship navigation. This approach constructs a wind speed model to describe the relationship between airship velocity and wind speed using the information output from ADS, and further establishes a mathematical model for SINS/GNSS/ADS integrated navigation. Based on these models, it also develops a constrained UPF to obtain system state estimation for SINS/GNSS/ADS integration. The proposed constrained UPF uses the wind speed model to constrain the UPF filtering process to effectively resist the influence of wind field on the navigation solution. Simulations and comparison analysis demonstrate that the proposed approach can achieve optimal state estimation for SINS/GNSS/ADS integrated airship navigation in the presence of wind field disturbance.

This paper presents a new Strap-down Inertial Navigation System/Spectrum Red-Shift/Star Sensor (SINS/SRS/SS) system integration methodology to improve the autonomy and reliability of spacecraft navigation using the spectrum red-shift information from natural celestial bodies such as the Sun, Jupiter and the Earth. The system models for SINS/SRS/SS integration are established. The information fusion of SINS/SRS/SS integration is designed as the structure of the federated Kalman filter to fuse the local estimations of SINS/SRS and SINS/SS integrated subsystems to generate the global state estimation for spacecraft navigation. A new robust adaptive unscented particle filter is also developed to obtain the local state estimations of SINS/SRS and SINS/SS integrated subsystems in a parallel manner. The simulation results demonstrate that the proposed methodology for SINS/SRS/SS integration can effectively calculate navigation solutions, leading to strong autonomy and high reliability for spacecraft navigation.

Increasingly, malicious Android apps use various methods to steal private user data without their knowledge. Detecting the leakage of private data is the focus of mobile information security. An initial investigation found that none of the existing security analysis systems can track the flow of information through Unix domain sockets to detect the leakage of private data through such sockets, which can result in zero-day exploits in the information security field. In this paper, we conduct the first systematic study on Unix domain sockets as applied in Android apps. Then, we identify scenarios in which such apps can leak private data through Unix domain sockets, which the existing dynamic taint analysis systems do not catch. Based on these insights, we propose and implement JDroid, a taint analysis system that can track information flows through Unix domain sockets effectively to detect such privacy leaks.