Abstract Digital twin (DT) bridge the gap between the real and virtual worlds, enhancing decision-making efficiency by facilitating task offloading in the industrial Internet of Things through comprehensive real-world status information. However, the often-overlooked discrepancies between DT and their real-world counterparts introduce high uncertainty into offloading decisions, potentially leading to unexpected outcomes. To address this issue, we adopt prospect theory to formulate a task offloading decision problem that integrates the behavioral tendencies of system participants, thereby maximizing participants’ utility and providing a realistic approach to managing offloading decisions. We reformulate this NP-hard problem as a potential game and demonstrate the existence of a Nash Equilibrium (NE). The finite improvement property is used to implement a decentralized algorithm that identifies the NE in the potential game as a solution to the offloading problem. Furthermore, we theoretically derive an upper bound on the algorithm’s convergence time. The superiority of our proposed scheme over existing schemes in terms of performance and scalability is evaluated and demonstrated through extensive simulations.

Heterogeneous Defect Prediction (HDP) refers to using labeled instance data from source projects to predict potential defect instances for the target project. This approach addresses the feature heterogeneity problem between different datasets, aiming to improve software quality actually. Notably, existing research on HDP addresses imbalance issues by focusing solely on minority classes and neglecting the influence of majority classes. Moreover, these studies fail to regard issues of feature consistency and differences in data distribution in depth while addressing heterogeneity in the feature space. Consequently, this paper proposes a 2-stage HDP approach, PEAPOD, based on dee P d Ensity s Ample and dee P d Omain a Daptation. First, PEAPOD creates a balanced dataset by transforming data into a latent space, selecting high-quality samples by density and enhancing the defective class with synthetic samples through feature-level oversampling. Next, PEAPOD aligns matching features utilizing KS-test and the Hungarian algorithm and subsequently reduces data distribution differences via a Domain Adversarial Neural Network (DANN). Extensive and rigorous experimental results on 23 projects from 5 public datasets demonstrate PEAPOD outperforms HDP baseline methods.

Distributed public ledger (e.g., Blockchain) has been proven to be a powerful technique that allows users to sign transactions in an untrusted environment, where identity-privacy disclosure is gaining attention in practice. Ring signatures can protect identities by providing anonymity property for users. However, a malicious anonymous user may generate multiple signatures on the same transaction, called double-spending attack. A unique ring signature avoids this attack by attaching a unique identifier to the transaction. In addition, future-proof cryptographic solutions are attracting attention in the quantum era. Thus, we aim to propose a post-quantum unique ring signature scheme for anonymous transactions, named . We initially provide verifiable random functions over lattices (L-VRF, in short) with tight security and optimize the proof size (compared with the work of Nguyen et al., ESORICS’ 22) using compression techniques. We then obtain from L-VRF inspired by the previous solution of Franklin-Zhang (FC’ 13) while enables to prevent of quantum computer attacks. Finally, is analyzed under the quantum random oracle model (QROM) while providing a prototype via C language. The performance evaluation shows offers a smaller communication load.

High-definition (HD) map caching at roadside units (RSUs) is an important component of localization for self-driving vehicles, HD map content delivery services must be efficient for various self-driving vehicles. Nevertheless, HD maps are dynamic files that must be updated and replenished in real time. Developing an effective content delivery strategy for different types of self-driving vehicles to require HD maps, while ensuring safe driving and minimizing bandwidth consumption, is challenging. To maximize the monetary utility of the vehicle system, in this article, we jointly optimize the HD map update strategy and the wireless bandwidth resource allocation strategy, considering service delay constraints and overall system risk. However, the optimization problem described above is an NP-hard mixed-integer nonlinear programming (MINLP) problem. Additionally, in a self-driving vehicle scenario, the highly dynamic character of HD maps, the diversity of self-driving vehicle types, and the randomness of vehicle trajectories are unknown to the vehicle system in advance. The intractable optimization problem and the highly uncertain nature of the driving environment make it difficult to find an existing method that allows vehicles to obtain HD maps that meet their localization requirements in a timely manner. To address the above issues, we propose DRL-MURA, which can learn HD map updates and implement a wireless bandwidth resource allocation strategy by constantly interacting with environment based on a deep reinforcement learning (DRL) algorithm. Finally, we prove the accuracy and effectiveness of our method through simulation experiments.

ABSTRACT Heterogeneous defect prediction (HDP) plays a crucial role in software engineering by enabling the early detection of software defects across projects with heterogeneous feature spaces. Recently, some mixed‐project HDP (MP‐HDP) methods have been proposed, which have demonstrated modest improvements in HDP performance. Nevertheless, existing MP‐HDP approaches fail to address feature redundancy and distribution inconsistency simultaneously. To overcome these limitations, this paper proposes a novel MP‐HDP approach, UFR‐OSFA, based on unified feature representation and oppositional structural feature alignment. Concretely, UFR‐OSFA first unifies these features by reducing the distribution differences between source and target projects through matching common features and the Hungarian algorithm based on the Kolmogorov–Smirnov (KS) test. Subsequently, utilizing a generator and two classifiers with oppositional structures, UFR‐OSFA separates the features of the source project and clusters those of the target project, addressing the issue of conditional distribution mismatch and enhancing the model's generalization ability in the target project. Extensive experiments on 23 projects from five datasets demonstrate that the proposed approach performs better or comparably to baseline methods.

The low latency of 5G networks greatly facilitates people's daily lives. However, the increases in latency caused by various factors, such as network configuration issues and environmental interference, may severely impact latency-sensitive applications. Therefore, accurately predicting network latency, especially bursty high latency, provides a new perspective for network operations and maintenance (O&M). Operators can proactively perform resource allocation and routing adjustments to prevent network faults. Nevertheless, network latency is a complex stochastic process, and the nonlinear correlations between network parameters and latency make latency prediction challenging. Imbalanced in latency further increases the difficulty of predicting bursty high latency. To address these challenges, we propose a latency prediction method called E-LDAC based on an LSTM model. First, by employing kernel density estimation (KDE), we improve the accuracy of the traditional training compensation process based on the empirical label density. Next, in the multiscale feature extraction stage, we realize the calibration of data features and improve the representation ability of data through multirate sampling and inverse transform-based interpolation. Then, to enhance the adaptability of the LSTM model to imbalanced data and explore the dynamic trends of latency, we design a jointly enhanced loss function with weight sharing. Finally, in a practical 5G multiservice network environment, we experimentally validate that the E-LDAC method achieves higher prediction accuracy than other methods in terms of overall latency, bursty high latency, and extended network fault prediction.

The large-scale coverage of LEO satellite networks and the enhancement of onboard satellite computing resources have emerged as a pivotal solution for meeting the computing and routing demands of remote sensing (RS) tasks in areas without ground network coverage. Nonetheles, efficiently leveraging LEO satellite network resources effective computation offloading and relay routing presents substantial challenges. In this study, we delve into the joint optimization of task offloading and routing path selection within LEO satellite networks, aiming to maximize long-term user quality of service while adhering to energy constraints. We propose a novel integrated modeling scheme for relay routing and computation offloading of RS tasks in LEO satellite networks, effectively reducing the scale of system decision problems. Furthermore, we devise a multi-step reward aggregation Deep Q-Learning (DQN-MCAR) algorithm for computing-aware routing, effectively addressing the sequential learning problem among the computing-aware routing decisions of subtasks. Finally, theoretical analysis confirms that our proposed algorithm has lower complexity, and the superiority of our scheme is validated by extensive simulation experiments.

Context: Cross-project defect prediction (CPDP), due to the potential of adaption by industry in realistic scenarios, had gained significant attention from the research community. Currently, existing CPDP studies use static statistical features designed by experts, which might not capture the semantic and structural aspects of software, resulting in low accuracy in defect prediction. Meanwhile, they tend to overlook the valuable iterative information brought about by version updates in mature software projects. Objective: This paper introduces DETECTOR, a novel CPDP approach based on coDE semanTic and cross-vErsion struCTural infORmation to leverage cross-versions features of the software and improve the performance of CPDP. Methods: DETECTOR parses source code to exploit Abstract Syntax Trees (ASTs) and cross-version software network (Cross-SN) that consists of internal class dependency network and cross-version class dependency edges. It utilizes Attention-based Bi-LSTM and simplified graph convolutional neural networks to automatically extract software features from ASTs and Cross-SN. The extracted features are fused using gate(⋅) to generate more effective cross-version features. Finally the source project is selected to carry out the data used to train the classifier to predict the defects. Results: Empirical studies on seven open-source Java projects, the experiment results show that: (1) DETECTOR outperforms the state-of-the-art models in CPDP; (2) our proposed cross-version dependent edges positively contribute to DETECTOR performance; (3) gate(⋅) outperforms existing strategies in fusion features; (4) more multi-versions information enhance DETECTOR’s performance. Conclusion: DETECTOR can predict more defects in CPDP and improve the accuracy and effectiveness of prediction.

As a solution for city-range integrated sensing and communication and intelligent positioning, 5G high-precision positioning is flooding into reality. Nevertheless, the underlying positioning security concerns have been overlooked, posing threats to more than a billion emerging 5G localization applications. In this work, we first identify a novel and far-reaching security vulnerability affecting current 5G positioning systems. Correspondingly, we introduce a threat model, called the selective-PRS-spoofing attack (SPS), which can cause substantial localization errors or even fully-hijacked positioning results at victims. The attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel. Different from traditional communication-oriented 5G attacks, SPS targets the localization and exerts real-world threats. More seriously, we confirm that SPS attacks can evade multiple latest 3GPP R18 defense, and analyze its great stealthiness from its precise spoofing feature. To tackle this challenge, a Deep Learning-based defence method called in-phase quadrature intra-attention network (IQIA-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate the effectiveness of our method and its good robustness to noise.

Deep learning (DL) greatly enhances cyber anomaly detection capabilities through effective statistical network characteristic. However, previous methods have not fully addressed two real-world scenario-driven challenges. 1) Frequent node access and disconnection sourced from free-bounded 5G/B5G cyberspace introduce unfamiliar communication behavior patterns, reducing the detection ability of the pre-trained DL model. 2) Low-frequency or sporadic communication behaviors lack stable patterns, posing a challenge for existing AI-driven models, including DL-based detection methods. To address these issues, we propose a cyber anomaly detection framework based on Continuous Temporal Graph (CTG) neural network from a new interaction-centered perspective. The proposed framework refines the concrete information interaction between network entities into the CTG evolution process, thereby naturally incorporating new node access behaviors into feature extraction on CTG neural network. We furthermore present a message aggregation scheme on CTG with fusion of spatio-temporal neighborhood, the actual time distribution and the historical state, thus transforming communication into a more stable pattern for the learning of low-frequency interactions. Extensive experiments on 4 novel datasets, including ToN-IoT, UNSWNB15, CIC-Dark2020, J.P. Morgan payment, demonstrate that our approach outperforms state-of-the-art methods, particularly in detecting new access and low-frequency behaviors.

Multi-access edge computing (MEC) has been booming in recent years, as it promises to fulfill the growing low-latency requirements of applications on large amounts of Internet of Things (IoT) devices. Nevertheless, as latency-sensitive applications tend to become more complicated, existing schemes are too sophisticated, which may result in exceeding the real-time requirements of IoT systems. In this paper, we investigate the task offloading problem for the multi-device multi-edge server IoT system integrated with MEC. Firstly, according to the performance gains obtained by offloading different subtasks, we formalize a system latency minimization problem with energy utilization consideration, which has been proven to be NP-hard. Then, to address it, we propose a heuristic computation offloading scheduling scheme, which offloads appropriate subtasks to edge servers such that the system latency is minimized. Additionally, we theoretically prove the upper and lower boundaries of the system latency. Extensive simulation results corroborate that the proposed algorithm is low-complexity yet effective in decreasing the system latency by 16.15% (and up to 51.18%), improving the energy efficiency of local devices by 8.97% (and up to 21.69%) and shortening the offloading strategy execution time by 96.33% (and up to 98.95%).

Witnessing its large swaths of success in various fields, digital twins (DTs) are considered a promising scheme for 6th Generation (6G) cellular systems, showing a leading edge in networking and communication modelling. However, another 6G core property ofhigh-precision positioningcan hardly be supported by existing 6G DT solutions due to the lack ofenvironmental modellingandsignal interactions with physical scenes. This shortcoming yields a series of challenges in 6G DT-enabled positioning, including positioning data acquisition, accuracy enhancement, and continuous optimization. In this regard, we propose a novel paradigm of localization-oriented DT (LocDT) with a compound architecture of 7 sub-DT layers to characterize the 6G integrated-localization-and-communication (ILAC) feature. LocDT starts from a physical environment sublayer to mirror 6G signal interactions within a real-world scenario, along with an ILAC baseband sublayer and a channel frequency Polar-coordinate (CFP) image construction method to provide finer-grained fingerprints. Furthermore, insight from LocDT reveals an interesting phenomenon: the channel features of Line-of-Sight (LoS) / None-Los (NLoS) gNodeBs makedifferentiated-contributionsto positioning accuracy, especially in wide-existingpartial-LoS-coveragescenarios. Benefiting from this, a DT-driven Artificial Intelligence (AI) positioning model, SSI-Net, is designed with a device-attention mechanism, achieving complementary improvements in accuracy. Evaluation results show LocDT and SSI-Net’s advantages from a position-of-strength in accuracy and time overhead, outperforming state-of-the-art models.

5G positioning systems, as a solution for city-range integrated-sensing-and-communication (ISAC), are flooding into reality. However, the positioning security aspects of such an ISAC system have been overlooked, bringing threats to more than a billion 5G users. In this paper, we propose a new threat model for 5G positioning scenarios, namely the selective-PRS-spoofing attack (SPS), disabling the latest security enhancement method reported in 3GPP R18. In our attack pattern, the attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel, which can introduce substantial localization errors at victims or even completely control the positioning results. Worse, such attacks are transparent to both the UE-end and the network-end due to their stealthiness and easily bypass the current 3GPP defense mechanisms. To solve this problem, a DL-based defense method called in-phase quadrature Network (IQ-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate that our method has 98% defense accuracy and good robustness to noise.

In recent years, the problem of discovering influence communities has received much attention. However, in practical applications, vertices are often associated with attributes that are significant for understanding community. This paper explores the problem of computing top-r influential communities in attributed networks. We present a new community model called AAC that aims to promote cohesiveness in both structure and attributes. In order to measure the impact of a community$C$, we have developed an influential score function called$\text{iScore}(C)$by balancing attribute and structure cohesiveness. We propose two baseline approaches with effective pruning techniques and an index-based approach to efficiently report communities in an attributed network. The experimental results on real attributed networks indicate the effectiveness of our model and the efficiency of our proposed online and index-based algorithms.

MOTIVATION: Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS: In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION: A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unmanned aerial vehicles (UAVs) facilitate services in civilian and industrial fields but suffer from a limited direct link operating range and unreliable satellite positioning in urban canyons. Fortunately, cellular-connected UAVs (CCUAVs) overcome these shortcomings, benefitting from the beyond 5th generation (B5G) network’scity-level coverageandhigh-precision positioning capabilities, and are considered a paradigm of 5G-advanced and beyond. However, in a challenging airspace (e.g., urban canyon), the CCUAV localization accuracy deteriorates due tolow signal-to-interference-plus-noise (SINR) air-ground channelsandstrong multipath effects. To solve these problems, we first construct channel amplitude-phase response (CAPR) images to characterize the cellular channel in a challenging airspace for CCUAV positioning. In particular, the effect of down-tilted antennas and high-dimensional channel features are embedded into CAPR images, to meet the relevant cellular communication criteria. Subsequently, a deep learning (DL) model, the scale-shared quarter network (SSQ-Net), is devised for CAPR image-based positioning, along with a robustness enhancement method. With this method, the multipath effects and interference in challenging environments are exploited to improve positioning accuracy and robustness, instead of being treated as detriments. Finally, the experimental results in a typical urban canyon show that our method outperforms state-of-the-art methods in terms of accuracy and robustness.

Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic peptides. Moreover, it is still challenging to utilize different properties features to predict the therapeutic peptides. In this study, a novel stacking framework PreTP-Stack is proposed for predicting different types of therapeutic peptide. PreTP-Stack is constructed based on ten different features and four predictors (Random Forest, Linear Discriminant Analysis, XGBoost and Support Vector Machine). Then the proposed method constructs an auto-weighted multi-view learning model as a final meta-classifier to enhance the performance of the basic models. Experimental results showed that the proposed method achieved better or highly comparable performance with the state-of-the-art methods for predicting eight types of therapeutic peptides A user-friendly web-server predictor is available at http://bliulab.net/PreTP-Stack.

Deep learning (DL) greatly enhances binary anomaly detection capabilities through effective statistical network characterization; nevertheless, the intrusion class differentiation performance is still insufficient. Two related challenges have not been fully explored. 1) Statistical attack characteristics are overemphasized while ignoring inherent attack topologies; sequence features are extracted from whole traffic flows, but the interaction evolution of each IP pair over time is rarely considered, such as in long short-term memory (LSTM) and gated recurrent units (GRUs). 2) Meeting the need for many high-quality labeled data samples is an expensive and labor-intensive task in large-scale, complex, and heterogeneous networks. To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning. Our model converts network traffic into a series of spatiotemporal graphs. A dynamic GNN (DGNN) is employed to extract spatial information from each discrete snapshot and capture the contextual evolution of communication between IP pairs through consecutive snapshots. Moreover, a line graph realizes edge embedding expressions corresponding to network communications and strengthens the message aggregation ability of graph convolution. Experiments on 6 novel datasets demonstrate that our approach achieves 98.15–99.8% accuracy in abnormality detection with fewer labeled samples. Meanwhile, state-of-the-art multiclass performance is achieved, e.g., the average detection accuracy for DDoS across the 6 datasets reaches 95.32%.

As numerous latency-sensitive applications have emerged with the popularization of 5G networking, accurate and rapid end-to-end latency measurement has come to play an essential role in network fault diagnosis and optimization. Although the Bloom hash-based timestamp aggregation method has been reported to be scalable and efficient, two shortcomings that reduce its accuracy have yet to be fully considered: frequent hash collisions and its fixed measurement interval. To address these challenges, we construct an end-to-end network latency measurement framework named Cuckoo Hash Adjustive Table exchange (CHAT). By employing an improved cuckoo filter, we decrease the number of hash collisions to assess the latency more accurately. Moreover, CHAT adjusts the receiver-side measurement interval dynamically based on a gain indicator, maximizing the total number of valid packets used for latency estimation. Additionally, the proposed measurement framework minimizes the number of packets transferred over links to avoid interfering with the end-to-end latency measurement in an actual network. Finally, extensive experiments on simulations and a practical real-world environment show the effectiveness and applicability of CHAT.

MOTIVATION: Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides. RESULTS: In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides. AVAILABILITY AND IMPLEMENTATION: The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Location-based services (LBSs) provide necessary infrastructure for daily life, from bicycle sharing to nursing care. In contrast to traditional positioning methods such as Wi-Fi, Bluetooth, and ultra-wideband (UWB), fifth-generation (5G) networking is defined as a paradigm ofintegrated sensing and communication(ISAC). With its advantages of wide-range coverage and indoor-outdoor integration, 5G is promising for high-precision positioning in indoor and urban canyon environments. However, 5G location studies face great obstacles due to the lack of commercialized 5G ISAC base stations that support positioning functions as well as publicly available datasets. In this paper, we first propose a dataset generation method, the Multilevel Feature Synthesis Method (Multilevel-FSM), to obtain positioning features. In particular, the features of a multiple-input multiple-output (MIMO) channel are flattened into a single image to increase the information density and improve feature expression, and data augmentation is performed to provide stronger robustness to noise. Subsequently, we devise a specially designed deep learning positioning method, Multipath Res-Inception (MPRI), trained on the proposed dataset to enhance positioning accuracy. Finally, the results of extensive experiments conducted in two typical 5G scenarios (indoors and urban canyon) show that Multilevel-FSM and MPRI outperform state-of-the-art works in accuracy, time overhead and robustness to noise.

Spatial auditory cues are important for many tasks in immersive virtual environments, especially guidance tasks. However, due to the limited fidelity of spatial sounds rendered by generic Head-Related Transfer Functions (HRTFs), sound localization usually has a limited accuracy, especially in elevation, which can potentially impact the effectiveness of auditory guidance. To address this issue, we explored whether integrating sonification with spatial audio can enhance the perceptions of auditory guidance cues so user performance in auditory guidance tasks can be improved. Specifically, we investigated the effects of sonification mapping strategy using a controlled experiment that compared four elevation sonification mapping strategies: absolute elevation mapping, unsigned relative elevation mapping, signed relative elevation mapping, and binary relative elevation mapping. In addition, we examined whether azimuth sonification mapping can further benefit the perception of spatial sounds. The results demonstrate that spatial auditory cues can be effectively enhanced by integrating elevation and azimuth sonification, where the accuracy and speed of guidance tasks can be significantly improved. In particular, the overall results suggest that binary relative elevation mapping is generally the most effective strategy among four elevation sonification mapping strategies, which indicates that auditory cues with clear directional information are key to efficient auditory guidance.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user's characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user's characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user's characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user's domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user's features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.

As a hyper-natural interaction technique in 3D user interfaces, non-isomorphic rotation has been considered an effective approach for rotation tasks, where a static or dynamic control-display gain can be applied to amplify or attenuate a rotation. However, it is not clear whether non-isomorphic rotation can benefit 6-degree-of-freedom (6-DOF) manipulation tasks in AR and VR. In this article, we extended the usability studies of non-isomorphic rotation from rotation-only tasks to 6-DOF manipulation tasks and analyzed the collected data using a 2-component model. Using a mixed reality (MR) simulation approach, we also investigated whether environment (AR or VR) had an impact on 3D manipulation tasks. The results reveal that although both static and dynamic non-isomorphic rotation techniques could save time and effort in ballistic phases, only dynamic non-isomorphic rotation was significantly faster than isomorphic rotation. Interestingly, while environment had no significant impact on overall user performance, we found evidence that it could affect fine-tuning in correction phases. We also found that most participants preferred AR over VR, indicating that environmental visual realism could be helpful to improve user experience.

Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides. In this study, a predictor called PreTP-EL has been proposed via employing the ensemble learning approach to fuse the different features and machine learning techniques in order to capture the different characteristics of various therapeutic peptides. Experimental results showed that PreTP-EL outperformed other competing methods. Availability and implementation: A user-friendly web-server of PreTP-EL predictor is available at http://bliulab.net/PreTP-EL.

Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.

High availability is one of the core properties of Infrastructure as a Service (IaaS) and ensures that users have anytime access to on-demand cloud services. However, significant variations of workflow and the presence of super-tasks, mean that heterogeneous workload can severely impact the availability of IaaS clouds. Although previous work has investigated global queues, VM deployment, and failure of PMs, two aspects are yet to be fully explored: one is the impact of task size and the other is the differing features across PMs such as the variable execution rate and capacity. To address these challenges we propose an attribute-based availability model of large scale IaaS developed in the formal modeling language CARMA. The size of tasks in our model can be a fixed integer value or follow the normal, uniform or log-normal distribution. Additionally, our model also provides an easy approach to investigating how to arrange the slack and normal resources in order to achieve availability levels. The two goals of our work are providing an analysis of the availability of IaaS and showing that the use of CARMA allows us to easily model complex phenomena that were not readily captured by other existing approaches.

Mobile data demand of users is soaring with the increasing number of smartphones, which brings huge challenges to cellular network providers. To meet the user traffic demand, we study the problem that the user traffic data is served by cellular and WiFi network concurrently, i.e., mobile data offloading between cellular and WiFi operators. Different from the existing work where the users or network operators possess the complete information about each other, we model the mobile data offloading problem as a multi-user multi-operator non-cooperative game with the incomplete information. In the proposed model, the users and operators are assumed to be rational, and each of them pursues its own maximum benefit. To address this problem, in a distributed way, we first develop a Marginal Utility-Based Traffic Allocation (MUBTA) algorithm to arrange the users' traffic among different networks. Then, a bidding model is built to adjust the transaction price between users and operators. In addition, a Nash equilibrium is proven to be existed by theoretical analysis. Simulation results show that the proposed approach achieves a near-optimal solution.

With the widespread adoption of location-based services, users are increasingly demanding high- precision indoor localization. However, the deployment of localization network elements, i.e., localization base stations (LBS), mostly depends on experiences which usually leads to an extreme deployment cost. We therefore develop an indoor localization simulation platform, which can obtain the error distributions of the localization system under different LBS deployments, and also provide a near-optimal LBS deployment in practice.

Device-to-device (D2D) communication is a promising technique for traffic offloading in next-generation cellular systems. In this paper, we study the D2D-assisted cellular traffic offloading (DACTO) problem, where Wi-Fi Direct technology is employed in D2D communication in consideration of its wide communication coverage and high transmission rate. Taking into account the user traffic demands and population distributions, we formulate the DACTO problem as a "Min-Max" problem, in which the operator energy consumption is minimized and meanwhile the user satisfaction is maximized. The DACTO is proven to be a NP-complete problem and is difficult to tackle with the increasing number of population. To achieve a feasible solution, we convert the DACTO problem into an approximate combination optimization problem, and develop a backpack algorithm combined with an improved Hungarian algorithm to solve it. Simulation results show that the proposed method achieves the near-optimal solution for the DACTO problem.

Dense network coding (NC) is widely used in wireless cooperative downloading systems. Wireless devices have limited computing resources. Researchers have recently found that dense NC is not suitable because of its high coding complexity, and it is necessary to use chunked NC in wireless environments. However, chunked NC can cause more communications, and the amount of communications is affected by the chunk size. Therefore, setting a suitable chunk size to improve the overall perfor-mance of chunked NC is a prerequisite for applying it in wireless cooperative downloading systems. Most of the existing studies on chunked NC focus on centralized wireless broadcasting systems, which are different from wireless cooperative downloading systems with distributed features. Accordingly, we study the performance of chunked NC based wireless cooperative downloading systems. First, an analysis model is established using a Markov process taking the distributed features into consideration, and then the block collection completion time of encoded blocks for cooperative downloading is optimized based on the analysis model. Furthermore, queuing theory is used to model the decoding process of the chunked NC. Combining queuing theory with the analysis model, the decoding completion time for cooperative downloading is optimized, and the optimal chunk size is derived. Numerical simulation shows that the block collection completion time and the decode completion time can be largely reduced after optimization.

In Distributed Virtualized Environment, service components on a dependable service path will be selected to implement service composition. Searching for the optimal dependable service path is the key to implement dependability assurance, which is a Multi-Constrained Optimal Path problem. However, the existing algorithms have disadvantages of high complexity and low performance, and lacking the consideration of trust relationships and evidence spread among service components during service construction and composition. We proposed the concept of QoD, the Quality of Dependability, introducing some attributes(e.g. component intimacy) to describe and restrict the dependable service path searching in distributed virtualized environment. We also applied Adaptive Bonus-Penalty Micro-canonical Annealing(ABP-MA) to dependable service path searching, and chose service components on the optimal dependable service path to satisfy users' demands for service dependability. The experimental results showed that ABP-MA has the advantages of fast convergence and high search success rate.

Mobile cloud computing (MCC) has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods (such as Monte-Carlo simulation) may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are defined to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

With the application and popularization of autonomic computing in the field of aerospace exploration, large-scale database management and critical network control, existing self-reflection models based on natural language or diagram can not meet the requirements of analysis and verification. In this article, two kinds of self-reflection models, one is the static and the other is dynamic, capable of model checking and quantification analysis are separately built using performance evaluation process algebra (PEPA). Besides, huge state-spaces of models cause that the traditional Markov chains implied are hard to solve, thus we use an approach of continuous state-space approximation, a recent breakthrough in the analysis of stochastic process algebra, to generate ordinary differential system (ODE) from the PEPA model avoiding state-space explosion. By analyzing the ODEs, it is found that reducing the latency time of monitoring as well as shortening the length of execution instructions plays an important role in improving the performance of self-reflection.

Wireless sensor networks (WSN) have become increasingly one of the most promising and interesting areas over the past few years. But the character of constrained resources makes it vulnerable to Denial of Service (DoS) attacks, resulting in a large number of compromised nodes. Until now, it is unrealistic to prevent DoS attacks, but compromised nodes may be designed to self-recover to reduce the harm of DoS attacks. In this paper, an agent based self-recovery model (ABSR) was presented on the use of autonomic computing, which can recover itself in the approach of anomaly monitoring, anomaly deciding and node recovery. Finally, the model is analyzed and verified with the Eclipse Plug-in PEPA Tool, which suggests that compromised nodes can self-recover effectively using ABSR.

In order to inspect the internal and external environment of software efficiently, more and more autonomic computing elements (AEs) are deployed in the autonomic computing architecture software, autonomic software for shorted, which usually causes a great resources waste. Based upon the queuing theory, this paper proposes a new model to process the software internal and external information, which aims at balancing the quantity of the AE and the cost resulted by running this software. The simulated results produced by the designed experiment show that this model has an outstanding capability of rationally assigning the number of AE according to the current internal and external environment of software. Moreover, the efficiency of the algorithm related to proposed model is quite high.

Autonomic computing has emerged as a paradigm for distributed computing systems to stem the tide of rapidly increasing complexity and evolution problem. In this paper, a two-layer self-reflection model for Autonomic Computing systems (ACs) based on pi-calculus is proposed from a theoretical point of view, which integrates self-awareness and context-awareness into a single model and provides a formal, verifiable basis for the development and further studies of ACs. According to the hierarchical structure, it does not only reduce the latency time of self-awareness in local domain but also gives consideration to the overall objectives of the system. In addition, the model is checked by MWB.