The storage and recall of factual associations in auto-regressive transformer language models (LMs) have drawn a great deal of attention, inspiring knowledge editing by directly modifying the located model weights. Most editing works achieve knowledge editing under the guidance of existing interpretations of knowledge recall that mainly focus on subject knowledge. However, these interpretations are seriously flawed, neglecting relation information and leading to the *over-generalizing* problem for editing. In this work, we discover a novel relation-focused perspective to interpret the knowledge recall of transformer LMs during inference and apply it on single knowledge editing to avoid over-generalizing. Experimental results on the dataset supplemented with a new R-Specificity criterion demonstrate that our editing approach significantly alleviates over-generalizing while remaining competitive on other criteria, breaking the domination of subject-focused editing for future research.

Large language models (LLMs) have demonstrated remarkable capabilities in many fields, especially in complex neural lagnuage processing tasks. Despite their impressive performance, the content generated by LLMs still suffers from the problem of hallucination, particularly in tasks that require real-time data or specialized domain knowledge. Knowledge graphs and multimodal unstructured data serve as important sources of knowledge that can help address the hallucination issues in LLMs. However, existing methods mostly utilize knowledge graphs or multimodal unstructured data in isolation, neglecting the interaction between the two and it is the interaction that contributes to the extraction of deep knowledge in the knowledge base. In this paper, we propose a novel framework called the Collaborative Framework of Multimodal Unstructured Data and Knowledge Graph (CoMuS-KG). This framework enhances the reasoning capabilities of LLMs by enabling interaction between multimodal unstructured data and knowledge graphs, extracting deep knowledge from unstructured data, and completing missing information in knowledge graphs. Specifically, CoMuS-KG first decompose the question posed to the LLMs into multiple sub-questions and convert these sub-questions into knowledge graph triplets with missing head entity, tail entity, or relation. And then the knowledge graph and multimodal unstructured data are used to complete these triplets. Finally, we use the completed triplets to answer the original question and the completed triplets can be updated back into the knowledge graph to assist in other reasoning tasks. Extensive experiments on three KGQA benchmark datasets demonstrate the question-answering performance and reasoning capabilities of CoMuS-KG. Our code is publicly available at: https://github.comlGuChongAnlCoMuS-KG

The Relational Triple Extraction (RTE) is a fundamental and essential task in information extraction and knowledge graph construction. Recently, the table filling RTE methods have attracted more and more attention due to its good performance. However, there are still some problems with this kind of methods, such as only focusing on local features and not making full use of the regional information of triples. To overcome these deficiencies, we propose a Supervised contrastive learning and Global feature-oriented based Object detection framework for Relational Triple Extraction (SGORTE). Specifically, we convert the table filling RTE task into an object detection task, introduce multiple positive examples and a penalty term through a designed supervised contrastive learning method to enhance the robustness of the framework. In addition, we combine vertices-based bounding box detection and global relational region detection to fully utilize the relevant information of the triples for extraction. We conduct extensive experiments on two widely used datasets, and the experimental results show that the proposed framework performs better than the state-of-the-art baselines, and has obvious performance improvements in a variety of complex scenarios.

Next point-of-interest (POI) recommendation aims to forecast users’ next POI visit based on their historical movement information. Existing methods typically explore latent transition patterns within complex human activity trajectories by sequential or graph-based paradigms. However, they essentially oversimplify transition patterns by treating users and POIs as nodes, learning uniform embeddings for each ID. This results in a vast of important irregular textual semantic information being overlooked. To this end, we propose a novel large language model-based Collaborative Semantics-Assisted model called CSA-Rec for next POI recommendation. Specifically, our model retains the irregular check-in information in its original format to prevent the loss of important contextual details. These textual information are subsequently mapped to new, meaningful POI IDs through a meticulously crafted dual-channel vector quantization module. Then, we devise two fine-tuning tasks to reinforce the integration of collaborative trajectory signals and POI semantic information in the large language models. Extensive experiments on several real-world datasets validate the effectiveness of our CSA-Rec.

In this paper, we propose IKG-Agent, an intent-driven knowledge graph agent framework designed to perform adaptive workflow reasoning for complex question answering. Unlike traditional approaches, IKG-Agent integrates semantic parsing (SP), subgraph retrieval (SR), and large language models (LLMs) into a unified system. When a user submits a query, IKG-Agent first identifies the underlying intent and constructs a tailored reasoning workflow. Based on task complexity and query requirements, the framework dynamically selects optimal reasoning paths and tools, adjusting workflows during execution. By leveraging a shared knowledge memory system to continuously evaluate information sufficiency at each step, IKG-Agent mitigates error accumulation in traditional SP/SR-based reasoning—particularly for long relational paths and complex multi-hop inference. Experimental results demonstrate that IKG-Agent outperforms state-of-the-art methods on multiple public datasets, achieving significant improvements in accuracy and reliability for tasks requiring multi-level reasoning. Our code and data will be publicly released.

Recommender systems have been widely adopted in real-world applications, yet they still face challenges in addressing the cold-start problem. Cross-domain recommendation (CDR) offers a promising solution to the cold-start problem by transferring user preferences across domains. However, existing methods often rely on overlapping users or biased embeddings, limiting their generalization and fairness. We propose PANDA-CDR, a unified CDR framework that integrates perturbation-guided contrastive bottleneck and dual-channel disentanglement to learn robust transferable semantics. To enhance fairness, especially under long-tail distributions, we further introduce adversarial domain alignment and exposure-aware reweighting to mitigate popularity bias. Extensive experiments on real-world benchmarks show that PANDA-CDR achieves state-of-the-art performance while improving cold-start and long-tail recommendation fairness in sparse, low-overlap scenarios.

How can robots learn and adapt to new tasks and situations with little data? Systematic exploration and simulation are crucial tools for efficient robot learning. We present a novel black-box policy search algorithm focused on data-efficient policy improvements. The algorithm learns directly on the robot and treats simulation as an additional information source to speed up the learning process. At the core of the algorithm, a probabilistic model learns the dependence between the policy parameters and the robot learning objective not only by performing experiments on the robot, but also by leveraging data from a simulator. This substantially reduces interaction time with the robot. Using the model, we can guarantee improvements with high probability for each policy update, thereby facilitating fast, goal-oriented learning. We evaluate our algorithm on simulated fine-tuning tasks and demonstrate the data-efficiency of the proposed dual-information source optimization algorithm. In a real robot learning experiment, we show fast and successful task learning on a robot manipulator with the aid of an imperfect simulator.

Given the substantial security risks associated with Deepfake technology, the identification of manipulated facial images has become a focal point of research. Regrettably, the majority of current Deepfake detection methods struggle to effectively discern forgery artifacts across various resolutions. Variations in image or video resolutions present substantial challenges to maintaining identity security in cooperative work environments. In this study, we introduce a Deepfake face detection model that relies on the identification of common forgery artifacts. Our model utilizes CFNet (Common Forgery Artifact Extraction Network) to automatically filter regions containing forged artifacts. These common forged artifacts are found in images of various resolutions, substantially enhancing the model’s accuracy in low-resolution images. Furthermore, our custom-designed multi-modal features ensure the model excels in high-resolution scenarios. Comprehensive experiments validate the efficacy of our model, achieving accuracy rates of 90.464% for Deepfakes, 75.520% for Face2Face, and 83.536% for FaceSwap within the Low Quality (LQ) category of the FF+ dataset.

In recent times, transformer-based deepfake detection networks have exhibited remarkable performance. However, the computational complexity and the number of parameters have constrained the practical application of these networks. To address these issues, we propose a knowledge distillation method for the Swin Transformer network. Specifically, this method utilizes the region prediction results of face images to distill the knowledge of the Swin Transformer in subregions, compensating for the deficiency of the small window size of the Swin Transformer in the early stage. Extensive experiments have demonstrated that our proposed distillation method not only reduces the parameters and computational effort of the model but also surpasses the teacher network in accuracy on low-resolution images. Our student network exhibits significantly lower computational complexity and fewer parameters than the teacher network, with reductions of only 17.96% and 20.44%, respectively. Despite this reduction in complexity and parameters, our student network has achieved state-of-the-art results on the FaceForensics++ dataset, surpassing the teacher network by 0.071%, 0.86%, and 8.339% on Raw/Raw, Raw/C23, and Raw/C40, respectively.

Compared with deterministic knowledge graphs, uncertain knowledge graphs can better describe the inherent uncertainty of relations facts in the real world. However, existing embedding models for uncertain knowledge graphs can not capture the direct effect of the confidence score on information propagation between different entities across different relations. This paper proposes a confidence-aware knowledge graph embedding model based on graph convolutional networks (CA-GCN). It enables the propagation of confidence information within the graph structure, allowing confidence scores to directly influence message passing, and ultimately improves the performance of unseen relation facts confidence prediction. To mitigate the adverse effects of false-negative samples during training, we design a collaborative mechanism between a confidence generator and a relation discriminator. The relation discriminator is used to determine if a given triplet is valid, and the confidence generator is used to generate confidence scores. In this way, confidence information of positive and negative samples can play unique roles in different encoders at different stages to avoid the influence of false negatives. Experiments on three widely used datasets show promising results with an obvious performance improvement compared to existing uncertain knowledge embedding models.

Open-world attribute extraction is one of the most important tasks of information extraction aiming to mine all the valuable attributes of entities and their corresponding values from unstructured texts, usually in the form of (entity, attribute, value) triplets. However, existing methods have difficulty extracting attribute triplets from open-world sparse corpora where the attribute names are not previously given, especially in the few- shot scenario with only few manual annotations available. To solve the above problems, we propose a two-stage Meta-pattern-Enhanced Generative Few-shot Attribute Extraction (MEGFAE) framework which can be used to discover utmost valuable attribute triplets from open-world sparse corpora in a generative manner. For evaluation on open-world sparse corpora, we introduce a benchmark dataset called OSN-51511The dataset is available in https://github.com/sunshower-liu/OSN-515.. Experimental results verifies the effectiveness of our framework and inspires future explorations on the text mining on sparse corpora.

Next point-of-interest (POI) recommendation has shown effectiveness in mining complex user preferences and transition patterns from sparse check-in data. Existing methods generally leverage auxiliary information like spatial-temporal context, POI categories, and social relationships to alleviate the problem of data sparsity. However, most of them overlook the fact that the check-in records collected from users are often incomplete, leading to effective information missing and inadequately modeling. To this end, we propose a novel method Adaptive Graph-Based Trajectory Data Augmentation (AG-TDA) for next POI recommendation, which perceives potential relations among POIs and auxiliary information based on adaptive graph structure learning. Specifically, we design an adaptive graph-based trajectory data augmentation module from global view, which automatically explores implicit uncertain relations among POIs, categories, and regions via similarity learning with fine-grained node embeddings to get more expressive representations. In local view, we extend the self-attention mechanism by the learned fine-grained representations and personalized spatial-temporal information for capturing user dynamic intentions. Extensive experiments on several real-world datasets demonstrate the effectiveness of AG-TDA.

This article addresses the robust trajectory tracking control strategy of differential-driven unmanned surface vessels, with the propeller servo loop taken into consideration. The proposed strategy takes duty cycles of propeller motors as control input and thereby can be directly applied in practice without any modification. Compared to the existing methods, the proposed method provides the framework of position control considering propeller servo loop and improves robustness to external disturbances. The controller design procedure is divided into three stages through backstepping technique. Disturbance observers are constructed to provide estimations of composite disturbances, which guarantee robustness in the presence of external disturbances and model uncertainties. An auxiliary system is introduced to handle the input saturation of duty cycles. With a linear growth condition of the output of propeller motors, a rigorous proof is presented to show tracking errors are uniformly ultimately bounded. Simulation and experiments illustrate the effectiveness of the proposed control strategy.

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge from an informative source domain to the target domain, which inevitably proposes stern challenges to data privacy and transferability during the transfer process. A small amount of recent CDR works have investigated privacy protection, while they still suffer from satisfying practical requirements (e.g., limited privacy-preserving ability) and preventing the potential risk of negative transfer. To address the above challenging problems, we propose a novel and unified privacy-preserving federated framework for dual-target CDR, namely P2FCDR. We design P2FCDR as peer-to-peer federated network architecture to ensure the local data storage and privacy protection of business partners. Specifically, for the special knowledge transfer process in CDR under federated settings, we initialize an optimizable orthogonal mapping matrix to learn the embedding transformation across domains and adopt the local differential privacy technique on the transformed embedding before exchanging across domains, which provides more reliable privacy protection. Furthermore, we exploit the similarity between in-domain and cross-domain embedding, and develop a gated selecting vector to refine the information fusion for more accurate dual transfer. Extensive experiments on three real-world datasets demonstrate that P2FCDR significantly outperforms the state-of-the-art methods and effectively protects data privacy.

Knowledge graph embedding, which aims to learn representations of entities and relations in knowledge graphs, finds applications in various downstream tasks. The key to success of knowledge graph embedding models are the ability to model relation patterns including symmetry/antisymmetry, inversion, commutative composition and non-commutative composition. Although existing methods fail in modeling the non-commutative composition patterns, several approaches support this pattern by modeling beyond Euclidean space and complex space. Nevertheless, expanding to complicated spaces such as quaternion can easily lead to a substantial increase in the amount of parameters, which greatly reduces the computational efficiency. In this paper, we propose a new knowledge graph embedding method called RotateCT, which first transforms the coordinates of each entity, and then represents each relation as a rotation from head entity to tail entity in complex space. By design, RotateCT can infer the non-commutative composition patterns and improve the computational efficiency. Experiments on multiple datasets empirically show that RotateCT outperforms most state-of-the-art methods on link prediction and path query answering.

Detecting manipulated facial images and videos on social networks has been an urgent problem to be solved. The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries. Hence, it is crucial to detect manipulated faces in videos of different quality. We propose a new multi-stream network architecture named GGViT, which utilizes global information to improve the generalization of the model. The embedding of the whole face extracted by ViT will guide each stream network. Through a large number of experiments, we have proved that our proposed model achieves state-of-the-art classification accuracy on FF++ dataset, and has been greatly improved on scenarios of different compression rates. The accuracy of Raw/C23, Raw/C40 and C23/C40 was increased by 24.34%, 15.08% and 10.14% respectively.

Abstract DPA Contest is a world-famous side-channel competition aiming at analyzing and evaluating the implementing security of some latest countermeasures. Improved Rotating S-box Masking Scheme (RSM2.0) is one of the most popular countermeasures designed during DPA Contest V4.2, which arms with both Low Entropy Masking Schemes and shuffling strategy to ensure the software security of AES-128, particularly the non-profiled security. Up to now, conducting high efficient non-profiled attacking scheme with low resource costs is still a challenge. In this paper, we first propose general and non-profiled leakage fingerprint attacks (named NP-LFA) for secret cracking and make use of it to crack RSM2.0 random masks with almost 100% accuracy. Further, we analyze the hidden vulnerabilities embedded in RSM2.0 implementation, and utilize them to bypass the shuffling defense and perform the master key recovery. Official evaluation results show that NP-LFA is capable of compromising RSM2.0 within 14 traces, each of which only costs 60 ms processing time. Such result validates the high efficiency and light-weighted characteristics of our attacking scheme, which has ranked the first in the official website till now. In addition, we discuss and put forward some possible strategies to mitigate our NP-LFA threats.

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

As a popular robot path planning algorithm, RRT (Rapid-exploring Random Tree) and various RRT-based extensions have achieved remarkable results. However, existing algorithms rely too much on randomness and often do not make use of known map information, this makes the growth of trees too blind. To this end, we introduce Bidirectional Homotopy-Guided RRT (BH-RRT) that combines Bidirectional RRT (Bi-RRT) with information obtained from obstacle contours. Compared with the previous methods, BH-RRT can reflect most of the map's information with a small number of feature points, and then form a set of points. This set can provide a better local goal point in each iteration, so that the tree can grow in a more favorable direction, rather than only being affected by the goal point. Experimental results show that BH-RRT outperforms RRT, HRRT and Bi-RRT in the success rate within a limited time.

This paper introduces a fishery ecology monitoring system for cultivation pools, and proposes a new stereo keypoint detection method followed by curve fitting analysis to estimate the fish posture and length. The system, which can be employed for aquaculture monitoring, is featured by its exploitation of underwater visual intelligence and deep neural-network architecture. As input, stereo image pairs are obtained by underwater binocular camera. A deep neural-network under Faster R-CNN architecture is built to detect fish from the stereo image inputs. Another network under Stacked Hourglass architecture is constructed to detect specific keypoints of each fish. For ecology monitoring, detected keypoints are used in the estimation of the fishes' posture and length. Unlike other size estimation methods which also apply a binocular camera, our method naturally bypasses the pixel-wise matching difficulty in global stereo matching algorithms. Experiment shows that our system is applicable for online fish ecology monitoring, with efficient and accurate estimation performance.

With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations. Although the massive check-in data bring a good opportunity, there are still many challenges in building personalized POI recommender systems based on geographical information. First, current coarse-grained geographical models provide considerably limited improvements on POI recommendations and fail to capture the overall impact of fine-grained geographical characteristics in LBSNs. Second, previous methods such as matrix factorization always give equal weight to each positive example and may not distinguish between their different contributions in learning the objective function. To cope with these challenges, we develop a fine-grained POI recommendation framework that makes full use of the geographical characteristics from both users’ and locations’ perspectives. For capturing the fine-grained geographical influence, we present a unified probability distribution model based on four key geographical characteristics. For mining more contribution information from positive examples, we assign a higher weight to highlight the contribution of a higher check-in frequency by employing a logistic matrix factorization. Finally, experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for single image dehazing, named SIDGAN. The proposed SIDGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed SIDGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.

User Alignment is to find users belonging to a same real person on different social networks and has become a fundamental task for many sequent applications such as cross-network recommendation systems. When matching users in multiple social networks, existing approaches always know some correctly matched users, which can be called seeds. Then, existing methods strongly depend on the neighboring users of each user to propagate alignment information from seeds and align probable matching users implicitly. However, the completeness and validity of original alignment information among seeds cannot be fully preserved when learning and aligning multiple user spaces. In this paper, we propose a unified framework named Jumping Seed Alignment Information Propagation (JSAIP) to flexibly leverage, for each user, complete and correct alignment information from seeds. Specifically, JSAIP learns a reasonable user space for each social network by preserving enough original network and label information. Then, JSAIP ensures the correct alignment among seeds and shared labels to reduce the diversity between different user spaces. Finally, JSAIP constructs jumping links from seeds to each user in each social network and ultilizes original seed alignment information to enhance or rectify the alignment information propagated from neighbors. Experiments on real world datasets demonstrate the effectiveness of our proposed JSAIP method compared to several state-of-the-art methods.

Point-of-interest (POI) recommendation has become an important service to help users discover attractive locations. A variety of available check-in data make it possible to build a personalized POI recommender system, but the extreme sparsity of check-in data poses a severe challenge for POI recommendation. Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data. However, these studies often apply shallow methods for the extra information and provide considerably limited improvements on POI recommendation. In this paper, we propose a fine-grained POI recommendation framework, called FGRec to capture the intrinsic influences of social, categorical and geographical information on the check-in behaviors of users. First, we study the social influence in depth by exploiting the multi-hop social friends and top-n nearest neighbor friends, not only the direct friends (i.e., 1-hop friends). Second, we investigate the categorical influence by factorizing both user-POI and user-category matrices simultaneously over the same user embedding space, rather than simply using the popularity of POI categories. Third, we explore the geographical influence by integrating two types of distance (i.e., the distance between user homes and POIs and the distance among POIs) into a unified probability distribution over check-in POIs, instead of modeling them separately. Finally, experimental results on two large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Embedding knowledge graphs (KGs) into continuous vector space is an essential problem in knowledge extraction. Current models continue to improve embedding by focusing on discriminating relation-specific information from entities with increasingly complex feature engineering. We noted that they ignored the inherent relevance between relations and tried to learn unique discriminate parameter set for each relation. Thus, these models potentially suffer from high time complexity and large parameters, preventing them from efficiently applying on real-world KGs. In this paper, we follow the thought of parameter sharing to simultaneously learn more expressive features, reduce parameters and avoid complex feature engineering. Based on gate structure from LSTM, we propose a novel model TransGate and develop shared discriminate mechanism, resulting in almost same space complexity as indiscriminate models. Furthermore, to develop a more effective and scalable model, we reconstruct the gate with weight vectors making our method has comparative time complexity against indiscriminate model. We conduct extensive experiments on link prediction and triplets classification. Experiments show that TransGate not only outperforms state-of-art baselines, but also reduces parameters greatly. For example, TransGate outperforms ConvE and RGCN with 6x and 17x fewer parameters, respectively. These results indicate that parameter sharing is a superior way to further optimize embedding and TransGate finds a better trade-off between complexity and expressivity.

With the popularity of smart devices such as mobile phones and tablets, the security problem of the widely used ARMv8-A processor has received more and more attention. Flush+Reload and Flush+Flush cache attacks have become two of the most important security threats due to their low noise and high resolution. In order to resist Flush+Reload and Flush+Flush attacks, researchers proposed many defense methods. However, these existing methods have various shortcomings. The runtime defense methods using hardware performance counters cannot detect attacks fast enough, effectively detect Flush+Flush or avoid a high false positive rate. Static code analysis schemes are powerless for obfuscation techniques. The approaches of permanently reducing the resolution can only be utilized on browser products and cannot be applied in the system. In this paper, we design two more secure collaborative APIs-flush operation API and high resolution time API-which can resist Flush+Reload and Flush+Flush attacks. When the flush operation API is called, the high resolution time API temporarily reduces its resolution and automatically restores. Moreover, the flush operation API also has the ability to detect and handle suspected Flush+Reload and Flush+Flush attacks. The attack and performance comparison experiments prove that the two APIs we designed are safer and the performance losses are acceptable.

Accurate intention understanding of the user inputs is the key to human-computer interaction (HCI). At present, more and more studies just focus on the improvement of algorithm efficiency and ignore the nature exploration of intention understanding. In humans, working memory is regarded as a cognitive system for handling a range of neuro-cognitive tasks. Because the intention understanding is a kind of human cognitive ability, in this paper we will explore the human cognitive execution mechanism and try to apply it to improve the machines' intention understanding level. First, we demonstrated a cognitive learning model called Cortico-Basal ganglia-Cerebella (CBC) loops plays an important role in the process of working memory. Then, based on the full understanding of the loops operation mechanism, we put forward a new model of intension understanding. Finally, we applied this model on speech data and compared it with other two methods. The results showed that the new model could help to get task-specific vectors and offer further gains in performance on intention understanding.

ARM-FPGA embedded SoCs have been widely used in the fields of drones, embedded and IoT devices due to its high performance and hardware design flexibility. However, ARM-FPGA embedded SoC suffers various types of security threats, one of which is flush-based cache attack. The proposed defense schemes either lead to a high false positive rate or a large performance loss. Due to the importance of high resolution time APIs in the system, schemes that permanently reduce the resolution of time APIs can only be implemented in specific applications such as browsers. Moreover, the method of protecting high resolution timers in software cannot defend against an attacker with root privileges. In this paper, we propose a more secure timer which is a hardware/software co-design on ARM-FPGA embedded SoC. When a software process calls the flush operation, the timer adaptively reduces its resolution and recover after a short period of time. In the case that the flush operation is not called, the impact of the timer on system performance is almost negligible. This hardware/software co-design guarantees the availability of a high resolution time API while defend against attackers with root privileges. The results of the attack experiments show that the success rates of Flush+Reload and flush-based Spectre attacks can be reduced to less than 1% when using the timer. Performance test results show that the timer access latency is 9.5% slower than the fastest PMCCNTR but 5% faster than the global timer of Cortex-A9 MPCore. The modified flush operation API for the design only increases the time consumption by about 12%.

The increasing prevalence of location-based social networks (LBSNs) poses a wonderful opportunity to build per-sonalized point-of-interest (POI) recommendations, which aim at recommending a top-N ranked list of POIs to users according to their preferences. Although previous studies on collaborative filtering are widely applied for POI recommendation, there are two significant challenges have not been solved perfectly. (1) These approaches cannot effectively and efficiently exploit unobserved feedback and are also unable to learn useful information from it. (2) How to seamlessly integrate multiple types of context information into these models is still under exploration. To cope with the aforementioned challenges, we develop a new Personalized pairwise Ranking Framework based on Poisson Factor factorization (PRFPF) that follows the assumption that users’ preferences for visited POIs are preferred over potential POIs, unvisited POIs are less preferred than potential POIs. The framework PRFPF is composed of two modules: candidate module and ranking module. Specifically, the candidate module is used to generate a series of potential POIs from unvisited POIs by incorporating multiple types of context information (e.g., social and geographical information). The ranking module learns the ultimate order of users’ preference by leveraging the potential POIs. Experimental results evaluated on two large-scale real-world datasets show that our framework outperforms other state-of-the-art approaches in terms of various metrics.

Abstract Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels.

Network embedding is to learn effective low-dimensional vector representations for nodes in a network and has attracted considerable attention in recent years. To date, existing methods mainly focus on network structure information and cannot leverage abundant label information, which is potentially valuable in learning better vector representations. Due to the noise and incompleteness of label information, it is intractable to integrate label information into the vector representations in a partially labeled network. To address this issue, we investigate the effects of label information based on the label homophily. Briefly, label homophily can not only drive nodes sharing similar labels to be connected to each other, but also produce a division of a network into densely-connected, homogeneous parts that are weakly connected to each other. Furthermore, we propose a novel Label Homophily Oriented Network Embedding (LHONE) model to make the best of label homophily by converting a partially labeled network to two bipartite networks, and learning vector representations combined with a Gaussian mixture model (GMM). Extensive experiments on two real-world networks demonstrate the effectiveness of LHONE compared to state-of-the-art network embedding approaches.

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.

Although the input validation vulnerabilities play a critical role in web application security, such vulnerabilities are so far largely neglected in the Android security research community. We found that due to the unique Framework Code layer, Android devices do need specific input validation vulnerability analysis in system services. In this work, we take the first steps to analyze Android specific input validation vulnerabilities. In particular, a) we take the first steps towards measuring the corresponding attack surface and reporting the current input validation status of Android system services. b) We developed a new input validation vulnerability scanner for Android devices. This tool fuzzes all the Android system services by sending requests with malformed arguments to them. Through comprehensive evaluation of Android system with over 90 system services and over 1,900 system service methods, we identified 16 vulnerabilities in Android system services. We have reported all the issues to Google and Google has confirmed them.

Based on the observations that spy-on-user attacks by calling Android APIs will be detected out by Android API auditing, we studied the possibility of a "transplantation attack", through which a malicious app can take privacy-harming pictures to spy on users without the Android API auditing being aware of it. Usually, to take a picture, apps need to call APIs of Android Camera Service which runs in mediaserver process. Transplantation attack is to transplant the picture taking code from mediaserver process to a malicious app process, and the malicious app can call this code to take a picture in its own address space without any IPC. As a result, the API auditing can be evaded. Our experiments confirm that transplantation attack indeed exists. Also, the transplantation attack makes the spy-on-user attack much more stealthy. The evaluation result shows that nearly a half of 69 smartphones (manufactured by 8 vendors) tested let the transplantation attack discovered by us succeed. Moreover, the attack can evade 7 Antivirus detectors, and Android Device Administration which is a set of APIs that can be used to carry out mobile device management in enterprise environments. The transplantation attack inspires us to uncover a subtle design/implementation deficiency of the Android security.

A varied weights (VW) method is proposed in this paper for the kinematic control of redundant manipulators with multiple constraints. A weight factor rule to reflect the transition of the state of constraint subtask between activeness and inactiveness is presented. Each constraint has a VW factor in such a way that every time only the main task and the active constraint subtasks are considered. A new concept of effective singular value is presented for the design of damping factors to avoid the pseudosingularity arising from the transition of weight factors. The experiments on the seven-degree-of-freedom (7-DOF) Ping-Pong manipulator illustrate the efficacy of the proposed VW method.

This paper proposed a task function method for the point-to-point (PTP) control problem of non-redundant robot manipulators. A task function to compress the conventional task space to a new one with lower dimensions increases the redundancy degree so that the original manipulator becomes a redundant one, such that we can apply redundant control laws to achieve online subtasks, such as obstacle avoidance without trajectory replanning. The four basic properties of the task function method which are necessary to ensure the good performance of the PTP control problem are presented. The experiment results verify the effectiveness of the presented method.

This paper presents the general-weighted least-norm (GWLN) method for the control of redundant manipulators by a new concept of virtual joints, which consist of the performance function of subtasks. The GWLN method enables the redundant manipulator to perform multiple subtasks with the assumption that all the subtasks can be represented by the inequalities as the joint limits avoidance subtask. The number of subtasks to be coped with might be even larger than the number of joints if they do not happen simultaneously. The simulations in contrast to the traditional gradient projection method (GPM) and the recent directional GPM have been made to manifest the advantages of the proposed method. An experiment on the seven-degree-of-freedom redundant manipulator illustrates the good performance for tracking a given trajectory of end-effector position, while both guaranteeing the obstacle free and not violating the joint limits.

Distributed systems today are very vulnerable to malicious attacks, either from insiders or outsiders. When an attacker controls a component of the system, he may steal some sensitive information, create some false information, or prevent legitimate users from using the system. An intrusion-tolerant distributed system is a system which is designed so that any intrusion into a part of the system will not endanger confidentiality, integrity and availability. This paper describes two such systems we are developing: an intrusion tolerance CA system and a survivable repository, which are highly resilient to both insider and outsider attacks that compromise one or more components.

Based on linear matrix inequality (LMI) technique, a sliding mode control approach is presented for a class of uncertain time-delay systems in the delay-independent and delay-dependent case. The corresponding sufficient conditions for the existence of sliding mode are proposed. Different from the reported results, the conclusion presented in this paper is only relating to the original system parameters and owns simple and legible form. A numerical example illustrates the effectiveness of the presented method.