Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first analysing concept drift through a bias-variance lens and proving that weighted ensemble reduces variance without increasing bias. These insights motivate DeepBooTS, a novel end-to-end dual-stream residual-decreasing boosting method that progressively reconstructs the intrinsic signal. In our design, each block of a deep model becomes an ensemble of learners with an auxiliary output branch forming a highway to the final prediction. The block‑wise outputs correct the residuals of previous blocks, leading to a learning‑driven decomposition of both inputs and targets. This method enhances versatility and interpretability while substantially improving robustness to concept drift. Extensive experiments, including those on large-scale datasets, show that the proposed method outperforms existing methods by a large margin, yielding an average performance improvement of 15.8% across various datasets, establishing a new benchmark for TS forecasting.

Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The proliferation of deepfake technology has precipitated a critical trust crisis in digital multimedia forensics, calling into question the reliability of existing detection systems. Current models predominantly rely on softmax-normalized probabilities, which exhibit heightened vulnerability to adversarial perturbations and out-of-distribution (OOD) samples. To address this deficiency and provide courts with quantifiably reliable forensic evidence, this paper proposes a Dual-Threshold Reliability Assessment framework (DTRA) grounded in class-conditional feature space analysis. The DTRA framework quantifies epistemic uncertainty through Mahalanobis distance-based inconsistency scoring computed from deep feature representations. Departing from conventional single-threshold paradigms, we independently calibrate optimal decision intervals for authentic and forged classes on a held-out calibration set. The interval optimization is formulated as a risk-adjusted utility maximization problem that trades off empirical precision against effective sample coverage. Specifically, an interval search algorithm identifies the most reliable subrange of inconsistency scores for each class via an odds ratio-weighted utility function, eschewing the restrictive assumption of a zero lower bound. DTRA serves as a conservative safeguard: when sample evidence is ambiguous, it abstains rather than forces a prediction. While this conservatism reduces coverage, the predictions it retains are significantly more reliable, thereby reducing the risk of high-confidence misjudgments.

Packet sampling is crucial for managing datacenter networks, serving fault diagnosis, traffic measurement, and intrusion detection functions. However, traditional sampling techniques, such as those based on sketches or ports, either lack packet–level granularity or provide insufficient visibility, leading to functional performance degradation. Recent research has employed the software-defined networking (SDN) model to enable flow-based packet sampling. However, these approaches often introduce substantial control and computation overhead, limiting their scalability. This paper presents Viper, a novel priority-based, high-visibility per-flow packet sampling mechanism tailored to address these challenges. Specifically, Viper leverages existing priority-based traffic scheduling mechanisms to prioritize shorter flows over longer ones. Then, a logical centralized controller orchestrates sampling policies for packets of different priorities. In-depth analysis indicates that the orchestration performed by the controller significantly impacts Viper’s performance. Consequently, we model this process as a nonlinear optimization problem, seeking to maximize the utility of sampling. Then, we propose an online primal–dual interior–point algorithm to address this optimization problem and prove the algorithm’s convergence, optimality, and efficiency. Experimental results show that Viper increases visibility by 3.83% to 8.3%, with negligible control overhead and a substantial reduction in sampling load by at least 20.51%.

Given the diversity and geographic distribution of data resources across multiple data centers, achieving unified storage and resource management presents significant challenges, complicating efforts to meet the demands for efficient cross-domain distributed data access. To address these challenges, this study proposes a cross-domain heterogeneous storage cluster architecture. Leveraging Alluxio as the foundational storage layer for data-intensive applications, this architecture facilitates unified management across various storage systems. For the storage system selection problem, this paper adopts a multi-objective approach with minimum cost, maximum reliability, shortest access distance, and load balancing as the target functions. An improved NSGA-III algorithm, incorporating generalized inverse learning and adaptive evolutionary mechanisms, is proposed for intelligently selecting the optimal storage scheme. The reasonableness and effectiveness of the improved algorithm are validated through a comparative analysis of the convergence and diversity metrics of the original and improved NSGA-III algorithms on benchmark problems, along with assessments of each objective's adaptability in real-world application scenarios.

Medical records are comprehensive repositories of patient health information and an essential tool for physicians to access medical histories. However, drafting medical records is a time-consuming process that contributes significantly to physician workload. Recent advances in Generative Artificial Intelligence (GAI) have shown strong potential in text summarization, yet most existing approaches rely on offline generation and singleturn interactions, failing to meet the real-time accuracy and user experience requirements of clinical practice. To address these limitations, we propose MedStructGen, a two-stage medical record generation framework that mirrors real-world clinical workflows. Stage 1 employs a simulation driven multiturn patient-physician dialogue model that adaptively refines its questioning strategy based on evolving symptom profiles, ensuring domain-complete diagnostic information capture. Stage 2 employs stage-specific fine-tuned LLMs with stepwise prompt engineering and entity alignment to generate EMRs that are compliant with standards and machine verifiable. This design not only improves contextual accuracy and completeness of information, but also achieves superior structural compliance, enabling seamless integration into Hospital Information Systems. Experiments on real-world hospital datasets demonstrate that our method achieves a 12.26% BLEU-4 improvement over one-stage baselines, with consistent gains in ROUGE and BERTScore. Located in a partner hospital, our system reduces physician documentation time by 1 to 1.5 hours per day, allowing more focus on patient care and personal well-being. The approach is generalizable and requires minimal customization for integration into other healthcare settings.

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction.However, there is a 'concept drift' problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios.For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios.We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell.Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams.The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing.

When a wheeled mobile robot (WMR) runs on loose soil (such as the planetary rover on surface of the planet), its wheels generally slip or skip. Since the slippage of the wheel directly affects the motion control and safety, it becomes urgent to effectively estimate the slippage. In this paper, an intuitive slippage estimation method is proposed based on wheel-ruts images, which aims to reduce the number of extra sensors and take advantages of the visual information effectively. Since the image samples sometimes are difficult to collect, a few-shot learning method is employed using distribution propagation graph network with dilated causal convolution layer (DCC-DPGN). The dilated causal convolution (DCC) layer is adopted in ResNet block to expand the receptive field and obtain the sequence information of wheel-ruts, which makes the model training more efficient. The proposed model is verified in the test set of images collected in real scene, which shows the potential of the proposed algorithm in slippage estimation.

Outlier detection in data streams is a critical component in numerous applications, such as network intrusion detection, financial fraud detection, and public health. To detect abnormal behaviors in real-time, these applications generally have stringent requirements for the performance of outlier detection. This paper proposes RTOD, a high-performance outlier detection approach that utilizes RT cores in modern GPUs for acceleration. RTOD transforms distance-based outlier detection in data streams into an efficient ray tracing job. By creating spheres centered at points within a window and casting rays from each point, RTOD identifies the outlier points according to the number of intersections between rays and spheres. Besides, we propose two optimization techniques, namely Grid Filtering and Ray-BVH Inversion, to further accelerate the detection efficiency of RT cores. Experimental results show that RTOD achieves up to 9.9× speedups over existing start-of-the-art outlier detection algorithms.

By leveraging deep neural networks, recent face swapping techniques have performed admirably in generating faces that maintain consistent identities. Nevertheless, while these methods accurately transfer source identities, they often struggle to preserve important attributes (such as head poses, expressions, and gaze directions) in the target faces. As a consequence, the current research in this domain has not resulted in satisfactory performance. In this paper, we propose an efficient attribute-preserving framework, called AP-Swap, for short, for face swapping. Our approach incorporates two innovative modules designed specifically to preserve critical facial attributes. First, we propose a global residual attribute-preserving encoder (GRAPE), which adaptively extracts globally complete attribute features from target faces. Second, in addition to the regular network streams for the source and target facial images, we introduce a network stream that takes into account the facial landmarks of the target faces. This additional stream enables our landmark-guided feature entanglement module (LFEM), which efficiently preserves fine-grained facial attributes by conducting a landmark-based attribute-preserving (LBAP) operation. Through extensive quantitative and qualitative experiments, we demonstrate the superiority of AP-Swap over other state-ofthe-art methods in terms of facial attribute preservation and model efficiency, along with satisfactory identity consistency performance

Video moment retrieval, i.e., localizing the specific video moments within a video given a description query, has attracted substantial attention over the past several years. Although great progress has been achieved thus far, most of existing methods are supervised, which require moment-level temporal annotation information. In contrast, weakly-supervised methods which only need video-level annotations remain largely unexplored. In this paper, we propose a novel end-to-end Siamese alignment network for weakly-supervised video moment retrieval. To be specific, we design a multi-scale Siamese module, which could progressively reduce the semantic gap between the visual and textual modality with the Siamese structure. In addition, we present a context-aware multiple instance learning module by considering the influence of adjacent contexts, enhancing the moment-query and video-query alignment simultaneously. By promoting the matching of both moment-level and video-level, our model can effectively improve the retrieval performance, even if only having weak video level annotations. Extensive experiments on two benchmark datasets, i.e., ActivityNet-Captions and Charades-STA, verify the superiority of our model compared with several state-of-the-art baselines.

In traditional networks, the multicast tree packing solutions usually aim to minimize the overall multicast tree cost, which can effectively improve network accommodation capacity but is disadvantageous to fully use network resources. In this article, we propose a delay-optimized multicast tree packing problem called delivery delay minimized multicast tree packing (DDMMTP), which aims to minimize the average source-destination delay, under constraints on the bandwidth and maximum source-destination delay, according to available network resources. A low source-destination delay is desirable because it improves the service quality, especially for time-sensitive applications. In practice, the DDMMTP is highly valuable for the software-defined network (SDN) mainly because this new network paradigm has the ability to rapidly rearrange multicast routes on demand. The DDMMTP problem is NP-hard. We solve it approximately by a batched multicast tree packing algorithm and a network accommodation capacity improvement algorithm that adjusts existing multicast paths on demand. We also propose a source-destination delay improvement algorithm to further reduce source-destination delays based on new available network resources.

Multimodal dialog system has attracted increasing attention from both academia and industry over recent years. Although existing methods have achieved some progress, they are still confronted with challenges in the aspect of question understanding (i.e., user intention comprehension). In this paper, we present a relational graph-based context-aware question understanding scheme, which enhances the user intention comprehension from local to global. Specifically, we first utilize multiple attribute matrices as the guidance information to fully exploit the product-related keywords from each textual sentence, strengthening the local representation of user intentions. Afterwards, we design a sparse graph attention network to adaptively aggregate effective context information for each utterance, completely understanding the user intentions from a global perspective. Moreover, extensive experiments over a benchmark dataset show the superiority of our model compared with several state-of-the-art baselines.

Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors.

Conversational image search, a revolutionary search mode, is able to interactively induce the user response to clarify their intents step by step. Several efforts have been dedicated to the conversation part, namely automatically asking the right question at the right time for user preference elicitation, while few studies focus on the image search part given the well-prepared conversational query. In this paper, we work towards conversational image search, which is much difficult compared to the traditional image search task, due to the following challenges: 1) understanding complex user intents from a multimodal conversational query; 2) utilizing multiform knowledge associated images from a memory network; and 3) enhancing the image representation with distilled knowledge. To address these problems, in this paper, we present a novel contextuaL imAge seaRch sCHeme (LARCH for short), consisting of three components. In the first component, we design a multimodal hierarchical graph-based neural network, which learns the conversational query embedding for better user intent understanding. As to the second one, we devise a multi-form knowledge embedding memory network to unify heterogeneous knowledge structures into a homogeneous base that greatly facilitates relevant knowledge retrieval. In the third component, we learn the knowledge-enhanced image representation via a novel gated neural network, which selects the useful knowledge from retrieved relevant one. Extensive experiments have shown that our LARCH yields significant performance over an extended benchmark dataset. As a side contribution, we have released the data, codes, and parameter settings to facilitate other researchers in the conversational image search community.

Online micro-video recommender systems aim to address the information explosion of micro-videos and make the personalized recommendation for users. However, the existing methods still have some limitations in learning representative user interests, since the multi-scale time effects, user interest group modeling, and false positive interactions are not taken into consideration. In view of this, we propose an end-to-end Multi-scale Time-aware user Interest modeling Network (MTIN). In particular, we first present an interest group routing algorithm to generate fine-grained user interest groups based on user's interaction sequence. Afterwards, to explore multi-scale time effects on user interests, we design a time-aware mask network and distill multiple temporal information by several parallel temporal masks. And then an interest mask network is introduced to aggregate fine-grained interest groups and generate the final user interest representation. At last, in the prediction unit, the user representation and micro-video candidates are fed into a deep neural network (DNN) for predictions. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, and the experimental results demonstrate that our proposed model achieves substantial gains over the state-of-the-art methods.

Nodes in wireless sensor networks (WSNs) are usually deployed in an unattended even hostile environment. What is worse, these nodes are equipped with limited battery, storage, computation, and communication resources. Therefore, it is challenging to ensure the security of a WSN without decreasing its network performance. Data aggregation (DA) combined with a security mechanism can provide a good scheme for solving the aforementioned problems. This article presents a comprehensive review of secure DA (SDA) in WSNs, including its security goals together with the existing problems. The traditional network topologies as well as new emerging ones are discussed and compared in order to indicate the application scenes and security levels of different topologies. Meanwhile, the contrastive analyses of security strategies are presented which divides SDA protocols into five categories according to different security mechanisms, security goals, and network topologies. Besides, the discussion points out some open issues which may be the valuable topics of SDA in the future.

Recent years have witnessed a flourishing of community-driven question answering (cQA), like Yahoo! Answers and AnswerBag, where people can seek precise information. After 2010, some novel cQA systems, including Quora and Zhihu, gained momentum. Besides interactions, the latter enables users to label the questions with topic tags that highlight the key points conveyed in the questions. In this article, we shed light on automatically annotating a newly posted question with topic tags that are predefined and preorganized into a directed acyclic graph. To accomplish this task, we present an end-to-end deep interactive embedding model to jointly learn the embeddings of questions and topics by projecting them into the same space for a similarity measure. In particular, we first learn the embeddings of questions and topic tags by two deep parallel models. Thereinto, we regularize the embeddings of topic tags via fully exploring their hierarchical structures, which is able to alleviate the problem of imbalanced topic distribution. Thereafter, we interact each question embedding with the topic tag matrix, i.e., all the topic tag embeddings. Following that, a sigmoid cross-entropy loss is appended to reward the positive question-topic pairs and penalize the negative ones. To justify our model, we have conducted extensive experiments on an unprecedented large-scale social QA dataset obtained from Zhihu.com, and the experimental results demonstrate that our model achieves superior performance to several state-of-the-art baselines.

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploits the item's multimodal features to estimate the user's special attention to different aspects of this item. The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item. The advantage of metric learning is that it can naturally overcome the problem of dot product similarity, which is adopted by matrix factorization (MF) based recommendation models but does not satisfy the triangle inequality property. In addition, it is worth mentioning that the attention mechanism cannot only help model user's diverse preferences towards different items, but also overcome the geometrically restrictive problem caused by collaborative metric learning. Extensive experiments on large-scale real-world datasets show that our model can substantially outperform the state-of-the-art baselines, demonstrating the potential of modeling user diverse preference for recommendation.

Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years. Although some progress has been achieved so far, several studies have pointed out that current VQA models are heavily affected by the language prior problem, which means they tend to answer questions based on the co-occurrence patterns of question keywords (e.g., how many) and answers (e.g., 2) instead of understanding images and questions. Existing methods attempt to solve this problem by either balancing the biased datasets or forcing models to better understand images. However, only marginal effects and even performance deterioration are observed for the first and second solution, respectively. In addition, another important issue is the lack of measurement to quantitatively measure the extent of the language prior effect, which severely hinders the advancement of related techniques.

Recently, as an essential part of people's daily life, clothing matching has gained increasing research attention. Most existing efforts focus on the numerical compatibility modeling between fashion items with advanced neural networks, and hence suffer from the poor interpretation, which makes them less applicable in real world applications. In fact, people prefer to know not only whether the given fashion items are compatible, but also the reasonable interpretations as well as suggestions regarding how to make the incompatible outfit harmonious. Considering that the research line of the comprehensively interpretable clothing matching is largely untapped, in this work, we propose a prototype-guided attribute-wise interpretable compatibility modeling (PAICM) scheme, which seamlessly integrates the latent compatible/incompatible prototype learning and compatibility modeling with the Bayesian personalized ranking (BPR) framework. In particular, the latent attribute interaction prototypes, learned by the non-negative matrix factorization (NMF), are treated as templates to interpret the discordant attribute and suggest the alternative item for each fashion item pair. Extensive experiments on the real-world dataset have demonstrated the effectiveness of our scheme.

Monitoring packet losses at links is beneficial to network management and service quality improvement. Software-defined networking can conveniently obtain flow statistics. However, the packet loss rate of a link or path cannot be directly measured by acquiring the statistics of an ongoing flow. In addition, monitoring the packet loss directly based on statistics inquiries brings considerable extra overhead to the controller. In this paper, we propose a two-way link-level packet loss monitoring solution for the software-defined networks. We propose a packet loss probe structure consisting of distributed rings. The proposed probe structure contains every monitored directed link once and only once, thereby avoiding the mutual interference between different probe flows and thereby improves probe accuracy. We further study two optimization problems of the ring-based packet loss probe structure. The two problems build probe structures, with minimal maximum ring delay, for all the network links and selected network links. The two optimization problems are very complex, and we approximately solve them in polynomial time. A packet loss positioning algorithm, based on flow statistic inquiries and the symmetry design of probe rings, is also proposed. Our proposed solution can effectively monitor link-level packet loss but brings low and controllable overhead to the controller.

E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well known that there are two types of preferences: long-term ones and short-term ones. The former refers to users’ inherent purchasing bias and evolves slowly. By contrast, the latter reflects users’ purchasing inclination in a relatively short period. They both affect users’ current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users’ current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.

Packet loss rate is an important consideration in the Quality of Service (QoS) measurement for a packet-switched network. The software-defined networking (SDN) technique can conveniently monitor flow statistics. However, the packet loss rate of a link or path cannot be directly measured by inquiring the statistics of an ongoing flow at the starting and ending points because it is impossible to accurately compute and control pairwise sampling moments. In this paper, we propose a two-way link-level packet loss measurement solution for software-defined networks. We solve the flow statistics sampling problem mentioned above by inquiring the statistics of a terminated probe flow. We propose a ring-based packet loss probe structure, which contains every measured directed link once and only once. The proposed probe structure effectively avoids the mutual interference between different probe flows, and thereby improves probe accuracy. The ring is implemented based on the flexible flow match capability of SDN. We further study an optimization problem of ring-based packet loss probe structure that strives to minimize the maximum delay of rings. This optimization problem is very complex, and we approximately solve it using a top-down-top graph partition method. A packet loss positioning method, based on flow statistic inquiries and the symmetry design of the probe ring, is also proposed herein.

This paper presents a hierarchical controller for an autonomous vehicle to track a reference path in the presence of uncertainties in both tire-road condition and external disturbance. The hierarchical control architecture consists of three layers: high, low, and intermediate levels. The upper-layer module deals with the vehicle motion control objective, which generates the desired longitudinal/lateral forces and yaw moment. The low-level module handles the braking control for each wheel based on the wheel slip dynamics. The intermediate-level controller generates the longitudinal slip reference for the low-level brake control module and the front-wheel steering angles. To cope with the unknown and nonuniform road condition parameters appearing in the actuator models, an adaptive law is designed for each wheel, and the convergence of the adaptive parameters is guaranteed under a certain persistency-of-excitation condition. The stability of the integrated control system is analyzed by utilizing a Lyapunov function approach. Simulation results are included to illustrate the proposed control scheme.

A good clustering algorithm can provide the basis for forming a good network topology and target location, improve routing efficiency and greatly reduce energy consumption. For the lack of GAF algorithm, an improved algorithm is putted forward. First of all, the monitored area is divided into many sections according to the intersecting circle structure. Then the nodes which meet specific criteria will be elected as cluster heads. Finally, some nodes in overlapping area of intersecting circles will be elected as the Mid Nodes to achieve multi-hops routing. As is shown in the experiment, the improved algorithm can greatly save energy consumption of nodes, and extend the lifetime of wireless sensor networks.

In recent years, many time synchronization protocols for wireless sensor networks are presented and reducing the number of synchronous data packets in transmission has become a new research breakthrough. In this paper, the nodes with amount of power held are placed in different areas where the two nodes may exchange the synchronization information. This way does not only efficiently use of energy during the synchronization process, but also reduce the overall timing messages. Based on inherent clock drift and clock offset problem, the Kalman Filter algorithm is used to optimize the clock deviation, which can minimize the mean-square error (MSE). The performance of the proposed time synchronization algorithm is described through the simulation results, there is not present relatively large deviation after optimizing comparing to clsscical TPSN algorithm for synchronization precision.

A constraint-based sensor network nodes particle swarm search localization algorithm (CPL) is presented. First of all, a constraint domain of an unknown node must be determined; Then the positions which meet specific criteria is searched out by particle swarm optimization algorithm and the searching results within the constraint domain are recorded; Finally, the unknown node's localization can be obtained by calculating the average recording results. As is shown in the experiment results, CPL has strong robustness, and comparing with normal schemes such as least square method (LS), CPL's positioning accuracy can improve 50% when the ranging error is 35%.

Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on different time sections respectively. Traffic flow forecasting usually is real-time and too many forecasting variables will reduce the real-time performance. So the selection of the most informative forecasting variable combination is significant. It can save computation cost and improve forecasting precision. In this paper, information bottleneck theory based on extended entropy is used to partition traffic flow of a day into different time sections. Corresponding to each time section, feature selection based on mutual information is generalized to regression problems and is used to select the most informative variable combination. Selected variables are input to Support Vector Machines (SVM) for traffic flow forecasting. Bayesian inference is used to determine the kernel parameters of SVM. The efficiency of the method is illustrated through analyzing the traffic data of Jinan urban transportation.

Summary form only given. Along with the rapid development and wide application of Internet technique, the research about communication security becomes a very important topic in theory and the practice. For the explicitness and large information content of images, much attention has been paid to the security of image storing and transmission in communication. As one of the key technique of digital image security problem, the digital image scrambling technique can be seem as one of the method for digital image encryption and used for digital image hiding, digital watermark, precondition, and after-treatment process of numerical resume for image deposit. Most digital image scrambling technique are based on the Arnold transformation, magic square, and fractal etc. By the Arnold transformation, the image becomes more and more complicity, but the image is recovered in a certain moment. In this way, the hiding purpose for image in communication can be reached. The periodicity of the two-dimensional Arnold transformation was discussed in many literatures. The necessary and sufficient condition about the existence of periodicity for the general Arnold matrix transformation had been given in literatures. The periodicity of n-dimensional Arnold transformation had been discussed in literatures. Some new image scrambling method based on three-dimensional Arnold transformation had been given in literatures. In this paper, we extend the conclusion about the periodicity of n-dimensional Arnold transformation, apply it in digital image scrambling, and give the key-dependent digital image scrambling scheme. For the n-dimensional transformation, the necessary and sufficient condition about the existence of periodicity is gcd(|A|, N)=1, where |A|=det(A) and A is the transformation matrix, N is module. We define the transformation satisfied above character to n-dimensional Arnold-type transformation. Obviously, we can derive the conclusion that the n-dimensional Arnold-type transformation is periodic. Using properties of the similar matrixes, we can derive the following conclusion. For a given module N, if the n-dimensional Arnold-type transformation matrix A is similar to B, then the period of transformation A is equal to that of transformation B. There is a shortcoming for the application of the Arnold transformation in digital image hiding. The security of the algorithm is based on the assumption that the attacker doesn't know about the algorithm (i.e. the attacker don't know the transformation matrix). If the attacker know about the algorithm, it is easy to recover the image. So this algorithm doesn't fill the requirement of the modern cryptography. However, we can apply the above n-dimensional Arnold-type transformation to avoid this lack. It can ensure the security of the digital image scrambling by the secrete key even the algorithm is public. We select a n-dimensional invertible matrix as the secrete key, then derive transformation matrix. In this paper, we extend the conclusion which about the Arnold transformation in the former paper using the property of the similar matrix and a digital image scrambling scheme is proposed by a kind of Arnold-type transformation in order to raise the security of the image scrambling.

CSCW in design maybe work in mobile ad hoc network and security of routing protocol in MANET is one key factor. The routing protocol for MANET uses cryptographic technology to heighten its security, which makes it possible to use formal method. Considered the property of routing protocol for MANET, the drawback of BAN logic is analyzed and the security is described based on improved BAN logic. The formalization of the protocol is described and the method is presented. Taken example of SADSR, the security of routing protocol is analyzed by using the method, which proves the method is valid. A method to attack SADSR is found

CSCW in design maybe work in mobile ad-hoc network and security of routing protocol in MANET is one key factor. Security analysis method for routing protocol in MANET was researched and the basic nodes behavior of MANET was analyzed and given in the paper. On the base of the behavior, formal analysis of attacking routing protocol is discussed. Six kinds of attacking mode are given. Each of the six modes is respectively discussed. Taken example of two protocols, DSR and SADSR, the security of routing protocol is analyzed by using the method, which proves that the method is valid.

Information security is very important in CSCW and digital signature is one of the valid methods to resolve these problems. Based on research on multisignature and proxy signature, we present a new type of signature: Multisignature with proxy, in which some signers can delegate their proxies to do the signatures. The common multisignature and the proxy multisignature are specific to this signature. Two new schemes of multisignatures mingled with proxy signers are introduced in this paper, and their security issues are analyzed as well.