Privacy-Preserving Mutual Authentication (PPMA) protocols utilize Physical Unclonable Function (PUF) and True Random Number Generator (TRNG) as security primitives to protect privacy. To ensure the security of Internet of Things (IoT) nodes in untrusted environments, PPMA keys and encrypted data must reside on the same chip. The concept of integrating PUF and TRNG on a single device has thus emerged as a new security paradigm. This paper proposes a novel lightweight, portable, multi-mode reconfigurable integrated PUF-TRNG architecture resistant to machine learning attacks. Through co-design, the architecture achieves the integration and switching among three modes: Arbiter Physical Unclonable Function (APUF), Ring Oscillator Physical Unclonable Function (RO PUF), and TRNG. The APUF mode leverages the RO PUF mode for assistance, endowing it with machine learning resistance, where the highest prediction rates using Logistic Regression (LR), Support Vector Machine (SVM), CMA (Comparative Model Analysis), and Deep Neural Network (DNN) algorithms are only around 60%. Additionally, a lightweight authentication protocol is proposed to further enhance resistance against machine learning attacks. In TRNG mode, the architecture has two outputs, each capable of generating random numbers at 800 Mbps, resulting in a total throughput of 1600 Mbps. The generated random numbers have successfully passed various tests, including NIST SP800-22, NIST SP800-90B, AIS-31, and TESTU01. In the NIST SP800-22 test, the pass rates for both outputs of the Artix-7 and Kintex-7 FPGAs are approximately 99%.

Abstract Large-scale atomic/molecular massively parallel simulator (LAMMPS) is a prevalent software package employed for molecular dynamics simulations, enabling the study of materials at the atomic and molecular scale. Its performance is paramount in numerous industrial applications, driving the need for ongoing enhancements in simulation speed and parallel efficiency. Previous works heavily rely on hardware accelerators, which lead to limited parallel and high costs. To address this, this work optimizes the message passing interface (MPI) and memory copy functions, while deploying LAMMPS on high-performance computing (HPC) systems. We propose a new adaptive broadcast algorithm to improve the parallelism efficiency of the interconnect topology. We also discuss how to realize the mutual hiding of computation and communication of the Packing algorithm in LAMMPS, and optimize the memory copy function and MPI operators to facilitate the execution of the program. The resulting components are integrated into the MPICH4 software and deployed on the MT-3000 HPC system. The experimental results show a significant performance improvement, with up to four orders of magnitude speedup on 1024, and more than 90% parallel efficiencies, demonstrating the effectiveness of our proposed optimization scheme. The adaptive broadcast algorithm and the portability of computation and communication hiding are also discussed. The adaptive broadcast algorithm is applied to SPEC MPI2007, and the average performance improvement is 23.91 and 27.29% on ARMv8 cluster and x86_64 cluster, respectively.

The true random number generator is capable of generating completely random and unpredictable sequences, and plays a crucial role in various fields such as cryptography, encryption communication, and random algorithms. To meet the demand for high-throughput true random number generators in modern high-speed systems, a lightweight TRNG design is proposed. It utilizes a mesh topology of coupled-XOR as entropy source and generates a highly compact and high throughput true random number generator by coupling oscillators in the network. The generated random sequences have successfully passed the NIST SP 800-22, TESTU01, NIST SP 800-90B, and AIS-31 tests. It achieved ultra-high throughput of 2.1Gbps and 2.4Gbps on the Xilinx Artix-7 and Kintex-7 series development boards, respectively, achieving efficient utilization of hardware resources. Compared with other works, this design has significant advantages in terms of resource utilization and throughput.

Limited data usually cause deep neural networks to hold poor performance after training, and many generative models are proposed to synthesize data to improve the performance of models. However, existing models ignore capturing the small defect details (e.g., features and locations), resulting in that most models cannot augment the Defect Location Sensitive Data (DLS data) in which the ratio of object size to the image size is small (e.g., 20%) and the locations of the defects are only on the object. In this paper, we propose a new augmentation model, named Defect Location Sensitive data augmentation GAN (DLS-GAN), to address DLS data augmentation problem. First, we modify the vanilla generator with two Encoder-Decoder models, and view the limited masked-images masked by labeling the defect-free pixels while remaining the defect pixels in defect images and many defect-free images as the input of the two models. The extracted feature map from the first Encoder-Decoder model provides the defect features and location information; the second one extracts the features of defect-free images, and integrates the two different features with a designed Defect Feature Transfer Module to synthesize images with desired defects. Second, we employ two discriminators to estimate the scores of both distribution matching degree and defect similarity between real data and generated ones. With the two modifications, we design a new loss function, and then prove that it makes our model get converged. Last, we conduct extensive experiments to demonstrate the significant performance improvement and generalizability of DLS-GAN on different types of DLS datasets. The experimental results show that our DLS-GAN outperforms the SOTA generative models in terms of synthesizing high quality images with desired defects.Note to Practitioners—Automated defect image detectors play an important role in the field of automated manufacturing. Training a detector with superior detection performance usually requires a large number of samples. However, it is difficult to collect many defect samples in practice. Although existing generative methods can synthesize realistic-like images, they cannot generate the Defect Location Sensitive Data (DLS Data) which refer to the samples that the defects appear at the specific locations in product objects, resulting in the synthesized images invalid. This paper proposes a new defect image generation model called DLS-GAN to address this problem, and validates its performance in different real-world industrial datasets ranging from DLS Data to Non-DLS Data. Such generated images can be adopted as useful resources for improving the detection performance of automated detector.

Mode collapse has been a persisting challenge in generative adversarial networks (GANs), and it directly affects the applications of GAN in many domains. Existing works that attempt to solve this problem have some serious limitations: models using optimal transport (OT) strategies (e.g., Wasserstein distance) lead to vanishing or exploding gradients; increasing the number of generators can cause several generators focusing on the same mode; and approaches that modify the loss also do not satisfactorily resolve mode collapse. In this article, we reduce mode collapse by formulating it as a Monge problem of OT map. We show that the Monge problem can be transformed to the distribution transformation problem in GAN, and a rectified affine neural network can be considered as a measurable function. In this way, we propose Monge GAN that uses this measurable function to transform the generated data distribution into the original data distribution. We utilize the Kantorovich formulation to obtain the OT cost, which is regarded as the OT distance between the two distributions. Finally, we conduct extensive experiments on both image and numerical datasets to validate our Monge GAN in reducing model collapse.

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.

We study the problem of distributed collision avoidance for mobile robotic systems, where a group of heterogeneous robots with different sizes and motion constraints avoid collisions with each other and static obstacles during the movements from their starting to goal locations. Existing methods mainly consider homogeneous robots and incur a high collision rate in environments with moving robots and static objects. Hence, we propose a distributedcollisionavoidance forheterogeneous mobile robots (Heter-CA), which allows each robot to independently avoid collisions considering the heterogeneity of robots and varying static obstacles. InHeter-CA, each robot predicts the trajectories of neighboring robots and estimates the varying size of static obstacles with the robots' range-finder sensors before motion planning, which enables each robot to avoid obstacles safely. Besides, we prove thatHeter-CAcan guarantee collision-free movement between heterogeneous robots by satisfying sufficient conditions. We evaluateHeter-CAin numerous simulated and real-world scenarios in which groups of heterogeneous robots perform navigation tasks. The experimental results demonstrate thatHeter-CAtakes$10\times$less computation time and achieves$5\%$less collision rate than baseline algorithms.

Color-tone represents the prominent color of an image, and training generative adversarial nets (GAN) to change color-tones of generated images is desirable in many applications. Advances such as HistoGAN can manipulate color-tones of generated images with a target image. Yet, there are challenges. Kullback-Leibler (KL) divergence adopted by HistoGAN might bring the color-tone mismatching, because it is possible to provide infinite score to a generator. Moreover, only relying on distribution estimation also produces images with lower fidelity in HistoGAN. To address these issues, we propose a new approach, named dynamic weights GAN (DW-GAN). We use two discriminators to estimate the distribution matching degree and details' similarity, with Laplacian operator and Hinge loss. Laplacian operator can help capture more image details, while Hinge loss is deduced from mean difference (MD) that could avoid the case of infinite score. To synthesize desired images, we combine the loss of the two discriminators with generator loss and set the weights of the two estimated scores to be dynamic through the previous discriminators' outputs, given that the training signal of a generator is from a discriminator. Besides, we innovatively integrate the dynamic weights into other GAN variants (e.g., HistoGAN and StyleGAN) to show the improved performance. Finally, we conduct extensive experiments on one industrial Fabric and seven public datasets to demonstrate the significant performance of DW-GAN in producing higher fidelity images and achieving the lowest Frechet inception distance (FID) scores over SOTA baselines.

Pipelines have become one of the most important infrastructures in the city. Over time, they are prone to aging, cracks, corrosion, and the demand for regular inspection is gradually increasing. Robotic solutions are effective methods for in-pipe inspection. However, existing In-pipe Inspection Robots (IPIR) require that the inner diameter of the pipe is fixed in the application scenarios, and need extra labor to control the robot and handle the cable. In this work, we design and develop a deformable robot to adapt to pipes with different inner diameters. Specifically, the passive elastic hinge is used by us to make the robot fully in contact with the pipe, generating enough friction to ensure that the robot is attached to the inner wall of the pipe. An edge device is deployed on the robot, generating velocity commands of wheels through the data from Inertial Measurement Unit (IMU), which eliminates the need for external devices. Experimental results demonstrate that the robot can move in horizontal and vertical pipelines, as well as traverse through pipe joints and scenarios where there is dirty or small obstacle.

Manipulator teams are frequently employed in various industrial applications to handle challenging cooperative tasks. The complicated interaction between manipulators makes it difficult to design applications from scratch. Although robotics middleware has emerged as the key to lowering the development complexity of manipulator applications, existing works still have limitations in controlling multiple manipulators to carry out tasks cooperatively. To overcome the limitations, middleware should provide programming abstraction support, coordination mechanism, and dynamic reconfiguration of motion controllers so that a team of manipulators can work together efficiently. This work proposes ManiWare, an easy-to-use middleware that provides the team-level programming abstraction and the manipulator-level plugin mechanism for programming and configuring manipulator applications. The team-level programming abstraction can facilitate the development process by invoking the functions from the fundamental cooperation components, which drives the developers to focus on designing application logic. Besides, the plugin mechanism dynamically configures and manages the motion controller of different parts of manipulators, making the reconfiguration feasible. This work implements ManiWare and evaluates the task execution performance with three case studies in the high-fidelity simulation platform. The experimental results demonstrate that ManiWare facilitates cooperative tasks with a high success rate, efficient completion time, and marginal runtime overhead. The source code is athttps://github.com/sundycoder/maniware.

Imbalanced data cause deep neural networks to output biased results, and it becomes more serious when facing extremely imbalanced data regarding the outliers with tiny size (the ratio of the outlier size to the image size is around 0.05%). Many data argumentation models are proposed to supplement imbalanced data to alleviate biased results. However, the existing augmentation models cannot synthesize tiny outliers, which make the generated data unavailable. In this article, we propose a new augmentation model named extremely imbalanced data augmentation generative adversarial nets (EID-GANs) to address the extremely imbalanced data augmentation problem. First, we design a new penalty function by subtracting the outliers from the cropped region of generated instance to guide the generator to learn the features of outliers. After this, we combine the output value of the penalty function with the generator loss to jointly update the generator’s parameters with backpropagation. Second, we propose a new evaluation approach that adopts two outlier detectors withk-fold cross-validation to assess the availability of generated instances. We conduct extensive experiments to demonstrate the significant performance improvement of EID-GAN on two extremely imbalanced datasets, which are the industrial Piston and the Fabric datasets, and one general imbalanced dataset, i.e., the public DAGM dataset. The experimental results show that our EID-GAN outperforms the state-of-the-art (SOTA) augmentation models on different imbalanced datasets.

The generative adversarial network (GAN) is usually built from the centralized, independent identically distributed (i.i.d.) training data to generate realistic-like instances. In real-world applications, however, the data may be distributed over multiple clients and hard to be gathered due to bandwidth, departmental coordination, or storage concerns. Although existing works, such as federated learning GAN (FL-GAN), adopt different distributed strategies to train GAN models, there are still limitations when data are distributed in a non-i.i.d. manner. These studies suffer from convergence difficulty, producing generated data with low quality. Fortunately, we found that these challenges are often due to the use of a federated averaging strategy to aggregate local GAN models' updates. In this article, we propose an alternative approach to tackling this problem, which learns a globally shared GAN model by aggregating locally trained generators' updates with maximum mean discrepancy (MMD). In this way, we term our approach improved FL-GAN (IFL-GAN). The MMD score helps each local GAN hold different weights, making the global GAN in IFL-GAN getting converged more rapidly than federated averaging. Extensive experiments on MNIST, CIFAR10, and SVHN datasets demonstrate the significant improvement of our IFL-GAN in both achieving the highest inception score and producing high-quality instances.

Manipulator teams are widely used to handle complex and cooperative tasks in various industrial applications. Although robotic middleware has emerged as the key to reducing the complexity of manipulator application development, existing works still have limitations in controlling multiple manipulators to perform tasks cooperatively. Therefore, middleware should provide programming abstraction support, coordination mechanism, and dynamic reconfiguration of motion controllers so that the manipulator teams can work efficiently. In this paper, we propose ManiWare, an easy-to-use middleware that provides a team-level programming abstraction and the manipulator-level plugin mechanism for programming and building manipulator applications. The team-level programming abstraction can facili-tate the development process, which drives the developers to focus on application logic development. Besides, the plugin mechanism configures and manages the motion controller of different components of manipulators. We implement the functional components in ManiWare and study two cases in the high-fidelity simulation engine. The utilization of ManiWare can reduce the construction difficulties of building manipulator applications.

Summary Multi‐robot systems are widely used to handle complex and cooperative missions in various industrial applications. Although robotic middleware has become the key to reducing the complexity of multi‐robot application development, existing works still have limitations in controlling multiple robots to perform missions cooperatively. To enable multi‐robot cooperation, middleware should provide high‐level abstraction support, dynamic configuration, communication, and synchronization. In this article, we proposeGraphWare, a novel middleware that provides a graph‐based programming abstraction and its underlying runtime kernel for programming and building multi‐robot cooperation applications. The graph‐based programming abstraction can express cooperative missions without exposing the complexity of managing multiple robots. The runtime kernel configures and manages multiple heterogeneous robots to intelligently perform cooperative missions. We implementGraphWareand evaluate its performance with ball collection missions which are cooperatively accomplished by a group of mobile robots, and study the fault‐tolerance, flexibility, and scalability of the middleware in the realistic simulation. The experimental results demonstrate thatGraphWarefacilitates the multi‐robot cooperative mission with efficient mission completion time, high success rate, and marginal runtime overhead.

Liquid fraud has plagued people with huge health risks. Liquid fraud detection can help to reduce the risk of liquid hazards. However, existing systems that use biochemical tools or radio frequency signals for liquid sensing are either expensive, intrusive, or inconvenient for public use. In this article, we propose HearLiquid, a low-cost and nonintrusive liquid fraud detection system using commodity acoustic devices. Our insight comes from the fact that acoustic impedance of different liquids results in distinct absorption of the acoustic signal across different frequencies when it travels through the liquid. In specific, we extract the liquid’s acoustic absorption and transmission curve (AATC) over multiple frequencies of the acoustic signal for liquid fraud detection. However, accurately measuring the AATC faces multiple challenges. First, due to the hardware diversity and imperfection, different acoustic devices introduce diverse frequency responses, which brings significant deviations to AATCs of the same liquid. Second, different relative positions between acoustic devices and the liquid container result in variations in the AATC, making the detection result inaccurate. To overcome these challenges, we first calibrate the AATC using a dedicated reference AATC to remove the effect of hardware diversity. To bear the variations in AATCs measured from different relative positions, we apply a well-orchestrated data augmentation technique to automatically generate sufficient AATCs for different positions using a small number of collected data. Finally, AATCs are used to train the liquid detection model. We conduct extensive experiments on many important liquid fraud cases and achieve liquid detection accuracy of 92%–97%.

Autonomous, arbitrary pattern formation is one of the most critical applications in multi-robot systems, where robots are required to form into circles, lines, and meshes or any other desired configuration. This task is important in military applications, search and rescue operations, and visual inspection of infrastructure and equipment tasks to name a few. Most existing works are very rigid, and only able to form certain shapes, where slight target changes can cause failure in the predefined pattern-specific rules and trigger algorithm redesign. We propose a novel, deep reinforcement learning-based method that generates general-purpose pattern formation strategies, in the form of deep neural networks (DNN), for any target pattern. Our method uses the trial-and-error feedback of each round of training to gradually generate the pattern formation strategy. Thus, robots are able to query the trained DNN model to select their optimal directions and speeds in a fully distributed manner. Considering that reinforcement learning models do not perform well with large state spaces and highly variant training samples, we employ auto-encoders to learn the condensed representation for each state and compute model-free policy gradients for arbitrary pattern formation. We experimentally show that groups of robots are able to form various general target patterns while minimizing the number of completion time steps.

Recently, much attention is paid to multi-robot systems due to their widespread applications such as warehouse robotics, persistent surveillance, and exploration of unknown environments. Although urgently required by the applications, coordination among multiple robots remains to be challenging. Among the problems of multi-robot coordination, pattern formation serves a fundamental one. It aims to control a group of robots to form a desired shape with some certain goals such as best formation quality, minimum makespan or minimum total distance. Existing works mainly focus on the formation of certain patterns, such as repeating squares or a circle. those approaches cannot be generalized to arbitrary pattern formation. In this paper, we propose a decentralized algorithm for a multi-robot system to generate a given formation with an arbitrary repeating pattern. We introduce basic pattern graph and assembling graph to define a repeating pattern and formation quality for measurement. Towards solving the repeating pattern formation problem, our approach is divided into two phases. The robots are grouped into multiple basic patterns in the first phase, and the patterns are assembled level by level in the second phase. Simulations and real-world experiments indicate the effectiveness and practicability of our approach.

Recently years, research in multi-robot systems has attracted increasingly attentions. One important research topic is to design programming models that can facilitate the developers to programme large-scale multi-robot systems. However, existing works fail to manage the robots to perform tasks with real-time requirements. To address this issue, we propose a new programming model called RMR (Real-time Multi-Robot). RMR is a logic programming model with real-time support. On the basis of the logic programming paradigm, RMR allows the code for multi-robot system to be written from a global perspective, rather than managing a large collection of independent robots. Moreover, RMR allows developers to set timing constraints on the behaviors of an ensemble of robots, which is not implemented by state of the art. After designing RMR, we further develop a compiler and a runtime system for distributed execution of RMR programs. To evaluate the performance of RMR, we deploy it in a simulator and a test-bed, and then demonstrate RMR based on several applications. Our results indicate that RMR greatly facilitates implementing correct collaborative multi-robot applications.

The performance of a depth-first frequent itemset (FI) miming algorithm is closely related to the total number of recursions. In previous approaches this is mainly decided by the total number of FIs, which results in poor performance when a large number of FIs are involved. To solve this problem, a three-strategy adaptive algorithm, bitmap itemset support counting (BISC), is presented. The core strategy, BISC1, is used in the innermost steps of the recursion. For a database D with only s frequent items, a depth-first approach need up to s levels of recursions to detect all the FIs (up to 2 s ). BISC1 completely replaces these recursions with a special summation that directly calculates the supports of all the possible 2 s candidate itemsets. With BISC1 the run-time is entirely independent of the database after one database scan, and the per-candidate cost is only s . To offset the exponential growth of cost (both time and space) with BISC1 as s increases, a second strategy, BISC2, is introduced to effectively double the acceptable range of s . BISC2 divides an itemset into prefix and suffix and improves the performance by pruning all the itemsets with infrequent prefixes. If the total number of frequent items in D is high, the classic database projection strategy is used. In this case for the first s items a single run of BISC (1 or 2) is applied. For each of the remaining items, a projected database is created and the mining process proceeds recursively. To achieve optimal performance, BISC adaptively decides which strategy to use based on the dataset and minimum support. Experiments show that BISC outperforms previous approaches in all the datasets tested. Even though this does not guarantee that BISC will always perform the best, the result is impressive given the fact that most existing algorithms are only efficient in some types of datasets. The memory usage of BISC is also comparable to those of other algorithms.

Traditional pattern growth-based approaches for sequential pattern mining derive length-(k+1) patterns based on the projected databases of length-k patterns recursively. At each level of recursion, they unidirectionally grow the length of detected patterns by one along the suffix of detected patterns, which needs k levels of recursion to find a length-k pattern. In this paper, a novel data structure, UpDown Directed Acyclic Graph (UDDAG), is invented for efficient sequential pattern mining. UDDAG allows bidirectional pattern growth along both ends of detected patterns. Thus, a length-k pattern can be detected in [log 2 k + 1] levels of recursion at best, which results in fewer levels of recursion and faster pattern growth. When minSup is large such that the average pattern length is close to 1, UDDAG and PrefixSpan have similar performance because the problem degrades into frequent item counting problem. However, UDDAG scales up much better. It often outperforms PrefixSpan by almost one order of magnitude in scalability tests. UDDAG is also considerably faster than Spade and LapinSpam. Except for extreme cases, UDDAG uses comparable memory to that of PrefixSpan and less memory than Spade and LapinSpam. Additionally, the special feature of UDDAG enables its extension toward applications involving searching in large spaces.

Web based question answering (QA) system is a valuable tool for improving e-learning. Many approaches use natural language processing technology to understand questions, which is incomplete and error-prone. Besides, instead of extracting detailed answer, many approaches simply return hyperlinks to potential answers, which is inconvenient for the users. In this paper we use template mapping technique to detect the type of a question, based on which an appropriate query for a specific search engine is built. We then extract detailed content blocks from relevant pages as answers. The content blocks are detected using a projection technique and clustered based on similarity. We select the answer cluster using the redundancy property of Web answers. The content blocks in the answer cluster are put into one page for easy comparison. In this way we can greatly improve the effectiveness of Web QA systems for e-learning.

Finding Contiguous Sequential Patterns (CSP) is an important problem in Web usage mining. In this paper we propose a new data structure, UpDown Tree, for CSP mining. An UpDown Tree combines suffix tree and prefix tree for efficient storage of all the sequences that contain a given item. The special structure of UpDown Tree ensures efficient detection of CSPs. Experiments show that UpDown Tree improves CSP mining in terms of both time and memory usage comparing to previous approaches.

Sequential patterns of d-gaps exist pervasively in inverted lists of Web document collection indices due to the cluster property. In this paper the information of d-gap sequential patterns is used as a new dimension for improving inverted index compression. We first detect d-gap sequential patterns using a novel data structure, UpDown Tree. Based on the detected patterns, we further substitute each pattern with its pattern Id in the inverted lists that contain it. The resulted inverted lists are then coded with an existing coding scheme. Experiments show that this approach can effectively improve the compression ratio of existing codes.

Web content function indicates authors' intension towards the purpose of the content and therefore plays an important role for Web information processing. In this paper we propose a generalized hidden Markov model which extends traditional hidden Markov model for Web content function detection. By incorporating multiple emission features and detecting state transition sequence based on layout structure, generalized hidden Markov model can effectively make use of Web-specific information and achieve better performance comparing to traditional hidden Markov model. Comparing to previous approaches on function detection, our approach has the advantages of domain-independency and extensibility for other applications. Experiments show promising results with our approach

In PCS networks, location management operation expends the limited wireless resources to keep track the location information of a mobile terminal. Various dynamic location update (LU) schemes have been proposed to improve the efficiency of location management. However, most of them only work for certain mobility patterns. In this paper, we propose a new scheme that the LU area is adaptively designed according to the mobility pattern and traffic parameters. The 2D Markov walk is used as the mobility model which describes a broad class of mobility patterns. A recursive algorithm is developed to compute the location management cost of a general LU area shape. An iterative greedy heuristic algorithm is then used to find the LU area shape with minimum location management cost. The effects of the mobility patterns and traffic parameters on the designed LU area shape are investigated. Experimental results show that the LU area designed by the heuristic algorithm can adaptively change according to the given mobility pattern and traffic parameters. Compared with some existing dynamic LU schemes, the proposed adaptive LU is more flexible and efficient for location management.

The clustering property of document collections in Web search engines provides valuable information for improving index compression. By clustering d-gaps of an inverted list and then encoding clustered and non-clustered d-gaps using different codes, we can tailor to the specific properties of different d-gaps and achieve better compression ratio. Further improvement on index compression can be achieved by adoptively adjusting the cluster threshold for inverted lists. Based on these ideas, in this paper we propose adaptive cluster based mixed codes for inverted file index compression. Experiment results show that codes using adaptive cluster based mixed approach have better performance in terms of compression ratio and lower complexity comparing to interpolative code which is considered as one of the most efficient bitwise codes at present

A generalized hidden Markov model (GHMM) which extends traditional HMMs by making use of Web-specific information for Web information extraction is presented in this paper. Web content blocks are used instead of content terms as basic extraction unit in our approach. Besides, instead of using the traditional sequential state transition order, the state transition orders of GHMMs are detected based on layout structures of the corresponding Web pages. Furthermore, multiple emission features are applied instead of single emission feature. In this way GHMMs can better accommodate Web information extraction. Experiments show promising results of GHMMs

Rapid advances in consumer electronics have led to the anomaly that consumer off the shelf (COTS) gaming hardware and software now provide better interactive graphics than military and other specialized systems costing orders of magnitude more. UTSAF is bridging software written to take advantage of the power of gaming systems by allowing them to participate in distributed simulations with military simulators such as OneSAF which use the DIS protocol. UTSAF parses DIS PDUs and uses the information gained to control entities within the Unreal game engine using the Game-Bots modification. This paper describes the advantages of game engine based simulation and the UTSAF bridging and control architecture. Our main contribution is to build a simulation bridge that enables affordable high-quality 3-D viewers for military simulations.

Content understanding is a crucial issue for website adaptation. In this paper we present a Function-based Object Model (FOM) that attempts to understand authors' intention by identifying Object function instead of semantic understanding. Every Object in a website serves for certain functions (Basic and Specific Function) which reflect authors' intention towards the purpose of an Object. Based on this consideration we have proposed the FOM model for website understanding. FOM includes two complementary parts: Basic FOM based on the basic functional properties of Object and Specific FOM based on the category of Object. An automatic approach to detect the functional properties and category of Object is presented for FOM generation. Two level adaptation rules (general rules and specific rules) based on FOM are combined for practical adaptation. A system for web content adaptation over Wireless Application Protocol (WAP) is developed as an application example of the proposed model. Experiments have shown satisfactory results and extensibility. Keywords Content Adaptation, Website Understanding, Content Function, HTML/WML Conversion 1.