HybridVis: An adaptive hybrid-scale visualization of multivariate graphs

Existing network visualizations support hierarchical exploration, which rely on user interactions to create and modify graph hierarchies based on the patterns in the data attributes. It will take a relatively long time for users to identify the impact of different attributes on the cluster structure. To address this problem, this paper proposes a visual analytical approach, called HybridVis, creating an interactive layout to reveal clusters of obvious characteristics on one or more attributes at different scales. HybridVis can help people gain social insight and better understand the roles of attributes within a cluster. First, an approximate optimal graph hierarchy based on an energy model is created, considering both data attributes and relationships among data items. Then a layout algorithm and a level-dependent perceptual view for multi-scale graphs are proposed to show the attribute-driven graph hierarchy. Several views, which interact with each other, are designed in HybridVis, including a graphical view of the relationships among clusters; a cluster tree revealing the cluster scales and the details of attributes on parallel coordinates augmented with histograms and interactions. From the meaningful and globally approximate optimal abstraction, users can navigate a large multivariate graph with an overview+detail to explore and rapidly find the potential correlations between the graph structure and the attributes of data items. Finally, experiments using two real world data sets are performed to demonstrate the effectiveness of our methods.     

A componential model of human interaction with graphs: VI. Cognitive engineering of pie graphs

This paper proposes and tests the following three-component model of reading a pie graph to estimate segment size: (a) selecting a mentally represented anchor segment (25%, 50%, or 75%), (b) mentally aligning representations of the anchor and target segments, and (c) mentally adjusting the size of the anchor to match the target. Experiment 1 showed that the size difference between the target and closest anchor and the angular displacement of the target from vertical predicted response times (RTs) and absolute error. Experiment 2 demonstrated that an aligned pie graph, designed to reduce the "align" portion of the process, produced faster RTs and lower error than did a regular pie graph. Experiment 3 showed that a pie graph labeled at the anchor values produced the same response times and absolute error as a regular pie graph but that a pie labeled off the anchor points produced a very different pattern of results. The discussion relates the results to the componential model and describes applications in increasing pie graph usability and developing design guidelines. Actual or potential applications of this research include guidelines for graph design and more usable pie graphs.     

Making data-centric storage adaptive and cost-optimal

We present an adaptive and cost-optimal mechanism called Tug-of-War (ToW) for data storage and query mechanism in wireless sensor networks. ToW is based on the concept of data-centric storage (DCS), where a unique home location is used to store events of a given type so that queries of the event type can be directed to the location without flooding. However, rather than using just a single home location, the mechanism can dynamically adjust the number of home locations according to the event and query rates ratio so as to minimize the total communication cost. We also generalize the framework so that different schemes can be used to construct home locations as well as to ensure rendezvous of events and queries, thereby to fine-tune the system performance.     

An adaptive system for dynamic storage allocation

We describe a technique (the adaptive creation of free lists) for dynamic storage allocation that is particularly suited to situations in which the distribution of sizes of blocks requested has one or more sharp peaks. We describe a particular dynamic storage allocation system and the environment in which it runs, and give the results of some experiments to determine the usefulness of the technique in this system. Our experiments also tested the efficacy of a technique suggested by Knuth for improving the performance of similar systems.     

Allocating task interaction graphs to processors in heterogeneousnetworks

The problem of allocating task interaction graphs (TIGs) to heterogeneous computing systems to minimize job completion time is investigated. The only restriction is that the interprocessor communication cost is the same for any pair of processors. This is suitable for local area network based systems, such as Ethernet, as well as fully interconnected multiprocessor systems. An optimal polynomial solution exists if sufficient homogeneous processors and communication capacity are available. This solution is generalized to obtain two faster heuristics, one for the case of homogeneous processors and the other for heterogeneous processors. The heuristics were tested extensively with 60,900 systematically generated random TIGs and shown to be stable independent of the size of the TIG. A performance model is also proposed to predict the performance of the heuristic algorithms, and it is successful in explaining the experimental results qualitatively     

An adaptive memetic algorithm for feature selection using proximity graphs

We propose a multivariate feature selection method that uses proximity graphs for assessing the quality of feature subsets. Initially, a complete graph is built, where nodes are the samples, and edge weights are calculated considering only the selected features. Next, a proximity graph is constructed on the basis of these weights and different fitness functions, calculated over the proximity graph, to evaluate the quality of the selected feature set. We propose an iterative methodology on the basis of a memetic algorithm for exploring the space of possible feature subsets aimed at maximizing a quality score. We designed multiple local search strategies, and we used an adaptive strategy for automatic balancing between the global and local search components of the memetic algorithm. The computational experiments were carried out using four well‐known data sets. We investigate the suitability of three different proximity graphs (minimum spanning tree, k‐nearest neighbors, and relative neighborhood graph) for the proposed approach. The selected features have been evaluated using a total of 49 classification methods from an open‐source data mining and machine learning package (WEKA). The computational results show that the proposed adaptive memetic algorithm can perform better than traditional genetic algorithms in finding more useful feature sets. Finally, we establish the competitiveness of our approach by comparing it with other well‐known feature selection methods.     

云存储中副本自适应一致性研究

在云存储系统中,为了保证系统可用性的同时尽可能提高系统性能,降低副本同步过程中网络带宽等资源的消耗。提出了基于文件热度的副本自适应一致性方法。该方法根据文件的热度不同,动态调整副本同步的一致性策略,对于热度高的文件在副本同步过程中采用强一致性,对于热度低的文件在副本同步过程中采用最终一致性。在计算文件热度时,考虑了文件访问的时间序列并结合LRFU算法,该热度值表示了文件将来可能的访问情况,并与文件的实际访问情况一致。实验表明该方法在保证系统可用性的同时显著降低了网络带宽资源的消耗,有效地平衡了系统的可用性和性能。     

多用户存储中自适应动态预取策略

通过分析多用户数据请求规律以及实时分解随机请求序列来获取顺序请求序列。基于对多用户顺序请求进行命令预分解和命中率统计,实现读预取长度自我学习。分析多用户预取率及系统负载与预取失效代价之间的关系,对常规自适应Cache策略进行优化,选择合适预取阈值等参数。与常规自适应预取策略相比,动态调整Cache策略的预取命中率提高了30%。有效解决了多用户访问共享存储系统的预取失效率高问题。     

Non-visual interaction with graphs assisted with directional-predictive sounds and vibrations: a comparative study

Blind and visually impaired students need special educational and developmental tools to allow them to interact with graphic entities on PDA and desktop platforms. In previous research, stylus movements regarding the hidden graph were sonified with three directional-predictive sound (DPS) signals, taking into account an exploration behavior and the concept of the capture radius. The results indicated that the scanpaths were by 24–40% shorter in length and task completion times decreased by 20–25%. The goal of the study presented in this paper was to measure and compare the subjective performance recorded with directional-predictive vibrations (DPV) regarding the subjective performance achieved when the hidden graphic images were explored with DPS. The study also aimed to find out which kind of feedback cues would require less cognitive efforts in interpreting their meaning. The prototype of vibro-tactile pen with embedded vibration motor was used to produce DPV instead of sounds. The performance of eight blindfolded subjects was investigated in terms of the number of both feedbacks used and the time spent to complete non-visual inspection of the hidden graphs. There was a statistically significant difference between the average number of DPS and vibrations and task completion time taken by the players to discover the features of hidden graphs being explored with different capture radius. The experimental findings confirmed the beneficial use of DPS signals in the task when cross-modal coordination should benefit the user in the absence of visual information when compared with DPV patterns.     

Energy-efficient and secure in-network storage and retrieval for WSNs: an adaptive approach

Storage and retrieval of data in Wireless Sensor Networks (WSNs) have been found to be challenging issues in recent studies. The two principal approaches used in almost all proposed schemes in this field are Centralized and Decentralized. Many investigations have considered the security and communication issues in centralized schemes where a central node exists as the sink and all collected data are sent to it to be processed or the central node provides access to the possible users. In decentralized systems, all nodes are equally responsible for the duties of the central node in centralized approaches. In cases where sensors cannot transmit data immediately, which is usually the case in distributed approaches, in-network storage and retrieval is an alternative with its own security and communication issues stemming from the properties of energy sensitive sensors. In this paper, a semi-centralized scheme is introduced that is based on a number of traditional techniques, namely, clustering, symmetric and asymmetric key-management, and threshold secret sharing. The proposed scheme provides an energy-efficient and secure in-network storage and retrieval that could be applied to WSNs. A predictive method is proposed to adaptively determine the proper parameters for the threshold secret sharing technique. Confidentiality, dependability, and integrity of the sensed data are enhanced in a distributed manner with fairly low communication and computation costs. Simulations were utilized to illustrate the effect of several network parameters on energy consumption and to come up with optimization recommendations for the parameters of the proposed secret sharing scheme.     

Communication and interaction strategies in automotive adaptive interfaces

Nowadays, drivers have to cope with a growing amount of information coming from on-board information messages, telematics and advanced driver assistance systems. The interaction between the driver and these systems is critical, since they may distract the driver from the primary task of driving. The paper, addressing this problem, aims at presenting the methodological framework for the optimization of human machine interfaces (HMI) in the automotive research area; thus, the proper communication and interaction strategies are designed, in order to deliver to the driver a message or a warning in the optimal way in terms of driver safety. The proposed methodology is adopted in the COMUNICAR project and relevant results are presented. Last but not the least, the AIDE integrated project and its vision is also proposed as the roadmap for future activities in the HMI sector.

New methods for interaction analysis of complex processes using weighted graphs

The selection of the structure of a controller in large scale industry processes usually requires extensive process knowledge. The aim of this paper is to report new results on recently suggested methods for the analysis of complex processes. These methods aid the designers in comprehending a process by representing structural and functional relationships from actuators and process disturbances to measured or estimated variables. The methods are formulated in a flexible framework based on graph theory, which can also be used for closed-loop analysis. Additionally, the sensitivity of the methods to scaling and time delays are discussed and resolved. It is also proposed how filtering can be used to restrict the analysis to a frequency region of interest.The feasibility of the methods is shown by the use of three case studies. A quadruple tank process is used to exemplify the methods and their use. Then the methods are applied on a real-life process, the stock preparation plant of a pulp and paper mill. The third study case analyzes a previously published example in closed loop.It is shown that the methods can be used to take efficient decisions on decentralized and sparse control structures, as well as assessing the channel interactions in a closed-loop system.     

基于分布结构自适应筛选的数据库存储优化设计

通过对数据库的存储结构优化设计,提高数据库的吞吐量。传统方法采用存储节点校验数据适应度分区的数据库存储模型,数据库中存在重复冗余数据,不能自适应滤除,导致数据存储开销较大。提出了一种基于分布结构自适应筛选的数据库存储优化模型,首先进行数据库的存储机制和分布式数据结构分析,采用相空间重构方法进行存储空间的结构分布重组,采用分布结构自适应筛选方法对提取的数据信息流进行重复冗余数据滤波处理,改善数据在数据库存储空间中的结构分布,实现数据库存储优化。仿真结果表明,采用改进的方法进行数据库构建,能提高数据库存储吞吐量,降低数据存储开销,提高数据库的访问和调度性能,展示较好的应用价值。     

An adaptive multiresolution method for combustion problems: application to flame ball-vortex interaction

In this article, we present numerical simulations of flame ball-vortex interactions using adaptive multiresolution methods. The numerical scheme for the convection-diffusion-reaction equations modelling this problem is based on a finite volume discretization coupled with a discrete multiresolution analysis. The grid in physical space is adapted dynamically to track the evolution of the solution in scale and space. Time integration is done by an explicit Runge-Kutta scheme. In space we use second-order centered schemes. The implementation is based on a graded tree data structure, which improves both CPU and memory performances, as no fine gridding is required in regions where the solution is smooth. To illustrate the features and the efficiency of the method, we compute several flame ball-vortex interactions and study the role played by the fluid flow on the evolution of the flame ball. We observe the roll-up of the flame ball around the vortex into a snail-like structure. We also put into evidence the flammability limit of the flame ball in function of both vortex and radiation intensities.     

Building adaptive E-catalog communities based on user interaction patterns

On the basis of observed customer interaction patterns, the WebCatalog/sup Pers/ system creates integrated product catalogs that continuously adapt and can be restructured in a dynamic environment.     

Stable haptic interaction based on adaptive hierarchical shape matching

Computational Visual Media - In this paper, we present a framework allowing users to interact with geometrically complex 3D deformable objects using (multiple) haptic devices based on an extended...     

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

一种基于肌电信号的自适应人机交互控制方法

针对踝关节康复机器人运动过程中的人机交互性问题,本文提出一种基于肌电信号的鲁棒自适应人机交互控制方法.针对患者难以保持某一动作、肌电信号微弱等特点,提出一种新的关节角度估计方法.该方法充分利用了踝关节运动时胫骨前肌与腓肠肌的拮抗关系,将踝关节的动作类型与单个肌肉群的收缩进行关联,利用归一化的特征值完成运动意图的辨识和运动角度的估计.为了保证人机交互的安全性,提出一种刚度、阻尼参数在线自适应调节的阻抗控制算法.基于交互力矩对机器人末端的运动角度与运动速度实时进行调节,使其对外表现出等效柔性.实验研究表明所提出的人机交互控制方法是有效的,并具有一定应用前景.