ASTERIX: towards a scalable,semistructured data platform for evolving-world models

ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal—the storage and analysis of data pertaining to evolving-world models. We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead.     

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.     

大规模3D并行分层可扩展矩阵乘法的递阶优化方法

为进一步提高大规模平台上可扩展矩阵乘法的并行计算效率,提出一种并行分层可扩展矩阵乘法的递阶优化方法。首先,在可扩展矩阵乘法算法(SMM)算法枢轴行和枢轴列通信研究基础上,利用分层方式在更高等级上对网格进行矩形群划分,实现矩阵乘法的二维计算向三维计算转变,并设计对应的集群内通信和集群间通信过程,实现SMM乘法的递阶并行优化(HSMM);其次,对所提HSMM算法进行理论分析,分情况对其通信成本进行分析和预测,推导出最佳计算成本的集群数选取方式;最后,通过在Grid5000和BlueGene/P测试平台实验,验证了所提算法有效性和理论分析的正确性。     

Collaborative learning activities at a distance via interactive videoconferencing in elementary schools: Parents’ attitudes

As schools are increasingly encouraging students to use the Internet and web-based technology at home and in the classroom, concerns among some parents have increased. Today’s parents have learned about computers as adults and did not receive guided participation as children either from their parents or from their teachers.     

Exact finite-dimensional filters via systems realization for a class of discrete-time nonlinear systems

We obtain necessary and sufficient conditions for the existence of a finite-dimensional filter for the discrete-time nonlinear system (ε x_k+1 =φ(x_k), y_k = h(x_k)+η(x_k)ν_k, K=0, 1,…. This system is distinguished by the absence of noise in the dynamic and by the correlation between the state and the intensity of noise in the observations.The necessary and sufficient condition provides an explicit formula for the minimal filter and various system-theoretic properties of (ε) and of the minimal filter.     

Parametric and nonparametric evolutionary computing with a content-based feature selection approach for parallel categorization

This paper proposes two modified evolutionary computing methods for genetic algorithms (GAs) and proves an effective content-based feature selection approach to improve clustering performance. The conventional GAs suffer from the problem of slow learning and are prone to be trapped into a local minimum due to a high dimensional exploration space. In this paper, we propose a parametric and a nonparametric evolutionary algorithms to properly adjust the operators of GA. In the parametric approach, several fuzzy control parameters are artificially defined to adaptively optimize the GA behaviors. By contrast, they are automatically adjusted by GA itself in the nonparametric approach. Moreover, a content-based feature selection (CFS) approach is demonstrated to create a robust semantic space and reduce the number of dimension which accelerates the speed of evolutionary computing. We take advantage of a parallel computing technology to improve the efficiency of clustering. The experimental results show that our methods enhance the performance of the standard GA and are more efficient than those implemented on a single processor. The CFS approach not only reduces the document dimension, but also indirectly advances clustering efficiency.     

A scalable delivery solution and a pricing model for commercial video-on-demand systems with video advertisements

This paper presents a scalable delivery solution for commercial near on-demand video streaming systems with an associated pricing model. The proposed delivery solution combines the benefits of periodic broadcasting and stream merging, thereby enabling scalable video delivery. Video advertisements are delivered to the clients prior to viewing the requested videos. The revenues generated from the ads are used to subsidize the price of the requested videos. The pricing is determined based on the total ad viewing time. The proposed solution includes an efficient ad allocation scheme and a new constraint-based scheduling approach. In addition, the paper investigates how targeted advertisements can be efficiently supported. Furthermore, we investigate the effectiveness of the overall solutions and analyze and compare the effectiveness of various scheduling policies and ad allocation alternatives in terms of several metrics, including client defection probability, average number of viewed ads per client, price, channel utilization, revenue, and profit.     

Stability analysis for a class of digital filters with single saturation nonlinearity

In this paper, a new stability criterion for a class of digital filters with single saturation nonlinearity is presented in the form of a linear matrix inequality. Numerical examples are given to show the effectiveness of the obtained results.     

Feedback control for a class of discrete event systems with critical time

This paper presents an algebraic approach for control laws synthesis of timed event graphs subjected to strict temporal constraints. This class of discrete event systems is deterministic, in the sense that its behaviour only depends on the initial marking and on the control that is applied. This behaviour can be modelled by a linear equations system in Min-Plus algebra. The temporal constraint is represented by an inequality that is also linear in the Min-Plus algebra. Then, a method for the synthesis of control laws ensuring the respect of constraints is described. We give explicit formulas characterising a control law, which ensures the validity of the temporal constraints. It is a causal feedback control, involving delays. The method is illustrated on an example.     

Controller synthesis for hybrid systems with a lower bound on event separation

A systematic procedure for synthesizing all full-state feedback controllers for a hybrid system subject to a safety (state-invariance) specification has been proposed in the literature. The interaction between the controller and a non- deterministic hybrid plant is viewed as a two-person game. The controller wins if it keeps the state of the closed-loop system within a specified set of good states; its adversarial environment tries to force the system outside the good set. The synthesis procedure iteratively augments the set of states from which the environment wins via either one additional discrete step, or one additional continuous flow. The key difficulty in carrying out the synthesis procedure lies in the computations for continuous flows. One must essentially solve a differential game in which the environment is trying to drive the system into its target set at the same time as avoiding the target set of the controller. In this paper, we study hybrid systems with lower bounds on the separation between occurrence times of consecutive discrete moves. These systems arise when modelling minimal delay times between events, either in the controller, or in the environment. For such systems, we provide techniques for solving the differential games in reduced state spaces. The main idea is to discretize information about whether discrete moves are enabled or not. We demonstrate our technique by successfully synthesizing the maximal set of controllers for a hybrid model of a heating system with discrete controls and disturbances and continuous controls and disturbances.     

Motion analysis for event detection and tracking with a mobile omnidirectional camera

A mobile platform mounted with omnidirectional vision sensor (ODVS) can be used to monitor large areas and detect interesting events such as independently moving persons and vehicles. To avoid false alarms due to extraneous features, the image motion induced by the moving platform should be compensated. This paper describes a formulation and application of parametric egomotion compensation for an ODVS. Omni images give 360 view of surroundings but undergo considerable image distortion. To account for these distortions, the parametric planar motion model is integrated with the transformations into omni image space. Prior knowledge of approximate camera calibration and camera speed is integrated with the estimation process using a Bayesian approach. Iterative, coarse-to-fine, gradient-based estimation is used to correct the motion parameters for vibrations and other inaccuracies in prior knowledge. Experiments with a camera mounted on various types of mobile platforms demonstrate successful detection of moving persons and vehicles.Published online: 11 October 2004     

Issues in an inference platform for generating deductive knowledge: a case study in cultural heritage digital libraries using the CIDOC CRM

Most information retrieval research focuses collecting documents that match the same set of concepts. This study considers a more advanced problem, namely how to discover knowledge not contained in a single source from combined historical facts. By using a well-designed core ontology in the cultural domain (CIDOC CRM, ISO21127), this study discusses the requirement for a robust inference platform for real-life knowledge discovery and integration over distributed sources. The methodology and design are justified in detail through functional requirements for an inference service with the capability of inferring new knowledge from combinations of facts distributed over different sources. A number of critical issues for developing such a robust inference platform are identified, namely (1) systematic accumulation of common concepts and inference rules; (2) extending the ontology with metaclasses; (3) accumulation of factual and categorical knowledge; (4) incorporation of fuzzy inference into the inference engine, and (5) improvement of performance and scalability in the inference engine.     

Enhancing mirror therapy via scaling and shared control: a novel open-source virtual reality platform for stroke rehabilitation

Virtual Reality - Mirror therapy is increasingly used in stroke rehabilitation to improve functional movements of the affected limb. However, the extent of mirroring in conventional mirror therapy...     

Validation of a QoS architecture for DVB–RCS satellite networks via the SATIP6 demonstration platform

Full integration of satellite technology in future terrestrial infrastructures requires support for high-quality broadband bi-directional communications. Research efforts in the field of satellite communications are currently oriented in the study of QoS-aware solutions for DVB–S and DVB–RCS which allow seamless deployment in the Internet. In this paper, the QoS architecture of the SATIP6 project, sponsored within the 5th EU Research Programme Framework, is presented, along with the implemented demonstrator and the obtained results. The QoS architecture is organized into two main modules, the Traffic Control and Access Control modules, whose aims are (i) to provide for differentiated service of conveyed IP flows and (ii) to achieve efficient utilization of uplink bandwidth, respectively. Experimental results obtained through the developed demonstration platform are reported and discussed to assess the effectiveness of the designed solution in terms of both service differentiation and efficient utilization of satellite resources.     

Deep autoencoders for feature learning with embeddings for recommendations: a novel recommender system solution

Neural Computing and Applications - We propose “Deep Autoencoders for Feature Learning in Recommender Systems,” a novel discriminative model based on the incorporation of features from...     

TuBeck: a novel peer-to-peer streaming system with Loopback-MDC for scalable H.264/AVC videos

Multimedia Tools and Applications - With the rapidly development of broadband networks, more and more users nowadays tend to acquire their desired videos from media-on-demand servers. How to...     

A Finite-State Machine for Collaborative Airlift with a Formation of Unmanned Air Vehicles

The article presents a finite-state machine, or FSM, for multi-vehicle airlift and experimental results of untethered multi-quadrotor flight. The rationale is as follows: before attaching a payload to a group of drones and deploying these vehicles in difficult conditions and environments, one must ensure that the FSM coordinates, for all the vehicles, the phases of airlift and assigns waypoints in a coherent manner and in real-time. The experiments demonstrate the feasibility of the proposed FSM from a high-level control point of view, under nominal and degraded conditions of flight, albeit without an actual payload. The experiments therefore provide a needed preliminary study on the validity of the FSM concept prior to further refinements and tests.