Soft data mining, computational theory of perceptions, and rough-fuzzy approach

Data mining and knowledge discovery is described from pattern recognition point of view along with the relevance of soft computing. Key features of the computational theory of perceptions and its significance in pattern recognition and knowledge discovery problems are explained. Role of fuzzy-granulation (f-granulation) in machine and human intelligence, and its modeling through rough-fuzzy integration are discussed. Merits of fuzzy granular computation, in terms of performance and computation time, for the task of case generation in large scale case-based reasoning systems are illustrated through an example

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

On the nature and impact of self-similarity in real-time systems

In real-time systems with highly variable task execution times simplistic task models are insufficient to accurately model and to analyze the system. Variability can be tackled using distributions rather than a single value, but the proper charac- terization depends on the degree of variability. Self-similarity is one of the deep- est kinds of variability. It characterizes the fact that a workload is not only highly variable, but it is also bursty on many time-scales. This paper identifies in which situations this source of indeterminism can appear in a real-time system: the com- bination of variability in task inter-arrival times and execution times. Although self- similarity is not a claim for all systems with variable execution times, it is not unusual in some applications with real-time requirements, like video processing, networking and gaming. The paper shows how to properly model and to analyze self-similar task sets and how improper modeling can mask deadline misses. The paper derives an analyti- cal expression for the dependence of the deadline miss ratio on the degree of self- similarity and proofs its negative impact on real-time systems performance through system¿s modeling and simulation. This study about the nature and impact of self- similarity on soft real-time systems can help to reduce its effects, to choose the proper scheduling policies, and to avoid its causes at system design time.This work was developed under a grant from the European Union (FRESCOR-FP6/2005/IST/5-03402).Enrique Hernández-Orallo; Vila Carbó, JA. (2012). On the nature and impact of self-similarity in real-time systems. Real-Time Systems. 48(3):294-319. doi:10.1007/s11241-012-9146-0S294319483Abdelzaher TF, Sharma V, Lu C (2004) A utilization bound for aperiodic tasks and priority driven scheduling. IEEE Trans Comput 53(3):334–350Abeni L, Buttazzo G (1999) QoS guarantee using probabilistic deadlines. 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In: Proc of the 23rd IEEE real-time systems symposium, pp 289–300Erramilli A, Narayan O, Willinger W (1996) Experimental queueing analysis with long-range dependent packet traffic. IEEE/ACM Trans Netw 4(2):209–223Erramilli A, Roughan M, Veitch D, Willinger W (2002) Self-similar traffic and network dynamics. Proc IEEE 90(5):800–819Gardner M (1999) Probabilistic analysis and scheduling of critical soft real-time systems. Phd thesis, University of Illinois, Urbana-ChampaignGarrett MW, Willinger W (1994) Analysis, modeling and generation of self-similar vbr video traffic. In: ACM SIGCOMMHarchol-Balter M (2002) Task assignment with unknown duration. J ACM 49(2):260–288Harchol-Balter M (2007) Foreword: Special issue on new perspective in scheduling. SIGMETRICS Perform Eval Rev 34(4):2–3Harchol-Balter M, Downey AB (1997) Exploiting process lifetime distributions for dynamic load balancing. ACM Trans Comput Syst 15(3):253–285Hernandez-Orallo E, Vila-Carbo J (2007) Network performance analysis based on histogram workload models. In: Proceedings of the 15th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS), pp 331–336Hernandez-Orallo E, Vila-Carbo J (2010) Analysis of self-similar workload on real-time systems. In: IEEE real-time and embedded technology and applications symposium (RTAS). IEEE Computer Society, Washington, pp 343–352Hernández-Orallo E, Vila-Carbó J (2010) Network queue and loss analysis using histogram-based traffic models. Comput Commun 33(2):190–201Hughes CJ, Kaul P, Adve SV, Jain R, Park C, Srinivasan J (2001) Variability in the execution of multimedia applications and implications for architecture. SIGARCH Comput Archit News 29(2):254–265Leland W, Ott TJ (1986) Load-balancing heuristics and process behavior. 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Willey, New York, Chap 1Paxson V, Floyd S (1995) Wide area traffic: the failure of Poisson modeling. IEEE/ACM Trans Netw 3(3):226–244Rolls DA, Michailidis G, Hernández-Campos F (2005) Queueing analysis of network traffic: methodology and visualization tools. Comput Netw 48(3):447–473Rose O (1995) Statistical properties of mpeg video traffic and their impact on traffic modeling in atm systems. In: Conference on local computer networksRoy N, Hamm N, Madhukar M, Schmidt DC, Dowdy L (2009) The impact of variability on soft real-time system scheduling. In: RTCSA ’09: Proceedings of the 2009 15th IEEE international conference on embedded and real-time computing systems and applications. IEEE Computer Society, Washington, pp 527–532Sha L, Abdelzaher T, Årzén KE, Cervin A, Baker T, Burns A, Buttazzo G, Caccamo M, Lehoczky J, Mok AK (2004) Real time scheduling theory: A historical perspective. 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Development of Multiple Growth Strategies for Use in Developing Traffic Forecasts: A Robustness Approach

Decisions that may be based on misleading forecasts may lead to a misallocation of funds and to under-performing projects during construction and operation. Poor projections of demographic and socioeconomic data are usually cited as the major source of poor traffic assignment projections and hence, unfavorably conceived planning and construction of street and highway infrastructure facilities. This report evaluated the accuracy of long range projections by using a transportation study done the in 1970s, projecting transportation demand 20 years into the future. The projected travel model inputs were compared with what actually happened after the horizon year had been reached and also compared the projected traffic volumes versus the actual ground counts at the same horizon year. The results of this study show that there is a poor correlation between what was forecasted and what actually happened in terms of socioeconomic and demographic data, which are the major inputs used by travel demand models to forecast future traffic volumes on road links. The projected traffic volumes were poorly correlated with the actual ground traffic counts for the same road links in the network. However, the end results of these projections, the estimated number of lanes required to accommodate the resulting traffic, were not adversely affected. It was found that 98 percent of the major streets had the number of lanes correctly estimated based on the 1994 Highway Capacity Manual (HCM) planning level of service (LOS) criteria. Robustness analysis is a technique with the potential in aiding decision makers in choosing transportation investment projects that more closely correlate to actual future development. In this report it has been demonstrated that robustness analysis can be successfully used in urban transportation planning in conjunction with urban travel demand software. The robustness analysis procedure emphasizes the need, under conditions of uncertainty, to make early decisions in a time-phased sequence, while preserving many future options until the choices are more definitive. The results of the robustness analysis indicate that the method is simple to understand, easy to use, minimizes future surprises in terms of expected future events not happening, and provides the flexibility required in typical urban planning problems where decision making has to be done under conditions of uncertainties. A general framework to be used in such cases is proposed

The first ICASE/LARC industry roundtable: Session proceedings

The first 'ICASE/LaRC Industry Roundtable' was held on October 3-4, 1994, in Williamsburg, Virginia. The main purpose of the roundtable was to draw attention of ICASE/LaRC scientists to industrial research agendas. The roundtable was attended by about 200 scientists, 30% from NASA Langley; 20% from universities; 17% NASA Langley contractors (including ICASE personnel); and the remainder from federal agencies other than NASA Langley. The technical areas covered reflected the major research programs in ICASE and closely associated NASA branches. About 80% of the speakers were from industry. This report is a compilation of the session summaries prepared by the session chairmen