Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space search, intelligent algorithms was used to found high quality rules and solve ARM issue. This paper deals with a cooperative multi-swarm bat algorithm for association rule mining. It is based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM). This latter suffers from absence of communication between bats in the population which lessen the exploration of search space. However, it has a powerful rule generation process which leads to perfect local search. Therefore, to maintain a good trade-off between diversification and intensification, in our proposed approach, we introduce cooperative strategies between the swarms that already proved their efficiency in multi-swarm optimization algorithm(Ring, Master-slave). Furthermore, we innovate a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in our earlier work [23]. A series of experiments are carried out on nine well known datasets in ARM field and the performance of proposed approach are evaluated and compared with those of other recently published methods. The results show a clear superiority of our proposal against its similar approaches in terms of time and rule quality. The analysis also shows a competitive outcomes in terms of quality in-face-of multi-objective optimization methods.     

Pairwise optimized Rocchio algorithm for text categorization

This paper examines the Rocchio algorithm and its application in text categorization. Existing approaches using global parameters optimization of Rocchio algorithm result in choosing one fixed prototype representing each category for multi-category text categorization problems. Therefore, they have limited discriminating power on different category’s distribution and their parameter optimization methods are based on weak representation ability of the negative samples consisting of several categories. We present a pairwise optimized Rocchio algorithm, which dynamically adjusts the prototype position between pairs of categories. Experiments were conducted on three benchmark corpora, the 20-Newsgroup, Reuters-21578 and TDT2. The results confirm that our proposed pairwise method achieves encouraging performance improvement over the conventional Rocchio method. A comparative study with the top notch text classifier Support Vector Machine (SVM) also shows the pairwise Rocchio method achieves competitive results.     

Relevant Timed Schedules/Clock Vectors for Constructing Time Petri Net Reachability Graphs

We consider here the time Petri nets (the TPN model) and its state space abstractions. We show that only some timed schedules/clock vectors (one per enabled transition) of the clock/firing domains are relevant to construct reachability graphs for the TPN. Moreover, we prove formally that the resulting graphs are smaller than the TPN reachability graphs proposed in the literature. Furthermore, these results establish a relation between dense time and discrete time analysis of time Petri nets and allow also improving discrete time analysis by considering only some elements of the clock/firing domains.     

A novel memory management scheme for residential gateways

Home gateways must manage services despite limited memory resources. In home gateway models (e.g., OSGi), services are implemented as software bundles (or plug-ins) that can be downloaded from the Internet and executed in the gateway. Services, in gateways, are not independent; they collaborate and complement each other. The problem we are solving is as follow: when the gateway runs out of memory, which service(s) will be stopped or kicked out of memory to start a new service? The problem was initially inspired by the FTTH (Fibre To The Home) trail project in Japan with NTT because of the limited memory in Panasonic set-top-box IP-STB. Note that stopping a given service means that all the services that depend on it will be stopped too. Because of the service dependencies, traditional memory management techniques, such as best fit, first fit, or worst fit, are not suitable. Our goal is to minimize the total number of stopped services while fulfilling the request of the new service. In this paper, we present two algorithms for service replacement and memory management in home gateways. The algorithms take into consideration the dependencies between different services, in addition to the amount of memory occupied by each service. The first one achieves optimal solution in O(n ²) time and O(nh) space, using dynamic programming. However, the optimal solution requires substantial memory and CPU resources. Then we propose a heuristic that compute solutions very close to the optimal but with much less time and space requirements. We carry simulation experiments to evaluate the effectiveness of the proposed techniques and compare them with traditional memory management techniques.     

NIST research in autonomous construction

The National Institute of Standards and Technology Construction Metrology and Automation Group is conducting ongoing research to provide standards, methodologies, and performance metrics that will assist the development of advanced systems to automate construction tasks. Initial research in this project, entitled “Performance of Innovative Technologies for Automated Construction,” has focused on autonomous large-scale pick-and-place operations using the assembly of structural steel as a test operation. An Automated Construction Testbed (ACT) is under development to research advanced concepts in crane automation, site metrology, laser-based 3D imaging, construction object identification and tracking, sensor-based data exchange, site status visualization, and design data integration for autonomous system planning. This paper provides an overview of this research effort.

Membrane permeability of some model compounds through macroporous membranes

Membranes consisting of various amounts of poly(2-vinylpyridine) (PVN) and its oxide PVNO were prepared by casting from ethanol and pyridine solutions. Resultant membranes exhibited effective diffusion coefficients in the range of 10^?9?10^?11 cm² s^?1. Effective diffusion coefficients were affected by the casting solvent, and by the hydrophilic polymer (PVNO) content.     

Efficient algorithm for automatic road sign recognition and its hardware implementation

The automatic detection of road signs is an application that alerts the vehicle’s driver of the presence of signals and invites him to react on time in the aim to avoid potential traffic accidents. This application can thus improve the road safety of persons and vehicles traveling in the road. Several techniques and algorithms allowing automatic detection of road signs are developed and implemented in software and do not allow embedded application. We propose in this work an efficient algorithm and its hardware implementation in an embedded system running in real time. In this paper we propose to implement the application of automatic recognition of road signs in real time by optimizing the techniques used in different phases of the recognition process. The system is implemented in a Virtex4 FPGA family which is connected to a camera mounted in the moving vehicle. The system can be integrated into the dashboard of the vehicle. The performance of the system shows a good compromise between speed and efficiency.     

Understanding hand gestures using approximate graph matching

We live in a society that depends on high-tech devices for assistance with everyday tasks, including everything from transportation to health care, communication, and entertainment. Tedious tactile input interfaces to these devices result in inefficient use of our time. Appropriate use of natural hand gestures will result in more efficient communication if the underlying meaning is understood. Overcoming natural hand gesture understanding challenges is vital to meet the needs of these increasingly pervasive devices in our every day lives. This work presents a graph-based approach to understand the meaning of hand gestures by associating dynamic hand gestures with known concepts and relevant knowledge. Conceptual-level processing is emphasized to robustly handle noise and ambiguity introduced during generation, data acquisition, and low-level recognition. A simple recognition stage is used to help relax scalability limitations of conventional stochastic language models. Experimental results show that this graph-based approach to hand gesture understanding is able to successfully understand the meaning of ambiguous sets of phrases consisting of three to five hand gestures. The presented approximate graph-matching technique to understand human hand gestures supports practical and efficient communication of complex intent to the increasingly pervasive high-tech devices in our society.     

Discrete cooperative particle swarm optimization for FPGA placement

Particle swarm optimization (PSO) is a stochastic optimization technique that has been inspired by the movement of birds. On the other hand, the placement problem in field programmable gate arrays (FPGAs) is crucial to achieve the best performance. Simulated annealing algorithms have been widely used to solve the FPGA placement problem. In this paper, a discrete PSO (DPSO) version is applied to the FPGA placement problem to find the optimum logic blocks and IO pins locations in order to minimize the total wire-length. Moreover, a co-operative version of the DPSO (DCPSO) is also proposed for the FPGA placement problem. The problem is entirely solved in the discrete search space and the proposed implementation is applied to several well-known FPGA benchmarks with different dimensionalities. The results are compared to those obtained by the academic versatile place and route (VPR) placement tool, which is based on simulated annealing. Results show that both the DPSO and DCPSO outperform the VPR tool for small and medium-sized problems, with DCPSO having a slight edge over the DPSO technique. For higher-dimensionality problems, the algorithms proposed provide very close results to those achieved by VPR.