Predicting the average size of blasted rocks in aggregate quarries using artificial neural networks

The prediction of the average size of fragments in blasted rock piles produced after blasting in aggregate quarries is essential for decresing the cost of crushing and secondary breaking. There are several conventional and advanced processes to estimate the size of blasted rocks. Among these, the empirical prediction of the expected fragmentation in most cases is carried out by Kuznetsov’s equation (Sov Min Sci 9:144–148, 1973), modified by Lilly (1986) and Cunningham (1987). The present research focuses on the effect of the engineering geological factors and blasting process on the blasted fragments using a more powerful, advanced computational tool, an artificial neural network. In particular, the blast database consists of the blastability index of limestone on the pit face, the quantities of the explosives and of the blasted rock pile, assessing the interaction of these parameters on the blasted rocks. The data were collected from two aggregate quarries, Drymos and Tagarades, near Thessaloniki, in the Central Macedonia region of Greece. This approach indicates significant performance stability, providing the fragmentation size with high accuracy.

     

Smart VideoText: a video data model based on conceptual graphs

An intelligent annotation-based video data model called Smart VideoText is introduced. It utilizes the conceptual graph knowledge representation formalism to capture the semantic associations among the concepts described in text annotations of video data. The aim is to achieve more effective query, retrieval, and browsing capabilities based on the semantic content of video data. Finally, a generic and modular video database architecture based on the Smart VideoText data model is described.     

An adaptive personalized news dissemination system

With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW ().

Monitoring water quality through a telematic sensor network and a fuzzy expert system

Abstract: In this paper we present an expert system that monitors seawater quality and pollution in northern Greece through a sensor network called Andromeda. The expert system monitors sensor data collected by local monitoring stations and reasons about the current level of water suitability for various aquatic uses, such as swimming and piscicultures. The aim of the expert system is to help the authorities in the decision-making process in the battle against pollution of the aquatic environment, which is vital for public health and the economy of northern Greece. The expert system determines, using fuzzy logic, when certain environmental parameters exceed certain pollution limits, which are specified either by the authorities or by environmental scientists, and flags up appropriate alerts.     

ExperNet: an intelligent multiagent system for WAN management

The authors describe ExperNet, an intelligent multiagent system developed to assist in managing large-scale data networks. The system assists network operators at various nodes of a WAN to detect and diagnose hardware failures and network traffic problems, suggesting the most feasible solution through a Web-based interface.     

DeTRACT: a decentralized,transparent, immutable and open PKI certificate framework

International Journal of Information Security - Public key infrastructure (PKI) is widely used over the Internet to secure and to encrypt communication among parties. PKI involves digital...     

Parallel planning via the distribution of operators

This paper describes Operator Distribution Method for parallel Planning (ODMP), a parallelization method for efficient heuristic planning. The method innovates in that it parallelizes the application of the available operators to the current state and the evaluation of the successor states using the heuristic function. In order to achieve better load balancing and a lift in the scalability of the algorithm, the operator set is initially enlarged, by grounding the first argument of each operator. Additional load balancing is achieved through the reordering of the operator set, based on the expected amount of imposed work. ODMP is effective for heuristic planners, but it can be applied to planners that embody other search strategies as well. It has been applied to GRT, a domain-independent heuristic planner, and CL, a heuristic planner for simple logistics problems, and has been thoroughly tested on a set of logistics problems adopted from the AIPS-98 planning competition, giving quite promising results.     

An ensemble uncertainty aware measure for directed hill climbing ensemble pruning

This paper proposes a new measure for ensemble pruning via directed hill climbing, dubbed Uncertainty Weighted Accuracy (UWA), which takes into account the uncertainty of the decision of the current ensemble. Empirical results on 30 data sets show that using the proposed measure to prune a heterogeneous ensemble leads to significantly better accuracy results compared to state-of-the-art measures and other baseline methods, while keeping only a small fraction of the original models. Besides the evaluation measure, the paper also studies two other parameters of directed hill climbing ensemble pruning methods, the search direction and the evaluation dataset, with interesting conclusions on appropriate values.     

Reinforcement learning agents providing advice in complex video games

This article introduces a teacher–student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.