Combined optimization of feature selection and algorithm parameters in machine learning of language

Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the 'right bias' to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons

Toward implementing a fully automated truck guidance system at a seaport: identifying the roles, costs and benefits of logistics stakeholders

The seaport-hinterland access infrastructures become more saturated leading to extra unpredictable costs for logistics operators. To keep their services competitive, infrastructure managers (such as port authorities) seek new approaches in managing and delivering information to their users. Starting from existing technological advancements and exploring the declared opportunities enabled by (near) future ones, a range of solutions (e.g. real-time information platforms, dynamic information boards or digital on-request notification solutions) can be pursued. This research investigates how an advanced truck guidance system (TGS) leverages on technology and leads to more efficient resource (infrastructure, equipment and labour) usage. The current paper merges two perspectives. The first conducts interviews with representatives of different logistics stakeholders to highlight their data needs. The second perspective is given by a review of information communication technology (ICT) innovative trends. These two perspectives provide an up-to-date overview of both needs (demand) and opportunities (supply) that challenge logistics stakeholders. This study proposes a TGS architecture that merges the two perspectives and identifies a stepwise approach to implement it. Therefore, the roles, costs and benefits brought to the logistics stakeholders are highlighted for each step. The key findings of this study show that road transport operators would benefit from operational savings and increased performance, while the other supply chain stakeholders can benefit from setting up a more reliable planning, better managing their infrastructure and developing added-value services for clients. To enable these benefits, the port administration could take the facilitator role and cover the TGS’s development costs

An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking

Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform

SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms

BACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. RESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. CONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data

Vehicular communication management framework : a flexible hybrid connectivity platform for CCAM services

In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems' (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity