Claudio Tomazzoli

Improving Energy Saving Techniques by Ambient Intelligence Scheduling

Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.

Energy Saving by Ambient Intelligence Techniques

Nowadays the problem of energy consumption is becoming a pressing problem. We present an innovative system named Elettra able to allow people to monitor and control energy consumption in one or more buildings. For improving Elettra we introduce different methods taken from ambient intelligence. Through these methods we can infer energy consumption, construct a plan for decreasing energy consumption, improve this plan and adopt it to the system. The implementation of these methods to Elettra helps its automation and increases its efficiency.

Defeasible Reasoning about Electric Consumptions

Conflicting rules and rules with exceptions are very common in natural language specification to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic defeasible logic, which corresponds to turned on or off devices.

Automatic Synthesis of Best Practices for Energy Consumptions

Conflicting rules and rules with exceptions are very common in natural language specification to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic defeasible logic, which corresponds to turned on or off devices. We define a procedure to achieve automatic synthesis of best practices, to be used as rules to obtain savings in electric consumptions.

A Multimodal Approach to Exploit Similarity in Documents

Automated document classification process extracts information with a systematic analysis of the content of documents. n nThis is an active research field of growing importance due to the large amount of electronic documents produced in the world wide web and available thanks to diffused technologies including mobile ones. n nSeveral application areas benefit from automated document classification, including document archiving, invoice processing in business environments, press releases and research engines. n nCurrent tools classify or tag either text or images separately.In this paper we show how, by linking image and text-based contents together, a technology improves fundamental document management tasks like retrieving information from a database or automated documents. n nWe present an investigation of a model of conceptual spaces for investigation using joint information sources from the text and the images forming complex documents. We present a formal model and the computable algorithms and the dataset from which we took a subset to make experiments and relative tests and results.

Towards a Logical Framework for Diagnostic Reasoning

Diagnosis is widely used in many different disciplines to identify the nature and cause of a certain phenomenon. We present (tmathsf {L}), a new logical framework able to formalise diagnostic reasoning, i.e., an hybrid learning technique based both on deduction and experiments. In this paper we introduce tL, a Labeled Modal Logic, garnishing with temporal and statistical information and a basic propositional language.

Future paradigms of automated processing of business documents

In this paper we summarize the results obtained so far in the communities interested in the development of automated processing techniques as applied to business documents, and devise a few evolutions that are demanded by the current stage of either those techniques by themselves or by collateral sector advancements. It emerges a clear picture of a field that has put an enormous effort in solving problems that changed a lot during the last 30 years, and is now rapidly evolving to incorporate document processing into workflow management systems on one side and to include features derived by the introduction of cloud computing technologies on the other side. We propose an architectural schema for business document processing that comes from the two above evolution lines.

Ubiquitous and Pervasive Computing for Real-Time Energy Management and Saving

In the present paper we investigate one of the emerging applicability fields of pervasive computing, that is, energy management and saving. We exploit innovative technologies to define a brand new system architecture for (i) centralized monitoring and (ii) real-time energy saving in distributed sub-networks of power consuming electric appliances. The architecture allows the definition of reactive and intelligent systems that take autonomous courses of action on electric devices. We also introduce a prototype which is an embodiment of such architecture, called (textit{MyElettra}).

Semantic Social Network Analysis Foresees Message Flows

Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph- theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.

A Multimodal Approach to Relevance and Pertinence of Documents

Automated document classification process extracts information with a systematical analysis of the content of documents. This is an active research field of growing importance due to the large amount of electronic documents produced in the world wide web and made readily available thanks to diffused technologies including mobile ones. Several application areas benefit from automated document classification, including document archiving, invoice processing in business environments, press releases and search engines. Current tools classify or “tag” either text or images separately. In this paper we show how, by linking image and text-based contents together, a technology improves fundamental document management tasks like retrieving information from a database or automatically routing documents. We present a formal definition of pertinence and relevance concepts, that apply to those documents types we name “multimodal”. These are based on a model of conceptual spaces we believe compulsory for document investigation while using joint information sources coming from text and images forming complex documents.