Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval

Relevance judgment in Information Retrieval is influenced by multiple factors. These include not only the topicality of the documents but also other user oriented factors like trust, user interest, etc. Recent works have identified and classified these various factors into seven dimensions of relevance. In a previous work, these relevance dimensions were quantified and user's cognitive state with respect to a document was represented as a state vector in a Hilbert Space, with each relevance dimension representing a basis. It was observed that relevance dimensions are incompatible in some documents, when making a judgment. Incompatibility being a fundamental feature of Quantum Theory, this motivated us to test the Quantum nature of relevance judgments using Bell type inequalities. However, none of the Bell-type inequalities tested have shown any violation. We discuss our methodology to construct incompatible basis for documents from real world query log data, the experiments to test Bell inequalities on this dataset and possible reasons for the lack of violation

Piezoelectric vibration energy harvesting from airflow in HVAC (Heating Ventilation and Air Conditioning) systems

This study focuses on the design and wind tunnel testing of a high efficiency Energy Harvesting device, based on piezoelectric materials, with possible applications for the sustainability of smart buildings, structures and infrastructures. The development of the device was supported by ESA (the European Space Agency) under a program for the space technology transfer in the period 2014-2016. The EH device harvests the airflow inside Heating, Ventilation and Air Conditioning (HVAC) systems, using a piezoelectric component and an appropriate customizable aerodynamic appendix or fin that takes advantage of specific airflow phenomena (vortex shedding and galloping), and can be implemented for optimizing the energy consumption inside buildings. Focus is given on several relevant aspects of wind tunnel testing: different configurations for the piezoelectric bender (rectangular, cylindrical and T-shaped) are tested and compared, and the effective energy harvesting potential of a working prototype device is assessed

Development of a piezoelectric energy-harvesting sensor: from concept to reality

This study focuses on the development and integrated design over a 24-month period of a high efficiency energy-harvesting (EH) temperature sensor, based on piezoelectric materials, with applications for the sustainability of smart buildings, structures and infrastructures. The EH sensor, harvests the airflow inside Heating, Ventilation and Air Conditioning (HVAC) systems, using a piezoelectric component and an appropriate customizable aerodynamic fin that takes advantage of specific air flow effects, and is implemented for optimizing the energy consumption in buildings. The project was divided in several work-packages (some running in parallel) that cover different aspects of the device development. Some of them focus on engineering aspects (starting from the numerical modeling, then prototyping, and concluding with experimental testing). Other aspects focus on the sensor promotion (including the development of a business plan, the intellectual property rights, the final design and the go-to-market actions). Considering the multidisciplinary character of the project (involving knowledge from fields such as wind engineering, electrical engineering, industrial design, entrepreneurship), this study tries to provide an insight on the complex design issues that arise when such complex, sometimes conflicting and overlapping aspects have to be managed within strict deadlines. In doing so, the most important design and development aspects are critically presented

Preserving Cultural Heritage Using Open Source Collection Management Tools

Open source software (OSS) popularity is growing steadily and many OSS systems could be used to preserve cultural heritage objects. Such solutions give the opportunity to organizations to afford the development of a digital collection. This paper focuses on reviewing two OSS tools, CollectionSpace and the Open Video Digital Library Toolkit and discuss on how these could be used for organizing digital replicas of cultural objects. The features of the software are presented and some examples are given

A Survey of Quantum Theory Inspired Approaches to Information Retrieval

Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions

An Analysis of Learning Bahaviour and Patterns in a Technology-Enhanced Learning Environment

Characterizing, structuring and supplying necessary knowledge for solving complex problems in today’s dynamically changing environments is a great challenge. This paper provides an introductory description of the STUDIO learning environment that supports learners in applying and evaluating knowledge and in adapting changes to their own context quickly. The focus of the current study is on analysing learning characteristics and behaviours of undergraduate students of a Management Information System course, who used the STUDIO to facilitate the acquisition of required knowledge. A detailed description of data analysis and the interpretation of results applying cognitive frameworks will be provided

Quantum Cognitively Motivated Context-Aware Multimodal Representation Learning for Human Language Analysis

A long-standing goal in the field of Artificial Intelligence (AI) is to develop systems that can perceive and understand human multimodal language. This requires both the consideration of context in the form of surrounding utterances in a conversation, i.e., context modelling, as well as the impact of different modalities (e.g., linguistic, visual acoustic), i.e., multimodal fusion. In the last few years, significant strides have been made towards the interpretation of human language due to simultaneous advancement in deep learning, data gathering and computing infrastructure. AI models have been investigated to either model interactions across distinct modalities, i.e., linguistic, visual and acoustic, or model interactions across parties in a conversation, achieving unprecedented levels of performance. However, AI models are often designed with only performance as their design target, leaving aside other essential factors such as transparency, interpretability, and how humans understand and reason about cognitive states. In line with this observation, in this dissertation, we develop quantum probabilistic neural models and techniques that allow us to capture rational and irrational cognitive biases, without requiring a priori understanding and identification of them. First, we present a comprehensive empirical comparison of state-of-the-art (SOTA) modality fusion strategies for video sentiment analysis. The findings provide us helpful insights into the development of more effective modality fusion models incorporating quantum-inspired components. Second, we introduce an end-to-end complex-valued neural model for video sentiment analysis, simulating quantum procedural steps, outside of physics, into the neural network modelling paradigm. Third, we investigate non-classical correlations across different modalities. In particular, we describe a methodology to model interactions between image and text for an information retrieval scenario. The results provide us with theoretical and empirical insights to develop a transparent end-to-end probabilistic neural model for video emotion detection in conversations, capturing non-classical correlations across distinct modalities. Fourth, we introduce a theoretical framework to model user's cognitive states underlying their multimodal decision perspectives, and propose a methodology to capture interference of modalities in decision making. Overall, we show that our models advance the SOTA on various affective analysis tasks, achieve high transparency due to the mapping to quantum physics meanings, and improve post-hoc interpretability, unearthing useful and explainable knowledge about cross-modal interactions

Ultimate capacity of diagrid systems for tall buildings in nominal configuration and damaged state

One of the evocative structural design solutions for tall buildings is recently embraced by the diagrid (diagonal grid) structural system. Diagrid, with a perimeter structural configuration characterized by a narrow grid of diagonal members involved both in gravity and in lateral load resistance, requires less structural steel than a conventional steel frame, provides for a more sustainable structure and has emerged as a new design trend for tall-shaped complex structures due to aesthetics and structural performance. The purpose of this study is twofold. First, to assess the optimal structural design of a diagrid tall-building, also compared to a typical outrigger building, focusing on the sustainability (the use of structural steel) and the structural safety and serviceability. To this aim, dierent diagrid geometries are tested and compared. Second, to provide some insight on the residual strength of diagrid structures, also in the damaged state (modelled by the elimination of diagonal grids). Both goals are accomplished using FEM nonlinear analyses