Information-Driven Housing

This paper suggests a new information-driven framework is needed to help consumers evaluate the sustainability of their housing options. The paper provides an outline of this new framework and how it would work

DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics

Smart human mobility in smart cities

Nowadays, society has challenged the scienti c community to nd solutions able to use technology to solve the gentri cation3 of city centers. Within this context, smart cities have had an important role because they view each citizen as a data source. In the same way, the Internet of Things network increases the number of physical devices generating peta-bytes of information into a Smart city architecture. Thus an appropriate Machine Learning approach is required to process and analyze collected data. In this paper, we apply three di erent Machine Learning techniques such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and a combined architecture, which we call CNN-LSTM, to the data generated by LinkNYC Kiosks devices | based on the city of New York |, and come to the conclusion the combined model gets better results in predicting human mobility

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Proposal for an Organic Web, The missing link between the Web and the Semantic Web, Part 1

A huge amount of information is produced in digital form. The Semantic Web stems from the realisation that dealing efficiently with this production requires getting better at interlinking digital informational resources together. Its focus is on linking data. Linking data isn't enough. We need to provide infrastructural support for linking all sorts of informational resources including resources whose understanding and fine interlinking requires domain-specific human expertise. At times when many problems scale to planetary dimensions, it is essential to scale coordination of information processing and information production, without giving up on expertise and depth of analysis, nor forcing languages and formalisms onto thinkers, decision-makers and innovators that are only suitable to some forms of intelligence. This article makes a proposal in this direction and in line with the idea of interlinking championed by the Semantic Web.Comment: Supplementary material by Guillaume Bouzige and Mathilde Noua

The role of bot squads in the political propaganda on Twitter

Nowadays, Social Media are a privileged channel for news spreading, information exchange, and fact checking. Unexpectedly for many users, automated accounts, known as social bots, contribute more and more to this process of information diffusion. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retweeting activity of such automated accounts amplifies the hubs’ messages

Auditor's Communication Identity in Carrying Out Audit Tasks

The purpose of this research is to find out how the identity of the auditor's communication when carrying out an examination task. The research was conducted with qualitative method using a case study  and interviews for for collecting the data. The research samples were five auditors at BPK RI Representatives of DIY Province. The research concluded using four layers, which are the personal layer, enacted layer, relational layer, and communal layer cited from the in the Theory of Communication of Identity by Michael Hecht. The theory stated that participants' personal identity is formed by several factors i.e. self-character, code of ethics, and environmental factors. Participants demonstrate their code of ethics through the communication process between the auditor and the audite, which is then manifested in the form of behavior when carrying out the audit tasks. By understanding each other's identity, it can create a harmonious relationship between the auditor and the audite through mutual support between the auditor and the audite. The attitudes and behaviors shown by the auditors when conducting the audit tasks reflect the identity of the BPK institution in the community as a free and independent audit institution, so that the negative stereotypes of auditors are minimized. There is also an identity gap in: (1) the personal layer and the personal layer, the personal and enacted layers, and the relational and enacted layers

HCC Architecture - Hormonal Communications and Control Architecture

This thesis aims to provide a novel framework for a multiagent system implementation. The major feature of the proposed architecture is the introduction of the biological concept of hormones. The hormones are passed via the communication network to convey limited global system state knowledge. The agents\u27 response to a hormone is interpreted depending on its own local agent state. The primary focus of this thesis is the development of the particulars of the architecture. Prior work of multiagent systems research is reviewed and studied for contributions. Biological studies of hormones are employed to draw out interaction rules and analyze control mechanisms in a biological organism. The hormonal communication and control architecture is constructed, with major components detailed by flowcharts. The proposal is tested with two simulations: A minesweeping problem that has been modeled by other models, and an application of the architecture to a hypothetical ant colony. Research on biological ants is presented to suggest the behavior and goals of a model configured to employ the HCC architecture. The model is fleshed out, and the decisions made by considerations to the architecture are explained. The implementation of the simulation programming with the SWARM programming libraries for the Objective-C language is discussed. The data from experimental runs are analyzed with attention to global action

Eurolanguages-2011: Innovations and Development

Збірник наукових студентських робіт призначено для широкого кола читачів, які цікавляться проблемами вивчення іноземних мов та перекладу в Україн