Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS

Urban surface uses for climate resilient and sustainable cities: A catalogue of solutions

Abstract In the current scenario of massive urbanization and global climate change, the urban surfaces and their characteristics have a key role, as they significantly influence the quality of life in urban areas, as well as their environmental conditions. To shed light on the role of urban surfaces in fostering climate resilient and sustainable cities, this paper proposes a catalogue of solutions for the urban surface use. The catalogue presents the main surface uses suitable for the built environment, and discusses the potential conflicts and synergies among them in the view of a multiple and integrated utilization of urban surfaces. Reviewing studies published in the last 15 years, this study aims to answer three major questions: (i) which solutions do exist, (ii) where can these be applied, and (iii) which benefits do they provide. The discussion demonstrates that the use of urban surfaces might lead the development of multiple opportunities for improving the existing urban environments and supporting not only environmental, but also social and economic resilience. Finally, it emphasizes the need for specific quantitative and qualitative approaches to address the multi-disciplinary challenges posed by the design and implementation of surface uses, and the evaluation of their contribution to site-specific objectives

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

The Quest for Bandwidth Estimation Techniques for large-scale Distributed Systems

In recent years the research community has developed many techniques to estimate the end-to-end available bandwidth of an Internet path. This important metric has been proposed for use in several distributed systems and, more recently, has even been considered to improve the congestion control mechanism of TCP. Thus, it has been suggested that some existing estimation techniques could be used for this purpose. However, existing tools were not designed for large-scale deployments and were mostly validated in controlled settings, considering only one measurement running at a time. In this paper, we argue that current tools, while offering good estimates when used alone, might not work in large-scale systems where several estimations severely interfere with each other. We analyze the properties of the measurement paradigms employed today and discuss their functioning, study their overhead and analyze their interference. Our testbed results show that current techniques are insufficient as they are. Finally, we will discuss and propose some principles that should be taken into account for including available bandwidth measurements in large-scale distributed systems. 1

Fatigue behavior of stiffener to cross beam joints in orthotropic steel decks

In the paper the possibility to evaluate the fatigue strength of stiffener to cross beam joints in orthotropic steel decks is discussed. The proposed methodology, based on Paris-Erdogan law, allows to derive a sound estimate of the stress intensity factor K combining the indirect approach, based on the Rice J-integral, with the direct one, based on the extrapolation of experimental or numerical data. The practical implementation of the proposed methodology allowed to predict correctly the actual fatigue life of a previously tested real scale specimen, so validating its potentialities

Static and dynamic accuracy of an innovative miniaturized wearable platform for short range distance measurements for human movement applications

Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60°). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0°, the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ±30°, and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ±60°. In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies

Learning from Errors: Detecting ZigBee Interference in WiFi Networks

—In this work we show how to detect ZigBee inter- ference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlled interference sources (training phase) and then run-time recognizing the most likely cause of error patterns. Experimental results prove the effectiveness of our approach for detecting ZigBee interference

Privacy-preserving Overgrid: Secure Data Collection for the Smart Grid

In this paper we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation ( extit{PP-Overgrid}). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of ``flexible'' energy consumption, i.e. the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildin

Experimental evaluation of privacy-preserving aggregation schemes on planetlab

New pervasive technologies often reveal many sen- sitive information about users’ habits, seriously compromising the privacy and sometimes even the personal security of people. To cope with this problem, researchers have developed the idea of privacy-preserving data mining which refers to the possibility of releasing aggregate information about the data provided by multiple users, without any information leakage about individual data. These techniques have different privacy levels and communication costs, but all of them can suffer when some users’ data becomes inaccessible during the operation of the privacy preserving protocols. It is thus interesting to validate the applicability of such architectures in real-world scenarios. In this paper we experimentally evaluate two promising privacy- preserving techniques on PlanetLab, analyzing the execution time and the failure rate that each scheme exhibits

Cross-technology WiFi/ZigBee communications: Dealing with channel insertions and deletions

In this paper we show how cross-technology in- terference can be exploited to set-up a low-rate bi-directional communication channel between heterogeneous WiFi and ZigBee networks. Because of the environment noise and receivers' imple- mentation, the cross-technology channel can be severely affected by insertions and deletions of symbols, whose effects need to be taken into account by the coding scheme and communication protocol