Mapping WiFi measurements on OpenStreetMap data for Wireless Street Coverage Analysis

The growing interest on smart cities and the deployment of an ever increasing number of smart objects in public locations, such as dumpsters, traffic lights, and manholes, requires ubiquitous connectivity for these devices to communicate data and to receive configurations. Opportunistic WiFi connectivity is a valid alternative both to ad hoc solutions, like LoRa, which require costly deployments, and to communicating through the mobile network, which is both pricey and battery power hungry. In this paper we present a tool to analyze the WiFi coverage of home Access Points (AP) on the city streets. It can be of interest to ISP or other providers which want to offer connectivity to Internet of Things smart objects deployed around the city. We describe a method for gathering WiFi measures around the city (by leveraging crowdsourcing) and an open source visualization and analysis web application to explore the accumulated data. More importantly, this framework can leverage the semantic information contained in OpenStreetMap data to extract further knowledge about the AP deployment in the city, for example we investigate the relationship between the AP density per square kilometer within the city and the WiFi street coverage ratio

Annotating Errors and Emotions in Human-Chatbot Interactions in Italian

This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall

Transparent Bandwidth Aggregation for Residential Access Networks

We propose, implement, and evaluate a bandwidth aggregation service for residential users that allows to improve the upload throughput of the asymmetric digital subscriber line connection by leveraging the unused bandwidth of neighboring users. The residential access gateway adopts the 802.11 radio interface to simultaneously serve the local home users and to share the broadband connectivity with neighboring access gateways. Differently from previous works, our aggregation scheme is transparent both for local users, who are not required to modify their applications or device drivers, and for neighboring users, who do not experience any meaningful performance degradation. To evaluate the achievable performance and tune the parameters driving the traffic balancing, we developed a fluid model that was shown experimentally to be very accurate. Our proposed scheme is amenable to efficient implementation on Linux networking stack. Indeed, we implemented it and tested in some realistic scenarios, showing an efficient exploitation of the whole available bandwidth, also for legacy cloud storage applications

Progettare chatbot: considerazioni e linee guida

Il lavoro si propone di delineare una serie di linee guida per la progettazione di chatbot e assistenti virtuali a partire dall’analisi degli attuali trend di progettazione e delle esigenze lato utente rilevate da precedenti lavori di rassegna della letteratura esistente. Il presente lavoro è stato svolto nell’ambito del progetto “Cognitive Solution for Intelligent Caring” di TIM.This work is focused on the current trends in designing chatbots and virtual assistants. We start from users’ needs identified in industrial surveys on chatbots. The result is a collection of guidelines and considerations which reflect the state of the art

Anticipating User Intentions in Customer Care Dialogue Systems

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users' requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users' specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users' information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge