We propose a new graph-based framework to reveal relationships among motivations, emotions and actions explicitly given natural language texts. A directed acyclic graph is designed to describe human's nature. Nurture beliefs are incorporated to connect outside events and the human's nature graph. No annotation resources are required due to the power of large language models. Amazon Fine Foods Reviews dataset is used as corpus and food-related motivations are focused. Totally 92,990 relationship graphs are generated, of which 63% make logical sense. We make further analysis to investigate error types for optimization direction in future research.

Robotic systems react to different environmental stimuli, potentially resulting in the dynamic reconfiguration of the software controlling such systems. One effect of such dynamism is the reconfiguration of the software architecture reconfiguration of the system at runtime. Such reconfigurations might severely impact the runtime properties of robotic systems, e.g., in terms of performance and energy efficiency. The ROS \emph{rosbag} package enables developers to record and store timestamped data related to the execution of robotic missions, implicitly containing relevant information about the architecture of the monitored system during its execution. In this study, we discuss about our approach for statically extracting (time-windowed) architectural information from ROS bag files. The proposed approach can support the robotics community in better discussing and reasoning the software architecture (and its runtime reconfigurations) of ROS-based systems. We evaluate our approach against hundreds of ROS bag files systematically mined from 4,434 public GitHub repositories.

In the broadest sense, it is the ability of computers to interpret and understand digital images. This includes everything from identifying objects in an image to understanding the meaning of an image. Computer vision is a rapidly growing field with many potential applications. It is already being used in a number of industries, including healthcare, automotive, and security. And as the technology continues to develop, the potential uses for computer vision are only going to increase.

In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, forest areas, rivers, lakes, etc. DL is a new paradigm for Big Data analytics and in particular for Computer Vision. The application of DL in images classification for land cover purposes has a great potential owing to the high degree of automation and computing performance. In particular, the invention of Convolution Neural Networks (CNNs) was a fundament for the advancements in this field. In [1], the Satellite Task Team of the UN Global Working Group describes the results achieved so far with respect to the use of earth observation for Official Statistics. However, in that study, CNNs have not yet been explored for automatic classification of imagery. This work investigates the usage of CNNs for the estimation of land cover indicators, providing evidence of the first promising results. In particular, the paper proposes a customized model, called Satellite-Net, able to reach an accuracy level up to 98% on test sets.

LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. In this paper, we study how to automatically extract such relationship through a sentence-level relation classifier and aggregating the scores of entity pairs from a large corpus. Also, we release two benchmark datasets for evaluation and future research.

Understanding a customer’s personal opinion is extremely important to initiate and maintain a meaningful conversation. In this paper, we propose an approach to extract latent emotional beliefs of customers and use them to tailor a chatbot’s conversation. We present a machine learning based mechanism to process customer complaints and extract sentiments like customer is sad, happy, upset, etc. Further, we also train a model that extract more fine grain sentiments like the customer is irritated, harassed etc. in context of a particular complaint scenario. This information helps to tailor the dialog according to customer’s emotional state and hence improve the overall effectiveness of the dialog system.

This repository contains the TSFRESH python package. "Time Series Feature extraction based on scalable hypothesis tests". The package contains many feature extraction methods and a robust feature selection algorithm. Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated.

Online “notice and choice” is an essential concept in the US FTC’s Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natural language processing and machine learning techniques. We define a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe supervised machine learning approaches for automatically extracting instances containing opt-out hyperlinks and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services. The focus of this paper, however, is to describe such methods to automatically extract useful opt-out hyperlinks from privacy policies.

Reasoning with commonsense knowledge plays an important role in various NLU tasks. Often the commonsense knowledge is needed to be extracted separately. In this paper we present our work of automatically extracting a certain type of commonsense knowledge. The knowledge resembles the kind that humans have about the events and the entities that participate in those events. One example of such knowledge is that "IF A bullying B causes T rescued Z THEN (possibly) Z = B ''. We call this knowledge an event-based conditional commonsense. Our approach involves semantic parsing of natural language sentences by using the Knowledge Parser (K-Parser) and extracting the knowledge, if found. We extracted about 19000 instances of such knowledge from the Open American National Corpus.

Technology offers new ways to measure the locations of the players and of the ball in sports. This translates to the trajectories the ball takes on the field as a result of the tactics the team applies. The challenge professionals in soccer are facing is to take the reverse path: given the trajectories of the ball is it possible to infer the underlying strategy/tactic of a team? We propose a method based on Dynamic Time Warping to reveal the tactics of a team through the analysis of repeating series of events. Based on the analysis of an entire season, we derive insights such as passing strategies for maintaining ball possession or counter attacks, and passing styles with a focus on the team or on the capabilities of the individual players.