Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This article introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval . Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions.

Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks.

We propose RL-Cargo, a revenue management approach for air-cargo that combines machine learning prediction with decision-making using deep reinforcement learning. This approach addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual amount received at departure time by the airline. The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in an overall loss of potential revenue for the airline. A DQN method using uncertainty bounds from prediction is proposed for decision making under a prescriptive learning framework. Parts of RL-Cargo have been deployed in the production environment of a large commercial airline company. We have validated the benefits of RL-Cargo using a real dataset. More specifically, we have carried out simulations seeded with real data to compare classical Dynamic Programming and Deep Reinforcement Learning techniques on offloading costs and revenue generation. Our results suggest that prescriptive learning which combines prediction with decision-making provides a principled approach for managing the air cargo revenue ecosystem. Furthermore, the proposed approach can be abstracted to many other application domains where decision making needs to be carried out in face of both data and behavioral uncertainty.

Employing an optimal traffic light control policy has the potential of having a positive impact, both economic and environmental, on urban mobility. Reinforcement learning techniques have shown promising results in optimizing control policies for basic intersections and low volume traffic. This paper addresses the traffic light control problem in a complex scenario, such as a signalized roundabout with heavy traffic volumes, with the aim of maximizing throughput and avoiding traffic jams. We formulate the environment with a realistic representation of states and actions and a capacity-based reward. We enforce episode terminal conditions to avoid unwanted states, such as long queues interfering with other junctions in the vehicular network. A time-dependent baseline is proposed to reduce the variance of Policy Gradient updates in the setting of episodic conditions, thus improving the algorithm convergence to an optimal solution. We evaluate the method on real data and highly congested traffic, implementing a signalized simulated roundabout with 11 phases. The proposed method is able to avoid traffic jams and achieves higher performance than traditional time-splitting policies and standard Policy Gradient on average delay and effective capacity, while drastically decreasing the emissions.

The current online digital world, consisting of thousands of newspapers, blogs, social media, and cloud file sharing services, is providing easy and unlimited access to a large treasure of text contents. Making copies of these text contents is simple and virtually costless. As a result, producers and owners of text content are interested in the protection of their intellectual property (IP) rights. Digital watermarking has become crucially important in the protection of digital contents. Out of all, text watermarking poses many challenges, since text is characterized by a low capacity to embed a watermark and allows only a restricted number of alternative syntactic and semantic permutations. This becomes even harder when authors want to protect not just a whole book or article, but each single sentence or paragraph, a problem well known to copyright law. In this paper, we present a fine-grain text watermarking method that protects even small portions of the digital content. The core method is based on homoglyph characters substitution for latin symbols and whitespaces. It allows to produce a watermarked version of the original text, preserving the anonymity of the users according to the right to privacy. In particular, the embedding and extraction algorithms allow to continuously protect the watermark through the whole document in a fine-grain fashion. It ensures visual indistinguishability and length preservation, meaning that it does not cause overhead to the original document, and it is robust to the copy and past of small excerpts of the text. We use a real dataset of 1.8 million New York articles to evaluate our method. We evaluate and compare the robustness against common attacks, and we propose a new measure for partial copy and paste robustness. The results show the effectiveness of our approach providing an average length of 101 characters needed to embed the watermark and allowing to protect paragraph-long excerpt or smaller the 94.5% of the times.

One of the most shared content in Social Media (SM) is text, making it vulnerable to copy and authorship misappropriation. Due to the low data noise, watermark embedding is very hard. This problem is exacerbated in the context of SM, where the amount of data in a single message can be extremely small, like in Twitter. Firstly, in this paper we investigate whether SM do applies watermarks on the texts. Then, we propose a text watermarking method able to work on all the SM platforms considered, while ensuring visual indistinguishability and length preservation of the original text and robustness to copy and paste. We conduct an extended evaluation on eighteen different SM platforms by using 6,000 posts from six public figures' profiles.

The temporal dimension of a text document defines the temporal scope of its narrated event. This temporal dimension acquires more importance in corpora created along several years of production, such as digital libraries. Temporal aspects of text have been the subject of many researches with specific tasks, notably information retrieval and event detection, while no studies have been conducted to quantify and analyze the richness of the temporal dimension of different text collections. Analysing thirteen text collections we show how the extent and characteristics of the time presence in text varies among collections that have different scopes, although time intervals are mentioned in almost all the text units analyzed. We found that unique intervals follow the same distribution, given by the Zipf's law, that holds for single words.

Digital watermarking has become crucially important in authentication and copyright protection of the digital contents, since more and more data are daily generated and shared online through digital archives, blogs and social networks. Out of all, text watermarking is a more difficult task in comparison to other media watermarking. Text cannot be always converted into image, it accounts for a far smaller amount of data (eg. social network posts) and the changes in short texts would strongly affect the meaning or the overall visual form. In this paper we propose a text watermarking technique based on homoglyph characters substitution for latin symbols1. The proposed method is able to efficiently embed a password based watermark in short texts by strictly preserving the content. In particular, it uses alternative Unicode symbols to ensure visual indistinguishability and length preservation, namely content-preservation. To evaluate our method, we use a real dataset of 1.8 million New York articles. The results show the effectiveness of our approach providing an average length of 101 characters needed to embed a 64bit password based watermark.