We present the first automatic emotion detection system for Cantonese. This system classifies input text into eight emotion classes: expectancy, joy, love, surprise, anxiety, sorrow, angry, or hate. While a number of emotion corpora and lexica for Mandarin Chinese have been developed, no emotion dataset is available for Cantonese. We leverage existing Mandarin Chinese emotion resources to build the system, with support from Cantonese-Mandarin lexical mappings from a machine translation system, as well as English-Mandarin lexical mappings to handle code-switching in Cantonese input. Evaluation on a set of Cantonese sentences from social media shows promising results.

Modern solutions for implicit discourse relation recognition largely build universal models to classify all of the different types of discourse relations. In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Expansion. We find semantic characteristics of each relation type and two cohesion devices---topic continuity and attribution---work together to contribute such linguistic properties. We encode those properties as complex features and feed them into a NaiveBayes classifier, bettering baselines(including deep neural network ones) to achieve a new state-of-the-art performance level. Over a strong, feature-based baseline, our system outperforms one-versus-other binary classification by 4.83% for Comparison relation, 3.94% for Contingency and 2.22% for four-way classification.

Social-based recommender systems have been recently proposed by incorporating social relations of users to alleviate sparsity issue of user-to-item rating data and to improve recommendation performance. Many of these social-based recommender systems linearly combine the multiplication of social features between users. However, these methods lack the ability to capture complex and intrinsic non-linear features from social relations. In this paper, we present a deep neural network based model to learn non-linear features of each user from social relations, and to integrate into probabilistic matrix factorization for rating prediction problem. Experiments demonstrate the advantages of the proposed method over state-of-the-art social-based recommender systems.

In this paper, we propose a fractional preference model for the facility location game with two facilities that serve the similar purpose on a line where each agent has his location information as well as fractional preference to indicate how well they prefer the facilities. The preference for each facility is in the range of [0, L] such that the sum of the preference for all facilities is equal to 1. The utility is measured by subtracting the sum of the cost of both facilities from the total length L where the cost of facilities is defined as the multiplication of the fractional preference and the distance between the agent and the facilities. We first show that the lower bound for the objective of minimizing total cost is at least Ω(n^1/3). Hence, we use the utility function to analyze the agents' satification. Our objective is to place two facilities on [0, L] to maximize the social utility or the minimum utility. For each objective function, we propose deterministic strategy-proof mechanisms. For the objective of maximizing the social utility, we present an optimal deterministic strategy-proof mechanism in the case where agents can only misreport their locations. In the case where agents can only misreport their preferences, we present a 2-approximation deterministic strategy-proof mechanism. Finally, we present a 4-approximation deterministic strategy-proof mechanism and a randomized strategy-proof mechanism with an approximation ratio of 2 where agents can misreport both the preference and location information. Moreover, we also give a lower-bound of 1.06. For the objective of maximizing the minimum utility, we give a lower-bound of 1.5 and present a 2-approximation deterministic strategy-proof mechanism where agents can misreport both the preference and location.

This study analyzes different methodological approaches followed in social media literature and their accuracy in predicting the general elections of four countries. Volumetric and unsupervised and supervised sentiment approaches are adopted for generating 12 metrics to compute predicted voteshares. The findings suggest that Twitter-based predictions can produce accurate results for elections, given the digital environment of a country. A cross-country analyses helps to evaluate the quality of predictions and the influence of different contexts, such as technological development and democratic setups. We recommend future scholars to combine volume, sentiment and network aspects of social media to model voting intentions in developing societies.

With over 50 million characters in over 1500 texts, the Chinese Buddhist Canon is a complex literary collection. Besides the Buddha himself, there is a myriad of characters including bodhisattvas, deities, disciples of Buddha, monks, lay Buddhists as well as kings. This paper analyzes the hierarchy among these characters by examining their verbal interactions. Exploiting techniques from natural language processing, we extract all direct speech from the text, and examine the relation between the speakers, listeners, and the quotative verbs used for reporting the speech. We show that a number of the quotative verbs indicate relative status between the speaker and the listener. We then use their usage patterns to induce a hierarchy of the characters in the Canon.

We study truthful mechanisms in the context of cake cutting when agents not only value their own pieces of cake but also care for the pieces assigned to other agents. In particular, agents derive benefits or costs from the pieces of cake assigned to other agents. This phenomenon is often referred to as positive or negative externalities. We propose and study the following model: given an allocation, externalities of agents are modeled as percentages of the reported values that other agents have for their pieces. We show that even in this restricted class of externalities, under some natural assumptions, no truthful cake cutting mechanisms exist when externalities are either positive or negative. However, when the percentages agents get from each other are small, we show that there exists a truthful cake cutting mechanism with other desired properties.

The defining characteristic the SDVRP, where vehicle capacity and customer demands of the SDVRP that distinguishes it from the classical are not required to be integer numbers, the number of vehicles vehicle routing problem (VRP) is that each customer is not limited to the minimum possible number, and can be served by more than one vehicle. Obviously, when the customer demands may exceed the vehicle capacity. The the demand of a customer is lager than the vehicle capacity, main contributions are threefold. First, we find a novel way it has to be split and the customer has to be visited more to represent the solutions of the SDVRP, which is the combination than once. As shown by (Dror and Trudeau 1989), when all of a set of vehicle routes and a forest. Second, based customer demands are less than or equal to the vehicle capacity, on this solution representation, we propose three classes of split delivery can also lead to substantial cost savings.

We study the problem of locating a single facility on a real line based on the reports of self-interested agents, when agents have double-peaked preferences, with the peaks being on opposite sides of their locations.We observe that double-peaked preferences capture real-life scenarios and thus complement the well-studied notion of single-peaked preferences. We mainly focus on the case where peaks are equidistant from the agents’ locations and discuss how our results extend to more general settings. We show that most of the results for single-peaked preferences do not directly apply to this setting; this makes the problem essentially more challenging. As our main contribution, we present a simple truthful-in-expectation mechanism that achieves an approximation ratio of 1+b/c for both the social and the maximum cost, where b is the distance of the agent from the peak and c is the minimum cost of an agent. For the latter case, we provide a 3/2 lower bound on the approximation ratio of any truthful-in-expectation mechanism. We also study deterministic mechanisms under some natural conditions, proving lower bounds and approximation guarantees. We prove that among a large class of reasonable mechanisms, there is no deterministic mechanism that outpeforms our truthful-in-expectation mechanism.

This paper describes how AI is used to plan, schedule, and optimize nightly engineering works for both the commuter and rapid transit lines in Hong Kong. The MTR Corporation Limited operates and manages all the rail lines in Hong Kong. Its “Engineering Works and Traffic Information Management System” (ETMS) is a mission critical system that manages all information related to engineering works and their related track possessions and engineering train movements. The AI Engine described in this paper is a component of this ETMS. In Hong Kong, the maintenance, inspection, repair, or installation works along the rail lines are done during the very short non-traffic hours (NTH) of roughly 4 to 5 hours each night. These engineering works can be along the running tracks, track-side, tunnel, freight yards, sub-depots, depot maintenance tracks, etc. The proper scheduling of necessary engineering works is crucial to maintaining a reliable and safe train service during normal hours. The AI Engine optimizes resource allocation to maximize the number of engineering works that can be performed, while ensuring all safety, environment, and operational rules and constraints are met. The work described is part of a project to redesign and replace the existing ETMS, deployed in 2004, with an updated technology platform and modern IT architecture, to provide a more robust and scalable system that potentially can be deployed to other cities around the world.