Understanding the expression of emotion and generating appropriate responses are key steps toward constructing emotional, conversational agents. In this article, we propose a framework for single-turn emotional conversation generation, and there are three main components in our model, namely, a sequence-to-sequence model with stacked encoders, a conditional variational autoencoder, and conditional generative adversarial networks. For the sequence-to-sequence model with stacked encoders, we designed a two-layer encoder by combining Transformer with gated recurrent units-based neural networks. Because of the flexibility of the sequence-to-sequence model, we adopted a conditional variational autoencoder in our framework, which uses latent variables to learn a distribution over potential responses and generates diverse responses. Furthermore, we regard a conditional variational autoencoder-based, sequence-to-sequence model as the generative model, and the training of the generative model is assisted by both a content discriminator and an emotion classifier, which assists our model in promoting content information and emotion expression. We use automated evaluation and human evaluation to evaluate our model and baselines on the NII Test Collections for IR Systems short-text conversation task Chinese emotional conversation generation Subtask dataset [ 44 ], and the experimental results demonstrate that our proposed framework can generate semantically reasonable and emotionally appropriate responses.

Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. However, most of the chatbots act as a simple question answering system that responds with formulated answers. Traditional conversational chatbots usually adopt a retrieval-based model that requires a large amount of conversational data for retrieving various intents. Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. We propose a method to build a task-oriented chatbot using a sentence generation model that generates sequences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence and a discriminator that judges the sentences by comparing the generated and the ground-truth sentences. In the generator, we combine the attention model with the sequence-to-sequence model using hierarchical long short-term memory to extract sentence information. For the discriminator, our reward mechanism assigns low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model that generates more diverse and information-rich sentences than those of the existing approaches.

This study aimed to examine the effect of an immersive virtual reality interactive feedback system on university students’ situational interest and learning achievement in a pour over coffee brewing lesson. A total of 103 university students participated in this experiment. They used the immersive virtual reality interactive feedback system to learn the steps of making pour over coffee. In addition, they were required to complete a prior knowledge test, a situational interest scale, and a learning achievement test. The results of this study indicate that the immersive virtual reality interactive feedback system can trigger sub-dimensions of situational interest to an average level except for the challenge dimension, while it also improves learners’ learning achievement. It is suggested that teachers can use an immersive virtual reality interactive feedback system to teach learners multiple-step lessons or trigger learners’ learning motivation.

Owing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and destination. The efficiency of this path planning function is critical for mobile users on roads due to various dynamic scenarios, such as a sudden change in driving direction, unexpected traffic conditions, lost or unstable GPS signals, and so on. In these scenarios, the path planning service needs to be delivered in a timely fashion. In this paper, we propose a system, namely, Path Planning by Caching (PPC), to answer a new path planning query in real time by efficiently caching and reusing historical queried-paths. Unlike the conventional cachebased path planning systems, where a queried-path in cache is used only when it matches perfectly with the new query, PPC leverages the partially matched queries to answer part(s) of the new query. Comprehensive experimentation on a real road network database shows that our system outperforms the state of-the-art path planning techniques by reducing 32% of the computation latency on average.

Efficient processing of skyline queries with partially ordered domains has been intensively addressed in recent years. To further reduce the query processing time to support high-responsive applications, the skyline queries that were previously processed with user preferences similar to those of the new query contribute useful candidate result points. Hence, the answered queries can be cached with both their results and the user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of cached queries with compatible user preferences. When caching a significant number of queries accumulated over time, it is essential to adopt effective access methods to index the cached queries to retrieve a set of relevant cached queries for facilitating the cache-based skyline query computations. In this paper, we propose an extended depth-first search indexing method (e-DFS for short) for accessing user preference profiles represented by directed acyclic graphs (DAGs), and emphasize the design of the e-DFS encoding that effectively encodes a user preference profile into a low-dimensional feature point which is eventually indexed by an R-tree. We obtain one or more traversal orders for each node in a DAG by traversing it through a modified version of the depth-first search which is utilized to examine the topology structure and dominance relations to measure closeness or similarity. As a result, e-DFS which combines the criteria of similarity evaluation is able to greatly reduce the search space by filtering out most of the irrelevant cached queries such that the query processor can avoid accessing the entire data set to compute the query results. Extensive experiments are presented to demonstrate the performance and utility of our indexing method, which outperforms the baseline planning techniques by reducing 37 percent of the computational time on average.

Owing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and destination. The efficiency of this path planning function is critical for mobile users on roads due to various dynamic scenarios, such as a sudden change in driving direction, unexpected traffic conditions, lost or unstable GPS signals, and so on. In these scenarios, the path planning service needs to be delivered in atimelyfashion. In this paper, we propose a system, namely,Path Planning by Caching (PPC), to answer a new path planning query in real time by efficiently caching and reusing historical queried-paths. Unlike the conventional cache-based path planning systems, where a queried-path in cache is used only when it matches perfectly with the new query, PPC leverages the partially matched queries to answer part(s) of the new query. As a result, the server only needs to compute the unmatched path segments, thus significantly reducing the overall system workload. Comprehensive experimentation on a real road network database shows that our system outperforms the state-of-the-art path planning techniques by reducing 32 percent of the computation latency on average.

Existing methods have addressed the issue of handling each individual skyline query performed on data sets with partially ordered domains. However, it is still very challenging to process such queries for on-line applications with low response time. In this paper, we introduce a cache-based framework, called CSS, for further reducing the query processing time to support high-responsive applications. Skyline queries that were previously processed with user preferences similar to those of the current query contribute useful candidate result points. Hence, the answered queries are cached with both their results and user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of selected queries with compatible user preferences. We introduce a similarity measure that establishes the level of similarity between the user preferences of a new query and a cached query; hence the system can start with the most similar candidates. Furthermore, if a new query is only partially answerable from the cache, then the query processor utilizes the partial result sets and performs less expensive constraint skyline queries guided by violated preferences. Furthermore, we introduce two access methods for cached queries indexed by their user preferences to only access a set of relevant cached queries for similarity measures. Extensive experiments are presented to demonstrate the performance and utility of our novel approach.

Location-based services (LBS) which bring so much convenience to our daily life have been intensively studied over the years. Generally, an LBS query processing can be categorized into snapshot and continuous queries which access user location information and return search results to the users. An LBS has full control of the location information, causing user privacy concerns. If an LBS provider has a malicious intention to breach the user privacy by tracking the users' routes to their destinations, it incurs a serious threat. Most existing techniques have addressed privacy protection mainly for snapshot queries. However, providing privacy protection for continuous queries is of importance, since a malicious LBS can easily obtain complete user privacy information by observing a sequence of successive query requests. In this paper, we propose a comprehensive trajectory privacy technique and combine ambient conditions to cloak location information based on the user privacy profile to avoid a malicious LBS reconstructing a user trajectory. We first propose an r-anonymity mechanism which preprocesses a set of similar trajectories R to blur the actual trajectory of a service user. We then combine k-anonymity with s road segments to protect the user's privacy. We introduce a novel time-obfuscated technique which breaks the sequence of the query issuing time for a service user to confuse the LBS so it does not know the user trajectory, by sending a query randomly from a set of locations residing at the different trajectories in R. Despite the randomness incurred from the obfuscation process for providing strong trajectory privacy protection, the experimental results show that our trajectory privacy technique maintains the correctness of the query results at a competitive computational cost.

The rapid development of the integration of cloud computing and location-based services have drawn so much attention currently. With the increasing number of users who own smart phones, significant amount of data that describe user surrounding information and interests have become widely available. However, significant attentions have been raised on the privacy issues. The existing approaches mainly focus on a centralized approach which brings tremendous security concerns. To prevent a centralized query processor from being attached by malicious hackers, we propose a decentralized approach to protect the sensitive location information of users who request for location-based services. Our system provides an approximate computing and an exact computing mechanism for different scenarios and requirements.

Numerous geographic information system applications need to retrieve spatial objects which bear user specified keywords close to a given location. In this research, we present efficient approaches to answer spatial keyword queries on spatial networks. In particular, we formally introduce definitions of Spatial Keyword k Nearest Neighbor (SKkNN) and Spatial Keyword Range (SKR) queries. Then, we present a framework of a spatial keyword query evaluation system which is comprised of Keyword Constraint Filter (KCF), Keyword and Spatial Refinement (KSR), and the spatial keyword ranker. KCF employs an inverted index to calculate keyword relevancy of spatial objects, and KSR refines intermediate results by considering both spatial and keyword constraints with the spatial keyword ranker. In addition, we design novel algorithms for evaluating SKkNN and SKR queries. These algorithms employ the inverted index technique, shortest path search algorithms, and network Voronoi diagrams. Our extensive simulations show that the proposed SKkNN and SKR algorithms can answer spatial keyword queries effectively and efficiently.

The results of skyline queries performed on data sets with partially-ordered domains vary depending on users' preference profiles specified for the partially-ordered domains. Existing work has addressed the issue of handling each individual query with some efficiency. However, processing large volumes of such queries for online applications with low response time is still very challenging. In this paper, we introduce a novel approach, termed CSS, to reduce the latency by caching query results with their unique user preferences. Of paramount importance in this case is that cached queries with compatible preference profiles need to be utilized. For this purpose, we introduce a similarity measure that establishes the level of a relation of a new query to each of the previously cached queries and profiles. The similarity measure allows the cached entries to be effectively ordered according to descending values; hence, query processing can start with the most promising candidates. If a new query is only partially answerable from the cache, the proposed method pursues a second optimization step. The query processor utilizes the partial result sets and augments them by performing less expensive constraint skyline queries guided by constraint violations between different query preference profiles. Extensive experiments are presented to demonstrate the performance and utility of our novel approach.

Skyline query processing has become an important feature in multi-dimensional, data-intensive applications. Such computations are especially challenging under dynamic conditions, when either snapshot queries need to be answered with short user response times or when continuous skyline queries need to be maintained efficiently over a set of objects that are frequently updated. To achieve high performance, we have recently designed the ESC algorithm, an Efficient update approach for Skyline Computations. ESC creates a pre-computed candidate skyline set behind the first skyline (a “second line of defense,” so to speak) that facilitates an incremental, two-stage skyline update strategy which results in a quicker query response time for the user. Our demonstration presents the two-threaded SkyEngine system that builds upon and extends the base-features of the ESC algorithm with innovative, user-oriented functionalities that are termed SkyAlert and AutoAdjust. These functions enable a data or service provider to be informed about and gain the opportunity of automatically promoting its data records to remain part of the skyline, if so desired. The SkyEngine demonstration includes both a server and a web browser based client. Finally, the SkyEngine system also provides visualizations that reveal its internal performance statistics.

The PLUS system is designed to efficiently track moving object locations on a road network and execute continuous spatial queries in support of location-based services. PLUS implements a novel lazy position update mechanism that significantly reduces the communication overhead and server indexing load related to frequent location updates in moving object and moving query scenarios. The contribution of this demo is to present how the lazy position update scheme can achieve message-efficiency under various conditions which can be interactively set via user-selectable parameters in a graphical user interface.

Continuous spatial queries posted within an environment of moving objects produce as their results a time-varying set of objects. In the most ambitious case both queries and data objects are dynamic, making it very challenging to find an efficient query evaluation strategy. The significant overhead related to frequent location updates from moving objects often results in poor performance. The most advanced existing techniques use the concept of simple geometric safe regions to delay or avoid location updates. We introduce a Partition-based Lazy Update (PLU) algorithm that elevates this idea further by adopting Location Information Tables (LIT) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. Among the significant advantages, our technique performs well even in very highly dynamic environments (with up to 100% mobility) where many other techniques deteriorate. PLU can be efficiently implemented and we demonstrate its query performance improvement of up to 28% over the current state-of-the-art.