Energy harvesting technologies offer a promising solution to sustainably power an ever-growing number of Internet of Things (IoT) devices. However, due to the weak and transient natures of energy harvesting, IoT devices have to work intermittently rendering conventional routing policies and energy allocation strategies impractical. To this end, this paper, for the very first time, developed a distributed multi-agent reinforcement algorithm known as global actor-critic policy (GAP) to address the problem of routing policy and energy allocation together for the energy harvesting powered IoT system. At the training stage, each IoT device is treated as an agent and one universal model is trained for all agents to save computing resources. At the inference stage, packet delivery rate can be maximized. The experimental results show that the proposed GAP algorithm achieves ~ 1.28× and ~ 1.24× data transmission rate than that of the Q-table and ESDSRAA algorithm, respectively.

Opinion mining or sentiment analysis is used for understanding the opinion of the community on a specific product or a service. In this paper, we investigate the modeling power of contextualized representations from pre-trained language model BERT. There are many methods and techniques used to explore these features from unstructured comments. We developed a different approach to target extraction task in sentiment analysis using a deep learning model combining Gated Recurrent Unit (GRU) and Conditional Random Field (CRF). The task is typically modeled as a sequence labeling problem and solved using state-of-the-art labelers such as CRF. This model is trained on labeled data to extract and classify feature sets. Experiments have been conducted on two benchmark datasets and our framework achieves consistently superior results.

Road damage detection is of importance to road maintenance, which typically requires huge amount of manual efforts. In this work, we leverage deep learning models to analyze the road images to detect road damages efficiently. Specifically, we train and test different deep learning methods to identify efficient models with high accuracy. Although two-stage detector, applied object classification and bounding-box regression on proposed regions, can achieve high accuracy in object detection, it runs slow due to its high computational cost. Instead, we adopt one-stage detectors, classification and bounding-box regression at a single stage, because they are faster than two-stage detectors. In this work, we have trained and tested several one-stage models and found a fast model called RetinaNet can detect road damages with relatively high accuracy. A user guide of the source code and predicted examples of trained model have been provided as a markdown document and jupyter notebook, respectively.

Ranking similar structure groups/subgraphs can contribute to importantly practical applications, such as finding the most suspicious tax evasion group within a heterogeneous information network. This problem, however lacks attentions in the current research community. Inspired by social network analysis, this paper coins a definition of a taxpayer interest interacted network (TPIIN), in which persons or companies act as nodes as well as the arcs act as the relationships between persons and/or companies, and the weight of an arc is equal to the interest affiliated degree (IAD) of a direct tie or link according to various economic behaviors between persons and/or companies. Moreover, we adopted four operations, Maximum, Minimum, Addition, and Multiply, to calculate the IAD of a trail, then propose a voting algorithm based on the four operations to compare the suspicion degrees of all groups containing the same trading relationship arc. Experiments are carried out and their results verify the proposed method.

To solve the problem of emotional illiteracy in current e-Learning environment, researches on sentiment analysis now get more attentions. This paper focuses on recognizing emotion from interactive Chinese texts (ICTs). Through observation, firstly, characteristics of ICTs are discussed. Then two kinds of feature sets, frequency based feature set and interaction related feature set, are presented. Finally, the corresponding feature extraction and selection for ICTs are presented. To validate the feature sets and choose the best method of sentiment analysis, we carry out a number of experiments. The experiments' results show that, combining with syntax based feature set, frequency based feature set and interaction related feature set can improve algorithm classification performance, and multi-class classifier and the tree based methods perform better than others.

An architecture of a group oriented intelligent tutoring system for rocket engineers and researchers is proposed, which combines the technologies of multi-agent and virtual reality with HLA. At the same time, a group oriented joint intention is introduced into the description of multi-agent cooperation in order to teach or guide trainers/beginners to learn right operation procedures or resolve faults. Then, an intelligent tutoring system for measuring and controlling rocket launch is implemented to verify the proposed method, which operates well at Jiuquan Satellite Launch Center (JSLC).

To help teachers know class/group members better in the case of large scale on-line textual interaction, this paper tried to display e-Learner's emotion combined with topics and group structure. For achieving this goal, a color palette of emotions based on Plutchik's color palette was presented, an extended cascaded PLSI algorithm using sliding window technique was proposed to detect and track topics in Chinese interactive texts, and multiple star-field variants were introduced to display the group structure.

A number of satellite image sources on meteorology and environment are available over the Gulf of Mexico (GOM). Those satellite images are taken periodically in order to observe the weather changes. However, users usually face several problems when they want to search, retrieve, and combine images from multiple data sources. Without an adequate system and personnel for managing data, the magnitude of the effort needed to deal with such large and complex data sets can be a substantial barrier to the GOM research community. As a result, a mediator-based data integration system is proposed and developed to retrieve and integrate the images of satellite observational systems over the GOM.

Automatic and accurate range image registration is often a prerequisite step for range image analysis and interpretation. Due to occlusion, appearance and disappearance of points in different images, outliers inevitably occur. In this case, various techniques to eliminate and model outliers have been proposed for accurate range image registration. The objective of this paper is to experimentally investigate which of the outlier elimination and modelling is more effective for the evaluation of possible correspondences established, so that a deep insight into how advanced range image registration algorithms will be developed can be obtained. The experimental results based on both synthetic data and real images show that the outlier modelling often outperforms the outlier elimination in the sense of producing more accurate and robust range image registration results.

In this paper, we propose using orientation difference length distribution (ODLD) to represent a 3D view of each object with a free form surface. ODLD has a number of advantages over the shape distribution. A comparative study of ODLD and the shape distribution has shown that the former is significantly more accurate for 3D free form surface matching than the latter.

In this paper, a novel algorithm is proposed to carry out automatic 3D shape matching with 3D shapes represented as sets of points. After the possible matches between the 3D shapes have been determined by the tradition ICP criterion, the novel approach employs the collinearity constraint to eliminate false matches based on a statistical model. A comparative study based on real range images has shown that the proposed algorithm is accurate, robust and efficient for the automatic matching of overlapping 3D shapes.

In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.

We present a new statistical method for evaluating search engines' precision performance based on sample queries. The method consists of relevance evaluation and statistical comparison. In relevance evaluation, we present two scoring algorithms: one is a term-based algorithm based on the vector space model, and the other is a new three-level algorithm modeled after manual methods commonly used in information retrieval studies. In statistical comparison, we apply a statistical metric probability of win, in ranking the search engines. Based on a set of sample queries, our method evaluates the relevance of the pages returned by the search engines and compares them statistically In the experiment, our method was applied to three search engines, AltaVista, Google, and InfoSeek, using two query sets derived from the domain of parallel and distributed processing. Our results show that the three-level scoring algorithm with a typical set of parameters obtained results consistent with those obtained using the manual method, whereas the term-based algorithm did not.