Internet of Things (IoT) is an important infrastructure for supporting vertical applications. However, existing IoT systems are still facing some challenges, e.g., lack of coverage in rural areas and lack of efficient data collection methods. To overcome these challenges, a satellite IoT framework is proposed in this study as a promising solution, and smart agriculture is used as a typical application scenario. A satellite edge computing workflow is further proposed, with computer vision as a case study. In the case study, a lightweight deep learning model named MobileViT is proven effective for aphid detection and infestation severity classification on lemon leaves.

Drought has been a global concern and an effective prediction method is needed. Meteorological data are seen as an efficient and economic approach. Challenges arise with the large volume and high nonlinearity between meteorological variables and the drought level. In this study, deep learning is proposed as an effective solution for drought level prediction as multivariate time series classification. The synthetic minority oversampling technique is further adopted to alleviate the class imbalance problem and improve the classification performance. Experimental results on an open dataset named DroughtED demonstrate the effectiveness of the proposed deep learning method.

To achieve a green heterogeneous 5G network, a promising approach is to shut off the light-loaded low-power nodes (LPNs) and transfer the load to the remote radio heads (RRHs) nearby. However, the RRHs may refuse to cooperate when there is no incentive. Considering a user will stay in an RRH for a period of time, how to provide proper long-term incentives for the potential RRHs and select the best one is an essential issue. Since the offloading capability of an RRH is private information, that is, unavailable to the LPNs, in this article, the RRHs’ collaboration incentive problem under such asymmetric information condition is modeled as a long-term contract design problem. In the formulated problem, the channel condition and traffic load, which represent both RRH’s instantaneous state and long-term state, are combined to capture the RRHs’ offloading capacity and used to classify their types. Due to the dynamic of state, an RRH’s type varies with time, which greatly complicates the contract design. We first study the state transition of RRHs and the long-term utilities of both parties, with which the contract-theoretic framework is formulated. Then, we theoretically analyze and simplify the individual rational and incentive-compatible constraints for a feasible long-term contract. Finally, we propose a low time complexity algorithm to find the optimal contract. Numerical results verify that the long-term contract-based incentive mechanism not only improves the utilities of both cooperation parties, but also is superior to the existing works in reducing handover cost and energy consumption.

For edge computing (EC) network, one critical problem is how to process computation-intensive task in time with efficient energy usage. However, existing works mainly study one aspect of the problem by computation offloading or energy cooperation. Considering that the computation offloading strategy and the energy cooperation strategy are coupled with each other, in this paper, we propose a new information-energy collaboration model for the EC network powered by renewable energy and stored energy. In this new model, an EC node can collaborate with the other EC nodes and the cloud for computation offloading. At the same time, the EC is also able to store energy and share energy with other EC nodes. In such a case, since the EC nodes can have a stable power supply to finish the computing tasks within the latency limit, we formulate the problem of deriving the information-energy collaboration strategy as the optimization problem of minimizing the cloud computing cost and the power purchase cost. To find the optimal offloading and energy cooperation strategy, we first analyze the sixteen cases of the offloading strategy depending on the computing tasks and the renewable energy. Then we further summarize four cases of energy collaboration strategy according to the different offloading strategies. To derive the collaboration strategy with low complexity, we propose the practical Hybrid Greedy Iterative Algorithm (HGIA) to the optimization problem. Finally, the simulation results demonstrate that our approach is effective and stable.

Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems. This paper presents a few-shot generative approach to conversational query rewriting. We develop two methods, based on rules and self-supervised learning, to generate weak supervision data using large amounts of ad hoc search sessions, and to fine-tune GPT-2 to rewrite conversational queries. On the TREC Conversational Assistance Track, our weakly supervised GPT-2 rewriter improves the state-of-the-art ranking accuracy by 12%, only using very limited amounts of manual query rewrites. In the zero-shot learning setting, the rewriter still gives a comparable result to previous state-of-the-art systems. Our analyses reveal that GPT-2 effectively picks up the task syntax and learns to capture context dependencies, even for hard cases that involve group references and long-turn dependencies.

Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension.Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA.However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages.Therefore, it is essential to investigate cross-lingual OpenQA.In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research.It consists of a training set in English as well as development and test sets in eight other languages.Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT).Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English.Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is.The XQA dataset is publicly available at http://github.com/thunlp/XQA.

The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data.In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions.In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system.We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers.Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer.Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.

OBJECTIVE: To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test. DESIGN: An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia. MEASUREMENTS: Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results. RESULTS: The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference. CONCLUSIONS: This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.