Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions.

Some recent news recommendation (NR) methods introduce a Pre-trained Language Model (PLM) to encode news representation by following the vanilla pre-train and fine-tune paradigm with carefully-designed recommendation-specific neural networks and objective functions. Due to the inconsistent task objective with that of PLM, we argue that their modeling paradigm has not well exploited the abundant semantic information and linguistic knowledge embedded in the pre-training process. Recently, the pre-train, prompt, and predict paradigm, called prompt learning, has achieved many successes in natural language processing domain. In this paper, we make the first trial of this new paradigm to develop a Prompt Learning for News Recommendation (Prompt4NR) framework, which transforms the task of predicting whether a user would click a candidate news as a cloze-style mask-prediction task. Specifically, we design a series of prompt templates, including discrete, continuous, and hybrid templates, and construct their corresponding answer spaces to examine the proposed Prompt4NR framework. Furthermore, we use the prompt ensembling to integrate predictions from multiple prompt templates. Extensive experiments on the MIND dataset validate the effectiveness of our Prompt4NR with a set of new benchmark results.

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the Global Positioning System (GPS) has been widely-used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and non-redundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.

The emergence of coverage holes will dramatically degrade the quality of service of the industrial wireless sensor networks(IWSNs). Based on the novel confident information coverage(CIC) model, this work focuses on how to heal the confident information coverage holes in hybrid IWSNs containing both stationary nodes and mobile nodes. We pinpoint The confident information coverage hole healing(CICHH) problem with the goal of selecting some randomly scattered mobile nodes to be dispatched to the CIC holes detected by the stationary nodes such that the CIC holes can be repaired and the confident information coverage performance can be satisfied, and prove its NP-completeness. For handling the CICHH problem, we devise two energy-efficient heuristic solutions including a centralized confident information coverage hole healing algorithm and a distributed one. Both the proposed schemes aim at efficiently healing the CIC holes while minimizing the total moving energy consumption of the dispatched mobile nodes, or maximizing the mobile nodes' average remaining energy after movement, or minimizing the maximum mobile energy consumption of each dispatched mobile node. Experimental simulation results show the proposed schemes can energy-efficiently heal the CIC holes and outperform three peer algorithms in terms of the energy efficiency and coverage ratio.

Recently, although radar sensors have been widely applied for Hand Gesture Recognition (HGR) tasks, conventional radar-based HGR systems still have two major challenges. Firstly, these systems rely on supervised learning approaches to learn gesture features, which normally requires a large-scale labeled dataset to address overfitting problem. However, the acquisition of such dataset is time-consuming. Secondly, the radar signature of hand movement is often influenced by micro motion caused by other body parts, which leads to distorted motion features, resulting in poor identification accuracy. To overcome these problems, we propose an unsupervised hand gesture feature learning method using deep convolutional auto-encoder network to analyze hand gesture signal collected by a Frequency Modulated Continuous Wave (FMCW) radar sensor. Firstly, via a convolutional encoder sub-network, input radar range profiles are transformed into lower dimensional representations. Then, the representations are expanded to reconstruct the corresponding input profiles by a deconvolutional decoder sub-network. In addition, to investigate the mechanisms of the proposed network and evaluate its performance, we conduct an in-depth study of the feature maps learned from various hand gesture experimental data and evaluate the corresponding classification performance. The results demonstrate that the proposed convolutional auto-encoder network is able to achieve high recognition accuracy with low training sample cost, which outperforms the state-of-the-art hand gesture recognition systems based on transfer learning VGGNet and fully connected based auto-encoder network.

Fingerprint crowdsourcing has recently been promoted as a promising solution for fingerprinting-based indoor localization systems to relieve the burden of site survey. When constructing an indoor localization map from crowdsourced samples, the following challenges should be addressed: inaccurate sample annotation, unequal sample dimensionality, measurement device diversity, and nonuniform spatial distribution. In this paper, we propose the RMapCS scheme to deal with these challenges, which consists of four main modules: outlier detection, source selection, fingerprint interpolation and device calibration. For each device in each grid, we first propose an improved clustering algorithm to remove outliers and use a threshold-based approach to select only those important signal sources. For a grid without enough samples, we propose a fingerprint interpolation algorithm to construct its device-specific fingerprints; Then we propose a device calibration algorithm to fuse samples from different devices to obtain grid fingerprints. Besides, we also propose a two-step online positioning algorithm consisting of both set comparison and similarity computation. We conduct field measurements and experiments to examine the localization performance. Results show that the proposed RMapCS can achieve significant improvements over the peer schemes and the average localization error can achieve around 1.5m by using only received signal strength-based fingerprints.