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In this paper, we study the task of template building in automatically generate NBA match reports from NBA live text. As a preliminary study, we collect and process the historical reports compiled by the editors and get different kinds of sentences. Our innovative proposal is to divide the NBA match reports into 11 categories, which covering almost all cases. We use different machine learning methods to classify sentences. Each class finally constructs a template library to service the next automatic writing. By comparing different methods, we get a higher accuracy classification structure. The evaluation results show that our method does construct a template library.

The Multimodal Named Entity Recognition (MNER) task enhances the text representations and improves the accuracy and robustness of named entity recognition by leveraging visual information from images. However, previous methods have two limitations: (i) the semantic mismatch between text and image modalities makes it challenging to establish accurate internal connections between words and visual representations. Besides, the limited number of characters in social media posts leads to semantic and contextual ambiguity, further exacerbating the semantic mismatch between modalities. (ii) Existing methods employ cross-modal attention mechanisms to facilitate interaction and fusion between different modalities, overlooking fine-grained correspondences between semantic units of text and images. To alleviate these issues, we propose a graph neural network approach for MNER (GNN-MNER), which promotes fine-grained alignment and interaction between semantic units of different modalities. Specifically, to mitigate the issue of semantic mismatch between modalities, we construct corresponding graph structures for text and images, and leverage graph convolutional networks to augment text and visual representations. For the second issue, we propose a multimodal interaction graph to explicitly represent the fine-grained semantic correspondences between text and visual objects. Based on this graph, we implement deep-level feature fusion between modalities utilizing graph attention networks. Compared with existing methods, our approach is the first to extend graph deep learning throughout the MNER task. Extensive experiments on the Twitter multimodal datasets validate the effectiveness of our GNN-MNER. [ABSTRACT FROM AUTHOR]

Nowadays, dust exposure pollution is receiving a lot of attention due to its significant impact on public health. To investigate the impact of dust particle size and human respiratory strength on respiratory particle deposition patterns, data was collected through on‐site surveys. The study analyzed the equivalent respiratory strength, dust environment characteristics, and bronchial particle escape and deposition patterns of humans in fully mechanized mining faces at various operating times. This was done using ergonomic energy consumption simulation experiments and a fluid–solid interaction method of CFD‐DEM. The findings revealed that as humans worked continuously for 5, 15, 30, 45, and 60 min, their respiratory intensity corresponded to 8, 18, 30, 42, and 50 L/min, respectively. According to the field investigation and particle size analysis, the particle size distribution of 1~5, 5~10, 10~20, 20~30, and 30~40 μm particles accounted for 36%, 26%, 15%, 11%, and 10%, respectively. In general, the deposition rate of dust was highest in the main bronchus of the respiratory tract, followed by the trachea area. Particles ranging from 5 to 10 μm in size were observed to have a higher likelihood of escaping from the tertiary bronchioles and entering the secondary bronchial regions. Conversely, particles larger than 20 μm exhibited a deposition rate of up to 80% in the tertiary bronchial regions. It was noted that the bronchial deposition rate of particles of varying sizes increased with respiratory strength, with smaller particles showing greater sensitivity to changes in respiratory strength in terms of the deposition fraction. Among the different particle sizes, the deposition rate of 5–10 μm particles exhibited the most variation with increasing respiration intensity, ranging up to 17%. [ABSTRACT FROM AUTHOR]

Continuous data scale growth increases energy consumption and operating cost that cannot be ignored in cloud storage systems. Previous studies have shown that analyzing the characteristics of I/O access and mining data features is effective for reasonable data distribution in storage systems. The granularity and criterion of classification are the key factors in determining the data distribution. To decrease energy consumption and operating cost, this paper puts forward a fine-grained framework of the climatic-season-based energy-aware in cloud storage system called CSEA. The framework concludes the following three aspects: (i) data feature mining. CSEA discovers potential data features by analyzing data access to provide help with data classification. (ii) K-means clustering algorithm. CSEA uses an unsupervised data classification algorithm in machine learning to divide data into categories based on seasonal characteristics by gathering real I/O access. (iii) data distribution of fine-grained. On the basis of seasonal features, CSEA fuses regional features to further refine the data distribution granularity to save on energy consumption and operating cost. Simulation experiments using extended CloudSimDisk and the constructed mathematical models indicate that CSEA reduces the energy consumption and operating cost compared with the single data classification standard and coarse-grained data distribution. [ABSTRACT FROM AUTHOR]

Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today's complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt a dual-layer strategy based on semantic similarity to extract seasonal features. Second, we put forward a cost-effective data classification storage framework based on text seasonal features. We compare our work with the data classification approach AS-H, which runs at full high performance. In addition, we also compare it with K-ear, which adopts K-means as the classification algorithm. The experimental results show that compared with AS-H and K-ear, our method reduces energy consumption by 9.51–13.35% and operating costs by 13.20–22.17%. [ABSTRACT FROM AUTHOR]

The 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and lightweight. Here, a LiteDEKR, a 2D pose estimation method that combines lightweight and accuracy, is proposed by designing a lightweight network based on DEKR and constructing two scientifically valid loss functions. The method, constructs a multi‐instance bias regression loss that matches the true distribution of keypoint bias, improves the accuracy of bias regression, and constructs a keypoint similarity loss with the object keypoint similarity index of keypoints as the optimization objective to achieve end‐to‐end training of the network. In addition, this paper has designed a lightweight DEKR, using LitePose as the backbone network. With the optimization of the above two loss functions, LiteDEKR not only achieves lightweight but also has high accuracy. Comparative experiments on the COCO and CrowdPose datasets show that compared to the current state‐of‐the‐art Contextual Instance Decoupling, LiteDEKR achieves a similar accuracy with only 10% of its network complexity. It also shows better robustness to low‐resolution input images. [ABSTRACT FROM AUTHOR]

Recently, head detection has been widely used in target detection, which has a great application value for improving security prevention and control in public places, as well as enhancing target tracking and identification in national defense, criminal investigation, and other fields. However, detecting small targets accurately at long distances is very difficult, and current methods often lack optimization of multi‐resolution features. Therefore, the authors propose a one‐stage detection network CFNet (cross‐layer feature fusion and fusion weight attention network), in which a fusion weight attention mechanism module (FWAM) is proposed to give different weights to the fused features in order to distinguish the importance of different features. The module increases the weights of features that contain strong information so that the fused features are focused on feature points that are beneficial for optimal head detection. Meanwhile, a cross‐layer feature fusion module is proposed to fuse information from different resolution feature maps to compensate for the decrease in detection accuracy caused by the omission of information features at low resolution, and a connection network for contextual information fusion is constructed, while weight parameter value settings are introduced to optimize the detection effect after fusion of different resolution features. In order to better reflect the effectiveness of the network, the experiments are performed on the SCUT‐HEAD PartA dataset and the Brainwash dataset; the results show that the network the authors proposed is better than the existing comparison methods, which proves the robustness and effectiveness of the network. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields. [ABSTRACT FROM AUTHOR]

Thanks to excellent reliability, availability, flexibility and scalability, r edundant a rrays of i ndependent (or i nexpensive) d isks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces |$\tau $| to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling. [ABSTRACT FROM AUTHOR]

Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy‐aware data clustering through a machine learning algorithm (K‐ear) is proposed. K‐ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three tidal categories according to its tidal access characteristics. The 15 classified categories are stored in different storage zones with different energy and performance modes. Simulation experiments using CloudSimDisk with the constructed mathematic models demonstrate that the proposed K‐ear algorithm is more energy‐efficient than the default data clustering algorithms in Hadoop and the classical data clustering storage strategy according to the data access frequency (Striping‐Based Energy‐Aware Strategy). [ABSTRACT FROM AUTHOR]

With the rapidly increasing popularity of social media sites, a large amount of user-generated data has been injected into the web. The data include a wide variety of real-world events. As a consequence, especially for social multimedia objects, it has become increasingly difficult to allow the browsing and organization of multimedia collections in a more effective manner. The approach we propose in this study addresses this problem, thus enabling the browsing and organization of multimedia collections in a natural way, i.e., by events. There have been some research studies on this problem. However, most of the previous approaches merge multiple types of features (e.g., textual content, visual content, user information and temporal information) of social media using a relatively simple mechanism. In this study, we merge multiple types of features in an integrated manner to identify the event associated with user-contributed social multimedia objects. We exploit the correlations between different types of features, i.e., textual content, visual content, user information and temporal information, to classify new social multimedia objects into their corresponding event categories. We accomplish this through a feature correlation graph ( FCG) that uses features as nodes and the correlations among these features as edges for each event and individual multimedia object. We then employ a probabilistic model based on Markov random field to connect each new multimedia object with the correct event. We evaluate the algorithm on large-scale, real-world datasets of event images downloaded from Flickr, and the experimental results confirm the superiority of our approach over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

With the availability of cheap location sensors, geotagging of images in online social media is very popular. With a large amount of geo-tagged social images, it is interesting to study how these images are shared across geographical regions and how the geographical language characteristics and vision patterns are distributed across different regions. Unlike textual document, geo-tagged social image contains multiple types of content, i.e., textual description, visual content, and geographical information. Existing approaches usually mine geographical characteristics using a subset of multiple types of image contents or combining those contents linearly, which ignore correlations between different types of contents, and their geographical distributions. Therefore, in this paper, we propose a novel method to discover geographical characteristics of geo-tagged social images using a geographical topic model called geographical topic model of social images (GTMSIs). GTMSI integrates multiple types of social image contents as well as the geographical distributions, in which image topics are modeled based on both vocabulary and visual features. In GTMSI, each region of the image would have its own topic distribution, and hence have its own language model and vision pattern. Experimental results show that our GTMSI could identify interesting topics and vision patterns, as well as provide location prediction and image tagging. [ABSTRACT FROM PUBLISHER]