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Implicit sentiment analysis as a challenging branch of sentiment analysis tasks faces issues such as lacking explicit sentiment cues and complex text semantics. Inspired by CoT, this paper proposed the TPISA. This method combined large language models with local pre-trained models, employing multi-level reasoning to extract sentiment elements such as aspects and potential opinions, which enables to more easily infer the final sentiment polarity. In the first two levels, it leveraged the extensive world knowledge embedded in LLMs to augment the sentiment information of sentiment sentences. Subsequently, the aspects and potential opinions acquired from these initial levels are interconnected with the context, forming inputs for the third level prompt. Concurrently, it constructed sentiment label words to empower the pre-trained model to assimilate profound semantic insights from the labeled vocabulary, thereby enhancing the model's learning capacity. The experimental results demonstrate that the proposed model achieves improvements of 5. 65 and 6.72 percentage points on the SemEval14 Laptop and Restaurant datasets, respectively, compared to the state-of-the-art models, verifying the progressiveness of this method. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to solve the problems in the data download and analysis process of CIR equipment (Cab Integrated Radio) in the railway industry. In response to the current challenges of long data download time and difficult manual analysis, etc., a design scheme for wireless data transmission and intelligent analysis system for CIR equipment is proposed. The current CIR equipment still relies on manual operations for downloading and analyzing data from recording units, which poses potential risks to railway transportation safety due to time lag and low efficiency. The system architecture designed in this article is divided into two parts: onboard acquisition equipment and trackside intelligent analysis system, which are connected through the existing LMD system for communication. The onboard data acquisition equipment is connected to the monitoring interface of the recording unit of locomotive CIR equipment through a dedicated cable, and the data is transmitted to the trackside server through wireless network. The trackside intelligent analysis system realizes functions such as data storage, intelligent analysis, and report statistics. Based on big data technology and distributed architecture, it improves the efficiency of data processing and analysis. The intelligent analysis module in the system can achieve functions such as remote monitoring of device status, intelligent analysis, intelligent evaluation of device usage, and standardized detection task evaluation. After on-site application verification, the system can shorten data download and analysis time, reduce labor costs, prevent CIR equipment failures in advance, and ensure railway transportation safety and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, significant advancements in large language models and visual foundation models in the field of artificial intelligence have attracted scholars' attention to the potential of general artificial intelligence technology in remote sensing. These studies have propelled a new paradigm in the research of large models for remote sensing information processing. Large remote sensing models, also known as pretrained foundation remote sensing models, are a kind of methodology that employs a vast amount of unlabeled remote sensing images to train large-scale deep learning models. The goal is to extract universal feature representations from remote sensing images, thereby enhancing the performance, efficiency, and versatility of remote sensing image analysis tasks. Research on large remote sensing models involves three key factors, including pre-training datasets, model parameters, and pre-training techniques. Among them, pretraining datasets and model parameters can be flexibly expanded with the increase in data and computational resources, while pre-training techniques are critical for improving the performance of large remote sensing models. This review focuses on the pre-training techniques of large remote sensing models and systematically analyzes the existing supervised single-modal pre-trained large remote sensing models, unsupervised single-modal pre-trained large remote sensing models, and visual-text joint multimodal pre-trained large remote sensing models. The conclusion section provides prospects for large remote sensing models in terms of integrating domain knowledge and physical constraint, enhancing data generalization, expanding application scenarios, and reducing data costs. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of improving the service quality of space-based information system in joint operations, the concept of precision service of space-based information is proposed. It selects specific combat tasks, and through task decom- position, service modeling, and information modeling, clarifies the relationship between meta-tasks, space-based information services, and space-based information. Through the roles and functions of warfighters in different positions in each combat portal node in combat tasks through analysis, the meta-tasks to be performed by each type of fighters are determined, and then the types of space-based information services they need are specified, and the demand matching of the space-based in- formation precision service system oriented to combat missions is realized. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research on the related analysis tasks of graph representation methods was carried out, starting from the formal definition, divide graph representation learning methods into five categories with different core technologies as the classification criteria, including dimensionality reduction analysis, matrix decomposition, random walk, neural network and other representation learning methods. Secondly, through induction and comparative analysis, the paper combed the development context of various technologies, and then showed the advantages and disadvantages of various graphic representation methods in a deep level. Then combined with the inductive analysis of common data sets, evaluation methods and application fields of graph representation learning, it carried out the four-dimensional analysis of dynamics, scalability, interpretability and analyticity. Finally, it summarized and looked forward to the future research trends and development directions of graph representation learning [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: With the increasing application of tag maps, there is an urgent need for research on the evaluation of tag weight expression strategies. Tag weight differences in tag maps are usually reflected by different font sizes. One of the common strategies is evaluated, where the sequence of font sizes is an arithmetic progression. Methods: The five countries with different, representative shapes are selected as study cases. Five corresponding tag maps are produced by randomly generated tags in alphabetical order from left to right and top to bottom. On this basis, a controlled experiment is conducted. Two application scenarios of un-purposed free browsing and purposeful reading analysis are set, and tag selection, recognition/search, recalling, and subjective evaluation tasks are assigned. The subjects' eye movement data and some other derived data are collected and statistically analyzed by the Kruskal-Wallis test and Mann Whitney U test methods. Results: The results show that: (1) Tags of different sizes do not show significant differences in terms of visual salience, visual attractiveness, weight recalling and search efficiency, reading efficiency, and cognitive load in recognition/search tasks. (2) Tags with font size at the upper level are easier to be recognized/searched and of higher interest to subjects than those with font size at the lower level, but it does not mean that tags with larger size are more likely to be recognized/searched and of higher interest to subjects. (3) The overall evaluation of tag maps using font size strategy of arithmetic progression is at a good level.Conclusions: The paper is helpful for map designers to further understand the characteristics of font size strategy. The font size is not a panacea in the tag weight expression of tag maps. In addition to font size, tag weight differences in tag maps can be shown by combining other visual attributes such as brightness, color, typeface, etc., or accompanying auxiliary charts according to the actual application. The interaction of the shape, area, the density of tags, and other visual attributes with the font size and some other font size strategies need to be studied for different users. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To achieve high efficiency with limitation of energy consumption is a key problem in the system of UAVs. In existing UAV task allocation methods, the relevance between tasks and UAV cruise directions is neglected, which may further influence the energy consumption and delay of task accomplishment. In view of this, this paper proposed an UAV task allocation method based on the relevance analysis between tasks and cruise directions. This method includes two phases: task screening phase and conflict resolution phase based on consensus. In the first phase, this method selects out tasks without turning back for an UAV according to angles between the cruise direction of the UAV and directions of these tasks, then designs an algorithm to further select candidate tasks before interaction from the tasks without turning back according to their energy consumption utility parameters and time urgency parameters. In the second phase, this method solves tasks conflicts between UAVs after exchanging of their candidate tasks according to energy consumption utility parameters and time delay parameters of these tasks in different cruise directions of these UAVs. Simulation results verify that the proposed method can achieve lower average task energy consumption and average task delay. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The drawbacks of existing task recommendation in mobile crowd sensing were as follows: on the one hand, not fully considering the influence of spatial-temporal information on worker preference led to low accuracy of recommendation; On the other hand, ignoring the impact of task popularity on recommendation led to poor recommendation coverage. To solve these drawbacks, this paper proposed a novel task recommendation approach based on spatial-temporal information and task popularity analysis in mobile crowd sensing. Firstly, this approach made full use of the relevant information contained in the worker execution record（e. g., the time and location of worker performing tasks） to accurately predict the preference of worker for performing tasks. Secondly, in order to reduce the impact of popular tasks on recommendation coverage, this paper analyzed task popularity based on worker reputation and task execution record, and designed appropriate task popularity penalty factor. Then, combining worker preference and task popularity penalty factor, this paper provided an appropriate task recommendation list for each worker. Finally, the experimental results show that compared with the existing baseline methods, the proposed method improves the recommendation accuracy by 3.5% and the recommendation coverage by 25%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, graph embedding has become a research hotspot in the field of graph neural networks. As an important means of graph task analysis, graph embedding encodes the high-dimensional non-Euclidean information of graph into low-dimensional vector space, so as to improve the performance and efficiency of downstream tasks. In order to keep abreast of the current research status of graph embedding methods based on random walks, the existing classical models are introduced and classified through induction and sorting, which are mainly divided into models based on classical random walks and models based on attribute walks. Then, the problems, algorithm ideas, model strategies, advantages and disadvantages and application scenarios solved by each model are summarized and analyzed in detail, and the performance of some models is evaluated on several common data sets. Through the research, it is found that the current graph embedding based on random walk needs to solve four problems: attribute selection, scalability, embedding dimension selection and interpretability. To solve these problems, graph embedding needs to establish a consistent theoretical framework to provide a reference standard for later research. [ABSTRACT FROM AUTHOR]

The network would continue to evolve with changes in nodes and connections over the time. Aiming at the problem that traditional network representation learning algorithms cannot handle dynamic networks correctly, a dynamic continuous-time network representation learning algorithm based on random walks (DCTNE) is proposed. By defining a flexible node timing neighbor concept, a biased random walk process is designed. According to the time information, it can effectively explore the neighbors of different time series of nodes and model the influence of different neighbors, and learn the network representation. The experiment proves the effectiveness of DCTNE dynamic network timing information. On the link prediction task, the AUC value of DCTNE is up to 50% gain compared with other algorithms. On the node classification task, DCTNE also has significantly improved the performance. The results show that modeling the time dependence in the network is helpful for subsequent network analysis tasks. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emotion cause pair extraction is a subtask in the sentiment analysis task, which aims to extract all emotion clauses in a given document and the cause clauses corresponding to the emotion. The previous work ignores the interrelationship between the emotion clause and the cause clause when generating the expression of the emotion clause and the cause clause. In order to solve the problem, based on the idea of graph-based dependency parsing and incorporating the graph attention mechanism, this paper proposed the GAT-ECPE model. When the model obtained the expression of the emotion clause and the reason clause, it used the sentence vector as a node into the graph attention layer to learn the information about the relationship between the clauses, and then performed biaffine transform to obtain the encoding of the emotion cause pair expression. And it set up a multi-task to establish a relationship between the extraction of emotions and causes. The experimental results on the ECPE data set prove that compared with the previous series of models, this model has improved evaluation indicators. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The discovery of network clustering patterns is an important task in network analysis. A good network layout should reflect the clustering characteristics of the network and allow users to explore the network structure from different levels. For this reason, based on the idea of community division and multi-level layout, this paper proposed a clustering feature hierarchical layout algorithm. First, used seed nodes and personalized PageRank to divide the network. Secondly, it coarsened the network according to the results of the division, and designed the initial layout of the coarsened network. Then it used the node degree information to improve the force-oriented model to complete the refinement. Finally, in order to verify the effectiveness of the algorithm, experiments from the whole to the part were carried out. The experiment shows that the algorithm can generate high-quality layout results within an effective time. Compared with the existing layout algorithms, the proposed algorithm can truly display the network. Clustering features, while taking into account the network microstructure, it can meet the needs of users to explore the network structure from different levels. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate sarcasm detection is crucial for sentiment analysis and other tasks. Traditional approaches rely heavily on discrete handcrafted features. Existing studies mostly formulate sarcasm detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. But supervised learning requires a lot of data, the collection and tagging of these data are difficult. Due to the limited target tasks, it may lead to the low performance of sarcasm detection. Therefore, this paper regarded sarcasm detection as a transfer learning task, combined the supervised learning of sarcasm labeled text with the knowledge transfer of external analytical resources. It improved the neural network model by transferring resource knowledge to improve the detection performance of target task. Experimental results on publicly available datasets show that the proposed sarcasm detection model based on migration learning is superior to the existing advanced sarcasm detection model. [ABSTRACT FROM AUTHOR]

The use of inquiry tasks is one of the features of the latest elementary social studies textbooks, and according to Taiwan's Curriculum Guidelines for Social Studies, at least one inquiry activity for each unit and one thematic inquiry and practice unit for each volume should be designed. This study, based on the perspective of inquiry-based design, explored how inquiry tasks are articulated in elementary social studies textbooks. Qualitative content analysis was adopted to analyze three versions of textbooks developed on the basis of the new Curriculum Guidelines for Social Studies. The findings were as follows: First, the content of the "thematic inquiry and practice unit" emphasized integrative materials and lacked the integration of social issues. Second, the inquiry tasks employed a question-oriented approach, but the inquiry paths and answers were too limited to encourage students' active exploration. Third, the inquiry tasks in the textbooks favored individual inquiry over collaborative inquiry, and peer collaboration was only included in part of the inquiry process. Finally, the inquiry tasks failed to prompt the active participation of students with a preference for structured inquiry forms. This paper also provided several suggestions for the design of inquiry tasks in elementary social studies textbooks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigated the following three associations in the university teaching context: (1) between genres of the documents faculty used to support their teaching and the tasks they performed to use these genres; (2) between these genres and the criteria they employed to assess these genres; and (3) between their tasks and criteria. The author first employed qualitative citation analysis to identify the genres faculty used based on citations in their teaching materials (e.g., syllabi and lecture slides). Semi-structured interviews were then implemented to explore how they assessed and used different genres. A total number of 27 faculty from different disciplines contributed 28 courses. Qualitative content analysis was employed to analyze interview transcripts. The results indicate the criteria faculty employed served as function enablers that bridged genres and tasks. The tasks they performed served as the inclusion and exclusion criteria that determined what genres were used or not. Tasks determined the information characteristics of genres that mattered in faculty’s task performance.

A task-structure-iteration fusion design method is proposed to improve the experience of operator manipulating mining excavator and the operation interface design. Questionnaire, field investigation and literature analysis are carried out to investigate the operator perceptive status to environment and information in the task and to find out the shortcomings of the existing operation interface, and the design points aiming at the defects are obtained. The mapping between modules and the design points is established by dividing the layout of operation interface into several modules with a structure split method. The arrangement methods of the console, control panel and control components are developed, with shading and anti-misoperation considered. The engineering software Jack is used to iteratively evaluate the visibility, accessibility and comfortability of mining excavator operation interface. The results show that the rationality of the operation interface is improved by the task-structure-iteration fusion method. [ABSTRACT FROM AUTHOR]

Present methods of opinion target extraction fail to extract multi-targets and compound target based on supervised learning model. This paper proposed an opinion method to deal with this issue based on syntactic structure and dependency relation. Firstly, it analyzed the dependency relationships between opinion targets and opinion words based on the different syntactic constituents of opinion targets and opinion words. Then,it defined features according to different dependencies. Finally, it selected the optimal feature combination by the greedy feature selection method to extract opinion targets with the conditional random field model. It conducted experiments in the evaluation data of task 3 of COAE2011. The results show that the value of F1 is 3% - 6% higher than that of the present methods of opinion target extraction, and the method can identify the opinion targets effectively. [ABSTRACT FROM AUTHOR]

Satellite resources are generalizations of limited capabilities of satellite-based Earth observation systems to produce remote sensing data to satisfy observational needs. We propose a satellite resource organization model to address the inconsistency of basic units for ground coverage characterization, the time - consuming process of area target access analysis,and the bottlenecks in abilities analysis in satellite coverage. This model provides a new path for improving the efficiency of various analytical methods. Firstly, it relies on the GeoSOT(geographical coordinate global subdivision grid with one-dimension-integer on two to nth power) reference frame, and then the global space - time coverage information of the satellite is calculated and stored in advance into a grid to form a spatial-temporal coverage grid to provide support for subsequent satellite coverage performance analysis or task scheduling. The traditional real-time complex computation process will turn into a process of “update once, search repeatedly” based on the model􀆳s large table. At the same time, the model uses a multi-scale grid system to characterize the satellite coverage area, which solves the problem brought by inconsistent basic coverage units, and also can provide technical support for simplifying the process of satellite cooperative scheduling. Finally, this paper chooses area target access analysis and regional simple coverage calculation as typical applications to verify that the performance im - provement of methods based on this model can be greater than that of the traditional methods. [ABSTRACT FROM AUTHOR]

The purpose of target-specific affective analysis is to predict the sentiment of a text from the perspective of different target words. The key is to assign appropriate affective words to a given target. When there are more than one affective word describing multiple target sentiments in a sentence, it may lead to the mismatch between the affective word and the target. This paper proposed a hybrid neural network based on CRT mechanism for target-specific sentiment analysis. The model used CNN layer to extract features from the word representation after BiLSTM transformation. It generated the specific target representation of the word by CRT component and saved the original context information from BiLSTM layer. Experiments on three open datasets show that the proposed model can significantly improve the accuracy and stability of target-specific affective analysis tasks compared with previous models. It is proved that the CRT mechanism can integrate the advantages of CNN and LSTM well, which is of great significance to the task of sentiment analysis for specific targets. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In ihe current aspect sentiment analysis task, the traditional attention-based deep learning model lacks the effective attention to the aspect information and the sentiment information. This paper put forward a new LSTM model, which combinin aspect information and deeper attention. Through the bidirectional LSTM with shared weights, it trained the aspect embeddin and the text embedding to gel the aspect feature and text feature to carry on the feature fusion, and after the deeper attention mechanism processing, it obtained the classification result of the corresponding aspect by the classifier. The experimental results of the SemEval-2014 task4 and SemEval-2017 task4 datasets show that this method has further improved the accuracy and stability of the attention-based sentiment analysis model in the aspect sentiment analysis. The introduction of aspect features and deeper attention mechanisms is of great significance to the task of sentiment analysis based on aspect, which provides method support for public opinion analysis, question answering system and text reasoning. [ABSTRACT FROM AUTHOR]

Since the self-structure of the DAG(directed acyclic graph)task is not considered， the schedulability analysis of the DAG parallel task model based on G-EDF(global earliest deadline first)is very pessimistic. The response time analysis of the DAG task set under the G-EDF scheduling strategy in multiprocessor systems was studied in this paper. First， a more accurate carry-in workload estimation method was proposed for the execution of the carry-in task instance. Then a method for estimating the problem window workload of completion time was put forward based on the carry-in workload estimation method. Based on the two proposed methods， this paper proposed a response time analyzing method to derive a response time upper bound of each task. The experiments show that the proposed method outperforms the state-of-the-art method by at least 15% and at most 25%. [ABSTRACT FROM AUTHOR]

The orientation analysis of judgment results is of great significance to the standardization of the judgment documents and the recommendation of litigation lawyers. However, there is no effective analysis model. In order to make full use of the massive data of the judgment documents ,this paper designed a model for orientation analysis. It extracted the key features from the semi-structured judgment documents, and identified and cleaned the multi-entities in the judgment results by fuzzy matching. It processed the results by deep learning neural network based on LSTM for analyzing orientation. By experimenting with the data set of three different kinds of cases, the accuracy of the model is up to 98. 3%,which verifies the validity of the model in the orientation analysis task of the judgment results. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to develop a higher level of usable and useful interactive system, designers need to analyze user's task, build task model, and then it is applied at all stages of systems development. Aiming at this problem, this paper introduced some conceptions of task world, presented an overview of the field of task analysis and modeling, compared typical task modeling methods from description ways, description ability, semantic and syntax, the relationship between tasks. At the end, it discussed the prospects for future development and suggestions for possible extensions. [ABSTRACT FROM AUTHOR]

Research for fast and energy-efficient task scheduling algorithm on heterogeneous cluster, this paper proposed a duplication-based task scheduling algorithm, which based on the initial allocation of tasks, duplicated tasks from two aspects of energy aware and performance-energy balance. It established mathematical models for the energy consumption caused by computing and communication, and did extensive experiments. Results show that compared with an existing algorithm-balanced energy-aware task allocation (BEATA), this method can significantly reduce the schedule length and energy consumption of processing parallel applications on heterogeneous cluster, and task duplication method can reduce the schedule length but meanwhile will increase energy consumption. Thus, the task scheduling method which can simultaneously reduce schedule length and energy consumption is the direction of future research. [ABSTRACT FROM AUTHOR]

For the reason that the existing algorithm on Hadoop doesn't have a high level of optimization, this paper presented a novel task scheduling algorithm based on data locality speculation. By calculating the time duration ratio of Map and Reduce task on each node combined with the local characteristics of tasks and data on different nodes, the algorithm introduced a more accurate task detection mechanism, and then launched backup tasks of slow tasks on fast nodes. For using computing migration instead of data migration, the algorithm can obtain higher efficiency. Experimental results in Hadoop show that compared with the existing scheduling algorithm, the algorithm proposed in this paper can shorten the task average operation time and reduce the network congestion caused by data exchange between cluster racks. It also can speed up the task execution efficiency. [ABSTRACT FROM AUTHOR]

Purpose: This study was done to identify job tasks and task elements of Korean nurse anesthetists according to type of medical institution. Methods: A job task scale which consisted of 9 job tasks and 40 task elements was developed. Data were collected from December, 2009 to February, 2010 from 182 nurse anesthetists who were working in medical institutions (response rate: 75.8%). Results: Forty-eight percent of nurse anesthetists were independent from anesthesiologists in anesthetic practice. Preanesthetic nursing assessment was much more frequent in small hospitals than in general hospitals (p(.O5), and anesthetic nursing intervention, administering the anesthetics, monitoring the patient's status during anesthesia, and provision of safety and compliance with anesthetic ethics were much more frequent in general hospitals than medical centers (p<.001). There were no differences among the medical institutions for job tasks in post-anesthetic nursing interventions (p=229), administering anesthetics (p=.354) and monitoring patients' status during anesthesia (p=.O99), providing safe anesthetic environment (p=.89Q), and management of ancillary personnel/equipment (p=.617). Conclusion: Results indicate that nurse anesthetists contribute significantly to anesthetic practice in small hospitals and general hospitals. Therefore, it recommended that nursing leaders make efforts to enact legal nurse anesthetist-related policies for safe and high quality anesthetic nursing care. [ABSTRACT FROM AUTHOR]

This study focuses on the effect of listening to music before simple repetitive tasks. Research on the Mozart effect suggests that listening to Mozart contributes to an enhanced arousal level and a positive mood which influences performance on a variety of tasks. From that research, this study hypothesizes that if music is expected to make workers' motivation rise, listening to music before doing a task could lead to inducing a positive affect and improving the performance on a simple repetitive task. The effectiveness of listening to music before tasks was examined through experiments. In the experiments, 44 subjects were required to do two sessions. In one session, they were required to do the simple repetitive task after listening to the music through which they expected their motivation could rise. In the other session, they were required to do the task without the music. There were 12 subjects who did the copying task in German, another 16 subjects who did the copying task in French, and another 16 subjects who did the mental rotation task as the simple repetitive task. The results of the experi- ments revealed that the positive mood of the subjects in the case of listening to the music before the task was significantly higher than in the case of no music, irrespective of the task. The results also revealed that the performance in the case of listening to the music before the task was significantly better than in the case of no music, irrespective of the task. These results revealed the effectiveness of listening to music before simple repetitive tasks in terms of positive mood and task performance. [ABSTRACT FROM AUTHOR]