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Discovering semantically related technical terms and web resources in Q&A discussions Research Articles

Junfang Jia, Valeriia Tumanian, Guoqiang Li,li.g@sjtu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 969-985 doi: 10.1631/FITEE.2000186

Abstract: A sheer number of techniques and are available for software engineering practice and this number continues to grow. Discovering semantically similar or related and offers the opportunity to design appealing services to facilitate information retrieval and information discovery. In this study, we extract and from a community of question and answer (A) discussions and propose an approach based on a neural language model to learn the semantic representations of and in a joint low-dimensional vector space. Our approach maps and to a semantic vector space based only on the surrounding and of a technical term (or web resource) in a discussion thread, without the need for mining the text content of the discussion. We apply our approach to Stack Overflow data dump of March 2018. Through both quantitative and qualitative analyses in the clustering, search, and semantic reasoning tasks, we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of and , and they can be exploited to support various search and semantic reasoning tasks, by means of simple -nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.

Keywords： 技术术语；网络资源；词语嵌入；问答网站；聚类任务；推荐任务    

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Joint entity–relation knowledge embedding via cost-sensitive learning Article

Sheng-kang YU, Xue-yi ZHAO, Xi LI, Zhong-fei ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1867-1873 doi: 10.1631/FITEE.1601255

Abstract: As a joint-optimization problem which simultaneously fulfills two different but correlated embedding tasks (i.e., entity embedding and relation embedding), knowledge embedding problem is solved in a joint embedding scheme. In this embedding scheme, we design a joint compatibility scoring function to quantitatively evaluate the relational facts with respect to entities and relations, and further incorporate the scoring function into the maxmargin structure learning process that explicitly learns the embedding vectors of entities and relations using the context information of the knowledge base. By optimizing the joint problem, our design is capable of effectively capturing the intrinsic topological structures in the learned embedding spaces. Experimental results demonstrate the effectiveness of our embedding scheme in characterizing the semantic correlations among different relation units, and in relation prediction for knowledge inference.

Keywords： Knowledge embedding     Joint embedding     Cost-sensitive learning    

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The Design and Analysis of Embedded Internet Control System

Zong Qun,Li Ran,Wang Bo

Strategic Study of CAE 2005, Volume 7, Issue 5,   Pages 53-56

Abstract:

An embedded Internet control system with embedded Internet nodes, of which the requirements and architecture is analyzed and studied, has been built up successfully and put into practice in REMS. Then the whole system is fully tested to prove that it can meet with the practical requirements well.

Keywords： embedded Internet control system     REMS     DS80C400     JAVA     TINI    

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Nagle algorithm and its application research in embedded Internet

Wang Baobao,Yu Shiming and Wang Zhenyu

Strategic Study of CAE 2014, Volume 16, Issue 2,   Pages 101-105

Abstract:

The existence of small packets in embedded Internet lead to low bandwidth efficiency and even congestion. The Nagle algorithm was applied by standard transmission control protocol（TCP）protocol to reduce the number of small packets. The paper builds embedded Internet network based on ARM7 32 bits micro control unit（MCU）and personal computer（PC）， analyses the principle and working mechanism of Nagle，and suggests an approach to resolve the temporary“deadlock”created by the interaction between the Nagle algorithm and the delayed ACK policy without modifying the Nagle algorithm through improving sampling frequency or filling the buffer in embedded system. The experimental results indicate that this approach is effective and reliable.

Keywords： Nagle algorithm     deadlock     delayed ACK policy     ARM7     embedded Internet    

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Multimethod Collaborative Optimization Algorithm Based on Embedding Collaboration

Luo Wencai,Luo Shishan,Wang Zhenguo

Strategic Study of CAE 2004, Volume 6, Issue 4,   Pages 51-55

Abstract:

Multi-method collaborative optimization algorithm based on embedding collaboration is advanced. It uses embedding structure to collaborate different kinds of optimization methods, and makes use of the collaboration effect among them to improve the optimization performance. A multimethod collaborative optimization algorithm base on embedding collaboration is designed, which is composed of genetic algorithm, pattern search method and Powell's method. Results show that multi-method collaborative optimization algorithm based on embedding collaboration obtains better global optimization performance than single optimization method.

Keywords： multimethod collaborative optimization algorithm     embedding collaboration     genetic algorithm     pattern search method     Powell's method    

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Reversible data hiding using a transformer predictor and an adaptive embedding strategy Research Article

Linna ZHOU, Zhigao LU, Weike YOU, Xiaofei FANG,zhoulinna@bupt.edu.cn,luchen@uir.edu.cn,ywk@bupt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1143-1155 doi: 10.1631/FITEE.2300041

Abstract: In the field of (RDH), designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects. In this paper, we propose a new RDH method, including a predictor based on a and a novel embedding strategy with multiple embedding rules. In the predictor part, we first design a -based predictor. Then, we propose an image division method to divide the image into four parts, which can use more pixels as context. Compared with other predictors, the -based predictor can extend the range of pixels for prediction from neighboring pixels to global ones, making it more accurate in reducing the embedding distortion. In the embedding strategy part, we first propose a complexity measurement with pixels in the target blocks. Then, we develop an improved prediction error ordering rule. Finally, we provide an embedding strategy including multiple embedding rules for the first time. The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images, and experimental results show that the performance of our RDH method is leading the field.

Keywords： Reversible data hiding     Transformer     Adaptive embedding strategy    

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An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring Research Articles

Da-peng TAN, Shu-ting CHEN, Guan-jun BAO, Li-bin ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 604-625 doi: 10.1631/FITEE.1601660

Abstract: Developing an efficient and robust lightweight graphic user interface (GUI) for industry process monitoring is always a challenging task. Current implementation methods for embedded GUI are with the matters of real-time processing and ergonomics performance. To address the issue, an embedded lightweight GUI component library design method based on quasar technology embedded (Qt/E) is proposed. First, an entity-relationship (E-R) model for the GUI library is developed to define the functional framework and data coupling relations. Second, a cross-compilation environment is constructed, and the Qt/E shared library files are tailored to satisfy the requirements of embedded target systems. Third, by using the signal-slot communication interfaces, a message mapping mechanism that does not require a call-back pointer is developed, and the context switching performance is improved. According to the multi-thread method, the parallel task processing capabilities for data collection, calculation, and display are enhanced, and the real-time performance and robustness are guaranteed. Finally, the human-computer interaction process is optimized by a scrolling page method, and the ergonomics performance is verified by the industrial psychology methods. Two numerical cases and five industrial experiments show that the proposed method can increase real-time read-write correction ratios by more than 26% and 29%, compared with Windows-CE-GUI and Android-GUI, respectively. The component library can be tailored to 900 KB and supports 12 hardware platforms. The average session switch time can be controlled within 0.6 s and six key indexes for ergonomics are verified by different industrial applications.

Keywords： Embedded lightweight graphic user interface (GUI)     Quasar technology embedded (Qt/E)     Industry process moni-toring     Multi-thread     Ergonomics performance    

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Design and Developing of the Network Device Driver on Embedded Access Point

Wang Zhili,Hu Aiqun,Song Yubo

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 91-94

Abstract:

This paper systematically introduces the main structure of network device driver based on embedded Linux. With the PCMCIA API, the software modules of embedded AP (access point) and the developing course are stressed. The testing results of the network device driver are given at the end of the paper.

Keywords： embedded linux     network device driver     PCMCIA    

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A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing Article

Yaoyao Bao, Yuanming Zhu, Feng Qian

Engineering 2022, Volume 18, Issue 11,   Pages 186-196 doi: 10.1016/j.eng.2022.04.025

Abstract:

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.

Keywords： Local quadratic embedding     Metric learning     Regression machine     Soft sensor    

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A Review of Recent Developments in “On-Chip” Embedded Cooling Technologies for Heterogeneous Integrated Applications Review

Srikanth Rangarajan, Scott Schiffres, Bahgat Sammakia

Engineering 2023, Volume 26, Issue 7,   Pages 185-197 doi: 10.1016/j.eng.2022.10.019

Abstract:

The electronics packaging community strongly believes that Moore’s law will continue for another few years due to recent technological efforts to build heterogeneously integrated packages. Heterogeneous integration (HI) can be at the chip level (a single chip with multiple hotspots), in multi-chip modules, or in vertically stacked three-dimensional (3D) integrated circuits. Flux values have increased exponentially with a simultaneous reduction in chip size and a significant increase in performance, leading to increased heat dissipation. The electronics industry and the academic research community have examined various solutions to tackle skyrocketing thermal-management challenges. Embedded cooling eliminates most sequential conduction resistance from the chip to the ambient, unlike separable cold plates/heat sinks. Although embedding the cooling solution onto an electronic chip results in a high heat transfer potential, technological risks and complexity are still associated with the implementation of these technologies and with uncertainty regarding which technologies will be adopted. This manuscript discusses recent advances in embedded cooling, fluid selection considerations, and conventional, immersion, and additive manufacturing-based embedded cooling technologies.

Keywords： Electronic cooling     Embedded cooling     Immersion cooling    

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Learning embeddings of a heterogeneous behavior network for potential behavior prediction Article

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 422-435 doi: 10.1631/FITEE.1800493

Abstract: Potential behavior prediction involves understanding the latent human behavior of specific groups, andcan assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

Keywords： Network embedding     Representation learning     Human behavior     Social networks     Heterogeneous information network     Attribute    

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An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression Research Papers

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 816-828 doi: 10.1631/FITEE.1800146

Abstract:

The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.

Keywords： Power grid     Artificial intelligence     Node importance     Text-associated DeepWalk     Network embedding     Support vector regression    

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The brain areas and the neural mechanism involved in the Chinese paired-word associated learning and memory in healthy volunteers——a brain functional magnetic resonance imaging study

Zheng Jinlong,Shu Siyun,Liu Songhao,Guo Zhouyi,Wu Yongming,Bao Xinmin,Zhang Zengqiang,Jin Mei,Ma Hanzhang

Strategic Study of CAE 2008, Volume 10, Issue 5,   Pages 38-45

Abstract:

This paper is to investigate the activated brain areas and the neuronal mechanism of Chinese paired-word associated learning and memory in healthy volunteers by functional magnetic resonance imaging (fMRI) technique. 16 right-handed normal volunteers participated in a test of paired-word associated learning and memory, while the fMRI data were recorded. Control tasks were performed for the block-design. SPM 99 was used to analyze the data and to get the activated brain regions. 14 volunteers passed the paired-word associated learning and memory task. Both cortex and subcortical structures were activated. The brain cortex areas include the bilateral frontal lobes, the bilateral parietal lobes, the bilateral occipital lobes, the bilateral cingulate gyrus and the bilateral parahippocampal gyrus with extremely left hemisphere predominance and the left temporal lobe were activated by both coding and retrieval stages of the paired-word associated learning and memory task. The subcortical structures including the striatum and its marginal division (MrD) were activated with left predominance, the caudate and the thalamus were also activated during the tasks. However, the left occipital lobe and the middle and inferior frontal gyrus of the left frontal lobe were more activative than others in scope and brightness during the coding stage of the paired-word associated learning and memory task, while the left parietal lobe and dorsolateral part of the middle frontal gyrus were more activative than others in scope and brightness during the retrieval stage of the paired-word associated learning and memory task. The left middle and inferior frontal gyrus of the frontal lobe, the left lateral parts of the occipital lobe, the left superior lobule and supramarginal gyrus and the angular gyrus of the parietal lobe might play more important roles in the paired-word associated learning and memory task than the rest of the cortex. The MrD of the striatum was mainly involved in coding stages of the paired-word associated learning and memory task. The results of this study revealed that the subcortical structures mainly the striatum as well as the cortex were involved in the associated learning and memory of language in human brain. The transform of activated brain areas from the coding stage to the retrieval stage of the Chinese paired-word learning and memory was described and its neural mechanism was discussed.

Keywords： functional magnetic resonance imaging (fMRI) of human brain     paired-word     language     associated learning and memory     cortex and subcortical structures    

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Non-iterative parameter estimation of the 2R-1Cmodel suitable for low-cost embedded hardware Article

Mitar SIMIĆ, Zdenka BABIĆ, Vladimir RISOJEVIĆ, Goran M. STOJANOVIĆ

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 476-490 doi: 10.1631/FITEE.1900112

Abstract: Parameter estimation of the 2R-1C model is usually performed using iterative methods that require high-performance processing units. Consequently, there is a strong motivation to develop less time-consuming and more power-efficient parameter estimation methods. Such low-complexity algorithms would be suitable for implementation in portable microcontroller-based devices. In this study, we propose the quadratic interpolation non-iterative parameter estimation (QINIPE) method, based on quadratic interpolation of the imaginary part of the measured impedance, which enables more accurate estimation of the characteristic frequency. The 2R-1C model parameters are subsequently calculated from the real and imaginary parts of the measured impedance using a set of closed-form expressions. Comparative analysis conducted on the impedance data of the 2R-1C model obtained in both simulation and measurements shows that the proposed QINIPE method reduces the number of required measurement points by 80% in comparison with our previously reported non-iterative parameter estimation (NIPE) method, while keeping the relative estimation error to less than 1% for all estimated parameters. Both non-iterative methods are implemented on a microcontroller-based device; the estimation accuracy, RAM, flash memory usage, and execution time are monitored. Experiments show that the QINIPE method slightly increases the execution time by 0.576 ms (about 6.7%), and requires 24% (1.2 KB) more flash memory and just 2.4% (32 bytes) more RAM in comparison to the NIPE method. However, the impedance root mean square errors (RMSEs) of the QINIPE method are decreased to 42.8% (for the real part) and 64.5% (for the imaginary part) of the corresponding RMSEs obtained using the NIPE method. Moreover, we compared the QINIPE and the complex nonlinear least squares (CNLS) estimation of the 2R-1C model parameters. The results obtained show that although the estimation accuracy of the QINIPE is somewhat lower than the estimation accuracy of the CNLS, it is still satisfactory for many practical purposes and its execution time reduces to 145−1 30.

Keywords： 2R-1C model     Embedded systems     Parameter estimation     Non-iterative methods     Quadratic interpolation    

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The Time Sequence Data Mining Techniques Based on Grey System Theory

Liu Bin,Liu Sifeng,Dang Yaoguo

Strategic Study of CAE 2003, Volume 5, Issue 9,   Pages 32-35

Abstract:

This paper expatiates the thoughts of data mining with embedded knowledge and the techniques status quo of data mining. Based on the thoughts and the grey system (GS) theory, it proposes the GS-based data mining method set (GDMS) for time sequence first. Then this paper introduces the idiographic arithmetic withGM(1,1) as an example. Last, it forecasts the total homes connecting with Internet in Shanghai in M02—2005 by the arithmetic.

Keywords： grey system theories     embedded knowledge     time sequeke data mining     GDMS     forecast    

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Title Author Date Type Operation

Discovering semantically related technical terms and web resources in Q&A discussions

Junfang Jia, Valeriia Tumanian, Guoqiang Li,li.g@sjtu.edu.cn

Journal Article

Joint entity–relation knowledge embedding via cost-sensitive learning

Sheng-kang YU, Xue-yi ZHAO, Xi LI, Zhong-fei ZHANG

Journal Article

The Design and Analysis of Embedded Internet Control System

Zong Qun,Li Ran,Wang Bo

Journal Article

Nagle algorithm and its application research in embedded Internet

Wang Baobao,Yu Shiming and Wang Zhenyu

Journal Article

Multimethod Collaborative Optimization Algorithm Based on Embedding Collaboration

Luo Wencai,Luo Shishan,Wang Zhenguo

Journal Article

Reversible data hiding using a transformer predictor and an adaptive embedding strategy

Linna ZHOU, Zhigao LU, Weike YOU, Xiaofei FANG,zhoulinna@bupt.edu.cn,luchen@uir.edu.cn,ywk@bupt.edu.cn

Journal Article

An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring

Da-peng TAN, Shu-ting CHEN, Guan-jun BAO, Li-bin ZHANG

Journal Article

Design and Developing of the Network Device Driver on Embedded Access Point

Wang Zhili,Hu Aiqun,Song Yubo

Journal Article

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Yaoyao Bao, Yuanming Zhu, Feng Qian

Journal Article

A Review of Recent Developments in “On-Chip” Embedded Cooling Technologies for Heterogeneous Integrated Applications

Srikanth Rangarajan, Scott Schiffres, Bahgat Sammakia

Journal Article

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

Journal Article

An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

Journal Article

The brain areas and the neural mechanism involved in the Chinese paired-word associated learning and memory in healthy volunteers——a brain functional magnetic resonance imaging study

Zheng Jinlong,Shu Siyun,Liu Songhao,Guo Zhouyi,Wu Yongming,Bao Xinmin,Zhang Zengqiang,Jin Mei,Ma Hanzhang

Journal Article

Non-iterative parameter estimation of the 2R-1Cmodel suitable for low-cost embedded hardware

Mitar SIMIĆ, Zdenka BABIĆ, Vladimir RISOJEVIĆ, Goran M. STOJANOVIĆ

Journal Article

The Time Sequence Data Mining Techniques Based on Grey System Theory

Liu Bin,Liu Sifeng,Dang Yaoguo

Journal Article