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Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network Research Article

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 388-402 doi: 10.1631/FITEE.2200151

Abstract: (FNE), which explores financially constrained small- and medium-sized enterprises (SMEs), has become increasingly important in industry for financial institutions to facilitate SMEs' development. In this paper, we first perform an insightful exploratory analysis to exploit the transfer phenomenon of financing needs among SMEs, which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE. The main challenge lies in modeling two kinds of heterogeneity, i.e., and SMEs' , under different relation types simultaneously. To address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapH aTtention network (M-RIGHT), which not only models the of financing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs' representations based on a translation mechanism on relational hyperplanes to distinguish SMEs' heterogeneous behaviors under different relation types. Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT's superiority over the state-of-the-art methods in the FNE task.

Keywords： Financing needs exploration     Graph representation learning     Transfer heterogeneity     Behavior heterogeneity    

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A cooperative heterogeneous vehicular clustering framework for efficiency improvement Research Articles

Iftikhar Ahmad, Rafidah Md Noor, Zaheed Ahmed, Umm-e-Habiba, Naveed Akram, Fausto Pedro García Márquez,ify_ia@yahoo.com,faustopedro.garcia@uclm.es

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1247-1259 doi: 10.1631/FITEE.2000260

Abstract: Heterogeneous ing integrates multiple types of communication networks to work efficiently for various vehicular applications. One popular form of heterogeneous network is the integration of long-term evolution (LTE) and dedicated short-range communication. The of such a network infrastructure and the non- involved in sharing cost/data are potential problems to solve. A ing framework is one solution to these problems, but the framework should be formally verified and validated before being deployed in the real world. To solve these issues, first, we present a heterogeneous framework, named destination and interest-aware clustering, for ing that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency. Then, we specify a model system of the proposed framework. The model is formally verified to evaluate its performance at the functional level using a model checking technique. To evaluate the performance of the proposed framework at the micro-level, a heterogeneous simulation environment is created by integrating state-of-the-art tools. The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.

Keywords： 车辆集群；异构性；协同；形式验证；系统模型    

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Avision of post-exascale programming None

Ji-dong ZHAI, Wen-guang CHEN

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 10,   Pages 1261-1266 doi: 10.1631/FITEE.1800442

Abstract:

Exascale systems have been under development for quite some time and will be available for use in a few years. It is time to think about future post-exascale systems. There are many main challenges with regard to future post-exascale systems, such as processor architecture, programming, storage, and interconnect. In this study, we discuss three significant programming challenges for future post-exascale systems: heterogeneity, parallelism, and fault tolerance. Based on our experience of programming on current large-scale systems, we propose several potential solutions for these challenges. Nevertheless, more research efforts are needed to solve these problems.

Keywords： Computing model     Fault-tolerance     Heterogeneous     Parallelism     Post-exascale    

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Programming bare-metal accelerators with heterogeneous threading models: a case study of Matrix-3000 Research Article

Jianbin FANG, Peng ZHANG, Chun HUANG, Tao TANG, Kai LU, Ruibo WANG, Zheng WANG,j.fang@nudt.edu.cn,zhangpeng13a@nudt.edu.cn,chunhuang@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 4,   Pages 509-520 doi: 10.1631/FITEE.2200359

Abstract: As the hardware industry moves toward using specialized heterogeneous many-core processors to avoid the effects of the power wall, software developers are finding it hard to deal with the complexity of these systems. In this paper, we share our experience of developing a programming model and its supporting compiler and libraries for Matrix-3000, which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization. To assist its software development, we have developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler. Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000, while the high-level model allows programmers to use the OpenCL programming standard. We detail our design choices and highlight the lessons learned from developing system software to enable the programming of bare-metal accelerators. Our programming models have been deployed in the production environment of an exascale prototype system.

Keywords： Heterogeneous computing     Parallel programming models     Programmability     Compilers     Runtime systems    

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The Progress and Application of the SKI Series Catalysts for Isomerization of C8 Aromatics

Qiao Yingbin

Strategic Study of CAE 1999, Volume 1, Issue 1,   Pages 73-77

Abstract:

The innovative idea and excellent performance of the SKI series catalysts for isomerization of C8 aromatics are elaborated. With the zeolite solid acid instead of halogen, not only the complicated chlorine supplement and the alkaline washing in the operation are eliminated but also the equipment corrosion is prevented and the operation environment is improved. As a result, the catalyst is environmental - friendly. The thermodynamic equilibrium can be approached for the isomerization of the mixed C8 aromatics with poor para - xylene by the SKI Series catalysts. The selectivity for the C8 aromatics is more than 97% and the catalyst life-span is more than 5 years. The achievement of the realistic productivity derived from the scientific and technological research was made through the substitution of the imported catalysts with the SKI series catalyst in the 7 imported commercial units

Keywords： isomerization     catalyst     xylene    

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Development and Commercial Application of FCC Process for Maximizing Iso-Paraffins (MIP) in Cracked Naphtha

Xu Youhao,Zhang Jiushun,Long Jun,He Mingyuan,Xu Hui,Hao Xiren

Strategic Study of CAE 2003, Volume 5, Issue 5,   Pages 55-58

Abstract:

A concept of two different reaction zones was proposed based on the FCC reaction mechanism. The concept was used to design a novel reactor with corresponding engineering measures. Experiments were conducted on the newly developed reaction system. The test run results of the 1.4 Mt/a MIP commercial unit showed that mass yield of dry gas was reduced by 0.41 % and mass yield of slurry was reduced by 0.99% , while mass yield of light ends was increased by 1.17%. The volume fraction of olefin in the cracked naphtha dropped by 14.1% while the volume fraction of isoparaffin increased by 12.9% and the mass fraction of sulfur was reduced by 26.5% with maintaining the octane rating index unchanged and a significant extension of the induction period compared with the conventional FCC process.

Keywords： catalytic cracking     cracked naphtha     olefin     isoparaffin     aromatic    

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Containment control for heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes Research Articles

Ya-ni Sun, Wen-cheng Zou, Jian Guo, Zheng-rong Xiang,xiangzr@njust.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000034

Abstract: We study the problem for high-order heterogeneous nonlinear under distributed event-triggered schemes. To achieve the objective and reduce communication consumption among agents, a scheme is proposed by applying the backstepping method, Lyapunov functional approach, and neural networks. Then, the results are extended to the self-triggered control case to avoid continuous monitoring of state errors. The developed protocols and triggered rules ensure that the output for each follower converges to the convex hull spanned by multi-leader signals within a bounded error. In addition, no agent exhibits . Two numerical simulations are finally presented to verify the correctness of the obtained results.

Keywords： Multi-agent systems     Distributed event-triggered control     Containment control     Heterogeneous nonlinear systems     Zeno behavior    

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Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives Research Article

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1390-1402 doi: 10.1631/FITEE.2300098

Abstract: (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data. However, researchers working on FL face several unique challenges, especially in the context of heterogeneity. Heterogeneity in data distributions, computational capabilities, and scenarios among clients necessitates the development of customized models and objectives in FL. Unfortunately, existing works such as FedAvg may not effectively accommodate the specific needs of each client. To address the challenges arising from heterogeneity in FL, we provide an overview of the heterogeneities in data, model, and objective (DMO). Furthermore, we propose a novel framework called federated mutual learning (FML), which enables each client to train a personalized model that accounts for the data heterogeneity (DH). A "meme model" serves as an intermediary between the personalized and global models to address model heterogeneity (MH). We introduce a technique called deep mutual learning (DML) to transfer knowledge between these two models on local data. To overcome objective heterogeneity (OH), we design a shared global model that includes only certain parts, and the personalized model is task-specific and enhanced through mutual learning with the meme model. We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios. The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting.

Keywords： Federated learning     Knowledge distillation     Privacy preserving     Heterogeneous environment    

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Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks Article

Chenxi Li, Lei Guan, Huaqing Wu, Nan Cheng, Zan Li, Xuemin Sherman Shen

Engineering 2022, Volume 17, Issue 10,   Pages 220-231 doi: 10.1016/j.eng.2021.04.019

Abstract:

Heterogeneous cellular networks (HCNs) are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity. However, the densified multi-tier network architecture introduces excessive intra- and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks. In this article, a dynamic spectrum control (DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity. Specifically, the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families, which represent the transmission decisions, by performing iterative and orthogonal sequence transformations. Based on the sequence families, multiple users can dynamically occupy different frequency slots for data transmission simultaneously. In addition, the collision probability of the data transmission is analyzed, which results in closed-form expressions of the reliable transmission probability and the secrecy probability. Then, the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities. Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance. Finally, the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.

Keywords： Heterogeneous cellular networks     Dynamic spectrum control     Transmission security     Efficient data transmission    

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Explainable data transformation recommendation for automatic visualization Research Article

Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, Jiazhi XIA,wzlzju@zju.edu.cn,chenvis@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 7,   Pages 1007-1027 doi: 10.1631/FITEE.2200409

Abstract: generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited s fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic s, the auto-generated transformations lack concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of s in . We summarize the space of feasible s and measures on of transformation operations with a literature review and a pilot study, respectively. A recommendation algorithm is designed to compute optimal transformations, which can reveal specified types of patterns and maintain . We demonstrate the effectiveness of our approach through two cases and a user study.

Keywords： Data transformation     Data transformation recommendation     Automatic visualization     Explainability    

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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Keywords： Independent and identically distributed (IID)     Non-IID     Heterogeneity     Coupling relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: The recent progress in multi-agent (MADRL) makes it more practical in real-world tasks, but its relatively poor scalability and the partially observable constraint raise more challenges for its performance and deployment. Based on our intuitive observation that human society could be regarded as a large-scale partially observable environment, where everyone has the functions of communicating with neighbors and remembering his/her own experience, we propose a novel network structure called the hierarchical graph recurrent network (HGRN) for multi-agent cooperation under . Specifically, we construct the multi-agent system as a graph, use a novel graph convolution structure to achieve communication between heterogeneous neighboring agents, and adopt a recurrent unit to enable agents to record historical information. To encourage exploration and improve robustness, we design a method that can learn stochastic policies of a configurable target action entropy. Based on the above technologies, we propose a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant called SAC-HGRN. Experimental results based on three homogeneous tasks and one heterogeneous environment not only show that our approach achieves clear improvements compared with four MADRL baselines, but also demonstrate the interpretability, scalability, and transferability of the proposed model.

Keywords： Deep reinforcement learning     Graph-based communication     Maximum-entropy learning     Partial observability     Heterogeneous settings    

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A novel confidence estimation method for heterogeneous implicit feedback Article

Jing WANG, Lan-fen LIN, Heng ZHANG, Jia-qi TU, Peng-hua YU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1817-1827 doi: 10.1631/FITEE.1601468

Abstract: Implicit feedback, which indirectly reflects opinion through user behaviors, has gained increasing attention in rec-ommender system communities due to its accessibility and richness in real-world applications. A major way of exploiting implicit feedback is to treat the data as an indication of positive and negative preferences associated with vastly varying confidence levels. Such algorithms assume that the numerical value of implicit feedback, such as time of watching, indicates confidence, rather than degree of preference, and a larger value indicates a higher confidence, although this works only when just one type of implicit feedback is available. However, in real-world applications, there are usually various types of implicit feedback, which can be referred to as heterogeneous implicit feedback. Existing methods cannot efficiently infer confidence levels from heterogeneous implicit feedback. In this paper, we propose a novel confidence estimation approach to infer the confidence level of user prefer-ence based on heterogeneous implicit feedback. Then we apply the inferred confidence to both point-wise and pair-wise matrix factorization models, and propose a more generic strategy to select effective training samples for pair-wise methods. Experiments on real-world e-commerce datasets from Tmall.com show that our methods outperform the state-of-the-art approaches, consid-ering several commonly used ranking-oriented evaluation criteria.

Keywords： Recommender systems     Heterogeneous implicit feedback     Confidence     Collaborative filtering     E-commerce    

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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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Title Author Date Type Operation

Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Journal Article

A cooperative heterogeneous vehicular clustering framework for efficiency improvement

Iftikhar Ahmad, Rafidah Md Noor, Zaheed Ahmed, Umm-e-Habiba, Naveed Akram, Fausto Pedro García Márquez,ify_ia@yahoo.com,faustopedro.garcia@uclm.es

Journal Article

Avision of post-exascale programming

Ji-dong ZHAI, Wen-guang CHEN

Journal Article

Programming bare-metal accelerators with heterogeneous threading models: a case study of Matrix-3000

Jianbin FANG, Peng ZHANG, Chun HUANG, Tao TANG, Kai LU, Ruibo WANG, Zheng WANG,j.fang@nudt.edu.cn,zhangpeng13a@nudt.edu.cn,chunhuang@nudt.edu.cn

Journal Article

The Progress and Application of the SKI Series Catalysts for Isomerization of C8 Aromatics

Qiao Yingbin

Journal Article

Development and Commercial Application of FCC Process for Maximizing Iso-Paraffins (MIP) in Cracked Naphtha

Xu Youhao,Zhang Jiushun,Long Jun,He Mingyuan,Xu Hui,Hao Xiren

Journal Article

Containment control for heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes

Ya-ni Sun, Wen-cheng Zou, Jian Guo, Zheng-rong Xiang,xiangzr@njust.edu.cn

Journal Article

Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Journal Article

Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks

Chenxi Li, Lei Guan, Huaqing Wu, Nan Cheng, Zan Li, Xuemin Sherman Shen

Journal Article

Explainable data transformation recommendation for automatic visualization

Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, Jiazhi XIA,wzlzju@zju.edu.cn,chenvis@zju.edu.cn

Journal Article

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article

A novel confidence estimation method for heterogeneous implicit feedback

Jing WANG, Lan-fen LIN, Heng ZHANG, Jia-qi TU, Peng-hua YU

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