Miracle-dz/UniFAR-FTUs huggingface dataset:
On the application of non-uniform water cooling for the fire safety of fuel storage tank farms

Compromising between application rate and spacing between tanks is one of the vague points regarding fire mitigation in hydrocarbon storage tanks. Codes and standards recommendations are not comprehensive and sometimes contradicting. The traditional, uniform, coolant application technique requires high storage capacities and over-consumes the coolant. This study investigates a non-traditional technique using non-uniform coolant distribution and illustrates the effect of this technique on coolant and land savings. A model is created to simulate fire propagation and mitigation through cooling water application under steady state conditions for surface pool fires in cylindrical hydrocarbon storage tanks. The model considers the types of fuel, the wind speed and direction, the smoke effect and the nozzles distribution, and the number of segments on the target tank surface. The model is applied on a real-life case study. The suggested technique achieved up to 30% reduction in cooling water consumption or 5.6% reduction in land area.
“It Was Really All About Books:” Speech-like Techno-Masculinity in the Rhetoric of Dot-Com Era Web Design Books

The future of Human-computer interaction (HCI) communication requires researchers to develop a strong understanding of the factors that influence design practitioners. As a step towards building that understanding, based on interviews conducted with veteran web designers, we analyze a corpus of popular web design books published during and shortly after the dot-com boom. Using a combination of ethnographic methods and discourse analysis, we identify the rhetorical strategies in these books and why they were successful in shaping our participants’ ideas about web design. We find that the books exhibit a particular style of technical writing defined by a speech-like techno-masculinity. Despite their short shelf-lives, the books and their writing style contributed to the disciplinary identity of web design which exists today. Studying the history of best practice books is an important opportunity to reflect on the genre of best practices in design, and how we should frame them in the future.
Research Context and Trend of Knowledge Spillovers at Home or Abroad: A Bibliometrics and Social Network Analysis

To characterize the home or abroad research context of knowledge spillovers and to forecast its future development trajectory. Bibliometrics and social network analysis methods are used based on the CNKI and WoS data. Results show that: the number of domestic and foreign literature is increasing by years, and foreign literature has a higher rate of co-authorship than domestic one; the analysis of the keyword co-occurrence network reveals that domestic and foreign literature shares a similar set of core keyword nodes, indicating that knowledge spillover literature could be classified according to its connotation and type, as well as its influencing factors and spillover effects; the analysis of the centrality index reveals that the nodes that can become domestic research trends are FDI technology spillovers, knowledge spillovers, spillover effects, and technology innovation, whereas the nodes that predict foreign research trends are not immediately apparent.
Floor Plan Semantic Segmentation Using Deep Learning with Boundary Attention Aggregated Mechanism

Floor plans play an essential role in the architecture design and construction. It serves as an important communication tool between engineers, architects and clients. Automatic identification of various design elements in a floor plan image can improve work efficiency and accuracy. Prior research has used image analysis and/or convolution neural network (CNN) to detect wall lines and segment rooms based on text annotations. However, text annotations may not be available and an efficient technique to automatically segment multiple elements of a floor plan is required. In this paper, a CNN-based technique is proposed to detect elements such as wall, door, and bedroom, and segment the floor plan by developing a room boundary attention aggregated mechanism. The room boundary prediction is performed simultaneously with the room type prediction. The attention mechanism makes use of the well-predicted room boundary feature to benefit the room type prediction. The analysis shows that a clear and well-shaped boundary appears in the attention model when the mechanism best improves the network performance. Experimental results show that the proposed technique can achieve a better performance than the state-of-the-art methods. The overall accuracy of the proposed technique for 9 categories classification at pixel-level is more than 92%.
Forest Fire Video Detection Based on Multi-scale Feature Fusion with Data Enhancement

Due to the lack of forest fire samples and the fact that the fire images contain many targets of different sizes and complex backgrounds, overfitting is easy to occur, leading to the problems of low accuracy and high false alarm rate of forest fire detection.In this paper, by constructing the data enhancement, multi-scale feature fusion, change the activation function is put forward based on the data of enhanced multi-scale feature fusion network framework, the method through data enhancement technique to solve the problem of insufficient samples, using the deep learning automatic feature extraction fire training sample produce identification model of flames and smoke, the smoke and flames for precise identification and positioning.In addition, the activation function in the backbone network Resnet with multi-scale feature fusion was changed to Leaky Relu activation function in this paper, which was more robust to the input image noise.Through the experimental comparison and analysis of the self-built data set in this paper, it is found that the average accuracy (MAP) of forest fire detection by the network framework in this paper is increased by 14.89% and the loss value is reduced by 0.94 compared with the original data.At the same time, the fire can be monitored in real time.
Research on Financial Risk Early Warning of Internet Financial Companies: - Analysis of Ant Group's Typical Case

Abstract: The 14th Five-Year Plan proposes to improve the financial risk prevention, early warning, disposal, accountability system. In the era of rapid development of the Internet, Internet finance occupies a place in the financial industry because of fast growth, high efficiency and wide coverage. As a method of financial risk management, early warning of financial risk can predict the financial risk of enterprises, find the problems that may exist in enterprises, and enable enterprises to take preventive measures in advance to avoid further expansion of risks. This paper selects Ant Group for financial risk early warning research. this paper comprehensively analyzes the financial data of Ant Group during 2017-2020 from the perspectives of profitability, debt paying capacity, operation capacity and cash flow capacity, and adopts factor analysis method to build a financial risk early warning model to deal with financial risks. According to the warning results, 2018 is at the high risk stage, 2019 and 2020 will still be at the significant risk level. Finally, according to the research conclusions, corresponding improvement measures are proposed to strengthen the financial risk coping ability of Internet finance companies and help them achieve sustainable development.
An Efficient and Accurate Mixed Dynamic Data Race Detection Method

∗ Data race is one of the main causes of concurrent errors in multi-threaded programs. It is difficult to accurately detect data race because of the uncertainty of threads scheduling. This paper proposes to maintain a fixed length history access information read and write queue for each shared variable, and adopts an adaptive lightweight representation method of read access. When the shared variable is in the read shared state switches to a vector clock to record read access information, otherwise only record its last read access timestamp. In the dynamic detection process, firstly verified the Happens-before relationship that two access operations whether have a partial order relationship, then use LockSet algo-rithm to judge the lock protection status of the shared variable, it is regarded as a data race error if shared variable is not protected by public lock set. Designed and implemented dynamic detection method HisInfoTrack, the detection results of the benchmarks in the Java Grande Forum showed that compared with Djit+, the average detection time and memory overhead of HisInfoTrack are reduced by 39.8% and 40% respectively. Compared with Eraser, the false positives rate is reduced by 89%, the false negative rate is reduced by 33% compared with FastTrack. This method effectively detects data race with low overhead, reduces false positives and false negatives and can be used as an effective new method for dynamic data race detection.
Persuasive Design for Sustainable Mobility

With the rising pollution levels due to the transport sector, managing the mobility of people has become one of the toughest environmental and social challenges. This makes sustainability the defining factor for the future of mobility. This paper aims to identify the problems faced by the people in using sustainable modes for daily travel. Digital assistance with special COVID-19 adaptions has been proposed to nudge people towards sustainable mobility by using seven persuasive strategies identified based on the insights from data analysis carried out on the survey undertaken by 90 participants. The results of the study have proved that the new strategies implemented, have a greater impact on changing people's attitudes towards sustainable mobility.
A Novel Optimal Scheduling Approach of Wind-storage Joint System Based on K-Means and MADDPG

Wind-storage combined system can effectively deal with the uncertainty of wind power output and improve the competitiveness of wind power manufacturers. However, traditional mathematical optimization methods and conventional reinforcement learning algorithms can not maximize the role of energy storage in optimizing wind power output, and maximize the benefits of energy storage combined system. Therefore, in order to realize the fine scheduling of energy storage, a day-ahead scheduling strategy of wind-storage combined system based on improved K-Means and multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed. Firstly, according to the improved K-Means clustering algorithm, energy storage is grouped according to characteristics. Then, wind power and grouped energy storage are modeled as different Agent to form a multi-agent system, which is solved by MADDPG algorithm. Finally, the effectiveness of the proposed method to stabilize the fluctuation of wind power output and improve the income of wind-storage combined system is verified by an example.
An Effective Image Enhancement Method for Color Fundus Images

A method is proposed for adaptive fundus image enhancement so as to restore the color images that are with extremely low or nonuniform brightness. Our proposed method is capable of increasing the brightness, contrast of images without losing original color hue. It includes three steps: luminance enhancement, contrast improvement, and color restoration. All procedures are adapted and modified from conventional image processing algorithms, which are proved simple and less time-consuming. Two public and commonly used fundus image database were partially employed to evaluate the performance of our method. It is concluded that the brightness of images was enhanced by 86.35 % and the contrast was improved by 132.37 % without color distortion.
ChromaBot - Prototyping Soft Robotic Actuators with Integrated Electrochromic Displays

Soft robotics - robots built from highly compliant materials that resemble soft biological materials - have recently become more popular especially in industry settings, given their ability to handle fragile objects. One problem however of these devices is that they can only communicate intend or the need for help through movement. To overcome this limitation, in this paper we present ChromaBot, a method towards prototyping soft robotic actuators with integrated printed electrochromic displays. Our method only degrades the longevity of the soft robotic actuator in an acceptable manner while at the same time allows for a more expressive soft robot. We present detailed instructions on how to prototype ChromaBot as well as an initial analysis of the durability, both of the display as well as of the actuator.
GRAE: Graph Recurrent Autoencoder for Multi-view Graph Clustering

Multi-view graph clustering aims to discover communities or groups in the graph with multiple views, which usually can supply more comprehensive information than that single-view graph clustering. With the increasing scale of complex data from the real world, multi-view graph clustering has drawn much attention. It has a solid theoretical foundation and high effectiveness in applications such as data mining and social network analysis. However, Most existing methods obtain the clustering result only through the shared feature representations, defectively overlooking the unique features of multiple views. To fill this gap, a Graph Recurrent AutoEncoder (GRAE) is proposed for attributed multi-view graph clustering, which can attain node representation well by learning different view features. Specifically, we first design a global graph autoencoder and a partial graph autoencoder to extract the shared features and the unique features of all views, respectively, which can better represent the nodes in the graph. Then, from the perspective of representation fusion, we adopt an adaptive weight learning method to fuse the different features according to the importance of features. Moreover, we investigate a self-training clustering method to optimize a clustering objective for improving the clustering effect. Finally, we conducted a large number of experiments on three real-world datasets, demonstrating the superior performance of our proposed GRAE model on the multi-view graph clustering task.
Service Quality Evaluation of O2O Platform for Catering Whole Industry Chain

Catering whole industry chain O2O platform is the new development direction of catering O2O. The research of catering whole industry chain platform has advantages on achieving catering and food safety supervision and creating a traceable and accessible internet catering platform. Based on the service quality theory, this paper first selects research dimensions and measurement indicators by literature research, JAVA crawling news and policies, and TextRank keyword extraction. Then we build a service quality evaluation model containing five dimensions and makes decisions through two rounds of Delphi method. Finally, four suggestions for developing a catering whole industry chain O2O platform are proposed based on the combination with model and real practice.
Multi-Agent Voice Assistants: An Investigation of User Experience

The use of voice assistants (VAs) is spreading widely. Most common VAs consist of a single, usually female voice that responds to the user’s inquiry. We designed a VA system appearing as a group of agents, each with a different voice and a specialized task domain. We conducted a quantitative user study comparing our multi-agent approach with a conventional single-agent assistant in a smart home scenario as virtual reality (VR) simulation. The results show significantly higher user experience ratings for the multi-agent concept. Based on our findings, we discuss the potentials and challenges of designing multi-party VA systems.
3D Reconstruction of Spatial Non Cooperative Target Based on Improved Traditional Algorithm

A new 3D reconstruction strategy is proposed. The strategy obtains the camera internal reference matrix with known camera focal length and field of view angle; Improves the SURF algorithm to add the color information of feature points for feature point extraction and matching; Then takes the imaging time order of the image as a priori information, and solves the 3D sparse point cloud structure of camera position and non-cooperative target by SFM (structure from motion); Finally, CMVS (clustering multi-view stereo) is used to cluster the images. , and PMVS (patch-based multi-view stereo) is used to generate the dense 3D point cloud structure by matching, expanding and filtering. The experimental results show that the improved 3D reconstruction strategy is effective to avoid the reconstruction errors caused by spatially non-cooperative targets due to no background texture, structural symmetry, and texture duplication.
Improved U-Net Network for Infrared Small Target Detection

Infrared small target detection is a key technology in infrared search and tracking systems. Despite the rapid development of computer vision in recent years, infrared small target detection based on deep learning is still few and far between due to the lack of texture information and detailed feature of infrared small target. In this paper, we propose an improved U-Net for infrared small target detection, which adds a multi-scale feature fusion module on top of the U-Net. In order to better capture the key information of infrared small target, the multi-scale feature module fuses the high-level semantic information in the convolutional neural network and the low-level small target detail feature across layers. Experimental results show that our proposed method is able to segment infrared small target well and reduce false alarms caused by complex backgrounds compared to traditional methods. In addition, our proposed method performs better than the original U-Net, demonstrating the effectiveness of our proposed improved U-Net.
Metaphoraction: Support Gesture-based Interaction Design with Metaphorical Meanings

Previous user experience research emphasizes meaning in interaction design beyond conventional interactive gestures. However, existing exemplars that successfully reify abstract meanings through interactions are usually case-specific, and it is currently unclear how to systematically create or extend meanings for general gesture-based interactions. We present Metaphoraction, a creativity support tool that formulates design ideas for gesture-based interactions to show metaphorical meanings with four interconnected components: gesture, action, object, and meaning. To represent the interaction design ideas with these four components, Metaphoraction links interactive gestures to actions based on the similarity of appearances, movements, and experiences; relates actions to objects by applying the immediate association; bridges objects and meanings by leveraging the metaphor TARGET-SOURCE mappings. We build a dataset containing 588,770 unique design idea candidates through surveying related research and conducting two crowdsourced studies to support meaningful gesture-based interaction design ideation. Five design experts validate that Metaphoraction can effectively support creativity and productivity during the ideation process. The paper concludes by presenting insights into meaningful gesture-based interaction design and discussing potential future uses of the tool.
Code Simulation and Verification of Pulse Tube Refrigerator

For the practical design needs of a pulse tube refrigerator,based on the thermodynamic nonsymmetrv theory,a Lagrange joint analysis method and related experienced formula about regeneraton is adopted and a code is programmed,which can fast simulate and evaluate the performance of a pulse tube refrigerator.It can simulate the orifice version,doubleinlet version and multibypass version at the same time. The comparison of calculation with experiment shows that the refrigeration power and pressure are well accorded with each other.This means the model in code is correct,on the other hand it proves that the aim of practical application is achieved.
A Real-time Fatigue Driving Detection Method Based on Facial Multi-Feature Fusion

Fatigue driving refers to the imbalance of physiological and psychological functions after driving continuously for a long time, which leads to the objective decline of driving skills. Fatigue driving is one of the main factors leading to traffic accidents, and most accidents related to fatigue driving can be avoided. Effective real-time detection of driver fatigue state and timely early warning prompt can greatly reduce and prevent this kind of traffic accidents and reduce casualties and property losses. In this paper, we present a real-time fatigue detection method based on the driver’s facial features, taking the state of the eye and mouth as the main fatigue judgment basis. The algorithm consists of three parts: Facial Feature Location, State Recognition, and Fatigue State Estimation. Firstly, the eye and mouth regions are extracted by combining the improved MTCNN and PFLD. Then, a general two-classification network was designed to recognize the state of eyes and mouth. Finally, a sliding window model is designed to record the driver’s state information in each frame of image, and to judge the driver’s fatigue state by combining PERCLOS, blinking frequency and yawning frequency. We tested the performance of the algorithm based on video data set and camera input. An accuracy of 95% was achieved on the entire data set, and the average detection time is 38.72 ms. The experimental results show that the proposed method has good real-time performance and high accuracy rate.
Tessellation-based Kernel Density Estimation

Kernel density estimation is a complex task that plays an essential role in a variety of applications. In this paper, we introduce an approach to the task that converts the problem of bandwidth evaluation in the Parzen-window-like framework into the non-parametric evaluation of a fine-grained density estimate which can then be scaled by means of the Scale-Space theory to achieve the desired level of smoothness. The detailed estimate is realized through the Delaunay space tessellation method and properties of its output simplices. Additionally, in the experimental part of the paper, we showcase the new method and demonstrate its outputs at various scales, reaching results that perceivably outperform its counterparts.
A Scheme for Electronic Evidence Sharing Based on Blockchain and Proxy Re-encryption

In order to solve the current problem of centralized storage of electronic evidence, to meet the requirements of confidentiality and transmission security of electronic evidence, this paper uses blockchain technology and applies distributed key generation technology to ciphertext policy attribute-based proxy re-encryption (CP-ABPRE), and designs a blockchain-based multi-centered data sharing scheme. The data sharing scheme is combined with In-terplanetary File System (IPFS) to proposes a model of electronic evidence sharing that can resist conspiracy attacks. By comparison and analysis, the scheme is able to achieve fine-grained access control and secure sharing of