Reviewerly/exHarmony huggingface dataset:
Spatially-Consistent Feature Matching and Learning for Heritage Image Analysis

Progress in the digitization of cultural assets leads to online databases that become too large for a human to analyze. Moreover, some analyses might be challenging, even for experts. In this paper, we explore two applications of computer vision to analyze historical data: watermark recognition and one-shot repeated pattern detection in artwork collections. Both problems present computer vision challenges which we believe to be representative of the ones encountered in cultural heritage applications: limited supervision is available, the tasks are fine-grained recognition, and the data comes in several different modalities. Both applications are also highly practical, as recognizing watermarks makes it possible to date and locate documents, while detecting repeated patterns allows exploring visual links between artworks. We demonstrate on both tasks the benefits of relying on deep mid-level features. More precisely, we define an image similarity score based on geometric verification of mid-level features and show how spatial consistency can be used to fine-tune out-of-the-box features for the target dataset with weak or no supervision. This paper relates and extends our previous works (Shen et al. in Discovering visual patterns in art collections with spatially-consistent feature learning, 2019; Shen et al. in Large-scale historical watermark recognition dataset and a new consistency-based approach, 2020). Our code and data are available at http://imagine.enpc.fr/~shenx/HisImgAnalysis/ .
Optimal Allocation of Higher Education Resources Based on Data Mining and Cloud Computing

With the explosion of information and ever-increasing information resources, humanity has entered a brand-new cloud computing era. We are confronted with a brand-new problem: how to quickly and accurately extract the required information from vast information resources. It is even possible to argue that university educational administration is inextricably linked to university teaching accomplishments. The teaching data mining (DM) technology was created in order to extract the required information from vast information resources. People’s ability to find data using cloud computing technology (IT) has improved. This paper analyzes the division and optimal allocation of talent types in universities, in combination with the development strategy and HR characteristics of universities, and provides strategies for the rational allocation of talents in universities, as well as support for the information relationship of HR in universities, using DM technology research. The emergence of DM technology alleviates the problem of data accumulation-induced information explosion. In today’s universities, there are an increasing number of talents. In terms of talent allocation, the information-based talent allocation scheme offers new approaches to talent acquisition and university education development in the modern era.
A Corpus-Driven Study on Three Elements of Third Personal Pronoun Endophora in English Abstracts of Chinese and Foreign Theses

The corpus is applied to compare and analyze three elements of the third personal pronoun endophora in English abstracts of Chinese and foreign theses. It is discovered that there is no significant difference in the total frequency of third personal pronoun endophora used by Chinese and foreign masters, but there is a significant difference in three elements of third personal pronoun endophora. Native Chinese-speaking masters tend to choose “it” as the anaphor, while native English-speaking masters are inclined to select “them” as the anaphor; antecedents used by native Chinese-speaking masters are mostly consistent with keywords of theses, while antecedents used by native English-speaking masters are rarely consistent with keywords of theses; distances between antecedents and anaphors set by native Chinese-speaking masters are much longer than those set by native English-speaking masters. These findings are beneficial for Chinese masters in terms of better understanding the writing conventions of English abstracts of foreign theses and further improving the language quality of English abstracts in their theses.
Cloud Application in the Construction of English Virtual Teaching Resources Based on Digital Three-Dimensional Technology

In order to improve the sharing effect of English teaching resources, based on digital three-dimensional technology, this paper constructs a cloud application in English virtual teaching resource database system based on digital three-dimensional technology. Moreover, this paper uses signal recognition technology to digitally process English teaching resources and establish a dynamic and changeable three-tier indicator system. In addition, this paper collects and summarizes the state data generated in the teaching operation of various departments of teaching management in real time and conducts scientific analysis and processing on it according to the management objectives to obtain valuable data information. Finally, this paper digitally processes English teaching resources into system-recognizable text to realize platform resource sharing.
Follow the Timeline! Generating an Abstractive and Extractive Timeline Summary in Chronological Order

Today, timestamped web documents related to a general news query flood the Internet, and timeline summarization targets this concisely by summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this article we propose our Unified Timeline Summarizer, which can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information retained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting a summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that our Unified Timeline Summarizer achieves state-of-the-art performance in terms of both automatic and human evaluations. 1
Achieving Performability and Reliability of Data Storage in the Internet of Things

Internet of things (IoT) includes a lot of key technologies; In this emerging field, wireless sensors have a key role to play in sensing and collecting measures on the surrounding environment.In the deployment of large-scale observation systems in remote areas, when there is not a permanent connection with the Internet, the network requires distributed storage techniques for increasing the amount of data storage which decreases the probability of data loss.Unlike conventional networked data storage, distributed storage is constrained by the limited resources of the sensors.In this research, we present a distributed data storage method with the combined K-means and PSO clustering mechanism organized with the binary decision tree C4.5 in the IoT area with considering efficiency and reliability approach.This scheme can provide reliability in responding to inquiries while minimizing the use of energy and computational resources.Simulation results and evaluations show that the proposed approach, due to the distributed data storage with minimal repeat publishing according to the decision tree structure, increases the reliability and availability, reduces the communication costs, and improves the Energy consumption, saving memory consumption without registering the same event and compared to other methods performed in this area have good results.
A Study of Regional Economic Disparities and Coordinated Development Based on Improving Grey Model Combinations

The positive promotion of regional economic growth is largely consistent with expectations, while there is also a promotion effect on the economic growth of neighboring regions. In this paper, the individual cognitive term of the particle swarm algorithm is introduced into the bath algorithm, and economic forecasting analysis is carried out based on the improved combination of grey model improvements. The main reason for the negative correlation between government investment and economic growth is that, although OFDI promotes economic growth, industrial upgrading, and optimizes industrial structure, it may also reduce regional export capacity, reduce employment opportunities, and increase agency costs. The estimated coefficients for human capital, labor input, capital stock, and urbanization rate are all positive, indicating that each variable is conducive to promoting regional economic growth.
Open document flow based on blockchane technology for cyber security of the accounting system

Introduction. To ensure cyber protection, the company’s management resorts to isolating accounting information containing trade secrets from other information flows. However, confidential document management attracts significant attention from internal users and outsiders in order to gain unauthorized access. Due to active cyber threats, the isolation practice in the implementation of electronic information communications is ineffective for cybersecurity, which requires the development of new methods documentation and document circulation at the enterprise.The purpose of the article is to study the benefits of open document management of enterprises based on blockchain technology to abandon the isolation of information flows in order to ensure cybersecurity of enterprises.Methods. In the process of researching the disadvantages and advantages of isolation and open document management, general theotrtical( empirical, logical and historical) methods of cognition of reality are used. The research is based on scientifical methods of dislosing the economic processes, facts and phenomena from the standpoint of accounting and cybersecurity of enterprises. The information base of the article is selected historical documents on the development of blockchain technology, scientific works of scientists on block-chain structuring of accounting information, etc.Results.The fundamental principles of blockchain technology in the conditions of its use for the organization of cybersecurity of accounting information are formed. The expediency of refusing to isolate information in favor of open document management in terms of maintaining trade secrets of the enterprise is substantiated. The method of documenting and document circulation on the principles of block-chain structuring of the database, which determines the order of fragmentation and recombination of accounting information at the internal and external levels of electronic communications, is studied. The procedure for using the electronic key system to gain access to confidential accounting information in accordance with the information needs of users and their classification in the enterprise management system.Perspectives. In order to organize cybersecurity of enterprises, it is important to improve the methodology of implementation of other elements of the method of accounting in the process of automated information processing, which requires further research.
Methods of College Education Reform under the Background of Wireless Communication and VR

With the rapid development of information technology, wireless communication and virtual reality technology have gradually been introduced to college education. Compared with traditional teaching methods, the use of information technology can make teaching more convenient and efficient. New teaching models such as online classrooms and shared notes have gradually shown their advantages, promoting the continuous advancement of higher education to intelligent development. Incorporating wireless communication and VR into our educational practice and selecting and innovating them according to the characteristics of colleges and universities and teaching content can better play the auxiliary role of technology. In this paper, the status quo of college education is studied through a questionnaire survey, and the advantages, existing problems, and solutions of wireless communication and virtual reality technology in college education reform are analyzed. The results show that wireless communication and virtual reality technology not only enhance the experience and motivation of students but also enhance the teaching quality of teachers. This provides an important reference value for college education reform.
Calibrating Histopathology Image Classifiers Using Label Smoothing

The classification of histopathology images fundamentally differs from traditional image classification tasks because histopathology images naturally exhibit a range of diagnostic features, resulting in a diverse range of annotator agreement levels. However, examples with high annotator disagreement are often either assigned the majority label or discarded entirely when training histopathology image classifiers. This widespread practice often yields classifiers that do not account for example difficulty and exhibit poor model calibration. In this paper, we ask: can we improve model calibration by endowing histopathology image classifiers with inductive biases about example difficulty? We propose several label smoothing methods that utilize per-image annotator agreement. Though our methods are simple, we find that they substantially improve model calibration, while maintaining (or even improving) accuracy. For colorectal polyp classification, a common yet challenging task in gastrointestinal pathology, we find that our proposed agreement-aware label smoothing methods reduce calibration error by almost 70%. Moreover, we find that using model confidence as a proxy for annotator agreement also improves calibration and accuracy, suggesting that datasets without multiple annotators can still benefit from our proposed label smoothing methods via our proposed confidence-aware label smoothing methods. Given the importance of calibration (especially in histopathology image analysis), the improvements from our proposed techniques merit further exploration and potential implementation in other histopathology image classification tasks.
GPU-Net: Lightweight U-Net with More Diverse Features

Image segmentation is an important task in the medical image field and many convolutional neural networks (CNNs) based methods have been proposed, among which U-Net and its variants show promising performance. In this paper, we propose GP-module (ghost pyramid pooling module) and GPU-Net based on U-Net, which can learn more diverse features by introducing Ghost module and atrous spatial pyramid pooling (ASPP). Our method achieves better performance with more than $$4\times $$ fewer parameters and $$2\times $$ fewer FLOPs, which provides a new potential direction for future research. Our plug-and-play module can also be applied to existing segmentation methods to further improve their performance.
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel type-aware model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP’s effectiveness.
Semantic-Based Data Augmentation for Math Word Problems

It’s hard for neural MWP solvers to deal with tiny local variances. In MWP task, some local changes conserve the original semantic while the others may totally change the underlying logic. Currently, existing datasets for MWP task contain limited samples which are key for neural models to learn to disambiguate different kinds of local variances in questions and solve the questions correctly. In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models. New samples are generated by knowledge guided entity replacement, and logic guided problem reorganization. The augmentation approaches are ensured to keep the consistency between the new data and their labels. Experimental results have shown the necessity and the effectiveness of our methods.
Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions

In serverless computing, applications are executed under lightweight virtualization and isolation environments, such as containers or micro virtual machines. Typically, their memory allocation is set by the user before deployment. All other resources, such as CPU, are allocated by the provider statically and proportionally to memory allocations. This contributes to either under-utilization or throttling. The former significantly impacts the provider, while the latter impacts the client. To solve this problem and accommodate both clients and providers, a solution is dynamic CPU allocation achieved through autoscaling. Autoscaling has been investigated for long-running applications using history-based techniques and prediction. However, serverless applications are short-running workloads, where such techniques are not well suited. In this paper, we investigate tiny autoscalers and how dy-namic CPU allocation techniques perform for short-running serverless workloads. We experiment with Kubernetes as the underlying platform and implement using its vertical pod au-toscaler several dynamic CPU rightsizing techniques. We compare these techniques using state-of-the-art serverless workloads. Our experiments show that dynamic CPU allocation for short-running serverless functions is feasible and can be achieved with lightweight algorithms that offer good performance.
Detection of Multicamera Pedestrian Trajectory Outliers in Geographic Scene

The abnormal detection of moving objects in intelligent video surveillance system plays an important role in early warning for man-made disasters. However, the current abnormal detection methods cannot effectively perceive the cross-camera abnormal movements of video objects. The main reason is that the existing methods ignore the spatial relationship between the fields of view of different cameras and blind areas among the fields of view. This condition prevents them to effectively infer and analyze the cross-camera movements of video objects combined with geospatial information. This paper proposes the detection of multicamera pedestrian trajectory outliers in geographic scene to address this problem. This approach first spatializes the video object trajectory and then realizes trajectory vectorization by extracting trajectory points with equal time difference. The position trajectory outliers are detected by constructing isolation forest and scoring trajectory vectors, and the velocity trajectory outliers are identified through vectors’ neighborhood comparison. Related experiments show that our method can effectively improve the efficiency and accuracy of detecting trajectory outliers, which can enhance the early warning capability of video surveillance systems for man-made disasters.
Teacher's Motivation on the Use of Virtual Learning Environment (VLE): The Effect of Social Factors, Self-Efficacy and Technological Support

Virtual learning environment or VLE has been introduced in educational institutions such as schools, colleges and universities to encourage a systematic online teaching and learning platform.The application of VLE is suggested as one of the mediators to vary the delivery process therefore teaching and learning process will be easier and more interesting.This paper aims to examine the effect of social factors, self-efficacy and technological support on the utilization of VLE in the educational process.A quantitative approach was employed and the data was collected through questionnaire administered to selected respondents.A total of 356 accounting teachers from secondary schools in Peninsular Malaysia were chosen as the samples.Reflective measurement model in Partial Least Square-Structural Equation Modelling was utilized to measure the effect of exogenous latent variable on the endogenous latent variable.Results indicated that social factor namely (colleague, administrators and school culture) significantly effect the use of VLE among teachers.The finding can be interpreted as teachers with higher value of social factors have higher intention to use VLE in his/her teaching and learning.Further, self-efficacy and technological support (facilities quality, internet access and technical support) positively influence teachers' to apply VLE during teaching.This paper proposes that teachers need to be given ongoing support and appropriate training to develop their skills in using digital technology.Further, the quality of technological facilities in schools need to be developed to promote effective educational processes and motivate teachers to use digital technology.
Morphace: An Integrated Approach for Designing Customizable and Transformative Facial Prosthetic Makeup

On-skin electronics are an emerging group of interactive devices, with challenges in both engineering functionalities and design aesthetics. One design approach that lacks extensive exploration is combining prosthetic makeup with transformative wearables that generate dynamic output modalities. We propose a design approach called Morphace that imbues prosthetic makeup with customizability and transformative properties, which allows wearables to ‘camouflage’ on the original face and transform it. We use a case study on the face for its rich affordance of expressions and high visibility, which emphasizes the appearance of epidermal electronics. We developed a three-step computational design and fabrication workflow that integrates the prosthetic makeup process to fabricate functional primitives. We further explore the utility of Morphace through interactive experiences in social communication, facial augmentation, and self-expression. We believe Morphace offers an integrative approach that enriches current wearable solutions and enables creative output modalities and affordances for designing future on-skin shape-changing interfaces.