Reviewerly/exHarmony huggingface dataset:
A Handwritten Signature Segmentation Approach for Multi-resolution and Complex Documents Acquired by Multiple Sources

Handwritten Signature is a biometric feature, which enables personal verification. Thus, it constitutes an alternative authentication used in several applications, such as bank checks, contracts, certificates, and forensic science. Signatures may be presented on a complex background with different textures, turning automatic signature segmentation into a difficult task. In this work, we propose an approach to locate and segment only the signature image pixels in documents with complex backgrounds, acquired by smartphone cameras in different environments, without any prior information about the signature location in these documents, the pen used by the signer, among others issues. Our approach is based on the U-net network architecture, combined with a pre-processing stage that allows dealing with images having different resolutions and distortions due to the document acquiring process. To make our model more robust to background and texture variations, we have generated a data set consisting of 20,000 document photos with different sizes, textures, and documents, named DSSigDataset-2. Our experiments show that the proposed method achieved encouraging results, over precision, recall, and F1-score measures, in all evaluated data sets (ours and benchmark ones).
Research on Multi-path and Multi-QoS Routing

Aiming at the heterogeneity, complexity and uncertainty of communication networks in industrial environment, a genetic variation neighborhood search algorithm (NSGA II-VNS) is proposed. The algorithm generates a set of optimal solutions as a multipath routing scheme to meet the redundant routing requirements of industrial networks. Due to the weak computing power of the node and the small storage space, the VNS method is integrated, and the pre-processing operation is performed in the generation stage. The strong function node stores the network information, and improves the algorithm's rapidity and the initial population diversity. The experimental results show that NSGA II-VNS can ensure the reasonable allocation of network resources, and effectively improve the reliability of the system.
Idea work online: shelters and crutches in remote collaborative autoethnography

Purpose The three authors of this paper are intrigued by ideas and how they are created. The purpose of this paper is to explore idea creation and work by means of remote collaborative autoethnography. Design/methodology/approach During the COVID-19 lockdown in 2020, the authors sent texts to each other, followed up on each other's thoughts and discussed them in online meetings. They shared, analyzed and eventually theorized their lived experiences in order to understand creating ideas as social and cultural experience. Findings The authors develop the notions of “shelter” and “crutch” to make sense of the complexity of creating ideas together; theorize how emotions and identities are entangled in idea work; and discuss how time, space and power relations condition it. Originality/value The authors contribute to understanding idea work in a remote collaborative autoethnography by highlighting its emotional, identity-related and power-laden nature.
Two-frame phase extraction technology based on vector normalization with single filter

This paper presents a fast and direct phase extraction method for two-frame fringe patterns. First, perform addition and subtraction operations on the two frames of phase shift fringe images, and only need to perform a single high-pass filter on the addition result to obtain a set of sine and cosine components. Then use the vector normalization method to achieve the vector orthogonality of the sine and cosine components. Finally, the phase to be measured of the interference fringe pattern is obtained by arctangent operation. The algorithm is compact in structure and efficient in processing. The accuracy and efficiency of the method are verified by comparing simulations with different methods and testing of experimental interferograms.
A double-phase search algorithm for sub-optimal path finding

Traditional optimal path finding algorithms are usually too complex for real world problems, motivating the need to find path with sub-optimality. Typically suboptimal algorithms use a single admissible heuristic value to decide how to find a path and bound the cost. Algorithms like Weighted A*(WA*), Convex upward parabola(XUP) and Convex downward parabola(XDP) have overcome the node re-expansion problem during search. However, this re-incur a balance between the quality of path and the speed of search. In this paper, we research the process of extending and put forward an algorithm that would more efficiently operate the search while on the same time not lower the quality of path. This algorithm includes two phase of search, the first phase is to fasten the process of path finding, while the second phase is to guarantee the quality of path. In most maps we choose from Dragon Age Origins(DAO), our algorithm performs better than WA*.
Distance learning of specialists in information and library activities in Russia and Kazakhstan

The article presents an analysis of opportunities of distance education organization for future librarians in the universities of Kazakhstan under the conditions of the Corona VIrus Disease-19 pandemic. These opportunities of distance and media education were actualized in the context of the coronavirus pandemic due to self-isolation of students. The process of modern library staff professional education has always been ahead of many humanities in the aspect of implementation of new information and communication technologies in the educational process with its focus on values of digital and virtual educational environment. In modern conditions, during the transition from an industrial society to a post-industrious information society of knowledge and innovations, the issues of extensive introduction of digital technologies have become more central in order to overcome the gap between the current need for information, the study of national and foreign experience, the tasks of new socio-cultural practices and the existing level of professional education of librarians. Therefore, the purpose of this study is to reveal the features of the formation of a culture of online distance learning, to identify positive experiences that pose a universal response to the crisis of pedagogical and psychological education, civil, personal and ethnic identities (ideals, beliefs, worldview) in the formation of professional competencies of librarians. The authors used the following scientific research methods: study of electronic content, observation, questionnaires, test polls, the Computer Assisted Web Interview method, conversations, focus groups, content analysis and others. In this study, the authors structured and characterized the main directions of the development of pedagogical culture of distance education, identified a positive experience, which poses a universal response to the crisis of librarian education. We analyzed the totality of objective, methodological, organizational and evaluative components of the system of distance education. The main attention is paid to the issues of organizing the educational process of distance education for librarians in the Republic of Kazakhstan and in Russia, student surveys, which showed that with a large selection of foreign and national platforms, they give preference to national educational programs developed by leading Kazakhstani researchers and teachers. An educational and pedagogical analysis of the leading platforms has been carried out. In the conclusion, the positive aspects of distance education are revealed and recommendations are given to organize and to improve the educational process and the development of the distance education system more effectively under the conditions of the pandemic and self-isolation.
Exploring the evidence-base for electronic access troubleshooting: Where research meets practice

The evidence-base of scholarly literature dedicated to e-resource access management and problem resolution maintains an important place in the discipline wherein librarians can share their knowledge, experience, and research dedicated to supporting users and minimizing interruptions. To date, no study has gathered and examined the literature surrounding e-access troubleshooting that exists in journals dedicated in scope to library technical services. Using a qualitative content analysis methodology, the author examines thirty-five articles pertaining to e-access troubleshooting in order to identify trends and illuminate gaps in the evidence base whereupon future studies may build.
On Greedy Approaches to Hierarchical Aggregation

We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs). The idea of HAG is to identify and remove redundancies in computations performed when training GNNs. The associated optimization problem is to identify and remove the most redundancies. Previous work introduced a greedy approach for the HAG problem and claimed a 1-1/e approximation factor. We show by example that this is not correct, and one cannot hope for better than a 1/2 approximation factor. We prove that this greedy algorithm does satisfy some (weaker) approximation guarantee, by showing a new connection between the HAG problem and maximum matching problems in hypergraphs. We also introduce a second greedy algorithm which can out-perform the first one, and we show how to implement it efficiently in some parameter regimes. Finally, we introduce some greedy heuristics that are much faster than the above greedy algorithms, and we demonstrate that they perform well on real-world graphs. A full version of this paper is accessible at: https://arxiv.org/abs/2102.01730
MMEnsemble: Imbalanced Classification Framework Using Metric Learning and Multi-sampling Ratio Ensemble

In classification, class imbalance is a factor that degrades the classification performance of many classification methods. Resampling is one widely accepted approach to the class imbalance; however, it still suffers from an insufficient data space, which also degrades performance. To overcome this, in this paper, an undersampling-based imbalanced classification framework, MMEnsemble, is proposed that incorporates metric learning into a multi-ratio undersampling-based ensemble. This framework also overcomes a problem with determining the appropriate sampling ratio in the multi-ratio ensemble method. It was evaluated by using 12 real-world datasets. It outperformed the state-of-the-art approaches of metric learning, undersampling, and oversampling in recall and ROC-AUC, and it performed comparably with them in terms of Gmean and F-measure metrics.
Unit Testing of Speech Recognition over Mobile Channels

When speech is transmitted over mobile channels, it gets degraded. In this paper, the speech to be transmitted is parameterized using feature extraction to create Mel cepstrum parameters. These are then compressed to lower the rate of data transmission. To this, error coding is added. At the receiver, these features are decoded to regenerate the front-end features. In this paper, the extraction and compression codes from ETSI are compiled on a Linux machine to test the correctness of distributed speech. Rigor is used to create suitable test vectors so that all the crucial parts are stimulated and the process helps reinforcing the conformance to the standard.
AI Guide Products System Based on Mental Model

In order to overcome the difficulties of visually impaired users to integrate into the society, and to meet the life needs of visually impaired users, this paper proposes a novel AI guide products system based on mental model. The system fully combines big data technology and artificial intelligence, and uses user analytic hierarchy process (AHP) to analyze user needs and build user psychological model. Based on this, the system can also give full play to the interaction of big data and artificial intelligence to obtain users' thinking mode and living habits, and then develop a series of exclusive services for users. The research results show that the system can develop the exclusive services according to the needs of each visually impaired user, and then meet the individual needs of visually impaired users to the maximum extent.
Improving Korean Emotion Classification via Colloquial-Adaptive Pretraining

Language models (LMs) pretrained on a large text corpus and fine-tuned on a task data have a remarkable performance for document classification task. Recently, an adaptive pretraining method that re-pretrains the pretrained LMs using an additional dataset in the same domain with the given task to make up the domain discrepancy has reported significant performance improvements. However, current adaptive pretraining methods only focus on the domain gap between pretraining data and fine-tuning data. The writing style is also different because the pretraining data, e.g., Wikipedia, is written in a literary style, but the task data, e.g., customer review, is usually written in a colloquial style. In this work, we propose a colloquial-adaptive pretraining method that re-pretrains the pretrained LM with informal sentences to generalize the LM to colloquial style. We verify the proposed method based on multi-emotion classification datasets. The experimental results show that the proposed method yields improved classification performance on both low- and high-resource data.
Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis—allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.
Generated Model Based Data Augmentations for Classification of HER2 Immunohistochemical Pathological Images in Breast Cancer

In recent years, deep learning technology is widely used in the study of pathological diagnosis. Deep learning technology has a very positive impact on pathology. It has a strong ability to learn pathological images and can solve complex problems. But in order for models using deep learning technology to achieve better performance, we need a large number of datasets to train. However, it is difficult for the model to obtain labeled pathological image training samples, which leads to a small dataset. This will lead to the occurrence of overfitting problem, which will reduce the generalization ability of the model [1].In this paper, we show that utilizing different data augmentation techniques can alleviate these problems. By comparing the effects of different data augmentation techniques on the classification results of HER2 immunohistochemical(IHC) pathological images dataset in breast cancer, we show that the use of data augmentations techniques can help improve the classification results of pathological images. In particular, we found that the generation model was most effective for HER2 IHC pathological image classification, where DCGAN showed stronger performance.
A One-stage Instance Segmentation Based on Polar Coordinates

Deep learning methods based on convolutional neural networks are widely used in computer vision and other fields. Instance segmentation is a popular branch of computer vision tasks. This paper focuses on the problem of inaccurate results of instance segmentation, and proposes a Local Receptive Field Expansion Module (LRFE Module), which makes up for the lack of down-sampling information and extracts more semantic information and detailed information without sacrificing the size of the feature map and without increasing the parameters. At the same time, the Irregular Convolution Kernel Sampling Module (ICKS Module) is added to the detection sub-network to make the convolution kernel closer to the target shape and size. Furthermore, use an adaptive optimizer to achieve a flexible and stable converge loss function. In addition, we also change the position of polar centerness to assist in improving the experiment effect. The experimental results show that the proposed method has a segmentation accuracy of 32.1% on the test set of the dataset MSCOCO2017, which is 1.7% higher than the basic method and proved its effectiveness.
Implementation and Evaluation of a Visual Programming Language in the Context of Blockchain

Programming is not a simple task. Besides, the learning of syntax, the obstacles often seem insurmountable for the beginner. Syntax errors frustrate and distract attention from the actual algorithms and logic that need to be understood. With the help of visual programming, syntax errors can be avoided as much as possible by using predefined graphical boxes, which, due to their shape, can only be connected correctly to form a program. Moreover, Smart Contracts are mainly designed to exclude the need to trust a third party. Here using Visual programming makes perfect sense by making the creation of Smart Contracts much simpler without the help of a developer. Another advantage of Visual Programming Language (VPL) in Smart Contract creation is that the terms and conditions in Contracts can be read and understood from non-developers. In this research we aim to implement a Visual Smart Contract Creator and to conduct an empirical evaluation, which on the one hand, measures the semantic understandability of a visual representation of a Smart Contract created with our tool and compares it to its textual representation in Ride programming language. On the other hand, we evaluate the user acceptance of the tool.
Haptic-go-round: A Surrounding Platform for Encounter-type Haptic in Virtual Reality Experiences

We present Haptic-go-round, a surrounding platform that allows deploying props and devices to provide haptic feedbacks in any direction in virtual reality experiences. The key component of Haptic-go-round is a motorized turntable that rotates the correct haptic device to the right direction at the right time to match what users are about to touch. We implemented a working platform including plug-and-play prop cartridges and a software interface that allow experience designers to agilely add their haptic components and use the platform for their applications.
Applications of blockchain in smart cities

In recent years, building a smart city happens with the collaboration of public and private enterprises, which provides efficient management and better services to the real world. Internet of Things (IoT), cloud computing, smart payment, transportation management, and high internet bandwidth helps to provide smart city infrastructure. To build smart cities, security, integrity, and flexibility are still some of the challenges in many countries. Several researchers are working to provide a better solution to the aforementioned challenges, and in addition, several steps have been adopted, developing different methods and algorithms for better comfort. Blockchain technology can be used for building of smart cities because of its high security and simplicity of working design. Blockchain is emerging as a solution in the real world because it eases the need for jobs and rectifies more complex issues. Today, many companies are involved in this blockchain research to build high security. The main aim of designing a blockchain cryptographic system is to provide security and transparency to every individual. In past years, many land registration has been done with forged documents with the support of brokers and a complete loss of record-keeping, where tracking is nearly impossible. One of the major challenges facing India is illegal land possession, where illegal land occupants generate fake documents to occupy land. Due to this, legal owners are suffering. Land registration can be done in using digitization, which consumes less time, but still many problems are associated in terms of tenuous land titles. In this chapter, we are trying to detect fake documents related to land registry and other revenue departments by using blockchain technology. Blockchain helps trace the property changes in different hands. For detecting the fake documents, first we analyze the details of property. It is very essential to know which documents are needed to consider. Here, three parties are taken into consideration: buyers, sellers, and land inspector. For implementation, we are using the Hyperledger fabric blockchain platform to develop the project. Finally, the main objective of this paper is to detect the fake documents, which are related to the land registry and maintain only the legal documents in land records and record all original documents for future use.
Generating Responses With a Given Syntactic Pattern in Chinese Dialogues

Recently, many efforts have been devoted to generating responses expressing a specific emotion or relating to a given topic in a controlled manner. However, limited attention has been given to generating responses with a specified syntactic pattern, which makes it possible to imitate someone's way of speaking in dialogue. To fulfill this goal, we propose two models to generate syntax-aware responses: a gross-constraint and a specific-constraint model. The former controls the syntactic patterns of generated responses at sentence-level, while the latter works at smaller language units, such as words or phrases, being capable of manipulating the syntactic structures of responses in a more subtle manner. The extensive experimental results on two different datasets show that both the two models not only can generate meaningful responses with a specific and coherent structure but also improve on the diversity of generated responses, with similar gains in readability, relevance, and diversity as measured by human judges.