Reviewerly/exHarmony huggingface dataset:
Gain more with less: Extracting information from business documents with small data

Information extraction (IE) is a vital step of digitization that reduces paperwork in offices. However, the adaptation of common IE systems to actual business cases faces two issues. First, the number of training samples is small (i.e. 100–200 examples). Second, span extraction models based on question answering formulation require a long time for training and inference. To overcome these issues, we introduce a new query-based model for the extraction of information from business documents. For data limitation, the model employs transfer learning which adapts the knowledge of pre-trained language models (i.e. BERT) to specific domains. To do that, we design a new CNN layer for the adaptation of the model to specific domains. For the speed, different from the encoding of normal span extraction methods (BERT-QA), the proposed model encodes short tags and context documents in two channels in parallel, which speeds up training and inference time. Information from short tags is fused with context documents learned from CNN by using attention to predict start and end positions of extracted spans. Promising results on five domain-specific datasets in English and Japanese indicate that the proposed model produces high-quality outputs and can be applied for business scenarios.
DLPM: A dynamic location protection mechanism supporting continuous queries

Summary Currently, the protection of users' location privacy, particularly for moveable users, is a major concern for both academia and business. In order to receive required services, a moveable user needs to constantly disclose his/her location information with an untrusted third party in his/her locations, which raises security and privacy issues. To settle the above problems, in this work, we creatively integrate local differential privacy (LDP) with conditional random field (CRF) to facilitate continuous location sharing among moveable users. Firstly, we advance a novel approach of employing CRF to represent users' mobility. After that, we establish a system to provide continuous location sharing by combining the ‐location set and ‐LDP. Finally, we evaluate the system performance on actual data sets. The experimental results indicate that our technique outperforms the planar isotropic mechanism (PIM) and AGENT.
Image analysis with logarithmic Zernike moments

As one of the most widely used orthogonal moments, Zernike moments (ZMs) have been applied in various fields. But low-order ZMs encounter problem for describing small size images. The cause of this problem lies in that zeros of ZMs' radial basis function (RBF) bias toward large radial distance from the origin. Fractional Zernike moments (FrZMs) can be utilized to deal with small size images by adjusting RBF's zeros distribution with fractional-order parameter. But high-order FrZMs will result in numerical instability. To address these problems, ZMs are generalized to transformed Zernike moments (TZMs) and logarithmic Zernike moments (LoZMs) are proposed. ZMs and FrZMs are only special cases of TZMs. LoZMs are constructed by a particularly designed transformed function such that zeros of LoZMs'RBF are near evenly distributed in interval [0,1]. As a result, small size images can be well represented with low-order LoZMs. Furthermore, amplitudes of LoZMs'RBF keep relatively stable and numerical instability of high-order moments is suppressed. Extensive experiments are conducted to demonstrate the superior performance of LoZMs in image reconstruction and pattern recognition tasks. In addition, a quaternion-based zero-watermarking algorithm also shows that LoZMs perform better than ZMs.
Social Network Analytic Methods as Part of Big Data Technology for the Process of Monitoring Public Information in Indonesia

The use of technology has changed the way they communicate and disseminate information. By utilising the internet, information circulating can be faster and almost unlimited by time and space. This medium makes the information circulating every day very much, ranging from information about politics, economics, technology, science, food, and others. The concept of social network analytics (SNA) as the concept of big data technology is very suitable to be applied, where SNA can manage vast and diverse data and process it into the desired information in a swift time. In addition to online and print media, social networking media is also a concern to be monitored. Because social media is extensive, it makes it an area that must be considered, especially to see public opinion. This study discusses the use of the SNA concept in monitoring public communication policies. The results of this study are a recommendation regarding the use of the SNA concept in managing public issues to support government policy.
The Application of Artificial Intelligence to the Process of Logistic During COVID-19

Artificial intelligence (AI) has become more widely used in many organizations, including logistics, such as driver assistance, parcel sorters, and inventory planners. In the times of COVID-19, the delivery drivers have become busier as more parcels are ordered, which affects their health. Due to poor management and outdated technology, the drivers cannot complete their tasks on time. However, if these organisations were to implement AI, the AI assistance would help with parcel sorting and save time on delivery packages. With the implementation of AI, the delivery time and route will also be predicted, easing the work of drivers. The customer and workers might positively accept the introduction of robotics. However, there might be some opposition from people who think that robots will take their jobs. The study used the case of logistic companies in South Korea. The study recommended that the deployment of AI could improve their performance and save the lives of drivers.
A dual-context sequent calculus for the constructive modal logic S4

Abstract The proof theory of the constructive modal logic S 4 (hereafter $\mathsf{CS4}$ ) has been settled since the beginning of this century by means of either standard natural deduction and sequent calculi or by the reconstruction of modal logic through hypothetical and categorical judgments à la Martin-Löf, an approach carried out by using a special kind of sequents, which keeps two separated contexts representing ordinary and enhanced hypotheses, intuitively interpreted as true and valid assumptions. These so-called dual-context sequents, originated in linear logic, are used to define a natural deduction system handling judgments of validity, truth, and possibility, resulting in a formalism equivalent to an axiomatic system for $\mathsf{CS4}$ . However, this proof-theoretical study of $\mathsf{CS4}$ lacks, to the best of our knowledge, its third fundamental constituent, namely a sequent calculus. In this paper, we define such a dual-context formalism, called ${\bf DG_{CS4}}$ , and provide detailed proofs of the admissibility for the ordinary cut rule as well as the elimination of a second cut rule, which manipulates enhanced hypotheses. Furthermore, we make available a formal verification of the equivalence of this proposal with the previously defined axiomatic and dual-context natural deduction systems for $\mathsf{CS4}$ , using the Coq proof-assistant.
Behavior of Flow of Channel Network of Dadahup Swampy Irrigation Area

Dadahup is a swampy irrigation area that belongs to the Indonesian national development program, namely the food estate project. The food estate project is a revitalization program for the former peatland development to increase national food security. Peatlands located in lowlands (less than +2.00 m above mean sea level) were a significant problem in their land development activities; besides, the land is generally infertile. The peatland development has been discontinuing, and farmers shifted the jobs to another job for a living. In terms of the water-related problems, The Dadahup swampy irrigation area development faces various challenges, some of which are flood inundation during the rainy season and difficulty in irrigating the land during the dry season. This paper evaluates the behavior flow of the channel network using the HEC-RAS software. The irrigation area has a channel network comprising a primary channel, a perimeter channel, and several secondary channels. It also has connections with three main rivers, i.e., the Barito, Mangkatip, and Kapuas Murung Rivers. The simulation results with several scenarios have contributed better understanding of the flow behavior. Further discussion has promoted a proper water management implementation strategy to overcome the inundation issue.
A shifted Mahler measure identity for Boyd’s family

Recently, the second author and Qin numerically verified some Mahler measure identities of genus 2 and 3 polynomial families. In this paper, we use the elliptic regulator to prove an identity involving shifted Mahler measure for Boyd’s family.
Long multispan prediction model for machine reading comprehension in healthcare domain

Machine reading comprehension (MRC) is a question answering task, in which a system provides appropriate answers to users queries in a given document. With large-scale language models and enough training datasets, recent MRC models have surpassed humans in well-designed intrinsic tests that require short and single span answers. However, they have performed poorly in real world applications that require long and multispan answers. In healthcare domain, users want to find long and detailed information (e.g., symptoms of an illness, causes of a disease, and effects of a drug) rather than short and simple ones (e.g., name of an illness, name of a virus, and date of discovery). To satisfy these needs, we propose an MRC model to extract nonconsecutive long text spans from a document. The proposed model detects long candidate answer spans consisting of sentences and determines multiple nonconsecutive spans by using a span matrix. In an experiment using long multispan datasets, namely, MASHQA (a healthcare domain dataset), the proposed model outperformed previous state of the art MRC models in terms of all evaluation parameters.
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition

Few-shot visual recognition aims to identify novel unseen classes with few labels while learning generalized prior knowledge from base classes. Recent ideas propose to explore this problem in an unsupervised setting, i.e., without any labels in base classes, which reduces the heavy consumption of manual annotations. In this paper, we build upon a self-supervised insight and propose a novel unsupervised learning approach that joints Invariant and Consistent (InCo) representation for the few-shot task. For the invariant representation operation, we present a geometric invariance module to construct the rotation prediction of each instance, which learns the intra-instance variance and improves the feature discrimination. To further build consistency representation of inter-instance, we propose a pairwise consistency module from two contrastive learning aspects: a holistic contrastive learning with historical training queues, and a local contrastive learning for enhancing the representation of current training samples. Moreover, to better facilitate contrastive learning among features, we introduce an asymmetric convolutional architecture to encode high-quality representations. Comprehensive experiments on 4 public benchmarks demonstrate the utility of our approach and the superiority compared to existing approaches.
Object tracking algorithm with multi-scale channel attention

In recent years, the application of Siamese networks for visual target tracking has brought a great improvement in tracker performance, allowing both accuracy and real-time performance. However, the accuracy of Siamese network trackers is largely limited. Object tracking algorithm with multi-scale channel attention is proposed based on the Siamese object tracking algorithm of fully convolutional classification and regression. Firstly, the backbone network ResNet50 is improved, which combined with multi-scale channel attention for feature extraction and enhancement. Then, adding a spatial attention mechanism to focus on information about the location features of the target image within each channel after the features are extracted from the template branch. At last, it performs fusion, classification and regression successfully. Compared with other advanced trackers, our approach achieves higher accuracy and success rate, especially in complex scenarios such as fast motion, occlusion, similarity interference, and scale changes.
Simple and effective multi-scale fusion strategy

for ground truths in different sizes can effectively improve the detection performance. This idea can be transplanted to classification tasks. Based on the inspiration of object detection, we re-examine the multi-scale information, simplify the steps of multi-scale fusion, and further propose two strategies for multi-scale information fusion based on weight redistribution. On classification tasks, our method maintains a certain degree of competitiveness with other finely designed fusion strategies, and improves the performance of the baseline by 1-7% on 6 datasets. Open code in: https://github.com/Clichong/StageCNN
A SAR image ship detection method combined with anchor-free and attention module

The detection of SAR images of ship recently become one of the important research direction, but the SAR image imaging feature is not obvious, the face of a complex scenario testing result is not ideal, can produce serious leakage inspection by mistake, and so on and so forth, in addition, the deployment in the actual situation, the detection accuracy and speed cannot be both at the same time, lead to much difficulty in the practical application. This paper designed a anchor-free detection algorithm TA-FCOS on the basis of FCOS, which can effectively reduce the number of parameters. Meanwhile, we proposed an attention module TA, which was embedded into the backbone network to effectively improve the detection accuracy. Finally, we conducted experiments on the HRSID data set. AP50 and mAP reach 85.9% and 57.8%, respectively. Experimental results confirm the effectiveness of our algorithm.
Research on classifier design of Chinese abstracts of postgraduate dissertations in library collection

At present, there are many researches and application fields of Chinese text classification, such as news classification, emotion classification, and spam identification and so on [1]. However, there are relatively few researches on the classification of Chinese abstracts of postgraduate dissertations. The abstract of postgraduate dissertation is a highly summary of the purpose, method, innovation, key and difficult points and results of the whole dissertation’s research. To recognize the abstracts of postgraduate dissertations in the library collection, and then to classify the dissertations in the library collection, is beneficial to the majority of teachers and students to consult the dissertations efficiently, and can better recommend the dissertations to readers according to their research background. Therefore, how to automatically classify postgraduate dissertations in library collection is an urgent problem to deal with. Assume that the abstract of postgraduate dissertation is written strictly according to the writing requirements, therefore, the abstract of the dissertation can represent the classification of the research field for the whole dissertation. Extract the abstracts of the dissertations as characteristic data X, label data Y was made based on the information of the school where the authors of the dissertations work in, based on the postgraduate dissertations collected in the library of the university. The dissertations studied were divided into eight categories, So Y can take integer values from one to eight. After preprocessing, the word vector and classifier were trained by the way of machine learning [2]. The results show that the classifiers can output the classification more accurately when a new abstract is randomly input to the models that has been trained.
Super-resolution reconstruction of face images based on iterative upsampling and downsampling layers

In order to solve the current problems of insufficient detail extraction and poor visual effect after high magnification reconstruction of face images, a super-resolution method is proposed for single images of faces based on generative adversarial networks. Channel attention is added to the generative network to extract richer facial details, and the idea of iterative up and down sampling layers in the depth inverse projection network is borrowed to make the reconstructed image with good visual effect after high magnification. For the discriminator network, the normalization layer, which would destroy the image contrast, is removed. The experimental results show that the reconstructed images are more realistic and the visual effects are improved compared with Bicubic, SRCNN, LapSRN and SRGAN.
A joint scoring and classification for rehabilitation training poses based on convolutional neural networks

Telemedicine is likely to be an increasingly available medical mean for modern patients and those who are inconvenient to go to hospitals. As a kind of Chinese traditional rehabilitation training, eight-section brocade is more suitable for patients of most ages and they can promote physical quality. However, it is difficult for non-professional patients to complete standard posture without doctor guidance. Therefore, in this work, a method of remote data collection, posture scoring, and classification based on convolutional neural network of Siamese Network will be presented. 1, 204 images are used as template pose data and other 1, 204 pictures are patient’s poses. To train the network to have a better understanding of the image's sophistication, a Convolutional Neural Network (CNN) is used in this work, which can take an input image and apply learnable weights and biases to the image's numerous objects. Meanwhile, Siamese network is mentioned as a method to compare two vectors with different outputs so that posture classification and evolution can be realized. The average similarity of the score is 0.2172, as can be shown. The score range is -1 to 1. The closer the score is to 0, the more the patient's activities resemble the template.
Research and application of artificial neural network prediction model in deep learning

In today's rapid development of artificial intelligence and big data, when people make decisions, they want to give guidance and advice through data-related information, such as: Will banks lose customers? Is the energy consumption data normal? How much is the price? These predictions and judgments can be built through deep learning models to obtain corresponding results. Through the research of artificial neural network in deep learning, this paper sorts out the key steps of its application, and proves through experimental data that the prediction model constructed based on artificial neural network has higher accuracy and less time-consuming.
Analysis of workspace of dual-arm robot in obstacled environment

The workspace of the dual-arm robot under the obstacle working environment is analyzed. An improved D-H coordinate system is established for the six-degree-of-freedom manipulator, and the coordinates of the end of each connecting rod are solved through the forward kinematic equation. Based on the Monte Carlo method, firstly analyze the ideal workspace without obstacles, further abstract the obstacles into a cylindrical envelope, and design a collision detection strategy to analyze the workspace that is closer to the actual work scene. The results show that the workspace analysis method using the collision detection strategy can delete a large number of collision points, which provides a basic optimization for the subsequent use of fast-expanding random tree and other manipulator path planning algorithms so that the algorithm avoids selecting invalid points during spatial sampling and reduces the number of collision points. Path search time.
Knowledge distillation for end-to-end speech recognition based on Conformer model

With the rise of deep learning, the end-to-end speech recognition model has received increasing attention. Currently, the performance of the end-to-end speech recognition model has been further updated on basis of on the proposal of the Conformer Framework, which has been widely used in the field of speech recognition. However, these models perform poorly on edge hardware due to large memory and computation requirements. This paper compresses the model by exploring the application of knowledge distillation in end-to-end speech recognition based on Conformer. By combining with the network structure of the model, the training loss function is redefined, and on this basis, the influence of different temperatures on the performance of the student model in knowledge distillation is explored.
Artificial neural network combined with support vector machine for breast cancer prediction

Breast cancer is a common cancer that threatens many people’s lives, especially women. Early and accurate diagnosis of breast cancer contribute much to improving life qualities of the patients. However, it is challenging to construct a reliable diagnosis model to diagnose breast cancer. In this study, to achieve better performance of the breast cancer diagnosis, a model called ANN-SVM model that hybridizes artificial neural network (ANN) and support vector machine (SVM) is proposed to categorize the breast tumours into benign or malignant. To be more specific, in the process of training artificial neural network, the hidden relationships between features and classification results are detected. The hidden layer in artificial neural network, which contains the information of these relationships can be extracted as input of support vector machine. The final classification results are given by SVM. ANN, SVM and the proposed ANN-SVM model were experimented on Breast Cancer Wisconsin (Diagnostic) Dataset. The performance of each model was evaluated based on accuracy, F1 score, and false-negative rate. From the results, the proposed ANN-SVM model reached the highest accuracy of 98.24% and F1 score of 0.9760, and it achieved the same false-negative rate of 1.17% as artificial neural network did, which was lower than the false-negative rate achieved by support vector machine. It is shown that the proposed ANNSVM model outperformed single ANN and SVM.
School safety early warning system based on machine vision

With the progress of society, people's needs are changing and higher requirements are being put forward for living standards and living environments. Therefore, the use of machine vision and information technology to solve the current problem of dangerous systems is a good solution. This paper explores the design and development of a school safety warning system based on machine vision algorithms. The school safety early warning system based on machine vision is an intelligent management system that can achieve real-time monitoring and early warning functions, which is important for achieving school safety management, by analyzing the existing system requirements and drawing conclusions.