Reviewerly/exHarmony huggingface dataset:
Chinese Entity Relation Extraction Based on Multi-level Gated Recurrent Mechanism and Self-attention

There is the influence of polysemy and segmentation quality on semantic understanding for Chinese entity relation extraction, we propose a Chinese entity relation extraction model based on a multi-level gated recurrent mechanism and self-attention (MGRSA). For alleviating segmentation errors, we design the framework of a multi-level gated recurrent mechanism that unites word-grained information into character-grained information. For reducing polysemy ambiguity, we utilize self-attention on two parts, including word vectors with external semantic knowledge. Compared with other models on two datasets, our model can learn more semantic information in the case of introducing less noise, which shows better advantages and robustness.
A simpler encoding of indexed types

In functional programming languages, generalized algebraic data types (GADTs) are very useful as the unnecessary pattern matching over them can be ruled out by the failure of unification of type arguments. In dependent type systems, this is usually called indexed types and it’s particularly useful as the identity type is a special case of it. However, pattern matching over indexed types is very complicated as it requires term unification in general. We study a simplified version of indexed types (called simpler indexed types) where we explicitly specify the selection process of constructors, and we discuss its expressiveness, limitations, and properties.
Exploration of Accounting Talents Training Mode under 1+X Certificate System

research-article Exploration of Accounting Talents Training Mode under 1+X Certificate System Share on Author: Dashan Wang Hainan College of Software Technology Hainan College of Software TechnologyView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021 Pages 549–553https://doi.org/10.1145/3452446.3452580Online:25 April 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Get to the Point: Content Classification of Animated Graphics Interchange Formats with Key-Frame Attention

Animated Graphics Interchange Formats (GIFS) are low-bandwidth short image sequences that can continuously display multiple frames without sound. In this paper, we focus on a new content classification task that is important in real-world applications. A key problem for this task is that some frames in an animated GF are irrelevant to the label, which may drastically reduce the classification performance. To this end, we first collect a new dataset of Web animated GIFS (WGF) that includes some typical samples in which only several key-frames are relevant to the ground truth. Then, an attention-based method is designed to learn to produce importance scores of the frames, and subsequently multi-frame predicted scores are merged to obtain the final prediction. Besides, an additional entropy loss is also used to sharpen the attention results to further emphasize the key-frames. Experimental results on WGF show that the proposed approach significantly outperforms various baseline methods.
W(e)aving: When maker literacies and diffraction collide

Inspired by posthumanist and new materialist perspectives, this article offers multi-perspective post-qualitative findings highlighting adults’ entanglements with environmentally oriented makerspace activities in higher education. The article draws on adult education studies and maker research to generate neomaterialist understandings of arts-based environmental education for adults. Using a methodology of w(e)aving as a way of listening, two posthuman vignettes are presented. The vignettes introduce Annike’s—an in-service elementary teacher—process of crafting a metaphorical representation of plastic waste in the ocean as part of a maker education and literacies module. The authors use diffraction, agential cuts, and ‘glow data’ to engage with Annike’s artefact, mappings, email communication, and interview data, as well as the affective flows that these generate. Posthuman methodologies point to findings that shed light on dynamic relationalities, orienting researchers towards more environmentally just futures. Posthuman makerspace research that employs the arts has the potential to work towards craftivism, emphasising the role of the arts in rendering humans accountable for matters of ethics, especially in situations where those adults are teachers.
Dependency-Aware Tensor Scheduler for Industrial AI Applications: Dymem—An Aggressive Data-Swapping Policy for Training Nonlinear Deep Neural Networks

Artificial intelligence (AI) applications based on deep neural networks (DNNs) have been widely applied in industry, e.g., in natural language processing and computer vision, among other fields. Researchers and industry practitioners typically use GPUs to train complex, hundred-layer deep learning (DL) networks. However, as the networks become wider and deeper, the limited GPU memory becomes a significant bottleneck, restricting the size of the networks to be trained. In the training of DNN-based AI applications, the intermediate layer outputs are the major contributors to the memory footprint. Various data-swapping techniques, such as the offloading and prefetching of intermediate layer outputs, are proposed to overcome the GPU memory shortage by utilizing the CPU dynamic random-access memory (DRAM) as an external buffer for the GPU.
Remote sensing image dense target detection based on rotating frame

Abstract Detecting densely arranged and arbitrarily oriented targets on optical remote sensing images is a challenge, and there is much room for improvement in existing algorithms. In this paper, an end-to-end two-stage rotating target detection model based on rotating frames is proposed. This model adds FPN + PAN feature fusion structure after the backbone network to obtain enhanced features that fuse the feature information of each layer, and secondly, fine-grained rotation detection is accomplished by introducing Oriented Region Proposal Network and Oriented Region of Interest Pooling layer. By comparing the performance of this paper’s algorithm with the current mainstream rotation detection algorithm on DOTA remote sensing dataset, this paper’s algorithm can solve the difficult problem of detecting remote sensing targets with tight arrangement and arbitrary orientation to a certain extent.
Wavelet Tree ensembles with Machine Learning and its classification

Abstract Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.
An Adaptive Industrial Human-Machine Interface to Optimise Operators Working Performance

Adaptive User Interfaces (AUI) have the potential to deliver advantageous solutions for a wide range of industrial applications. Their ability to adapt to operator interaction patterns and achieve a more personalised interaction can lead to greater efficiency and productivity in the manufacturing process. However, in certain industrial contexts multiple operators interact with the system, rendering it impossible to detect each individual and propose an operator-personalised adaptation. In this paper we propose a data-driven methodology to generate temporal adaptation rules for a multi-operator industrial process. Through the use of machine learning (ML), the methodology: i) analyzes the interaction of different operators with the same machine, ii) selects the most representative adaptations, and iii) generates a set of temporal adaptation rules. The methodology was validated in a real industrial setting resulting in over 40% shorter operator interaction time, and almost 60% number of clicks reduction, thus decreasing the occurrence of interaction errors.
Diseño automático de W-operadores mediante el uso de funciones de pertenencia para la segmentación de leucocitos [Not available in English]

No English translation of this document was provided by the author or conference organizers.
TOOP Trust Architecture

Abstract While information security nowadays represents a core concern for any organization, Trust Management is usually less elaborated and is only important when two or more organizations cooperate towards a common objective. The overall Once-Only Principle Project (TOOP) architecture relies on the concept of trusted sources of information and on the existence of a secure exchange channel between the Data Providers and the Data Consumers in this interaction framework. Trust and information security are two cross-cutting concerns of paramount importance. These two concerns are overlapping, but not identical and they span all of the interoperability layers, from the legal down to the technical, passing through organizational and semantic layers. While information security aims at the preservation of confidentiality, integrity and availability of information, trust establishment guarantees that the origin and the destination of the data and documents are authentic (authenticity) and trustworthy (trustworthiness), and that data and documents are secured against any modification by untrusted parties (integrity). In this chapter, the TOOP Trust Architecture is presented, starting from a simple abstract model of interaction between two agents down to the detailed end-to-end trust establishment architecture, modeled onto the Toop Reference Architecture presented in the previous chapter.
Class-level Aware Network for Human Parsing

Having shown great performance in human parsing, convolutional neural networks(CNNs) come with much computation budget. In this paper, a novel class-level aware network(CANet), which employs an asymmetric encoder-decoder architecture, is presented to achieve reliable human parsing results in a memory friendly way. To achieve the trade-off between speed and accuracy in human parsing, we design group-split-bottleneck(GS-bt) block, where group convolution and channel split are utilized in the residual block. In decoder network, the attention pyramid pooling module(APPM) is proposed to recovering the details of human parsing. Moreover, a multi-class classification branch is developed to extract class-level information and revise human parsing results. Compared to current models, our model has less parameters and experiments demonstrate that the proposed CANet can reach state-of-the-art results on PASCAL-Person-Part dataset.
Recognition Method of Electrical Components Based on Improved YOLOv3

research-article Recognition Method of Electrical Components Based on Improved YOLOv3 Share on Authors: Jishuang Yan Dalian University of Technology, China Dalian University of Technology, ChinaView Profile , Yingguang Hao Dalian University of Technology, China Dalian University of Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 146Pages 1–5https://doi.org/10.1145/3469213.3470349Online:18 August 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Feed Formula Optimization Based on Improved Tabu Search Algorithm

The profitability of the livestock industry largely depends on cost-effective feed formula as feed accounts for a large proportion of production costs. Recently, it is one of research hotspots that investigation on how to scientifically formulate livestock feed reducing the cost. In this work, an Improved Tabu Search (ITS) algorithm is proposed to study the pig feed formula optimization method. The proposed ITS algorithm focuses on combination of the tabu search algorithm and intelligent optimization algorithms, which can obtain advantages of global and local optimization search from traditional optimization algorithms. The experimental results show that the ITS algorithm can achieve higher precision search and performs better than other optimization algorithms.
Android Based Student Monitoring Information System

Information systems play a very important role in the world of education to convey academic and non-academic information. At SMPN 2 Sedati, information is conveyed to parents about academic and non-academic achievements at the end of the semester. There is no information system that can provide development for students with mobile devices. The purpose of this research is to make a student monitoring information system to make it easier for teachers and bk in providing information on grades, presence, achievement, violations to parents of students efficiently and quickly. System testing is carried out by the blackbox method with 100% results without any errors, while user testing using the User Aceptance Testing method gets results for teachers and bk according to the material needed, for parents the application is very easy to use and the information obtained is very useful. It can be concluded that the application of the Android-Based Student Monitoring Information System is very useful and makes it easy for teachers and Bk to provide information to parents of students about the development of their children.
Classification algorithm using halftone features of counterfeit bills and CNN

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non-destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment.
Semi-Supervised Medical Image Semantic Segmentation with Multi-scale Graph Cut Loss

Most semantic segmentation methods are based on supervised convolutional neural networks which require large amounts of labeled data. However, the acquisition of a large number of high-quality labels is time-consuming and of high annotation cost for medical images. In this paper, we propose a semi-supervised learning framework based on a novel multi-scale graph cut loss function. Firstly, the multi-scale features obtained from the segmentation network are utilized to construct the graph in non-Euclidean space. Then the long-distance information between voxels at different scales can be captured through the graph embedding module. After that, the graph cut loss is calculated according to the final latent features. Only a few labeled data is needed in our proposed method, which is of significance in the practical clinic. The experiments on the BrainWeb20 dataset and the IBSR18 dataset demonstrate the effectiveness of the proposed method compared to the well-known state-of-the-art methods.
Scale-Invariant Salient Edge Detection

In this paper, we propose a Scale-Invariant Salient Edge Detection framework (SISED) using Hadamard-Product operation. Current HED-like edge detection approaches fuse multiple side outputs to produce the final edge map, which contains noise and unwanted edge details. Scale-invariant salience provides strong and convincing evidence for precisely describing object contour. The normalized Hadamard-Product is able to find and extract scale-invariant features by multiplying a set of side outputs. The computed Scale-Invariant Salient Edge (SISE) maps capture the hierarchical structure of contour details and can be utilized to improve the detection accuracy. The experimental results show SISED reaches state-of-the-art performance.
A Generalized Net Model of the Abdominal Aorta and Its Branches as a Part of the Vascular System

In a series of papers, Generalized Net (GN) models of the ways of functioning of the different systems and organs in the human body are described in general. Each GN model of a particular system or organ can be detailed and made more complex. In this paper a GN model of the abdominal aorta and its branches of the vascular system is proposed.
Machine Learning Algorithm for Developing Classroom Attendance Management System Based on Haar Cascade Frontal Face

The face is an essential characteristic of a person; it is used to distinguish between the personalities of two or more people in the real world. In recent years, different parts of the body have been manipulated to ensure that only the right person has access to their respective accounts, both physical and virtual. Biometrics, which involves identification such as fingerprints, palm veins, DNA, palm print, and face recognition, is one of the methods that has been developed. Similarly, this study would demonstrate how to use image processing to apply facial identification and recognition algorithms to create a device that can identify and recognize the frontal faces of students in a classroom. A face is a front part of a person’s head from the forehead to the chin or the corresponding part of an animal. The face is the most significant element in human experiences since it holds important details about a person or entity. Humans have the potential to recognize others depending on their faces. This paper proposed work to develop a working prototype of a system that will facilitate class control for Technical Informatics College of Akre lecturers in a classroom by detecting the frontal faces of students from an image taken in a classroom. Face recognition and identification technologies have been established in recent years as a result of testing. Many of them are used on social networking sites, financial applications, and government offices such as the Metropolitan Police Service, Facebook, and others.
Online detection system for electric bike in elevator or corridors based on multi-scale fusion

The traditional elevator building room electric vehicle monitoring system adopts manual monitoring, which has disadvantages such as easy fatigue of personnel and difficulty in monitoring each video channel; to address this situation, we propose an online detection system for elevator building room electric vehicles based on the improved YOLOv4-tiny algorithm, which first crawls pictures of electric vehicles as positive samples, and crawls pictures with relevant backgrounds or similarity in order to reduce the error detection rate and leakage detection rate of the system. The experimental results show that the map of the online detection system based on the improved YOLOv4-tiny algorithm is 94.07%, which is about 2 percentage points higher than the original algorithm.
Tool for Evaluating Subjective Answers using AI (TESA)

Modes of evaluation can be bifurcated into two main types, namely, objective answer evaluation and subjective answer evaluation. The technique of objective answer evaluation is widely used for most entrance examinations. The reason behind doing so is that it tests the analytical as well as the reasoning ability of a student while also being extremely accurate and efficient. Examinations such as the GATE exam are known to have never repeated questions. Although this technique is liable, it is close to impossible to implement such a system at the institute level for evaluating the semester exams of engineering students. One of the major limitations of the objective examinations is that it fails to analyze how well a student has grasped a particular subject. There is hence a need for a system that automizes the task of repetitive answer sheet corrections and provides optimal accuracy. This proposed model has come up with a reliable system based on previous work that formulates the marks scored by the student based on the sentence similarity, Jaccard similarity, and grammar of the model answer and student answer.
Dimensionality Reduction: Is Feature Selection More Effective Than Random Selection?

The advent of Big Data has brought with it an unprecedented and overwhelming increase in data volume, not only in samples but also in available features. Feature selection, the process of selecting the relevant features and discarding the irrelevant ones, has been successfully applied over the last decades to reduce the dimensionality of the datasets. However, there is a great number of feature selection methods available in the literature, and choosing the right one for a given problem is not a trivial decision. In this paper we will try to determine which of the multiple methods in the literature are the best suited for a particular type of problem, and study their effectiveness when comparing them with a random selection. In our experiments we will use an extensive number of datasets that allow us to work on a wide variety of problems from the real world that need to be dealt with in this field. Seven popular feature selection methods were used, as well as five different classifiers to evaluate their performance. The experimental results suggest that feature selection is, in general, a powerful tool in machine learning, being correlation-based feature selection the best option with independence of the scenario. Also, we found out that the choice of an inappropriate threshold when using ranker methods leads to results as poor as when randomly selecting a subset of features.