Reviewerly/exHarmony huggingface dataset:
Edge-Labeling Based Directed Gated Graph Network for Few-Shot Learning

Existing graph-network-based few-shot learning methods obtain similarity between nodes through a convolution neural network (CNN). However, the CNN is designed for image data with spatial information rather than vector form node feature. In this paper, we proposed an edge-labeling-based directed gated graph network (DGGN) for few-shot learning, which utilizes gated recurrent units to implicitly update the similarity between nodes. DGGN is composed of a gated node aggregation module and an improved gated recurrent unit (GRU) based edge update module. Specifically, the node update module adopts a gate mechanism using activation of edge feature, making a learnable node aggregation process. Besides, improved GRU cells are employed in the edge update procedure to compute the similarity between nodes. Further, this mechanism is beneficial to gradient backpropagation through the GRU sequence across layers. Experiment results conducted on two benchmark datasets show that our DGGN achieves a comparable performance to the-state-of-art methods.
Construction and Application of College English Teaching Management System Based on Computer Software

Abstract The foundation of English education in China is relatively solid. China’s compulsory education policy also tends to high-quality English education. Many parents hope their children can receive more advanced English education. They even spend a lot of money on English education and training institutions. With the increasing trend of internationalization, English language has become an important language way for us to communicate with international friends. This paper briefly introduces the historical process of English teaching. The research of English teaching management system based on computer software technology has become an important topic for many linguists.
Employment of New Cryptography Algorithm by the Use of Spur Gear Dimensional Formula and NATO Phonetic Alphabet

Cryptography is the branch of science. In cryptography, the techniques that are being used to protect data are derived from mathematical theories and a collection of calculations known as algorithms to process messages in many ways to encode it. In this paper, new approach to cryptography algorithm is mentioned in which the equation and numerals are adopted. This new finding is established on the unique amalgamation of mechanical concept and traces of alphabets used in NATO (North Atlantic Treaty Organization) phonetics alphabet.
Research and Analysis of Image Enhancement Algorithm in the Classification of Rock Thin Section Images

The structure of rock flakes is complex and difficult to be classified accurately. The proposed method to solve the problem is to use an image enhancement algorithm to enhance the rock slice image. In the study, the neural network ResNet50, which has a significant effect on fine-grained classification, was used to construct the rock cast thin section image classifier, and three image enhancement algorithms, CutOut, MixUp, and CutMix, were used to enhance the rock thin section image. The rock slice images used in the data set are from Ordos, and are divided into five categories according to the size of the rock. The experimental result obtained was that the CutOut algorithm performs well on the data set, and the accuracy of the classifier was as high as 93.39%, which is 1.3% higher than the result of only using ResNet50 for classification. The experimental results show the effectiveness of the image enhancement algorithm in the classification of rock slice images.
An Ensemble Approach on Scientific Paper Reviews for Sentiment Analysis

Sentiment analysis and opinion mining have developed significantly throughout the last decades. This study aims to assess, amongst many other aspects, people's feelings, opinions, and emotions of someone or something. This review's principal objective is to automatically assess the direction of the review and contrast this with the evaluation made by the reviewers. This will allow scientists to categorize and evaluate reviews cross-sectionally and encourage a scientific article's overall evaluation more critically. An EM-VC ensemble approach has been proposed for analyzing the reviews. This approach uses POS to achieve a syntactic sentence structure. With dictionaries, this syntactic structure enables the semantic orientation of the analysis via the scoring algorithm to be determined. To test the proposed approach's efficiency and performance about the baseline method, some tests have been performed using typical performance metrics like precision, accuracy, F1-score, and recall. The findings reveal that improvements are achieved in binary classification, ternary classification, & five-point classification compared to the existing HS-SVM method. Still, it provides a slight improvement in accuracy in the context of multiclass classification compared to others.
The KDD 2021 Workshop on Causal Discovery (CD2021)

As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore, there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.
Disentangling Latent Groups Of Factors

This paper proposes a framework for training variational autoencoders (VAEs) for image distributions that have latent groups of factors. Our key idea is to introduce a mechanism to predict the factor group an image belongs to while simultaneously disentangling factors in it. More specifically, we propose an architecture consisting of three components: an encoder, a decoder, and a factor-group prediction header. The first two components are trained with a VAE objective, and the last one is trained with the proposed algorithm using the loss of unsupervised contrastive learning. In experiments, we designed a task in which more than one group of factors were entangled by combining multiple datasets and demonstrated the effectiveness of the proposed framework. The Mutual Information Gap score was improved from 0.089 to 0.125 on a merged dataset of Color-dSprites, 3DShapes, and MPI3D.
College Mental Health Education Course Based on Ecological Experience Mode

There are many ways to construct ecological civilization, and education plays a fundamental role in the construction of ecological civilization. This article mainly researches the development of college mental health education courses based on the ecological experience model. In the specific method of index extraction, a combination of comprehensive method and factor decomposition is adopted. On the one hand, the comprehensive method is used for the primary selection of indicators. On the other hand, on the basis of extensive research and acquisition of a large amount of information and expert interviews, factor decomposition is used. In this article, the method of simple valuation weight is directly adopted. The ecological diversity of the evaluation subject should consider the ecological subject in many aspects, including the learning subject student, the teaching subject teacher and the teaching carrier. The data shows that about 60% of students show a recognized attitude towards college mental health education courses. The results show that the college mental health education course based on the ecological experience model promotes the construction of students' environmental awareness and the cultivation of environmental behavior ability.
Helping Professionals Select Persona Interview Questions Using Natural Language Processing

Personas are often created based on user interviews. Yet, researchers rarely make their interview questions publicly available or justify how they were chosen. We manually extract 276 interview questions and categorize them into 10 themes, making this list publicly available for researchers and practitioners. We also demonstrate an approach of using natural language processing to assist in selecting persona interview questions for a given use case.
A Computational Model Based on Neural Network of Visual Cortex with Conceptors for Image Classification

Artificial neural networks, especially for deep learning, has made great progress in image recognition in recent years. However, deep learning neural networks have the disadvantage of biologically implausibility and the excessive consumption of energy because of the non-local transmission of real-valued error signals and weights. With the rapid development of theories and applications in brain science, more and more researchers are paying attention to brain-inspired computational models in recent years. In this paper, we propose a novel computational model for image classification with conceptor networks and two visual cortex neural networks, including the primary visual layer (V1) and the feature orientation layer (V2). We have examined the performance of this model on the MNIST database, ORL face databases and CASIA-3D FaceV1 databases. Our model can achieve the same high classification accuracy with much fewer training samples than the other methods. Our results demonstrate that both of the orientation-selective characteristics of V2 layer and the feature detection of conceptors can provide remarkable contributions for efficient classification with small training samples.
Construction of an Ecological Model of Blended Assistant Teaching of College Oral English Based on “Internet +” Technology

Abstract According to the input and output of the language as the theoretical basis, from the learning of oral English in college, construct the oral English teaching mode of students, teachers, and the Internet. Different forms of teaching modes help students increase their interest in learning English. Improve the use rate of expressing English, let students develop the role of autonomous learning and deep learning, stimulate students’ initiative through the help of the Internet, and give play to the teacher’s auxiliary guidance, students’ autonomy and diversity in oral English learning The Internet is an integrated education model.
BOUNCE: An Efficient Selective Enumeration Approach for Nested Named Entity Recognition

The scenario that one entity contains other entities is known as nested entities. Nested named entity recognition is a fundamental and challenging task in various NLP applications. The state-of-the-art nested NER approach first enumerates all the text spans in a sentence and then performs classification. We realize that a large proportion of entities contain only one token which cannot be nested, and most text spans in a sentence are not entities and the full enumeration is thus costly and unnecessary. In this paper, we propose an efficient selective enumeration approach named BOUNCE. We decompose the nested NER task into two subtasks for identifying unit-length entities and the others respectively. We develop a delicate model for each subtask and perform joint training for both of them. To improve the efficiency, we employ a head detection module to locate the start points of entities, which acts as a filtering step before enumeration. We provide a detailed analysis on the time complexity of the existing nested NER techniques and conduct extensive experiments on two datasets. The results demonstrate that BOUNCE outperforms various nested NER techniques and achieves higher efficiency than the state-of-the-art method with comparable accuracy performance.
Expert System for Diagnosing Early Symptoms of Stroke Using the Fuzzy Mamdani Method

One of the dangerous diseases is stroke because it can disrupt the nervous system system in humans so that sufferers of this disease often experience paralysis in their body parts. Therefore, an application is needed to assist medical personnel when doctors are not available or are not on duty. Expert system applications are needed in early diagnosing the characteristics of the disease suffered by a patient. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques to solve problems. The purpose of this study is to make it easier for researchers to identify problems with stroke based on the symptoms experienced by the patient or a person. The research method used is the fuzzy mamdani method. Data collection techniques are quantitative data techniques. The results that have been achieved from this study are that it can prevent and reduce the risk of stroke that can occur as early as possible in sufferers or the community and reduce the death rate in stroke patients.
Tales from the Materialverse: Comic-Based Narratives and Character Cut-Outs for Co-Design Fiction

Sequential art in the form of comics is a powerful and effective vehicle for graphic storytelling and communication, rendering it a suitable means for design fiction. Few, dispersed examples introduce comic-based design fictions in HCI research, yet little is reported on the design and implementation process or the use of paper cut-outs. In this paper, we present our process of crafting and implementing comics and character cut-outs for facilitating co-designing fictions. Our utopian visions, “Tales from the Materialverse”, informed by our interest in future smart materials and their applications, were used as a provocative communication tool to mediate discussions and encourage critical thinking. Based on learnings derived from our process and an expert workshop, we propose suggestions for designing design fiction comics and cut-outs as an alternative co-design fiction approach.
Object Detector Friendly Frame Memory Compression Based on 1-D DWT and SPIHT

Frame memory compression (FMC) based on embedded compression (EC) modules is widely used to reduce the memory bandwidth (BW) in object detectors based on convolutional neural networks (CNNs), but the accuracy drop of the object detectors is inevitable due to due to image distortion caused by lossy EC. This paper proposes an integrated platform that equips FMC based on 1-D discrete wavelet transform (DWT) and set partitioning in hierarchical trees (SPIHT) to YOLOv2, a representative object detector. Furthermore, we propose a technique to improve the compression efficiency of 1-D DWT-SPIHT in order to maximize latency and power savings by reducing memory BW while minimizing the accuracy degradation caused by FMC. In particular, while most of the existing FMC studies have focused on improving the image quality, this study has a distinction in that it has focused on the accuracy of object detectors. The proposed method reduces the memory access of the input image by 50%, improving the throughput and reducing power consumption in proportion to the compression ratio with only a 0.01% reduction in mAP compared to baseline YOLOv2 without FMC.
Testing the Performance of Some New Hybrid Metaheuristic Algorithms for High-Dimensional Optimization Problems

This chapter tests the performance of five new firefly-based hybrid algorithms to solve unconstrained high-dimensional as well as fixed-dimensional optimization problems. Firefly algorithm has been successfully combined with bacterial foraging, flower pollination, pattern search, and grey wolf optimizer to present these high performing computing algorithms. Three types of benchmark functions are taken up to justify each of the hybrid propositions. The first two sets of test functions, namely, the unimodal and the multimodal functions, are employed to validate the exploitation and exploration features, respectively, of the suggested techniques. The convergence plots show good promise in terms of convergence speed and accuracy than the existing algorithms. Even the nonparametric test, viz., the Kruskal–Wallis diagram, was used to validate the test results. The third type of test functions constituting some fixed-dimensional multimodal functions is also evaluated using the integrated methods. The statistical measures of the error function are also performed considering 50 independent test runs. The rank-sum test of Wilcoxon is carried out to validate the test outcomes. The proposed methods can also be suitably utilized to solve constrained as well as multi-objective optimization problems.
Virtual Learning Objects in the teaching of Financial Accounting II of the Accounting and Auditing Career at Técnica del Norte University

Just as the use of Information and Communication Technologies has increased in education, one of the problems still present is the ignorance of strategies such as the use of (VLO) Virtual Learning Objects: their characteristics, construction process and strategies to achieve incorporation into academic activities; which implies a challenge for professors when introducing them into the classroom. This research proposes interactive strategies using VLO for the Accounting and Auditing Career at Técnica del Norte University (UTN). Through a quantitative study of documentary, descriptive scope, non-experimental and comparative nature, it was possible to show that the way students learn is different; because the master class is not enough to strengthen their learning, but it requires instruments that allow them to interact, collaborate and participate actively. As a result, an implementation proposal was generated, which integrates the VLO for the Financial Accounting II subject and its use in the teaching-learning processes inside and outside the classroom, creating a learning environment that mixes face-to-face and virtual activities that promote self-learning, motivation and collaborative work: this requires the professors to put their best effort into strengthening their digital and social skills that allow them to generate a more active collaboration environment to achieve comprehensive learning to design, create and apply new interactive strategies.
Literature Reading and Writing Proficiency in Class II Elementary School Students Through Online Learning

This study aims to describe the development of reading and writing literacy skills for second-grade students of SD Islam Al-Azhar 13 Rawamangun, East Jakarta. This research is a case study research that utilizes observation, interviews, document studies, and tests as data collection techniques. The development of reading and writing literacy for elementary school students in grade II begins with the emergency curriculum review process, syllabus and lesson plans design, learning implementation, and ends with an evaluation. The results of reading learning activities which consist of three activities of reading aloud reading texts, reading comprehension, and reading poetry texts indicate a very good level of students' reading skills. Writing lessons focused on writing simple poems according to pictures and writing simple texts according to pictures using punctuation marks and capital letters also showed excellent student skills. Thus, it is concluded that the online learning conducted by Al-Azhar Islamic Elementary School 13 Rawamangun to improve students' reading and writing literacy skills has been effective with various supporting factors.
Research on the Voltage Regulation of Distribution Network with Distributed Photovoltaic

research-article Research on the Voltage Regulation of Distribution Network with Distributed Photovoltaic Share on Authors: YU Xiaodong Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile , Cao Kan Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile , Yan Bingke Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile , Cai Defu Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile , Zhou Kunpeng Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile , Wan Li Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, China Electric Power Research Institute, State Grid Hubei Electric Power Co, Ltd, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 357Pages 1–6https://doi.org/10.1145/3469213.3471390Online:18 August 2021Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks1 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
A Handheld 3D Surface Measurement System

GelSight Mobile is a handheld, high-resolution, 3D surface measurement system. The system uses an elastomeric gel with a painted membrane that conforms to the inspection surface together with a camera and custom algorithms. The device is capable of quantifying surface geometry down to one micrometer on any rigid material, including metals, composites, plastics, and glass, regardless of the optical properties of the surface. A detailed 3D surface is reconstructed within seconds. GelSight, Inc., has validated the performance results for spatial (X-Y) and depth (Z) measurements, and developed several novel measurement algorithms of specific interest in the aerospace industry, including fastener flushness.
Visual Saliency Prediction Using Attention-based Cross-modal Integration Network in RGB-D Images

Saliency prediction has recently gained a large number of attention for the sake of the rapid development of deep neural networks in computer vision tasks. However, there are still dilemmas that need to be addressed. In this paper, we design a visual saliency prediction model using attention-based cross-model integration strategies in RGB-D images. Unlike other symmetric feature extraction networks, we exploit asymmetric networks to effectively extract depth features as the complementary information of RGB information. Then we propose attention modules to integrate cross-modal feature information and emphasize the feature representation of salient regions, meanwhile neglect the surrounding unimportant pixels, so as to reduce the lost of channel details during the feature extraction. Moreover, we contribute successive dilated convolution modules to reduce training parameters and to attain multi-scale reception fields by using dilated convolution layers, also, the successive dilated convolution modules can promote the interaction of two complementary information. Finally, we build the decoder process to explore the continuity and attributes of different levels of enhanced features by gradually concatenating outputs of proposed modules and obtaining final high-quality saliency prediction maps. Experimental results on two widely-agreed datasets demonstrate that our model outperforms than other six state-of-the-art saliency models according to four measure metrics.