Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation via Efficient Attention Augmented Convolutional Networks

Self attention can extract global information by operating on the whole input while convolution layer only operates on a local neighborhood. So concatenating the outputs of convolution and self attention can augment the ability of collecting the contextual information of convolutional networks. However, the complexities of memory and computation of self attention will grow quadratically with the input size, which hinders its applicability on high-resolution images. Thus, we propose the efficient attention augmented convolution module to solve the complexity problem caused by self attention. In this module, there are three branches of operations, which are convolution, efficient attention and column-row attention respectively. Efficient attention has linear complexities with input size by switching the order of matrix multiplication of self attention. Column-row attention is a column attention operation followed by a row attention operation, which is used to collect the spatial information that efficient attention lack of for flattening its input. And the output of this module is the combination of the outputs of these three operations. We replace several convolution layers in fully convolutional networks with this augmentation module and get the efficient attention augmented convolutional networks. Then we test it on PASCAL VOC 2012 semantic segmentation task, and the experimental results show that all the augmented models have improvements on performance compared with those baselines not being augmented.
High SNR Processing for Low-Light Images

This image enhancement on a single image for better visibility and higher SNR (signal-to-noise ratio) is an under-constrained problem, which becomes more challenging as this image is under the low-light condition. In order to solve the above problems, we firstly analyze the reason that classical Retinex enhancement result has low SNR due to ignoring the influence of noise in reflectance. To this end, combined with the actual illumination-reflectance prior, a new convex optimization function is proposed to estimate noise-suppressed/detail-preserved reflectance and spatial piece-wise smoothed illumination simultaneously. Especially, although the objective of the proposed method is a hybrid non-smooth problem, by subtly decomposition of the objective, we can solve it effectively. The final low-light enhancement result with pleasant visual performance and high SNR can be effectively obtained according to recompose the adjusting illumination and reflectance. Qualitative and quantitative experiments have illustrated that the proposed method has a higher SNR and more pleasing visual effect than state-of-the-art image enhanced techniques under low-light conditions.
Task-oriented system test parameter automatic detection technology

research-article Task-oriented system test parameter automatic detection technology Share on Authors: Ruchang Huang Chinese Flight Test Establishment, China Chinese Flight Test Establishment, ChinaView Profile , Xiaopeng Qi Chinese Flight Test Establishment, China Chinese Flight Test Establishment, ChinaView Profile , Jing He Chinese Flight Test Establishment, China Chinese Flight Test Establishment, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 52Pages 1–8https://doi.org/10.1145/3469213.3470252Online:18 August 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 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
Action-Insensitive Embodied Visual Navigation

Embodied visual navigation is an important task that the agent learns to navigate to a specific target object based on egocentric visual observations, by performing specific actions in the environment. However, there exists a problem of mismatch between the training and testing action spaces through learning methods, and methods used to solve this problem have been scarcely developed. In this paper, we propose a novel problem of the action-insensitive embodied visual navigation task with different action spaces of the agent between the training and testing process. A robust adversary learning framework is built to learn a general and robust policy that can adapt properly to different action spaces. The proposed model in the first-stage adversary training learns a robust feature representation of the agent’s states and transfers the trained strategy to new action spaces with fewer training samples in the second-stage adaptation training. Experiments on 3D indoor scenes validate the effectiveness of the proposed approach.
Optimization of training time for self-conducting classes by cadet managers using a computer program in everyday activities

The paper describes the process of training sessions, where cadets-leaders are trained to independently teach military personnel general military regulations with an emphasis on the procedure for monitoring training time. Observations of military personnel conducted by the authors during the training showed that the issue of monitoring the methodological parts and the breaks between the hours of the class makes it necessary to distract the attention of military personnel from training with a certain frequency. Moreover, the switching of attention occurs among the cadet-head and the duty officer for the training group. The authors propose to solve this problem by automating the control of the chronology of the lesson through the use of computer technologies. As a result, a computer program was developed; it performs operations of monitoring training time in automatic mode and, if necessary, visualizes its timing. Testing of the developed program for military personnel training in the course of daily activities confirmed the hypothesis of the study and showed that after a period of cadets adaptation to the automation of training time control, its optimization is achieved due to a more complete concentration of students on the training process.
A Brief Talk about the Modern Application of Computer Software Technology and the Development Trend of Research

Abstract With the progress of the times, we find that with the development of high technology and the dawn of a new era of high technology, the modern society has become more and more dependent on computers, if suddenly lose computers, the world will be in a panic. Therefore, the development of computer software technology in the future is the trend, will get more attention and development. Therefore, the purpose of this paper is to explore the modern application of computer software technology and future development trends. After searching for the industry about the penetration of computer software technology and the annual growth of high-tech enterprises and network data on computer technology, this paper uses K NN algorithm to conduct comprehensive analysis and evaluation, and discusses the modernization of computer software technology and the future development trend of computer software technology. The experimental results show that the modern application of computer software technology in the future is the trend, which will get the key help and full development of the country.
Quality Assessment of Screen Content Images Based on Convolutional Neural Network with Dual Pathways

To simulate the characteristics of perceiving things from binocular vision, a dual-pathway convolutional neural network (CNN) for quality assessment of screen content images (SCIs) is proposed. Considering the different sensitivity of retinal photoreceptor cells to RGB colors and the human visual attention mechanism, we employ a convolutional block attention module (CBAM) to weight the RGB channels and their spatial position on each channel. And 3D convolution considering inter-frame information is used to extract the correlation features between RGB channels. Moreover, because of the important role of optic chiasm in binocular vision, we design its simulation strategy in the proposed network. Furthermore, since the characteristics of multi-scale and multi-level are indispensable to perception of any objects in human visual system (HVS), a new multi-scale and multi-level feature fusion (MSMLFF) module is built to obtain perceptual features of different scales and levels. Experimental results show that the proposed method is superior to several mainstream SCIs metrics on publicly accessible databases.
Open-Set Domain Generalization VIA Metric Learning

In this study, we address open-set domain generalization, which aims to reject unknown class samples while classifying known class samples in unseen domains. Conventional domain generalization has the problem of unknown class samples being classified as known classes because domain generalization methods align feature distributions without distinction between known and unknown classes. To tackle this problem, we propose a decoupling loss that diffuses the feature representations of unknown samples. The loss allows us to construct a feature space that can better distinguish unknown samples. We demonstrate the effectiveness of decoupling loss using open-set domain generalization benchmarks.
Analysis on interaction experiences with intelligent technology and preference of technology use in nursing care

The shortage of human resources in Japan has become a serious social issue in nursing care services owing to the rapid increase in the demand and the declining working population. Although the use of intelligent technologies such as robots, AI, and IT is expected to offer solutions, little progress has been made in the introduction and extensive use of these technologies in nursing care services. In this study, we aimed to clarify the factors that increase the preference for technology use in nursing care services from the perspective of trust. We conducted an online survey on attitudes towards nursing care and technologies in Japan, and statistically analyzed the relationships among the preference for technology use in nursing care, trust in technologies, and personal characteristics. The results revealed that those with high trust in intelligent technologies were more likely to prefer the use of such technologies in nursing care services.
Deep Learning Based Sentiment Analysis for Phishing SMS Detection

Sentiment analysis works on the principle of categorizing and identifying the text-based content and the process of classifying documents into one of the predefined classes commonly known as text classification. Hackers deploy a strategy by sending malicious content as an advertisement link and attack the user system to gain information. For protecting the system from this type of phishing attack, one needs to classify the spam data. This chapter is based on a discussion and comparison of various classification models that are used for phishing SMS detection through sentiment analysis. In this chapter, SMS data is collected from Kaggle, which is classified as ham or spam; while implementing the deep learning techniques like Convolutional Neural Network (CNN), CNN with 7 layers, and CNN with 11 layers, different results are generated. For evaluating these results, different machine learning techniques are used as a baseline algorithm like Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Artificial Neural Network (ANN). After evaluation, CNN showed the highest accuracy of 99.47% as a classification model.
Using Deep Learning Techniques for Endoscopic Image Analysis

The method for the diagnosis of inflammatory bowel disease by classifying endoscopic images using deep learning techniques was developed. The developed model based on convolutional neural network was trained, evaluated and tested on a dataset of endoscopic images. The results obtained indicate the possible effective use of the model in the diagnostic process.
Discussion on the Problems of Computer Assisted English Translation

Abstract As a mainstream subject, English is the second category of linguistics which is attached great importance to in China. English translation is a form of language connection between Chinese culture and international culture [1]. With the continuous progress of today’s computer technology, the emergence of a huge amount of translation makes the efficiency of old manual translation reach the upper limit. In order to meet the social needs of English language translation, scholars put forward the form of computer-aided translation. Through a lot of practice, it is found that this form of translation can greatly improve the efficiency of English translation. However, it gradually in the use of various problems also appeared. This paper briefly describes the advantages and disadvantages of computer-aided translation. On this basis, this paper also puts forward some strategies to solve the problem.
On The Development Planning Of Teaching Management and Teaching Evaluation Information System in Colleges and Universities in the Environment of Massive Data

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
HalftimeHash: Modern Hashing Without 64-Bit Multipliers or Finite Fields

HalftimeHash is a new algorithm for hashing long strings. The goals are few collisions (different inputs that produce identical output hash values) and high performance. Compared to the fastest universal hash functions on long strings (clhash and UMASH), HalftimeHash decreases collision probability while also increasing performance by over 50%, exceeding 16 bytes per cycle. In addition, HalftimeHash does not use any widening 64-bit multiplications or any finite field arithmetic that could limit its portability.
Development of a method for calculation of camera parameters using feature points for which 3D coordinates are known outside and unknown inside the control volume

The purpose of this study was to develop a method for calculation of camera parameters using feature points outside the control volume (known three-dimensional coordinates) and feature points inside the control volume (unknown three-dimensional coordinates, as filmed by multiple cameras). The film process used in this study was as follows: (1) The camera platform position and focal length for properly filming the control volume were selected. (2) Without changing the camera platform position and focal length, the cameras were panned and tilted to film the feature points outside the control volume (number of images = 4). (3) The camera platform joints were adjusted and fixed to the film control volume. Finally, (4) the feature points inside the control volume were filmed by the cameras. From the images obtained, the digitizer coordinates of the feature points inside and outside the control volume were collected. The focal length and camera platform parameters (such as the position, lean, and joint angle) were estimated using hybrid particle swarm optimization and a genetic algorithm to minimize the sum of the reprojective errors of the feature points outside and inside the control volume. The camera parameters at the moment of filming the control volume were calculated using the focal length and camera platform parameters. Three-dimensional coordinates as accuracy verification markers (70 points) inside the control volume were reconstructed using the estimated camera parameters. The RMS errors of the accuracy verification markers in the CCUP method and a DLT method developed in a previous study were 10.5 and 4.4 mm, respectively. These results reveal that the CCUP method can be adapted for collection of three-dimensional coordinates.
Weakly-Supervised Multiple Object Tracking Via A Masked Center Point Warping Loss

Multiple object tracking (MOT), a popular subject in computer vision with broad application areas, aims to detect and track multiple objects across an input video. However, recent learning-based MOT methods require strong supervision on both the bounding box and the ID of each object for every frame used during training, which induces a heightened cost for obtaining labeled data. In this paper, we propose a weakly-supervised MOT framework that enables the accurate tracking of multiple objects while being trained without object ID ground truth labels. Our model is trained only with the bounding box information with a novel masked warping loss that drives the network to indirectly learn how to track objects through a video. Specifically, valid object center points in the current frame are warped with the predicted offset vector and enforced to be equal to the valid object center points in the previous frame. With this approach, we obtain an MOT accuracy on par with those of the state-of-the-art fully supervised MOT models, which use both the bounding boxes and object ID as ground truth labels, on the MOT17 dataset.
Innovative Application of Ancient Chinese in the Teaching of Classical Chinese Reading in Middle Schools

At present, the requirements for the teaching of classical Chinese in the new Chinese curriculum standards for middle schools pay more attention to students' emotions, attitudes and values. The college entrance examination is also more inclined to examine students' classical Chinese reading ability and students' classical Chinese literacy. Based on the above background, the research purpose of this article is the innovative application of ancient Chinese in the teaching of classical Chinese reading in middle schools. Ancient Chinese, as a basic and tool course for Chinese language and literature majors in colleges and universities, plays an important role in the whole Chinese language and literature majors. For more than sixty years, the construction of ancient Chinese textbooks in colleges and universities has grown out of nothing, with fruitful results, and has continued to adapt to the needs of teaching and reformed the textbooks. This article first puts forward the strategies of applying ancient Chinese in the teaching of classical Chinese reading in middle schools. First of all, it discusses the necessity of using ancient Chinese in the teaching of classical Chinese reading in middle school from the characteristics of the selected chapters of classical Chinese in middle school and the advantages of ancient Chinese method in classical Chinese reading in middle school. And then put forward the principles of ancient Chinese in the teaching of classical Chinese reading in middle schools, namely the principle of stimulating interest and paying attention to the subject, the principle of rationality, and the principle of moderation. Finally, according to the characteristics of classical Chinese in middle school, a feasible method and strategy are summarized.
An Image Retrieving Method Based on Strong Constrained Manifold-embedded Hashing

Abstract In recent years hashing methods have attracted considerable attention. The main procedure of manifold hashing methods is to convert the high dimension features into binary codes and then use manifold distance to approximate the original Euclidean distance. However, there still is a problem that how to directly preserve the manifold structure by hashing. Besides, because of information loss, the effect of using existing hashing methods to retrieve images is not ideal when the length of binary code is very short. In order to solve these problems, we proposed a strong constrained manifold-embedded hash (SCDMH), which can not only explore the nonlinear manifold structure of data, but also make full use of the semantic information in the data. Additionally, the objective function we proposed reconstructs the original features of the datasets and the hash representation mutually to preserve the semantic information. Our experimental results on three large benchmark datasets demonstrate that SCDMH outperforms other state-of-the-art baselines.
A Belief Propagation-based Quantum Joint-Detection Receiver for Superadditive Optical Communications

We design a quantum joint-detection receiver for binary-phase-shift-keyed optical communications using belief propagation with quantum messages. For an exemplary tree code, the receiver attains the block-Helstrom limit in discriminating the codewords and achieves superadditive capacity.
Temporal-scope grammars for polyphonic music generation

We present temporal-scope grammars for automatic composition of polyphonic music. In the context of this work, polyphony can refer to any arrangement of musical entities (notes, chords, measures, etc.) that is not purely sequential in the time dimension. Given that the natural output of a grammar is a sequence, the generation of sequential structures, such as melodies, harmonic progressions, and rhythmic patterns, follows intuitively. By contrast, we associate each musical entity with an independent temporal scope, allowing the representation of arbitrary note arrangements on every level of the grammar. With overlapping entities we can model chords, drum patterns, and parallel voices -- polyphony on small and large scales. We further propose the propagation of sub-grammar results through the derivation tree for synchronizing independently generated voices. For example, we can synchronize the notes of a melody and bass line by reading from a shared harmonic progression.
UNSUPERVISED ANOMALY DETECTION FOR TIME SERIES WITH OUTLIER EXPOSURE

It is of great practical significance to accurately model and analyze abnormal events in time series. For example, the identification of anomaly patterns on infrastructure sensor curves helps locate equipment failures. In this paper, we propose an unsupervised anomaly detection approach for time series, which can comprehensively consider both point anomalies and subsequence anomalies. We innovatively introduce RNN into the architecture of Adversarial Autoencoder to better analyze anomaly events based on overall relationship of time series. In addition, we innovatively apply the Outlier Exposure technique for the performance optimization of anomaly detector. Meanwhile, a WGAN-based method is utilized to generate anomaly datasets through normal distribution learning. Finally, we apply the proposed method for fraud detection on a financial statement dataset and intrusion detection on a network traffic dataset. Experimental results demonstrates that our model can comprehensively consider different anomaly types in time series, and achieve promising detection performance overall. In the experiment of fraud detection, the LSTM integrated AAE model achieves an F1 score of 0.810, while the Outlier Exposure enhanced model achieves an F1 score of 0.894. This indicates that our method can improve the performance of current audit systems and facilitate discovering malicious behaviors.