Reviewerly/exHarmony huggingface dataset:
Multi-Scale Semi-Coupled Convolutional Sparse Coding for the Super-Resolution Reconstruction of Remote Sensing Image

The traditional convolutional sparse coding super-resolution method only introduces the linear projection relationship in the feature space conversion and fails to consider the local detail in the feature map learning, which causes the reconstruction results to be unsatisfactory in terms of edges and details. The convolutional sparse coding theory is introduced into the super-resolution reconstruction framework of remote sensing images and a multi-scale semi-coupled convolutional sparse coding super-resolution reconstruction method is proposed. Firstly, the input image is decomposed by multi-scale to extract smooth components and multi-scale texture components, and the final smoothing components are reconstructed by bicubic interpolation. Then, the texture components of each scale are reconstructed by semi-coupled convolution sparse coding. The nonlinear convolution operator is used as the projection function between the high-resolution feature map and the low-resolution feature map of texture component at each scale and the non-local self-similarity structure in the feature map learning for constrained optimization is introduced to better reconstruct the texture image at each scale. Finally, the reconstructed smooth component and the texture component at each scale are superimposed to obtain the final reconstructed image. Remote sensing images from 4 different sensors are used as experimental images and the state-of-the-art super-resolution reconstruction methods are compared. Experimental results show that the reconstructed images obtained by the proposed method are better than other methods in quantitative analysis index PSNR and FSIM and show clearer boundary and detailed information and have a certain anti-noise performance.
Early Experiences of Noise-Sensitivity Performance Analysis of a Distributed Deep Learning Framework

Deep Learning (DL) applications are used to solve complex problems efficiently. These applications require complex neural network models composed of millions of parameters and huge amounts of data for proper training. This is only possible by parallelizing the necessary computations by so-called distributed deep learning (DDL) frameworks over many GPUs distributed over multiple nodes of a HPC cluster. These frameworks mostly utilize the compute power of the GPUs and use only a small portion of the available compute power of the CPUs in the nodes for I/O and inter-process communication, leaving many CPU cores idle and unused. The more powerful the base CPU in the cluster nodes, the more compute resources are wasted. In this paper, we investigate how much of this unutilized compute resources could be used for executing other applications without lowering the performance of the DDL frameworks. In our experiments, we executed a noise-generation application, which generates a very-high memory, network or I/O load, in parallel with DDL frameworks, and use HPC profiling and tracing techniques to determine whether and how the generated noise is affecting the performance of the DDL frameworks. Early results indicate that it might be possible to utilize the idle cores for jobs of other users without affecting the performance of the DDL applications in a negative way.
3dcascade-Gan: Shape Completion from Single-View Depth Images

Depth images can be easily acquired using depth cameras. However, these images only contain partial information about the shape due to unavoidable self-occlusion. Thanks to the availability of large datasets of shapes, it is possible to use a learning-based approach to produce complete shapes from single depth images. State-of-the-art generative adversarial network (GAN) architectures can produce reasonable results. However, the use of relatively local convolutions restricts GAN architectures from producing globally plausible shapes. In this study, we develop a novel dynamic latent code selection mechanism in which the model learns to select only important codes from the latent space. Furthermore, a novel 3D self-attention (3DSA) layer is introduced that is able to capture non-local relationships across the 3D space. We further design a GAN architecture that uses a multistage encoder-decoder to recover the shape, where our 3DSA layer is introduced to the discriminator to help attend to global features, which stabilizes the model learning and encourages shape refinement, making our reconstruction more structurally plausible. Through extensive experiments, we demonstrate that our method outperforms other state-of-the-art methods for single depth image 3D reconstruction.
Itemization framework of requirements using machine reading comprehension

In practice, it is very important to determine the requirements items of the proposed software system from the requirements document. However, due to the problems of ambiguity, redundancy, ambiguity, and difficulty in traceability of changes in the requirements documents described in natural language, the estimation results are subject to a certain degree, and the process is labor-intensive and cost-intensive. In this paper, we propose a new method to automatically extract requirement entries from requirement text by leveraging a set of natural language processing techniques and machine learning models. Our method is inspired by imitating the process of expert extraction of itemized requirements, which usually consists of three main processes: locating requirement locations and boundaries, building models, and extracting fine-grained requirement semantics. We performed evaluations in the field of military arguments, and the results showed that our model was nearly 80 percent accurate. Our approach can provide sound advice to help industry practitioners extract requirements items faster and easier.
Pressure as Propaganda

Abstract This chapter investigates how Chinese state and commercial media has historically dealt with international condemnation of human rights in the country, from Mao’s 1959 crackdown in Tibet, through the Tiananmen Square crackdown, to its aftermath of sanctions and UN resolutions in the 1990s and 2000s. The chapter shows the dilemmas of foreign pressure. State media has heavily censored external condemnation immediately after major instances of repression but reported it to a surprising degree and level of detail at other times, framing it as an attack on China from hostile and hypocritical rivals. The chapter illustrates how authorities’ censorship and rhetoric in response to pressure developed over time, as they became used to periodic condemnation from the international community.
Anomaly Detection in Dynamic Graph based on Deep Graph Auto-encoder

Dynamic networks are ubiquitous in daily life, such as the power data center and social network. Anomalies in dynamic networks seriously endanger the security of the network. Therefore, it is a critical task to detect anomalies in dynamic networks. This paper proposes an anomaly detection system based on network embedding learning, which encodes the dynamic network, learns the embedding vector of each node in the network, and performs anomaly detection by clustering the embedding vector. We propose a depth graph autoencoder model to learn the dynamic node embedding vectors. The we calculate the anomaly score based on the distance of the node to its nearest cluster center. Extensive experiments on real-life datasets are conducted to illustrate that proposed method outperforms state-of-the-art baselines. Compared with the existing methods, the method in this paper improves the AUC by up to 11%.
Area information resources of I. Franko Crimean Republican Universal Scientific Library

The key vectors of area studies and services of the Crimean central library under regional and interregional projects in the web-environment are discussed. These efforts are to expand the traditional methods of area information popularization, which has practical evidences. The study is of practical importance.
A feature attention based multi-stage network for image deblurring

A feature attention based multi-stage network for image deblurring is proposed. The feature attention module is introduced into the model. This module is composed of channel attention and pixel attention mechanism. More attention is focused on the blurred pixels and important channel information, solving the problem of uneven blurred distribution in images effectively. We also introduce the atrous residual block and the context module between the encoder and the decoder. The atrous convolution are combined with the residual, and the context module adopts the multi-layer atrous convolution, which effectively increase the receptive field of the network and better capture the multi-scale contextual information. Experiments were conducted on the public dataset GoPro to evaluate the performance of our method. The results show that the PSNR of the proposed model reaches 30.51, and the processing speed reaches 0.035s, which outperform that of the most current deblurring methods.
A Bayesian Nonparametric Test for Cross-Group Differences Relative to a Control

Gutiérrez, Iván Gutiérrez, Luis Alvares, DaniloWe propose a new Bayesian nonparametric multivariate testing procedure for comparing several treatments against a control. The test is based on a general model where the distribution of each treatment group can be identical to (or different from) the control group distribution, depending on the value of a latent binary vector. This vector is endowed with a spike-and-slab prior distribution carefully chosen to ensure a multiplicity correction. Group distributions are modeled in a flexible way using a dependent Dirichlet process. Monte Carlo experiments suggest that our proposal performs better than state-of-the-art frequentist alternatives for small sample sizes.
The use of a digital educational and information system and innovative techniques in improving the literacy of foreign students of technical specialties

The article describes the course "Speak Russian Correctly", which is included in the portfolio of university-wide disciplines. The course is based on the author's method of applying innovative techniques of graphical representation of information Fishbone and Mindmap, which makes it especially attractive for students of technical specialties who better perceive information presented in the form of diagrams and algorithms. Training is carried out while implementing a communicative approach. The course is posted in the telecommunication educational and information system of the Peoples' Friendship University of Russia (Russia, Moscow). The opportunities provided by digital technologies allow you to study it in any format: face-to-face, intramural and distance learning. The article also presents algorithms for drawing up Fishbone and Mindmap diagrams, author's exercises for working both with a teacher and independently, a telecommunication educational information system toolkit, which helps to increase the literacy of foreign students and their motivation to learn the Russian language.
GAMIFICATION OF DISCUSSOO: AN ONLINE AI-BASED FORUM FOR SERIOUS DISCUSSIONS

Engagement in the discussion process is one of the common challenges of asynchronous online forums.It becomes especially crucial if the discussion is organized over a serious topic about a complex problem with a group of diverse stakeholders.Gamification gives much promise in addressing this challenge.In this paper, we propose possible game design solutions to the engagement challenge for an existing online AI-based platform Discussoo and reflect on the results from the expert interviews and an experiment with students.
ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency.
Combine Clasification Algorithm and Centernet Model to Predict Trafic Density

Nowadays, the traffic situation is very complicated in Vietnam. Traffic jams happen frequently in densely populated or peak hours. So, it is necessary for an automatic warning system to police officers about traffic status in time and effectively. In this research, the system automatically estimates the vehicle motion rate and the number of vehicles. This system is useful for roads with lots of hard-to-distinguish traffic. The method is based on computer vision such as background subtraction and deep learning such as the CNN network model and the CenterNet object detection model. Experimental data are taken from videos in Vietnam with the view in front of the Kien Giang and Da Nang hospitals. The achieved classification model results can predict with 91.7% accuracy on the test set, the precision of crowded road predictions is 81.9% precision and 70.4% recall. When the CenterNet model is applied to estimate the number of vehicles, the model reached 1.261 MAE. The speed of the system when it is run experimentally on hardware using Nvidia Geforce GTX 1070 GPU reached 4.6 FPS.
A 3D reconstruction algorithm based on adaptive cost aggregation and depth map optimization

Aiming at the problems of large memory consumption, long calculation time and difficult reconstruction in weak texture and occlusion areas of the existing deep learning 3D reconstruction network, this paper proposes a cascade 3D reconstruction network algorithm based on phased estimation of resolution. First, a visibility-aware adaptive cost aggregation method is introduced to aggregate the cost volume, and the visibility of pixels in the view is obtained through the network; Secondly, the disparity range per pixel is predicted based on variance to construct a spatially varying depth hypothesis surface; Finally, an improved depth map fusion algorithm is used to check the consistency of the re-projection error between the pixel point and the 3D point, and a dense point cloud is obtained. The quantitative and qualitative comparison with other methods on the DTU data set shows that the algorithm in this paper can reconstruct scenes with better performance in detail and reduce GPU memory consumption and computing time.
A Span-Based Joint Model for Measurable Quantitative Information Extraction

As a set of essential information related to the quantity of objects, measurable quantitative information widely exists in various texts. This paper proposes a new span-based joint model with lexicon enhanced BERT for measurable quantitative information extraction, which incorporates both measurable quantitative information recognition and association within one model. A standard dataset containing 3,106 Chinese text sentences and a clinical dataset containing 1,359 Chinese electronic medical records are used for evaluation. Experiment results show that our joint model achieves an F1-score of 97.44% on the clinical dataset and outperforms baseline models by an improvement of 3.2% for recognition and 5.1% for association in F1-score on the standard dataset.
Image enhancement with luminance preserving is a false proposition and others

The “image enhancement with luminance preservation” is a self-conflict proposition. Researchers, including propositional proposer Kim, gave the index AMBE (absolute mean luminance error) to judge if the “luminance preserving”, however, which just proved that the image enhancement with “luminance preserving” cannot be realized. We have proved that the visual quality of an image is a function of the image average luminance with convex feature. The image cannot be enhanced with luminance preservation, and the luminance cannot be preserved with image being enhanced. The visual perception quality of an image is not equal to the image contrast. We can experimentally prove what the higher the contrast of the image, the better the visual quality of the image is not true.
Didactic provision ofteaching geography by means of media technologies

The educational portal is designed to provide students and teachers with completely new educational and methodological tools. The main purpose of using teaching materials and electronic textbooks created on the basis of computer software on the educational portal is to form a modern information and educational style. They will become not only learners, but also researchers who can search, analyze news from authentic Internet and additional sources. Currently, the number of electronic textbooks on geography is not at the level of meeting existing needs, furthermore there us a need for a perfect and complete development of methodological literature based on individual criteria.
Curriculum Learning in Peristaltic Sortation Machine

This paper presents a novel approach to train a Reinforcement Learning (RL) agent faster for transportation of parcels in a Peristaltic Sortation Machine (PSM) using curriculum learning (CL). The PSM was developed as a means to transport parcels using an actuator and a flexible film where a RL agent is trained to control the actuator. In a previous paper, training of the actuator was done on a Discrete Element Method (DEM) simulation environment of the PSM developed using an open-source DEM library called LIGGGHTS, which reduced the training time of the transportation task compared to the real machine. But it still took days to train the agent. The objective of this paper is to reduce the training time to hours. To overcome this problem, we developed a faster but lower fidelity python simulation environment (PSE) capable of simulating the transportation task of PSM. And we used it with a curriculum learning approach to accelerate training the agent in the transportation process. The RL agent is trained in two steps in the PSE: 1. with a fixed set of goal positions, 2. with randomized goal positions. Additionally, we also use Gradient Monitoring (GM), a gradient regularization method, which provides additional trust region constraints in the policy updates of the RL agent when switching between tasks. The agent so trained is then deployed and tested in the DEM environment where the agent has not been trained before. The results obtained show that the RL agent trained using CL and PSE successfully completes the tasks in the DEM environment without any loss in performance, while using only a fraction of the training time (1.87%) per episode. This will allow for faster prototyping of algorithms to be tested on the PSM in future.
Hand Gesture Recognizer Based on Artificial Intelligence Algorithms

As there is a significant barrier between the user and the machine, using a physical device for human-computer interaction.In this paper one of the Hand Gesture Recognizer Media Players is developed.It was initially built using machine learning.The Ai algorithms are used in our project to find gestures.It uses computer vision to count the number of fingers and executes the pre-defined actions.By saving time and energy, the major goal of completing this project is to simplify life.
Garbage classification and detection method based on improved YOLOX

In order to solve the problems of low efficiency and strong subjectivity of manual garbage classification, this paper proposes a garbage classification detection method based on improved YOLOX to improve the efficiency and accuracy of garbage classification. By training YOLOX network with self-made garbage classification dataset, garbage detection and classification can be realized. In order to get better test results, a YOLOX algorithm with ECA-Net attention mechanism is proposed to improve feature extraction ability and information transmission among features. Experimental results show that [email protected] of the improved algorithm is 89.2%, and the number of detected frames per second is 63.1. Compared with YOLOX algorithm, [email protected] improves by 3.3%, while the number of detected frames per second only decreases by 0.3. Compared with the original algorithm, the accuracy is greatly improved without loss of performance, which can meet the requirements of real-time detection.
Speech Recognition System Using DeepSpeech Architecture Method on VHF Radio Communication for Tanker Ship Officers at Indonesian Sea Ports

The loading and unloading of fuel oil cargo by tanker ships at ports in Indonesia has a problem in terms of time efficiency and speed. A virtual robotic is created to increase the time efficiency of the loading and unloading process. However, the robot needs a way to communicate with the tanker ship officers during the process. Because the port where the loading and unloading process took place is considered as a dangerous and explosive location, the only communication allowed is through voice communication via marine Very High Frequency (VHF) radio. The solution to overcome this problem is to design a technology that can perform speech recognition via marine VHF radio, one of which is using the Deep Learning method with DeepSpeech architecture. This paper has simulated speech recognition system using DeepSpeeh architecture method on VHF radio communication for tanker ship officers at sea ports. This paper has tested the DeepSpeech architecture to produce a speech recognition model with an average Word Error Rate (WER) value of 0.335 and an average Character Error Rate (CER) value of 0.263. This paper also analyzes the effect of variations in learning rate, dropout rate, and epoch value to get the best speech recognition system model.
Prediction of Unknown Terrorist Group Names Responsible for Attacks in Turkey

In this paper, the dataset of real incidents that occurred in Turkey between 2013 and 2017 and are regarded as acts of terrorism without any doubt according to Global Terrorism Database (GTD) are used to predict the group names responsible for unknown attacks. Principal Component Analysis (PCA) technique was used for feature selection. A novel voting method between five classification algorithms such as Random Forests, Logistic Regression, AdaBoost, Neural Network, and Support Vector Machine was used to predict the names. The results clearly demonstrate that the classification accuracy of all classifiers studied in this paper improved when PCA was used to select features as compared to selecting features without using PCA. The prediction of terrorist group names with PCA based feature reduction and the original features is carried out and the results are compared.