Reviewerly/exHarmony huggingface dataset:
Rectifying Mode Collapse in GANs

GANs or Generative Adversarial Models have done exceptionally good in the field of generative models. It involves two adversarial networks Generator and Discriminator compete against one another. They have a lot of applications which involve data generation, image-to-image translation, text-to-image translation etc. However GANs have the anomaly of mode collapse. The generator over-optimizes for a specific discriminator with each iteration, and the discriminator never learns how to get out of the trap. As a result, the generators switch between a small number of output types. In this paper we are going to compare the different ways in which the issue of mode collapse is tackled while implementing GANs.
Metaphysical Conflict Evolution in Ivan Vyrypaev’s Dramaturgy

The aim of the study is to determine the artistic features of the conflict in Ivan Vyrypaev’s dramaturgy in the context of the “new drama” and to trace its evolution. The scientific novelty lies in determining the nature of the conflict in Ivan Vyrypaev’s dramaturgy and describing its evolution from the early plays to the modern ones. As a result, based on a comparative analysis of the early and recent periods plays (2002 and 2020), the commonality of I. A. Vyrypaev’s and other new drama authors’ attitudes towards the metaphysical nature of the conflict is ascertained. At the same time, the individual specificity of the conflict in Vyrypaev’s dramaturgy is revealed, which distinguishes him from other representatives of the school: his conflicts are of discursive nature and are realized, as a rule, in the characters’ imagination, which actualizes the “theatre in the theatre” technique.
A Non-Entity Approach for Intent-Based Classification: A Case Study of Thai News

Conjunction and stop words are normally ignored in text classification task that is content-based, such as classifying news into entertainment or sports. However, they are useful in this study, since the content and the intention of the document are independent. This paper studies intent-based classification that specifically desires to classify the author’s intention of Thai news article into three intents, ‘inform’, ‘explain’, and ‘give solution’. These three intents subtly co-exist with the content of the article and thus is our classification challenge. Our experiments confirm that intent-based classification needs a different approach from those techniques used for content-based classification. Accordingly, we propose a new pipeline for Thai intent-based classification such that conjunction and others can play a significant role above entity. Our contributions include (1) proving the need for a new methodology to handle intent-based classification and (2) proposing the Non-Entity data processing approach to managing intent-based classification problems. The proposed methodology shows partially promising results. Nonetheless, flags for enhancement are also discussed in the conclusion for future works.
Boosting remote multi-user AR privacy through a magic rope

In remote Multi-user AR (MuAR), a user shares with remote users his/her physical environment, which is enhanced by computer-generated perceptual information. Despite its important role in Metaverse, a user may have serious privacy concerns for MuAR: the user may want to share only a portion, or may want to block certain areas in their environment. Today's object detection and AR tracking techniques fall short of reliably, efficiently, and accurately supporting this use case, in particular on an (already overloaded) headset without offloading to an edge/cloud server for privacy preservation purposes.
A Roberta-Seq2Seq based model for Chinese text abstractive summarization

In order to overcome the limitations of existing generative text summary generation algorithms in Chinese text and improve feature extraction ability of traditional deep learning models, a generative Chinese text summary generation model based on RoBERTa-Seq2Seq is proposed. The pre-training model RoBERTa is used to learn the dynamic meaning of current words in a specific context, so as to improve the semantic representation of words. Based on the Seq2Seq model, Luong Attention is used to further enhance global information. The experimental results show that our model’s ROUGE score is higher than some other traditional Seq2Seq models, which indicates that our RoBERTa-Seq2Seq based model can effectively improve the semantic representation ability of the generated summary in Chinese text and improve feature extraction ability of the traditional deep learning model.
A Novel Approach to Image Colorization Algorithm using Convolutional Neural Networks

This work tackles the topic of imagining a noticeable color rendition of a picture using a grayscale image as input. Because this issue is plainly unconnected, earlier solutions have generally depended on extensive user engagement or resulting in underexposed clarifications. In this paper, a fully automated method is developed to create rich as well as lifelike colors. It presents the challenge as a classification job to recognize the intrinsic ambiguity of the problem and employ rearranging classes throughout the training period to improve the variety of colors within results. At the time of testing, the system was set up as a feed-forward pass to Convolutional Neural Network (CNN) as well as trained on over one million color pictures. It marks the program by utilizing the “algorithm Turing test,” which asks individuals to tell the difference between our program's graphics and real-world photos. Our approach effectively deceived people in 32% of tests, which is far higher than prior methods. The CNN will be used to enhance grayscale conversion as well as auto colorization algorithms. Predicting brilliant and realistic colors will be done using a feed-forward, two-phase system based on CNN.
Sentiment Analysis Using K Means Clustering on Microblogging Data Focused on Only the Important Sentiments

The aim of this paper is to perform sentiment analysis on a microblogging data (Twitter data is taken for the experiment) using k means clustering algorithm. The main intention here is to study the conditions that may favor or not, sentiment analysis using k means clustering considering only two major sentiments as positive and negative. The other sentiments which are not of major interest are made into another cluster and the accuracy of the clusters thus formed is analyzed and meaningful conclusions from the same can be obtained.
Color Image Restoration from Low Resolution Based on Multiscale Decomposition

A successful method for improving the clarity of dim light image that has been compromised by unbalanced lightning is to evaluate and compress the lightning portion. Due to inaccurate calculation, some previous approaches like edges are affected in improved image. The weighted least square multi scale image decomposition model's goal is to separate the light layer as effectively as possible. Later enhancement effect is obtained by adjusting the illumination layer. The proposed decomposition model is used to the value channel in HSV space in order to avoid colour artefacts caused by illumination adjustments and reduce computing complexity. Experiments reveal that the advance approach works well with a outspread range of dim images.
IndieSign: A Learning Module for Indian Sign Language using Supervised Machine Learning Techniques

Globalization is moving at a rapid pace with virtually no borders amongst various cultures and societies, the hearing-impaired face a monumental challenge to be included in the multi-faceted society. There has been a recent push towards introducing inclusiveness policies so that the hearing impaired shall have an easier way to be included in the society. However, as hearing-impaired people are stigmatized as being “diseased”, people do not interact with them. This affects their mental well-being and discourages future interactions. With a recent push towards digitization, it is believed that providing an online learning module can promote inclusiveness between the two parties. The issue of not being taught the concept of a word makes the learning incomplete. This is not only restricted to day-to-day concepts but also niche categories like technology, agriculture etc. To bridge this gap, this paper presents a unique model through which any person would be able to learn the words along with its concepts and their signage. As people with no hearing impairment tend to not learn sign language, this paper discusses methods through which learning sign language can be made unexacting.
Covid19 Vaccination and Lifestyle Counsel using Machine Learning algorithms

Machine Learning is a predominant area in Artificial Intelligence. It gets the ability to make predictions by learning the past observed values and information. This learning process is Machine Learning. A large amount of data is accessed and processed to gain more accurate results. Nowadays anyone around the world can use any Machine Learning algorithm to obtain competitive and accurate results. The main objective of this project is to recommend the Life style modification of the people after covid19 and to predict whether the particular person needs for the vaccination intake or not by accessing thousands of patient details. Hence the accuracy rate is very high compared to other predicting processes. These techniques are used to predict the current health conditions of the people.
Vehicle-mounted multi-object tracking based on self-query

The on-board visual multi-object tracking task is a basic function of vehicle intelligent driving, which plays a connecting role in various applications and research fields such as traffic control, automatic driving and human-computer interaction. However, neither the method of using detection technology to locate and then using data association technology to generate target trajectory nor the method of combining detection and tracking fail to solve the problem that tracking performance (i.e. MOTA, MOTP, IDSW and other indicators) and tracking speed cannot coexist. This also makes it difficult for the existing multi-target tracking algorithms to be widely used in smart vehicles. To this end, we have designed an end-to-end deep learning multi-object tracking method that can be used in the vehicle, namely the self-query tracker, SQT. Specifically, the input of the algorithm consists of two parts :the current frame, and the tracking result of the previous frame. Firstly, the current frame is input to the backbone network to obtain the feature graph A. The feature graph A is input into the detection branch, and the detection object of the current frame can be quickly obtained through the regression between the heat map and the frame. Then the feature map A of the current frame is flattened and input to the coding-decoding network composed of Transformer. The tracking result of the previous frame is used as the query vector to obtain the position map of the tracking object of the previous frame in the current feature map. The final tracking result can be obtained by matching the two results (the detection result and the position mapping of the tracking object in the previous frame in the current feature graph). The training and verification based on MOT20 data set show that the inference time of each frame is about 44ms, and the multi-target tracking accuracy is 58.9%. The model is integrated into intelligent vehicle ROS platform for testing, and the test results show that the proposed algorithm can realize multi-target real-time tracking in complex traffic scenarios, and the algorithm has good practical application value. On the platform using RTX 2080Ti, the proposed method reached 15+ FPS and the MOTA score was 58.9 on the MOT20 dataset.
Robotic Trash Collector Boat Using Artificial Intelligent Techniques

The main aim of the proposed work is to remove the floating trashes that are disposed in the river banks, lakes, ponds, estuaries and urban drain. In this proposed system, Artificial Intelligence enabled robotic trash boat is designed to harvest these floating trashes without any manual intervention. The system is integrated with the microcontroller unit which is connected with the camera, ultrasonic sensor, GSM module, power supply, motor driver and servo motor. The camera is used to detect the wastes and captures its image with the help of image processing. The ultrasonic sensor is used to detect the obstacles in order to drive the boat properly. The robotic arm which is driven by servo motor is programmed to find and collect the floating trash. When the Power supply is fed to the microcontroller, the motor which is coupled with the propeller and conveyor starts to operate where the trashes are collected by the conveyor and dumped in the bin present inside the boat. Once the trashes are filled, the information is sent to the GSM module and the boat would return to the hub with the help of GPS and it will throws out the waste. The information that is received from the GSM module can be sent to the mobile phone for future purpose.
Comparison and Analysis of Proposed KNN-based Colorization Methods in Myanmar Movie Clip using 8-neighboring Pixels and Color Features Extraction

With the rapid development of information technology and increasing image data, the study of image colorization has become an important role. There is no doubt that colors make a lot of difference in the viewing experience, so colorizing black and white movies was the hot topic in 1980s. For decades, many movie creators opposed the idea of colorizing their black and white movies, and thought of it as vandalism of their art. Today it is accepted as an enhancement to the art form. There have been different ways to colorize images and videos. In this paper, the two KNN-based colorization method using 8-neighboring pixels and color features are presented to colorize and their performances analyzed.
Graph attentive transfer learning for few-shot session-based recommendation

The session-based recommendation predicts the user’s next-click preference with anonymous temporal sessions, which meets the considerable challenges in scarce item recommendation or the newly-established e-commerce platform. Those practical limitations severely constrain the performance of existing session-based recommendation models, which is called by few-shot session-based recommendation task. This paper proposes a Graph Attentive Transfer Learning (GATL) approach involving a source domain with sufficient data to distill useful knowledge into the target domain with limited sessions. Concretely, GATL contains an intra-session attentive feature learning module to explore the correlations among different items in each session and a cross-domain inter-session interactive feature learning with an adversarial transfer learning strategy to solve the few-shot learning in target session-based recommendation. The proposed modules ensure GATL can extract the intra- and inter-session graph feature vectors and feed them into an improved prediction layer for overall item prediction. Experimental results on two datasets (Diginetica and Retailrocket) demonstrate the effectiveness of our proposed GATL model on the few-shot session-based recommendation task.
THE SCRUTINY OF AI, ML, BIG DATA,DEEP LEARNING AND OTHER TECHNICAL VOWS AND CALLS IN NEPHROLOGY

Telepathology, which was first used in the 1960s, established the possibility of data exchange and communication for diagnostic evaluation. Human kidney biopsies done on individuals with primary renal failure are the major source of benign renal histopathology data. Most AI applications are still in the conceptual design phase. From computer vision to genomic data gathering, deep learning models are progressively have been used to analyze biological data. Initiatives such as CNN, FCNN, GAN deep hierarchical learning, and recurrent neural networks seem to be well. Over the next five years, the biomedicine material is expected to quadruple every 18–24 months. Going to follow the coronavirus pandemic, digitalization of treatments to intensify, especially in those societies that are always heavily reliant on technology, With Rising numbers of biopsies, data held in archives throughout the world are hardly digitized, and metadata is not standardized. To make matters harder, the development of robust algorithms that are sensitive to inter-laboratory differences necessitates the use of multicenter samples. In biology and wellness, ethical norms and data safeguards that do not endanger patient value are badly required. The kidney domain lags below most fields in terms of big data use in studies. This void, on the other hand, presents a chance for experts to pursue a career in nephrology and have a substantial impact on the discipline.
Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection

Summary Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail.
Computer Vision Application in Automobile Error Detection

This paper describes the authentic AI application using the computer vision model in the automotive sector, to find out the error in the automobile by scanning the picture or dashboard and vehicle parts. Since the automobile has begun a complex system over the years and it became very difficult to know and understand each part of its function. So, this AI application would help automobile owners and users to find out the system errors when in case the automobile has gone bad and not functioning in the middle of the fleet. The automobile is a fascinating innovation of the last two centuries and more and more developments have been seen in this sector. The safety and performance increased so drastically by the introduction of processing the functions through electronics and software sensing and actuation, thereby the complexity of the vehicle been increased and self-diagnosis of errors and automobile service been the huge opportunity there and been addressing and lots of gaps still in the addressable area. This paper describes such gap-filling applications in the automobile service sector using AI applications.
An airport object tracking method in foggy weather based on Siamese network

With the increasing number of passengers and the continuous expansion of the scale of the airport, the security of the airport has gradually attracted people’s attention. Aiming at the security needs of airports in foggy weather, an improved object tracking method based on SiamRPN is proposed, which solves the problems of drift and occlusion in the process of object tracking in foggy weather. First, a video pre-processing module plays the role of processing the input video, improving the target's visibility to be tracked in the video. Then, a global attention module is proposed and introduced in the feature extraction network. The global context information is integrated into the backbone network. The Region Proposal Network (RPN) network performs classification and regression operations and finally calculates the tracking results. The method is tested on a private data set that contains 20 airport videos. The results show that compared with SaimRPN, the improved method has more competitive performance. The AUC of success rate and precision has increased 6.5% and 3.1% compared with SiamRPN.
Semi-Supervised Cell Detection with Reliable Pseudo-Labels

Pathological images play an important role in the diagnosis, treatment, and prognosis of cancer. Usually, pathological images contain complex environments and cells of different shapes. Pathologists consume a lot of time and labor costs when analyzing and discriminating the cells in the images. Therefore, fully annotated pathological image data sets are not easy to obtain. In view of the problem of insufficient labeled data, we input a large number of unlabeled images into the pretrained model to generate accurate pseudo-labels. In this article, we propose two methods to improve the quality of pseudo-labels, namely, the pseudo-labeling based on adaptive threshold and the pseudo-labeling based on cell count. These two pseudo-labeling methods take into account the distribution of cells in different pathological images when removing background noise, and ensure that accurate pseudo-labels are generated for each unlabeled image. Meanwhile, when pseudo-labels are used for model retraining, we perform data distillation on the feature maps of unlabeled images through an attention mechanism, which further improves the quality of training data. In addition, we also propose a multi-task learning model, which learns the cell detection task and the cell count task simultaneously, and improves the performance of cell detection through feature sharing. We verified the above methods on three different data sets, and the results show that the detection effect of the model with a large number of unlabeled images involved in retraining is improved by 9%-13% compared with the model that only uses a small number of labeled images for pretraining. Moreover, our methods have good applicability on the three data sets.
A high-performance nuisance SMS recognition model

At present, nuisance SMS is getting more and more intense, and its forms are diversified, hidden and malicious. Not only does it waste cell phone resources, but it also affects people's normal use of cell phones. If the situation is bad, it will also affect the normal social order. In this paper, we use Word2Vec algorithm and LightGBM algorithm to select feature words and build a nuisance SMS identification model. By using the classical nuisance SMS training set for training, the results show that our model has an accuracy rate of over 99%. And our model has higher performance compared with the popular classification models.
A Geometric Method for Extracting Images of PDF Files

PDF is a format of the file that is used for showing research articles mostly. PDFs are actually easy to create, but extracting the data from PDF files is a challenging task. Images contain important as well as useful information which cannot be represented by text. The difficult concepts are better explained by an image than the text. In this paper, a feasible way to extract images from the PDF file is explored. This work is attempted to extract images from scholarly research publications of PDF files. The structure of a PDF file is entirely different from other file formats. A geometric method is proposed to get the position of an image in the PDF document and extracted all the images in that document. These images are saved in JPG or PNG or GIF or BMP format as a separate file. The resolution and size of extracted image are the same as the original image and are suitable for printing.