Reviewerly/exHarmony huggingface dataset:
Multi-entity Topic Modeling and Aspect-Based Sentiment Classification Using Machine Learning Approach

Online reviews are essential for measuring the quality of business using sentiment analysis, opinion mining, and sentiment classification, which has achieved much popularity in recent years. The product quality has been evaluated based on a large number of user’s comments on social media. To evaluate such a kind of large data is a very tedious ask task in data mining. Choosing any product or service after checking existing users’ opinion is the general thing for new users. Still, it is too hard to evaluate such kind of large in single features. Such techniques have already been proposed by existing analysis, but numerous systems are facing negation handing, accuracy-related issues, and eliminating those problems. We proposed sentiment classification on restaurant review dataset using naive Bayes classification. This work also carried out data preprocessing using natural language processing. Various feature extraction technique has been used to collect unique and important features itself. Different cross-validation techniques are also used for experimental investigation. The dataset consists of approximately 3000 records. Each form is being analyzed utilizing the hybrid naive Bayes procedure, using a succession of NLP and a WordNet dictionary; this is a probabilistic method. Eventually, the result is a concern in the form of a matrix. The correctness of the intended approach is yet calculating with the help of the confusion matrix. The system provides better classification accuracy according to given aspect for real time and synthetic dataset.
Size-Sensitive Optimization of Loss Function on Vision-based Object Detection

Vision-based object detection is constantly seeking a solution of higher accuracy and faster speed. Loss function of object detection algorithm directly influences a lot on backpropagation of training and counts to the result of detection. To design a better loss function, different sizes of bounding boxes are much more considered in this paper. In detail, instead of the absolute distance, the location loss with the relative distance of bounding box sizes is calculated and an L2 regularization is added to the loss function. It is trained with VOC datasets and finally it comes out to the 58 frames per second (FPS) detection speed with 416×416 image and 82.38% mean average precision (mAP) on VOC validation dataset with the network backbone of Darknet-53. It is proved efficiently in surrounding information lost and overlapped daily objects detection and has good performance in real-time detection with 720p webcam.
ConvLSTM-based Neural Network for Video Semantic Segmentation

We propose a convolutional long short-term memory(ConvLSTM)-based neural network for video semantic segmentation. The network can capture the timing information between frames through the ConvLSTM module to improve the prediction accuracy. The back-bone network uses dense connection, atrous convolution, and pooling pyramid structure to expand the receptive field. During the training, to avoid over fitting, data augmentation and learning rate attenuation strategies were used. The proposed method is end-to-end trainable and evaluated on the street scene benchmark Cityscapes dataset. Experimental results show that, benefit from the ConvLSTM module, the proposed network can extract temporal information between frames effectively, and thus improve the accuracy of video semantic segmentation, especially for dynamic objects and small obiects. such as truck. pedestrian and pole.
HMER-Image To LaTeX : A Variational Dropout Approach

A lot of research has been done to convert textual information into digital format but cannot obtain the desired results when applied to handwritten mathematical expressions. It is mainly due to complex 2D structure and spatial relations of the expressions. Meanwhile, many models were developed using encoder-decoder-based approaches to solve the problem. In this paper, we present a novel approach based on an encoder-decoder model with variational dropout. The model takes an image of a handwritten mathematical expression as input and provides the one-dimensional LaTeX code as output. It achieved 35.8% and 34.6% accuracy on CROHME 2014 and 2016 datasets, respectively.
SDE-DualENet: A Novel Dual Efficient Convolutional Neural Network for Robust Stereo Depth Estimation

Stereo depth estimation is dependent on optimal correspondence matching between pixels of stereo-pair image to infer depth. In this paper, we attempt to revisit the stereo depth estimation problem in a simple dual convolutional neural network (CNN) based on EfficientNet that avoids the construction of a cost volume in stereo matching. This has been performed by considering different weights in otherwise identical towers of the CNN. The proposed algorithm is dubbed as SDE-DualENet. The architecture of SDE-DualENet eliminates the construction of cost-volume by learning to match correspondence between pixels with a different set of weights in the dual towers. The results are demonstrated on complex scenes with high details and large depth variations. The SDE-DualENet depth prediction network outperforms state-of-the-art monocular and stereo depth estimation methods, both qualitatively and quantitatively on challenging scene flow dataset. The code and pre-trained models will be made publicly available.
A Novel Upgraded Uniform Embedding Technique for JPEG Steganography

JPEG steganography finds extensive applications in the area of concealing the intelligent data in images. Minimal distortion embedding-based steganographic technique provides a precise distortion measure with feasible coding. This paper presents a new Upgraded Uniform Embedding Distortion (U2ED) for JPEG steganography that tries to maintain the statistical imperceptibility in both spatial and DCT domains. A new distortion measure is proposed for both AC modes and DCT blocks in the proposed U2ED scheme that aims to restrict the secret data into coarse and noisy regions, thus making it tough to be detected by the steganalyzer. Syndrome trellis codes are utilized to accomplish minimum distortion due to embedding of secret message. Effectiveness of the proposed scheme is analysed using BOSS base image database as used in literature using GFR steganalyzer combined with ensemble classifier. Experimental result based on 100 testing images indicates the goodness of the proposed U2ED against Uniform Embedding Revisited Distortion (UERD) at different payloads in terms of probability of error. Also, the proposed scheme competes well with JPEG Universal Wavelet Relative Distortion (J-UNIWARD) for quality factor 75 and tends to outperform it for quality factor 95.
PETS2021: Through-foliage detection and tracking challenge and evaluation

This paper presents the outcomes of the PETS2021 challenge held in conjunction with AVSS2021 and sponsored by the EU FOLDOUT project. The challenge comprises the publication of a novel video surveillance dataset on through-foliage detection, the defined challenges addressing person detection and tracking in fragmented occlusion scenarios, and quantitative and qualitative performance evaluation of challenge results submitted by six worldwide participants. The results show that while several detection and tracking methods achieve overall good results, through-foliage detection and tracking remains a challenging task for surveillance systems especially as it serves as the input to behaviour (threat) recognition.
Investigation of Serverless Architecture

Serverless computing is a new and still evolving type of cloud computing, which brings a new approach to the development of information systems. The main idea of serverless is to give an approach of doing computing without dealing with a server to a user. Such approach allows to reduce the cost of the system building and system support. It allows small companies to concentrate on their own system designing instead of thinking about infrastructure building and supporting. Also, a big problem of providing the system security on high level is on cloud’s provider engineering support service. Serverless approach allows to start business quickly without huge initial investment. There is an attempt to completely analyze features, benefits and drawbacks of serverless approach, its use cases and main patterns of Serverless architecture. What is more, different providers have been analyzed.
Using Co-evolution of Artefacts in Git Repository to Establish Test-to-Code Traceability Links on Method-Level

Test-to-code traceability links describe the test relationship between the test codes and the product codes. It is beneficial for maintaining the test codes during developing, but to manually create and manage such links are time-consuming and fragile. The automatic link establishing strategies can properly solve such an issue.
An Adaptive Cloud Bursting Job Scheduler based on Deep Reinforcement Learning

Cloud bursting is a possible solution to the continuously increasing demand for computing resources in high performance computing clusters or supercomputers. It offloads on-premises workloads to cloud resources scaled out on demand. In the supercomputers with the cloud bursting functionality, scheduler systems take an important role in terms of how much the computing workload is offloaded under the limited budget and how much the users&#x2019; job waiting time is slashed. However, the scheduler systems available today are not able to take full advantage of cloud bursting because they do not either assume a hybrid computing environment of on-premises and cloud resources or consider the cost of cloud usage. In this paper, we propose a deep reinforcement learning-based job scheduling algorithm that optimizes both job waiting time and the cost of cloud usage. The optimization can be conducted in an adaptive fashion to dynamic changes in the computing environment such as job arrival rate. Moreover, the proposed algorithm allows the administrators of the supercomputers to flexibly control the two objectives, i.e., the job waiting time and the cost of cloud usage by adjusting parameters in a reward function, which leads to a desirable cost-performance. The simulation results show that the proposed algorithm successfully learns the optimal job scheduling policy by maximizing the reward function even when the computing environment changes. We also confirm that the job waiting time and the cost of cloud usage can be optimized with the administrator&#x2019;s intended balance.
A Survey on Visual Data Mining Techniques and Applications

In the era of extensive data collection, mining and analysis, the use of data visualization tools has become very urgent. To better understand the visualization of data mining, we discuss the previous work in this field from the perspective of theoretical research and related application platforms. Firstly, the concepts of data mining and visualization are defined. Then, based on the classification system of data mining visualization, the application of visualization in the three data mining processes is reviewed in detail. Finally, on this basis, the development prospect in the future is studied.
Car-Like Robot Path Planning Based on Voronoi and Q-Learning Algorithms

This paper discusses a differential path planning issue for the mobile robot depending on Voronoi diagram (VD) and Q-learning algorithms (QL). The issues with re-arranging paths in a dynamic environment with obstructions are treated as an issue of looking for the best route between the start and target stage. Since the car-like robot is differentially system and its mathematically involved with the inward state of impediment because of that some modification will be embedded like the orientation method. This is a unique instance of a solitary vehicle, which just goes ahead at a consistent speed and can just turn left also, right. Voronoi diagram presents the world encompassing brilliant specialists for robots, computer games, and military issues, so improving the dependability, of arranging the environment using it will help and decrease Q learning calculations by re-updating the Q-table, according to the new state, also decreasing both existence intricacy, relies upon earlier information that came from the environment. The work arrangement was tested for a 2D environment. The result of the proposed work showed better performance in time, speed, and length of the path also it can be utilized as an alternate style of guides. A comparison with other related works is performed and the result of these comparisons showed that our work provides a good trajectory with performance.
Comparative Study of Deep Learning Models For Automatic Detection of Metastases in H&amp;E Stained Images

Deep learning has gained the attention of researchers around the world. We used several deep learning models to check whether they could potentially be used as a clinical utility by pathologists to detect lymph node metastases in tissue sections of women with breast cancer to save time and reduce the burden on healthcare. For that purpose, we selected three deep learning models of different depths. The deepest one (DenseNet201), medium depth (Resent50), lightweight (Mobie/Net) and trained them in different experiments under various hyperparameters settings from scratch. The test set consists of 32000 histopathological images. We used absolute accuracy, sensitivity, specificity, and F1-score as a performance measure. Apart from these measures, we observe the effect of data augmentation and the number of epochs on the classification performance. We achieved an average accuracy of 85.6&#x0025;, 87.5&#x0025;, and 88.4&#x0025; for MobileNet V<inf>1</inf>, ResNet50, and DenseNet201, respectively, which further improves to 91.1 &#x0025;, 93.8&#x0025;, and 95.7<sup>&#x0025;</sup>, respectively, when augmented images are used in the training set. The performance of all deep learning models was on par with that of expert pathologists reported in different studies. We may expect that deep learning could be an exceptional utility for clinicians in real-time diagnosis too.
Text Language Classification Based on Dynamic Word Vector and Attention Mechanism

With the continuous development of Internet information processing technology, artificial intelligence and machine learning technology have been applied to text classification, making the development of text classification technology more rapid. The deep neural network suitable for text classification can also be used in tasks such as text sentiment classification and text sentiment analysis because it can well extract the deep and complex features of text. By designing and implementing a dynamic word vector algorithm model to better identify text language features, to improve the system's ability to recognize text language features, it has very important value and significance. The purpose of this paper is to study the text language classification of dynamic word vector and attention mechanism. This article first summarizes the basic theory of dynamic word vector, and then extends the core algorithm of dynamic word vector, and analyze the status quo of text language classification. This paper systematically expounds the algorithm flow and model of dynamic word vector and attention mechanism. And through the comparative method, observation method and other research forms to carry out the experimental research on the text language classification based on dynamic words and attention mechanism. Research shows that the text language classification algorithm based on dynamic word vector and attention posture studied in this paper is more efficient in many aspects.
Concatenated Dynamic Reinforcement Learning for Multi-staged Tasks (MST)

There exists a class of complex tasks that can be distinctly separated into multiple concatenated simpler tasks [1-2]. This class of complex and continuous motion processes can then be considered as multiple motion sub-processes, and a switch that brings these sub-processes together in accordance to an input function. This paper validates the potential value of this approach by proposing a method to train a complex task through a series of sub-tasks, and then concatenating them through a switch. Through the classic simulation physical task of OpenAI Gym[3]: cartpole, the paper demonstrates the viability and value of this approach. An example complex task of moving the pendulum back and forth between to points was used. A set of reward and switching functions were developed to achieve the complex task though the use of Deep Q-network (DQN) [4]. Results show that the proposed approach achieves the goal within a limited number of training cycles that otherwise cannot be met by other traditional means. Alternative reinforcement learning methods would result in more input parameters, larger deep learning networks, and larger accumulative training cycles.
Reduced Model Size Deep Convolutional Neural Networks for Small-Footprint Keyword Spotting

This paper discussed the application of Densely Connected Convolutional Networks (DenseNet), group convolution, and squeeze-and-excitation Networks (SENet) in keyword spotting tasks. We validated the network using the Google Speech Commands Dataset. Our proposed network has better accuracy than other networks even with less number of parameters and floating-point operations (FLOPs). In addition, we varied the depth and width of the network to build a compact variant network. It also outperforms other compact variants.
Meta-analytical Comparison Of SVM and KNN for Text Classification

Text classification is a crucial method for Intelligent and AI based systems as the amount of text data increases from year to year. As sentiment analysis which is a prominent technique used by many companies and governments to understand the societies, it has become important to select the efficient and accurate text analysis algorithm to be used the text classification system. In this paper we present a meta-analytical study aimed at comparing two text classification machine learning algorithms namely KNN and SVM in terms of F-score. In addition to the meta-analytical study, our study presents a literature review for machine learning based text classification algorithms. For the meta-analysis, random and fixed models were used. The results of the meta-analysis using 95%-CI proved that there is no significant f1 performance difference between KNN and SVM in text classification tasks.
GEU-Net: Rethinking the information transmission in the skip connection of U-Net architecture

With the wide application of deep learning technology in medical image processing, the performance of medical image segmentation has been improved in a breakthrough. U-Net architecture has excellent performance in medical image segmentation tasks. In order to solve the problem of image signal loss caused by the autoencoder structure, U-Net has added skip connections to its network to transfer the low-level features of the encoder path to the decoder path. Although this method can roughly solve the problem of image information loss, while it introduces a new problem, that is, the simple feature fusion method causes the high-level semantic information to be diluted. In order to solve the problem that the simple fusion of low-level edge information and high-level semantic information creates the semantic gap and dilutes high-level semantic information, we propose a novel U-shaped architecture, namely GEU-Net. GEU-Net utilizes ensemble learning methods to obtain better segmentation performance with a small computational cost. In addition, We propose a multi-scale group convolution block namely Group Residual (GR) module to reduce the semantic gap between encoder and decoder. We have evaluated our model on the BraTS 2020 Challenge, and have achieved competitive segmentation results.
Strategy of Public Organization on Governance of ESG: Focusing on KOTRA’s Governance Strategy

ESG is one of the most remarkable management trend which evaluates the sustainability using environmental, social and governance factors. In this study, we introduce the strategy and activities of KOTRA after defining major factors in terms of Governance area of ESG in public organization. KOTRA arranges the acting organization and operation system under the board of directors, utilizes external experts to enhance the independency and expertise of audits, and adhere to the announcement guidelines of the government. It also makes an effort to settle down the culture of ethical management with integrity and transparency while keeping up with the standards of the citizen. In addition, KOTRA is building up and intensifying the supporting actions for small and medium sized enterprises to cope with global ESG trend and the related supply chain management of global companies.
Data Acquisition based Seizure Record Framework for Digital Forensics Investigations

In the computer era, various digital devices are used along with networking technology for data communication in secured manner. But sometimes these systems are misused by the attackers. Information security with the high efficiency devices, tools are utilized for protecting the communication media and valuable data. In case of any unwanted incidents and security breaches, digital forensics methods and measures are well utilized for detecting the type of attacks, sources of attacks, their purposes. By utilizing information related to security measures, digital forensics evidences with suitable methodologies, digital forensics investigators detect the cyber-crimes. It is also necessary to prove the cyber-crimes before the law enforcement department. During this process investigators type to collect different types of information from the digital devices concerned to the cyber-attack. One of the major tasks of the digital investigator is collecting and managing the seizure records from the crime-scene. The present paper discusses the seizure record framework for digital forensics investigations.
Backpropagation-Based Decoding for Multimodal Machine Translation

People are able to describe images using thousands of languages, but languages share only one visual world. The aim of this work is to use the learned intermediate visual representations from a deep convolutional neural network to transfer information across languages for which paired data is not available in any form. Our work proposes using backpropagation-based decoding coupled with transformer-based multilingual-multimodal language models in order to obtain translations between any languages used during training. We particularly show the capabilities of this approach in the translation of German-Japanese and Japanese-German sentence pairs, given a training data of images freely associated with text in English, German, and Japanese but for which no single image contains annotations in both Japanese and German. Moreover, we demonstrate that our approach is also generally useful in the multilingual image captioning task when sentences in a second language are available at test time. The results of our method also compare favorably in the Multi30k dataset against recently proposed methods that are also aiming to leverage images as an intermediate source of translations.