Reviewerly/exHarmony huggingface dataset:
Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in English Translation Evaluation

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation.
Improved Efficient Privacy-Preserving Certificateless Provable Data Possession Scheme for Cloud Storage

Cloud storage technology is evolving at a high speed; effectively auditing the cloud data’s integrity has become a focal point. Recently, Ming and Shi proposed a certificateless integrity auditing scheme with a privacy protection function. The scheme used the certificateless cryptosystem to solve the certificate management problem of the auditing schemes based on public key infrastructure and the key escrow problem of the identity-based auditing schemes. Although their scheme is novel and efficient, we found that their scheme was not secure and could not achieve integrity auditing of cloud data. The malicious cloud server can generate the proof through the blocks and tags sent by the user. On the basis of the original scheme, we propose an improved auditing scheme; our new scheme is more secure and effective. In addition, for the problem of idle tags in the existing cloud data integrity auditing scheme, we propose the idea of intermediate tags and we applied the idea to the improved scheme to improve audit efficiency.
A Fast Scheduling Method for Massive Oblique Photography 3D Models

In view of the large amount of oblique photography 3D model data and the fact that high-performance hardware devices cannot load and render massive oblique photography 3D models at one time, this paper provides a fast scheduling method for massive oblique photography 3D models: get the tile radius, side length, scheduling range, and other parameters from the original data, normalize the original tile to a hundred grid, use a regular grid + quadtree + hexadecimal tree to build a tile spatial index, resample the tile data at each level to generate the elevation model DEM and the real radiographic image TDOM, and use the DEM+TDOM method to reconstruct the layered and block top-level 3D model to achieve rapid scheduling and rendering of massive oblique photography 3D models. Experiments show that the method is efficient and feasible and can meet the visualization and real-time interaction requirements of massive urban 3D models.
Design of an Automatic Evaluation System for English Translation Based on Artificial Intelligence Fusion Control Algorithm

In the process of globalization, language plays an increasingly important role in international communication. As a global language, English involves many fields, so the translation of English is very important. Not all translations can be used, and the quality of the translation must be accurate. Therefore, it is necessary to evaluate the quality of the translation of the translation system. Only when it reaches a certain level can it meet the application standard. The automatic evaluation system is a specific application of intelligent information processing technology. The system can automatically complete the compilation and feed back the system space-time resource information consumed by the user to the user in time, so that the user can check the pros and cons of the algorithm. The purpose of this article is to study the design and research of the English translation automatic evaluation system based on the artificial intelligence fusion control algorithm, and it was expected to combine the artificial intelligence fusion algorithm with the automatic evaluation of the English translation system. In this way, the traditional evaluation efficiency of translation could be further improved and the consumption of human resources can be reduced. Data fusion refers to an information processing technology that uses a computer to automatically analyze and synthesize several observational information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. This article proposed an automatic terminology translation method based on central word constraints aimed at the different linguistic characteristics of English and Chinese terms; the materials were sequenced before translation. The macroevaluation index was also introduced into the machine translation fusion method to achieve a balance between the robustness and effectiveness of the translation fusion technology. The experimental results of this article showed that under the traditional evaluation system, the response time of the system was 5.5 s and 6.3 s when the number of translations to be judged was 9 sentences and 11 sentences, respectively, whereas under the fusion control method, it was 4.1 s and 4.6 s, respectively. Therefore, the system evaluation efficiency under the fusion control algorithm was higher.
Multiple Classifiers Based Adversarial Training for Unsupervised Domain Adaptation

Adversarial training based on the maximum clas-sifier discrepancy between two classifier structures has achieved great success in unsupervised domain adaptation tasks for image classification. The approach adopts the structure of two classifiers, though simple and intuitive, the learned classification boundary may not well represent the data property in the new domain. In this paper, we propose to extend the structure to multiple classifiers to further boost its performance. To this end, we develop a very straightforward approach to adding more classifiers. We employ the principle that the classifiers are different from each other to construct a discrepancy loss function for multiple classifiers. The proposed construction method of loss function makes it possible to add any number of classifiers to the original framework. The proposed approach is validated through extensive experimental evaluations. We demonstrate that, on average, adopting the structure of three classifiers normally yields the best performance as a trade-off between accuracy and efficiency. With minimum extra computational costs, the proposed approach can significantly improve the performance of the original algorithm. The source code of the proposed approach can be downloaded from https://github.com/rucv/MMCD&#x005F;DA.
Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers

In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Vision Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the <tex>$8\times 8$</tex> patch level, and the inference resolution can be adjusted to balance prediction granularity and real-time perception constraints. We study how best to adapt a ViT to our task and environment, and find that some lightweight architectures can yield good single-image segmentations at a usable frame rate, even on CPU. The resulting perception model is used as the backbone for a simple yet robust visual servoing agent, which we deploy on a differential drive mobile robot to perform two tasks: lane following and obstacle avoidance.
Performance Analysis of AWS and GCP Cloud Providers

Cloud servers are currently in high demand because of their low cost and wide range of service providers. Businesses use cloud servers because of their quick response times and ability to meet the needs of their customers. Users frequently require recommendations on which service provider best meets their needs. Using the Golang Framework (Gorilla Mux) and an SQLite database, we examine how powerful AWS and GCP are in CPU processing, latency, and throughput. This study aims to help users select a cloud provider based on their needs. JMeter is used to load tests to the APIs. Requests are sent to the API via the GET method and are saved as parameter query data in the SQLite database. We discovered that the success rate on AWS was higher than the success rate on GCP.
Credit card fraud detection system using machine learning technique

Over the years, with the development of e-commerce, people are mostly making online transactions, and the risk of getting scammed has also increased. This in turn forces the financial institutions to improve continuously and upgrade their model. Machine Learning techniques were used to detect fraud in credit card transactions, but working with real-time data can be tough for machine learning to handle. Thus, implementation of blockchain techniques with machine learning to improve the efficiency and accuracy of the model. In the proposed model, Ethereum dataset has been used to check the fraudulent transaction and secure it with the help of machine learning algorithms. Out of all the classifiers XGBoost has attained the highest accuracy of 99.21% for the stated dataset.
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos

Distracted driving is one of the major reasons for vehicle accidents. Therefore, detecting distracted driving behaviours is of paramount importance to reduce the millions of deaths and injuries occurring worldwide. Distracted or anomalous driving behaviours are deviations from &#x2018;normal&#x2019; driving that need to be identified correctly to alert the driver. However, these driving behaviours do not comprise one specific type of driving style and their distribution can be different during the training and test phases of a classifier. We formulate this problem as a supervised contrastive learning approach to learn a visual representation to detect normal, and seen and unseen anomalous driving behaviours. We made a change to the standard contrastive loss function to adjust the similarity of negative pairs to aid the optimization. Normally, in a (self) supervised contrastive framework, the projection head layers are omitted during the test phase as the encoding layers are considered to contain general visual representative information. However, we assert that for a video-based supervised contrastive learning task, including a projection head can be beneficial. We showed our results on a driver anomaly detection dataset that contains 783 minutes of video recordings of normal and anomalous driving behaviours of 31 drivers from various top and front cameras (both depth and infrared). We also performed an extra step of fine tuning the labels in this dataset. Out of 9 video modalities combinations, our proposed contrastive approach improved the ROC AUC on 6 in comparison to the baseline models (from 4.23&#x0025; to 8.9 1&#x0025; for different modalities). We performed statistical tests that showed evidence that our proposed method performs better than the baseline contrastive learning setup. Finally, the results showed that the fusion of depth and infrared modalities from top and front view achieved the best AUC ROC of 0.9738 and AUC PR of 0.9772.
Measuring Plagiarism in Introductory Programming Course Assignments

Measuring plagiarism in programming assignments is an essential task to the educational procedure. This paper discusses the methods of plagiarism and its detection in introductory programming course assignments written in C++. A small corpus of assignments is made publicly available. A general framework to compute the similarity between a solution pair is developed that uses the three token-based similarity methods as features and predicts if the solution is plagiarized. The importance of each feature is also measured, which in return ranks the effectiveness of each method in use. Finally, the artificially generated dataset improves the results compared to the original data. We achieved an F1 score of 0.955 and 0.971 on original and synthetic datasets.
Occluded Text Detection and Recognition in the Wild

The performance of existing deep-learning scene text recognition-based methods fails significantly on occluded text instances or even partially occluded characters in a text due to their reliance on the visibility of the target characters in images. This failure is often due to features generated by the current architectures with limited robustness to occlusion, which opens the possibility of improving the feature extractors and/or the learning models to better handle these severe occlusions. In this paper, we first evaluate the performance of the current scene text detection, scene text recognition, and scene text spotting models using two publicly-available occlusion datasets: Occlusion Scene Text (OST) that is designed explicitly for scene text recognition, and we also prepare an Occluded Character-level using the Total-Text (OCTT) dataset for evaluating the scene text spotting and detection models. Then we utilize a very recent Transformer-based framework in deep learning, namely Masked Auto Encoder (MAE), as a backbone for scene text detection and recognition pipelines to mitigate the occlusion problem. The performance of our scene text recognition and end-to-end scene text spotting models improves by transfer learning on the pre-trained MAE backbone. For example, our recognition model witnessed a 4&#x0025; word recognition accuracy on the OST dataset. Our end-to-end text spotting model achieved 68.5&#x0025; F-measure performance outperforming the stat-of-the-art methods when equipped with an MAE backbone compared to a convolutional neural network (CNN) backbone on the OCTT dataset.
An Exact Fast Fourier Method for Morphological Dilation and Erosion Using the Umbra Technique

In this paper we consider the fundamental operations dilation and erosion of mathematical morphology. It is well known that many powerful image filtering operations can be constructed by their combinations. We propose a fast and novel algorithm based on the Fast Fourier Transform to compute grey-value morphological operations on an image. The novel method may deal with non-flat filters and incorporates no restrictions on shape and size of the filtering window, in contrast to many other fast methods in the field. Unlike fast Fourier techniques from previous works, the novel method gives exact results and is not an approximation. The key aspect which allows to achieve this is to explore here for the first time in this context the umbra formulation of images and filters. We show that the new method is in practice particularly suitable for filtering images with small tonal range or when employing large filter sizes.
Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural network is a cumbersome task. As such, unsupervised methods are preferred as a common approach to solve this task. Deep autoencoders have been broadly adopted as a base of many unsupervised anomaly detection methods. However, a notable shortcoming of deep autoencoders is that they provide insufficient representations for anomaly detection by generalizing to reconstruct outliers. In this work, we have designed an adversarial framework consisting of two competing components, an Adversarial Distorter, and an Autoencoder. The Adversarial Distorter is a convolutional encoder that learns to produce effective perturbations and the autoencoder is a deep convolutional neural network that aims to reconstruct the images from the perturbed latent feature space. The networks are trained with opposing goals in which the Adversarial Distorter produces perturbations that are applied to the en-coder&#x0027;s latent feature space to maximize the reconstruction error and the autoencoder tries to neutralize the effect of these perturbations to minimize it. When applied to anomaly detection, the proposed method learns semantically richer representations due to applying perturbations to the feature space. The proposed method outperforms the existing state-of-the-art methods in anomaly detection on image and video datasets.
Semi-supervised Grounding Alignment for Multi-modal Feature Learning

Self-supervised transformer-based architectures, such as ViLBERT [1] and others, have recently emerged as dominant paradigms for multi-modal feature learning. Such architectures leverage large-scale datasets (e.g., Conceptual Captions [2]) and, typically, image-sentence pairings, for self-supervision. However, conventional multi-modal feature learning requires huge datasets and computing for both pre-training and fine-tuning to the target task. In this paper, we illustrate that more granular semi-supervised alignment at a region-phrase level is an additional useful cue and can further improve the performance of such representations. To this end, we propose a novel semi-supervised grounding alignment loss, which leverages an off-the-shelf pre-trained phrase grounding model for pseudo-supervision (by producing region-phrase alignments). This semi-supervised formulation enables better feature learning in the absence of any additional human annotations on the large-scale (Conceptual Captions) dataset. Further, it shows an even larger margin of improvement on smaller data splits, leading to effective data-efficient feature learning. We illustrate the superiority of the learned features by fine-tuning the resulting models to multiple vision-language downstream tasks: visual question answering (VQA), visual commonsense reasoning (VCR), and visual grounding. Experiments on the VQA, VCR, and grounding benchmarks demonstrate the improvement of up to 1.3&#x0025; in accuracy (in visual grounding) with large-scale training; up to 5.9&#x0025; (in VQA) with 1/8 of the data for pre-training and fine-tuning<sup>1</sup><sup>1</sup>We will release the code and all pre-trained models upon acceptance..
Review of Data Visualization Techniques in IoT Data

Data visualization is considered the most efficient solution in IoT. It visualizes the endless stream of information collected by the respective smart systems to exponentially increase its value due to the meaningful insights it provides. Notably, numerous tools and techniques visualize the meaningful information obtained. It is, however, prudent that the analysis of this information considers the possibilities of anomalies within the data. The proper mechanisms must be implemented to detect these anomalies to ensure that the visualized information is accurate and reliable. This research discussion delves into reviewing current literature on the different tools and techniques that could detect anomalies in IoT. The review would reveal that the machine learning and deep learning approaches are the most-used approaches for anomaly detection. Several images would emerge regarding the visualization tools, including Grafana, Tableau, Orange, Power Bi tool, Kibabana, and Plotly. Tableau was identified as the popular and leading software used by numerous entities for visualizing the data in meaningful insights leading to powerful decisions. Some of its salient features include offering Api's and sophisticated developer tools such that different companies could use to meet specific needs, it has a less learning curve and has the ability to handle with extensive amounts of data generated in the network. Notably, the fusion of Big Data and IoT will continue to develop and advance both the existing and new visualization tools. These developments will arise because the IoT networks will continue to obtain data from various sources as the number of smart devices increases tremendously.
A Comparative Analysis of Deep Learning Models for Detecting Malaria Disease Through LBP Features

Malaria is a parasitic infection spread by the plasmodium parasite. Malaria continues to be a major threat to world health, with an estimated 200 million cases and over 400,000 fatalities each year. When exposed to this disease, symptoms develop 10–15 days after the parasite enters the body. This disease becomes chronic if it is not treated medically, and it eventually leads to death. Using spatial information collected from microscopic images, several techniques based on image processing and machine learning have been utilized to diagnose malaria. Using the Local Binary Pattern (LBP) texture feature as a feature extraction approach, this study contributes to the development of a predictive and high-accuracy deep learning model by testing multiple Deep Learning models and determining which model delivers the best accuracy. To be specific, we tested frequently used baseline methods, namely ResNet34, VGG16, Inception V3, and EfficientNet. The results demonstrate that EfficientNet has a 91 percent outstanding accuracy rate, compared to 87 percent for VGG16, 81 percent for Resnet34, and 77 percent for InceptionV3, respectively.
Image Classification of Starlings Using Artificial Neural Network and Decision Tree

Starlings are famous animals in Indonesia. Therefore, many in Indonesia maintain and cultivate starlings. Almost every region in Indonesia has different types of starlings. Therefore, the researchers used Artificial Neural Networks and Decision Trees to classify starlings. Both methods are useful for obtaining the accuracy values generated in the classification of the starlings. In this comparison, the Artificial Neural Network has a precision of 0.870, the highest recall value is 0.600, the f-measure is 0.865, and the accuracy is 93% at a split ratio of 90:10. The Decision Tree has resulted in the classification of starlings on features, shapes, and colours with the highest texture value at a precision of 1,000, recall reaching 1,000, f-measure reaching 1,000, and accuracy reaching 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> at a split ratio 90:10. The tests carried out show that the Decision Tree can classify starling images based on 3 feature levels. And in this case, it can be proven that the Decision Tree is more accurate in classifying starlings images. The method of this Decision Tree can make it easier to find the right accuracy value in classifying starling species.