Reviewerly/exHarmony huggingface dataset:
ED-NET: Educational Teaching Video Classification Network

Convolutional neural networks (CNNs) are widely used in computer vision-based problems. Video and image data are dominating the Internet. This has led to extensive use of deep learning (DL)-based models in solving tasks like image recognition, image segmentation, video classification, etc. Encouraged by the enhanced performance of CNNs, we have developed ED-NET in order to classify videos as teaching videos or non-teaching videos. Along with the model, we have developed a novel dataset, Teach-VID, containing teaching videos. The data is collected through our e-learning platform Gyaan, an online end-to-end teaching platform developed by our organization, GahanAI. The purpose is to make sure we can restrict non-teaching videos from being played on our portal. The models proposed along with the dataset provide benchmarking results. There are two models presented one that makes use of 3D-CNN and the other uses 2D-CNN and LSTM. The results suggest that the models can be used in real-time settings. The model based on 3D-CNN has reached an accuracy of 98.87%, and the model based on 2D-CNN has reached an accuracy of 96.34%. The loss graph of both models suggests that there is no issue of overfitting and underfitting. The proposed model and dataset can provide useful results in the field of video classification regarding teaching versus non-teaching videos.
Document Image Binarization in JPEG Compressed Domain Using Dual Discriminator Generative Adversarial Networks

Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Analysis (DIA) applications like word spotting, document retrieval, and OCR. Most of the existing techniques focus on feeding pixel images into the convolution neural networks to accomplish document binarization, which may not produce effective results when working with compressed images that need to be processed without full decompression. Therefore in this research paper, the idea of document image binarization directly using JPEG compressed stream of document images is proposed by employing Dual Discriminator Generative Adversarial Networks (DD-GANs). Here the two discriminator networks—Global and Local work on different image ratios and use focal loss as generator loss. The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibers. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.
Auxiliary Label Embedding for Multi-label Learning with Missing Labels

Label correlation has been exploited for multi-label learning in different ways. Existing approaches presume that label correlation information is available as a prior, but for multi-label datasets having incomplete labels, the assumption is violated. In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients. The approach recovers missing labels and simultaneously guides the construction of model coefficients from the learnt label correlations. Empirical results on multi-label datasets from diverse domains such as image & music substantiate the correlation embedding approach for missing label scenario. The proposed approach performs favorably over four popular multi-label learning techniques using five multi-label evaluation metrics.
Semi-supervised Semantic Segmentation of Effusion Cytology Images Using Adversarial Training

In pleural effusion, an excessive amount of fluid gets accumulated inside the pleural cavity along with signs of inflammation, infections, malignancies, etc. Usually, a manual cytological test is performed to detect and diagnose pleural effusion. The deep learning solutions for effusion cytology include a fully supervised model trained on effusion cytology images with the help of output maps. The low-resolution cytology images are harder to label and require the supervision of an expert, the labeling process time-consuming and expensive. Therefore, we have tried to use some portion of data without any labels for training our models using the proposed semi-supervised training methodology. In this paper, we proposed an adversarial network-based semi-supervised image segmentation approach to automate effusion cytology. The semi-supervised methodology with U-Net as the generator shows nearly 12% of absolute improvement in the f-score of benign class, 8% improvement in the f-score of malignant class, and 5% improvement in mIoU score as compared to a fully supervised U-Net model. With ResUNet++ as a generator, a similar improvement in the f-score of 1% for benign class, 8% for the malignant class, and 1% in the mIoU score is observed as compared to a fully supervised ResUNet++ model.
Multimodal Controller for Generative Models

Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-and-play module named `multimodal controller' to generate multimodal data without introducing additional learning parameters. In the absence of the controllers, our model reduces to non-conditional generative models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and Omniglot benchmark datasets. We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with conditional generative models. Moreover, we show that multimodal controlled models can also create novel modalities of images.
Detection of Motion Vector-Based Stegomalware in Video Files

Cybercriminals are increasingly using steganography to launch attacks on devices. The cyberattack is more threatening as steganography hides the embedded malware, if any, making it harder to detect by various anti-virus tools. Such malware is called stegomalware. Since video files are larger and have a complex structure, they also have a high capacity for hiding malware. Motion vector (MV)-based steganography techniques do not cause much distortion in video files. Therefore, it remains to be one of the leading video steganography techniques. This paper deals with a lightweight solution for MV-based stegomalware detection in video files. Our model is compatible with state-of-the-art video coding standards having variable macroblock sizes and different motion vector resolutions. The proposed method obtained an accuracy of 95.8% on testing H.264 videos with various embedding rates. The 81-D spatial and temporal features result in the high performance of the proposed model.
Extractive Text Summarization Using Statistical Approach

Nowadays, text summarization is an important research area because of its large requirements in society. The usage is increasing in different areas such as education, health care, and legal. In the past, researchers have carried a lot of work on different text summarization techniques. Also, they propose machine learning/deep learning-based approaches for text summarization. This study proposed a novel statistical approach for extractive multi-document text summarization. The proposed approach is based on weight assignment of keywords, in local files and global files. We evaluate the approach of the publicly available “Multi-news” dataset. Results show that the proposed approach outperforms 1.08% in the ROUGE-L F1 score on the state-of-the-art method.
Cross-Media Topic Detection: Approaches, Challenges, and Applications

Developing technologies and new social media platforms (Facebook, YouTube, Flickr, and Twitter) are transforming how people communicate with each other and scrutinize information. Different social media users prefer different mode of information for expressing their views. For instance, Twitter users prefer short text and informally taken photos; YouTube viewers tend to discuss breaking news in the form of short videos with rough editing, and professional journalists usually produce standardized news stories with substantial text and well chosen images. Every day, a large number of videos, images, and text are shared on social media platforms. The cross-media data provide credible insights into public opinion, which are extremely beneficial to commercial firms, governments, and any organization concerned with societal opinions. As a result, assessing such voluminous data is beneficial. When cross-media information is combined, it can provide a more comprehensive and effective description of a given event or issue, as well as improve system performance. Also, in addition to being a significant source of information, cross-media information provided by various media is more robust, has a larger audience, and appears to be a more accurate reflection of real-world events. Consequently, robustly identifying topics from multimodal data from multiple media is a useful and sensible extension of cross-media topic identification. This paper presents a review of various cross-media topic detection approaches.
Domain Adaptation: A Survey

In computer vision, domain shifts are a typical issue. A classifier that has been trained on a source domain will not be able to perform well on a target domain. As a result, a source classifier taught to discriminate based on a particular distribution will struggle to classify new data from a different distribution. Domain adaptation is a hot area of research due to the plethora of applications available from this technique. Many developments have been made in this direction in recent decades. In light of this, we have compiled a summary of domain adaptation research, concentrating on work done in the last few years (2015–2022) for the benefit of the research community. We have categorically placed the important research works in DA under the chosen methodologies and have critically assessed the performances of these techniques. The study covers these features at length, and thorough descriptions of representative methods for each group are provided.
Motion Prior-Based Dual Markov Decision Processes for Multi-airplane Tracking

Multi-airplane tracking (MAT) is the foundation of airport video surveillance. Besides common problems in tracking such as occlusion, this task is further complicated by the specific challenges in airport. For example, due to the low-compact shape of the airplane, its appearance will change drastically when turning, frequently resulting in failures in data association. In this paper, airplane motion prior-based dual Markov decision processes (DMDPs) are proposed for MAT. In airport surface, the airplane has only two direction modes such as straight and turn and two velocity modes such as acceleration and uniform speed. Such motion prior can be used to cope with the challenges in MAT. Firstly, we create a motion state MDP (msMDP) where each target has three motion patterns: straight/constant velocity (S/CV), straight/constant acceleration (S/CA), and curve/constant velocity (C/CV). This is consistent with the motion of airplane. Secondly, after the computation of affinity matrix based on msMDP, another tracking state MDP (tsMDP) which manages the lifetime of airplane is use to assist the data association step within the DMDPs framework. Finally, the effectiveness of the presented algorithm is verified based on the AGVS-T1(a new change detection dataset for airport ground video surveillance benchmark).
A Comparison of Model Confidence Metrics on Visual Manufacturing Quality Data

After ground-breaking achievements through the application of modern deep learning, there is a considerable push towards using machine learning systems for visual inspection tasks part of most industrial manufacturing processes. But whilst there exist a lot of successful proof-of-concept implementations, productive use proves problematic. Whilst missing interpretability is one concern, the constant presence of data drift is another. Changes in pre-materials or process and degradation of sensors or product redesigns impose constant change towards statically trained machine learning models. To handle these kind of changes, a measurement of system confidence is needed. Since pure model output probabilities often lack in this concern better solutions are required. In this work, we compare and contrast several pre-existing methods used to describe model confidence. In contrast to previous works, they are evaluated on a large set of real-world manufacturing data. It is shown that utilizing an approach based on auto-encoder reconstruction error proves to be most promising in all scenarios tested.
Kullback–Leibler Distance-Based Fuzzy K-Plane Clustering Approach for Noisy Human Brain MRI Image Segmentation

MR images are complex as the data distribution of tissues in MR images is non-spherical and overlapping in nature. The fuzzy k-plane clustering method (FkPC) is the most suitable soft clustering method to cluster non-spherical shape data and solve the overlapping problem of MR tissues. Generally, MR images are corrupted with imaging artifacts such as noise and intensity inhomogeneity. The FkPC method performs poor in the presence of noise. In this work, a new objective function for handling noise in the plane-based clustering approach is discussed with application to the MR image segmentation problem. We have utilised Kullback–Leibler (KL) distance measure to dampen the effect of noise in the k-plane clustering approach for the image segmentation problem. The proposed method is termed as KL distance-based fuzzy k-plane clustering (KLFkPC) method. Three publicly available MRI image datasets were utilised in the study to assess the efficiency of the proposed KLFkPC approach. The results are evaluated using a variety of performance metrics that demonstrate the efficacy of the proposed method over the ten existing related methods.
URL Classification on Extracted Feature Using Deep Learning

The widespread adoption of the World Wide Web (WWW) has brought about a monumental transition toward e-commerce, online banking, and social media. This popularity has presented attackers with newer opportunities to scam the unsuspecting—malicious URLs are among the most common forms of attack. These URLs host unsolicited content and perpetrate cybercrimes. Hence classifying a malicious URL from a benign URL is crucial to enable a secure browsing experience. Blacklists have traditionally been used to classify URLs, however, blacklists are not exhaustive and do not perform well against unknown URLs. This necessitates the use of machine learning/deep learning as they improve the generality of the solution. In this paper, we employ a novel feature extraction algorithm using ‘urllib.parse’, ‘tld’, and ‘re’ libraries to extract static and dynamic lexical features from the URL text. IPv4 and IPv6 address groups and the use of shortening services are detected and used as features. Static features like https/http protocols used show a high correlation with the target variable. Various machine learning and deep learning algorithms were implemented and evaluated for the binary classification of URLs. Experimentation and evaluation were based on 450,176 unique URLs where MLP and Conv1D gave the best overall results with 99.73% and 99.72% accuracies and F1 Scores of 0.9981 and 0.9983, respectively.
Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms

The research herein applies text classification with which to categorize Khmer news articles.News articles were collected from three online websites through web scraping and grouped into nine categories.After text preprocessing, the dataset was split into training and testing sets.We then evaluated the performance of the ensemble learning method via machine learning classifiers with kfold validation.Various machine learning classifiers were employed, namely logistic regression, Complement Naive Bayes, Bernoulli Naive Bayes, k-nearest neighbours, perceptron, support vector machines, stochastic gradient descent, AdaBoost, decision tree, and random forest were employed.Accuracy was improved for the categorization of Khmer news articles, in which Grid Search CV was used to find the optimal hyperparameters for each machine learning classifier with feature extraction TF-IDF and Delta TF-IDF.The results determined that the highest accuracy was achieved through the ensemble learning method in the support vector machine with the optimal hyperparameters (C = 10, kernel = rbf), using feature extraction TF-IDF and Delta TF-IDF, at 83.47% and 83.40%, respectively.The model establishes that Khmer news articles can be accurately categorized.
ICT: Attendance and Contact Tracing During a Pandemic

This paper proposes the use of a web tool to collect attendance for on-campus and online students. This allows for a single interface for tracking participation of students using either modality while also capturing position information of any on-campus students for use in contact tracing. While the need for contact tracing has been reduced in the years following 2020, the tool still quickly captures generic attendance information across modalities.
Machine Learning: Fake Product Prediction System

Product review plays a vital role in hopping, especially for the online customers. Some base their buying decisions on the review; hence a fake review is a major concern (Jadhav and Gore, Int J Comput Sci Inform Technol 5(2):1447–1450, 2014). Competitions appear to facilitate Fake product malicious agents, a major challenge in the e-commerce industry. This paper intends to use machine learning to explore a predict fake or genuine products system by feeding the products into the model.
Tuning Neural Networks for Superior Accuracy on Resource-Constrained Edge Microcontrollers

The approaches to tune Artificial neural networks (ANN) for running on edge devices, such as weight quantization, knowledge distillation, weight low-rank approximation, and network pruning, usually reduce their accuracy (gap 1). Moreover, they usually require at least 32-bit microcontrollers, leaving out of the equation widely used and much cheaper platforms mostly based on 8-bit microcontrollers (e.g., ATMega328p and ATMega2560), such as Arduino (gap 2). Those microcontrollers can cost between $0.01 to $0.10 on a large scale and can make viable extending IoT applications to a wider range of cheaper personal objects, such as bottles, cans, and cups. In this context, the present study addresses those two identified gaps by proposing and evaluating a technique for tuning ANN to run on 8-bit microcontrollers. 16,000 ANN with distinct configurations were trained and tuned with four widely used datasets and evaluated on two 8-bit microcontrollers. Using less than 3.5Kbytes, the embedded ANN average accuracies outperformed their benchmarks on a 64-bit computer.
Towards Specifying and Evaluating the Trustworthiness of an AI-enabled System

We propose a method to specify and evaluate the trustworthiness of AI-based systems using scenarios and design tactics. Using our trustworthiness scenarios and design tactics, we can analyze the architectural design of AI-enabled systems to ensure trustworthiness has been properly achieved. Trustworthiness scenarios allow for the specification of trustworthiness, and design tactics are used to achieve the desired level of trustworthiness in the system. We illustrate the validity of our proposal through the design of the software architecture of a pollination robot. We find that our method opens discussions on ways to achieve trustworthiness and leads to the discovery of any weaknesses in the design concerning the trustworthiness of the AI system. Furthermore, our method allows for designing an AI system with trustworthiness in mind and therefore leads to greater analysis and identification of the sub-attributes that affect the trustworthiness of an AI system.
Analysis of News Article Various Countries on a Specific Event Using Semantic Network Analysis

People today obtain news and information from many sources. This has been made possible by the exponential growth of the number of news outlets due to the development in the scope and speed of the Internet. As a result, people can access news articles in various ways, not only through domestic media, but also through media from other countries. There are limited ways to check news reports from overseas on events and their differences from the news content of one’s own country. By collecting and analyzing news related to the Russian-Ukraine war, we conduct experiments and analyze contents reported in countries related to this war and the focus of their reports. Our study analyzes whether all the news related to this war reported the same content and what it focused on. The rationale for this study is that the more access people have to different kinds of news, the more opportunities they have for thinking differently. It takes a large amount of effort to collect, translate, and analyze news reports in various languages across various countries. For this analysis, the news must be collected from the media in various countries, and translations must be carried out if the content was not in English. The main purpose of the data is to obtain meaningful results through various methods of analysis rather than a single method. To address these problems, we propose a system for translating, analyzing, and comparing news collected from various countries. We collected news from the United States, Germany, Russia, and China; we then applied LDA to extract the topics for comparison and analysis. We aim to use semantic network analysis to obtain betweenness centrality scores and calculate sentiments scores.
Explaining Multimodal Image Retrieval Using A Vision and Language Task Model

Humans can provide arguments, cite proof, and express confidence in their predictions. They may also say, “I don’t know,” if there isn’t enough information. However, only some of the multimodal image retrieval techniques now in use have these characteristics, making the models extremely opaque and unreliable. Additionally, the vision-language task should pay more attention to multimodal aspects. The Vison-language concept has recently become very popular because of all the potential uses. It is crucial to understand the output of the vision and language models using various use cases, such as visual captioning, visual question answering, and image retrieval. In this article, we examine the image retrieval task, where the input query consists of an image and a text description of the image. With the help of the Transformer model, which concentrates on both modalities and merges embedded features using the feature fusion technique, the system launches a query prompted by the input image and text. We applied two different techniques based on the traits taken out of the fusion layer. We first trained the K Nearest Neighbor (KNN) algorithm to discover a similar image on training data. Second, we computed the nearest neighbor points and clustered the center using the clustering algorithm and a support vector machine to see a comparable image. In our work, we choose the VisualBert model for Multimodal Image Retrieval and apply the SHAP algorithm in the NLVR2 JSON dataset to explain language features based on features importance. The algorithm visualizes both positive and negative shapley values for the input text.