Reviewerly/exHarmony huggingface dataset:
Dzongkha Handwritten Digit Recognition using Machine Learning Techniques

Handwritten digit recognition has recently gained importance, attracting many researchers due to its use in various machine learning and computer vision applications. As technology and science progressing, there is a need for a system to recognize the handwritten script in several real-time applications to reduce human effort. There a lot of work has been done on the recognition and generation of handwritten digits of high-resource languages such as English. However, insufficient work has been done on Dzongkha digits recognition, as Dzongkha digits are low-resource and more complex than English patterns. This paper aims to perform handwritten character recognition of Dzongkha digit using several machine learning techniques. The unavailability of the Dzongkha handwritten digit dataset is the prime motivation behind this work. To facilitates the recognition of Dzongkha handwritten digit, we have collected the data of Dzongkha handwritten digit from indigenous and non-indigenous people of Bhutan and provided the dataset for further research. Moreover, we have used several machine algorithms, including a support vector machine, K-nearest neighbor, and decision tree. Among these algorithms, the support vector machine classification algorithm has achieved a remarkable result with an accuracy of 98.29%.
An investigation of videos for abnormal behavior detection

The identification of abnormal behavior has several applications. There are several ways, ranging from classical to deep learning based. It may be used to monitor campuses, banks, transportation, and airports. In many circumstances, the context determines whether real-life events are common or unusual. Recent video surveillance anomaly detection systems are good enough, but they come at a significant computational cost and require particular hardware resources. When it comes to real-time anomaly detection, extra emphasis must be paid on lowering model complexity, which causes computational and memory demands. This study attempts to find real-world abnormalities in CCTV recordings, such as violence, detention, property destruction, assault, burglary, blast and theft. The impact of these abnormalities on public safety is enormous. The study also provides a low-cost algorithm for detecting crowd irregularities. The proposed model uses the convolutional neural network based on DenseNet121 as the feature extractor. Our suggested framework has an AUC of 86.63 percent on the UCF-Crime dataset and obtains new stateof-the-art performance
Question Tags or Text for Topic Modeling: Which is better

Topic modelling is a probabilistic based statistical model used to find the latent topics that best depicts the content of the documents. Community Question Answering websites such as Quora, Stack Overflow and Yahoo! Answers have been prevalently in use, performs topic modeling as lot of queries pour in on daily basis which make it challenging to understand, summarize and synthesize the main topic of discussions. On these websites there are basically two sources of information that are available to analyze the key latent topics: questions text and tags. Questions are in textual format and tags are the keywords or tokens that are related to the question being asked which describes the content of the question. In past studies, most of the researchers have used question text for the purpose of topic modeling. It is still unclear why tag is not being considered for topic modeling. To combat this issue, this paper performs topic modeling using both question tags and text. The topic modeling based on tags has been compared with text based on two metrics namely coherence and perplexity. Experiment has been conducted on three real time datasets namely Artificial intelligence, Software Engineering and quantum computing from Stack exchange website. At high level tag-based topic modelling looked promising but closer observation revealed the opposite. It has been found that topic modeling using question text is preferable as topic modelling using tags collapses after a certain number of topics.
Performance of Optimizers in Text Summarization for News Articles

Text summarization involves selecting the most important phrases from a given paragraph to produce a concise and pertinent summary. There are two primary approaches to text summarization. While the second is abstractive, the first is extractive. Extractive-based text summarization has reached its peak and as a result, its scope has shrunk, instead of that, let's look at the contrast Text summarization based on abstract concepts, which is inherently dynamic, has attracted a great deal of attention from academics in recent years. The production of a human-like summary has historically required the use of multiple optimizers. These optimizers have expanded text summarization's capabilities. As a result, this paper compares and assesses the effectiveness of two optimizers on a variety of datasets. The two utilized optimizers are adam and rmsprop. This paper compares their performance on various datasets as they are widely employed in text summarization.
Enhanced Colorization of Thermal Images for Pedestrian Detection using Deep Convolutional Neural Networks

Colorization of a thermal image into realistic picture is a challenging task. Thermal cameras can detect objects in environments when human vision cannot (in case of fog, rains, dark surrounding, etc). It is difficult to analyse the thermal image and to identify potential impediments simply with human vision. In industries working on autonomous vehicles, drones, etc., the use of thermal cameras to capture the images for any kind of surveillance is very beneficial and in such use, image colorization of thermal pictures is very crucial. The research presents a translation method with enhancement to convert thermal infrared to visual colour picture by utilising a custom-tailored architecture for a Convolutional Neural Network. The pedestrian detection system is designed that provides Image colorization, Image enhancement and object detection functionalities. The colorized and enhanced images are fed to the detection model by using a pre-trained YOLOv5 (You Only Look Once) architecture. Bounding boxes are drawn on the resultant images that refer to the border's coordinates enclosing the pedestrians. The proposed models are trained on a publicly accessible CAMEL thermal dataset. Such integrated model has achieved test accuracy of 92.1% for pedestrian detection. The performance of the colorization and deblurring model are quantitatively evaluated using metrics such as Root Mean Squared Error (RMSE), Structural/feature Similarity Indexes, Peak signal to noise ratio (PSNR). The performance is further confirmed with qualitative assessment by performing apparent comparison of resultant images with ground truth. Higher value up to 84 and lower value up to 0.006 are obtained for PSNR and RMSE respectively, that is evident for reasonable similarity between ground truth and resultant colorized, enhanced image.
Forensic Sketch to Real Image Using DCGAN

Converting pencil sketches to real image is required in forensic analysis for identifying a person. In a reported crime, the testimony explains the physical features of the accused by recalling them from memory. As the human face is so diverse and variable, it's tough to predict the person from a forensic sketch expert's low-quality sketch. In this paper we propose a deeplearning model using DCGAN to convert the sketch to real image. The prepared sketch is used as input for training, and after a set number of epochs, our deep learning model learns and produces a realistic high-resolution image of the suspect. The face recognition system is a powerful tool which can be used for a wide range of purposes, from security to entertainment. Our proposed method's final average SSIM is 0.587 where as in the existing methods the average SSIM is 0.554.
WSD based Ontology Learning from Unstructured Text using Transformer

Representation of knowledge and making it machine comprehensible has become a necessity in modern times but with the large amount of data being generated nowadays, this process has to be automated as much as possible. In this work, we propose a deep-learning based model to build an RDF based Ontology from Unstructured Text. We aim to evaluate the proposed model by creating a general knowledge ontology from newspaper article corpora. The proposed model is based on transformer, Natural Language Processing and contains a Relation Extraction model and novel implementation of RDF mapping algorithm. The main highlight of our model is its ability to handle the Word Sense Disambiguation problem. The model was able to perform well and achieved very high accuracy scores.
Encoder-Decoder Architecture for Image Steganography using Skip Connections.

Data security has become a basic need in today's world as it shifts more and more toward an all-online mode of operation. All sorts of data and information are now passed from one entity to another over a variety of transmission channels as well as stored on virtual online repositories, and a large chunk of that information is highly private. Over this stream of technological advancements, various data security techniques like encryption, data hiding, etc. have been developed to provide security while transmitting or storing data. This paper proposes an approach to hiding one image inside another image by using CNN in for feature extraction and processing. The proposed model works on the principles of the auto-encoder architecture and is made up of two modules one of which is responsible for the task of processing the secret image and extracting n features and the incorporating those features in the cover image ion such a manner that the cover image retains its original visual qualities and also the secret image gets hidden in it, that module is the Hiding network/Encoder. The other part of this network is the one responsible to then extract the hidden secret image and them create back to its original, this paper is the Reveal network/Decoder. The proposed model is implemented on ImageNet and Pascal-VOC datasets with images of different sizes. In order to judge the models ability to hide the image and to extract is back, PSNR and SSIM are used as performance evaluation Metrics.
Performance Analysis and Deployment of Partitioning Strategies in Apache Spark

Data is flourishing day by day to a large extent, the data that need to be analyzed are not just large, but it may be high dimensional, heterogeneous, complex, unstructured, incomplete and noisy as well. Apache Spark framework is used for high-performance computing of Big Data. The proper division of the dataset can impact the degree of parallelism achieved. In Apache Spark, partitioning techniques help to manage the dataset in a distributed fashion. The optimal number of partitions and associated data with these partitions ensures the appropriate storage and quick access to data. Inbuilt libraries and flexibility utilizing the available techniques help Apache Spark to divide the dataset as a convenience. In this study, partitioning techniques available in Spark have been discussed and implemented. The pros and cons of the partitioning techniques have been highlighted. Furthermore, the performance of partitioning techniques has been compared in terms of execution time which help to select the suitable partitioning strategy for the different jobs.
Prediction of Need for Cyber Training for University Students Using Artificial Neural Networks

In recent years, predictive analysis has attracted a lot of interest due to advances in technology, particularly in the large data and machine learning fields. The advancement in technology has also scored the cases of cybercrimes. The majority of the time, a mathematical model that captures important trends is built using data that is historical. The predictive model is used to analyze data to forecast certain parameters or trends or to advise actions to achieve the best results. Artificial neural network (ANN) is an effective technique for such predictions. In this paper, ANN is used for prediction from a survey-based dataset that has been created from the students of universities of J&K Union Territory. The dataset that is used has been created with a dire need to understand the level of students in terms of their knowledge and understanding of cybercrimes. A very imperative way to prevent cyber against people is to make people aware of crimes and by training them for the same. For such a problem, a model is proposed to predict the need for cyber training among students using ANN. In this paper, ANN with back-propagation is proposed as a tool to predict whether there is a need for cyber training for individuals. Numerous cases have been reported in the cyber cell of J&K where cybercrimes have been registered by individuals. It is the need of the hour to train the student community on cybercrime schemes and behavior.
An Efficient Search Space Exploration Technique for High Utility Itemset Mining

Data mining techniques discover the potentially helpful pattern concealed in a vast database for the decision support system in various real-life applications. One such technique is association rule mining. It finds the pattern from the transaction database to understand customer behavior. Frequent itemset mining (FIM) identifies a set of frequently purchased items together. One key drawback of FIM is that it ignores the item's importance. An item's importance plays a pivotal role in a real-world application. Therefore, it is necessary to discover the essential itemset from the transaction database that generate the high profit called the HUIM (High-Utility Itemset Mining) problem. From a transaction database, a variety of strategies can be used to find high utility itemset. The HUIM techniques that use the Utility list are recent and have better performance in terms of memory utilization and running time. The key limitation of these algorithms is performing costly utility list join operations. This paper proposes an efficient search space exploration technique to lessen the number of comparisons performed for utility list join operations. Hence, it reduces the cost of utility list join operations. In addition, the execution time of the proposed method is evaluated. Further, the same is compared with appropriate existing state-of-the-art methods. Finally, extensive experiments carried out on publicly available benchmark datasets show that the proposed support count ascending order (SCAO) based approach performs much better as oblivious to the state-of-the-art methods.
CPU-side comparison for Key Agreement between Tree Parity Machines and standard Cryptographic Primitives

Information Security has become a crucial aspect nowadays in every domains. In order to protect these several domains, various cryptographic primitives have been implemented. To address this issue, in this paper we provide a key contribution. We compare three cryptographic primitives: Tree Parity Machines (TPM), Diffie-Hellman (DH) and Elliptic-curve Diffie-Hellman (ECDH) and show that TPMs is the best choice, based on cpu-side instructions, to make the key agreement between two counterparts <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> . Regarding DH and ECDH, tests have been performed using authenticated and unauthenticated versions. DH-Unified and Elliptic Curve Fully Hashed Menezes-Qu-Vanstone (ECFHQV) represent the authenticated version of DH and ECDH, respectively. We performed the comparisons for key agreement using programs compiled with a native programming language and a terminal tool to gather statistical information, such as some CPU-side evens, time required for synchronization, and information to be sent to the unsafe channel. Our detailed analysis, both formal and experimental, shows that cpu instruction-side TPM networks are ideal candidates for execute a key agreement between two counterparts <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> .
Is Your Model Sensitive? SPEDAC: A New Resource for the Automatic Classification of Sensitive Personal Data

In recent years, there has been an exponential growth of applications, including dialogue systems, that handle sensitive personal information. This has brought to light the extremely important issue of personal data protection in virtual environments. Sensitive Information Detection (SID) approaches different domains and languages in literature. However, if we refer to the personal data domain, a shared benchmark or the absence of an available labeled resource makes comparison with the state-of-the-art difficult. We introduce and release SPeDaC , a new annotated resource for the identification of sensitive personal data categories in the English language. SPeDaC enables the evaluation of computational models for three different SID subtasks with increasing levels of complexity. SPeDaC 1 regards binary classification, a model has to detect if a sentence contains sensitive information or not; whereas, in SPeDaC 2 we collected labeled sentences using 5 categories that relate to macro-domains of personal information; in SPeDaC 3, the labeling is fine-grained (61 personal data categories). We conduct an extensive evaluation of the resource using different state-of-the-art-classifiers. The results show that SPeDaC is challenging, particularly with regard to fine-grained classification. The transformer models achieve the best results (acc. RoBERTa on SPeDaC 1 = 98.20%, DeBERTa on SPeDaC 2 = 95.81% and SPeDaC 3 = 77.63%).
A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using our new publicly-available Challenging Dataset for Underwater Image Enhancement (CDUIE). The collected dataset is comprised of 85 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/ashrafrepo/underwater-image-enhancement</uri> .
MLP-JCG: Multi-Layer Perceptron With Joint-Coordinate Gating for Efficient 3D Human Pose Estimation

Various structural relations/dependencies exist among human body joints, which makes it possible to estimate 3D poses from 2D sources. The current research on 3D human pose estimation (3D-HPE for short) mainly focuses on structural information from a specific perspective. However, this information cannot facilitate 2D-to-3D pose lifting. This paper presents a novel and efficient multi-layer perceptron with a joint-coordinate gating (MLP-JCG) model, exploring and utilizing both the local and global structural information to perform 3D pose estimations. Specifically, MLP-JCG contains two independent MLP blocks, i.e., joint-mixing MLP and coordinate-mixing MLP, which solely act on the joint and coordinate axes in modelling their local structural information. For the global structural information, we first explore two kinds of global statistics from the pose matrix embeddings, which are referred to as the dynamics aggregated along the joint/coordinate axis. Then, we propose two kinds of gating units to elementwisely contextualize the features learned from MLP blocks. All the model components are designed based on MLP, making the MLP-JCG easy to implement and train. We conduct experiments on three 3D-HPE benchmarks, and the results demonstrate the superior effectiveness and efficiency of the proposed approach.
Hate Crime during the COVID-19 Pandemic: A Qualitative Study of the Experiences of an Ethnically Diverse University Student Population

The COVID-19 pandemic coincided with a rise in reports of hate crime against East and Southeast Asian minorities. Political rhetoric blaming China for causing the pandemic was tantamount to a ‘permission to hate’, making COVID-19 a catalyst of hate crime against Chinese people which also fuelled overt prejudice against other ethnic minorities. Researching experiences of hate offences in an ethnically diverse university student population in the United Kingdom during COVID-19, this qualitative study found that actual reported cases potentially underestimate the problem of hate crime. Analysing data from semi-structured interviews, we argue that a focus on reported cases alone risks obscuring the full extent of pandemic-related harassment and the insidious nature of hate crimes more generally. Minority ethnic interview participants lived with varying degrees of fear of victimisation, even if they were not personally subjected to any actual incidents during the pandemic. Accounts of pre-pandemic experiences, along with vicarious experiences involving victims with similar characteristics as interview participants, confirm the status of hate crime as a ‘message crime’. Third-party bystander accounts involving the victimisation of others whose identity research participants did not share afford additional insights into the nature and extent of pandemic-related hate.
Interpretable tabular data generation

Abstract Generative adversarial network () models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as , , , etc. are based on models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model— G enerative A dversarial Network modelling inspired from N aive B ayes and L ogistic R egression’s relationship ( $${ { \texttt {GANBLR} } }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>GANBLR</mml:mi> </mml:math> ), which not only address the interpretation limitation of existing tabular -based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate $${ { \texttt {GANBLR} } }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>GANBLR</mml:mi> </mml:math> ’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.