Reviewerly/exHarmony huggingface dataset:
A Comparative Study on the Evaluation of k-mer Indexing in Genome Sequence Compression

Low-cost and faster next-generation sequencing (NGS) technology generates huge sequence data for living organisms in the terabyte range. Storing, transferring, and analyzing these data is a real challenge for researchers. An efficient compression algorithm is the ultimate solution to this challenge. Three benchmark methods are tested on the Amazon Web Services (AWS) virtual cloud platform in this article: High-performance referential genome compression (HiRGC), High-efficiency referential genome compression (SCCG), and Hybrid referential compression method (HRCM). Eight benchmark human genomes, coronavirus genome, and a few additional species in FAST-ALL (FASTA) and Raw formats are used to test these algorithms. The widely-used FASTA format, which is utilized in GenBank, makes data analysis and reading easier for researchers. A very fast k-mer hashing method is used for indexing, which is efficient for pairwise and batch-wise compression. HiRGC offers a good trade-off between compression ratio and time, according to experimental data. The SCCG technique takes longer to compress data but significantly reduces the amount of available space. HRCM does not perform as well for pairwise compression, but it makes significant strides for batch processing. These facts motivate us to propose an improved and efficient compression algorithm in the future.
An Automatic POS Tagger System for Code Mixed Indian Social Media Text

For a range of Natural Language Processing (NLP) applications, including Sentiment Analysis, Sarcasm Detection, Information Retrieval, Question Answering, and Named Entity Identification, text derived from multiple users’ posts and what they comment on social media constitute significant information (IR). All such applications require part-of-speech (POS) tagging to add tag information to the raw text. Code-mixing, a social media user’s natural desire to submit content in multiple languages, presents a difficulty to POS tagging. In addition, sophisticated and freestyle writing increases the intricacy of the issue. For POS tagging of Code-Mixed Indian social media text, a supervised algorithm using Hidden Markov Model (HMM) with the Viterbi algorithm has been developed to address the problem. The suggested system has been trained and tested using publicly accessible social media text in Indian languages (ILs), particularly Bengali, Telugu, English, and Hindi. On the basis of the F-measure, the accuracy of the system-annotated tags have been assessed.
EDGE-Based Image Steganography

Steganography is a technique of hiding information in another data. In image steganography, the secret message is embedded inside one digital image. The most significant traffic comes from images on the internet, so hiding the data in images is most suitable. A digital image is represented as a grid of pixels, and generally, images have a large number of pixels, so a large amount of data can be hidden inside an Image. It is done so that there is less distortion with the image. The secret message is embedded in the image so that the human eye cannot detect it. Edge of Image means a sudden change in the image pixels. It captures the boundary present in some images, so edges are the most suitable place to hide the data in an image. In this paper, we will do some qualitative and quantitative analysis of various edge-based steganography methods, and we will compare this with the existing steganography methods available.
An Introduction to KDB: Knowledge Discovery in Biodiversity

The most basic method of experimentation using data mining algorithms is the command prompt. A convenient approach of interactive graphical user interfaces can be supplied for data exploration to build up complex studies. A graphical user interface gives an upgraded status for experimental data mining. An innovative proposal for employing data mining methodology on biodiversity data is shown in this article through the KDB (Knowledge Discovery in Biodiversity). It provides a platform for domain researchers to apply their datasets to domain-specific data mining algorithms for further analysis. A convenient interactive graphical user interface is provided for data exploration for the biodiversity domain to build up complex studies. The proposed data mining methods are developed in Java, while the website is built in PHP.
Load Balancing in a Heterogeneous Cloud Environment with a New Cloudlet Scheduling Strategy

The idea of cloud computing has completely changed the digital world in the age of the internet. Cloud computing offers a variety of cloud services, including software (Software as a Service), platforms (Platform as a Service), and infrastructure (Infrastructure as a Service). Pay-as-you-go is the primary business model used by cloud service providers (CSP). Services are offered that are in demand. Therefore, it is the only obligation of the cloud service provider to guarantee a terrific, continuous, and seamless service to its clients. To ensure an increase in service quality, load balancing is highly solicited. This paper mainly focused on task scheduling in a heterogeneous cloud environment so that no VM gets overloaded or under loaded. Users of cloud services typically submit tasks to CSPs. We mainly focused on enhancing the tasks’ completion rates and turnaround times. Enhancing the overall completion time of all jobs unquestionably contributes significantly to raising system throughput. This is accomplished by assigning a specific task to a particular virtual machine so that all machine loads are nearly equal and all tasks have nearly equal priority. Finally, we compare our task scheduling algorithm by substituting the original task scheduler with the one we have proposed. We then compare the results with the load-balancing techniques currently in use. The results are quite encouraging and have significantly improved the load balancer’s efficiency.
A Novel Approach of Fragile Watermarking for Authentication and Tamper Detection Exploiting Local Binary Pattern (LBP)

Data communication and transmission through internet has been rapidly increased now a days specially during and after COVID-19 pandemic situation. So, it is essential to preserve multimedia data from outlawed access. Here, a novel approach of fragile watermarking for authentication and tamper detection has been suggested. Firstly, an input image is split up into non-overlapping blocks of sized $$3\times 3$$ . A reference block has been chosen randomly to get the watermark which is hidden at the LSBs (Least Significant Bits) of the non-reference blocks of input image. The proposed scheme takes the advantage of Local Binary Pattern (LBP) feature of the image which is used to generate the watermark. The proposed scheme maintains high quality watermarked images with 51.68 dB average PSNR and 0.9955 average SSIM. The proposed scheme has been analysed against different attacks like cropping and copy-move forgery which shows 100% Tamper Detection Rate (TDR) for cropping attack and 99.39% for copy-move forgery attack. The presented scheme also exceeds other methods with respect to PSNR and SSIM value as depicted in comparative analysis.
Blockchain-Enabled IoV Framework for Establishing Automated Communication Using Smart Contract

A global network known as the Internet of Things (IoT) connects all sentient objects and allows inter-object communication. Internet of Vehicles (IoV) refers to the IoT-on-Wheels, wherein moving objects exchange information that could mean the difference between life and death to one another and the nearby environment to ensure highly dependable and efficient traffic flows. IoV is a method used in intelligent transportation systems to enable vehicle communications. The leading intent of IoV is to offer a safer, more efficient, and ecologically friendly transportation system. In this paper, we first look at a thorough literature review of the core notion of IoV, its evolution, its architecture, and the underlying principles of VANET technology. Many analyses on IoV have been explored to identify and categorize the current issues with adopting and deploying IoV in urban cities. A seven-layered IoV architecture is proposed, with each layer’s functionality and representation is taken into account. This paper presents a blockchain-enabled IoV framework to enable trusted and disseminated communications among vehicles. In this framework, each vehicle act as a blockchain node and should be verified by smart contracts automatically. The proposed work concludes with a Blockchain-based security framework for the security and smooth functioning of IoV.
Design of a Vehicle Overspeed Management System Using Blockchain-Assisted IoV

The Internet of Vehicles (IoV) is a network of vehicles integrated with sensors, software, and intermediary technologies to connect and exchange data over the Internet according to predetermined standards. Traditional vehicular ad-hoc networks (VANET) have evolved into the Internet of Vehicles as a result of recent significant breakthroughs in the new but promising paradigm of the Internet of Things (IoT) and its fusion with cutting-edge wireless communication technology. Recent data shows traffic accidents are a big cause of concern. In recent years, speeding has been a factor in 60% of road accidents. Road transport must eventually transition to the Internet of Vehicles to solve this problem and many others related to road safety. With the help of the internet of vehicles, driving will significantly change. Blockchain secures the entire process of sharing information among IoVs and adds features like traceability, thus people can be confident that the system is secure and reliable. This paper highlights the problem that road transport in India is very unsafe, and put forward a proposed model of blockchain-assisted IoVs as a solution to tackle this issue.
Real-Time Human Fall Detection Using a Lightweight Pose Estimation Technique

The elderly population is increasing rapidly around the world. There are no enough caretakers for them. Use of AI-based in-home medical care systems is gaining momentum due to this. Human fall detection is one of the most important tasks of medical care system for the aged people. Human fall is a common problem among elderly people. Detection of a fall and providing medical help as early as possible is very important to reduce any further complexity. The chances of death and other medical complications can be reduced by detecting and providing medical help as early as possible after the fall. There are many state-of-the-art fall detection techniques available these days, but the majority of them need very high computing power. In this paper, we proposed a lightweight and fast human fall detection system using pose estimation. We used ‘Movenet’ for human joins key-points extraction. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject. We used two datasets ‘GMDCSA’ and ‘URFD’ for the experiment. We got the sensitivity value of 0.9375 and 0.9167 for the dataset ‘GMDCSA’ and ‘URFD’ respectively. The source code and the dataset GMDCSA of our work are available online to access.
Analysis of Quantum Cryptology and the RSA Algorithms Defense Against Attacks Using Shor’s Algorithm in a Post Quantum Environment

Over the course of the previous twenty years, quantum cryptanalysis has become an important topic of research. The development of two well-known algorithms one for speedier factoring and the other for quicker searching has brought about a change in the conventional technique of computation. These algorithms are responsible for the change. A great number of the algorithms that were used in the conventional way of computing were really risk-free, precise, speedy, and output-oriented. In the post-quantum environment, it is absolutely necessary to investigate the behaviour of various classical algorithms when they are under attack. The behaviour of the RSA algorithm in terms of its resilience to attacks is the subject of investigation in this work. Deciphering the RSA codes requires the application of the Shor algorithm. In order to conduct the experiment, both the Qiskit simulator and the IBM quantum environment are utilised. The amount of qubit capacity now available is the primary determinant of how simple it is to break the RSA algorithm. One can draw the conclusion that the capacity of any quantum computer to decode RSA codes is directly dependent on the number of bits used in those codes.
Contextualized word senses: from attention to compositionality

The neural architectures of language models are becoming increasingly complex, especially that of Transformers, based on the attention mechanism. Although their application to numerous natural language processing tasks has proven to be very fruitful, they continue to be models with little or no interpretability and explainability. One of the tasks for which they are best suited is the encoding of the contextual sense of words using contextualized embeddings. In this paper we propose a transparent, interpretable, and linguistically motivated strategy for encoding the contextual sense of words by modeling semantic compositionality. Particular attention is given to dependency relations and semantic notions such as selection preferences and paradigmatic classes. A partial implementation of the proposed model is carried out and compared with Transformer-based architectures for a given semantic task, namely the similarity calculation of word senses in context. The results obtained show that it is possible to be competitive with linguistically motivated models instead of using the black boxes underlying complex neural architectures.
Analysis of the SAMR Model and Psychological Approach on Duolingo Livestreams by Hololive VTubers in Learning Japanese

This qualitative research endeavors to investigate the innovative intersection of technology, persona, and psychology in language education through a unique lens—the world of VTuber Hololive Duolingo livestreams. The study's primary objective is to analyze the transformative potential of this approach to language learning, guided by the SAMR (Substitution, Augmentation, Modification, Redefinition) model and psychological perspectives. Employing purposive sampling, the research centers around six Hololive VTubers: Ceres Fauna, Ouro Kronii, Nanashi Mumei, Gawr Gura, Ninomae Ina'nis, and Moona Hoshinova. The investigation uncovers how these VTubers, each with distinct personas, employ the Duolingo application to teach Japanese on livestreams and its impact on viewers' language acquisition. The findings illuminate the multifaceted effects of technology integration, ranging from familiarity to transformative language learning experiences using SAMR, accompanied by psychological engagement and motivation. This study contributes to the evolving landscape of language education in the digital age and offers a foundation for future research in this captivating domain.
The Influence of Soil Saturation and Soil Strain on Landslides

Landslides are the movement of masses of rock, regolith and soil from high to low places due to the influence of gravitational forces, due to the weakness of the soil structure when saturated. Humidity on a certain slope is an initial indication of a landslide. By using a humidity measuring device you can get a landslide indication value, apart from humidity there is also another indication, namely ground movement which is detected with a strain sensor. Thus, these 2 main parameters are expected to become new findings in the field of landslide disasters as an initial disaster instrumentation system.
Classification of Offensive Tweet in Marathi Language Using Machine Learning Models

Offensive language identification is essential to make social media a safe and clean place to share one’s view. In this work, a model is proposed to automatically classify offensive tweets into offensive and not offensive classes of low-resource language. Marathi is spoken in Western India. Marathi being a low-resource language, lacks a comprehensive list of stopwords and proper stammer. To fill this gap, we created a list of stopwords for stopword removal and a list of suffixes to identify the root word in the Marathi language. Two different methods, Label Vectorizer and term frequency-inverse document frequency (TF-IDF) Vectorizer, are used to extract features from the text and then these features are used with six different conventional machine learning classifiers to classify a Marathi tweet into offensive or non-offensive.
Hold surrounding’s key-you only look once version 7: a real-time pedestrian and vehicle detection algorithm in the low-signal-to-noise ratio infrared image

Compared with visible light imaging technology, infrared optical imaging technology (see Fig. 1) is less affected by weather and illumination and is a potential auxiliary solution method for the future of the autonomous driving system. Based on the above characteristics, this study proposes the hold surrounding’s key (HSK)-you only look once (YOLOv7) algorithm. First, based on the characteristics of the infrared image, this study optimizes the network structure of YOLOv7 and proposes a vehicle and pedestrian detection algorithm based on the improved YOLOv7. Aiming at the problem that the reasoning speed and detection accuracy of pedestrian and vehicle detection algorithms based on infrared images are challenging to balance, occupy large storage space, and are difficult to deploy and run in real-time in low and medium-performance devices, the MPConv is added to replace the Conv structure in YOLOv7. In view of the false detection, missed detection, mutual occlusion and overlap of detected objects, and other situations that the YOLOv7 algorithm is prone to cause in the actual deployment environment easily, a tiny object detection layer is added. At the same time, to solve the problem that infrared optical imaging systems are prone to noise caused by external factors, this study introduces the TRPCA method for image denoising in the preprocessing process of the YOLOv7 algorithm. In the end, the HSK-YOLOv7 algorithm is verified using the self-made infrared traffic object detection dataset and the publicly available FLIR dataset to verify the detection effect of the HSK-YOLOv7 algorithm on near-infrared images and thermal infrared images. The parameter quantity of our algorithm is 37.3M, and the computing throughput is 107.5 GFLOPs. The detection speed on the self-made dataset and FLIR dataset reaches 163 frames per second (FPS) and 71.8 FPS, respectively, and the mAP@0.5 indicator reaches 94.08% and 61.3%, respectively. In general, HSK-YOLOv7 can meet the real-time requirements of the autonomous driving system while ensuring detection accuracy.
Methods for Compositional Data

You work with compounds of a whole (and, of course, including missing values), for example, measurements of parts per million of chemical elements of a sample, or chemical concentrations of elements in general, or time-use per day, or expenditures, wages or income for different components, or any data where the rows add up to constants. Then you should get familiar with compositional data analysis, log-ratio analysis, and corresponding imputation methods. Your dataset especially includes zeros, and therefore you cannot apply log-ratio techniques, since this would result in a division by zero. The concentration of some compositions on some chemical elements was too low to measure it (rounded zero), and you are not sure if you should replace these concentrations with a positive constant. Then you should get familiar with rounded zeros and their treatment with compositional zero-replacement methods. This chapter attempts to provide answers to these questions and discusses compositional data analysis methods for imputing missing values and rounded zeros. Before we discuss imputation methods for compositional data, we will introduce compositional data as well as log-ratio analysis.
Nonlinear Methods

So far, we have exclusively considered model-free and linear models for regression, since (1) the theory is simple(er), (2) (imputation) models are easier to interpret, and (3) for small n and/or large p, linear models are often the only way to avoid overfitting the data. What is new in this chapter is the consideration of nonlinearities between variables, that is, those that do not disappear by transforming variables or including quadratic terms, by adding new features, or by specifying interactions between predictors. Starting with tree-based methods such as the imputation with random forests or XGboost, we also introduce GAMs and show how ANNs can be used to impute missing values.
Feature Selection Approaches in Online Bangla Handwriting Recognition

Any feature selection technique aims to identify a smaller subset of essential characteristics from a larger collection by eliminating those that are redundant, noisy, or irrelevant. Feature selection techniques have proven to be a major ground-breaking technique to save computing time while strengthening prediction accuracy and data interpretation and better cognition of data in machine learning as well as pattern recognition algorithms. In the present experiment, we have compared four well-known feature selection techniques, Harmony Search, Krill Herd Algorithm, Principal Component Analysis and mRMR Algorithm when applied on the feature set produced by combining the frechet distance and distance-based features (256-dimension feature vector). The experiment has been performed on a 10000 online handwritten Bangla character database. The experimental outcome metrics and the analysis of the performances with respect to the percentage of dimensionality reduction and achieved accuracy are presented in a nutshell.
Qualitative and Quantitative Analysis of Modifications in Playfair Cipher

Security in digital world has become a paramount issue, even more so in the post pandemic world when the digital footprint of the world has increased. Multiple encryption techniques are used to ensure the same. Certain famous classical ciphers which were in prevalent use during ancient times are now not robust enough to keep up with security requirements of modern world. This paper analyses the different modifications proposed to one such cipher i.e., Playfair Cipher and how these modifications enhance it’s performance thus making it usable in modern world.
Imitation learning by state-only distribution matching

Abstract Imitation Learning from observation describes policy learning in a similar way to human learning. An agent’s policy is trained by observing an expert performing a task. Although many state-only imitation learning approaches are based on adversarial imitation learning, one main drawback is that adversarial training is often unstable and lacks a reliable convergence estimator. If the true environment reward is unknown and cannot be used to select the best-performing model, this can result in bad real-world policy performance. We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric. Our training objective minimizes the Kulback-Leibler divergence (KLD) between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion. Such methods demonstrate improved robustness when learned density models guide the optimization. We further improve the sample efficiency by rewriting the KLD minimization as the Soft Actor Critic objective based on a modified reward using additional density models that estimate the environment’s forward and backward dynamics. Finally, we evaluate the effectiveness of our approach on well-known continuous control environments and show state-of-the-art performance while having a reliable performance estimator compared to several recent learning-from-observation methods.