Reviewerly/exHarmony huggingface dataset:
EfficientNet-based Image Captioning System

An individual might take an instant glimpse of an image or environment and express it quite precisely and effectively. Image captioning i.e., automatic labelling of images is a well-known complex problem in the area of image processing. The rationale for this is because the convergence of Natural Language Processing (NLP) and Computer Vision (CV) should be capable of developing and providing the mankind with an appropriate depiction of a picture. In this study, we attempt to develop an EfficientNet-based model that requires fewer characteristics and produces excellent predictions. We utilise the very famous and well-known MSCOCO Dataset i.e., Microsoft Common Objects in Context, which includes near about 330K pictures, 1.5M object instances, and 80 different classifications of objects. We use the BLEU-4 scoring for the analysis of the system, that specifies how close the predicted caption is to the reference caption. Having BLEU score nearer to 1 denotes more comparable captions. The suggested approach comprehends the picture efficiently and produces more intended outcomes.
Analysis of Attack Patterns and Cyber Threats in Healthcare Sector

Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector.
Machine Learning Thyroid Model for Prediction System

Deep learning and cytopathology identification have trended in recent decades. Whole slide images (WSI) from clinic e-scanners help researchers classify slides (benign or malignant). In a thyroid WSI, the crucial area that supports the detection result may be smaller, and only the worldwide label may be gathered, making the supervised learning framework impracticable. Clinical thyroid cell identification requires many visual cues across several scales, therefore standard feature extraction may not work. This study proposes a poorly guided multi-instance-based learning technique for cytopathological thyroid diagnosis using Multi-Scale Feature Fusion (MSF) and Convolutional Neural Network (CNN). WSIs are pouches with numerous occurrences for each section. Architecture learns to automatically categorise important sections. A feature fusion architecture that merges minimum features in the feature map with an instance-level awareness model improves classification results. The suggested model, trained and verified on clinical data, outperforms all existing methods with 93.2% accuracy. Our model outperformed ultra-modern deep multi-instance technique on a publically accessible histopathology dataset.
Security in Internet of Things: An Overview

The Internet of Things (IoTs) has been one of the innovations that have gained the most traction in recent years across a range of applications. For communications, analysis, computation, and monitoring of various environments, the smart devices are wired/wirelessly connected. The items are diverse, have little storage, and become less powerful. Since traditional-based existing security mechanisms are incompatible with the IoTs, implementing the IoTs system poses security and privacy issues.In this article, in-depth layer-by-layer identification of the security concerns in the IoTs system is done. A thorough analysis of related technologies, including blockchain incorporation with the IoTs security, artificial intelligence, and machine learning, is conducted. In the study of this survey’s findings, security issues resolved by machine learning, artificial intelligence, and blockchain technology are mentioned along with challenging issues.
Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection.
LiteGaze: Neural architecture search for efficient gaze estimation

Gaze estimation plays a critical role in human-centered vision applications such as human-computer interaction and virtual reality. Although significant progress has been made in automatic gaze estimation by deep convolutional neural networks, it is still difficult to directly deploy deep learning based gaze estimation models across different edge devices, due to the high computational cost and various resource constraints. This work proposes LiteGaze, a deep learning framework to learn architectures for efficient gaze estimation via neural architecture search (NAS). Inspired by the once-for-all model (Cai et al., 2020), this work decouples the model training and architecture search into two different stages. In particular, a supernet is trained to support diverse architectural settings. Then specialized sub-networks are selected from the obtained supernet, given different efficiency constraints. Extensive experiments are performed on two gaze estimation datasets and demonstrate the superiority of the proposed method over previous works, advancing the real-time gaze estimation on edge devices.
FPGA Implementation of Chaotic Oscillators

In this work, FPGA implementation of Chaotic Oscillators based on Lü-Chen, Lorenz, Chen, Pehlivan-Wei and Rössler Chaotic Systems has been carried out using two different numerical methods, namely, Euler’s Numerical Method and Heun’s Numerical Method. This work consists of a concise description of the algorithms of these Chaotic Systems, which is followed by a brief explanation of the two numerical methods. A detailed account about the FPGA implementation of the Chaotic Oscillators is also presented. The 32-bit Signed Fixed Point Number Format has been employed for the representation of all the system parameters and values of inputs and outputs. In addition to Chaotic Oscillators, Pseudo Random Number Generators (PRNGs) based on each of the Chaotic Oscillators are also implemented, that produces an output at each clock cycle along with the Chaotic Oscillator. The algorithm of this implemented PRNG has also been explained. The designed system has been simulated and synthesized targeting the Xilinx Artix-7 family of FPGA. Subsequently, Chaotic Oscillators have been compared in terms of Resource Utilisation, Power and Speed. This work elucidates the details of the simulation and testing process of the PRNGs based on the Chaotic Oscillators. The Frequency (Monobit) Test, which is the first test of NIST Statistical Test Suite, has been performed on the pseudorandom bit sequences generated by the PRNGs using MATLAB. Lastly, the results of the testing of PRNGs have been explained along with the conclusion and scope of future work.
Mammographic mass Classification using DL based ROI segmentation and ML based Classification

Mammography is said to be the initial examination for analysis of breast cancer masses in women at the age of 38 or above. Breast cancer masses may be benign or malignant. Various CAD systems have been designed to differentiate benign and malignant breast cancer masses. Breast cancer masses can be easily classified as benign or malignant considering their features. Features from mammographic images can be efficiently extracted using shape and texture features. Both the shape and texture features contribute a vital role in the classification of mammographic masses. Enormous Computer-assisted design systems have been employed for the classification of masses in mammographic images. In the present research work, the performance of the CAD system with DL based ROI segmentation and ML based mass classification of SFM mammographic images has been analyzed using 518 mammographic images from the DDSM dataset. DL based encode-decoder segmentation model ResNet50 is used for ROI segmentation. The shape and texture feature sets are extracted and concatenated, yielding a combined feature set to be fed to ML based classifiers namely ANFC-LH and PCA-SVM for binary classification of mammographic masses. The ANFC-LH and PCA-SVM classifier yields 73.0 % and 72.0 %, accuracy for the binary classification of SFM mass images.
Anomaly Detection for Automated Vehicles Integrating Continuous Wavelet Transform and Convolutional Neural Network

Connected and automated vehicles (CAVs) involving massive advanced sensors and electronic control units (ECUs) bring intelligentization to the transportation system and conveniences to human mobility. Unfortunately, these automated vehicles face security threats due to complexity and connectivity. Especially, the existing in-vehicle network protocols (e.g., controller area network) lack security consideration, which is vulnerable to malicious attacks and puts people at large-scale severe risks. In this paper, we propose a novel anomaly detection model that integrates a continuous wavelet transform (CWT) and convolutional neural network (CNN) for an in-vehicle network. By transforming in-vehicle sensor signals in different segments, we adopt CWT to calculate wavelet coefficients for vehicle state image construction so that the model exploits both the time and frequency domain characteristics of the raw data, which can demonstrate more hidden patterns of vehicle events and improve the accuracy of the follow-up detection process. Our model constructs a two-dimensional continuous wavelet transform scalogram (CWTS) and utilizes it as an input into our optimized CNN. The proposed model is able to provide local transient characteristics of the signals so that it can detect anomaly deviations caused by malicious behaviors, and the model is effective for coping with various vehicle anomalies. The experiments show the superior performance of our proposed model under different anomaly scenarios. Compared with related works, the average accuracy and F1 score are improved by 2.51% and 2.46%.
Variational Bayesian analysis of nonhomogeneous hidden Markov models with long and ultralong sequences

Nonhomogeneous hidden Markov models (NHMMs) are useful in modeling sequential and autocorrelated data. Bayesian approaches, particularly Markov chain Monte Carlo (MCMC) methods, are principal statistical inference tools for NHMMs. However, MCMC sampling is computationally demanding, especially for long observation sequences. We develop a variational Bayes (VB) method for NHMMs, which utilizes a structured variational family of Gaussian distributions with factorized covariance matrices to approximate target posteriors, combining a forward-backward algorithm and stochastic gradient ascent in estimation. To improve efficiency and handle ultralong sequences, we further propose a subsequence VB (SVB) method that works on subsamples. The SVB method exploits the memory decay property of NHMMs and uses buffers to control for bias caused by breaking sequential dependence from subsampling. We highlight that the local nonhomogeneity of NHMMs substantially affects the required buffer lengths and propose the use of local Lyapunov exponents that characterize local memory decay rates of NHMMs and adaptively determine buffer lengths. Our methods are validated in simulation studies and in modeling ultralong sequences of customers’ telecom records to uncover the relationship between their mobile Internet usage behaviors and conventional telecommunication behaviors.
A Relational Semantics for Ockham’s Modalities

This article aims at providing some extension of the modal square of opposition in the light of Ockham’s account of modal operators. Moreover, we set forth some significant remarks on the de re–de dicto distinction and on the modal operator of contingency by means of a set-theoretic algebra called numbering semantics. This generalization starting from Ockham’s account of modalities will allow us to take into consideration whether Ockham’s account holds water or not, and in which case it should be changed.
HPnet: Hybrid Parallel Network for Human Pose Estimation

Hybrid models which combine the convolution and transformer model achieve impressive performance on human pose estimation. However, the existing hybrid models on human pose estimation, which typically stack self-attention modules after convolution, are prone to mutual conflict. The mutual conflict enforces one type of module to dominate over these hybrid sequential models. Consequently, the performance of higher-precision keypoints localization is not consistent with overall performance. To alleviate this mutual conflict, we developed a hybrid parallel network by parallelizing the self-attention modules and the convolution modules, which conduce to leverage the complementary capabilities effectively. The parallel network ensures that the self-attention branch tends to model the long-range dependency to enhance the semantic representation, whereas the local sensitivity of the convolution branch contributes to high-precision localization simultaneously. To further mitigate the conflict, we proposed a cross-branches attention module to gate the features generated by both branches along the channel dimension. The hybrid parallel network achieves 75.6% and 75.4%AP on COCO validation and test-dev sets and achieves consistent performance on both higher-precision localization and overall performance. The experiments show that our hybrid parallel network is on par with the state-of-the-art human pose estimation models.
MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts

In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.
PETRARCH IN THE ARTISTIC INTERPRETATION OF OSIP MANDELSTAM: TRANSLATION STRATEGIES AND DECIPHERING BIOGRAPHICAL SUBTEXTS

Osip Mandelstam translates Francesco Petrarch’s sonnets between December 1933 and January 1934. We consider the reason for the poet’s addressing these sonnets, their place of in his own poetry and the specific features of Mandelstam’s translation. The Russian texts are close to the original in terms of syntax and rhythm, however, lexically, Mandelstam introduces into the text images close to his own poetics. Petrarch wrote sonnets dedicated to Laura during the formation of the literary Italian language: the language that he uses is a kind of interlinguistic substance, a search for a path from archaism to novelty. For the modern reader, this language is not just archaic, it gives the impression of a kind of tonguetied language, attempts to create words for a poetic work in a situation where words are not enough. Accordingly, Mandelstam compensates for this linguistic effect by including in the text the author’s neologisms. An analysis of the four sonnets that Mandelstam chose for translation shows that the translator’s vocabulary goes back to his own creative images. Mandelstam, as it were, appropriates these sonnets, makes them his own in the literal sense of the word, and it is not by chance that though he designates them as translations, he nevertheless publishes them among his poems.
Using an explicit query and a topic model for scientific article recommendation

The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher's productivity. This article proposes a new method of recommending scientific articles that takes advantage of content-based filtering. The challenge is to target relevant information that meets a researcher's needs, regardless of their research domain. Our recommendation method is based on semantic exploration using latent factors. Our goal is to achieve an optimal topic model that will serve as the basis for the recommendation process. Our experiences confirm our performance expectations, showing relevance and objectivity in the results.
APLIKASI PENGAJUAN KOMPLAIN NASABAH BANK MELALUI NOTIFIKASI WHATSAPP

During the covid 19 pandemic, the use of website technology facilities is very important because it reduces direct interaction between banks and customers to prevent the spread of covid 19. PT Bank Pembangunan Daerah Sumatera Selatan and Bangka Belitung were established on November 6, 1957 under the name PT Bank Pembangunan South Sumatra. Complaints faced by Bank Sumsel Babel from customers such as money not being received but debited account, insufficient money received, not making a transaction but account being debited twice, failed transfer to another bank, debited account, transfer via M-Banking failed to debit account, etc. The Complaint Process for South Sumatra Babel customers now has to come directly to the office, customers will take a long time if they are far from the Bank Sumsel office, Customers queue for a long time and have to complete the file manually (physically). The solution to overcome these problems is to build a Customer Complaint File Submission Service system at Bank Sumsel which can be accessed online with the results of the notification of the resolution of the problem obtained by the customer via WhatsApp. This can provide service quality and customer satisfaction. The system development method used is the waterfall model. The purpose of this study is to build an information system for filing complaints at Bank Sumsel Babel which will make it easier to process data for filing complaints
SISTEM INFORMASI WISATA BERBASIS WEBSITE MENGGUNAKAN METODE PROTOTYPE

Tourism activities cannot be missed, they are a basic need for every human being to eliminate boredom every day. The tourism sector plays an important role for Indonesia, which is currently growing in various regions. By combining information technology and natural resource development business is a form of domestic tourism development strategy. The problem that often occurs when tourists are going on a tour is they lack information on the right location and estimated prices when traveling on the Thousand Islands. As we know, the tour package booking system is still conventional, customers still have to come to the crossing port and the payment system still uses cash. To overcome this problem, an information system was created to facilitate ordering tour packages online which aims to expand marketing and increase profits in the tourism sector. This website-based information system is built using the prototype method. In designing this information system using the PHP programming language, and using the Laravel framework as coding, for the database using MySQL. The results of this study are a tourist information system that can present complete tourist information, order tour packages online, pay using payment gateways, and manage visitor data more quickly and accurately.