Reviewerly/exHarmony huggingface dataset:
Analysis of Syndrome-Based Iterative Decoder Failure of QLDPC Codes

Iterative decoder failures of quantum low density parity check (QLDPC) codes are attributed to substructures in the code’s graph, known as trapping sets, as well as degenerate errors that can arise in quantum codes. Failure inducing sets are subsets of codeword coordinates that, when initially in error, lead to decoding failure in a trapping set. In this paper we examine the failure inducing sets of QLDPC codes under syndrome-based iterative decoding, and their connection to absorbing sets in classical LDPC codes.
Food Genie, Recipe Search Algorithm Using Web Scraping

Coping up with our busy lifestyle and unhealthy eating habits, we are compelled to eat diet foods containing specific ingredients and recipes. It's not easy to ask the chef to make a range of recipes that contain the required ingredients. There are several recipe websites containing a wide range of recipes to choose from. This allows the chef to make new meals containing the specified calories and other nutritional values. The challenge is to find a list of all recipes containing the required ingredients along with the maximum calories allowed without having any data in-hand. This paper presents a method to get a list of all recipes along with its details like methods, ingredients, nutritional information which contains all or some of the mentioned ingredients. The method will allow users to search for recipes easily without worrying about the ingredients. Web scraping using Scrapy and Neo4j graph database is used to implement the algorithm. Web scraping takes the contents of a web page and saves them in a Neo4j graph database using the Python scrapy package. This database will be used to do more study on the food genie application in order to improve search results. Chefs may use the online app to prepare a variety of recipes using particular components to maintain a healthy lifestyle.
Dynamic Optimization of Provider-Based Scheduling for HPC Workloads

The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.
Breast Cancer Recognition Using Integrated Lasso Based Artificial Intelligence Approach

Breast cancer is the most found disease in the women's which spreads throughout the body if it doesn't concentrated well. There are more technologies has been introduced earlier for the detection of breast cancer. However there is no efficient technology to find the breast cancer in the severe stage and reaches distant metastasis. In the existing work, it is done by using the various machine learning algorithms, which will predict the disease occurrence by learning the various genes associated with them by Analysing the gene structure. However those techniques are complex in nature and cannot predict the diseases in the accurate way due to its varying characteristics. These issues are resolved in the proposed methodology by introducing the method namely Lasso based Artificial Intelligence Approach (LAIA) for breast cancer Recognition which is in the distant metastasis stage. In the proposed research work, input datasets were pre-processed first which is then given as input to the LAIA method which can detect the disease more accurately. The analysis were done in the matlab simulation environment and then the comparison has been made against the various existing techniques such as adaboost and XGBoost algorithms. The metrics considered for the evaluation are accuracy, F1 Score, Positive Predictive Value, Sensitivity and AUC values. The comparison analysis proved that the proposed methodology attains better performance improvement than the existing methodologies.
Bangla Hate Speech Detection System Using Transformer-Based NLP and Deep Learning Techniques

Hate speech is a form of discriminatory communication disrupts community standards and breaches the line of self-limitation, causing harm to others and occasionally leading to cyberbullying. Hate speech spreads hatred toward a person or a particular group based on various characteristics, e.g., race, religion, gender, and so on is referred to as bias. The offensive speech detection system is the frontier where researchers are battling to provide secure internet using natural language processing and machine learning approaches. In this research, we strive to create an automatic Bangla hate speech detection system using natural language processing (NLP) and deep learning approaches. We utilized our custom dataset and some labeled data from an open-access repository in this work. 4,978 data from both sources were merged and implemented in our proposed model. Different data preprocessing techniques, tokenization, stemming, and removal of stopwords have been applied. Four deep learning and NLP-based classifiers have been applied to detect Bangla hate speech. Google API has been employed to convert text from Bangla to English. The emojis were removed from the datasets and the data were translated into Bangla. The GRU and Attention techniques performed best with 98.87% and 98% accuracies, respectively.
Recognition System for Malarial Parasites Causing Protozoa Infections in Thin Blood Smears

Fungi, bacteria, and protozoa are the primary agents of infectious diseases. Bacteria are the prokaryote kingdom, and yeast mold belongs to the fungi kingdom. Protists are the kingdom of protozoa. The malaria parasite is nothing but Plasmodium, a genus of parasitic protozoa. Parasites are organisms that use another organism directly to their benefit. This research article uses a computer-aided diagnostic tool to recognize malarial parasites that result in protozoa infections in thin blood smears. Six different machine learning algorithms were used, out of which the support vector machine resulted in 89.94% accuracy. The results are validated using the proposed Convolution Neural Network (CNN) model. With convolution and pooling processes for each pixel, CNN learns more compelling features, thus resulting in a much better decision hyperplane. The different layers were designed to get better accuracy. The accuracy of 96.46% is attained using CNN, which results in a 6.76 % accuracy improvement.
Automated 20-20-20 Timers Using Facial Detection and Facial Recognition

This paper proposes two automated 20-20-20 timers using facial detection and facial recognition respectively. Computer Vision Syndrome (CVS) is a common phenomenon these days, resulting in eye irritation and headaches. A possible prevention of CVS is adopting the 20-20-20 rule. Two types of 20-20-20 timer exist in the market, but both have scopes of improvement. While one type of timer needs to be manually set by the user like a stopwatch, the other type of timer detects the ON -screen time of monitors as a parameter. Both these models fail to detect whether the user is actually looking at the screen, and interrupt the work schedule of the users. The authors have developed a smart automated 20-20-20 timer that uses haar-cascade classifiers for detecting the face and accordingly adapt to the situation. This timer is able to detect the facial features in real time, and alert the user after 20 minutes of screen time. Furthermore, the authors have developed another smart 20-20-20 timer using YOLOv5s. This timer can recognise multiple users looking at the screen and run the timer independently for each person. While the haar-cascade enabled timer is preferred for homes and personal laptops, desktops and PCs; the YOLOv5s enabled timer is suited for offices, schools, colleges and libraries. This paper proposes an automated smart 20-20-20 timer system.
Fuzzy Inference System Based Intelligent Food Recommender System

The personalized suggestions using the intelligent methods are becoming promising ways to change eating habits to-ward more desirable diets. This has necessitated the requirements of intelligent food recommender system (FRS). This paper has designed a FRS using the Fuzzy Inference System (FIS) to suggest the food preference (FP). This is achieved using the different system blocks such as Rule Base, Database, Decision-making Unit, Fuzzification Interface Unit (FIU), and Defuzzification Interface Unit (DIU). The FP is decided by processing the input variables such as Age, Income and Calorific values. This is achieved with an accuracy of higher than 95%. Efficacy of proposed FRS is established by comparing the performance with the rule based FRS reported in literature. Study is performed using the coding environment of MATLAB.
Sentiment and Context-Aware Recurrent Convolutional Neural Network for Sentiment Analysis

Sentiment analysis is a Natural Language Processing (NLP) task that involves using machine learning techniques, particularly deep learning, to determine the sentiment or emotion expressed in a piece of text. The goal of sentiment analysis is to understand whether the text expresses a positive, negative, or neutral sentiment towards a particular subject, product, service, or topic. A deep learning technique called Sentiment and Context-Aware Hybrid Deep Neural Network (SCA-HDNN) was proposed for sentiment analysis. In SCA-HDNN, Convolutional Neural Network (CNN) was used for sentiment classification. However, CNNs require fixed-sized inputs, which can be a limitation when dealing with sequences of different lengths. So in this paper, Recurrent CNN (RCNN) is introduced for sentiment classification. The incorporation of recurrent connections in each convolutional layer of RCNN enables handling variable-length sequences. RCNNs can be more computationally efficient than CNN. The combination of CNNs and RNNs allows for parallelization and shared computations across time steps, which can speed up training and inference for sentiment analysis. This method is named as SCA-RCNN. The results from the experiment demonstrate that the suggested SCA-RCNN method achieves high levels of accuracy, precision, and recall in sentiment analysis.
An Efficient Approach to Classify Lung Cancer Tissue Cells Using Transfer Learning Techniques

One of the deadliest malignancies in the world is lung cancer. In this paper, we are using histopathological images of lung tissue to classify them into cancerous and non-cancerous. We proposed different transfer learning approaches like VGG16, ResNet50, and DenseNet121 to classify the images of tissues into healthy and two types of cancerous tissues. Of the three models, the ResNet50 and DenseNet121 provided better accuracies in training and testing almost reaches to 96%. VGG16 provided 91% accuracy because its complex layers take a long time to be trained. With a high computational capacity system and better training, this model can perform much better. All three proposed models gave good accuracy compared to the basic CNN model and KNN model.
Machine Vision Enabled Fall Detection System for Specially Abled People in Limited Visibility Environment

In this study, a unique fall detection system with machine vision capabilities is presented. It is intended to help people with special needs in environments with low visibility. People with disabilities are far more likely to fall, so it is important to spot such accidents right once for their safety and wellbeing. However, current fall detection technologies sometimes have trouble operating effectively in dimly lit environments. Our suggested method uses cutting-edge computer vision algorithms to detect falls in real-time, even in environments with poor sight, in order to solve this problem. The device collects visual data using infrared sensors and a low-light camera, and uses a deep learning algorithm to identify falls. The deep learning model can learn complicated patterns and traits related to falls because it was trained on a huge dataset of annotated fall and non-fall samples. The model takes the collected photos and extracts the pertinent elements, then categorises them as autumn events or non-fall events. The system also uses a threshold-based methodology to distinguish between falls and other behaviours that could cause false alarms. To assess the system's performance, numerous tests were run, including ones in controlled low lighting and in real-world situations. The outcomes show that the suggested system can detect falls reliably even in difficult lighting situations, attaining high detection accuracy and a low false alarm rate. The machine vision enabled fall detection system has a great deal of promise to improve the safety and independence of people with disabilities by offering prompt help in the event of falls, especially in dimly light areas. The system provides a dependable and efficient way to meet the particular requirements of people with disabilities in low-light situations and can be deployed in a variety of venues, including homes, hospitals, or care facilities.
Intelligent Recognition of Erroneous Movements in Athlete Training Based on Computer Vision Technology

Human motion recognition has great practical value in the field of computer vision, but it is also extremely complex and a hot spot and difficult area of computer vision research. The purpose of this paper is to study the intelligent recognition of erroneous movements in athlete training based on computer vision technology. The principle of YOLO algorithm is analysed, and a window segmentation method based on a combination of sliding window and action window is proposed for action recognition based on a double-layer classifier in terms of mobile data window segmentation, and applied to an intelligent error action recognition system. Finally, it is demonstrated that the method is able to accurately identify action types with a final false action recognition rate of 95%.
Mathematics Online Teaching Quality Test System Based on Optimization Algorithm

Optimization problem has always been a difficult and hot topic. With the development of mathematical science, people have a deeper understanding of it, and the idea of optimization into various fields. Optimization theory has become an important subject in recent years and has been widely used in many important fields. With the improvement of science and technology, computer and network technology has been popularized in every corner of social life. Since the spring of 2020, in the face of the impact of the novel coronavirus epidemic, the Ministry of Education has issued a notice of “using online platforms to suspend classes without stopping school”, and large, primary and secondary schools across the country are taking part in online teaching. The implementation of online teaching has rapidly changed the teaching methods of teachers and students. The changes of learning environment, learning platform and teaching methods have also brought new challenges to teachers. Therefore, in order to solve the problems accompanying online teaching, improve the effect of online teaching of mathematics, in order to help mathematics teachers more accurate and efficient online teaching work. In this paper, an online mathematics teaching quality testing system based on optimization algorithm is proposed. The AMM algorithm is used in the numerical experiment. Thirdly, the improved algorithm proposed in this paper is compared with the classical gradient descent optimization algorithm, which proves that the algorithm is feasible and efficient. The experimental results show that the combination of optimization algorithm and big data technology can better realize the online teaching quality detection, but also help students to obtain better learning experience and effect.
Heritage of India: Advanced Monuments Classification using Artificial Intelligence

Tourism is India's most lucrative service industry. Many historical buildings and cultural practices originated in India. Every year, millions of people go to tourist hotspots, and millions more share photos they have taken there on social media and mobile devices with friends and family. The promotion and dissemination of cultural assets as well as tourism activities and landmarks in urban settings might benefit greatly from the use of artificial intelligence methods and machine learning algorithms, which are already being used in a wide variety of settings. Automatic identification and categorization of landmarks and tourist attractions might be an intriguing use case for these methods in the cultural and touristic sector. This study makes an effort to categorize India's historic sites. Hotels, travel companies, restaurants, government organizations, etc. all benefit from the classification of monuments so that they may better plan for the future and provide facilities that are up to date with visitor expectations. Using a deep learning technique, the suggested approach aims to retrieve characteristics and categorize the Indian landmarks that visitors visited.
An Attempt at Zero-shot Ancient Documents Restoration Based on Diffusion Models

The virtual restoration of ancient documents using deep learning is an emergency and an expected work. However, GANs-based image-to-image translation approaches hit the degradation data shortage, a hardness to build one-to-many restoration models, and a limitation for large deformation. In this study, we apply zero-shot restoration based on Diffusion models to ancient degraded documents, specifically, leverage inpainting of Denoing Diffusion Restoration Models (DDRM) for missing ancient characters. Furthermore, we introduce a noise masking method, which limits the attention area of predicted noise images in the reverse process. Noise masking forces DDRM to generate faithful objects following mask images, so that has high usability without re-training of deep neural networks. The zero-shot restoration and noise masking prompt GUI-connecting restoration of missing characters, leading to realizing a cooperative application with humans for ancient document restoration.
Artificial Intelligence Based Approach to Identify Plastic Bottle for Environment Sustainability

Several classification problems have been resolved with computer vision and deep learning technology. Despite this, no dedicated work has been carried out to classify plastic bottles from non-bottle objects. Therefore, this paper is concerned to resolve issue to classify waste plastic bottle from non-bottle objects. Here a dataset of 52000 images of bottle and non-bottle has been used to classify different objects based on their size. Each image contains a single bottle and non-bottle object. In this paper, various pre-trained models have been employed to classify these objects based on their size. The pretrained models have been trained with bottle dataset and attained 97%, 92% and 91 % accuracy. The comparison shows that the ResNet-18 outperforms.
Research on Machine Learning and Signal Processing of IQ Signals

IQ signals are a core technology of the zero-IF architecture in modern communication technology, but they are often unstable due to factors such as amplitude and phase imbalances. Utilizing the characteristics of IQ two-way signals, common IQ signal calibration algorithms use compensation, frequency domain calibration, etc. This article uses the characteristics of Machine Learning and Signal Processing to train the existing IQ signals and predict the signals based on the training set. Through simulation experiments and actual data measurement, it can be known that different prediction models will have different correction effects. Among them, the Neural Networks model, which is mainly adopted in this article, has a relatively ideal effect on signal processing and prediction, and can also significantly make the signal smoother.
Towards Highly Compressed CNN Models for Human Activity Recognition in Wearable Devices

The widespread usage of wearables set the foundations for many new applications that process the wearable sensor data. Human Activity Recognition (HAR) is a well-studied application that targets to classify the data captured by multiple wearable sensors into one of a predefined set of activity classes. Earlier approaches in the area relied on Machine Learning (ML) classifiers, while many researchers proposed solutions based on Deep Neural Networks (DNNs), e.g., using Long Short-Term Memory (LSTMs) or Convolution Neural Network (CNNs). Even though the usage of DNNs for HAR is a widely explored field, the problem of compressing (thus reducing the computational requirements) the resulting DNN models has been disregarded by the research community. This is of paramount importance if we consider that wearables are characterized by scarce memory and computational resources. In this work, we first analyze the impact of the size of the various CNN components (focusing on the convolution and dense layers) to the model validation performance. As a second step, we focus on two CNN models with diverse memory characteristics (the lean and the fat model) and we employ three different compression techniques (Filter-Based Pruning –FBP,– Low Rank Factorization –LRF,– and dynamic range quantization) assuming various compressibility scenarios.
Estimating Power Consumption of Collocated Workloads in a Real-World Data Center

Carbon-aware computing is emerging as one of the sustainable ways to meet the projected rise in IT energy demand. Carbon-aware computing methods require workload-level power consumption as one of the inputs. To this end, we conducted a novel study involving a real-world data center (DC) to attribute power consumption to individual workloads collocated on servers. In this paper, we propose a machine learning-based modeling approach to estimate server-level power consumption and then adapt the server-level model to estimate the power consumption of individual workloads running on the server. The proposed method aims to solve two problems: the diversity of workloads in a real-world data center concerning the magnitude of power consumed and the lack of ground-truth for workload-level power consumption. For the first problem, we use short-range power models instead of a single model for the entire power range, which reduces the estimation error by about 28 to 30 percentage points on average compared to the conventional method. We use a top-down modeling approach for the second problem, using dominant feature-based scaling to transform server-level model estimations into workload-level power attributions. According to our knowledge, this study is a first of its kind where we proposed a method to attribute power consumption to individual workloads in a real-world DC.