Reviewerly/exHarmony huggingface dataset:
Approach to Predict Software Vulnerability Based on Multiple-Level <i>N</i>-gram Feature Extraction and Heterogeneous Ensemble Learning

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.
evaluation study of face detection by Viola-Jones algorithm

Face detection technology is an essential step in almost all face related analysis applications such as face feature extraction, face alignment, face verification, face identification, face parsing, face recognition, age recognition, and gender classification. Numerous algorithms were introduced for face detection, one of which is the Viola-Jones algorithm being introduced in 2001. This algorithm is still widely used due to its simplicity and ability of detection in real-time with relatively high accuracy and low computational power requirements compared to other recent algorithms such as deep learning based algorithms. In this paper, Viola-Jones algorithm is implemented and evaluated through different tests. And its strengths, limitations, and affecting factors are provided according to the obtained results. This paper concentrates on the algorithm limitations and the reasons of these limitations, and suggests some solutions if possible. This can help in enhancing the algorithm performance by increasing the detection accuracy or reducing the time taken for detection or training…etc.
Automated Perpetrator Identification by Face Recognition

In the present scenario, the perpetrator identification procedure is done by the police force with the help of automated systems. Many buildings and streets have surveillance cameras installed to monitor the activities that occur within the focus. The videos recorded in these cameras have become one among the shreds of evidence for the police force to investigate the crime. Recognizing the person face from the captured videos is the most challenging task. The objective of this paper is to propose a face recognition model that can detect and then recognize the perpetrator’s faces automatically from the videos captured using the surveillance camera. The system implements three components: Face detection, facial features extraction, and face recognition. Haar cascades is used for face detection. Algorithms like local binary pattern histograms, fisherface, and eigenface are used for implementing the face recognition and the results obtained are plotted.
A Survey on Identification of Grocery Store Items Using Deep Learning in Retail Store

The work for distinguishing an item on a retail store’s rack could be an essential human capacity. Machine vision frameworks confront diverse deterrents whereas managing with the same acknowledgment issue. The automatic item location on a retail store’s racks moves forward the shopper encounter with additional esteem too whereas advertising retailers will advantage financially. Machine vision-based protest acknowledgment approaches have the next victory rate than the programmed discovery of retail stock in a store environment. In this paper, we display to consider device vision-primarily based retail object acknowledgment frameworks in addition to a cutting-edge medical category for the subject. Besides, we discuss about the problem’s troubles. We see the highlights that have been utilized in state-of-the-art endeavors in this comprehensive investigation of distributed works. The report closes by proposing to encourage inquire about subjects in related areas.
A Novel Marathi Speech-Based Question and Answer Chatbot for the Educational Domain

In any interactive agent, providing a suitable answer plays a vital role in determining the success of that chatbot. In the present research work, speech recognition API and Google Translate API are used to recognize the user’s voice and text translation respectively. We propose the design and implementation of a novel Marathi speech-based educational chatbot using Naïve Bayes classifier. After performing preprocessing, Naive Bayes classifier is used to determine and evaluate the highest probable class. More than 1500 words are used to train the model. The proposed approach is tested with some questions and their topics in the dataset. Brute force keyword matching and string similarity algorithms are implemented to fetch the suitable answer. Confusion matrix is generated to evaluate the performance of classification model. The obtained results prove the robustness of the proposed system.
A deep learning‐based framework for fast generation of photorealistic hair animations

Hair is the most important but onerous step for depicting dynamic 3D virtual characters. The photorealistic hair animation requires high-quality simulation and rendering models. These models are based on complex calculations of mechanics and optics. Because of the huge time budget, it is difficult to apply in the interactive scene. A promising solution to overcome the time budget is the reduced model that struggles to reduce the computation of physical details by various interpolation methods. However, current reduced models compromise too much reality. This research intends to achieve photorealistic hair animation in a fast way. Building a deep learning-based framework to synthesize photorealistic hair is aimed at. Furthermore, this research also presents a pipeline for hair merging into the scene. This new framework enables the model to significantly improve the appearances of hair animation while adding little computation overhead.
A Text Classification Optimization Framework for Prodigious Datasets

This paper introduces Tunicate Swarm Algorithm-based Hierarchical Attention Network (TSA-HAN). TSA-HAN is the combination of Tunicate Swarm Optimization Algorithm (TSA) that uses jet propulsion and Swarm Intelligence and Hierarchical Attention Network (HAN) which makes use of leveled document structure. The proposed optimized algorithm is used for text classification. The performance of TSA-HAN is evaluated based on five parameters, namely accuracy, TPR, TNR, precision, and FNR. For this, purpose self-created dataset named real-time dataset consisting of 5000 documents and popular datasets, i.e., Reuters datasets and 20-Newsgroup datasets, have been utilized and the potency of the said optimized algorithm has been further compared with an existing improved sine cosine algorithm (ISCA). The comparative analysis results show that TSA-HAN performs slightly better than ISCA.
A Survey of Methods and Techniques in Offline Telugu Character Segmentation and Recognition

Telugu is one of South India’s oldest languages. It has a complicated orthography with various different character shapes. For so many decades, offline character recognition has always been a popular area of study. Handwriting segmentation and recognition are difficult, which has stimulated the interest of industry and academic researchers. Methods for recognizing handwriting have become much more prominent in recent years. Until now, the connected component approach, vertical and horizontal profiles, and other approaches were presented in the literature for the segmentation of printed character's work. The selection of the best discriminative features is an important concern in character recognition. Various statistical and structural features, as well as various combinations of them, are discussed in the research work. Researchers used ANN, SVM, KNN, and CNN to classify offline Telugu characters. The purpose of this article is to closely analyze various feature extraction methods and classification models to understand the problem and challenges encountered by earlier research. This identification is meant to provide numerous recommendations for advancements and scope.
A novel deep neural network with adaptive sine cosine crow search (DNN-ASCCS) model for content based medical image reterival

Tumor is the second major cause of death in women worldwide. Breast cancer diagnosis and treatment can be difficult for radiologists. As a result, primary care helps to avoid disease and mortality. The study’s main goal is to improve treatment choices and to save lives by detecting breast cancer earlier. For classification problems, we propose a DNN-ASCC architecture in this study. The Fast Non-Local Means Filter completes the initial preprocessing stage. The binary grasshopper optimization algorithm (BGOA) and the grey-level run length matrix are utilized to choose the best features for the feature extraction operation. The suggested hybrid classifier (DNN-ASCCS) is critical for identifying normal and malignant tumors. Breast cancer is accurately detected by the suggested hybrid classifier. The recommended (DNN-ASCCS) was developed using MATLAB and datasets from the BIDCIDRI. The results of the simulation showed that the proposed technique has an accurate results in classification (99.17 percent) and robustness analysis is also done. When compared to alternative approaches, experimental results show that the suggested method is efficient.
Spatial feature point measurement on large-curved surfaces: A portable mobile measurement method based on monocular multi-angle points

This research introduces a precise and efficient detection method for feature-points distance measurement on the curved surface of large components based on tightly-coupled visual- Inertial measurement unit (referred to as IMU) such as forging, bridge, underwater pier, nuclear power and others manufacturing and quality monitoring sceneries. Basic algorithms and mathematical principles of the tightly-coupled monocular camera-IMU combined positioning module are utilized and real-time estimation model of the relative spatial attitude of the camera in the time domain is proposed. We study the calibration algorithm and mathematical principles of monocular camera external parameters during the measurement process and establish the estimation model of the relative spatial pose of the imaging surface and the object surface. The distance calculation model of the point to be measured on the space surface is established by fusing the camera's internal and external imaging parameters, imaging features and the theory of no distortion of the camera's optical axis. The distance measurement experiment device and the measurement system frame are constructed. The measurement results are validated by comparison against auxiliary target, image stitching and other measurement methods data. The target error within 20 m measured by the proposed method is controlled within 10 mm.
NUMERICAL SIMULATION OF THE STRESS-STRAIN STATE IN THE INTERFACE ZONE OF THE FLOOR SLAB AND THE COLUMN OF A MONOLITHIC REINFORCED CONCRETE FRAME

Scientific-educational and applied journal UNIVERSITY NEWS NORTH-CAUCASIAN REGION TECHNICAL SCIENCES SERIES. NUMERICAL SIMULATION OF THE STRESS-STRAIN STATE IN THE INTERFACE ZONE OF THE FLOOR SLAB AND THE COLUMN OF A MONOLITHIC REINFORCED CONCRETE FRAME
Mixup gamblers+: Learning interpolated pseudo “uncertainty” in latent feature space for reliable inference

We introduce Mixup Gamblers+, a method for building a deep learning classifier capable of self-evaluating the reliability of inference results. In the proposed method, samples with high uncertainty are generated virtually through data interpolation in the feature space embedded by the deep learning model, and the classifier is trained to detect them. Moreover, we introduce metric learning for feature representation learning to correlate the distance in the latent feature space with the similarity of the samples. This enables us to estimate the uncertainty of the pseudo data based on the distance in the latent feature space and enhances the training of our proposed method. The use of data interpolation in the latent feature space makes the proposed method a general-purpose method for learning uncertainty estimation models for inference, independent of the dataset and problem settings. The proposed method improves the accuracy of the inference and achieves state-of-the-art inferential uncertainty estimation results.
Animal Recognition Application System for Early Childhood Using Augmented Reality Method

Currently the level of interest in reading in children is very low. They prefer to play gadgets rather than reading books to add insight and knowledge. By reading, they can see and understand the contents of the reading. Especially learning about various animals around the world, not those around the house or surroundings, which they may not recognize. The aim of the researcher is to help children to learn about the animals around them and the world. Instead of children just playing with gadgets, it's better if their gadgets are used as learning tools. The marker-based Augmented Reality (AR) method is used as a method of recognizing the animal, because it can provide an overview of 3D visual objects that can provide information to make it more attractive to children. And AR is able to provide appropriate information in the real world. This is because Augmented Reality can provide interactive and more efficient learning, because children not only learn theoretically, but also see objects being taught in 3 dimensions. In the study, it can be seen that Augmented Reality can be used as a medium for introducing animals around the house and the world, it is hoped that this application can then be developed for various platforms and can also contain more information about all animals in the world.
Animal Recognition Application Based on Food Type Using Android Based Augmented Reality

Augmented reality (AR) is a technology with the concept of combining the dimensions of the real world with the dimensions of the virtual world that is displayed in real time. Augmented reality is not like virtual reality which replaces the real world augmented reality only adds and complete this ,this is done by drawing there-dimensional object on the marker, which is a pattern that is unique and can be recognized by its application this method often called the single marker and 3D method tracking objects smartphones allowed the development of augmented reality applications easily inexpensively and can be accessed by many users augmented reality can be utilized in various fields for example for the means of developing children’s education by playing and learning using augmented reality such as using a smartphone as learning medium by using augmented reality android based reality with a marker object to bring up 3d animal objects as a medium for learning and introducing animals bases on the type of food.
Muhammadiyah 1 Taman SMA Library Information System

The library is a work unit to store, manage collections of books or other libraries so that many people can use them as sources of information, previously when in the library, if you want to find the desired book or want to borrow books, then you have to look for the books one by one. But now everything has developed rapidly, where with the existence of information technology, everything can be done without having to be complicated. SMA Muhammadiyah 1 Taman is one of the institutions that has not implemented information technology in the library sector, the SMA Muhammadiyah 1 Taman library still uses conventional methods in terms of borrowing books, by writing loans on paper at the back of the book, the problem that often occurs is that the note paper is lost and also books that are lost because of the large number of books, therefore the purpose of this research is to create a system that can facilitate the process of borrowing and returning books and can maintain the availability of existing books. The method used for making the system is the waterfall method and has two stages of testing, alpha and beta. Therefore, a library information system for SMA Muhammadiyah 1 Taman was created which has two roles, members and librarian which can make it easier for librarian to serve students in borrowing books, and students can also find out the availability of the desired book without having to take the book directly into the library rack.
Analysis and interpretation

This chapter explains the analysis of the skull and soft tissue. Using nonmetric features and metric measurements the uniqueness of the face can be revealed. Knowing how these intersections and learning how to interpret how they affect the face the helps to provide a fuller picture of the individual.
Clinical applications of deep learning in neurology and its enhancements with future directions

The health care industry is known to generate a lot of medical data which serve as a great opportunity for increased health care data-driven environment utilizing artificial intelligence for optimization, clinical pathways, preclinical research, patient-care, population-level applications. Artificial neural network like the deep learning can be utilized for complex analysis of data derived from various brain structures for accurate prediction of neurological outcomes. Clinical uses of artificial Intelligence encompass many areas such as radiology, pathology, dermatology and physiology to enhance fast automated disease detection, medical imaging, data conversion, interpretation, decision support systems, optimizing rehabilitation, prediction of neurological outcomes, digitization of histology slides to generate high-resolution images, early stroke prediction, neuroimaging, modification of genomics-informed precision medicine, response to diverse neurochemicals, drugs and external factors to enhance tailored treatment. This chapter will discuss this topic and provide an overview of deep learning's application to neuroimaging techniques in clinical practice, as well as machine learning's future prospects in medical imaging and precision medicine.
Meta learning by optimization

This chapter introduces the optimization-based approaches to meta learning, which model the inner loop of meta learning as solving an optimization problem. The central observation is that the vanilla stochastic gradient descent algorithm is unsuitable for optimizing learning models under data scarcity constraints. Optimization-based meta learning algorithms address this limitation by seeking effective update rules or initialization that allows efficient adaptation to novel tasks with few training samples. We also discuss the impact of inner loop optimization on meta learning performance.
Introduction to meta learning

Meta learning has become an increasingly popular framework for quickly adjusting artificial intelligence systems to novel tasks or data distributions. This chapter first provides a history of meta learning and the reasons for its recent renaissance. We then introduce the mathematical formulations of meta learning under three different perspectives. Finally, this chapter concisely discusses the taxonomy of different meta learning categories, including metric learning, optimization, and model-based approaches.
AGILE - a meta learning framework for few-shot brain cell classification

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the metatraining process. In this chapter, we propose a tAsk-auGmented actIve meta LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta learning model trained from scratch. We show that the proposed task-augmented meta learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and few update steps, we achieved 90% accuracy on the new cell type classification task on the rat brain cell dataset, and 87% accuracy on the human brain cell dataset, 7 ∼ 50% points improvement over a state-of-the-art meta learning algorithm.
Quantum Computing Chip with Error-Correction Encoding

We design and fabricate a quantum photonic circuit to implement a quantum error correction code. A single logical qubit is encoded with 4 physical qubits to demonstrate its capability of detecting and correcting a single-bit error with an average state fidelity of 86%. We further extend the scheme to demonstrate a fault-tolerant teleportation process.
Note-Taking Technique Achievement In Narrative Text At Senior High School

The researcher researched note-taking techniques in teaching reading comprehension of English subjects. This research would like to know how students’ note-taking technique ability results are in narrative text. The research does in senior high school. This research does during three months by using 22 populations. The research design is qualitative research. The research instrument is a test. Based on the results of this study, the researcher concludes that the use of note-taking techniques has an influence on the ease of students or writers in writing and compiling summaries. The results of using note-taking techniques in writing also affect the ease of readers to understand the contents of the entire text from the summary made by students or writers. However, the results of this study also show deficiencies in the discussion to find out about the differences in students' thinking models in understanding sentences and students' writing creativity so that there are differences in the quality of students' writing. So, researchers can accept and give suggestions to future researchers to conduct similar and more complex research.