Reviewerly/exHarmony huggingface dataset:
Robust Two-Dimensional T$\ell _{1}$-Norm Linear Discriminant Analysis for Image Recognition

Two-dimensional linear discriminant analysis (2DLDA) is an effective supervised feature extraction technique without converting image data to vectors. It can not only alleviate the small sample size (SSS) problem but also make it full of structural information. However, 2DLDA behaves badly when processing the data with noise and outliers since it employs squared F-norm as the distance metric. Considering T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm is bounded and Lipschitz-continuous, we combine T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm with 2DLDA to overcome this problem and propose a novel method, named 2DLDA-T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> . As far as we know, this is the first time to introduce T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm into 2DLDA. Since 2DLDA-T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> is a non-convex and non-smooth optimization model, it is difficult to solve. So, we design a spherical gradient ascent method, which can guarantee the unit constraint and the convergence of the objective function. Due to the boundedness and Lipschitz-continuity of T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm, it can be expected that 2DLDA-T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> should have better performance than the existing related methods, including 2DLDA, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -2DLDA, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{p}$</tex-math></inline-formula> -2DLDA, F2DLDA, and 2DBLDA. Moreover, experimental results on two real image datasets verify the effectiveness of 2DLDA-T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> .
Image Aesthetics Assessment With Attribute-Assisted Multimodal Memory Network

Image aesthetics assessment (IAA) has attracted growing interest in recent years but is still challenging due to its highly abstract nature. Nowadays, more and more people tend to comment images shared on the social networks, which can provide rich aesthetics-aware semantic information from different aspects. Therefore, user comments of an image can be exploited as supplementary information for enhancing aesthetic representation learning. Previous researches have demonstrated that aesthetic attributes make significant effect on image aesthetic quality and humans’ aesthetic perception. Typically, people are used to give comments on an image from the perspective of aesthetic attributes, based on which the aesthetic quality of images can be inferred. Motivated by this, this paper presents an Attribute-assisted Multimodal Memory Network (AMM-Net) for image aesthetics assessment, which utilizes aesthetic attributes to model the interactions between visual and textual modalities. Specifically, we design two memory networks to capture the attribute-aware information most related to the image and associated comments respectively. Further, with multiple memory hops, attribute semantics shared by the two modalities are refined and cross-modal interactions are enhanced progressively. Finally, more discriminative aesthetic representations can be obtained for IAA. The experimental results and comparisons on two public multimodal IAA datasets demonstrate the superiority of the proposed model over the state-of-the-art methods. The source code is available at https://github.com/zhutong0219/AMM-Net.
Mathematical Induction and Recursion

This chapter discusses mathematical induction and recursion. Induction is a common proof technique in mathematics, and there are two parts to a proof by induction (the base case and the inductive step). We discuss strong and weak induction, and we discuss how recursion is used to define sets, sequences, and functions. This leads us to structural induction, which is used to prove properties of recursively defined structures.
Propositional and Predicate Logic

This chapter provides an introduction to propositional and predicate logic. Propositonal logicPropositonal logic may be used to encode simple arguments that are expressed in natural language and to determine their validity. The nature of mathematical proof is discussed, and we present proof by truth tables, semantic tableaux, and natural deduction. Predicate logicPredicate logic allows complex facts about the world to be represented, and new facts may be determined via deductive reasoning. Predicate calculus includes predicates, variables, and quantifiers, and a predicate is a characteristic or property that the subject of a statement can have.
Advanced Topics in Logic

This chapter presents some advanced topics in logic including fuzzy logic, temporal logic, intuitionistic logic, undefined values, and the applications of logic to AI. Fuzzy logic is an extension of classical logic that acts as a mathematical model for vagueness. Temporal logic is concerned with the expression of properties that have time dependencies, and it allows temporal properties about the past, present, and future to be expressed. Intuitionism was a controversial theory on the foundations of mathematics based on a rejection of the law of the excluded middle and an insistence on constructive existence. We discuss three approaches to deal with undefined values, including the logic of partial functions; Diijkstra’s approach; and Parnas’s approach which preserves a classical two-valued logic.
Fundamentals of Software Engineering

The first chapter presents a broad overview of software engineering, and the birth of the discipline was at the Garmisch conference in Germany in the late 1960s. We discuss various software lifecycles and the phases in software development. We discuss requirements gathering and specification, software design, implementation, testing, and maintenance. The lightweight Agile methodology is introduced, and it has become very popular in industry. Mathematics may potentially assist software engineers in delivering high-quality software products that are safe to use, and the extent to which mathematics should be employed remains a topic of active debate.
Language Theory and Semantics

This chapter discusses language theory and includes a discussion on the syntax of languages including grammars, parse trees, and derivations from grammar. The important area of programming language semantics is discussed, including a discussion on axiomatic, denotational, and operational semantics.
The Nature of Theorem Proving

This chapter discusses the nature of proof and theorem proving, and we discuss automated and interactive theorem provers. We discuss the nature of rigorous mathematical proof and formal mathematical proof.
Theoretics on the Indeterminacy of Economic Decisions

The purpose of this paper is to validate G.L.S Shackle's theory of Potential surprise in economic decisions.The critique against potential surprise is its subjective process of making economic decisions.Attempts at formalizing the theory in mathematical sense have seen the inductive use of sub-additive probabilities and infinite alleles with the frame of discernment (θ) for evidence theory.The theory, generally seems inseparable from set theory and probability regarding how belief function Bel(H) is derivably obtained from the set operation of empirical evidence m(n) and frame of discernment.In this paper, an algebraic approach is proposed using abductive analysis of the bounded space of [ ] , µ ℜ .Test of sequence in chaos economic state (H) space showed Cauchy compliance and proved that ℜ and μ commute conjugatively.Consequently, a derivation for limiting state of economic decisions as indeterminate from the propensity of potential surprise between the bounds of awful with implausible outcome ( ℜ ) and astounding with implausible outcome (μ) is theorized.
MAXIMUM SUM ARRAY FIRST SEARCH ALGORITHM FOR GRAPH TRAVERSAL

Graphs are the commonly used data structures that describe a set of objects as nodes and the connections between them as edges. Graph traversal is a technique to find all nodes reachable from a given set of root nodes. To traverse a graph is to process every node in the graph exactly once. The two most widely used algorithms used for traversing a graph are Breadth First Search and Depth First Search. This paper presents a new algorithm namely maximum sum array first search algorithm for traversing a graph. In this algorithm the node with the maximum number of edges if processed first and then its neighboring nodes are processed. This paper presents an algorithm to traverse an undirected or a directed graph and calculates the time and space complexity of the algorithm. The objective of proposed algorithm is to find a new alternative algorithm that can be applied to all types of graphs.
Task Scheduling in Cloud Computing using Mean Ant Colony Optimization

In the context of cloud computing, the scheduling of tasks has been a very crucial component. Numerous guidelines can be subjected as a memorandum of understanding between cloud consumers and organizations that must be obeyed to remedy the task scheduling problem. This agreement indicates the level of service the user expects from the stakeholders. Given the enormous number of tasks co-occurring at the provider’s end, providers must deliver good Quality of Service(QoS) to consumers by the agreement. To sustain good goals for tasks, one could think of the challenges of task scheduling as the search or discovery of an optimal mapping having a set of subtasks consisting of various functions over a range of resources. The approach termed Ant Colony optimization (ACO), which stands for “random optimization search”, is used to spread incoming jobs across virtualized resources. In the research that is being presented, a solution to the issue of job scheduling in cloud computing is proposed in the form of mean ant colony optimization(MACO) method. This MACO shortens the amount of time required to complete the study as a whole by estimating the rate of evaporation along with improvement in the pheromone update function. With the simulation tool CloudSim, the objectives like response time and total time of the proposed method are assessed and compared with other existing algorithms like FCFS, SJF, RR, and PSO. In terms of expenses, resources required, scheduling time, energy consumption, makespan, and degree of imbalance, we aim to build a strategy that can perform at a greater level than the traditional ACO algorithm.
Application of Bayesian Theorem for Precise Analysis of Clinical Test Results

Bayes theorem a seventeen-century powerful theorem generally used to find conditional probabilities. But now a day this theorem has a wide use especially in medical – Clinical science. The right analysis and the probabilistic information give an idea about the seriousness of the situation and then give direction to doctor/ hospital even to the Nation how to deal with the situation to avoid the panic. This paper studies the importance of Bayes theorem in health care especially in analysis of clinical results. Few examples are given which puts lights on the strength of the theorem in correct meaningful interpretation of test results. This paper aimed to analysis the clinical test results with the help of Bayesian theorem. Finally, Bayesian theorem gives milestone towards the analysis of healthcare domain for test results of diseases.
A Survey of Software Defects Research Based on Deep Learning

In the process of software development, software defects are inevitable. How to quickly and accurately identify defects and accurately deliver bug reports to the most appropriate repair personnel is very important for defect repair. In recent years, with the development of artificial intelligence, deep learning has been widely used in software defect research. This paper summarizes and analyzes the progress of software defect research based on deep learning in the past three years from four perspectives of software defect prediction, software defect identification, software defect analysis and bug report assignment. They are software defect prediction technology based on the TextCNN model and TextRNN model, a software defect identification technology based on LSTM, BiLSTM, DNCC, a software defect analysis technology based on the DAKSM model, and a software bug report assignment technology based on Atten-CRNN model. And compared these mentioned deep learning-based techniques with previous techniques in the field. After experiments, it is concluded that these models have good accuracy. At the same time, the related technologies involved are introduced in detail, as the problems that may be encountered in future research in this field and the development prospects prospect.
Improving the quality of Monocular Depth Estimation using Ensemble learning

Estimation of the distance of objects present in the surrounding environment from the visual sensor is the classical research problem in Computer Vision and related areas. The traditional approaches that have been used for depth estimation generally used a pair of images captured through a stereo camera to obtain a disparity map through triangulation method. Later on, researchers have proposed and developed deep learning based methods that can generate the depth map using only a RGB image captured through a monocular camera. However, the recent approaches based on deep convolutional neural networks have shown remarkable development with feasible results. The CNN architecture used for depth estimation consists of two components: Encoder and Decoder. The encoder network is responsible for the extraction of dense features from the input RGB image and the decoder network is used for the upsampling of the encoded depth map. In this paper, we first analyze the depth estimation results obtained using three different pretrained CNN models which are used as an encoder in the depth estimation network, then in order to improve the results further, we leverage the ensemble learning technique, in which these depth estimation models are combined to form an ensemble and the average of all the depths predicted by the individual models in the ensemble is considered as the prediction of the ensemble. The results presented in the paper have shown that the proposed scheme of forming the ensemble of individual encoder-decoder based depth estimation models outperforms the existing benchmark monocular depth estimation methods.
A Hybrid GA-FF based Ranking Method for Reorganizing Website Structure

Today we live in a developing era, where we all have access to the internet and depend on it to collect various information in our daily life. As we know the Internet is a very vast topic, which is a collection of various websites, and its size is increasing day by day that it becomes hard for users to differentiate between real and duplicate content present on the websites. It is a challenging task for users to search for significant and required data from such a huge dataset of information present over the internet. When a person searches for any information or site the information that is on the top are those which are most viewed or top rated, but some sites are underrated but the content area is worth viewing and using. This paper aims to reorganize the website using a hybrid metaheuristic optimization algorithm GA and FF to prioritize web pages for reorganizing the websites. Experimentally it has been seen that the proposed hybrid approach is able to get better quality web pages in comparison to several existing approaches proposed in the literature. It will help in enhancing the user’s experience on websites.
Comparative Analysis of Applications of Machine Learning in Credit Card Fraud Detection

Recent statistics show that Credit card fraud had increased by a hefty 20% in 2021, marking a stark rise in criminal activity from the previous year. Several techniques have been applied so far to counter the rise of fraudulent activity. However, there still lies scope for improvement in the techniques applied. One of the many ways that a fraud detection system can be improved is by the introduction of machine learning into its applications. The key purpose of a card fraud detection program is to identify fraudulent or suspicious activity and report them to an analyst whilst letting the normal transactions pass through. With the application of supervised algorithms, like Logistic regression and K-Nearest Neighbors, the efficiency for detecting fraudulent cases, by systems that have trained on detection of suspicious transactions, becomes all the more efficient. In this paper, we present a comparative analysis detection of fraud cases through usage of supervised algorithms, an AutoEncoder (neural network architecture), and one more model, OCSVM (One-Class Support vector Machine), from a library built specifically for outlier detection. Metrics like precision, recall and F1-score are used to evaluate the models and select the optimal algorithm for credit card fraud detection, which results in a balanced combination of the metrics used to evaluate them. The selected algorithm will help prevent such suspicious activities with regard to credit card transactions, with prompt accuracy and precision.
A Hybrid Recommendation System of Upcoming Movies Using Improved version of SOM with Hybrid Filtering Methods

Using a hybrid recommendation system and optimization strategy based on weighted classification and user collaborative filtering algorithms, this study proposes a solution to the problem that conventional recommendation systems cannot accurately reflect user preferences, as the single model of a conventional recommendation system cannot. The top-N personalized movie suggestions are generated depending on the combination of the weighted classification model, the local recommendation model, which is trained depending on the clustering of users, and the sparse linear model, which is a fundamental recommendation model. The target user is assigned to the cluster that is closest to each cluster after the scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, and then the cluster centers are identified, the distances between each cluster center and the target user are calculated, and the target user is then assigned to each cluster. To create the suggestion list, the next step is to use the collaborative filtering technique that anticipates the ratings for the unrated items for the target user. In order to further reduce the sparsity of the data, a high-dimensional rating matrix is converted to a low-dimensional item category preference matrix. After that, the items are categorized in accordance with the categories selected by the data analysis system. The recommendation algorithm presented in this article solves some of the drawbacks of a single algorithm model. It also enhances the suggestion effect, according to experiments using the MovieLens movie dataset.
User Adaptive Video Summarization

Video Summarization shortens a video content by extracting the most significant part from it and presenting the extracted contents in a summarized form that maybe a collection of keyframes or key shots in temporal sequence. In the recent past, various techniques have been suggested for automatic summarization of videos. It has been observed that summarization of videos is a subjective task and the traditional approaches of summarization though, are capable of generating generic summaries but are often incapable of generating the most appropriate and customized summary as desired by the user. A user intuitive and adaptive approach enables to summarize the video as per the preference of the user. In this paper, we discuss various frameworks for generating a user preference-based summary from a video. We explore the possible approaches and techniques available for generating a user adaptive video summary and present a comparative analysis of the techniques to provide an insight to the researchers working in this area.
Post-Processing Deblocking Technique for Reduction of Blocking Artifacts

Image compression is a challenging problem that degrades the quality of an image. In last few decades, several post-processing methods/techniques have been designed for the removal of blocking artifacts. However, these techniques generated satisfactory results but it cannot maintain the perceptual quality of reconstructed images. Moreover, blocking artifacts destroy the quality of an image and information in reconstructed image that totally different from original image. In this paper, a deblocking technique has been proposed to alleviates different kinds of artifacts such as grid noise, stair noise, ringing artifacts, blurring artifacts and corner outliers and maintain an important information of reconstructed images. The efficiency of novel post-processing technique is measured on the bases of Peak Signal to Noise Ratio including Blocking Effects (PSNR-B), Structural Similarity Index (SSIM) and Mean Opinion Score (MOS). According to Experimental results, the proposed method demonstrates better performance as compared to existing method in terms of various parameters at different bit rates.
A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds

Today, all cloud-based social media applications are data-intensive, and the large amounts of data they generate are managed on the public cloud, which compromises user data security and privacy. Many government and nongovernment organizations that manage users’ sensitive and personal data do not choose to use the public cloud to administer their databases because of the numerous security risks it has. However, these organizations are jeopardizing the performance of the application by handling the user’s data on the private cloud. Without jeopardizing user data security and privacy or application performance, a security solution will encourage these organizations to manage their databases on the public cloud. This paper presents an L-PNR computational model for the hardware-level security of cloud databases in a multi-tenant public cloud environment. The L-PNR model’s performance has been simulated and evaluated using VMware vCenter Simulator. According to experimental findings, the proposed model outperforms the current models THR-RS, LR-MU, DDPA, and DSRBACA in terms of performance and security. The suggested L-PNR model may be used by cloud service providers (CSPs) to offer data security over the public cloud, where organizations can affordably manage their sensitive data.