Reviewerly/exHarmony huggingface dataset:
A Frequency Learning Approach Based on the Hartley Transform for Texture Classification

Convolutional neural networks (CNNs) significantly impacted challenging real-world tasks, such as image and audio processing, giving rise to solutions with impressive results. Notwithstanding, in some scenarios, discriminative features may be more easily retrieved when data samples are represented in a different domain, and are processed with the aid of convenient operations in that domain, instead of the usual temporal/spatial convolution explored in CNNs. In this work, a learning model that operates entirely in the frequency domain with the aid of the discrete Hartley transform (DHT) is proposed. In particular, a frequency layer is developed based on the DHT property related to convolution, as well as a proper frequency pooling stage to reduce the number of parameters. The proposed DHT-based model was analyzed in the context of texture classification, which presents a natural appeal to the frequency domain, and it achieved competitive results in terms of accuracy with state-of-the-art CNNs, but with the advantage of requiring a much smaller number of trainable parameters, thus offering a promising and lightweight solution.
An Accurate and Energy-Efficient Anomaly Detection in Edge-Cloud Networks

With the wide-adoption of edge-cloud networks in various domains, anomaly detection is a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Traditionally, sensory data are gathered at the network edge and completely routed to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of certain anomaly. Considering the occurrence infrequency of anomalies, this strategy may route relatively large-volume of sensory data, which reflects a healthy situation indeed, to the cloud. To reduce these anomaly-irrelevant sensory data transmitted in the network, this paper proposes an accurate and energy-efficient anomaly detection mechanism in three-Tier IoT-Edge-Cloud (3T-IEC) networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge network, the edge node applies the Marching Squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are routed to the cloud. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are derived by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered to refine the boundary. Extensive experiments are conducted, where an air quality hazardous gas dataset acquired from Towards Data Science is applied. Evaluation results show that our technique outperforms the state-of-the-art ones in boundary accuracy and energy-efficiency.
Novel Elimination Method of Baseline Drift Basedon Improved Least Square Method

With the development of brain computer interface (BCI), the application of bioelectrical signals in the field of intelligent medical devices has been flourishing. Currently, how to remove the interference in the electrical signal and improve the recognition accuracy has attracted great attention. In the process of electromyographic (EMG) signal acquisition, baseline drift is a serious issue, which can affect the signal recognition accuracy, the traditional least square method (LSM) cannot remove the filtered baseline drift component within the window. To address this issue efficiently, a modified least method is designed in this paper, which employs a polynomial fit to remove the baseline drift component within the window by the curvature of the polynomial. The designed method can not only retain the advantages of the LSM in terms of small operation size, but also improve the baseline drift removal capability, providing a solution for a high-precision embedded bioelectric signal acquisition device. Experimental results show that the improved least square method (ILSM) improves the baseline drift removal capability by about 5% over the LSM. In addition; In addition, compared to LSM, the ILSM can reduced the number of window openings.
The Comparison Study of Matrix Factorization on Collaborative Filtering Recommender System

The recommendation system has been a vital study topic in recent years, so many scientists and academics across the world are interested in researching the subject. Music, movies, books, news, commercial items, and search inquiries are all examples of applying the recommendation system. One of the most common and successful strategies in recommendation systems is collaborative filtering. This method seeks to find similar users who are active to recommend an item. The author proposes to compare model-based collaborative filtering techniques using matrix factorization algorithms, such as alternating least squares (ALS), singular value decomposition (SVD), Alternating Least Squares weight regularization (ALS-WR) and SVD++. Furthermore, the quality of the recommendation system is determined using three datasets with various features. According to the research findings of four algorithms (ALS, ALS-WR, SVD, SVD++), only the MovieLens dataset can overcome the sparsity problem, as seen by the RMSE score below 1.
Symbolic Minimization on Relational Data

The current wave of AI is heavily driven by data, especially for cognitive capabilities. Minimization of data semantics not only reveals core information but also becomes a guide in a wide range of domains. However, scalability is theoretically weak in pure semantic methodologies. In order to cooperate with large DBs, expressiveness is over-sacrificed in existing techniques. Thus, the quality of discovered patterns and redundancies are far from satisfactory. In this article, we formalize symbolic minimization on relational DBs and prove its NP-Completeness. A lossless technique is proposed by inducing generic first-order Horn rules that infer a subset of records from the others. More importantly, we further improve the scalability via effective caching and pruning without sacrificing the expressiveness of first-order Horn rules. A concrete system is implemented and comprehensively evaluated. Experiments show that our technique removes up to 70% contents and outperforms the state-of-the-art on minimization and scalability. The optimizations reduce up to 96% memory consumption and accelerate the performance by two orders. Our technique shows the practicality of pure semantic approaches in database mining.
Dictionary Based Arabic Text Compression and Encryption Utilizing Two-Dimensional Random Binary Shuffling Operations

This paper developed Arabic text encryption and compression based on dictionary indexing algorithm. The proposed algorithm includes encoding the Arabic text utilizing Arabic words dictionary, mapping encoded binary stream to a two-dimensional binary matrix, utilizing randomized variable size encryption key, applying randomly binary shuffling functions on the two-dimensional matrix, and mapping back the two-dimensional binary matrix into a sequential binary stream. The decryption algorithm at the receiver side implements the encryption steps reversely, utilizing the encryption key and the shared Arabic word dictionary. In this dictionary, the words of the formal Arabic language are classified into four categories according to the word length and sorted alphabetically. Each dictionary category is given an index size that is large enough to fit all words in that category. The proposed algorithm shuffles adjacent bits away from each other in random fashion through utilizing randomized variable length encryption key, two-dimensional shuffling functions, and repetition loop. Moreover, the index size is selected not to be multiple bytes to destroy any statistical feature that may be utilized to break the algorithm. The proposed algorithm analysis concluded that it could be broken after 3.215∗109 years. Moreover, the proposed algorithm achieved a less than 30% compression ratio.
Auto-tuning of AI/ML Graphs for Optimal Performance in a Heterogenous Processor System

High-performance parallel computing is very important to ensure the efficient execution of deep learning algorithms. However, current research focuses more on parallel computing of homogeneous processors. Due to the differences in memory, communication units and computing capabilities of heterogeneous architectures, existing deep learning parallel computing architectures cannot achieve good performance improvement in heterogeneous environment. Current solutions use serial methods to execute algorithms on heterogeneous architectures, rely on developer’s development experience or use simple search strategies to perform tuning to achieve parallel computation. The existing solutions either cannot fully utilize the hardware resources of heterogeneous architectures to achieve performance optimization or cannot maximize performance due to the design of limited search space and low efficient search strategies.In our solution, we designed a new neural network automatic horizontal partitioning framework, which automatically splits the neural network horizontally into multiple sub-graphs based on a given target device or target device cluster, so that the neural network can be running sub-graphs in a pipelined or parallel manner on a target device or target device cluster. Our solution creates a larger search space compared to existing solutions and uses efficient exploration and cost evaluation methods.
Investment In Human Capital In Promoting Economic Growth And The Importance Of Financial Inclusion In The Russian Economy

This study aims to investigate human development and financial inclusion in Russia. The global bank provided the secondary data for this study, which covered the years 1990 to 2021. This study uses vector analysis. We found that the development of human capital in Russia not only promotes financial inclusion that drives the economy, but also provides a direct impetus to the growth of the Russian economy. The role of human capital is very important in today's modern human development. It is proven that human development through human capital investment provides awareness of the importance of financial inclusion and provides productive human qualities so as to be able to provide an impetus for growth in the economy.This research contributes to adding references related to financial studies and human development including human capital. This research contributes to providing a simulation and an overview of the importance of human capital investment in promoting economic growth and financial inclusion.
A Shared Interactive Space in Mixed Reality for Collaborative Digital Tower Operations

Air transportation is undergoing a metamorphic transformation with digital towers (DTs) replacing physical air traffic control (ATC) towers by reproducing live camera feeds of out-of-tower (OOT) views using massive immersive spatial displays. With the recent pandemic-induced decline in air traffic, the quest to introduce cost-effective alternatives for DTs has gained importance. Recent developments in mixed reality (MR)-based visualizations allow air traffic systems to be better represented stereoscopically in an interactive virtual space. Moreover, MR offers customizable immersion, allowing air traffic control officers (ATCOs) to maintain their view of both environment and OOT view, as in a physical tower. Air traffic control, is, however, fundamentally a collaborative activity that requires clear and concise coordination between controllers and pilots to ensure an orderly flow of traffic. This paper presents a collaborative DT prototype system visualized in MR, that allows multiple controllers wearing MR headsets, such as Microsoft HoloLens 2, to view and interact with a shared visualization of the aerodrome, even if they are remotely located. We introduce collaborative routing capabilities to the system, whereby two controllers can collaboratively decide on taxiway routing for an arriving aircraft by interactively selecting and approving or rejecting routes in a shared MR space. We evaluate the prototype system through a preliminary assessment to investigate controllers' perceptions of situation awareness and workload while using the system. This paper presents initial results of our evaluation. Key findings include a faster Time-To-Decision and higher situation awareness when controllers collaborated by using the proposed system. The results demonstrate promising potential for MR-based collaboration for ATC operations, and for the proposed prototype to mature into a fully self-contained, interactive and portable air traffic control system.
Research on Visual Servoing control of metal objects in complex and variable lighting environment

The problem of recognizing metal parts under complex and variable lighting conditions in conventional visual servoing is addressed. In this paper, we combine the deep learning semantic segmentation network with the visual servoing algorithm to significantly reduce the dependence of target recognition on stable illumination. A semantic segmentation model is used to extract the pixel regions of the target features, and then these features are input into the visual servoing controller to move the robotic arm to the area around the target.
A Light-Weighted Model for Encrypted Images Retrieval Under Cloud Computing

Because a large number of images are generated every day and the storage capacity of mobile terminals is limited, people have to upload private images to cloud storage services. Although this choice solves the problem of image storage, it brings another problem: how to protect the privacy of the image owner. At present, although some searchable encryption methods have been proposed, most of them are all based on large-scale calculations, and most of the user's devices do not have the corresponding processing capabilities. Therefore, this paper propose a lightweight model for encrypted image retrieval under cloud computing. This model would solve the problem of limited capabilities of user equipment through image semantic extraction, image semantic tree construction, and separation of feature extraction and encryption processes, while providing users with a feasible image encryption scheme that can be retrieved.
Research on Big Data Collection and Application of Building Material Price Based on Focused Web Crawler

In order to collect building material price information correctly and effectively, a focused crawler that directionally grabs web resources related to building materials was proposed in this paper. First, clarify building material price data requirements and design a database structure to restore data. Then design a focused web crawler system architecture for large data collection and analysis of building material prices. On the basis of the establishment of the database, various applications can be carried out.
Preface

Extract About this book This book arose out of the desire to enable students, especially those in philosophy, who have only a minimal background in mathematics and logic, to appreciate the most important results of classical proof theory. Most proof theory textbooks begin at a rather demanding level. The more accessible among them only cover structural proof theory, and those that also cover ordinal proof theory assume a strong mathematical background. Unfortunately, there is no good, elementary introduction to proof theory in English that does both. Our book is meant to fill this gap. We assume that the reader is familiar with both the propositional and predicate calculus, as is covered in most introductory courses on formal logic taught in philosophy departments. We do not assume familiarity with any specific deductive system. Indeed, the first few chapters are for the most part introductions to three of them: axiomatic derivations, natural deduction, and sequent calculus. We also do not assume familiarity with the metatheory of logic, not even the most elementary principle of reasoning about our calculi, namely the principle of induction on the natural numbers (ℕ)⁠. We try to ease the reader into the mechanics of inductive proofs, first presenting simple induction on ℕ⁠, then double induction, and finally induction along more complicated well-orderings such as ε0.
COVID-19 Semantic Search Engine Using Sentence-Transformer Models

With the onset of COVID-19, enormous research papers are being published with unprecedented information. It is impractical for the stake holders in medical domain to keep in pace with the new knowledge being generated by reading the entire research papers and articles in order to keep pace with new information. In this work, a semantic search engine is proposed that utilises different sentence transformer models such as BERT, DistilBERT, RoBERTa, ALBERT and DistilRoBERTa for semantic retrieval of information based on the query provided by the user. These models begin by collecting COVID-19-related research papers and are used as an input to the pre-trained sentence transformer models. The collected research papers are then converted into embedded paragraphs, and the input query is sent to the same model, which in turn delivers the embedded query. The model uses cosine similarity to compare both embedded paragraphs and the embedded query. Consequently, it returns the top K most similar paragraphs, together with their paper ID, title, abstract, and abstract summary. The bidirectional nature of the sentence transformer models allows them to read text sequences from both directions, making the text sequence more meaningful. Using these models, COVID-19 semantic search engine has been developed and deployed for efficient query processing. The similarity score for each model was computed by averaging the top 100 query scores. As a result, the RoBERTa model is faster, generates a higher score of similarity, and consumes less runtime.
Cycles Improve Conditional Generators: Synthesis and Augmentation for Data Mining

Conditional Generative Adversarial Networks (CGANs) are diversely utilized for data synthesis in applied sciences and natural image tasks. Conditional generative models extend upon data generation to account for labeled data by estimating joint distributions of samples and labels. We present a family of modified CGANs which demonstrate the inclusion of reconstructive cycles between prior and data spaces inspired by BiGAN and CycleGAN improves upon baselines for natural image synthesis with three primary contributions. The first is a study proposing three incremental architectures for conditional data generation which demonstrate improvement on baseline generation quality for a natural image data set across multiple generative metrics. The second is a novel approach to structure latent representations by learning a paired structured condition space and weakly structured variation space with desirable sampling and supervised learning properties. The third is a proposed utilization of conditional image synthesis for supervised learner data set augmentation as an alternative generation metric. Additional experiments demonstrate the successes of inducing cycles in conditional GANs for both image synthesis and image classification over comparable models with no additional tweaks or modifications. We release our source code, models, and experiments here: https://github.com/alexander-moore/Cycles-Improve-Conditional-Generators .
IBE-Net: Low-Light Image Enhancement Based on Image Brightness Estimation

In this paper, we proposed a low-light image enhancement method based on image brightness estimation which is divided into two steps: In the brightness estimation step, a separate convolutional auto-encoder is designed to extract the brightness and color features of each channel and entire image. In the detail reconstruction step, a decoder designed by concat-add block is used to preserve the detailed features of the input images as much as possible. Moreover, the Convolutional neural network (CNN) is used to supplement the texture features of the enhanced image. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods; our method achieves better results in visual perception and qualitative analysis.
Interest Level Estimation Using Behavior Information through Multi-view Feature Integration Considering Partial and Ordered Labels

This paper presents interest level estimation using behavior information through multi-view feature integration considering partial and ordered labels. User behavior information is effective for estimating interest levels on content. However, behavior information is noisy, and a noise-robust integration method is needed. We propose a novel multi-view feature integration method based on multi-modal Gaussian process latent variable model (mGPLVM). The effectiveness of our method is that it can apply interest levels assigned to some samples and the order of the levels to integrate content and behavior features. Consequently, we verify the effectiveness of our proposed method through the experiments.
bNaming: An Intelligent Application to Assist Brand Names Definition

Relevant branding projects start with a significant name, so the naming process is essential for creating a successful brand. However, not everyone has access to branding experts to guide this naming process. This paper proposes an accessible solution that makes a technical evaluation of brand names, supporting and helping people to decide the best name for their business. For this, our work aims to develop a mobile application that uses machine learning models trained on a database created by naming experts that evaluates many brand names accordingly to ten technical criteria. Thus, we provide a mobile application that makes the machine learning models accessible to the user in a simple way, allowing him/her to make rational decisions based on technical knowledge. Furthermore, we demonstrate the accuracy of our models on predicting the evaluation of the brand names. For the vast majority of the evaluation criteria, our application automatically generated a score matching with the output given by the branding experts within a positive/negative deviation of one point in more than 80% of the cases. Finally, an MVP is developed that already presents satisfactory results.
Embedded front vehicle detection system based on fusion of machine learning and traditional methods

For the detection of remote and fuzzy small targets, machine learning detection has a higher probability of missed detection. It is more dangerous when applied to vehicle detection. Therefore, this paper proposes and uses a detection method that combines machine learning and traditional methods to deal with the problem that the vehicle in front with a high risk factor is missed. Through the designed prediction mechanism for fusion, the video frame images will be processed by different modules. Compared with target detection using machine learning only, the missed detection rate was reduced by 29.1%. Finally, we deploy it in an embedded device.