Reviewerly/exHarmony huggingface dataset:
Language-Augmented Pixel Embedding for Generalized Zero-Shot Learning

Zero-shot Learning (ZSL) aims to recognize novel classes through seen knowledge. The canonical approach to ZSL leverages a visual-to-semantic embedding to map the global features of an image sample to its semantic representation. These global features usually overlook the fine-grained information which is vital for knowledge transfer between seen and unseen classes, rendering these features sub-optimal for ZSL task, especially the more realistic Generalized Zero-shot Learning (GZSL) task where global features of similar classes could hardly be separated. To provide a remedy to this problem, we propose Language-Augmented Pixel Embedding (LAPE) that directly bridges the visual and semantic spaces in a pixel-based manner. To this end, we map the local features of each pixel to different attributes and then extract each semantic attribute from the corresponding pixel. However, the lack of pixel-level annotation conduces to an inefficient pixel-based knowledge transfer. To mitigate this dilemma, we adopt the text information of each attribute to augment the local features of image pixels which are related to the semantic attributes. Experiments on four ZSL benchmarks demonstrate that LAPE outperforms current state-of-the-art methods. Comprehensive ablation studies and analyses are provided to dissect what factors lead to this success.
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

&lt;span lang="EN-US"&gt;Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.&lt;/span&gt;
Proposed system for data security in distributed computing in using ‎triple data encryption standard and ‎Rivest Shamir ‎Adlemen

&lt;span lang="EN-US"&gt;Cloud computing is considered a distributed computing paradigm in which resources ‎are ‎provided as services. In cloud computing, the ‎applications do not run ‎from a user’s personal computer but are run and stored on distributed ‎servers on the Internet. The ‎resources of the cloud infrastructures are shared on cloud ‎computing on the Internet in the open ‎environment. This increases the security problems in ‎security such as data confidentiality, data ‎integrity and data availability, so the solution of such ‎problems are conducted by adopting data ‎encryption is very important for securing users data. ‎In this paper, a comparative ‎study is done between the two security algorithms on a cloud ‎platform called eyeOS. From the ‎comparative study it was found that the Rivest Shamir ‎Adlemen ‎(3kRSA) algorithm ‎outperforms that triple data encryption standard (3DES) algorithm with ‎respect to the complexity, and output bytes. The main ‎drawback of the 3kRSA algorithm is its ‎computation time, while 3DES is faster than that ‎‎3kRSA. This is useful for storing large amounts of ‎data used in the cloud computing, the key ‎distribution and authentication of the asymmetric ‎encryption, speed, data integrity and data ‎confidentiality of the symmetric encryption are also ‎important also it enables to execute ‎required computations on this encrypted data.‎&lt;/span&gt;
Social distance and face mask detector system exploiting transfer learning

&lt;p class="IJASEITAbtract"&gt;&lt;span lang="EN-US"&gt;As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.&lt;/span&gt;&lt;/p&gt;
A web content mining application for detecting relevant pages using Jaccard similarity

&lt;span lang="EN-US"&gt;The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.&lt;/span&gt;
Hybrid information security system via combination of compression, cryptography, and image steganography

&lt;span lang="EN-US"&gt;Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.&lt;/span&gt;
A systematic literature review of automatic ontology construction

In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.
Causality between financial development, economic growth, and income inequality in EU countries

This study investigates the causality between financial development, economic growth, and income inequality using panel data for 23 European Union countries over the period 1987-2017. Various proxies of financial development are chosen to represent the depth, efficiency, and stability of the banking system and stock markets. For the empirical analysis, the study performs the Granger non-causality test in heterogeneous panels. The findings are contradictory and sensitive to the measures of financial development. Most importantly, the results reveal a one-way causality from financial development to economic growth when private credit, stock market capitalization, net margin interest rate, and Z-score are chosen as financial development indicators. In addition, a two-way causality exists between bank assets, liquid liabilities, non-performing loans, and economic growth, and a one-way causality from economic growth to value traded and turnover ratio. However, the results show no causality between stock price volatility and economic growth. The results indicate a one-way causality running from income inequality to economic growth. Finally, a one-way causality runs from income inequality to financial development for most measures of financial development except for a one-way causality running from private credit to income inequality, a two-way causality between bank assets and inequality, and an absence of causality between income inequality and turnover ratio, Z-score and stock price volatility.
Performance evaluation of automatic speech recognition systems on integrated noise-network distorted speech

In VoIP applications, such as Interactive Voice Response and VoIP-phone conversation transcription, speech signals are degraded not only by environmental noise but also by transmission network quality, and distortions induced by encoding and decoding algorithms. Therefore, there is a need for automatic speech recognition (ASR) systems to handle integrated noise-network distorted speech. In this study, we present a comparative analysis of a speech-to-text system trained on clean speech against one trained on integrated noise-network distorted speech. Training an ASR model on noise-network distorted speech dataset improves its robustness. Although the performance of an ASR model trained on clean speech depends on noise type, this is not the case when noise is further distorted by network transmission. The model trained on noise-network distorted speech exhibited a 60% improvement rate in the word error rate (WER), word match rate (MER), and word information lost (WIL) over the model trained on clean speech. Furthermore, the ASR model trained with noise-network distorted speech could tolerate a jitter of less than 20% and a packet loss of less than 15%, without a decrease in performance. However, WER, MER, and WIL increased in proportion to the jitter and packet loss as they exceeded 20% and 15%, respectively. Additionally, the model trained on noise-network distorted speech exhibited higher robustness compared to that trained on clean speech. The ASR model trained on noise-network distorted speech can also tolerate signal-to-noise (SNR) values of 5 dB and above, without the loss of performance, independent of noise type.
An Efficient Information-Reinforced Lidar Deep Completion Network without RGB Guided

Due to the sparsity of point clouds obtained by LIDAR, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.
A Faithful Description of ECMAScript Algorithms

We present an ongoing formalization of algorithms of ECMAScript, the specification describing the semantics of JavaScript, in a tiny functional meta-language. We show that this formalization is concise, readable, maintainable, and textually close to the specification. We extract an OCaml interpreter from our description and run small JavaScript programs whose semantics is based on these algorithms.
Optimization enabled deep learning method in container-based architecture of hybrid cloud for portability and interoperability-based application migration

Virtualisation is a major part of the cloud as it permits the deployment of several virtual servers over the same physical layer. Due to the adaption of cloud services, the count of the application running on repositories increases, resulting in overload. However, the application migration in the cloud with optimal resource allocation is still a challenging task. The application migration is employed to reduce the dilemma of resource allocation. Hence, this paper proposes a technique for portability and interoperability-based application migration in the cloud platform. The cloud simulation is done with the Physical Machine (PM), Virtual Machine (VM), and container. The interoperable application migration is provided using the newly devised Lion-based shuffled shepherd (Lion-SS) optimisation algorithm. The Lion-SS algorithm combines the shuffled shepherd optimisation algorithm (SSOA) and the Lion optimisation algorithm (LOA). The new objective function is devised based on predicted load, demand, transmission cost, and resource capacity. Besides, the prediction of the load is performed using Deep long short-term memory (Deep LSTM). The proposed technique obtained the minimal load of 0.007 and resource capacity of 0.342.
Denoising Single Images by Feature Ensemble Revisited

Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and a concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture's number of parameters remains smaller than in most of the previous networks and still achieves significant improvements over the current state-of-the-art networks.
Understanding Unsupervised Deep Learning for Text Line Segmentation

We propose an unsupervised feature learning approach for segmenting text lines of handwritten document images with no labelling effort. Humans can easily group local text line features to global coarse patterns. We leverage this coherent visual perception of text lines as a supervising signal by formulating the feature learning as a global pattern differentiation task. The machine is trained to detect whether a document patch contains a similar global text line pattern with its identity or neighbours, and a different global text line pattern with its 90-degree-rotated identity or neighbours. Clustering the central windows of document image patches using their extracted features, forms blob lines which strike through the text lines. The blob lines guide an energy minimization function for extracting text lines in a binary image and guide a seam carving function for detecting baselines in a colour image. In identifying the aspect of the input patch that supports the actual prediction and clustering, we contribute toward the understanding of input patch functionality. We evaluate the method on several variants of text line segmentation datasets to demonstrate its effectiveness, visualize what it has learned, and enable it to comprehend its clustering strategy from a human perspective.
Enabling Product Recognition and Tracking Based on Text Detection for Mobile Augmented Reality

We propose a system that supports real-time product recognition and tracking based on text detection for mobile augmented reality. To accurately distinguish products with visually similar packages, we develop a method that recognizes product names by utilizing the characteristics of texts printed on the product packages. It first filters out irrelevant products and effectively ranks candidate products through an inverted index search. We significantly reduce processing overhead by selectively performing product name recognition. In addition, we present an optical-flow-based method that enables efficient and responsive product tracking. Our evaluation shows that the proposed system achieves significantly better product recognition accuracy (80%) compared to alternative solutions, Vuforia (55.4%) and MobileNetV2 (69.6%). We also show that it achieves reasonable tracking accuracy and processing latency to support quality mobile AR experiences.
Underwater Navigation of AUVs Using Uncorrelated Measurement Error Model of USBL

&lt;i&gt;This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.&lt;/i&gt;
Exploratory Analysis on Adaptive Reasoning of Undergraduate Student in Statistical Inference

In statistical inference, adaptive reasoning is defined as logical thinking to determine what can be inferred from data or statistical results and whether the justifications led to valid conclusions.Accordingly, adaptive reasoning is a mathematical proficiency required in statistical inference.This study aims to discover the association between adaptive reasoning and the initial statistical competence of undergraduate students.For this purpose, we performed mixedmethods research conducted by sequential exploratory analysis.This study involved 66 participants selected from undergraduate students in the Statistical Inference course offered by the mathematics education department at one university in Indonesia.The qualitative result describes the characteristics of students' adaptive reasoning proficiency at each grade.The proportion of students from grade 1 to grade 4 is 4.55%, 21.21%, 48.48%, and 25.76%, respectively.The quantitative result based on the chi-squared statistics test shows a significant association between adaptive reasoning proficiency and initial statistical competence.The correspondence analysis solution depicts that a high level of statistical competence is strongly associated with a high grade of adaptive reasoning proficiency, and conversely.Generally, the results provide evidence that the mastery of initial statistical competence is an important aspect in developing students' adaptive reasoning proficiency.The study provides some recommendations that will benefit the lecturer to develop adaptive reasoning proficiency in the Statistical Inference courses.