Reviewerly/exHarmony huggingface dataset:
SELECTION OF DISCRIMINATIVE FEATURES FOR ARABIC PHONEME’S MISPRONUNCIATION DETECTION

Pronunciation training is an important part of Computer Assisted Pronunciation Training (CAPT) systems. Mispronunciation detection systems recognized pronunciation mistakes from user’s speech and provided them feedback about their pronunciation. Acoustic phonetic features plays a vital role in speech classification based applications. This research work investigated the suitability of various acoustic features: pitch, energy, spectrum flux, zero-crossing, Entropy and MelFrequency Cepstral Coefficients (MFCCs). Sequential Forward Selection (SFS) was used to find out most suitable acoustic features from the computed feature set. This study used K-Nearest Neighbors (K-NN) classifier was used to detect the pronunciation mistakes from Arabic phonemes. This research selected the set of most discriminative acoustic features for each phoneme. K-NN achieved accuracy of 92.15% for mispronunciation detection of Arabic Phonemes.
AN EFFICIENT ALGORITHMIC SOLUTION FOR HIGH PAYLOAD GOOD IMPERCEPTIBILITY HYBRID IMAGE STEGANOGRAPHY

&#x0D; &#x0D; &#x0D; A hybrid steganographic technique has been proposed to enlarge the size of secret message and to hide several images in one cover image. Initially, the secret images were compressed using JPEG 2000 before embedding them to the cover image. Second least significant bit plane of cover image was entirely replaced by secret message while tri-ways-pixel-value-differencing (TPVD) was used to hide secret message in remaining seven-bit cover image. The quality of stego image was enhanced by residual image coding (RIC). The cover image was divided in four sub-images, three of them provided cover for secret message using proposed technique while fourth sub-image was used to embed RIC of stego image using only TPVD, to ensure better quality in terms of peak signal to noise ratio (PSNR). Proposed technique provided the opportunity to transmit secret data without detection by different steganalysis techniques like dual statistics steganalysis. Experimental results verified that proposed technique achieved high payload with better image quality than TPVD scheme.&#x0D; &#x0D; &#x0D;
Automated Breast Cancer Detection Systems (Analytical Study)

The introduction of information technology in medicine in general and artificial intelligence in particular has increased the diagnosis accuracy and reduced the number of errors. Breast cancer mortality is decreasing with time in women, due to the prevention and the early detection. Mammography is considered the best tool to detect the presence of breast cancer, image reading and cancer diagnosis requires high skills. CAD is an automated system that uses the artificial intelligence algorithms to help detecting breast cancer from mammogram image processing. In this paper, we perform a comparison between the artificial intelligence tools used in breast cancer classification in terms of accuracy rate. Our results show that ANN algorithm is the best for breast cancer detection in terms of accuracy rate where it can define the type of cancer with accuracy range from 93 to 99.4%.
A Survey on Sentiment Analysis in Health Care: New Opportunities and Challenges

Twitter is increasingly being used as a venue for medical research because of the large number of unstructured and free-text tweets sent there on healthcare-related topics. In natural language processing, sentiment analysis is one sort of data mining that may be used to assess the direction of a person's personality. Computational linguistics is used to the analysis of text to infer and assess conceptual understanding of the internet, social media, and related topics. Healthcare information is also widely available online in the form of personal blogs, social media, and websites that rate medical conditions, but this data was not compiled in a systematic fashion. A few of the numerous advantages of sentiment analysis include better healthcare outcomes and more efficient medical practice. In this paper, we explore possible new opportunities for those researchers who want to do work in the domain of sentiment analysis in the medical field and. We explore many recent and existing papers and find out the strength and research gaps of these papers in terms of methodologies, datasets used, and different machine learning and deep learning models. These tabular forms give new direction for research in this domain.
Leveraging Artificial Intelligence for Model-based Software Analysis and Design

Fundamental decisions are made in the early phases of software development. The typical outcomes of these phases are models of different kinds, such as architectural models, data models, and process models. Automation support is required to efficiently and effectively handle large models and conduct continuous quality improvement processes. Thus, several approaches have been proposed that integrate modeling with Artificial Intelligence (AI) methods such as Genetic Algorithms (GAs), among others. These approaches, e.g., transform models to improve their quality by searching for good solutions within the potential solution space. In this chapter, we first review existing applications of AI methods to model-based software engineering problems. Subsequently, we show a representative use case of how a model-based software analysis and design problem can be solved using GAs. In particular, we focus on the well-known and challenging modularization problem: splitting an overarching, monolithic model into smaller modules. We present two encodings, the model-based and the transformation-based encoding, which are both applied for the modularization of Entity-Relationship (ER) diagrams. We further discuss how these encodings may be adapted to other structural models and conclude with an outlook on future research lines related to software modeling intelligence.
Binary Tumbleweed Algorithm for Application of Feature Selection

The tumbleweed algorithm (TA) is a new swarm intelligence optimization algorithm based on tumbleweed seedling growth and seed propagation. In this paper, a binary tumbleweed algorithm (BTA) is proposed to solve the binary optimization problem. The continuous to binary conversion is critical, the binary tumbleweed algorithm uses transfer function to implement the continuous to binary conversion. To verify the effectiveness of the proposed algorithm, this paper uses 23 benchmark functions to test. The proposed BTA has strong competitiveness on 23 benchmark functions. Finally, the algorithm was successfully used for feature selection in the intrusion detection dataset and achieved satisfactory results.
Study of Some Properties and Behavior of the Group of Signed Quadratic Residues and Its Applications to Cryptography

The application of the group of signed quadratic residues (SQR) is first introduced by Hofheinz and Kiltz. The group SQR satisfies most of all properties of the group of quadratic residues, however elements of this group are easily recognizable. Because of these properties SQR can be used in cryptography. The group SQR is an example of “Gap-group” in which hardness of the computational problem is equivalent to factoring a composite number, whereas the corresponding decisional problem is easy. In the present work, we will discuss its properties for the implementation of cryptography. Despite its various interesting property, they have found very few cryptographic applications till now. Here, we attempt to bring out some applications of SQR in cryptography to motivate the researchers.
Cybersecurity Requirements for Management Information Systems

Cybersecurity is therefore one of the most important elements of security in developed countries.Especially since there is an overall trend towards cybersecurity in all aspects of life, I have found that the idea of cybersecurity is based on protecting critical facilities: The nation's information infrastructure.Information systems, including e-government management systems, are managed by key state agencies.As with economic, scientific, commercial, and other systems, threats are threats to a nation's national security.We have therefore found that many countries are preparing institutions capable of integrating cybersecurity into protection, development, and information security.This concept has become the most important concern of developed countries, which have secured all scientific possibilities and systems to achieve it.The electronic information network has become an integral part of today's daily lives in all places.In addition to personal uses, digital information is used, processed, stored, and shared.As this information increases and spreads, we have found that its protection has become more vital and has an effective impact on national security and technical progress.
Factors Influencing Employees on Compliance with Cybersecurity Policies and Their Implications for Protection of Information and Technology Assets in Saudi Arabia

In the current digital era, it is difficult to preserve the confidentiality, integrity, and availability of an organization's information and technology assets against cyber attacks.Organizations cannot rely solely on technical solutions for defense, since many cyber attacks attempt to exploit non-technical vulnerabilities such as how well employees comply with the organization's cybersecurity policies.This study surveyed 245 randomly selected employees of government organizations in the Kingdom of Saudi Arabia with an electronically distributed questionnaire about factors that influence employees' compliance with cybersecurity policies.The study found that ethical factors had the most influence on employee compliance with cybersecurity policies, followed in decreasing order of influence by legislative factors, technical factors, and administrative factors.
A Novel Model for Elementary Particles: Light Charges and Their Motion in 5D Space-Time

This paper proposes a novel model for elementary particles, introducing the concept of "light charges", as fundamental entities that exhibit intriguing behavior in 5-dimensional space-time.Light charges are postulated to possess two velocity vectors: one moving at variable velocity, and one moving at the speed of light, that remain fixed relative to each other at perpendicular angles.These light charges interact with each other based on their charges, leading to changes in their motion over time.This paper derives equations based on the proposed model to investigate the behavior of light charges in various scenarios.Remarkably, the model is capable of reproducing known properties of elementary particles, including values for their charge, mass, and spin.This paper further discusses the implications of this model for our understanding of fundamental particles, including the potential connections to quantum mechanics and relativity.The proposed light charge model offers a new perspective on the behavior of elementary particles.While further research and validation are needed, this model may pave the way for a deeper understanding of the fundamental nature of matter and energy in the universe.
A game theory approach for joint blind deconvolution and inpainting

In this paper we propose a new mathematical model for joint Blind Deconvolution and Inpainting. The main objective is the treatment of blurred images with missing parts, through the game theory framework, in particular, a Nash game, we define two players: Player 1 handles the image intensity while Player 2, operates on the blur kernel. The two engage in a game until the equilibrium is reached. Finally, we provide some numerical examples: we compare the efficiency of our proposed approach to other existing methods in the literature that deals with Blind Deconvolution and Inpainting separately.
Quest as a gamified tool for developing students’ media competence in the post-truth era

The current issue of the development of media literacy among modern students is considered. In the era of social networks and the intensification of “fake news”, teaching young people the rules of countering disinformation is the first step towards creating a safe information field. The subject of the research is a quest as a tool for the development of media competence. The purpose is to identify the potential of the quest as a tool for developing critical thinking. We consider the gamified format of media literacy development optimal for today’s students, representatives of generation Z, since it is the zoomers who appreciate the introduction of game elements into the learning process as a reference to their rich gaming experience. The stages of creating an educational quest are considered, the technology of its implementation based on the author’s case “Media-Compass” is described. The conclusion is made about the high potential of the quest as a dynamic, emotionally saturated tool for the development of media competence among the modern generation of students in the post-truth era.
(De)Coding of Abbreviations from the Last Two Decades

The publication examines abbreviations and combinations with an abbreviated element registered in the public domain in the last two decades, as well as the re-actualization of long-known abbreviations. The system analysis unfolds in several aspects: structural (mono-, bi- and polyelement, cut off abbreviations), temporal (neo-abbreviations and renewal of already known abbreviations, renomination), etymological (Bulgarian – regional and national, loan words, international abbreviations), functional (with distribution in written and/or usual utterances), stylistic (neutral, stylized, occasionally transposed, result of language play). The two main approaches that are advanced and woven into the analysis of the above-mentioned groups are the coding of significant nominations by means of various types of abbreviations and the connotative decoding (desabbreviation) of part of the abbreviation formations. In the study, cases of syngraphy, abbreviated homonymy and synonymy, archaization, competition between acronyms, which are registered in modern language practice, are marked.
Feature screening algorithm for high dimensional data

Currently, feature screening is becoming an important topic in the fields of machine learning and high-dimensional data analysis. Filtering out irrelevant features from a set of variables is considered to be an important preliminary step that should be performed before any data analysis. Many approaches have been proposed to the same topic after the work of Fan and Lv (J. Royal Stat. Soc., Ser. B. 70 (5), 849–911 (2008)), who introduced the sure screening property. However, the performance of these methods differs from one paper to another. In this work, we aim to add to this list a new algorithm performing feature screening inspired by the Kendall interaction filter (J. Appl. Stat. 50 (7), 1496–1514 (2020)) when the response variable is continuous. The good behavior of our algorithm is proved through a comparison with an existing method, proposed in this work under several simulation scenarios.
Blockchain Crowdfunding: A New Era in Democratizing Investment Opportunities

One way to collect funds for a project or business endeavor is through crowdfunding. This approach involves gathering small donations from a vast number of people, which is usually done using an online platform. It has become increasingly popular in recent years as a means of financing everything from creative projects to social causes, start-ups, and small businesses. Blockchain is a decentralized digital ledger that records transactions across a network of computers in a secure and transparent way. Unlike traditional centralized ledgers, blockchain technology does not rely on a central authority to validate transactions or manage data. Instead, it uses cryptographic algorithms and consensus mechanisms to maintain the integrity of the ledger. Overall, using blockchain for crowdfunding provides a secure, transparent, and efficient way to raise funds for a variety of projects and initiatives. With the ongoing development and increasing availability of technology, it is probable that we will observe an upsurge in the utilization of blockchain advantages by crowdfunding platforms. Third-party API named VerifyAadhaarNumber is used to verify Aadhaar the fundraisers before starting any venture. The documents of the fundraisers are stored on the InterPlanetary File System (IPFS) of ThirdWeb. As a result of the implementation, one can use our crowdfunding platform using blockchain to raise funds as well as invest funds to ventures, the funds can be either Ethereum coins called Ether (ETH) or ERC20 tokens. Crowdfunding using blockchain is adequate for raising funds for small projects; however, more features like getting equity in exchange are required.
Streamlining Heritage Tourism: A Mobile App for Booking and QR Ticketing of Heritage Sites

Culture can be defined as the distinctive way of life that a society or community adheres to, including its customs, traditions, beliefs, and practices. In India, tourism has always been a major industry, with cultural tourism being a significant part of it. The emergence of the digital era has revolutionized the way travelers plan and book their trips, and India's tourism industry was quick to adapt to this change. The integration of technology into the traditional cultural tourism experience has significantly enhanced the overall experience for visitors. Furthermore, the integration of technology has made the process of booking accommodations and transportation more streamlined, allowing citizens to plan their trips with ease. With the use of our mobile app, travelers can now access information on cultural events and festivals happening in different parts of the country, and even book tickets for popular cultural performances. Hence, the objective of our system is to maximize the digitization of the Indian travel industry has greatly enhanced the experience of cultural tourism in the country. With the implementation of our system in real, people can now access a wealth of information and immersive experiences, making it easier to appreciate and explore the rich cultural heritage of India.
Evolution from Handcrafted to Learned Representation Methods for Vision-Based Activity Recognition

With several applications in the disciplines of healthcare, human–computer interaction, assistive learning, and many others, Human Actions Recognition, or HAR, is a trending research area. Vision-based methods have shown remarkable accuracy in recognizing the daily activities of a human being. This paper presents the evolution of HAR from handcrafted techniques to learned representation, i.e., supervised and unsupervised representations. Merits and limitations of each technique are also presented to provide a clear vision of the evolution. It has been analyzed from the literature that depending on the application area, any single model or combination of handcrafted and learned representations can be employed.
BIRD: A Binary Intermediate Representation for Formally Verified Decompilation of X86-64 Binaries

We present BIRD: A Binary Intermediate Representation for formally verified Decompilation of x86-64 binaries. BIRD is a generic language capable of representing a binary program at various stages of decompilation. Decompilation can consist of various small translation passes, each raising the abstraction level from assembly to source code. Where most decompilation frameworks do not guarantee that their translations preserve the program's operational semantics or even provide any formal semantics, translation passes built on top of BIRD must prove their output to be bisimilar to their input. This work presents the mathematical machinery needed to define BIRD. Moreover, it provides two instantiations - one representing x86-64 assembly, and one where registers have been replaced by variables—as well as a formally proven correct translation pass between them. This translation serves both as a practical first step in trustworthy decompilation as well as a proof of concept that semantic preserving translations of low-level programs are feasible. The entire effort has been formalized in the Coq theorem prover. As such, it does not only provide a mathematical formalism but can also be exported as executable code to be used in a decompiler. We envision BIRD to be used to define provably correct binary-level analyses and program transformations.
A Fast Approach for Adversarial Training by Fleeing the Illness Parameter Space

Although adversarial training is successful to improve the adversarial robustness under various attacks, the time-consuming is 3–30 times as long as standard training. The main challenge of fast adversarial training is catastrophic overfitting, which breaks the robustness of the model in one training epoch. Although many works have been devoted to solving this problem, a challenging adversarial setting is still not available for these methods. In this paper, we provide a new view of the relationship between the roughness of the adversarial loss and catastrophic overfitting and propose a method with nearly zero cost for times and memories. Our accuracy and robustness can be comparable to the state of the art in the common $$\epsilon $$ adversarial settings. Furthermore, our method can prevent catastrophic overfitting when training and testing under large $$\epsilon $$ adversarial settings, because our method can choose a larger range of hyper-parameters to adapt to the strong adversarial setting.
Multi-secret Sharing Visual Cryptography for Grayscale Images by Polarization

Multi-secret sharing visual cryptography improves the efficiency of information transmission. However, the current multi-secret sharing schemes still have room for improvement. For example, the quality of the reconstructed image is relatively poor; the pixel expansion is large; and the decoding usually requires computation, etc. In this paper, we propose a multi-secret sharing visual cryptography scheme for grayscale images by polarization (PMSVC). PMSVC has no pixel expansion and decoding is computationally free. Two shares can encrypt two grayscale images. When two shares are collected and stacked in two different ways between two horizontal polarizers, two reconstructed images are obtained. The reconstructed images are different from previous schemes that can only achieve halftone at most. In PMSVC, the reconstructed images have five tones. Therefore, the quality of the reconstructed image is greatly improved.
A Nonconvex Total Variation Based Method for SAR Image Despeckling

Speckle noise usually deteriorate the synthetic aperture radar (SAR) imaging system severely, which results in great difficulties to the post-processing. In this paper, we propose a novel despeckling method based on Fisher-Tippett (FT) distribution and nonconvex total variation (NTV) model to tackle the problem. First, we introduce a FT distribution-based Bayesian framework to develop speckle noise removal models. Secondly, we propose a new nonconvex TV model which is more appropriate to preserve edges/textures than the conventional model. Meanwhile, we give a simple method to solve the nonconvex TV model. Thirdly, a new iterative regularization scheme-based despeckling algorithm is proposed, in which the NTV model filtered residual is added back to the despeckling image. Experiments conducted on both simulated and real SAR images demonstrate that the proposed method can effectively remove speckle while preserving edges and textures compared with some state-of-the-art methods.