Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Design Using a Particle Swarm Optimization for Face Recognition

In this work, the combination of convolutional neural networks (CNNs) and particle swarm optimization techniques to automatically design CNN architectures is proposed. The proposed particle swarm optimization aims at finding CNN parameters: the number of fully connected layers with their number of neurons, the number of convolutional layers with the number and filters size, batch size, and the epochs number. This method is applied and tested with face recognition datasets. The particular databases used in this work are the ORL and Yale, where the principal goal of this work is to reduce the recognition error. Experiments are performed using different images for the training phase to observe how much information is needed to obtain a good recognition. The simulation results show a face recognition rate of 100% with 7 (ORL) and 4 (Yale) images for the training phase, respectively. The obtained results are contrasted with other works to observe the behavior of the proposed method.
Data Security Using Cryptography and Steganography Technique on the Cloud

Cloud computing technology is on the rise in our world today. It has become a sought-after technology due to its flexibility for users to access their data at any time of the day. From anywhere they are provided, there is an Internet connection. One of the challenges this technology faces is security, due to this reason, some organization has refused to embrace this technology for the safety of their data. Researchers have done quite a lot of work in addressing this issue of which in this paper a novel approach is also introduced. Our proposed model has to do with a hybrid cryptography method using the blowfish algorithm for encryption and the RSA algorithm for the encryption of the secret key, and then the Replace R in RGB steganography algorithm will be used for the cover image in other to strengthen the security on the cloud.
Merging Reinforcement Learning and Inverse Reinforcement Learning via Auxiliary Reward System

In recent years, learning from demonstration has become one of the promising methods in robotics and interactive systems. Learning from demonstration is a model by which an agent learns by observing an expert. The expert could be a pre-trained agent or human. The main problem with learning from demonstrations is the difference between the reward representation in the demonstrations and the actual environment. During the construction of the demonstrations, it is easy to add new rewards to enhancement the agent’s performance. In contrast, it is not easy to do that in an actual environment. This work is built upon our previous work to solve this problem. In previous work, the agent uses Reinforcement Learning algorithms to learn how to play video games from demonstrations. The agent was supplied with an external reward to solve the problem of missing rewards in the hard exploration environments. In this work, Inverse Reinforcement Learning uses to extract the external rewards from the demonstration and make them available during the interaction period. The results showed that inverse learning enables the agent to interact with the environment after the pre-training. Furthermore, the performance of the agent becomes more stable.
Factors Affecting Academic Achievement in SW Education

Software industry is becoming the core of technology trend. SW-based changes in education, industry, and social perception are continuously increasing, and thus, computing power is recognized as an essential requirement in higher education. Globally, research on software (SW) education methodology is being actively conducted, and from this point of view, the socio-economic importance of software education is increasing. This study conducted a study on how to identify the educational characteristics and motivations of students. In this study, a method to deal with the motivation for SW education from the perspective of student characteristics was reviewed. For this analysis, the relationship between educational achievement and factors is analyzed, and factors that can affect student achievement are explored. Factors for the methodology are identified and verified with a statistical methodology.
Detecting Non-Spherical Clusters Using Modified CURE Algorithm

Clustering using representatives (CURE) algorithm is a robust hierarchical clustering algorithm which is dealing with noise and outliers. CURE algorithm merges and divides the clusters in some datasets which are not separate enough or have density difference between them. The obtained results show that CURE clustering is sensitive to input parameters. In this paper, the advantages of density-based cluster center detection are represented, and a modified CURE clustering algorithm is developed. The new algorithm determines the cluster centers and doesn't allow merging the clusters which contain cluster centers in data points. Experimental results show that the proposed algorithm has the capability to extract clusters more efficiently than the traditional CURE algorithm.
Systematic reviews of elderly exercise barriers

The objective of this study is to conducting studies and research to understand the barriers to exercise among the elderly. This research is a qualitative study. by collecting information from academic documents in the Scopus, google scholar, PubMed another involving research database using the keyword “Physical activity” or “exercise” or “motivation” and “barrier” analysis by used of extensive literature reviews/retrieval, and analysis using of systematic reviews methods The results showed the factors affecting physical activity in the elderly. Study from 129 documents and can screen to 10 publication which results consistent to the problem finding, then can be divided into six areas of barriers: physical, medical, no incentive to exercise. economic problems Cognitive problems, etc. In conclusion, it was found that the main obstacles in exercising in the elderly correlated with various factors both in terms of people and the environment but obstacles in the field of exercise in the elderly are important. In this research study is ignorance of the correct exercise method or cognitive problems. This leads to further studies to develop problem-solving models using knowledge management methods. This will be an important point that should be a good starting point for solving exercise problems in the elderly.
Toward Accurate Fall Detection with a Combined Use of Wearable and Ambient Sensors

This paper proposes the use of relative barometric pressure sensors for enhancing the accuracy of fall detection based on a tri-axial accelerometer. In this study, a device consisting of a tri-axial accelerometer and a barometric pressure sensor was placed on a subject's waist and another reference barometric pressure sensor is placed on the wall. The data is collected while the subject is performing a sequence of 22 fall and non-fall activities 12 times. We compare activity classification with features extracted from these following datasets: 1) acceleration data, 2) acceleration and on-body barometric pressure, and 3) acceleration, on-body barometric pressure, and the reference barometric pressure. Three classifiers are compared, i.e., k-Nearest Neighbors, Decision Tree, and Random Forest. The results show that Random Forest yields the highest classification accuracy among the three classification algorithms and accuracy enhancement can be achieved with the relative barometric pressure information. The highest classification accuracy of 96.6% can be achieved with the Random Forest algorithm when acceleration, on-body barometric pressure, and the reference barometric pressure information on the wall are used.
Analytical solution and hidden symmetry operators of asymmetric two-mode quantum Rabi model

Abstract The asymmetric two-mode quantum Rabi model (tmQRM) breaks <?CDATA ${\mathbb{Z}}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> -symmetry and also implies hidden symmetry. In this paper, the energy spectrum are analytically computed with Bogoliubov transformation and SU (1, 1) Lie algebra. The analytical results are in good agreement with those obtained by numerical calculation. We identify the condition for the bias parameter such that level crossings appear in the spectrum which are caused by the hidden symmetry. This condition is related not only to the field frequency, but also to the coupling strength between the field and qubit. The level crossing points could be determined by the pole energy and recurrence coefficients. This is tested and verified in the low energy levels. The generators of the hidden <?CDATA ${\mathbb{Z}}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> -symmetry, commuting with the Hamiltonian, are derived within the Bogoliubov operator approach. The present results may provide more information to the hidden symmetry in the asymmetric tmQRM.
Investigation of Multi-task Learning for Object Detection

Deep learning-based object detection is one of the most popular topics in computer vision. The generalization issue is particularly challenging due to the limited amount of labeled data and the difficulty in merging datasets in various domains. Multi-task learning is a promising way to leverage different datasets to enhance the generalization ability of networks, but it is rarely applied in object detection. In this paper, we focus on the effect of multi-task learning for one-stage object detection. Results show that the performance of the model trained in a small dataset can be improved significantly if it is trained jointly with a large dataset. The generalization of models trained with labeled data in a single domain can also be improved when trained jointly with different other domains.
Deceptive Product Review Identification Framework Using Opinion Mining and Machine Learning

Reviews of products are an integral part of the marketing and branding of online retailers. They help build trust and loyalty and generally define what separates the goods from others. The competition is so high that at times the company is forced to rely on the third party in producing deceptive reviews to influence readers’ opinions and hence to enhance the sales. This misleads the ethics and purpose of online shopping. This paper proposes a model to identify fake product reviews. The model uses Naive Bayes classifier and Support Vector Machine to classify the fake and genuine reviews. The model uses a set of parameters such as length of the review, usage of personal pronouns, nature of the review, verified purchase status, rating of the review and the type of the product to extract the features for the classification. The experimental results show that the model is working efficiently with a high classification accuracy rate.
F-Drop&amp;Match: GANs with a Dead Zone in the High-Frequency Domain

Generative adversarial networks built from deep convolutional neural networks (GANs) lack the ability to exactly replicate the high-frequency components of natural images. To alleviate this issue, we introduce two novel training techniques called frequency dropping (F-Drop) and frequency matching (F-Match). The key idea of F-Drop is to filter out unnecessary high-frequency components from the input images of the discriminators. This simple modification prevents the discriminators from being confused by perturbations of the high-frequency components. In addition, F- Drop makes the GANs focus on fitting in the low-frequency domain, in which there are the dominant components of natural images. F-Match minimizes the difference between real and fake images in the frequency domain for generating more realistic images. F-Match is implemented as a regularization term in the objective functions of the generators; it penalizes the batch mean error in the frequency domain. F-Match helps the generators to fit in the high-frequency domain filtered out by F-Drop to the real image. We experimentally demonstrate that the combination of F-Drop and F-Match improves the generative performance of GANs in both the frequency and spatial domain on multiple image benchmarks.
Speckle Noise Reduction from Medical Images Using Gaussian Fuzzy Membership Function

The primary concept of this paper is a fuzzy membership function-based filter with the expertise to reduce speckle noise that is often utilized in clinical diagnosis such as ultrasonography. In medical science, image sharpening is a highly effective approach for recognizing human inner organs and body tissues. Speckle noise is multiplicative in nature and it highly affects the image and reduces some useful information. The target of image noise reduction is to extract the genuine image from a noisy speckled image. In this paper, a Gaussian fuzzy-based system has been developed that is fast and provides good accuracy depending upon the membership function. Also, experimental results demonstrate that the proposed algorithm ensures better filtering quality and image restoration ability in comparison with others.
Text classification of COVID-19 reviews based on pre-training language model

This experiment analyzed 100,000 epidemic-related microblogs officially provided by the CCF. Using Enhanced Representation through Knowledge Integration (ERNIE), the effect of pre-training model on extracting Chinese semantic information was improved. After that, the deep pyramid network (DPCNN) was merged with ERNIE to save computing costs. Enhanced feature extraction performance for long-distance text. This model was the most effective in the comparison test of six emotional three-category tasks, which improved the accuracy of BERT pre-training model by 7%.
Review on Augmented Reality Technology

Augmented Reality (AR) and Virtual Reality (VR) based treatment has proven that it is easy to use, also motivating and more attractive, and enjoyable as most of the studies proved that. Furthermore, the cost of human resources and the equipment with standard rehabilitation therapy is high to some extent. Whereas, the AR and VR systems are low cost and easy to popularize, beside the unbelievable number of the saved time. This paper presents the comprehensive review on Augmented Reality Technology.
Defining discovery: Is Google Scholar a discovery platform? An essay on the need for a new approach to scholarly discovery

<ns3:p>This essay discusses the concept of discovery, intended as content discovery, and defines it in the new context of Open Science, with a focus on Social Sciences and Humanities (SSH). Starting from the example of Google Scholar, the authors argue that this well-established service does not address the current needs, practices, and variety of discovery. Alternatives in terms of technical choices, features, and governance, do however exist, offering richer and more open discovery. The paper presents, in particular, the implementations and research work of the H2020 project TRIPLE (Transforming Research through Innovative Practices for Linked Interdisciplinary Exploration). Dedicated to the building of a discovery platform for the SSH, the project is meant to address the specificities and evolution of discovery in this field. Prevailing scholarly resource platforms like Google Scholar limit discovery by focussing only on publications, and favouring through their algorithm well-cited papers, English content, and discipline-specific resources. A limitation in the context of cross-disciplinary and collaborative Open Science, such a service more specifically hinders discovery in the SSH. Characterized by a fragmented landscape, a variety of languages, data types, and outputs, research in the SSH requires services that fully exploit discovery potentialities. Moreover, a survey conducted within the TRIPLE project showed that most SSH researchers use Google Scholar as their starting point, and that they recognise the lack of control they have with this system. Beyond the extension of features and content, transparency is the other important criterion for the building of an open infrastructure serving the research community. In light of this, we present the GoTriple platform, which exploits today’s technological potential and incorporates the best known functionalities, in order to unveil more and innovative scholarly outputs and lead to international and interdisciplinary research project collaborations.</ns3:p>
Automatic Math Word Problem Generation With Topic-Expression Co-Attention Mechanism and Reinforcement Learning

Taking several topic words and a math expression as input, the aim of math word problem generation is to generate a problem that can be answered by the given expression and related to these topic words. Considerable progress has been achieved by sequence-to-sequence neural network models in many natural language generation tasks, but these models do not effectively consider the characteristics of the math word problem generation task. They may generate problems that are unrelated to the topic words and expressions, and problems that cannot be solved. In this paper, we propose a new model, MWPGen, for automatically generating math word problems. MWPGen has a topic-expression co-attention mechanism to extract relevant information between topic words and expressions. Further, we fine-tune MWPGen with the solving result of the generated problem as the reward for reinforcement learning. MWPGen shows improved performance in popular automatic evaluation metrics and improves the solvability of generated problems.
A hybrid algorithm to solve linear systems of equations with limited qubit resources

The solution of linear systems of equations is a very frequent operation and thus important in many fields. The complexity using classical methods increases linearly with the size of equations. The HHL algorithm proposed by Harrow et al. achieves exponential acceleration compared with the best classical algorithm. However, it has a relatively high demand for qubit resources and the solution $\left| x \right\rangle $ is in a normalized form. Assuming that the eigenvalues of the coefficient matrix of the linear systems of equations can be represented perfectly by finite binary number strings, three hybrid iterative phase estimation algorithms (HIPEA) are designed based on the iterative phase estimation algorithm in this paper. The complexity is transferred to the measurement operation in an iterative way, and thus the demand of qubit resources is reduced in our hybrid algorithms. Moreover, the solution is stored in a classical register instead of a quantum register, so the exact unnormalized solution can be obtained. The required qubit resources in the three HIPEA algorithms are different. HIPEA-1 only needs one single ancillary qubit. The number of ancillary qubits in HIPEA-2 is equal to the number of nondegenerate eigenvalues of the coefficient matrix of linear systems of equations. HIPEA-3 is designed with a flexible number of ancillary qubits. The HIPEA algorithms proposed in this paper broadens the application range of quantum computation in solving linear systems of equations by avoiding the problem that quantum programs may not be used to solve linear systems of equations due to the lack of qubit resources.
A Modified Weighed Histogram Approach for Image Enhancement Using Optimized Alpha Parameter

Enhancement of images is a critical part of Computer vision for the processing of images. The objects with a low contrast ratio and low intensities are difficult to interpret by the system models for their further processing. In the present work, we have emphasized our approach in finding the correct methodology for the enhancement of images collected from different data sources. In the proposed approach, we have considered the maritime satellite imagery data and the images captured through camera (especially of ships). We have proposed a novel approach Modified Weighted Histogram Equalization method in which the alpha parameter is adjusted to the best optimized value and the presented work have been compared with other existing approaches such as adaptive gamma correction weighted distribution in terms of PSNR (Peak Signal to Noise Ratio), AMBE (Absolute mean brightness error) and processing time. The work gives the best accuracy results in enhancing the images which further may be used for different application of computer vision such as object detection, vessel classification, etc.
Mining Contextual Item Similarity without Concept Hierarchy

In the modern era, data is precious. Therefore, a huge amount of data is being generated every moment and data mining extracts insight from this data. Item similarity mining is a special domain of data mining that helps discover inherent and important characteristics of a dataset. It is a popular research problem with application in numerous domains. In this work, we propose a novel, symmetric, null-invariant measure of similarity that can evaluate contextual similarity between items, without any additional metadata. We also propose an optimal algorithm for calculating this measure. Moreover, as the optimal algorithm has comparatively high runtime complexity, we propose a heuristic algorithm which generates an approximate result without sacrificing much accuracy. This similarity can be used for mining localized associations and discovering object relationships in large datasets. The results obtained using the proposed measure in six real-life datasets confirm the measure&#x2019;s effectiveness and versatility in data of varying nature.
Handling Non-Fuctional Requirements in IoT-based Machine Learning Systems

In recent years, IoT and machine learning solutions have garnered immense popularity with the rise of industry 4.0. It is extremely important to document system requirements to deliver solutions that meet customer demands and increase satisfaction. However, the inclusion of non-functional requirements (NFRs) in software systems is either outright neglected or they are introduced in the later stages of the software development lifecycle, which often leads to architectural debt and, in the worst-case scenario, project failure. In the machine learning context, the poor handling of non-functional requirements affects architectural decision-making, which can potentially cripple the resulting system. Furthermore, our knowledge of existing NFRs no longer applies in this context due to the unique quality attribute concerns presented by the ML systems. This work aims to review the relationship between requirements engineering and software architecture w.r.t machine learning and present recently proposed methodologies for documenting and handling NFRs to deliver quality software systems. Three recent methodologies were also highlighted and compared.
Local Gamma Correction using Bi-linear Function for Dark Image Enhancement

Enhancement of dark images with embedded light sources still remains a challenging task. The classical methods are effective in enhancing the dark regions of an image but results in over enhancement of the bright regions. The over enhancement leads to the loss of original information and degradation of image quality. To effectively enhance the dark and bright regions of an image, a Bi-linear piecewise gamma correction method is proposed here. The proposed method employs two linear functions for simultaneous enhancement of dark and bright regions. The Bi-linear piecewise mapping function is used for the estimation of local gamma factor as per the local mean intensity so as to enhance different local intensity regions effectively. Experimental results show that the proposed method works better than many of the existing enhancement techniques in terms of objective and subjective measures.