Reviewerly/exHarmony huggingface dataset:
Speech Enabled Visual Question Answering using LSTM and CNN with Real Time Image Capturing for assisting the Visually Impaired

The proposed work benefits visually impaired individuals in identifying objects and visualizing scenarios around them independent of any external support. In such a situation, the user can capture a real time image of the surrounding and ask an open-ended question, classification question, counting question or yes/no question to the application by speech input. The proposed application uses Visual Question Answering (VQA) to integrate image processing and natural language processing which is also capable of speech to text translation and vice versa that helps to identify, recognize and thus obtain details of any particular image. The work uses a classical CNN-LSTM model where image features and language features are computed separately and combined at a later stage using image features and word embedding obtained from the question and runs a multilayer perceptron on the combined features to obtain the results. The model achieves an accuracy of 57 per cent. The model can also be utilized to develop cognitive interpretation better in kids. As the application is speech enabled it is best suited for the visually impaired with an easy to use GUI.
Intelligent control of spacecraft attitude using reinforcement leaning

The aim of this paper is to develop an effective algorithm for intelligent control of spacecraft based on reinforcement learning (RL) methods.In the development and analysis of the algorithm, methods of theoretical mechanics, automatic control and stability theories, machine learning, and computer simulation were used.To increase the RL efficiency, a statistical model of spacecraft dynamics based on the concept of Gaussian processes was used.On the one hand, such a model allows one to use a priori information about the plant and is sufficiently flexible, and on the other hand, it characterizes uncertainty in the dynamics in the form of confidence intervals and can be refined during the spacecraft operation.In this case, the problem of control/state space analysis reduces to obtaining such measurements that narrow the confidence intervals.The familiar quadratic criterion, which allows one to take into account both the accuracy requirements and the control cost, was used as the reinforcement signal.An RL-based search for control actions was made using a control law iterative algorithm.To implement the regulator and evaluate the cost function, neural network approximators were used.Spacecraft motion stability guarantees were obtained using the Lyapunov function method with account for the uncertainty in the spacecraft dynamics.The cost function was chosen as a candidate Lyapunov function, To simplify the stability test on the basis of this methodology, the dynamics of the plant was assumed to be Lipschitz continuous, which made it possible to use the Lagrange multiplier method for searching for control actions with account for the constraints formulated using the upper uncertainty bound and Lipschitz dynamics constants.The efficiency of the proposed algorithm is illustrated by computer simulation results.The approach makes it possible to develop control systems that can improve their performance as data are accumulated during the operation of a specific object, thus allowing one to reduce the requirements for its elements (sensors, actuators), do without special test equipment, and reduce the development time and cost.
Designing Guitar Tuning Software Using Karplus Strong Algorithm

Abstract Tuning is the process of adjusting strings on stringed musical instruments such as guitars. If the instrument is played frequently, the settings will change. For this reason, the tuning process is usually carried out to get the right settings so when the notes are produced the standard becomes. In addition to how to listen, then tuning can also be used in the tuning process. The tuning technique must also be adjusted because there are usually several types of tuning for musical instruments. The implementation of this paper is design tuning program for musical guitar instruments with tuning settings for 7 types along with 36 tuning types. In addition, this program also provides customized tuning inputs for other stringed instruments.
VISE

This article presents VISE, an interface that enables VIsual Search and Exploration across collections of approximately 836,000 museum objects extracted from the websites of the National Museums Scotland and the Rijksmuseum in the Netherlands. VISE provides an interactive visual summary of information relating to the museum to address online users with no domain knowledge challenges of exploring large museum collection. User satisfaction evaluation was conducted to measure the user satisfaction level for using VISE as the interface for search and exploration of information from large museum collection when compared to non-visual search interface. The evaluation of the visual interface revealed that users are more satisfied and attracted to explore museum objects via VISE than via the system with no visual search interface. Users with little or no domain knowledge find it easier to explore collections and find objects of interest while using VISE in contrast to the system with no visual interface.
A New Approach to Image Enhancement Based on Modified Histogram Equalization

The problem of improving the efficiency of contrast enhancement for complex low-contrast images in an automatic mode with an acceptable level of computational costs is considered. A new approach to image enhancement by parameter-free intensity transformation based on an estimate of the bivariate distribution of brightness is proposed. Analysis of the bivariate distribution of brightness enables to significantly improve the efficiency of image contrast enhancement. Based on this approach, a new generalized description for the procedure of intensity transformation based on the analysis of the marginal cumulative distribution function of image brightness is proposed. The relationship between the proposed generalized description of the intensity transformation and the traditional definition of histogram equalization is shown. Based on proposed description, a new technique of modified histogram equalization is proposed. The research confirms the effectiveness of the proposed technique. The proposed technique of modified histogram equalization provides an increase in the efficiency of contrast enhancement compared to the well-known traditional technique of histogram equalization.
Mini CNC Design to Increase Students’ Programming and Control CNC Competencies

Practical tools used in learning must support and adapt to the needs of the Industrial Revolution 4.0 era with the characteristics of working automatically, 3D printing, internet-based work of things (IoT) and Data of Things. One practice tool that works with these principles is a CNC machine. Mini CNC design that has been made has been equipped with modern computing technology. Mini CNC learning media aims to make students not only skilled in terms of operating the machine, but students can develop their skills in the design and programming of a CNC machine. This research was conducted based on the ADDIE model, namely Analysis, Design, Development, Implementation and Evaluation. The results of the design and manufacture of IoT-based Mini CNC 3D printers for learning media consist of designing hardware and software. Linearity test results showed there was no significant difference between the setting value and the measured value of the results of the work process of the tool and being able to do the cutting process from wood, fiber or aluminum, so that the Mini CNC was suitable for use as a learning media.
A Decision Support System for Role Assignment in Software Project Management with Evaluation of Personality Types

Recent studies show that personal factors in software engineering are effective on team performance, motivation and job quality. Forming team members or incorporation of a new member into the team that currently working on a software project directly affects the project team’s work performance and hence the progress of the project. In this study, a decision support system was developed to provide the ability to select team members according to personal characteristics in order to improve the performance of software project teams. The developed decision support system, determines the project roles that can be appropriate by analyzing the personality type of the project team members. The fuzzy c-means method, one of the fuzzy clustering methods, was used when the IPI personality inventory and personality type team members were determined to be appropriate to their personality type.
An Automatic Image Segmentation Model Integrating Fuzzy Clustering with Level Set Method

Automatic segmentation for medical images is a fundamental task for scientific research and clinical application. Most reports focus on the active contour model or fuzzy clustering method independently, while integrating two methods properly may play an unexpected better results. A new method is proposed in this paper, which integrates fuzzy clustering with level set method through an additional term in our new energy functional. It is able to use the results of fuzzy clustering directly, which can control the level set evolution automatically. Such algorithm eliminates the manual operation and leads to more robust segmentation results. With the split Bregman method, the minimization of the energy functional is fast. The proposed algorithm was tested on some medical images. Experimental results show its effectiveness for medical image segmentation.
Creating the Opportunity and Ability to Shape the Future: A Social and Cultural Response to the Challenges of Modern Education

This study is highly relevant today, as it responds to the need for a social and cultural strategy aimed at a systemic education overhaul in Russia, which would guarantee a high-quality education, boost the domestic economy, and help improve community life, culture, and overall well-being in the country. Therefore, the main goal of the paper is a critical analysis of the Club of Rome model and the New Enlightenment concept, representing future literacy. The core idea of our study is to propose an original alternative to the Club of Rome model, which differs from the latter and promotes the concept of providing students with the opportunity and ability to shape their future. We based this paper on systemic comparative practices and the retrospective method, which allowed us to single out and evaluate different viewpoints on the objectives, means, and forms of modernizing secondary and tertiary professional education in Russia and abroad, as well as set the methodological criteria for evaluating the role of education in society’s social and cultural development. We interpret the key components of changing the education paradigm by using an original content model, with adjustments for the criteria that we are profiling. The paper’s approach reveals the obstacles on the path towards developing Russian education, as well as the ways of overcoming them. The research is practice-oriented, and its results will address the needs of experts working for government authorities on a national and local level, and secondary and tertiary educational institutions, as well as experts on forecasting, introducing, and promoting digital technology (IT), and the creators of teaching methodologies and education process managers.
Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.
Boundary Strategies for Self-organizing Migrating Algorithm Analyzed Using CEC’17 Benchmark

This paper is focused on the influence of boundary strategies for the popular swarm-intelligence based optimization algorithm: Self-organizing Migrating Algorithm (SOMA). A similar extensive study was already performed for the most famous representative of swarm-based algorithm, which is Particle Swarm Optimization (PSO), and showed the importance of related research for other swarm-based techniques, like SOMA. The current CEC’17 benchmark suite is used for the performance comparison of the case studies, and the results are compared and tested for statistical significance using the Friedman Rank test.
Fast and Reliable Facial Landmarks Localization in Non Frontal Images

The goal of this paper is to introduce a pipeline able to precisely and rapidly localize a set of facial landmarks on a face image. The proposed method is also able to estimate the pose of the face portrayed on the image independently from its orientation and it is robust to partial occlusions. This method is based on an optimized version of Part Mixture Model, it is able to fit a facial landmarks configuration to faces dealing with out-of-plane rotations and is suitable for real time processing. Moreover, the proposed method also uses a double scale approach to speed up the pose estimation phase and a posteriori configuration regularization based on Principal Component Analysis (PCA).
Using Engeström’s Activity Triangle Model to Design and Develop a Technology-based HIV Intervention for the Youth

Recent reports claim there is a significant decline in terms of new HIV infections recorded among adults and children. In spite of this global decline, the Philippines continue to face an increased number of new HIV cases, especially among the youth. HIV testing, an important step towards HIV detection and medication, can be aided with mobile technology specifically smartphones. Given the advanced capabilities of smartphones and its wide reach among the youth, it can be a potential tool for delivering HIV health care intervention. This paper outlines how TimesApp - an Android-based prototype for anonymously scheduling an HIV counseling and testing appointment was developed using Engestrom’s second generation activity triangle model.
Recognizing Fine-Grained Home Contexts Using Multiple Cognitive APIs

To implement fine-grained context recognition affordable for general households, we are studying techniques that integrate image-based cognitive API and light-weight machine learning. Specifically, our method first captures images of a target space in different context, then sends them to the cognitive API. For each image, the API returns a set of words, called tags, representing concepts recognized in the picture. Regarding these tags as features of the target context, we apply the supervised machine learning. Our preliminary results with a commercial API showed that the overall accuracy was more than 90%, however, the accuracy decreased for contexts with multiple people (e.g., "General meeting", "Dining together" and "Play games"). The goal of this paper is to improve the recognition accuracy of such difficult contexts, with preserving the affordability to general households. In the proposed method, we use multiple cognitive APIs. For each API, we construct an independent recognition model. Then, the context is determined by majority voting among results of the independent models. Experimental evaluation with five commercial APIs shows that the ensemble of the five independent models achieved 98% of overall accuracy, where the individual models complement mutual limits of recognition.
English Automatic Dictionary Creation with Natural Language Processing

Studies in the area of language lexicography are focused on automatic dictionary creation. In this article, an English document is given as an initial reference. In the study, meaningful words representing the reference document were identified. For this purpose, the Helmholtz Principle has been applied. The first dictionary words consist of the meaningful words of the reference document we call this seed. Then, with a loop, Web search is performed in the Azure Web Cognitive Web Search system using meaningful words from the most recently processed document. The first document from the search result has meaningful words with the Helmholtz Principle as applied to the reference document. The meaningful words found during the cycle are not added directly to the dictionary this time, and using the WordNet dictionary to avoid deviations, the similarity of each meaningful word with the dictionary formed is calculated. The meaningful words with similarity values higher than a certain threshold value are added to the dictionary and the search cycle is repeated using these words, and finally, when the desired number of words for the dictionary is reached, ends. In order to measure the performance of the dictionary, WordNet similarity calculation was used. Dictionaries with an average of % 38,93 similarity can be generated in tests performed with reference documents given in different subjects.
Virtual Reality Arcades: A Study on Usage Habits with Emphasis on Digital Gaming

Virtual reality (VR) and VR gaming have seen substantial advancement during the recent years both in terms of technological development and the number of users. A notable rise has also occurred in the number of VR arcades. Despite the growing academic interest towards VR and VR gaming, there is still a dearth of understanding on the usage aspects of VR gaming and VR arcades. To address this gap, this study explored the habits of using VR arcades with emphasis on digital gaming. Specific focus was set on investigating differences between gender, age, and physical activity background. The study was exploratory in nature and based on a quantitative analysis of data collected from 126 respondents in Finland. The findings of this study bring new knowledge to this emerging subject, and thus, extend our understanding on the habits of using VR arcades and VR games. The study shows that digital gaming is clearly the most popular form of entertainment in VR arcades. Also, for the vast majority, the main purpose to visit VR arcades is gaming for fun, and it is more popular to visit VR arcades together with others than alone. The findings also demonstrate how VR gaming can be a new frontier for exergaming. Gender, age, and physical activity background differences are discussed. As a practical contribution, the findings are used to present implications for VR gaming and VR arcade stakeholders.
Air Sanitization Using AESA Radar

This paper proposes a system of modeling an air volume as a collection of cells and computing the cumulative probability of detection at each cell. The paper considers how a cell increases in detection confidence with multiple beams as well as how that confidence degrades over time and in the adjacent presence of other sanitized cells. The paper will make the argument that a sanitized area of sufficient confidence can only degrade at the boundary edge and therefore it is only that area boundary that requires subsequent beam attention. When beams are no longer spent sweeping an area as fast as possible, the result is a sensor that can be used for other activities (e.g. tracking threats, synthetic aperture radar (SAR) mapping) or in reduced detectability.
A Fronthaul Signal Compression Method Based on Trellis Coded Quantization

In the C-RAN architecture, there is a very high requirement of data rate for the fronthaul due to the characteristics and the high number of signals. One of the solutions relies on compression techniques to alleviate this requirement. Therefore, in this work, we propose a compression technique based on Trellis Coded Quantization. We use a resampling of 2/3, block scaling, TCQ quantization, and entropy coding. The results show that improves EVM performance in comparison with the scalar quantization and presents a lower computational cost than vector quantization.
Survey on Various Approaches of Saliency Detection

Saliency detection is one of the most attractive and active topics in the field of computer vision. Just as the name suggests, saliency detection is an idea to find out the most impressive part of an image or a video. Although it contributes to many of nowadays technology, the idea of Saliency detection appeared as early as the end of 20th century with the introduction of the Itti Model. However, with a 20-year development, current method of deep learning becomes an important part of researching on saliency detection. With the participation of convolutional neutral network, the accuracy and efficiency of saliency detection has improved a lot recently. In this paper at first, some classic traditional methods (without deep learning) are introduced first. Then, there will be some more developed recent algorithms which adjust with the idea of convolutional neutral network. Finally, an evaluation of both kinds of methods as well as a brief conclusion will be given.
Differential Evolution Algorithm Using Enhance-Based Adaption Mutant Vector

Nature-inspired optimization isSingh, Shailendra Pratap the field of study forSingh, Deepak Kumar planning, simulation, and execution of problems using scientific methodologies. In this paper, a novel mutation-based modified differential evolution (DE) algorithm has been proposed. Enhance-based adaption mutation operator helps in avoiding the local optimum problem. The proposed approach is named as enhance-based adaption (EBA) in the existing mutation vector to provide more diversity for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escaping the situation of the local optimum problem. Comparisons with other DE variants such as CPI-DE, TSDE, ToPDE, MPEDE, and JADEcr establish that the proposed Environment adaption-based operator is able to improve the performance of differential evolution algorithms.