Reviewerly/exHarmony huggingface dataset:
Modified Fizeau interferometer with the 4th order polynomial smoothing algorithm

The modified Fizeau interferometer that allows to diagnose plane and spherical optical elements with the diameter ranging from 10 to 100 mm is discussed. The method of interference patterns reconstruction based on reference lines is modified, the algorithm of 4th order polynomial smoothing is implemented. The modified method increases the reliability and accuracy of interferometric pattern reconstruction and suppress the influence of incoming noise. Accuracy of the measurements is about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda/10 (\lambda=0.63\ \text{um})$</tex> .
The Development of Digital Competence of Future Teachers in the Process of Distance Learning

The article digital competence is considered, on the one hand, as a component of professional competence, and on the other – as a level of personal digital culture. The structure of the studied phenomenon defines the motivational component (motivation to use digital technologies in professional activities, focus on collective interaction, the desire to succeed through the possession of information technology); cognitive component (level of knowledge on the application of digital technologies in professional activities); activity component (skills and abilities to use digital technologies for the organization of educational activities and solving professional problems); personal component (ability to self-study, professional self-development in a digital society).&#x0D; The pedagogical conditions for the development of digital competence of future teachers are substantiated: creation and maintenance of the digital environment of a higher education institution; activation of independent educational and cognitive activity of students by means of information and communication technologies; actualization of innovative directions and technologies of development of digital competence of future teachers in distance learning. The research was conducted with the students of the first (bachelor's) level of higher pedagogical education. The results of diagnostic sections have been generalized, statistical analysis of which testifies to the effectiveness of the developed system of development of digital competence of future teachers.
Augmented reality in education: a systematic study on technical and usability issues

In recent years, augmented reality (AR) is being used in creating distinctive learning environments for students' education. Our paper gives a systematic literature review on AR in educational environments. We considered the features, advantages and challenges of using AR in educational settings. One of the main advantages of AR is that it increases students' motivation, thus improving their learning experiences. Technical and usability issues are reported as the challenges experienced while using AR. There are other advantages and challenges identified from the literature survey, which are discussed in this paper. The paper also reports some of the existing studies of AR for education. Furthermore, it details the present state of art and the opportunities for additional research in AR in education.
Elderly Care System for Classification and Recognition of Sitting Posture

Technological advancements in medical field have caused increase in life expectancy of humankind. Recent societal changes and other conditions cause most of the elder population to live alone. Health and safety of such elders have become a burning issue nowadays. Growth in Computer vision and sensor technology has presented a prominent solution for this problem. In this paper, a study on elderly monitoring system is presented with a proposed posture monitoring model. Proposed model takes video frames as input. Model works on the concept of Bag of Visual Words (BoVW). Model uses Oriented FAST and rotated BRIEF (ORB) to obtain features from input images. Subsequently, K means method is deployed for feature reduction. Reduced dimension vectors are used to classify various posture of person in frame. In this study provides major emphasis on posture recognition of sitting, standing and transitional postures. this non-intrusive, efficient monitoring system is tested on various datasets which have yielded good results.
A User Location Privacy Protection Mechanism for LBS using Third Party-based Architectures

Location Based Services are becoming more and more popular as users are enjoying a wide range of services such as tracking friends, finding the nearest cash machine, querying for nearby restaurants, etc. However, protecting location privacy is becoming a growing concern, as principals are not willing to reveal any sensitive information about their locations, which can lead to disclosure of confidential and personal information. In addition, principals aim to be provided with a different privacy protection level mechanism based on their relationships. For example, some principals may need a low or medium privacy protection level when interacting with close friends or friends respectively. However, a high privacy protection level is required when interacting with public users. In this paper, we propose a privacy aware access control model using third party-based architectures. The proposed mechanism provides principals with different location privacy protection levels whenever they are willing to reveal whether they are visible and accessible or not. The proposed user location privacy mechanism is based on elliptic curve cryptography concepts, which can preserve the location privacy of the principals. The protocol is shown to achieve privacy protection for users' location. Through the informal security analysis, the proposed protocol is shown to be secure against many well-known location privacy threats. In addition, the proposed mechanism provides an efficient solution to overcome dynamic access control problems.
Comparison of feature selection algorithms for Data classification problems

This article discusses various feature selection algorithms, namely SelectKBest with different statistical criteria and Random Forest algorithm, compares classification accuracy with and without feature selection algorithms on different datasets. As a result, it was found that in the general case, Random Forest algorithm shows the best classification, but on some datasets it is better to use SelectKBest with certain statistical criteria.
Object Detection Analysis Study in Images based on Deep Learning Algorithm

Deep learning is a subfield of machine learning. Computer vision is one of the technological advances that utilizes deep learning in image processing, object classification, and object detection. In the Object Detection, there have been various models that can detect objects with different characteristics, and with so many models that have been developed, it takes longer to determine which model is suitable for the needs of a project because it requires comparisons between each model. In this study, an analysis was conducted by comparing three models that utilize Deep Learning to detect car and bus objects, namely Faster-RCNN with ResNet50, SSD with MobileNet, and EfficientDet with D0. Each model is run using TensorFlow Object Detection. The models will be trained using a custom dataset containing of 52 images and will be trained in 3000 steps. Based on experiments, it is known that from the comparison of mAP, Faster-RCNN ResNet50 has the highest score of 0.453, and the lowest is EfficientDet D0 with 0.274; for the comparison of Average Recall, Faster-RCNN ResNet50 has the highest score with 0.337, and the lowest is EfficientDet D0 with 0.190, as well as for model size comparison, EfficientDet D0 has the smallest size with 290 MB, and the largest is Faster-RCNN ResNet50 with 1280 MB.
Revolutionizing Healthcare

Healthcare has become an indispensable part of our daily life. The buzzwords of technology such as Internet of Things (IoT), Cloud Computing, Big Data and blockchain have made their way into the healthcare arena. Miniaturization of electronic equipment and advanced wireless technologies has forced human lives to land up in a world of smart ecosystems that enhance healthcare service. This has also helped medical practitioners to streamline their process and diagnosis. Technology brings with it a positivity in the form of improvisation by making the healthcare system smart through Internet of Medical Things. However, one cannot deny the persistent privacy risk. This chapter focuses on blockchain technology in healthcare, which enhances the security for data handling that also demands high computational bandwidth and power. Hence, integration of blockchain and IoT is a herculean task because of the varying demand and might not be suitable for all IoT devices. This chapter presents the usage of decentralized data management of IoT devices using blockchain technologies in the healthcare sector. A safe and secure healthcare system using blockchain technologies can be embedded for future direction, which may increase the scalability and versatility of IoT devices.
Scalable compression for machine and human vision tasks via multi-branch shared module

The existing image compression methods are mainly aimed at human perception tasks instead of machine vision tasks. Rich features learned by the shallow and deep layers of pre-trained visual geometry group (VGG)-net can serve human perception and machine vision tasks, respectively. To improve the machine analysis capabilities of the human-targeted compression method, we propose a scalable image compression framework at low bit-rates. Specifically, the scalable compression framework is composed of a base layer (BL) and an enhancement layer (EL), which utilize the correlation between the above features to perform machine analysis and human perception tasks, respectively. For effectively utilizing the above two types of features, a multi-branch shared module that utilizes the complementarity of multi-branch convolution kernels to retain compact information for the above features to support BL and EL tasks is proposed. In addition, for further improving the accuracy of machine analysis, the machine-vision importance map is introduced in BL; it adaptively utilizes the spatial and channel information from the deepest layer of VGG-net to guide local bit allocation. When the bit-rate is limited to 0.2 bpp, the average recognition accuracy (Top-1) of the BL of proposed method is 5.2%, 13.4%, 6.0%, and 7.4% higher than that of BPG, Webp, Mentzer, and NIC, respectively, on the ILSVRC2012 verification dataset. Meanwhile, the EL provides good visual experience.
Garnet composition from kimberlites of the Arkhangelsk region as a proxy for diamond prospectivity

We showed the effectiveness of the various existing methods applied for the determination of the diamond prospectivity of kimberlite pipes based on the composition of garnets on the examples of high-grade diamond-bearing kimberlite pipes V. Grib and Arkhangelsk and low-grade kimberlite pipe TsNIGRI-Arkhangelsk, located in the Arkhangelsk diamondiferous province. The reasons for the low diamond grade of the TsNIGRI-Arkhangelskaya pipe are revealed. The results of this work allowed us to determine the analytical procedure for studying the garnet grains extracted from panned samples and magmatic bodies in order to assess the potential diamond grade of prospected areas in order to plan subsequent exploration and appraisal work in the Arkhangelsk region.
Learn Basic Skills and Reuse

Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems. For example, the basic skills "dig hole," "put tree," "backfill" and "watering" compose a complex skill "plant a tree". Besides, some basic skills can be reused for solving other problems. For example, the basic skill "dig hole" not only can be used for planting a tree, but also can be used for mining treasures, building a drain, or landfilling. The ability to learn basic skills and reuse them for various tasks is very important for humans because it helps to avoid learning too many skills for solving each individual task, and makes it possible to solve a compositional number of tasks by learning just a few number of basic skills, which saves a considerable amount of memory and computation in the human brain. We believe that machine intelligence should also capture the ability of learning basic skills and reusing them by composing into complex skills. In computer science language, each basic skill is a "module", which is a reusable network of a concrete meaning and performs a specific basic operation. The modules are assembled into a bigger "model" for doing a more complex task. The assembling procedure is adaptive to the input or task, i.e., for a given task, the modules should be assembled into the best model for solving the task. As a result, different inputs or tasks could have different assembled models, which enables Auto-Assembling AI (AAAI). In this work, we propose Modularized Adaptive Neural Architecture Search (MANAS) to demonstrate the above idea. Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures. Further experiments and empirical analysis provide insights to the effectiveness of MANAS.
Graph Neural Network-based Node Classification with Hard Sample Strategy

Existing graph neural networks (GNNs) usually use a balanced class distribution to learn node embeddings over graph data. When dealing with an imbalanced class distribution, they tend to bias to nodes in majority classes, while nodes from minority classes are under-represented. To meet this challenge, this paper introduces an effective GNN-based node classification model with Hard Sample Strategy (GNN-HSS) to handle class-imbalanced graph data. The proposed GNN-HSS model first uses a two-layer graph convolutional network (GCN) to get node embeddings, and then performs a clustering analysis procedure to make the node embeddings more representative and easier to classify. In particular, a hard sample strategy is given to ensure that the embeddings of hard nodes are correctly represented. The experiments show that GNN-HSS outperforms state-of-the-art methods in node classification tasks.
CONFIGURING REVIT – VISUAL STUDIO INTEROPERABILITY

The purpose of this article is to develop a methodology for setting up the interaction of Revit and Visual Studio programs, which provides a quick process for creating, debugging, testing and supporting program modules. The research hypothesis suggests that there is a method for efficient and optimized interaction between Visual Studio and Revit. As a result of the study, the authors described additional settings that will allow Visual Studio and Revit to interact more productively, which will speed up the process of creating additional software modules.
Teaching professional competence – an indication of performance and efficiency in the process of professionalizing teaching career

For any field of activity, the notion of competence is the criterion that guarantees performance and efficiency. This article proposes a structured analysis of the concept of competence and professional competence of teacher, being identified some classifications and types of professional competence necessary in the process of professionalization of the teaching career.
Design of Innovation and Entrepreneurship Achievement Transformation Platform based on PHP and Data Center Model

Design of the innovation and entrepreneurship achievement transformation platform based on PHP and data center model is studied in the paper. It is mainly oriented to Internet. JSP is based on Java Servlet, but the fast running speed of Java is realized by using class resident memory, so it consumes a lot of memory. JSP also requires additional disk space to store a series of. java files and. class file. Considering these basic conditions, this paper then use the data center with SSM model to achieve the efficient searching. In order to improve the security of network programs, in the development process of the actual website, it is necessary to consider encrypting the relevant data such as the password of the user registration information and with these actions, the robustness of the system is then improved. Through the analysis on the efficiency, the different models are tested, and the performance is validated.
An Empirical Study on Rule Violation History of JavaScript Code Blocks on Stack Overflow

JavaScript code blocks on Stack Overflow (SO) are often used in software projects. However, little is known about the issue of rule violation risk in SO JavaScript code blocks. Rule violation is one of the factors which degrades the quality of Java Script code. To prevent prevalence of rule violation by reusing SO JavaScript code blocks, it is needed to investigate how secure SO JavaScript code blocks are against rule violation. To examine the issue, we performed a quantitative analysis to investigate how many rule violations are, when first rule violation occurs and what is the trend of rule violations in evolution history of Stack Overflow JavaScript code blocks. We collected SO posts related to JavaScript and extracted the code blocks contained in the posts. By using ESLint, the most popular rule violation detection tool, we identified rule violations in the evolution history of our target code blocks. We then performed quantitative analyses on the identified rule violations. As the results of the analyses, we found that: (1) 60% of the studied code blocks evolve with any rule violations. (2) In the rule violated code blocks, 92% of the code blocks get first rule violation occurrence in the early phase of their evolution. (3) 80% of the rule violated code blocks never fix existing rule violations during their evolution. Our findings suggest that SO should provide a policy which can reduce rule violations in submitted JavaScript code blocks. The findings can also make SO users attend to rule violations when reusing SO JavaScript code blocks.
Physics-Informed Machine Learning

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Towards generation of indistinguishable coherent states

We have addressed the characteristic distinguishability of coherent states in the temporal domain from a directly modulated quantum well-based gain-switched laser diode. Using small-signal and large-signal models, we identify tunable parameters to generate indistinguishable coherent states from an electrically pumped semiconductor laser. The experiment confirms the generation of indistinguishable signal and decoy coherent states as predicted by the numerical simulation.
Advancements in Quantum-PSO and Its Application in Sustainable Development

Optimization is an inevitable part of sustainable development. Optimization techniques are employed to improve sustainability and achieve the goals of sustainable development. Quantum inspired Particle Swarm Optimization (QPSO) is a probabilistic optimization algorithm based on the quantum mechanics and trajectory analysis of PSO. Quantum mechanics provides a system of uncertainty letting the particle appear anywhere in the feasible space with certain probability. Further, QPSO has only one parameter that controls creativity and imagination of particles during evolution making it easy to control. Because of ease to control and global searching ability, QPSO has gained popularity among researchers who have contributed a lot in finding its application in sustainable development in varied domains as well as suggesting strategies for the improvements in its performance. This paper provides an integrated and synthesized overview of the advancements in QPSO algorithm since its advent. A state-of-the-art snapshot of different improvement strategies categorized according to the adapted method and a review of the QPSO algorithms used in sustainable development related to social, economical, environmental, and energy resources sustainability has been presented. Aim is to provide the researchers a platform for development of efficient QPSO based algorithms suitable for different real-life optimization problems.
Enhancing Speaker Diarization for Audio-Only Systems Using Deep Learning

The Speaker Diarization domain has grown rapidly in the last few years. It has benefited many businesses may it be Broadcasting, Legal, Forensics, Marketing, Healthcare, or Education. After integrating with deep learning and neural networks diarization systems enhanced its different modules of each stage. From the perspective of sustainable development, most of the solutions given for the issues faced in daily lives use artificial intelligence in some or another way. To cater needs of future generations effectively intelligent solutions should be generated without harnessing the resources completely. The major challenge faced by these intelligent systems is reachability. To overcome this issue speech processing is one of the effective ways out. Speech command is the easiest way to communicate in this real-world environment. But in a multi-speaker environment, it becomes difficult at times to recognize who is speaking what? So, the background noise while having a conversation creates a lot of disturbances that need to be looked after. It makes conversation difficult to understand or decode. The proposed system suggests a solution that will overcome this challenge effectively on a VoxConverse diarization corpus. The diarization error rate (DER) improved significantly after the noise removal.
What Machine Learning Can Learn From Software Modularity

In the last couple of years we have witnessed an enormous increase of machine learning (ML) applications. More and more program functions are no longer written in code, but learnt from a huge amount of data samples using an ML algorithm. However, what is often overlooked is the complexity of managing the resulting ML models as well as bringing these into a real production system. In software engineering, we have spent decades on developing tools and methodologies to create, manage and assemble complex software modules. We present an overview of current techniques to manage complex software, and how this applies to ML models.