Reviewerly/exHarmony huggingface dataset:
Conceptual Model for Component Selection: A Research Review on Existing Techniques

Nowadays, software developers pay more attention to component-related innovations with the implementation of software reuse; most of which have been used in the development of large-scale complex applications to improve software development efficiency and speed up time to market. The development of component-based software is a well-known technique that establishes the reusability of software and cost-effectively reduces development. The major issue, however, is determining how to select a component. The principle rationale of this paper is to give a reference highlight future exploration by ordering and arranging distinctive categories of component selection methods and accentuating their individual qualities and shortcomings. To achieve this, the rationale for employing these techniques, as well as the use of a hybrid mechanism in the component selection method, is demonstrated. Finally, a conceptual approach for component selection is proposed which utilizes existing methodologies. Hopefully, it can help researches to locate the current status of this issue and fill in as a reason for future exercises.
Lattice Pattern Analysis

Beyond morphology, studies on the spatial positioning and arrangements of smaller scale elements within bulk materials are of great interest to material scientists, because analysis of such arrangements could yield insights concerning the functionalities of materials. Towards that end, Chap. 5 presents the location and dispersion analysis of nanomaterials, whereas this chapter is dedicated to the lattice pattern analysis. A lattice, a term in geometry, describes here for material science purpose the symmetric arrangement of atoms in crystals. Naturally, lattice pattern analysis refers to the study of symmetric arrangement of atoms and their deviation from the symmetry. Lattice pattern analysis plays an important role in material property characterization. In fact, the field of crystallographic research at atomic length scales is meant to locate individual atoms, and identify symmetries, dislocations, and defects in atom’s locations. Quantification of these spatial features allows material scientists to map material performance as a function of the features. In this chapter, we introduce the relevant state-of-the-art approaches regarding the lattice pattern analysis.
Facial Expression Recognition Through CNN

Humans have many types of Facial expressions but there are 7 basic facial expressions (happy, sad, anger, surprise, neutral, disgust, and fear). This paper utilizes the algorithms that detect, fetch, and evaluate those facial expressions from the images and videos and detect that facial expression. This paper is based on image processing and machine learning. In the application image taken from a webcam/camera and compared that image with the trained dataset, which is stored in the application and then the application will display the emotional state of the image. The proposed application is focused on some points of human's face to identify the expression, that are distance between the eyes, width of the nose, depth of the eye socket, eyebrow, jaw line, chin, lips etc. with the help of these points application can identify the expression by the use of Convolution Neural Network (CNN).
Semantic Understanding of Source and Binary Code based on Natural Language Processing

With the development of open source projects, a large number of open source codes will be reused in binary software, and bugs in source codes will also be introduced into binary codes. In order to detect the reused open source codes in binary codes, it is sometimes necessary to compare and analyze the similarity between source codes and binary codes. One of the main challenge is that the compilation process can generate different binary code representations for the same source code, such as different compiler versions, compilation optimization options and target architectures, which greatly increases the difficulty of semantic similarity detection between source code and binary code. In order to solve the influence of the compilation process on the comparison of semantic similarity of codes, this paper transforms the source code and binary code into LLVM intermediate representation (LLVM IR), which is a universal intermediate representation independent of source code and binary code. We carry out semantic feature extraction and embedding training on LLVM IR based on natural language processing model. Experimental results show that LLVM IR eliminates the influence of compilation on the syntax differences between source code and binary code, and the semantic features of code are well represented and preserved.
Teaching sine wave concept through simulation for elementary school students

Abstract In the Natural Science subject for elementary school, one learns about the waves topic which is derived material from sound energy. Students have been taught that waves have periods and frequencies, including the theoretical application of its application in real life. However, the teacher requires a high effort to explain it because it is quite complicated with a short teaching duration. With the help of information computer technology such as an EDA-based software tool, elementary school students can more quickly understand the sine wave material. This paper reveals on how to teach a sine wave material for children with the following topics, i.e., 1) fundamental concept; 2) the introduction of the instrumental tool; 3) introduction of the used Simulator, periods and frequencies measurement using Oscilloscope instruments; 4) Measurement example; and 5) Practicum. These topics are packaged in a new course outside formal schools. The Circuit Wizard simulator was chosen as a teaching tool; we hope to make abstract contents (ex. sine waves) more exact for children and serve many benefits to improve the teaching quality. This paper focuses on the output frequency measurement using Frequency Generator (FG) &amp; analog Oscilloscope pair under the Circuit Wizard simulation. This course can be taken by 4 th – 6 th grade (10 – 12 years old children).
Novel variational technique for mixed Poisson-Gaussian noise filtering

A variational framework for mixed noise removal is proposed in this article. The considered variational model is based on an energy functional minimization that leads to a nonlinear second-order anisotropic diffusion-based model. This PDE (partial differential equation) model is then solved numerically by applying an approximation algorithm that is constructed using the finite difference-based method. The obtained explicit numerical approximation scheme is able to restore successfully the images corrupted by both Gaussian and Poisson noise, its effectiveness being illustrated by the described mixed denoising experiments.
Vision of the Teacher Education in NEP 2020: Problems and Prospects

The present paper focuses on the very fundamental changes set in the NEP 2020 document in teacher education. The paper discusses some of the issues in implementing these changes in the teacher education field in the country. It also offers possible solutions to meet these issues.
DNA-based authentication to access internet of things-based healthcare data

Data security and privacy are always considered as critical aspects, especially in healthcare. The advent of technologies such as the Internet of Things (IoT) has encouraged a great deal of attention in this digital era and helped to improve e-health services. IoT-based services in healthcare and their applications have led to potential growth of quality services in healthcare. However, the sensitive nature of healthcare data and IoT devices which store and collect real-time data makes it even more vulnerable to various attacks. With the development of digitalized data and IoT-based e-health systems, authentication mechanisms are essential to ensure both usability and security. Considering these aspects, a novel, secure user authentication scheme is proposed that uses user ID, unique ID (AADHAAR), password, DNA steganography, and hash function. An One Time Password method is also proposed to strengthen the device authentication. The Scyther tool is used for security analysis and to validate the claims.
MODY-Net: Moving Object Detection Using Multiscale Output Ensemble Y-Network

Detecting the moving objects in a video is a challenging task. Most of the currently existing methods work on a pixel level. Working on a pixel level is usually inaccurate because the pixel intensity level may change due to illumination, dynamic background, camera movement, and many other challenges. This is why, in this article, a deep network is proposed, which learns to extract features that are invariant to these challenges and then detect moving objects based on the extracted features instead of pixel intensities. The proposed network MOD-YNet consists of two main parts: an encoder and a decoder. The encoder learns to extract suitable features from both the background frame and the current frame. Then, the decoder uses the extracted features from both frames and learns to extract the objects that exist in the current frame and does not exist in the background frame. MODY-Net is evaluated on a benchmark data set called ChangeDetectionNet, using the evaluation metrics precision, recall, F1-measure, false-positive rate, and false-negative rate. The proposed model almost outperforms the state-of-the-art methods in moving object detection in all evaluation metrics by a huge gap.
Contour Mask and Matting Driven Face Image Generation

This chapter proposes a face generation model based on contour mask and matting, which can well control the distribution quality of generated images. In the generated face model, a mask labeled with a face contour is input to ensure that the generated face conforms to the distribution of the corresponding face contour, and the distribution quality of the generated image is improved. Face generation based on contour mask mainly considers the generation of contour information provided by the mask and the generation of style information composed of other parts of the face. In this chapter, masks are considered as two types of semantic labels with face and background images. This guarantees the accuracy of semantic contour features, and then performs detailed style processing on faces to generate high-quality pictures. Therefore, the generator has both semantic generation and meticulous style information. Finally, inspired by image matting, this chapter improves the face generation model based on contour mask, adds matting information to the input and constraints, and improves the generation effect of the edge part of the face. By comparing with existing unconditional face generation models and semantic label-guided face generation models on the same dataset, it is found that our model proposed in this chapter performs better.
Many facets of interferometry: a deceptively simple and powerful measurement technique

Interferometry is a deceptively simple and yet powerful optical concept, just as Dean Wyant is an unpretentious man who achieved successes by wisely practicing his art. We take two coherent waves and generate an interferometric pattern that gives us an intensity distribution with varying spatial frequencies. We apply several modalities in our laboratory in the CIO: vectorial shearing interferometry to probe a surface in an arbitrary direction, rotational shearing interferometry to detect a faint off-axis source in the presence of a strong, rotationally symmetric one, and interferometry with ballistic photons to diagnose abnormal tissue.
Cross-Domain Few-Shot Classification through Diversified Feature Transformation Layers

The purpose of the few-shot classification is to classify new categories, and each category contains few labeled samples. The currently popular cross-domain few-shot classification uses a feature transformation layer to transform features to achieve the feature enhancement, so as to simulate various feature distributions in different domains during the training process. However, due to the large differences in the distribution of cross-domain features, a single feature transformation layer cannot perform multiple feature transformations. To obtain the change of the feature distribution in different domains, a diversified feature transformation is proposed based on the original feature transformation layer to solve the metric-based cross-domain few-shot classification problem Simulation results are obtained based on these five datasets commonly used in few-shot classification: mini-ImageNet, CUB, Cars, Places and Plantae. The simulation results show that the proposed diversified feature transformation layer can achieve good results in the metric-based model.
Learning quantized neural nets by coarse gradient method for nonlinear classification

Abstract Quantized or low-bit neural networks are attractive due to their inference efficiency. However, training deep neural networks with quantized activations involves minimizing a discontinuous and piecewise constant loss function. Such a loss function has zero gradient almost everywhere (a.e.), which makes the conventional gradient-based algorithms inapplicable. To this end, we study a novel class of biased first-order oracle, termed coarse gradient, for overcoming the vanished gradient issue. A coarse gradient is generated by replacing the a.e. zero derivative of quantized (i.e., staircase) ReLU activation composited in the chain rule with some heuristic proxy derivative called straight-through estimator (STE). Although having been widely used in training quantized networks empirically, fundamental questions like when and why the ad hoc STE trick works, still lack theoretical understanding. In this paper, we propose a class of STEs with certain monotonicity and consider their applications to the training of a two-linear-layer network with quantized activation functions for nonlinear multi-category classification. We establish performance guarantees for the proposed STEs by showing that the corresponding coarse gradient methods converge to the global minimum, which leads to a perfect classification. Lastly, we present experimental results on synthetic data as well as MNIST dataset to verify our theoretical findings and demonstrate the effectiveness of our proposed STEs.
The Research on Multi-level Performance Optimization of Course Selection System Based on Full Credit System

The full credit system is a trend in the teaching reform of Chinese universities in recent years. The comprehensive elective system is the core of the full credit system. For comprehensive universities, with the continuous expansion of enrollment scale, the number of majors, classes, and students has increased dramatically, and the amount of information about students' course selection has also doubled, making it very difficult to implement a comprehensive course selection system. In view of the characteristics of the comprehensive course selection system, this article starts with the basic needs of the course selection business, deeply analyzes the complexity of the course selection system, and adjusts the course selection strategy. At the same time, in view of the performance problems of the course selection system, a multi-level architecture optimization scheme was proposed, using data caching, data level segmentation, cloud platform and other technologies, which greatly reduced the concentration of data processing during the course selection. Practice shows that the performance of the optimized course selection system has been greatly improved, which can better meet the course selection requirements of comprehensive universities.
The application of modular architecture on apartment buildings

Abstract Housing crisis become one of the world problems, where many people cannot afford a decent shelter. This problem is mostly experienced by young people, where young people have a middle-level income, so they feel it difficult to get suitable housing for them and their needs. Research using descriptive methods with literature study approach. The study is aimed to provide affordable housing for young people with developing financial condition. The aspect to be considered is about how the application and method of modular architecture can provide affordable housing for young people in the form of vertical housing or apartment. This study will discuss about a comparison between modular and conventional construction and the impact of applying modular architecture to apartments to produce affordable apartment units. The results of this study discuss about how the influence of modular architecture to provide an affordable apartment.
Subjective and objective study of sharpness enhanced UGC video quality

With the popularity of video sharing applications and video conferencing systems, there has been a growth of interest to measure and enhance the quality of videos captured and transmitted by those applications. While assessing the quality of UGC videos itself is still an open question, it is even challenging to enhance the perceptual quality of UGC videos with unknown characteristics. In this work, we study the potential to enhance the quality of UGC videos by increasing the sharpen effects. To this end, we construct a subjective dataset by conducting a massive online crowdsourcing. The dataset consists of 1200 sharpness enhanced UGC videos processed from 200 UGC source videos. During subjective test, each processed video is compared with its source to capture finegrained quality difference. We propose a statistical model to precisely measure whether the quality is enhanced or degraded. Moreover, we benchmark state-of-the-art No-Reference image or video quality metrics with the collected subjective data. It is observed that most metrics do not correlate well with subjective score. This indicates the need to develop more reliable objective metrics for UGC videos.
Dynamic analysis of customer needs using opinion mining and fuzzy time series approaches

In the traditional research of customer survey, the basic research methods are interview or questionnaire. For the relevant data obtained, researchers can use a variety of analysis methods for comparative analysis, so as to get important research conclusions. At this stage, with the improvement of online platform and technology level, there are many online reviews of products. The main reason for the popularity of online reviews is that they can contain a lot of customer expectation information and customer opinion requirements. If we summarize, sort out, compare and analyze these comments, we can get important analysis results, which can fully show the needs of customers and play a very important reference role in decision-making. However, a lot of research on this aspect, for the prediction of customer demand is still not deep enough, there is a lot of room for improvement. Based on this development status, this paper develops and designs an online review content mining system based on opinion mining and fuzzy time series method. Opinion mining can effectively mine the important customer needs hidden behind the online review content, as well as the different weights of various customer needs. Fuzzy time series method can predict the future importance weights based on the mined information. At the same time, a case study is taken to compare the effectiveness and feasibility of this method with exponential smoothing method, simple moving average method and fuzzy moving average method in the online review analysis of electric iron. It is found that the method studied and designed in this paper has higher accuracy and reliability.
Fast parameter free Smart particle swarm optimization (SPSO)

The qualitative value of an optimization technique depends upon the way it explores and exploits the search region. The proposed Smart PSO (SPSO) is a new version of PSO that uses the swarm particles smart behavior in the search space to solve global optimization problems. Unlike the traditional PSO algorithm, SPSO does not depends upon the initial velocities of the particles during successive iteration. It converges the particle towards global optima using cognitive and social knowledge only. This personal environment and social information gives the algorithm a fast capability of exploitation and exploration This fact has been supported by empirical simulation of seven standard benchmark functions and the comparative results clearly evident that SPSO is better than DIW-PSO, Rand-PSO and QPSO.
Adversarially Smoothed Feature Alignment for Visual Domain Adaptation

Recent approaches to unsupervised domain adaptation focus on transferring knowledge from source (labeled) data to target (unlabeled) data. Both data types share the same class space but originate from different domains. One way to achieve this transfer is tasking two classifiers to detect target features that diverge from source features. Meanwhile, a feature generator is adversarially refined to match the detected features with the source ones. However, the aligned features are still subject to ambiguity. Samples that are not smoothly distributed on the latent manifold are often missed in training. Moreover, target data may not be sufficient for adversarial learning. To overcome these problems, our proposed Adversarially Smoothed Feature Alignment (AdvSFA) model is designed to identify ambiguous target inputs by maximizing classifiers discrepancy in an extended class space. This enables the generator to receive valuable feedback from the classifiers and consequently learn more discriminative and smooth representation. Imposing smoothness on the latent manifold is a desirable property to improve model generalization and avoid having neighboring samples of different classes. To further promote such property, we not only task the generator to conduct feature alignment on target examples and but also in-between them. By adopting these constraints, our method shows a remarkable improvement across different adaptation tasks using two benchmark datasets.
Enhancing Data Applied in the Research of Partial Discharge Systematic Diagnosis

Abstract The existing problems in the current partial discharge PRPS sample enhancement method have been summarized in this artcle, and a GIS UHF partial discharge data enhancement method has been proposed which is based on noise coupling, Gaussian blur and so on. It solves the problem of unable to collect a large number of defect maps and form a sample set due to the lack of GIS partial discharge cases, which limits the application of intelligent algorithms such as deep neural networks. This partial discharge PRPS sample enhancement method has been proved through application that it can effectively enhance the partial discharge sample set, make the samples evenly distributed, and meet the training needs of intelligent algorithms such as deep neural networks. The intelligent diagnosis algorithm trained using the enhanced sample set has a high diagnostic accuracy rate of 95%, which has practical application value.
Deep Learning based Object Detection using Mask RCNN

Object detection aims to recognize all instances of a known class of objects in an image, such as people, vehicles, or faces. In recent times deep learning techniques was applied for detecting objects and those existing methods have some problems with viewpoint changes and occlusion. This paper proposes a solution for automatic object detection by implementing instance segmentation at a pixel level, and several RCNN techniques were also inferred in this paper. The proposed Mask RCNN detects the images and marks them with bounding boxes, class labels as well as masks. The implemented mask RCNN model was trained and tested with Plasmodium vivax dataset so that the model is capable to detect cells in a congested image. The model was also tested in a custom dataset to check the model performance, and the model obtained 94% mAP for the custom dataset. This paper also mentions the future scope of this work so that the robustness and the reliability of the model can be improved further.