Reviewerly/exHarmony huggingface dataset:
Literasi Mitigasi Bencana Covid-19

Corona Virus (Covid-19) is a new type of virus that is transmitted from human to human through the mouth, nose and eyes. This virus attacks the respiratory system which can lead to death. This virus makes the world restless, because it is a new type of virus, so many parties do not know and do not understand how to deal with it. One thing that can be done to reduce the risk of being exposed to Covid-19 is to be disciplined in implementing health protocols. Health protocols are only limited to efforts to protect themselves from patient droplets, but do not provide full protection. SMAN 2 Mataram is one of the schools with 1685 students and 36 study groups. The large number of students and groups makes SMAN 2 Mataram vulnerable to Covid-19 transmission. There is no other choice, except to prepare students as early as possible to have awareness in mitigating the Covid-19 disaster at school. One of the efforts made is literacy education for the Covid-19 disaster mitigation. Implementation of Covid-19 disaster mitigation literacy education through Covid-19 disaster mitigation learning with the Covid-19 disaster mitigation literacy learning model. The research was conducted in class X MIPA1 SMAN 2 Mataram which resulted in students' awareness of the Covid-19 disaster mitigation in the form of inadequate knowledge and application of health protocols, even though they received sufficient information and knowledge about Covid-19. The role of the school in the form of a task force must be optimized to control students in implementing health protocols so that students feel safe when doing activities at school.
Smart Management: Traditional and Distance Learning in Modern Higher School

Modern distance learning technologies open up real prospects for improving the quality of knowledge and efficiency of the educational process, for solving various social problems related to the functioning of the institute of education. In recent years, higher educational institutions began to actively develop their own training courses using online learning technologies with the traditional form. However, the question arises on which areas of training a mix of these two forms of education, or exclusively e-learning can be applied? If we take, for example, some humanitarian areas, such as musical and instrumental art, the art of singing. It is absolutely impossible only to train online doctors of various profiles, as well as specialists in the explosive business. Or if we take a number of natural sciences, such as physics, especially oil and gas business and a number of other technical areas. If we summarize all of the above-mentioned, then it is necessary to determine how and in what situations new forms of training should be introduced. And also where to leave the traditional approach. That is, it is necessary to find the medium that allows to combine these two forms of training in the best way.
Research on Logo Design of New Energy Vehicle Friend Club Based on Computer 3D Image Processing Technology in Digital Era

With the progress and development of society, new energy vehicles have gradually entered the public’s field of vision. However, the rapid development of automobile industry can not be separated from its engineering and design factors, because engineering and design factors affect the quality of automobile use, while design factors affect the user experience. In this paper, the Vehicle fan friend club logo design of new energy vehicles based on computer three-dimensional image processing technology in the digital age is studied, aiming at rationally applying computer graphics and image processing technology in different fields and giving full play to its maximum application value. In this paper, on the basis of color pre-correction of digital image based on logo color blocks, according to the characteristics of image color gamut, iterative self-organizing clustering method is adopted, and the color blocks of training samples are selected adaptively in combination with Chinese color system, so that the training samples are more targeted. The RANSAC(RANdom SAmple Consensus) algorithm is applied to image feature matching, which is used to quickly obtain accurate image matching points and solve the basic matrix, so that it can be more effectively applied to the three-dimensional reconstruction of images.
An analysis of property inference methods

Abstract Property inference involves predicting properties for a word from its distributional representation. We focus on human-generated resources that link words to their properties and on the task of predicting these properties for unseen words. We introduce the use of label propagation, a semi-supervised machine learning approach, for this task and, in the first systematic study of models for this task, find that label propagation achieves state-of-the-art results. For more variety in the kinds of properties tested, we introduce two new property datasets.
Effective object detection using Tensorflow facilitated YOLOv3 model

Object detection forms an important area of research where the efforts are still being put forth to improve the accuracy of detection. Several approaches have been made which also include R-CNN and DNN. Whereas they have rendered the detection of object to be more cumbersome as each component has to be trained separately. This also poses a challenge while optimizing and hence takes more time for single detection. Whereas, in the present study, an attempt is made to effectively locate and detect the objects as a single regression problem, thereby reducing the time for image detection. The technique incorporated is YOLOv3 with significant assistance from tensorflow. An in – depth understanding is achieved and the performance of the model is assessed. Python is used for processing the images and to prepare the YOLOv3 model. The training of the model is facilitated by Pascal Visual Object Classes(VOC) dataset, which comprises of nearly 11000 images for testing, 5717 images for training and 5823 images for validation, which are found to be quite sufficient to attain good detection accuracy. The training is carried out initially for a learning rate of 1e – 4 with first 20 epochs, followed by 30 epochs for a learning rate of 1e – 6. The model is trained with the help of available Common Objects in Context (COCO) weights. The precision of each object detected is evaluated and the mean Average Precision (mAP) is found to be around 79%.
An absolute Optical Character Recognition system for Bangla script Utilizing a captured image

Character recognition from a captured image is a significant field of research because there are 230 million native speakers in Bangladesh and India. In addition, there are many signboards, billboards, and many other image sources that contain Bangla Script. Since mid-1980, researchers started to recognize Bangla characters from scanned images. However, they already tried different kinds of methods to identify characters and examine the performance of recognition. This paper focuses on developing an eclectic OCR system that can recognize and extract Bangla text. This recognition process predestines captured images by digital camera or scanner containing Bangla scripts. Preprocessing steps include binarization, segmentation, noise cleaning, scaling characters by font size, skew detection, and correction. Freeman chain code represents a character from the image after feature extraction from a scaled character. A multilayer feedforward neural network-based recognition scheme is constructed to recognize and classify the unknown character and samples. We concluded that the success rate is approximately 99% in identifying characters and demonstrating the Unicode text from experimental results.
MR brain tissue classification based on the spatial information enhanced Gaussian mixture model

Classifying T1-weighted Magnetic Resonance brain scans into cerebrospinal fluid, gray matter and white matter is one of the most critical tasks in neurodegenerative disease analysis. Since manual delineation is a labor-intensive and time-consuming process, automated methods have been widely adopted for this purpose. One group of commonly used method by biomedical researchers are based on Gaussian mixture model. The main drawbacks of this model include complex computational cost and parameter selection with the presence of imaging defects such as intensity inhomogeneity and noise.To alleviate these aspects, an improved Gaussian mixture model-based method is proposed in this work.Standard mixture model was used to formulate individual voxel intensity. A set of spatial weightings were created to represent local tissue characteristics. The emphasis of this method is its "lite" and robust implementation mode highlighted by a dedicated entropy term. The Expectation-Maximization algorithm was then iteratively executed to estimate model parameters. The Maximum a Posteriori criterion was employed to determine for each voxel if it belongs to a certain tissue.The proposed method was validated on both simulated and real MR scans. The averaged Dice coefficient of segmented brain tissues on each dataset ranged between [66.41, 87.42] for cerebrospinal fluid, [80.57, 85.35] for gray matter, and [83.17, 85.63] for white matter.Experiments illustrated the effectiveness and reliability in tissue classification against imaging defects compared with manually constructed reference standard.
Expressing the Day of the Month in Biblical Hebrew: A Diachronic Perspective

Biblical Hebrew (henceforth BH), like most languages, possesses two different kinds of numerals, cardinals, e.g., sheloshah (three) and ordinals, e.g., shelishi (third). While BH can generate a cardinal of any size, the ordinal paradigm in BH only goes up to ten, and there is no morphological or syntactic mechanism for combining ordinals to express higher numbers. BH compensates for its lack of higher ordinals by employing ordinal numerals in dedicated syntactic constructions. The present study is a diachronic corpus-based analysis of the syntax of one kind of cardinal phrase with ordinal meaning, the day-of-the-month expression. This study takes the syntactic and semantic accounts in Rothstein and Moshavi (2021) as its points of departure, and includes a construction not discussed in that publication. The study examines the synchronic and diachronic distributions of four constructions that account for nearly all occurrences of cardinal constructions with ordinal meaning: the appositional phrase (AP), the reduplicative construct phrase (RCP), the construct phrase (CP), and the bare numeral phrase (BN). The data presented reveals noteworthy developments in day-of-the-month expressions in Late Biblical Hebrew (LBH) that are also reflected in the extra-biblical Hebrew corpora of the Second Temple period. These results establish the syntactic expression of ordinality as a topic of significance for the broader endeavour of characterising the linguistic features of exilic and Second Temple Hebrew.
An Intelligent Writing Training System of Current Situation Appraisal Composition for Higher Vocational Language Teaching

Due to the lack of subject model selection in traditional intelligent writing training systems, the functions of vocabulary and sentence recommendation in current writing training are poor. Therefore, this paper designs an intelligent writing training system of current situation review composition for higher vocational language teaching. In the hardware design system, the overall structure optimizes the data memory. In the software, the subject model is established, the database structure is redesigned, and the intelligent writing training system is designed. After testing, the performance of the system is better than that of the traditional system in the recommendation of words and sentences.
Applying Multidimensional Framework in Agile Software Development (MDFA)

Software development is a complex and expensive job. As software systems become more complex day by day because of several aspects e.g. innovation, business Automation, data privacy, and security issues. To develop a quality product and construct software that satisfies customer needs, we are required to develop a system that fulfills the needs of users. For developing a perfect system we also need a perfect framework or architecture. The proposed framework is just not a framework, but it provides us a complete pattern and flows to develop a software system. It includes initial steps from requirements gathering till its final steps like implementation and testing. This research is based on such a multidimensional framework for software development which covers six dimensions that are used implicitly or explicitly. There are four viewpoints that represent different stakeholders. These dimensions and viewpoints are integrated with the scrum process to increase the quality of software systems. The suggested framework is evaluated by a case study.
Quantum Circuit for Regression Learning with Backpropagation

This work reports the experiment on building quantum circuit for regression learning using qiskit library. The circuit is based on Watabe&#x0027;s work with some modification based on our interpretation. The parameters and results are stored and used in the quantum circuits. We performed the experiments on fitting the linear function and quadratic function. From the experiment, learning the linear function is more accurate than quadratic function within 120 times of training. The initial parameters effect on the number of rounds of training and the accuracy of the result are investigated.
Classification of Sentiments of the Roman Urdu Reviews of Daraz Products using Natural Language Processing Approach

Sentiment Analysis plays a significant part in determining how the audience feels about a specific product or topic. It is an essential fragment in the machine learning and natural language processing domain. In this paper, sentiment analysis of the reviews was performed. The dataset was obtained from Kaggle containing the reviews of different products in Roman Urdu text posted on Daraz E-Commerce site. The data is collected and then processed. The finalized dataset is then analyzed for the classification of the sentiments using different machine learning classifiers in Python. The results and accuracies reported by the algorithms are also presented and discussed in detail in the paper. Logistic Regression outperforms with an accuracy of 75%
Detection of Single and Multi-character Tulu Text Blocks

Segmentation of line into words, characters is another important exercise of OCR pipeline. In south Indian handwritten script named Tulu, it is an imperative research problem as it involves complex structural characteristics. The degree of processing aggravates even more in the presence of touching/overlapping character blocks along with usual operational intricacies. In the perspective of optimizing the touching/overlapping character segmentation of Tulu script, an intermediate processing step to simplify the segmentation process has been introduced in this work based on text block characteristics.
Voxel-based Deep Learning for Image Super-resolution of Areal Density Maps of Carbon-nanotube Sheets

Miralon sheets are an advanced carbon-based product that provides outstanding physical properties which enable strong, lightweight, and environmentally resistant solutions for aerospace and industrial applications. For quality control, beta transmission equipment is used to generate areal density maps to ensure the uniformity of the Miralon sheets. However, the original areal density maps obtained from the beta transmission equipment is insufficient to identify variation and defects at the required level. As areal density maps can be interpreted as images, current work in image super-resolution deep convolutional neural network (CNN) promises to reconstruct high-resolution areal density maps with fine texture details. However, for this promise to be realized two challenges need to be overcome. First, generating areal density maps is expensive, and only limited training data is available. Second, current 2D super-resolution neural networks cannot provide sufficient accuracy in the vertical axis (thickness) direction. Therefore, we propose a 3-layer voxel representation to adapt current deep CNNs to the particular application of Miralon sheets. In particular, our results demonstrate that appropriate data augmentation is essential to achieving accurate results. Using our proposed techniques, a high-resolution areal density map, which is rich in 3D information, can be obtained by leveraging 2D image super-resolution CNNs.
Experiments on Quantum Circuits for Compact Genetic Algorithm

Programming a quantum computer is still a challenge for researchers. Quantum computers operate based on a fundamentally different paradigm from classical computers. Programming a quantum computer today is somewhat like programming a classical computer in the 1950s, in a manner close to assembly language. This paper presents a Grover-assisted compact genetic algorithm to solve some real-world problems on a quantum computer. This method is a combination of Grover's search algorithm initializing with the probability distribution, and a compact genetic algorithm with elite (cGA*), which keeps the current best individual. The compact genetic algorithm (cGA) is a powerful and popular method in evolutionary computation. The results of the proposed algorithm are compared with the cGA running on classical computers in terms of the solution quality and the number of function evaluations. The compared results illustrate that the proposed methodology can successfully reach 100% accuracy of the solution with a higher number of function evaluations than the cGA.
Automatic Generation of Diagnostic Content Feedback in Spoken Language Learning and Assessment

This study aims to explore an effective way to automati-cally generate actionable diagnostic feedback targeting the content development skill within the context of spoken language learning and assessment. There is one type of test question widely used in speaking assessment, which requires test takers to first listen to and/or read stimulus material and then create a spontaneous response to a question related to the stimulus. In a high-proficiency response, critical content from the stimulus - referred to as “key points” - should be properly covered. We propose Transformer-based models to automatically detect the location of key point spans within a response, or if no key points are covered, to detect their absence. Additionally, we introduce a multi-task learning approach for assigning a “quality score” to each key point span, measuring how well the key point is rendered within the response. Experimental results demonstrate that the pro-posed models can surpass human expert performance based on human transcriptions. In contrast, based on automatic speech recognition hypotheses, models underperform human expert performance with an F1-score at 66.6% (vs. human agreement of 68.8%) on span detection and a Pearson corre-lation coefficient at 0.579 (vs. human agreement of 0.610) on quality score prediction.
AI education matters

We introduce a Model AI Assignment (Neller et al., 2021) where students combine various techniques from a deep learning course to build a denoising autoencoder (Shen, Mueller, Barzilay, &amp; Jaakkola, 2020) for news headlines. Students then use this denoising autoencoder to query similar headlines, and interpolate between headlines. Building this denoising autoencoder requires students to apply many course concepts, including data augmentation, word and sentence embeddings, autoencoders, recurrent neural networks, sequence-to-sequence networks, and temperature. As such, this assignment can be ideal as a final assessment that synthesizes many topics. This assignment is written in PyTorch, uses the torchtext package, and is intended to be completed on the Google Colab platform.
Adversarial Examples of SAR Images for Deep Learning based Automatic Target Recognition

With the development of deep neural network techniques, an explosion of neural network structures are designed and gained great success in the field of image-based SAR automatic target recognition. Recent studies have shown that machine learning networks are vulnerable to adversarial attacks, which may cause severe security issue. In this paper, a convolution neural network (CNN) for SAR target classification is designed and then attacked by adversarial examples using fast gradient sign method (FGSM) and basic iteration method (BIM). Experiments shown that although the classification accuracy of the model is quite high for real SAR image dataset, elaborately designed tiny perturbation on the data will magnificently reduce the classification performance.
Cosine Based Stereointerpolation

Considered the future development of the computer procedure for stereo interpolation, implemented on the basis of two cosine waves with a variable period. The proposed procedure is designed to create and visualize a 3D curve passing through three arbitrary points in the stereo vision space, specified by the researcher using a stereo cursor. The feature of this method is the implemented mode in which the tangent drawn to the curve at the midpoint is always parallel to the line segment connecting the end points. The two cosine waves merge harmoniously around the midpoint, and this gives the researcher the feeling that the synthesized curve stretches for the midpoint as it moves.
Intelligent Evaluation Model of the Talent Training Quality of Engineering Cost Specialty Based on a Genetic Algorithm

The quality evaluation of personnel training is of great significance for guiding the formulation of professional personnel training programs and improving the teaching level of schools. At present, the quality of personnel training is mainly qualitative evaluation, and some indicators are quantified by using the analytic hierarchy process (AHP). Due to the subjectivity in the selection of indicators, there is a large deviation in the evaluation results, and the generalization ability of the model is poor. In view of the above problems, we will study and construct an intelligent evaluation model based on a genetic algorithm for the training quality of engineering cost professionals. According to the particularity of the engineering cost specialty, under the 1+ X certificate system, the quality evaluation index system of personnel training is established. The genetic algorithm is used to calculate the index weight, and the fuzzy comprehensive evaluation model is used to evaluate the quality of personnel training. By way of example verification, it is verified that the evaluation accuracy of the constructed evaluation model is higher than 90%, and it has strong generalization ability and better evaluation effect.
Command and Control System in Intelligentized Warfare

With the development of artificial intelligence (AI) technologies, the intelligentized warfare shows the trend of “being easier to attack than to defend” and the trend of “all-domain operation”. The intelligentized warfare is characterized by joint operation in which decentralized C2 is required to obtain decision-making advantages, so that frontline commanders can act independently according to the intention of their superiors. Driven by military demands, the edge C2 came into being as a central power. This paper explores the C2 system in intelligentized warfare, analyzes its differences from traditional C2 systems, discusses the decision-making advantages of IT-enabled edge C2 in information acquisition, transmission, processing and application and its operating mechanisms, and predicts the development trend of the intelligent C2 system, which is of great significance to the construction of intelligent operation system.