Reviewerly/exHarmony huggingface dataset:
SDGs 연계 교육에서 예비교사들의 탄소 발자국 인식

We are to introduce an example of ESD in connection with SDGs through liberal arts courses opened at the College of Education. We analyzed the awareness of the carbon footprint expressed by 42 preservice teachers in a class designed in connection with SDGs 13 goal, 'climate action'. The pre-service teachers wrote a carbon diet diary, a checklist for the level of practice that emits carbon in daily life, and freely expressed the source and proportion of their carbon emissions through the carbon footprint drawing activity. In items of electricity use, public transportation use, and garbage disposal, the level of practice was positive, but water use was analyzed in a negative way. The pre-service teachers who expressed the carbon footprint in 2-3 items reached a majority, showing limitations in recognizing the carbon emission situation in their daily life. Pre-service teachers will be the main actors of education on the environmental issues of the earth at the school site, and the carbon literacy of pre-service teachers will directly or indirectly affect students. We hope that various ESD programs linked to the 17 SDGs will be developed and applied to the educational field to contribute to sustainable global environmental education.
Multi-lingual Transfer Learning for Intent Classification

Intent Classification (IC) is critical in Natural Language Understanding (NLU) in dialogue systems in which separate models are usually required for different languages. However, the maintenance and deployment of multiple models for a number of languages are costly and inefficient. Moreover, there are issues in the data collection of different languages for training these models, such as imbalanced feature distribution. In this paper, we develop and experiment with monolingual and language-agnostic joint models in IC. In these experiments, we compare the performances of the two schemes and conclude with the advantages of the language-agnostic model for three languages. In addition, we study language similarities through transfer learning and visualizing word piece embedding projections. The results show that the language-agnostic model performs close to monolingual models with the advantages of being easy to maintain. From transfer learning and embedding visualization, we also confirm that our analysis on language similarity aligns with language family partition.
How To Avoid Peter and His Principle

Does the current recommended computing curricula prepare our students to work in the business world? Technology does not exist in a silo. Technical employees work in departments, where they manage projects, people, and budgets. Many computing disciplines solely focus on the technological skills, completely ignoring the other skills necessary to be successful and to achieve upward mobility in the business world.
Enhanced N Cut and Watershed based Model for Brain MRI Segmentation

Segmentation of an image is most important and essential task in medical image processing, specifically while analyzing magnetic resonance (MR) image of brain clinically. during the clinical investigation of brain MRI images. Lot of research has been carried out for MRI segmentation but still it is challenging task. Hybrid approach which uses enhanced normalized cut and watershed transform to segment brain MRI images is developed in this paper. Watershed transform is used for the initial partitioning of the MRI, which creates primitive regions. In the next stage these primitive regions resembled for graph depiction and then the normalized cut method is used for segmenting an image. Variety of simulated and actual MR images are being segmented by using proposed algorithm to test its efficiency, in addition to it segmentation results are also compared with the other available techniques of brain MRI segmentation.
Research on Algorithm of DRC Catalog Generation Based on Machine Learning

Abstract Aiming at the current logic and expansion issues in data governance caused by data scheduling in two directions: business retrieval and data processing. Data Oriented Architecture proposes a Registration Intermediate Library(RIL) and a Catalog Intermediate Library(CIL) to achieve separation of management and application. Among them, the business-oriented catalog intermediate library automatically classifies and labels data registration information through machine learning, and realizes the automatic generation of catalogs. There are many text classification methods based on machine learning, but the model obtained through unbalanced data set training often has performance degradation. In order to solve this problem, category weights are introduced in the feature vector to reduce the influence of most samples on the model parameters. Experiments on THUCNews text classification data set show that the proposed method can effectively improve the performance of baseline system, solve the problem of unbalanced training data categories and solve the problem of automatic generation of DRC directory.
The Case Against Sentiment Analysis for Natural Text

Natural language processing is a broad field that encompasses several sub-tasks. One problem that has gained visibility over the past several years is that of Sentiment Analysis. This is the process of determining the attitude of an author towards some subject across some spectrum, typically “positive” or “negative,” by analyzing the textual information. Whereas the field started with simple counting of words with certain characteristics, it has grown in complexity with the advent of deep learning and neural network based language models. Typically, datasets used to train and evaluate these models consist of text with appropriate labels, such as movie reviews with an accompanied star rating. However, the applicability of those results to other scenarios, such as unstructured or natural text has not been clear. In this paper, we demonstrate a clear and simple case that shows that the problem of sentiment analysis is fundamentally unsuitable for natural text. We consider state-of-the-art black box models developed and hosted by 3 of the largest companies in this field: Amazon, Google and IBM.
Managing Gene Expression in Evolutionary Algorithms with Gene Regulatory Networks

This paper evaluates the effectiveness of using gene regulatory networks to manage gene expression in evolutionary algorithms for the purpose of balancing exploitation versus exploration. This builds on previous work that has shown that the introduction of non-coding genes can improve the ability of an evolutionary algorithm to adapt to change in the environment. As part of the paper an algorithm is developed and a prototype is implemented. The developed algorithm is compared to the standard genetic algorithm and previously developed methods for managing gene expression. Results show that the developed algorithm can outperform the standard genetic algorithm in dynamic environments. The algorithm is however not able to outperform all the other developed methods of managing gene expression and avenues for future improvement will be explored.
Strategies for Integrating Two-Year Information Technology and Cybersecurity Programs

To meet the high demand for trained Cybersecurity professionals, many two-year colleges which have been offering degree programs in Information Technology (IT) are expanding or revising their course offerings to include Cybersecurity courses. The increasing importance of Cybersecurity skills has led to an increase in Cybersecurity requirements for IT majors. Due to considerations related to competency overlap, vendor-based academy content, government designations, faculty qualifications, general education requirements, enrollment capacity, and other factors, two-year colleges are evaluating various options for how to effectively offer both IT and Cybersecurity content. Options include separate IT and Cybersecurity degrees, IT and Cybersecurity concentrations using a common core of general Computing courses, and multiple IT degree concentrations based on a common IT core. Colleges should evaluate the competencies expected of completers to determine how to best structure their programs.
Bug Prediction Capability of Primitive Enthusiasm Metrics

Bugs in software development life cycle are unavoidable. Manually finding these bugs is not always the most effective way. To aid this, various bug prediction approaches which are using code metrics are developed and are also still in active development.In a previous work, the Primitive Enthusiasm code metrics were introduced to add detection capabilities for Primitive Obsession code smells. This paper explores the usability of the Primitive Enthusiasm metrics in a bug prediction scenario. To evaluate the new metrics, an existing source code bug data set was used. The correlation between existing metrics and the new PE metrics was investigated. Furthermore the effectiveness of bug prediction is investigated by building training models with and without the new metrics. Using a cross-project and a project version-based evaluation the results show that adding PE metrics can be beneficial for bug prediction.
Question Classification for the Travel Domain using Deep Contextualized Word Embedding Models

Question answering can be considered as a key area in Natural Language Processing and Information Retrieval, where users construct queries in natural language and receive suitable answers in return. In the travel domain, most questions are “content questions”, where the expected answer is not the equivalent of “yes” or “no”, but rather factual information. Replying to a free-form factual question based on a large collection of text is challenging. Previous research has shown that the accuracy of question answering systems can be improved by adding a classification phase based on the expected answer type. This paper focuses on implementing a multi-level, multi-class question classification system focusing on the travel domain. Existing research for the travel domain is conducted using language-specific features and traditional Machine Learning models. In contrast, this research employs transformer-based state-of-the-art deep contextualized word embedding models for question classification. The proposed method improves the coarse class Micro F1-Score by 5.43% compared to the baseline. Fine-grain Micro F1-Score has also improved by 3.8%. We also present an empirical analysis of the effectiveness of different transformer-based deep contextualized word embedding models for multi-level multi-class classification.
Improved k-means Algorithm using Weight Estimation

Clustering is an unsupervised method where similar data points are belonged in the same group so that maximum similarity are obtained among them. The K-means is a widely used algorithm to perform clustering. This algorithm is used for various purposes because it is simple and efficient. One of its main limitations is that it first chooses initial cluster centers randomly. In this paper, we propose a method, namely, IK-meansWE (Improved k-means using weight estimation), which overcomes the main limitations of the algorithm. This algorithm uses weight estimation method to transform multi-dimensional data into one-dimensional data. The one dimensional data are used to make ascending order which are divide into k parts. Then k initial cluster centers are chosen from k different groups. These selected initial centers are used to perform clustering solution in k-means clustering process. We have used both artificially generated and real world data-sets for experiments. The results of clustering using IK-meansWE algorithm are better than several K-means type algorithms, which shows the enhancement of the performance of K-means algorithm.
Effect of High-Energy Voiced Speech Segments and Speaker Gender on Shouted Speech Detection

Shouted speech detection is an essential preprocessing task in many conventional speech processing systems. Mostly, shouted speech has been studied in terms of the characterization of vocal tract and excitation source features. Previous works have also established the significance of voiced segments in shouted speech detection. This work posits that a significant emphasis is given to a portion of the voiced segments during shouted speech production. These emphasized voiced regions have significant energy. This work analyzes the effect of high-energy voiced segments on shouted speech detection. Moreover, fundamental frequency is a crucial characteristic of both shouted speech and speaker gender. Authors believe that gender has a significant effect on shouted speech detection. Therefore, the present work also studies the impact of gender on the current task. The classification between normal and shouted speech is performed using a DNN based classifier. A statistical significance test of the features extracted from high-energy voiced segments is also performed. The results support the claim that high-energy voiced segments carry highly discriminating information. Additionally, classification results of gender experiments show that gender has a notable effect on shouted speech detection.
The Implementation of Prioritized Task Scheduling for VOIP Data Package Processing in Cloud Computing

The rapid growth of computer technology makes researchers try to solve every problem faced. Cloud Computing arises as one of the solutions for those problems. Currently, one of the problems available to Cloud Computing is the limited resource. Considering its limited source, the VOIP data package processing can be one solution to utilize. Prioritized Task Scheduling, an algorithm that can prioritize data transmission on Cloud, is needed to create a reliable Cloud server. This research aims to develop Cloud Computing technology with a concept of virtualization. The result shows that the implementation of the Prioritized Task Scheduling for Voice Over Internet Protocol (VOIP) data package processing on Cloud Computing works as expected in the design of the system. The Quality of Service (QoS) performance value of the proposed method shows good and stable results in terms of delay, packet loss, and throughput. According to the standardization used on this paper, the packet loss result with 0% is categorized as "very good" and the biggest average delay with 12.11ms is also categorized as "very good".
Improving sound of clarinet by coating lacquerware (Wajima-nuri)

Thick films of lacquers by Wajima-nuri were coated only on a barrel and a bell of a clarinet. Significant change of sound characteristics has been observed on the frequency spectra analyzed by FFT. For octave below on A3 (220 Hz), the resonant peaks appear in 8– 10 kHz only coated both on the outer and inner surfaces. For octave above on A5 (880 Hz), dissonances found before the coating disappeared by the coating. The half width of the resonant peaks becomes narrower, resulting in the sharp sounds after being Wajima-nuri. At 15– 20 kHz, the width of the peaks becomes sharp, and the sound pressures are extraordinarily strong if coated on both surfaces. All peaks of sound pressures were shifted to higher frequencies by Wajima-nuri. The improvement of sound characteristics is most effective coated on both inside and outside the barrel and bell of clarinet.
Sistem Pakar Diagnosa Penyakit Alergi Pada Anak Dengan Menggunakan Microsoft Visual Basic Net Studi Kasus Klinik Dokter Yunita Siregar

Food allergies are health problems that arise as a result of a specific immune response to food. The prevalence of food allergy in children is 6%, while in adults it is 3 - 4%. Food allergy prevention strategies that are not yet optimal are one of the causes of the increasing incidence. Immunopathogenesis in food allergies involves reactions between allergens and antibodies that are mediated by immunoglobulin E, non-immunoglobulin E, or both. The clinical implications of food allergy in adults can affect the gastrointestinal, cutaneous, respiratory, and systemic systems. The gold standard for the diagnosis of food allergy is the oral food challenge. The most appropriate treatment is to avoid trigger factors. Specific and nonspecific therapy against allergens can be given although it is still under debate.
Balancing detectability and performance of attacks on the control channel of Markov Decision Processes

We investigate the problem of designing optimal stealthy poisoning attacks on the control channel of Markov decision processes (MDPs). This research is motivated by the recent interest of the research community for adversarial and poisoning attacks applied to MDPs, and reinforcement learning (RL) methods. The policies resulting from these methods have been shown to be vulnerable to attacks perturbing the observations of the decision-maker. In such an attack, drawing inspiration from adversarial examples used in supervised learning, the amplitude of the adversarial perturbation is limited according to some norm, with the hope that this constraint will make the attack imperceptible. However, such constraints do not grant any level of undetectability and do not take into account the dynamic nature of the underlying Markov process. In this paper, we propose a new attack formulation, based on information-theoretical quantities, that considers the objective of minimizing the detectability of the attack as well as the performance of the controlled process. We analyze the trade-off between the efficiency of the attack and its detectability. We conclude with examples and numerical simulations illustrating this trade-off.
Team Topologies in Software Teams: A Multivocal Literature Review

Team topologies have been frequently associated with organization’s success for effective software delivery in the shape of right product. Achieving excellence for software development highly depends upon the team’s coordination and communication between them. Team topologies are methodology helping an organization deliberately think about different teams’ purposes (fundamental topologies) and how and when they should interact with each other (core interaction mode). Authors aim to investigate the concept and the impact of team topologies. Using a Multivocal Literature Review we found significant impact of team’s goals over the individual’s goal in an organization. As expected organization formed using fundamental topologies and core interaction mode lead to high success rate.
The Concatenated Dynamic Convolutional and Sparse Coding on Image Artifacts Reduction

In order to enhance compressed JPEG image, a deep convolutional sparse coding network is proposed in this article. The network integrates state-of-the-art dynamic convolution to extract multi-scale image features, and uses convolutional sparse coding to separate image artifacts to generate coded feature for the final image reconstruction. Since this architecture consolidates model-based convolutional sparse coding with deep neural network, that allow this method has more interpretability. Also, compared with the existing network, which uses a dilated convolution as a feature extraction approach, this proposed concatenated dynamic method has improved de-blocking result in both numerical experiments and visual effect. Besides, in the higher compressed quality task, the proposed model has more pronounced improvement in reconstructed image quality evaluations.
Path Planning Approach for a Quadrotor Unmanned Aerial Vehicle

A path planning method for an unmanned aerial system type quadrotor is proposed in this work. It is based on Dubins curves. Therefore, different points (initial and ending) are set for generation of several paths. Additionally, to validate the proposed model a computational resource is applied. Also, some flight dynamics limits and orientation angles computations are considered to be able to determine a simplified Dubins model. Dubins paths are commonly divided into low, medium and high altitude gains. It will depend on the altitude established for the start and end points and other configurations.
Convolutional Normalizing Flows for Deep Gaussian Processes

Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart. However, it is impossible to perform exact inference in DGPs, which has motivated the recent development of variational inference-based methods. Unfortunately, either these methods yield a biased posterior belief or it is difficult to evaluate their convergence. This paper introduces a new approach for specifying flexible, arbitrarily complex, and scalable approximate posterior distributions. The posterior distribution is constructed through a normalizing flow (NF) which transforms a simple initial probability into a more complex one through a sequence of invertible transformations. Moreover, a novel convolutional normalizing flow (CNF) is developed to improve the time efficiency and capture dependency between layers. Empirical evaluation shows that CNF DGP outperforms the state-of-the-art approximation methods for DGPs.
Human 3D Pose Estimation Based on Inception Architecture

Researchers provide us with newer and deeper structures of neural networks every year, confirming their effectiveness concerning earlier versions of their architectures. A common assumption in creating current structures is to ignore performance issues. On the one hand, we theoretically obtain better and better classification and regression methods. On the other hand, despite the significant development of mobile devices, we lose the possibility of their implementation in systems accessible to humans, such as mobile phones. Being aware of social expectations towards the technologies being developed, we should create algorithms that improve our previous versions’ operation and optimize performance. The study compares the operation of Inception-V3 and V4 networks in precision and speed in the regression process. The estimation ability was determined as part of studying the position of human joints in 3D space. The LoCO architecture, one of the leading 3D human pose estimation methods, was used for the experiments.
Research on Image Classification Algorithm Based on Convolutional Neural Network-TensorFlow

Abstract Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards of image Classification. And convolutional neural networks (CNN) are increasingly used to solve the problem of image classification. This paper briefly ces the convolutional neural network, which is constructed using the TensorFlow deep learning framework, and conducts experiments on 2000 pieces of custom data set of 10 classification obtained by crawler technology. At the same time, a comparative experiment is conducted on the traditional image classification KNN algorithm. Experimental results show that the application of convintroduolutional neural network in image classification has relatively large advantages, and the accuracy has been greatly improved.
Research on the Construction Status and Smart Application of the Community Elderly "Physical and Medical Integration" Center------Based on the Typical Case of the Community Elderly "Physical and Medical Integration" Center in Yinhang Street, Shanghai

The COVID-19 epidemic has brought a huge impact to the people's health concept across the country. In the post-epidemic era, how to protect the health of residents by providing better public service facilities and spaces for sports health, launching health education activities, and formulating comprehensive sports health management plans is a major issue before us. This paper analyzes the Shanghai Yangpu Yinghang street community elderly "body fusion" medical center for the construction of the background, present and describes the Yinghang street community elderly "body medical fusion center six core area layout, the function of four important innovations and five clinical typical cases, and finally summarizes the Yinghang street community elderly" body medical fusion center to improve the situation of health among the elderly.