Reviewerly/exHarmony huggingface dataset:
Acting on the invisible: Computational tools and community action in the landscapes of air quality

This article proposes that designers and planners can better manage wicked problems by developing a strategic alignment of computational technology with a theory of change. Together with an understanding of the most effective places to intervene in a system, designers’ informed use of technology enables them to orchestrate community action and leverage large-scale environmental change. Aligning technology with a theory of change deepens the relevance of computational tools and suggests that technologies or tools that augment one’s ability to perceive, understand relevance, or prioritize raise the potential for action; technologies or tools that aggregate information on collective beliefs or actions help to build a community of concern; and technologies that elevate community capacity and create a sense of identity can contribute to the long-term transformation of values. Through a case study, this article demonstrates a nested approach to computation, which enhances public awareness and enables action in a small community which is trying to manage an extra-territorial problem of air quality. This article also proposes that while computational tools have extended the reach and effectiveness of advocacy, designers should continue to push for expanded application. By aggregating lessons learned from technological networks, such as the emerging clean air network described in this article, we can add another socio-ecological dimension to the practices of landscape and urbanism.
Quality Analysis of Management Accounting Information Systems and Organizational Structures on Management Decision Making

Management decision making is expected to provide great benefits in the business world. Management of a company requires a variety of fast information and accuracy needed for various management decisions. Without the support of relevant data and information, the management's decisions at various levels of the organization can be wrong and not according to needs. Phenomena in the field regarding management in decision making do not think about problems that will occur in the future that can harm the company and management to make decisions unilaterally based on the input of information received that is not yet relevant. This study aims to determine the effect of the quality of management accounting information systems and organizational structure on management decision-making. The method used in this study is a descriptive method with a quantitative approach through data collection techniques with questionnaires that are distributed directly throughout the section heads in PT. Macrosentra Niagaboga. The statistical test instrument used in this study is the Sructural Equation Model (SEM) with Partial Least Square (PLS) assessment. Application of quality management accounting information systems, organizational structure, and management decision making at PT. Macrosentra Niagaboga is in good criteria. The results of this study show 1) There is a significant influence on the quality of management accounting information systems on management decision making. 2) organizational structure has a significant influence on management decision making.
Transforming and Projecting Images into Class-conditional Generative Networks

We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias and color bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better editability of these images.
Artificial Intelligence (AI) Collides with Patent Law

Artificial intelligence and laws are coming to appoint where revision of laws is required to keep up with the technological advancements. Artificial intelligence is developing rapidly and there are possibilities that it may take over a lot of human endeavours one of which may also be a large part of law profession. Lawyers with their teams spend a lot of time in understanding and placing Patent claims. Hours are spent in investigating and analysing the novelty, utility and non-obvious nature of products and processes for which a claim is to be presented. A substantial expenditure goes into the process of patenting something. Facilitating laws by including AI in the whole process may substantially save us time and money involved in the process. This article explores some of the aspects of IP laws that particularly deal with Patent processes which are placed in the introduction. This article explores a model that may reduce the fear that people have for different biases that may be picked up by the artificial intelligence.
Health Monitoring and Alert Systems Using Internet of Things

We are presenting a platform where considering the human life can be saved by giving quick response time which enables the caretakers and doctor can be know about patients health and various parameters if it falls below certain threshold range. In the analysis of the experiment we came to know if the patients health were in crucial situations its impossible to intimate to their beloved ones. By intimating through messages or through automated voice we can able to alert them. This could be done by their smart phone consisting of GSM to intimate to the caretakers even if the patients are in any range. The patients e-health is also noticed if they are in any hospitals and by viewing that they can able to revive the patients health as soon as possible. This was done by fitting the various sensors in physical contact with the patients body like chest, wrist. This can able to design in the fashion accessories and nothing to be consider as prestige. This can be developed in MEMs and we can able to design that in smaller and less battery consumption devices and it can able to operate for long time. This experiment not only ensure to diagnose but also to advice about the patients parameters to maintain in the safe section by regular exercise and diet. Finally this can be implemented all over the entire world to take the world in to peaceful and save environment.
On the Generalization Effects of Linear Transformations in Data Augmentation

Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations which preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations which mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms RandAugment by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR datasets.
Using Response Cards Media to Improve the Students' Narrative Text Reading Skill of the Tenth Graders of MAN 1 Magelang in the School Year 2018/2019.

Reading is one of the important skills in English because by mastering reading students can get much information and knowledge. However, the tenth graders of MAN 1Magelang in the school year 2018/2019 still found difficulties in understanding reading English test of narrative text. It made their average score only 68. There are two main objectives of this research. First, it is to know the increase of the learning motivation in students’ narrative text reading skill of the tenth graders of MAN 1 Magelang in the school year 2018/2019. Second, it is to measure the improvement in students’ narrative text reading skill of the tenth graders of MAN 1 Magelang in the school year 2018/2019 who are taught using Response Cards media.  The subject of this research was the tenth graders of MAN 1Magelang in the school year 2018/2019 of X IPS 7. The writer conducted a classroom action research that was divided into some cycles. As the results in Pre-Cycle, the students still had low learning motivation in the reading class. In Cycle I, many students were interested in using Response Cards media. In Cycle II, the students had better participation in teaching learning process. The writer concludes that the use of Response Cards media can increase the motivation in learning narrative text reading skill and there is a great improvement in students’ narrative text reading skill of the tenth graders of MAN 1 Magelang in the school year 2018/2019.
A cloud broker architecture for cloud service selection based on multi-criteria decision making and rough set theory

Cloud computing is a rising field providing computation resources. It represents a new paradigm of utility computing and enormously growing phenomenon in the present IT industry and economy hype. The companies which provide services to customers are called as cloud service providers. The cloud users (CUs) increase and require secure, reliable and trustworthy cloud service providers (CSPs) from the market. So, it's a challenge for a new user to choose the best provider. In this paper, we propose cloud broker architecture to help a new customer to find out the best CP. This architecture is based on a combination of rough set theory and multi-criteria analysis of some parameters related to the quality of service of the available providers. A mathematical model is used to do this analysis; it integrates multi-agent systems to present an intelligent, distributed and collaborative method in order to add assistance to each actor in cloud computing environment.
Secure Image Hiding Scheme Based on Magic Signet

Image data hiding technology is secret communications that carry hidden data in such a way that no one except the sender and intended recipients can even realize there is a hidden message. High embedding capacity, good images quality, and security are three important essentials. In this paper, each confidential hexadecimal will be carried by two cover pixels based on a magic matrix generated from a square template to reach the goal of higher embedding capacity while keeping good image visualization. Experimental results reveal that the proposed scheme guarantees higher embedding capacity of 2 bits per pixel and has the peak signal-to-noise ratio (PSNR) of 44.7 dB on average. Moreover, secret keys are used to ensure security consideration.
MOBILE TECHNOLOGIES IN THE CONTEXT OF PROFESSIONAL SPEECH CULTURE FORMATION OF FUTURE TEACHERS

The article is devoted to mobile technologies as main and defining components of speech culture formation of future primary school teachers, is focused on practically oriented online resources (The Ukrainian orthography, Rules of the Ukrainian language, The Ukrainian defining dictionary, The defining dictionary of Ukrainian, Spell check, Dictionary of synonyms, Dropword, Words from Word, Mova, Viber, QR-Code, etc.). It is highlighted that mobile technologies can provide comfortable continuous and lifelong learning, which is equally important for maintaining our knowledge at the current level. In order to trace the latest innovations and achievements in a particular field of human activity, one must always work on improving their knowledge and skills. Social media are also important, they allow you to be connected with peers, friends, teachers that create the perfect virtual space environment. At the same time, using mobile learning technologies apart from other technologies is inappropriate. It is necessary not only to artificially combine mobile learning with traditional learning methods, but also to develop new ways of modern information transmission using mobile devices and integrate mobile learning with other competently oriented technologies that will maximize the effect. It is emphasized that mobile application is one of the fields in mobile learning, which is an effective, progressive tool in socio-cultural communication, since modern education requires teachers’ formation of progressive generation that is educated, sociable with creative potential, following the culture of professional speech.
Recommendation of Diet Using Hybrid Collaborative Filtering Learning Methods

These days, various recommender systems exist for online advertisement services which recommend the products considering users’ interests. Similarly, health recommendation systems are becoming most important component in individual’s life. Due to the modernization and busy schedule, people give less concern to their eating patterns. This leads to various health issues like obesity, thyroid disorder, diabetes and others. Every individual has different health issues and food habits. Therefore, diet recommendations should be suggested by considering their personal health profile and food preferences. So, it becomes essential to analyze individuals’ health concerns before recommending the diet with required nutrient values. Thus, it helps people to minimize the further risks associated with the current health conditions. The proposed diet and exercise recommender framework suggests a balanced diet for thyroid patients. It takes care of the food intake with necessary nutrients requirement based on thyroid disorders. This paper applies K-nearest neighbor collaborative filtering models using various similarity measures. The paper assessed two-hybrid learning methods, KNN with alternating least squares: KNN-ALS and KNN with stochastic gradient decent: KNN-SGD. The experimental setup analyzed and evaluated the performances of all algorithms using mean absolute error (MAE) and root mean squared error (RMSE) values.
Development of University Convergence Major Standards to Promote Key Competencies

4차 산업혁명 시대에 인간에게 필요한 것은 시대적 변혁에 유연하게 대처하고 지식을 융복합적으로 결합시킬 수 있게 하는 핵심역량이다. 대학융합전공은 사회와 산업의 변화에 신속하게 대응하고 대학생의 핵심역량을 함양시키도록 하는 유용한 전공 방식이다. 하지만 융합전공에 대한 이해가 부족하고, 대학마다 융합전공 운영방식이 달라 융합전공의 효과적인 운영방식에 대한 논의가 필요하다. 이에 본 연구는 핵심역량을 함양하기 위해 대학 융합전공의 목표 등을 제시하는 대학융합전공기준을 개발하는 것을 목적으로 한다. ‘가교 교육과정기준’ 형식으로 핵심역량 함양 대학융합전공기준을 가안-초안-수정안-최종안의 4단계에 걸쳐 개발하였다. 본 연구에서 개발된 핵심역량 함양 대학융합전공기준이 학교현장에 안착하고 실효성을 갖기 위해서는 핵심역량과 융합전공에 대한 학내 구성원의 이해와 적극적인 협조, 대학융합전공을 촉진할 수 있는 인프라와 대내외 네트워크 구축, 융합전공 교수자의 전문성 함양 등이 요구된다.What humans need in the era of the Fourth Industrial Revolution is key competencies to cope flexibly with the changes of the times and to combine various knowledge in a flexible manner. Convergence majors are implemented as a method designed to respond and adapt quickly to rapid changes made in society and industry and to promote key competencies of undergraduate. However, there is a lack of understanding of convergence majors and different universities have different ways of operating convergence majors, which requires discussion on the effective way of operation of convergence majors. The purpose of this study is to develop university standards of convergence major that suggest goals of university convergence major in order to promote key competencies. University Convergence Major Standards to promote Key Competencies are developed in the form of ‘Bridging Curriculum Standards through four stages: preliminary draft, initial version, revised version, and final version. In order for the university convergence major standards to promote key competencies developed in this study to settle in the university curriculum, understandings and cooperations among university members on the needs for convergence education and convergence majors, building infrastructure and networks that can promote university convergence majors, the professors’ expertise in teaching university convergence majors are required.
Fraud Analysis Using Machine Learning Algorithms

Abstract In the previous studies done by others, the methods of picking abnormal numbers and data have been developed. This paper will focus on using machine-learning method to find abnormal value. Machine learning has been applied in all kinds of fields, including picture identifying and regression which is widely used in finding anomaly. The author finds some of the anomalies during the investigation and the fraud detection rate is higher than 80 percent. This paper not only aims at using traditional way of finding abnormal value, but also aims at using the modern computer science to deal with huge amount of data. Traditionally, when it comes to the topic of finding anomalies, people tend to think about t-checking or f-checking[1,2], which shares the same limitation that the target has to be fixed when being measured. However, a more realistic problem is that the target and the measuring environment is always changing. To fix the problem, this paper uses machine learning to do regressions of the environment and the target. Not only can it help us find the relationship between our target and ‘environment’, but also it can be written into computer elegantly so that it could be able to deal with huge mount of data.
Two variants of one vector graphics tutorial (CorelDRAW vs Inkscape)

Currently, the problem of choice between proprietary and free software for use in the educational process is relevant. The article describes the experience of implementing laboratory works focused on the use of the proprietary vector editor CorelDRAW and the free editor Inkscape. A brief comparison of the main terms and tools of these editors is presented, which will facilitate the transition from CorelDRAW to Inkscape or from Inkscape to CorelDRAW. The description of the laboratory work is a step-by-step instruction with screenshots for creating a vector logo equipped.
Research on Information-Based Construction of Audit Culture Based on Improved Particle Swarm Optimization Algorithm

In order to improve the information-based construction level of audit culture in Anhui of China, the improved optimization algorithm of particle swarm is applied in it. Firstly, the current situation analysis of audit culture of Anhui in China is summarized. Secondly, the basic theory of particle swarm algorithm is analyzed. Thirdly, the information-based construction model of audit culture is established, and then the optimization procedure of information-based construction of audit culture based on particle swarm algorithm is designed. Finally, the simulation analysis is carried out for information-based construction of audit culture in Anhui Province of China, and the results show that the improved particle swarm optimization algorithm is an effective method for promoting information-based construction of audit culture.
Enhancement of Anisotropic Diffusion Filtered Cardiac MR Images Using Contrast-Based Fuzzy Approach

Image denoising is an important preprocessing step done with MRI images to remove various kinds of noise like speckle noise, Gaussian noise, pepper and salt noise, etc. Some filtering mechanisms have been eliminating the required parts of the image along with the noisy pixels of the image, a phenomenon called over-filtering. Anisotropic diffusion is a denoising technique having an iterative process that computes a set of functions to acquire a good degree of smoothening without loss of actual contents of the images. A filtering technique using anisotropic diffusion and application of fuzzy logic has been presented in this paper as it has given a better sharpness of the image, with a good PSNR while it was simulated over 33 MRI cardiac images.
Ontology-based knowledge model for human-robot interactive services

Recently, increasing interest in service robots with artificial intelligence has triggered several studies on developing service robots as human assistants. To perform automated tasks, service robots are not only required to recognize various attributes of the service environment, but they must also determine, which tasks and behaviors to perform, according to their internal system specifications and those of the individual situation. To perform tasks in such a generalized manner efficiently, the service robot must abstractly understand the recognition data obtained from the external environment and plan its tasks by combining the data and existing knowledge. Thus, an abstract and integrated knowledge management of low-level external recognition data and symbolic-level knowledge is indispensable. This study proposes a knowledge model for an integrated robot framework, which is used to provide human-robot interactive services. Our knowledge model is based on an ontology that contains general knowledge which provides clear definitions of common concepts and domain knowledge which provides specific concepts required to understand the information about agents(users and robots), and the environments about human-robot interactive services. An exemplary experiment is given in the context of a social robot service, which shows the usability of our knowledge model.
Optimization of hierarchical reinforcement learning relationship extraction model

Purpose Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation. Design/methodology/approach This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model. Findings Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction. Originality/value The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.
Cost Effective Optimization for Cost-related Hyperparameters.

The increasing demand for democratizing machine learning algorithms for general software developers calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters, which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incapable to properly control cost during the optimization process. To address this problem, we develop a cost effective HPO solution. The core of our solution is a new randomized direct-search method. We prove a convergence rate of $O(\frac{\sqrt{d}}{\sqrt{K}})$ and provide an analysis on how it can be used to control evaluation cost under reasonable assumptions. Extensive evaluation using a latest AutoML benchmark shows a strong any time performance of the proposed HPO method when tuning cost-related hyperparameters.