Reviewerly/exHarmony huggingface dataset:
Information Design for Differential Privacy

Firms and statistical agencies that publish aggregate data face practical and legal requirements to protect the privacy of individuals. Increasingly, these organizations meet these standards by using publication mechanisms which satisfy differential privacy. We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users. We show that this is equivalent to a constrained information design problem, and characterize its solution. Moreover, by introducing a new order on information structures and showing that it ranks them by their usefulness to agents with supermodular payoffs, we show that the simple geometric mechanism is optimal whenever data users face supermodular decision problems.
PEMETAAN PEMAHAMAN SISWA TENTANG LAHAN BASAH DALAM PEMBELAJARAN BAHASA INGGRIS TINGKAT SEKOLAH MENENGAH ATAS JURUSAN IPS DI KOTA BANJARMASIN

This study aims to find out the students' views dealing with wetlands in learning English based on wetland character and to map the basic draft of English based wetland characters. Based on its objectives, this study can be categorized as a descriptive research design to find out the students' views regarding wetlands in English learning by collecting as much data as possible relating to students' understanding of English language learning strategies based on wetlands so that a large collection of ideas or large preconceptions of wetland character-based English learning strategies is obtained through questionnaire as a data collection tool. The subjects of this study were 112 grade XI students spread across three public senior high schools, namely SMAN 5, SMAN 8, and SMAN 11. The data was collected through questionnaires to obtain their views of wetlands relating to English learning. The data analysis is by describing students' views regarding wetlands related to learning English. The findings show that they consider it very important to understand the river as an environmental basis in living life. River life, which is a buffer for life in wetlands, needs to be cared for and maintained. Integrating river life topics in English subject is needed as a formal step so that the students can become curriculum bound in learning about the river. There is an internalization of the values that are instilled in schools and their impact can be on the development of the habits of life of students outside of school. This is expected to build language awareness based on the wetland environment. Enhancing vocabularies, texts, and topics about river is needed to strengthen students' views regarding wetlands in learning English.
Formation electronic countermeasure equipment capability assessment based on training data

In order to analyze the capability of electronic countermeasure equipment of naval formation and improve the combat capability of naval formation, the paper constructs electronic countermeasure equipment capability index system which based on AHP and training data. Meanwhile, the paper analyzes the weight of some indexes, establishes the index models, then finally gets the electronic countermeasure equipment capability. The conclusion may provide some references for improving the combat capability of naval formation.
Clustering of Association Rules for Big Datasets using Hadoop MapReduce

Mining association rules is essential in the discovery of knowledge hidden in datasets.There are many efficient association rule mining algorithms.However, they may suffer from generating large number of rules when applied to big datasets.Large number of rules makes knowledge discovery a daunting task because too many rules are difficult to understand, interpret or visualize.To reduce the number of discovered rules, researchers proposed approaches, such as rules pruning, summarizing, or clustering.For the flourishing field of big data and Internet-of-Things (IoT), more effective solutions are crucial to cope with the rapid evolution of data.In this paper, we are proposing a novel parallel association rule clustering approach which is based on Hadoop MapReduce.We ran many experiments to study the performance of the proposed approach, and promising results have been demonstrated, e.g. the lowest scaleup was 77%.
Bayes without Bayesian Learning for Resisting Adversarial Attacks

Recent studies have been shown the vulnerability of Deep neural networks (DNNs) by a small adversarial perturbation in images that humans cannot distinguish and a well-trained neural network misclassifies. There are many defense methods, e.g. randomness, ensemble, and adversarial training that can improve the robustness of neural networks with respect to adversaries. Among them, adversarial training has been being the most outstanding defense. However, the training cost becomes a big challenge to adversarial learning. In this paper, we propose a new defense algorithm called Bayes without Bayesian Learning, which does not add the training phase in resisting adversarial attacks. Our method has based on the stochastic components of Bayesian Convolutional Neural Network (BCNN) to prevent the forceful gradient based attacks and generate the ensemble model to enhance the performance of models. In order to avoid adversarial training, we utilize pretrained CNN models on both natural and perturbed data. Therefore, this approach can be applied to any model with learned parameters to accelerate the computation of the defensive method. Our defensive algorithm has significantly improved the accuracy of pretrained CNN models under the high level of attacks. For instance, on ImageNet with Resnet50 network, we achieve 57% prediction accuracy in the top5, under strong PGD attack. We also confirm 3% improvement when combining our method with adversarial training. Experimental results prove the efficiency of our BCNN model that does not need Bayesian learning but still resists adversarial perturbation on both small and large scale datasets as CIFAR10, CIFAR100, and ImageNet.
Electronic dictionary of Indonesian sign systems (ED-ISS) based on Raspberry Pi as learning media for deaf students

This study aims to develop an Electronic Dictionary of Indonesian Sign Systems (ED-ISS) based on Raspberry Pi. The ED-ISS consists of input modules (RFID card, keyboard, and microphone); a raspberry pi as process module; and LCD display as well as audio as output modules. ED-ISS visualizes words into sign language (hand motion) that can be understood by deaf students. The results of the study reveal that the content validity of the ED-ISS is 89.7% (according to learning material experts), while the construct validity is 87.95% (according to media experts). Moreover, deaf students value the ED-ISS as 87.90% effective to translate words into sign language (hand motion).
Design and independent training of composable and reusable neural modules

Monolithic neural networks and end-to-end training have become the dominating trend in the field of deep learning, but the steady increase in complexity and training costs has raised concerns about the effectiveness and efficiency of this approach. We propose modular training as an alternative strategy for building modular neural networks by composing neural modules that can be trained independently and then kept for future use. We analyse the requirements and challenges regarding modularity and compositionality and, with that information in hand, we provide a detailed design and implementation guideline. We show experimental results of applying this modular approach to a Visual Question Answering (VQA) task parting from a previously published modular network and we evaluate its impact on the final performance, with respect to a baseline trained end-to-end. We also perform compositionality tests on CLEVR.
Cultivating Good Test Writing Skills

Writing automated unit tests is a skill that must be honed and nurtured just as carefully as the skills developers acquire over their careers writing production software. Indeed, a new programming model is involved with writing tests, one where the prime directive is to insure the stability and accuracy of production software. This chapter addresses some of the topics related to the development of those skills.
A Nonlocal Spatial Coherent Fuzzy C-Means Clustering for Bias Field Correction in Brain MR Images

Bias field in magnetic resonance (MR) images is caused due to the varying gradient driven eddy current in the MR image scanner. This results in intensity inhomogeneity (IIH) in the MR image. Therefore, bias field correction is a fundamental requisite in the tissue investigation procedure. The existing solutions used fuzzy c-means (FCM) algorithm with non-local spatial information. However, the use of only local spatial information may lead to poor segmentation of tissue regions. In this paper, we suggest a nonlocal spatial coherent FCM clustering scheme for bias field correction. A similarity measure is computed using a Gaussian kernel for local spatial information. A nonlocal coherence factor is incorporated into the objective function and membership matrix for separating the non-discriminable tissue regions in the brain MR images. The suggested scheme is analyzed in comparison with different existing methods. It is experimented with different modalities of synthetic and clinical brain MR images. The method is validated using standard cluster validation and quality evaluation indices. The results indicate the superiority of the proposed methodology compared with state-of-the-art approaches.
Teaching Material for Deepen Understanding of Strength of Materials Using Finite Element Method

Since the finite element method software can display stress distribution and deformation, it is considered to be effective for education of the strength of materials. However, the solution of the strength of materials does not consider the effect of the fixed end, so the solution of the finite element method does not agree with the solution of the strength of materials. In this study, we devised a method to match the solution of the strength of materials by devising the boundary conditions of the finite element method. From the class evaluation questionnaire, it can be said that this teaching material is effective in learning the strength of materials.
Research on the Internal Governance Structure of American Research Universities

Based on studying the internal governance structure of American research universities, this paper discussed all stakeholders' responsibilities and interests in American research universities' internal governance structure. Meanwhile, the types and characteristics of the internal governance structure of American research universities are discussed, which has strong significance for the internal governance models of other autonomous universities.
Usability Evaluation on Mobile Devices, Practical Case

Usability is an attribute of software quality that needs special attention in app development. In the present document, we analyze aspects like visual clarity and error prevention in a massive consumer app for passenger transport. With the purpose of knowing the influence of these aspects in the success or failure of an app we submit a survey to 3 developers and 40 users inquiring about these aspects. The results reflect the vitality of the usability principles by both users and developers. We hope the obtained results will help future developers, researchers and/or students.
A Lindström theorem for intuitionistic first-order logic

We extend the main result of (G. Badia and G. Olkhovikov. A Lindstrom theorem for intuitionistic propositional logic. Notre Dame Journal of Formal Logic, 61 (1): 11--30 (2020)) to the first-order intuitionistic logic (with and without equality), showing that it is the maximal (with respect to expressive power) abstract logic satisfying a certain form of compactness, the Tarski union property and preservation under asimulations. A similar result is also shown for the intuitionistic logic of constant domains.
Comparison between Autoregression model and Gaussian Process model on stock price prediction: a case study with the Microsoft stock

Prediction of the stock price is an important topic in time-series analysis field. Two methods have been compared in this paper: Autoregression model (AR) and Gaussian Process (GP) with Microsoft stock price data to illustrate the performances of these two algorithms. From the empirical results, GP model outperforms the AR model in terms of both MSE and predictive accuracy, and it also provides the uncertainty quantification of the prediction, which makes it a clear winner under this case. However, the GP model also requires higher computational cost and is more sensitive to hyperparameters.
No-Reference Quality Assessment of an Image Resizing Algorithms

In this paper, a No-Reference method is proposed for quality assessment of an image resizing methods based on interpolation algorithms. Quality refers to the image blur. To this end, it is proposed to use a previously developed measure based on a statistical assessment of the Weibull distribution shape parameter, adopted as a model for gradient magnitude of an image. The results of numerical experiments are presented that allow us to evaluate and compare the quality of various image resizing algorithms.
Outlook and Summary

The scaling of the track finder demonstrator to the proposed final size system is discussed. It is shown how the Hough transform and Kalman filter FPGA implementations scale to the latest generation of FPGA devices that allow for 25 Gb/s transceivers and many more logic resources per chip.
The Impact of Aggregation Window Width on Properties of Contextual Neural Networks with Constant Field of Attention

Artificial Neural Networks are quickly developing machine learning models with numerous applications. This includes also contextual neural networks (CxNNs). In this paper it is analyzed how the width of the aggregation window of the Constant Field of Attention (CFA) aggregation function changes the basic properties of the CxNN models, with the focus on classification accuracy and activity of hidden connections. Both aspects were analyzed in the custom H2O environment for real-life microarray data of ALL-AML leukemia gene expression and selected benchmarks hosted by the UCI ML repository. The results of presented study confirmed that increased length of the aggregation window of the CFA function can lead to similar or better classification accuracy than in the case of the Sigma-if aggregation. On the other hand, the activity of hidden connections in the majority of analyzed data sets resulted considerably higher. Presented analysis can be used as a guideline in further research on contextual neural networks.
Automated Multi-document Text Summarization from Heterogeneous Data Sources

We are currently witnessing an exponential increase of data which emanate from varied sources such as different types of records in companies, online social networks, videos, unstructured text in web pages, and others. It is very challenging to process this sparse, noisy and domain specific data. For instance, BT, a technology company in the UK and the PhD project sponsor, has a significant workforce of field engineers, desk-based agents, and customer support services who generate, collect and manage large volumes of temporally organized unstructured and semi-structured information every day. The problem that they face is effectively and efficiently answering client questions regarding order status live. The main challenge in my PhD is to propose computational models that could effectively and efficiently distil relevant information for the user who could answer the client’s questions from various technical order record documents which are very noisy and follow no structural pattern. To this end, what would be useful is to automatically summarize and derive meaningful information in the form of short answers to queries from this vast source of distributed occurring data. The solution that my thesis proposes is based on a computational model that jointly learns extractive and abstractive summarization techniques with a temporal structure. Besides, the model interplays with a question-answering framework to help answer questions.
Internet of Things (IoT) and Security: Challenges Ahead

Network security issues on the Internet of things (IoT) are linked directly to an extensive application of its system. Starting from the current research trends and emerging threats of network security, this paper explores several network and cloud threats, mitigation techniques of IoT devices and applications that exist in the network layer structure and in other elements as well. This paper further showcases with future recommendations and technical challenges including public key infrastructure (PKI) and perception of network design, algorithm security, digital certificates, privacy and finally two-way authentication, etc.
Model for Application of Optical Passive SFM Method in Reconstruction of 3D Space and Objects

Reconstruction of objects and space-based on images is the topic of active research in a recent decade. The reason for that is because high-quality reconstruction is hard to achieve, but if achieved successfully there is a wide range of possible applications. Particularly in disruptive technologies such as virtual reality and augmented reality. Advancement in computer and optical hardware has enabled development of acceptable reconstruction that can be applied for some type of purposes. Depending on the goal of reconstruction there is a wide variety of possible approaches and methods. This thesis is based on structure from a motion approach that can be used for reconstruction of a single object, room, building, street, or city. The method uses a set of images that have a targeted object or space for achieving reconstruction. Structure for motion is based on feature extraction, camera registration and stereo vision. The method is not designed for real-time reconstruction.
METHODICAL SUPPORT OF TEACHERS OF CHILDREN'S EDUCATIONAL INSTITUTIONS ON ORGANIZATION OF CHILDREN'S EXPERIMENTATION

In modern conditions of development of Russian education, methodological work with teachers in pre-school educational institutions is of particular importance. Methodological support of a teacher can be the most effective way to organize children's experimentation. The article deals with the problem of methodological support for teachers of preschool educational institutions in the organization of children's experimentation. The authors ' views on the essential characteristics of the concept of "experimentation" and "Child experimentation"are studied and analyzed. And on the problem of methodological support of teachers in a secondary school educational institution. The purpose, tasks, and methods of the ascertaining stage of research of experimental work in the framework of the research topic are presented. The article analyzes the organizational and pedagogical conditions of a preschool educational organization that contribute to the effectiveness of methodological support for teachers of preschool educational institutions in organizing children's experimentation. The study involved teachers in the number of 10 people, as well as children. In the course of the study, the main methods were: conversation, observation, and questioning. The results of studying the organization of children's experimentation in a secondary school educational institution are presented.
Path Authentication Protocol: Based on a Lightweight MAC and a Nonlinear Filter Generator

In this research, the authors propose a functionality based on message authentication code (MAC) to ensure the correctness of its path from a source to a destination. It aims to use a controller area network (CAN) over driving system. In the construction, MAC and a pseudorandom number generator having an intentional group structure are employed so that the correct user can authenticate a message and its path. The proposed method is experimentally evaluated by observing the computational time over IoT devices. As a result, it is found that the results show that it is possible to implement as an additional function that gives path authentication with additional time at most 3 times for the corresponding computation.
Relation Between Rank and Frequency

Part II mainly considers the characteristics of a population of linguistic elements, such as words. A word has a frequency in a text, and the vocabulary of the text forms a population, which this part analyzes.
A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries

Software maintenance contributes the majority of software system life cycle costs.However, existing approaches with automated code analysis are limited by accuracy and scope.Using human-assessed methods or implementing quality standards are more comprehensive alternatives, but they are much more costly for smaller organizations, especially in opensource software projects.Instead, bugs are generally used to assess software quality, such as using bug fixing time as an estimate of maintenance effort.Although associated bug reports contain useful information that describe software faults, the content of these bug reports are rarely used.In this paper, we incorporate quality standards with natural language processing techniques to provide insight into software maintainability using the content of bug reports and feature requests.These issues are classified with an automated approach into various maintainability concerns whose generalizability has been validated against over 6000 issue summaries extracted from nine open source projects in previous works.Using this approach, we perform a large empirical study of 229,329 issue summaries from 61 different projects.We evaluate the differences in expressed maintainability concerns between domains, ecosystems, and types of issues.We have found differences in relative proportions across ecosystem, domain and issue severity.Further, we evaluate the evolution of maintainability across several versions in a case study of Apache Tomcat, identifying some trends within different versions and over time.In summary, our contributions include a refinement of definitions from the original empirical study on maintainability related issues, an automated approach and associated rules for identifying maintainability related quality concerns, identification of trends in the characteristics of maintainability related issue summaries through a large-scale empirical study across two major open source ecosystems, and a case study on changes in maintainability over versions in Apache Tomcat.
Combining decision making algorithms to improve accuracy of the course assessment rank

Abstract Assessment activity is important to keep the quality of student’s knowledge. This study aims to combine decision making algorithms to improve accuracy of the course assessment activities rank to evaluate the student’s knowledge automatically. This study use Analytical Hierarchy Process (AHP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) as decision making algorithms. AHP and VIKOR are Multi-Criteria Decision Making (MCDM), where VIKOR is compromise ranking method that is expected to improve the accuracy of the ranking results. AHP and VIKOR algorithm which are combined in this study are named AVI. The experiment result with 6 types of assessment as criteria shows that AVI capable to increase the accuracy value of assessment ranking result until 1.75 times more accurate/higher percentage than using AHP or VIKOR only. This result has implications for the similar research of decision support systems, for different case studies, it can be applied the AVI combination method to improve the accuracy of ranking results.