Reviewerly/exHarmony huggingface dataset:
Adversarially Robust Decision Trees Against User-Specified Threat Models

In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as decision trees are understandable, they suffer from adversarial attacks. When a decision tree is used to differentiate between a user's benign and malicious behavior, an adversarial attack allows the user to effectively evade the model by perturbing the inputs the model receives. We can use algorithms that take adversarial attacks into account to fit trees that are more robust. In this work we propose an algorithm that is two orders of magnitudes faster and scores 4.3% better on accuracy against adversaries moving all samples than the state-of-the-art work while accepting an intuitive and permissible threat model. Where previous threat models were limited to distance norms, we allow each feature to be perturbed with a user-specified threat model specifying either a maximum distance or constraints on the direction of perturbation. Additionally we introduce two hyperparameters rho and phi that can control the trade-off between accuracy vs robustness and accuracy vs fairness respectively. Using the hyperparameters we can train models with less than 5% difference in false positive rate between population groups while scoring on average 2.4% higher on accuracy against adversarial attacks. Lastly, we show that our decision trees perform similarly to more complex random forests of fair and robust decision trees.
Strengthening students’ academic digital competencies through development of learning patterns

Today students are met with expectations of being able to navigate in a broad spectrum of digital challenges. The STAK project aims at developing specific digital didactic designs in the form of learning patterns that support and develop students' digital competencies, within areas such as digital sharing, open access, online collaboration and other emerging technological opportunities and challenges.The concept of “Learning Patterns” covers a method used to capture experience about best practice from educators and other experts, and disseminate these into concrete learning patterns and activities. These concrete learning patterns are then systematised and described in such a way that it is possible for others to understand them and reuse them. As a product, learning patterns can be described as a “how-to”-formula that you can transfer to your own teaching.The STAK project have developed more than 100 learning patterns within four categories: Digital information competencies, digital participatory competencies, digital production competencies, and digital responsibility-and security competencies. The concrete learning patterns are available on an OER (open educational resource), https://open-tdm.au.dk/blogs/stak/,along with models and templates for how to develop learning patterns. In the presentation, we focus on our work processes with developing learning patterns in the project and present a concrete learning pattern. We are also going to provide you with insight into how you can use this method to develop your own learning patterns. The STAK project (Studerendes Akademiske Digitale Kompetencer på videregående uddannelser) is a Danish national project where six education - and research libraries collaborate with CUDIM (Centre for Teaching Development and Digital Media) at Aarhus University on developing learning patterns to strengthen students’ digital competencies.
ANALYSIS OF TEACHING METHODS WITH THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN THE STUDY OF HIGHER MATHEMATICS BY TECHNICAL STUDENTS

The researchers of methodological tools for differentiation of training higher mathematics at technical universities are chosen different methods approach to teaching mathematics, which provides the organization of mastering the content of professionally directed training of mathematics preparation students through the introduction of a teaching productive learning method. To identify methodological features of using productive learning methods of training higher mathematics engineer students at technical universities. We can’t underestimate the important role of mathematical training in the professional development of personality of the students in technical universities. Objectives of higher technical education is to produce creative person, able to work in a rapidly changing environment. Therefore, the use of productive learning methods of training higher mathematics engineer students at technical universities such as project method, multilevel studies, case-method, method teaching in groups, training in cooperation, the method portfolio, the method of problem-based learning, heuristic method, research training and so contributes to personal development, able to quickly make the right decisions. The author analyzed, examined the particular use of productive learning methods as multi-level learning, method of teaching in groups, training in cooperation, heuristic method, problem-based learning. The features of these methods use the example parts of Mathematics «Indefinite and definite integrals». Of the proposed methods of teaching higher mathematics promotes students' cognitive activity, ability to work independently with new information, the ability to analyze data subcritical. These methods can be used in the process of learning about blended learning model both individually and in combination with each other. Are the same methods the teacher can use in different ways, directing the activities of students or to reproduce previously acquired knowledge (reproductive activities) or meet new independent learning tasks (creative activity).
2021년 EBS 연계교재와 대학수학능력시험의 듣기 및 읽기 지문의 어휘 범위

The present study aims to investigate lexical coverage of General Service List (GSL)/Academic Word List (AWL), 2015 Basic Vocabulary of Ministry of Education, and 10 BNC/COCA word frequency lists in 2021 EBS-linked textbooks and CSAT. For the purpose of the study, AntWordProfiler was used to analyze the data. First of all, the word tokens in CSAT included higher percentage of GSL than those in EBS books in both reading and listening corpora, but the word tokens in both EBS and CSAT had high percentage of AWL(7-9%) in the reading corpora. Secondly, the word tokens in the reading corpora of both EBS and CSAT cover between 94% and 96% of 2015 Basic Vocabulary of Ministry of Education, and those in the listening counterparts also cover between 97% and 98%. Finally, Korean high school students need to know 4,000 and 6,000 word families of BNC/COCA to understand the reading corpora of EBS textbooks, compared with 2,000 and 3,000 families in the CSAT counterparts respectively, based on 95% and 98% figures. On the other hand, based on 95% and 98% lexical coverage figures, they need to know 3,000 and 4,000 word families for listening comprehension, while they need 2,000 and 3,000 word families respectively. Findings and implications are discussed at the end.
The Effectiveness of Brainstorming and Brainwriting Strategies to Teach Writing for Students with High and Low Interest in the Academic Year of 2019/2020

The aim of this research is to investigate how effective was brainstorming and brainwriting strategy to teach writing to students with different interest. This paper showed that the result of experimental research on writing skills to students with high and low interest at MTs Ma’arif Nu 1 Kembaran. A 2x2 factorial design of experimental research was used to collect the data from 60 students which divided into two groups; they were experimental group one and experimental group two. They were treated with different strategies: Brainstorming and strategy and Brainwriting strategy. The instrument of this research was observation checklist, questionnaire, and test (pre-test and post-test). The data was analysed using ANOVA to prove the hypothesis. The result showed that brainwriting was more effective to teach writing to students with high and low interest. Based on the analysis of ANOVA, there were no interaction among strategies, writing skills and students’ interest.
A Codec Simulation for Low-rate Speech Coding with Radial Neural Networks

A brief review of low-rate coding methods with the inclusion of neural networks in the codec structure is carried out. The issue of using radial neural networks for solving problems of low-speed coding and reducing the energy consumption of information transmission systems without communication quality degradation. The structure of a codec using radial neural networks in Matlab code is presented. Simulation of three-channel transmission system was performed for two signal type: human voice and music. Codec structure for each signal is the same, but neural network for music processing contains 69 neurons in hidden layer, while for voice 27 neurons is enough. The simulation shows that the application of a radial neural network as part of a predictive codec can reduce the dynamic range of signals by up to 70% while maintaining the communication quality for each of the used types of signal. At the same time, the simulation shows that codec with neural networks causes additional latency in signal processing.
MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES

Abstract. The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.
Digitizing Art or How to Broaden the Viewer’s Experience

As all of us, know 2020 gave a new meaning to the phrase "Go fully digital".For many institutions, this new era of online entertainment became a true challenge.If earlier digital art was an experimental practice, which not everyone wanted to understand, in today's situation it became an impossible opinion due to the fact that all of us did our best to master the digital sphere while working from home.The paper seeks to demonstrate how digital documentation and digital exhibitions became an important part of contemporary exhibition life.Through different examples of video, media, and other types of "digitized" art the author will show digital exhibits may create a place where all viewers are able to come from any location in the world.The main focus of the paper is a case study of three digital exhibitionstwo made at the Russian State Art Library as a part of the "RSAL Studio" program, one made by students of the first Russian Digital Art MA program "-Da.Digital Art".Each exhibit has a different approach to the concept of demonstration of art and methods of its creation.The paper will be followed by an image demonstration, which the author will prepare in case of the proposal approval.
Revisiting Disruptive Technology and the Innovator’s Dilemma in the Age of Cybersecurity

On November 23, 2020, Dr. Michael A. Hennessy presented on the topic of Revisiting Disruptive Technology and the Innovator’s Dilemma in the Age of Cybersecurity at the 2020 CASIS West Coast Security Conference. The presentation was followed by a moderated question and answer period. Key points of discussion included: the difficulties of embracing major technological change in modern military bureaucracies; how organisational culture is an impediment to change; and how leadership and management in military organisations is out of step with the pace of change.
Evaluasi Service Operation dan Pembuatan Dokumen Service Strategy Menggunakan Framwork ITIL Versi 3 (Studi Kasus : BPPW Jabar)

BPPW JABAR (Balai Prasarana Permukiman Wilayah Jawa Barat) under the Ministry of Public Works and Public Housing Directorate General Cipta Karya, is a representative of the Central Government to support the Coordination function with the Regional Government,&#x0D; In the BPPW JABAR organizational structure, there is an Individual Consultant who is placed specifically to deal with obstacles experienced by the organization in the use of IT services, but in its application, there has never been an evaluation for any process of repairing the constraints experienced and documentation for the process of repairing IT service constraints, resulting in every obstacle never experienced repeated with the same case. That is because the number of people who handle IT problems is only 1 person and there is no IT work structure so that work is carried out based on continuous orders, if it continues to occur it will result in obstruction of existing business processes and processes, due to problem handling IT services are not completed quickly and precisely because of the limitations of people and lack of documentation for any problems that have been experienced and resolved. Not just repairing IT service constraints. In the BPPW JABAR organization the dependence on centralized data and secure data storage becomes a high need because every existing data is important and confidential data because it is related to planning data and the implementation of development in the field of Cipta Karya, so the need for an IT service for data storage centralized and can be shared specifically for the work environment of the BPPW JABAR organization and people who have access to the data.&#x0D; The steps taken are evaluating IT services in service operations to assess the level of readiness of the IT problem improvement process experienced by the organization while using IT services and to conduct a GAP analysis between the existing conditions of IT service improvement with the level of maturity expected by the organization in the process of improving IT services and design service strategy for related centralized data and can be shared.
Towards a Curry-Howard Correspondence for Linear, Reversible Computation

In this paper, we present a linear and reversible language with inductive and coinductive types, together with a Curry-Howard correspondence with the positive fragment of the logic µMALL: linear logic extended with least fixed points allowing inductive statements. Linear, reversible computation makes an important sub-class of quantum computation without measurement. In the latter, the notion of purely quantum recursive type is not yet well understood. Moreover, models for reasoning about quantum algorithms only provide complex types for classical datatypes: there are usually no types for purely quantum objects beside tensors of quantum bits. This work is a first step towards understanding purely quantum recursive types.
MENINGKATKAN KETERAMPILAN MENULIS, KETERAMPILAN BERKOMUNIKASI LISAN, DAN MOTIVASI BELAJAR SISWA MELALUI PENDEKATAN PROSES MENULIS [THE EVALUATION OF THE WRITING PROCESS APPROACH TO INCREASE WRITTEN SKILLS, ORAL COMMUNICATION SKILLS, AND LEARNING MOTIVATION OF STUDENTS WITHIN AN INDONESIAN CLASSROOM]

The writing process approach is used to help students to write more competently through stages of the approach. The design of Static-Group Pretest-Postest is used to investigate the increase on students' writing skills, oral communication skills and learning motivation in Indonesian classes. The subject of the study was 41 Grade 8 students consisting of 21 students in experiment group and 20 students in control group. The study was conducted by implementing the writing process approach as the treatment in experiment class and conventional approach in the control class. The data was analysed using descriptive statistics and Wilcoxon test by comparing the results between pretest and posttest, and Mann Whitney test was used to find the difference between two groups. The findings show that there were increase on writing skills in middle category (N gain =0.48; p =0.000), oral communication skills in middle category (N gain = 0,47; p = 0,000), and learning motivation in low category (N gain = 0,25; p = 0,000) after the implementation of the writing process.
Professional Skills and Soft Skills for LIS Professional in ICT Era

ICT tools empowered web-based library, which provides services to its user throughout computer networks. LIS Professional plays an important role in the field of Information Generation and dissemination to the end users based on information storage and retrieval process tools and techniques. In this paper the author explain the list of skill and its impact on LIS fields. This paper discusses the professional skills and soft skills required for the library professionals to provide effective services to the user’s community. Further it deliberates, the competencies required by the professionals such user’s needs assessment, application of new technology and development the non-print collection and also discusses the soft skills one should have to serve the users effectively such as communication skills, leadership skills, interpersonal skills, presentation skills etc.
Clustering Emotional Features using Machine Learning in Public Opinion during the 2019 Presidential Candidate Debates in Indonesia

This research has produced a description of the emotions of streaming-video viewers of presidential candidate debates broadcasted on Youtube. In the first presidential candidate debate, the emotions of viewers were still neutral and tended to be feelings of pleasure and happiness. In the second to fifth presidential candidate debates, the dominant emotions were happy, angry, and sad. This research is known as emotion analysis, using comments from viewers of presidential candidate debates on Youtube as the data. Those comments were downloaded and pre-processed for data cleaning, emotion feature extraction, and clustering using K-Means based on six basic types of emotions: anger, sadness, happiness, fear, surprise, and disgust. The aims to be achieved are to determine a more homogeneous cluster for each opinion in the presidential candidate debate videos and to provide an emotional label for each cluster formed. The results of the research are five clusters that have distinctive homogeneity, namely happiness, anger, neutral, surprise-angry-disgust, and sadness. Each cluster member was labeled according to its characteristics. After being divided for each stage of the presidential candidate debate, it can be seen that the journey from the first debate to the next debate period tended to increase the emotion of anger and reduce the emotion of neutral.
과제중심 학습과 디지털 텍스트를 활용한 영미시 교육

This paper aims to examine some limitations of today’s English poetry classes and suggest more responsive ways of teaching poetry to college-level students in Korea. The number of English poetry classes in Korean universities has been rapidly decreasing because of students'' low interest in literature and teachers'' unchallenging poetry lessons. This research points out major weaknesses of previous strategies for teaching poetry and proposes the Task-Based Learning (TBL) method using digital texts to reinvigorate poetry classes. Digital texts, which are widely spreading through IT devices, are more responsive to shifting social trends and students'' interests. The TBL method, which emphasizes student-centered learning through active class participation, motivates students to think creatively and independently. The TBL method and digital texts incorporated in English poetry classes are creative ways to encourage students'' interests in poetry in the changing educational environment of our times. (Chungnam National University)
Boosting unsupervised monocular depth estimation with auxiliary semantic information

Learning-based multi-task models have been widely used in various scene understanding tasks, and complement each other, i.e., they allow us to consider prior semantic information to better infer depth. We boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary task. To address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training, we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset, which not only provides a new way to conduct multi-task training, but also helps us to evaluate results compared with those of other algorithms. In addition, in order to comprehensively use the extracted features of the two tasks in the early perception stage, we use a strategy of sharing weights in the network to fuse cross-domain features, and introduce a novel multi-task loss function to further smooth the depth values. Extensive experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task, as well improved semantic segmentation.
Study On Barcode Technology And Other Biometrics Technology

This study has been undertaken to understand the barcode technology and various other biometrics technology. The study reveals that the use of these technologies reduces the human effort and also reduces error occur during the handling of records. Comparison of different biometric technologies is done and various applications reviews are discussed. This paper analyses the advantages of using barcode technology.
Evaluation of a NoSQL Database for Storing Big Geospatial Raster Data

Database systems capable of efficiently storing geospatial data are widespread.However, recent developments in earth observation systems, remote sensing, mobile mapping, and crowd sourcing lead to large amounts of geospatial mass data that can hardly be handled efficiently with the existing solutions.Especially large geospatial raster data require novel concepts for well-organized data storage.A concept for storage of large geospatially and temporally referenced image data using the NoSQL graph database system Neo4j as a research subject of the project "RiverView®" is introduced.New strategies and access structures have been developed to ensure the persistence and performant access to image data in Neo4j.These strategies are compared with the up-and download performance of the widespread Rasdaman array database system.