Reviewerly/exHarmony huggingface dataset:
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.
Why do Geodetic Data need DOIs? First ideas of the GGOS DOI Working Group

&amp;lt;p&amp;gt;Only four years after the implementation of digital object identifiers (DOIs) for unambiguously identifying and linking to online articles, the first DOI for digital datasets was registered in 2004. Originally developed with the purpose of providing permanent access to (static) datasets described in scholarly literature (to allow reproducibility and scrutiny of research results), DOIs today are increasingly used for dynamic datasets (e.g. time series from observational networks, where new data values are added frequently given that the originally published data will not change), collections or networks. These DOIs (and other persistent identifiers) are mainly assigned for providing a citable and traceable reference to various types of sources (data, software, samples, equipment) and means of rewarding the originators and institutions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As a result of international groups, like the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS) and the Enabling FAIR Data project, datasets with assigned DOIs are now fully citable in scholarly literature - many journals require the data underlying a publication to be available before accepting an article. Initial metrics for data citation are available and allow data providers to demonstrate the value of the data collected by institutes and individual scientists &amp;amp;#8211; which makes them even more attractive.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This is especially relevant in the framework of evaluation criteria for institutions and researchers, that usually only consider scientific output in the form of scholarly literature and citation numbers. Compared to other scientific disciplines, geodesy researchers appear to be producing less &amp;amp;#8220;countable scientific&amp;amp;#8221; output. Geodesy researchers, however, are much more involved in operational aspects and data provision than researchers in other fields might be. Geodesy data and equipment therefore require a structured and well-documented mechanism which will enable citability, scientific recognition and reward that can be provided by assigning DOI to data, data products and scientific software.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To address these challenges and to identify opportunities for improved coordination and advocacy within the geodetic community, the International Association of Geodesy&amp;amp;#8217;s (IAG) Global Geodetic Observing System (GGOS) has established a Working Group on &amp;amp;#8220;Digital Object Identifiers (DOIs) for Geodetic Data Sets&amp;amp;#8221;. The GGOS DOI Working Group (with more than 20 members) officially started with a first meeting during December 2019, co-located to the AGU Fall Meeting. Beginning with an assessment of DOI minting strategies that are already implemented, the GGOS DOI Working Group is designated to establish best practices and advocate for the consistent implementation of DOIs across all IAG Services and in the greater geodetic community.&amp;lt;/p&amp;gt;
LibGuides: CRM 430: Correctional Systems: Websites

This course is designed to be a comprehensive overview of our government's response to convicted criminal offenders. The origins, evolution, processes, and current problems of correctional systems will be the topics of study. More specifically, the course
THE USE OF AUDIO VISUAL VIDEO MEDIA ON UNDERSTANDING THE CONCEPT OF DISASTER MITIGATION OF GEOGRAPHIC EDUCATION STUDENTS OF VETERAN BANGUN NUSANTARA SUKOHARJO UNIVERSITY

The learning of Disaster Mitigation Material needs to be given to student teacher’s candidate, so the college students have the understanding about the importance of disaster Mitigation because Indonesia is included in the Pacific Ring of Fire. This research aims to analyze the understanding of disaster mitigation concept of students before and after given learning material by using Audio Visual Video media to the early level college students. This uses quantitative research with an experimental approach. The population on this research are college students at semester 1 &amp; 3 of Geography Education in the odd semester of 2017/2018 academic year with 24 participants. The data collecting techniques use test, student’s worksheet and questionnaire. Data analysis technique uses normality test statistic, homogeneity and T test with the aid of SPSS statistic 21. The results of this research shows that 1) there are differences in the results of student’s understanding of mitigation concepts by using audio-visual video media before and after treatment, 2) by using video, students give a positive respond on the learning process.
More Problems in AI Research and How the SP System May Help to Solve Them (Technical Report)

This technical report, an adjunct to the paper in AI research ..., describes some problems in AI research and how the {\em System} (meaning the SP Theory of Intelligence and its realisation in the SP Computer Model) may help to solve them. It also contains a fairly detailed outline of the System. Most of the problems considered in this report are described by leading researchers in AI in interviews with science writer Martin Ford, and presented in his book Architects of Intelligence. Problems and their potential solutions that are described in this report are: the need for more emphasis in research on the use of top-down strategies is met by the way has been developed entirely within a top-down framework; the risk of accidents with self-driving vehicles may be minimised via the theory of generalisation within the System; the need for strong compositionality in the structure of knowledge is met by processes within the Computer Model for unsupervised learning and the organisation of knowledge; although commonsense reasoning and commonsense knowledge are challenges for all theories of AI, the System has some promising features; the programme of research is one of very few working to establishing the key importance of information compression in AI research; Likewise, the programme of research is one of relatively few AI-related research programmes attaching much importance to the biological foundations of intelligence; the System lends weight to 'localist' (as compared with 'distributed') views of how knowledge is stored in the brain; compared with deep neural networks, the System offers much more scope for adaptation and the representation of knowledge; reasons are given for why the important subjects of motivations and emotions have not so far been considered in the programme of research. Evidence in this report, and in AI research ..., suggests that ***the System provides a relatively promising foundation for the development of artificial general intelligence***.
AIR-Act2Act: Human-human interaction dataset for teaching non-verbal social behaviors to robots

To better interact with users, a social robot should understand the users' behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human-human interaction videos. However, human-human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly-care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human-human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and two college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes and 3D skeletal data that are captured with three Microsoft Kinect v2 cameras. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful python scripts are available for download at https://github.com/ai4r/AIR-Act2Act. It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.
Dialogue with Hou Ying: A Dance Artist’s Reflections on the Crisis of Human Civilization

Creative Arts Educ Ther (2020) 6(1):50–60 DOI: 10.15212/CAET/2020/6/16 Dialogue with Hou Ying: A Dance Artist’s Reflections on the Crisis of Human Civilization 对话侯莹：一个舞蹈艺术家对人类文明危机的思考 Abstract In March 2019, Hou Ying presented a site-specific dance performance called “Immanent fall” as part of the “Civilization: The Way We Live Now” series to an audience in the labyrinth-like exhibition hall in the Center for Contemporary Art in Beijing. Her creation seems to have had an amazing predictive function which was almost identical to what is happening in the world today, the outbreak of COVID-19. In an in-depth interview with Hou Ying and Aimee T. Liu, Hou shared her inspiration for creating the work “Immanent Fall”, and explored her reflections on the crisis that hit human civilization on 23 February, 2020. Keywords: COVID-19, crisis, dance, Hou Ying, human civilization. 摘要 2019年3月，在北京尤伦斯当代艺术中心（UCCA）特别项目《文明：当代生活启示录》迷宫般的展厅中，侯莹为观众呈现了一部特定场域的舞蹈作品《坠入内在》，作品中情景与当下世界正在爆发的2019冠状病毒病疫情场景，简直如出一辙，她的创作似乎具有一种神奇的预言功能。2020年2月23日，在CAET编辑刘婷对侯莹的采访中，她分享了自己创作《坠入内在》的灵感源泉，并探讨了一个舞蹈艺术家对人类文明危机的思考。 关键词：2019冠状病毒病，危机，舞蹈，侯莹，人类文明
Exploring social interactions of headphone users : Behaviour studies and concept design

The development of wireless headphones has put us in a new situation, both considering the product itself and how it is used. Headphones are today used everywhere, by everyone and are more and more ...
Variation Rate of the Value Sense of English Language the Sasak People in the Village Gereneng Subdistrict East Sakra

This Research is a sociolinguistic study aimed to describe the form of variations in the level of language on the value of a sense of language in society Gereneng Sasak village District of East Sakra.Collecting data used observation, interview, and documentation.Analysis of the data used a unified method ekstralingual.These results indicate that a) language Sasak has a very strategic position as a communication tool and unifying role in society of Sasak, b) in addition to functioning as a communication tool, Sasak can also be used as a means of regulating the harmonious relationship within a community and as a filter for the negative impact of advances in science and technology as well as development of the transformation information, and c) use of language Sasak lies in the use of communicative language and maintain the unity of meaning and information by sticking to the principles of language which was based on the identity of the people of Sasak by promoting the value of a sense of language, mutual respect, and civility in relationships.
Time Series Mining at Petascale Performance.

The mining of time series data plays an important role in modern information retrieval and analysis systems. In particular, the identification of similarities within and across time series has garnered significant attention and effort over the last few years. For this task, the class of matrix profile algorithms, which create a generic structure that encodes correlations among records and dimensions—the matrix profile—is a promising approach, as it allows simplified post-processing and analysis steps by examining the resulting matrix profile structure. However, it is expensive to create a matrix profile: it requires significant computational power to evaluate the distance among all subsequence pairs in a time series, especially for very long and multi-dimensional time series with a large dimensionality. Existing approaches are limited in their scalability, as they do not target High Performance Computing systems, and—for most realistic problems—are suited only for datasets with a small dimensionality.
Cloud Computing: Considering Trust as Part of the User Quality of Experience

This chapter shifts focus to the technology and discusses the use of cloud computing and how this is a trend that has many positive aspects for its users. However, the idea of trust comes into play because cloud computing technology is a very abstract concept for many users, and details of its operation are often not revealed. The chapter discusses the technology overall, but quickly shifts focus to a specific example and explores the interaction between a cloud provider and a cloud user from the perspective of trust. Trust between the two interacting parties can affect the user positive experience. This is even a bigger factor when considering a recurring interaction, or an interaction that is continuous over time. The chapter focuses mainly on the perspective of the user. The cloud provider is also significant in the discussion, as well as the elements of chance and overall policy. The overall aim is to discuss the motivation for an interaction that will provide a positive experience for the user and a positive payoff for the cloud service provider, as the interaction progresses in time.
Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems

Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically "black boxes" that work on mathematical models of the problem to solve. However, many problems are difficult to fully specify, and require a "human in the loop" who collaborates with the algorithm by refining the model and guiding the search to produce acceptable solutions. Recently, the Problem-Solving Loop was introduced as a high-level model of such interactive optimisation. Here, we present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop. They range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions. We first examined the applicability of the recommendations by investigating how well they had been supported in previous interactive optimisation tools. We then evaluated the recommendations in the context of the vehicle routing problem with time windows (VRPTW). To do so we built a sophisticated interactive visual system for solving VRPTW that was informed by the recommendations. Ten participants then used this system to solve a variety of routing problems. We report on participant comments and interaction patterns with the tool. These showed the tool was regarded as highly usable and the results generally supported the usefulness of the underlying recommendations.
Literasi Baru Sebagai Bentuk Penanaman Nilai-Nilai Karakter dalam Pembelajaran Bahasa Indonesia MI di Era Disrupsi

Disruption is an era of massive changes in all areas of life as a result of a modern technology. The existence of digital technology support has provided many facilities and innovations obtained for education, services have become faster and more efficient and a wide range of connections. The negative impact also arises from the digitalization of the program, for example the role of humans has been largely taken over by robots or automotive machinery which causes more unemployment.&#x0D; This reality is a big problem, therefore the government needs to answer the challenges of the industrial revolution 4.0, one of which is by providing new literacy reinforcement in the world of education. New literacy is a data on human resources technology or humanism which is a complement to the old literacy, which is read-write-archive.
On the Query Complexity of Constructing PRFs from Non-adaptive PRFs

This paper studies constructions of pseudorandom functions (PRFs) from non-adaptive PRFs (naPRFs), i.e., PRFs which are secure only against distinguishers issuing all of their queries at once. Berman and Haitner (Journal of Cryptology, ’15) gave a one-call construction which, however, is not hardness preserving – to obtain a secure PRF (against polynomial-time distinguishers), they need to rely on a naPRF secure against superpolynomial-time distinguishers; in contrast, all known hardness-preserving constructions require $$\omega (1)$$ calls. This leaves open the question of whether a stronger superpolynomial-time assumption is necessary for one-call (or constant-call) approaches. Here, we show that a large class of one-call constructions (which in particular includes the one of Berman and Haitner) cannot be proved to be a secure PRF under a black-box reduction to the (polynomial-time) naPRF security of the underlying function. Our result complements existing impossibility results (Myers, EUROCRYPT ’04; Pietrzak, CRYPTO ’05) ruling out natural specific approaches, such as parallel and sequential composition. Furthermore, we show that our techniques extend to rule out a natural class of constructions making parallel but arbitrary number of calls which in particular includes parallel composition and the two-call, cuckoo-hashing based construction of Berman et al. (Journal of Cryptology, ’19).
About Graph Degeneracy, Representation Learning and Scalability

Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, vertex classification and missing edge detection have very interesting applications, ranging from drug discovery to recommender systems. To achieve such tasks, tremendous work has been accomplished to learn embedding of nodes and edges into finite-dimension vector spaces. This task is called Graph Representation Learning. However, Graph Representation Learning techniques often display prohibitive time and memory complexities, preventing their use in real-time with business size graphs. In this paper, we address this issue by leveraging a degeneracy property of Graphs - the K-Core Decomposition. We present two techniques taking advantage of this decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets. Our code is available at https://github.com/SBrandeis/kcore-embedding
LibGuides: Data Resources for Poltical Science and Political Scientists: Data Support and Contacts

Services available for students and faculty who want to use datasets, statistical information, and/or geographic data in their work, and the contacts for them. If you need more help or want more information, these are the people to contact.
Shaping Our Common Digital Future

Presents the introductory editorial for this issue of the publication.
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation

The use of AI and ML for scientific applications is currently a very exciting and dynamic field. Much of this excitement for HPC has focused on ML applications whose analysis and classification generate very large numbers of flops. Others seek to replace scientific simulations with data-driven surrogate models. But another important use case lies in the combination application of ML to improve simulation accuracy. To that end, we present an anomaly problem which arises from a core-collapse supernovae simulation. We discuss strategies and early successes in applying anomaly detection techniques from machine learning to this scientific simulation, as well as current challenges and future possibilities.
Research on Application Model of Marine Geological Sampling Model Based on Object-Oriented Relational Mapping

Abstract In the study of marine geology, the sampling technology and preservation are very important links. Marine geology is developed on the basis of marine sediment sampling technology. ORM (Object-Relation Mapping) technology is an effective method to solve the “impedance mismatch” between object model and relation model. The research on object-relation mapping technology has become a research hotspot in the field of software architecture. The development of object-relational database has enhanced the ability of database to support complex data types, making the integrated storage of spatial data, topological relations and attribute data a reality. This paper proposes a class hierarchy based on the technical framework of object-relational mapping, and explains all kinds of classes. The application of this framework can simplify the mapping mechanism on the premise of meeting the requirements of marine geological sampling, thus providing users with an object-oriented way to intuitively access the database.
Six Preludes for Solo Piano

Author(s): Neil, William | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
The Libraries’ contributing to the Victory in the Great Patriotic War

Review of the book: Mazuritsky A. M. Libraries in the years of Great Patriotic War: A learning guide. – Orel, 2020. – 139 p. The reviewed textbook introduces to the history of Russian librarianship during the Great Patriotic War. The structure of the manual is overviewed. The didactic analysis demonstrates the presentation logic which facilitates learning and is of great ethical educative value. In terms of scientific discussion, recommendations are given for further development of this subject scope to cover the following issues: 1) library support facilities for high morale and combative mood of the Red Army (the front-line and navy libraries); 2) library support of healing and bibliotherapy (hospital libraries); 3) library and information support of promising research and development projects (scientific and technical libraries of plants and factories); 4) libraries in the system of cultural, educational, moral and psychological support for the residents of blockaded Leningrad; 5) library support of high morale of the population and its faith in the victory over the enemy. The significance and relevance of the learning guide is emphasized. The reviewer recommends to prepare a textbook on the history of library construction during the Great Patriotic War.