Reviewerly/exHarmony huggingface dataset:
Mapping of folds and faults in metamorphic complexes of the Central Aldan and Prikolyma terranes using geophysical data

The study of the structure of the Precambrian crystalline complexes raised and exposed along the margins of the North Asian craton allows us to characterize the geological features of various stages of the evolution of the lithosphere. Based on these data identification of the promising metallogenic zones and local potentially ore-bearing objects can also be achieved. The goal of the performed research was to create objective models of the areas characterized by the presence of mineral deposits to identify characteristics and promising structures. Using modern geoinformation systems, the maps of the high gradient zones of the local components of geophysical fields have been created. These maps reflect the boundaries of geological complexes with contrasting physical properties. For clarity, the geophysical background was removed from the maps, and only the zones of high gradients of various geophysical fields were left. A combination of maps was performed and it was found that high-gradient zones of the various geophysical fields complement each other along the strike. Based on the obtained geophysical schemes, geological maps and field observations of geological structures, already known and new folds and faults have been deciphered. As a result, models of folds and faults that control the location of mineral resources were created. Models allow finding the common features and evaluating the minerals prospects of areas. In the framing of the Upper-Timpton dome of the Central-Aldan superterrane and in the Shamanikhin fault zone of the Prikolyma terrane, there are many high-gradient zones of the various geophysical fields observed. New ore objects can be expected to be discovered in these areas.
Machine Learning for Building Literary Mapping Geoinformation System

The article is devoted to the development of a web-oriented geoinformation system intended for literary mapping. Interest in literary mapping has recently increased, as this method of exploring the space in which characters operate not only provides a key to understanding the author's intent, but also allows the reader to take a new look at the artistic text. With the help of mapping, the reader can see how the space created in the artwork is superimposed on the prototype place, trace the routes of characters, and highlight spatial objects that are important for the development of the action. In addition, mapping can serve the purpose of creating an image of a territory through its associated territories. The methods of text vectorization and corpora linguistics are used for analyzing artistic texts. Machine learning methods such as the "transformers" neural network and production knowledge bases are used to extract structural elements of a text and group texts by various attributes. The tool is in the prototype state and is planned to be placed in the public domain.
4D-DCT Hardware Architecture for JPEG Pleno Light Field Coding

This paper presents a 4D-DCT hardware architecture for Light Field Coding according to the JPEG Pleno standard. It is composed of two instances of 2D-DCT engines and a novel 4D Transposition Memory organization. Experimentally-defined fixed-point representation and LSB pruning techniques are employed do reduce hardware area and power dissipation. The proposed architecture operates over 4D-hypercubes of up to 8x8x8x8 samples and reaches performance to process 30 Lytro-like light fields per second dissipating 145.32mW at 825.75MHz. This is the first known 4D-DCT hardware architecture for light field coding and demonstrates the feasibility of such solutions on real-world systems.
How colored passwords can improve the usability of coercion-resistant internet voting systems

Coercion is an inherent problem of any Internet election. Because there is no voting booth to protect the voter while she is voting, a coercer can easily influence them to choose his candidates. In order to mitigate these problems, coercion-resistant voting protocols employ the idea of voting credentials. Voters use their legitimate credentials to authorize the corresponding votes that will appear in the final tally. When voters are under coercion, they use fake credentials to deceive adversaries about their true vote intention. Credentials, however, are usually composed of large numeric values and have low usability to be used in real world elections. As a result, voters can incorrectly manage them and thus undermine the coercion-resistance. In this context, this work proposes a novel method to improve the credential usability called colored passwords. The new method is based on a known password technique and abstracts the numeric value of the credential. To show the application of the novel method, this work also applies the new method to a coercion-resistant voting system.
Who Cares Where? A Pivotal Ethnographic Study for Italian Hospital Homecare

The case study presented is an in‐depth view on the project “Casa nel Parco” (translated as “the House in the Park”), a three‐year, European‐funded project (ERDF Funds 2014‐2020) in the Italian region of Piedmont that involves 4 hospitals, 2 large companies, 14 small‐medium enterprises, 2 universities, and 2 private research centers. The goal is to research and innovate hospital‐homecare services for elderly and ALS patients, as well as their caregivers, through the implementation of e‐health solutions. The uniqueness of our case study lays on the fact that our ethnographic work was pivotal in shifting the narrative of closed hospital ecosystems (Goffman 1961); where those outside of the hospital environment are not viewed as credible or essential sources for improving the care system. In this study we share how we built trust and negotiated a complex network of stakeholders and technical systems to successfully influence the design of homecare services. Ethnography played a major role in identifying user requirements (patients, caregivers, healthcare professionals) and in building scenarios for product and service prototype development, technology adaptation, and testing. In this way, ethnography and design were used as holistic and critical approaches to addressing health challenges and change. Successful implementation could therefore be considered as a socio‐technical design challenge, rather than a pure technological design challenge, emphasizing a non‐conflationary approach in which the social and the material are held apart for the purpose of exploring their interplay (Mutch, 2013). The key success that ethnography brought was in identifying how technical systems that were designed in the abstract could be used for the very real and specific problems faced by the elderly and ALS patients needing homecare.
Long cycle of random permutations with polynomially growing cycle weights

We study random permutations of n objects with respect to multiplicative measures with polynomial growing cycle weights. We determine in this paper the asymptotic behavior of the long cycles under this measure and also prove that the cumulative cycle numbers converge in the region of the long cycles to a Poisson process.
Enforcing Corporate Governance's Internal Controls and Audit in the Cloud

More and more organizations are today using the cloud for their business as a quite convenient alternative to in-house solutions for storing, processing, and managing data. Cloud-based solutions are then permeating almost all aspects of business organizations, resulting appealing also for functions that, already in-house, may result sensitive or security critical, and whose enforcement in the cloud requires then particular care. In this paper, we provide an approach for securely relying on cloud-based services for the enforcement of Internal Controls and Audit (ICA) functions for corporate governance. Our approach is based on the use of selective encryption and of tags to provide a level of self-protection to data and for enabling only authorized parties to access data and perform operations on them, providing privacy and integrity guarantees, as well as accountability and non-repudiation.
High-Throughput Image Alignment for Connectomics using Frugal Snap Judgments

The accuracy and computational efficiency of image alignment directly affects the advancement of connectomics, a field which seeks to understand the structure of the brain through electron microscopy. We introduce the algorithms Quilter and Stacker that are designed to perform 2D and 3D alignment respectively on petabyte-scale data sets from connectomics. Quilter and Stacker are efficient, scalable, and can run on hardware ranging from a researcher's laptop to a large computing cluster. On a single 18-core cloud machine each algorithm achieves throughputs of more than 1 TB/hr; when combined the algorithms produce an end-to-end alignment pipeline that processes data at a rate of 0.82 TB/hr - an over 10x improvement from previous systems. This efficiency comes from both traditional optimizations and from the use of “Frugal Snap Judgments” to judiciously exploit performance-accuracy trade-offs. A high-throughput image-alignment pipeline was implemented using the Quilter and Stacker algorithms and its performance was evaluated using three datasets whose size ranged from 550 GB to 38 TB. The full alignment pipeline achieved a throughput of 0.6-0.8 TB/hr and 1.4-1.5 TB/hr on an 18-core and 112-core shared-memory multicore, respectively. On a supercomputing cluster with 200 nodes and 1600 total cores, the pipeline achieved a throughput of 21.4 TB/hr. We introduce the algorithms Quilter and Stacker that are designed to perform 2D and 3D alignment respectively on petabyte-scale data sets from connectomics. Quilter and Stacker are efficient, scalable, and can run on hardware ranging from a researcher's laptop to a large computing cluster. On a single 18-core cloud machine each algorithm achieves throughputs of more than 1 TB/hr; when combined the algorithms produce an end-to-end alignment pipeline that processes data at a rate of 0.82 TB/hr - an over 10x improvement from previous systems. This efficiency comes from both traditional optimizations and from the use of “Frugal Snap Judgments” to judiciously exploit performance-accuracy trade-offs. A high-throughput image-alignment pipeline was implemented using the Quilter and Stacker algorithms and its performance was evaluated using three datasets whose size ranged from 550 GB to 38 TB. The full alignment pipeline achieved a throughput of 0.6-0.8 TB/hr and 1.4-1.5 TB/hr on an 18-core and 112-core shared-memory multicore, respectively. On a supercomputing cluster with 200 nodes and 1600 total cores, the pipeline achieved a throughput of 21.4 TB/hr. A high-throughput image-alignment pipeline was implemented using the Quilter and Stacker algorithms and its performance was evaluated using three datasets whose size ranged from 550 GB to 38 TB. The full alignment pipeline achieved a throughput of 0.6-0.8 TB/hr and 1.4-1.5 TB/hr on an 18-core and 112-core shared-memory multicore, respectively. On a supercomputing cluster with 200 nodes and 1600 total cores, the pipeline achieved a throughput of 21.4 TB/hr.
B/S Transformation of Power Communication Integrated Network Management System Based on RIA/Flex

In order to adapt for the existing power network management system business application model to the new conditions and to take full advantage of the hardware and software investment on the same time, based on the business logic reuse of the spring/ActiveMQ extension methods under J2EE architecture, we manage to realize browser-interface B/S transformation based on the RIA/Flex demonstration technology for the management system, which improves the maintainability and usefulness of the original function of the system, and provides a useful reference for other systems B/S transformation.
Minimizing Cost and Maximizing Performance for Cloud Platforms

We are witnessing the rapid growth of cloud computing with the proliferation of tenants adopting cloud for elasticity, availability, and flexibility for a plethora of applications. To efficiently cater for different tenant requirements, cloud providers have steadily evolved to offer a myriad of resource and service types which inherently complicates the cloud adoption process. On the other hand, the perpetuating growth of cloud tenants in turn impel providers to expand datacenters to cope with the tenant demand. The objective of this proposal is to maximize the performance and minimize the cost for both tenants and cloud providers, by providing efficient means of managing resource allocations for their applications. Towards this, the proposal comprises of three intertwined tasks. First, we start from a tenant perspective, with the first two tasks aimed at investigating the primary reasons for performance-cost inefficiency. Second, from a provider perspective, the third task investigates the primary reasons for performance-energy inefficiency in datacenters. All the three tasks can collectively improve the performance and cost efficiency of emerging applications in next generation cloud platforms.
Convolutional Neural Networks: A Novel Approach for Sinhala Sign Recognition System

Communication is a well-needed factor in the community to convey messages to each other. Communication between the hearing impaired people and the ordinary people can be considered as one of the major barriers in today's communication arena. This issue has been addressed in many situations globally, but in a country like Sri Lanka, the problem still exists and no feasible solutions have been introduced yet. Even though there are some proposed solutions, they are not feasible enough to be implemented in order to overcome this communication bottleneck. Considering the real-world scenarios and to address the above mentioned issues and limitations, the authors propose a novel vision-based approach for sign gesture recognition to fulfill the communication barrier. In this approach, a Convolutional Neural Network has been used to enable the realworld knowledge, how a human really detects the images and identify the reality. This CNN based system is a subcategory of neural networks that have proven extremely effective for tasks such as image recognition and classification. Our proposed solution has been able to achieve high accuracy of 89.9% in the image classification process with an error rate of 10.1%.
Graph-based Deep Learning Analysis and Instance Selection

While deep learning is a powerful tool for many applications, there has been only limited research about selection of data for training, i.e., instance selection, which enhances deep learning scalability by saving computational resources. This can be attributed in part to the difficulty of interpreting deep learning models. While some graph-based methods have been proposed to improve performance and interpret behavior of deep learning, the instance selection problem has not been addressed from a graph perspective. In this paper, we analyze the behavior of deep learning outputs by using the K-nearest neighbor (KNN) graph construction. We observe that when a directed KNN graph is constructed, instead of the more conventional undirected KNN, a large number of instances become isolated nodes, i.e., they do not belong to the directed neighborhoods of any other nodes. Based on this, we propose two new instance selection methods, that both lead to fewer isolated nodes, by either directly eliminating them (minimization approach) or by connecting them more strongly to other points (maximization). Our experiments show that our proposed maximization method leads to better performance than random selection and recent methods for instance selection.
LEARN Board Game

Software Architecture specifies the set of decisions about the software components, their external properties and their relationships with other elements. These activities have a direct impact on the software quality, requiring architects and developers to know and to apply appropriately the architectural concepts and standards for their decision making involving the Software Architecture. Due to this, we identified a need for designing an approach that would dynamize the teaching and learning process in Software Architecture. Educational games are an alternative for teaching in several Computing areas. Therefore, this research proposes the creation and application of LEARN (LEarning software ARchitecture fundameNtals) board game, a game for teaching architectural concepts and standards in an interactive way. We created the game using the Design Science Research methodology. We evaluated the game through a case study with undergraduate students in Software Engineering, attending the Software Architecture discipline. We used a questionnaire based on the MEEGA+ model for data collection. The results show that LEARN board game has a good usability and provided a good learning experience for the students. The LEARN new version is available for adoption in Software Architecture learning.
Using information and communication technologies in teaching the academic discipline “Lifecycle management of information systems”

Abstract The results of the research on the application of information and communication technologies in teaching the discipline “Lifecycle management of information systems” in higher education institutions majoring in “Business Informatics” are presented in the article. The choice of methods, notations and modeling tools supporting the life cycle of information systems creation was carried out on the basis of peer inspections. The information for the execution of an examination was systematized in the form of passports of methods, notations and modeling tools. The package of educational materials, including interactive lectures, laboratory work, business games, independent tasks that form students’ professional competencies, was created using the selected techniques, notations and modeling tools. It is proposed to include elements of a package of training materials when teaching other engineering disciplines related to the creation of various automated systems, including information and software systems.
Distributed Principal Component Analysis for Real-time Big Data Processing

Real-time big data analytics, which is the combination of real-time analytics and big data, works on processing large scale data as it arrives and strives to obtain insights from it without exceeding a limited time period. Massive amount of data is being generated every moment through web sites, social networks, scientific experiments, etc. which is stored in the cloud. When decision making requires an insight of the raw data in real-time (often by applying machine learning algorithms such as dimensionality reduction), these algorithms fail to analyze these incessantly flowing high volume data i.e dynamic big data. Our work proposes a variant of scalable principal component analysis (PCA) which is suited for real-time big data applications. We maintain a sliding window (representing incoming data in real-time) over the most recent data and project every incoming data into lower dimensional subspace. Our goal is to minimize the reconstruction error of the output from the input and keep updating the principal components depending on it. We have implemented our scalable algorithm on popular Spark framework for distributed platform and performed extensive experiments on datasets from a variety of real-time applications e.g. activity recognition, customer expenditure, etc. Furthermore, we have demonstrated that our algorithm can capture the changing distributions of real-life datasets, thus enabling real-time PCA. We have also compared the performances of distributed and non-distributed versions of our algorithm over a variety of window sizes and showed that our distributed algorithm is scalable and performs better when window size and target dimension increase.
Universal Adversarial Perturbations and Image Spam Classifiers

As the name suggests, image spam is spam email that has been embedded in an image. Image spam was developed in an effort to evade text-based filters. Modern deep learning-based classifiers perform well in detecting typical image spam that is seen in the wild. In this chapter, we evaluate numerous adversarial techniques for the purpose of attacking deep learning-based image spam classifiers. Of the techniques tested, we find that universal perturbation performs best. Using universal adversarial perturbations, we propose and analyze a new transformation-based adversarial attack that enables us to create tailored “natural perturbations” in image spam. The resulting spam images benefit from both the presence of concentrated natural features and a universal adversarial perturbation. We show that the proposed technique outperforms existing adversarial attacks in terms of accuracy reduction, computation time per example, and perturbation distance. We apply our technique to create a dataset of adversarial spam images, which can serve as a challenge dataset for future research in image spam detection.
Pelatihan penggunaan aplikasi raport dan e-learning untuk perwakilan guru SD/MI pada Badan Kerja Sama Sekolah (BKS) SD/MI Muhammadiyah/ Aisyiyah Kabupaten Bantul

Management of final semester grades or report cards is one of the routine activities carried out by every educator. Based on the results of questionnaires from teachers in SD / MI Bantul Regency, the management of report cards so far is still experiencing difficulties because they still use MS Excel, the time waiting for grades from other teachers, makes the management of grades less effective and efficient. In addition to that there are limitations to teachers in managing classes online, teachers still use WhatsApp media in distributing materials and managing classes. Therefore, in this service, the development and training of report cards application and E-learning training were carried out. The method used in this service is pre-implementation, training, mentoring and evaluation. Pre-implementation includes making a report card application, video tutorials, training modules. The training was held for 4 days starting on 2, 3, 5, and 6 October 2020 from 08.00 to 12.00 and took place at Aisyiyah Bantul Primary School. Mentoring is carried out by distributing video tutorials and training modules to assist teachers in filling out report cards and the sustainable use of E-learning. Evaluation is used to make improvements for the next service.The result of this service is the existence of a report card application and learning videos for each teacher. With this training, it has a big enough impact on teachers, namely 80% of the teachers find it easier to use the report card application and feel helped in managing the class using E-Learning.
An Efficient Compression Scheme for Bangla Natural Text by 16 Bit Unicode Transformation

Text compression is an important area of research for natural language processing community. Due to huge number of characters in Unicode, it takes 16 bits to manage the total codes. Each of the language has a block of codes. We describe a technique to compress the 16 bit Unicode Bangla natural text along with English characters by 8-bit encoding. The encoding and decoding schemes consist of two steps. In step1 we replace some frequently used Bangla phrases with the extended ASCII characters and in the second step we assign the ASCII characters by an 8-bit binary value. We create a bit stream by appending the sequence of the binary values that we get after encoding the text. From this bit stream every 16 bit is taken and converted into a Unicode character. It creates an arbitrary sequence of Unicode characters with the values of the encoded bit stream. The scheme guarantees at least 50% compression in the worst case and 63% in the average case. This Unicode character sequence is then stored or transmitted. To decode it, we use the reverse process that was used for encoding. Finally we compare our result with well-known Huffman and LZW algorithm. Experimental result shows the effectiveness of the scheme.