Reviewerly/exHarmony huggingface dataset:
Dashed Strings and the Replace(-all) Constraint

Dashed strings are a formalism for modelling the domain of string variables when solving combinatorial problems with string constraints. In this work we focus on (variants of) the Replace constraint, which aims to find the first occurrence of a query string in a target string, and (possibly) replaces it with a new string. We define a Replace propagator which can also handle Replace-Last (for replacing the last occurrence) and Replace-All (for replacing all the occurrences). Empirical results clearly show that string constraint solving can draw great benefit from this approach.
Quality of Education According to the Assessment of University’s Key Partners

The article addresses the problems of the quality of education in general, including from the perspective of employers. The authors believe that the mechanism for assessing the quality of education should be implemented through the active participation of the University’s key partners and employers in the educational process. The article presents the results of a study of the level of satisfaction with the quality of education of graduates estimated by enterprises that are the key partners of Orenburg State University (2020). The first set of questions involved an analysis of the degree of participation of employer organizations in the educational process and assessment of the level of training of university graduates. The second block included an assessment of personnel needs and demands on graduates when applying for a job, as well as an analysis of the prospects for developing forms of cooperation between employers in the region and the University. In conclusion, we offer a list of recommendations for improving the effectiveness of internal independent assessment of the quality of education with the involvement of representatives of employers. The list includes recommendations aimed at enhancing their participation in improving the system of training for the region’s economy.
NEW APPROACH TO BUILDING A COMPETENCY MODEL FOR LIBRARY PROFESSIONALS

The purpose of the article is to present a new approach to building a competency model for a specialist in library and information activities. Methods used in the work: analysis and generalization of the content of domestic and foreign sources on the problem of professional education. The article presents an analysis of the lists of competencies of the Federal State Educational Standard, opinions of specialists in the field of library and information activities, an approximate list of the scientific (academic) library specialist’s competencies, prepared in 2017 by employees of SPSL SB RAS at the request of the St. Petersburg State Institute of Culture representatives. It is concluded that, taking into account numerous discussions about FSES and the professional standard of a specialist in library and information activities, it is necessary to combine the efforts of representatives of the professional community for to achieve consensus in this area and update the existing regulatory framework. It is also advisable to consider the possibility of creating competency profiles that would form the basis for the above-mentioned documents and ensure interests of organizations providing training in the field of library and information activities and institutions-employers. Novelty. Complex approach is proposed to build a competency model for a specialist in library and information activities, based on integration of approved regulatory documents and employers’ requests: for each group of competencies the basic minimum should be determined (approved by the federal authorities responsible for the state supervision and ensuring compliance of requirements in composition of labor functions, knowledge and skills in approved documents) and the variable part, ensuring compliance of competencies being formed with particularities of libraries of various types (public, academic, special). This requires development of a regulatory framework in the field of library and information activities (updating existing and developing new documents). The proposed competency model correlates with regulatory documents. Practical significance: a competency model is proposed, within which educational organizations, institutions of the library and information sphere can jointly form an effective process for training specialists.
ASP (): Answer Set Programming with Algebraic Constraints

Weighted Logic is a powerful tool for the specification of calculations over semirings that depend on qualitative information. Using a novel combination of Weighted Logic and Here-and-There (HT) Logic, in which this dependence is based on intuitionistic grounds, we introduce Answer Set Programming with Algebraic Constraints (ASP(AC)), where rules may contain constraints that compare semiring values to weighted formula evaluations. Such constraints provide streamlined access to a manifold of constructs available in ASP, like aggregates, choice constraints, and arithmetic operators. They extend some of them and provide a generic framework for defining programs with algebraic computation, which can be fruitfully used e.g. for provenance semantics of datalog programs. While undecidable in general, expressive fragments of ASP(AC) can be exploited for effective problem-solving in a rich framework. This work is under consideration for acceptance in Theory and Practice of Logic Programming.
Defining TestOps: Collaborative Behaviors and Technology-Driven Workflows Seen as Enablers of Effective Software Testing in DevOps

Abstract Context: DevOps is an increasingly popular approach to software development and software operations. Being understood as mutually integrated, both activities have been re-united under one single label. In contrast to traditional software development activities, DevOps promotes numerous fundamental changes, and the area of software testing is not an exception. Yet, the exact appearance of software testing within DevOps is poorly understood, so is the notion of TestOps. Objective: This paper explores TestOps as a concept rooted in industrial practice. Method: To provide a pluralist outline of practitioners’ views on What is TestOps , the YouTube platform was searched for digital content containing either “TestOps” or “DevTestOps” in the content title. Through a qualitative lens, the resulting set was systematically annotated and thematically analyzed in an inductive manner. Results: Referring to DevOps, practitioners use the notion of TestOps when characterizing a conceptual shift that occurs within the area of software testing. As a matter of fact, two dominant categories were found in the data: (i) TestOps as a new organizational philosophy; (ii) TestOps as an innovative software technique (i.e. process supported by technology). A set of high-level themes within each of these categories was identified and described. Conclusion: The study outlines an inconsistency in practitioner perspectives on the nature of TestOps. To decrease the identified conceptual ambiguity, the proposed model posits two complementary meanings of TestOps.
Recursive Quad-Tree Block Partitioning for Data Embedding in Images

In this paper, we propose to embed messages in raw images using a recursive block partitioning technique adopted from HEVC video coding technology. This work introduces a quad-tree partitioning solution in which square blocks of pixels are partitioned recursively according to a homogeneity criterion. As such, large block sizes are allocated for homogenous areas of the image and smaller block sizes are used elsewhere. For this to work, the variable block sizes should cover the whole image without gaps. Additionally, the criterion used for determining the block partitioning should work for both message embedding and message extraction. As such, the extractor process can reproduce the same variable block sizes. Matrix encoding is used for message embedding and extraction, as such, a maximum of one-pixel value is changed per block. The proposed solution is assessed in terms of percentage of changed pixels, PSNR, SSIM, histogram changes and blind steganalysis. Comparison with existing work reveals that the proposed solution reduces both the image distortions and pixel change rates. With embedding rates ranging from 2.5 to 38%, the average PSNR of the cover images ranged from 72 to 60 dB, and the average normalized histogram differences ranged from 3 to 12 pixels. It is also shown that the proposed solution is less detectible when tested with blind steganalysis in comparison to existing solutions where the average detection accuracy is 49.8%.
Bidirectional LSTM with Attention Mechanism for Automatic Bangla News Categorization in Terms of News Captions

The aim of any classification problem is to create a set of models that can classify the class of different texts and objects. Text classification is known as one such application. This problem can be used in various classification tasks, e.g., news category classification, identifying language, classification of text genre, recommendation systems, etc. In this paper, we propose a text classification method using bidirectional LSTM with attention mechanism to classify Bangla news articles. This news articles are collected from a renowned a news portal Prothom Alo. The dataset consists of in total 383,304 news articles and there were total number of 12 different categories. Traditionally, news classification task is done in terms of news content. But in our work, we have performed classification based on the news captions which takes lesser amount of training time. We have achieved 91.37% accuracy using our approach. This is the state-of-the-art result that has achieved on this dataset.
The Identification of Micro-hydro Power Plants Potential in Irrigation Areas Based on Unmanned Air Vehicle (UAV) Image Processing

Estimation of the energy demand in Indonesia will increase by 8.15 % annually until 2030. Indonesia is committed to preventing climate change through mix energy (new and renewable energy). One of the sources of this energy is waterpower plants with a potential of 75 091 MW have been utilized 6.4 %, mini-hydro, and micro-hydro with a potential of 19 385 MW have been utilized 1 %. In the Industrial Revolution 4.0, it is doable to identify such potential using drone. This study was conducted in the Kalibawang irrigation area, Special Region of Yogyakarta, Indonesia. The procedure starts with the generation of detailed Aerial Photography using Drone DJI Phantom 4. The images from aerial photography were then processed into high-resolution Digital Elevation Model (DEM). DEM data represent the study area topography and can be used to identify the stream gradient potential or height difference (H). Six potential points were identified with height differences between 3 m to 8 m, resulting in the potential generated power ranging from 125 kW to 334 kW. This study demonstrates the identification of micro-hydro energy potential points, height difference, and generated power using Drone. Six points were identified with total potential energy 1 418 kW.
Multi-Adversarial Variational Autoencoder Nets for Simultaneous Image Generation and Classification

Discriminative deep-learning models are often reliant on copious labeled training data. By contrast, from relatively small corpora of training data, deep generative models can learn to generate realistic images approximating real-world distributions. In particular, the proper training of Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) enables them to perform semi-supervised image classification. Combining the power of these two models, we introduce Multi-Adversarial Variational autoEncoder Networks (MAVENs), a novel deep generative model that incorporates an ensemble of discriminators in a VAE-GAN network in order to perform simultaneous adversarial learning and variational inference. We apply MAVENs to the generation of synthetic images and propose a new distribution measure to quantify the quality of these images. Our experimental results with only 10% labeled training data from the computer vision and medical imaging domains demonstrate performance competitive to state-of-the-art semi-supervised models in simultaneous image generation and classification tasks.
Hiding Patient Information in Medical Images: A Robust Watermarking Algorithm for Healthcare System

Electronically transmission of the medical images is an essential prerequisite in a Healthcare system. During transmission, the medical images can be hacked and altered either partially or in totality, unless aptly safeguarded. Digital watermarking schemes are being utilized to protect the integrity of the medical image. Digital watermarking is a technique of embedding watermark in a medical image, to maintain confidentiality and security to the inserted watermark. The watermark used in the medical image can either be an image, text, audio, or video. Electronic Patient Record (EPR) in an image form is used as a watermark. A non-blind watermarking scheme for healthcare system using Discrete Cosine Transform (DCT) is presented in this paper. Block-based Cosine transformation is applied to the host medical image to obtain different frequency coefficients. The middle frequency band of Cosine transformation is considered for embedding watermark, as this frequency band provides an extra robust resistant to watermark. Before hiding the EPR information in the image, binary EPR information is encoded using Convolution Error Correcting Code (ECC) and decoded using Viterbi decoder, to enhance the accuracy of the detection process. To provide additional robust to the coded EPR data, M-ary modulation is further applied before embedding. The imperceptibility and robust performance evaluation of the scheme is tested on standard online Digital Imaging and Communications in Medicine (DICOM) brain image database by changing the watermark embedding factor. The proposed scheme is tested using various performance measures and is observed to be highly robust and imperceptible.
Enhancing cloud computing security by paillier homomorphic encryption

In recent years, the trend has increased for the use of cloud computing, which provides broad capabilities with the sharing of resources, and thus it is possible to store and process data in the cloud remotely, but this (cloud) is untrusted because some parties can connect to the network such as the internet and read or change data because it is not protected, therefore, protecting data security and privacy is one of the challenges that must be addressed when using cloud computing. Encryption is interested in the field of security, confidentiality and integrity of information that sent by a secure connection between individuals or institutions regardless of the method used to prepare this connection. But using the traditional encryption methods to encrypt the data before sending it will force the data provider to send his private key to the server to decrypt the data to perform computations on it. In this paper we present a proposal to secure banking data transmission through the cloud by using partially homomorphic encryption algorithms such as (paillier, RSA algorithm) that allow performing mathematical operations on encrypted data without needing to decryption. A proxy server will also use for performing re-encryption process to enhance security.
LBD Server: Visualising Building Graphs in Web-Based Environments Using Semantic Graphs and GlTF-Models

Over the last years, Building Information Modelling (BIM) has been increasingly adopted in building projects for its benefits to collaboration and associated risk mitigation. Recent work is trying to make this data available over the web (web-based BIM applications). Most notable in making building data web-ready is the linked building data (LBD) initiative. This group has worked on a number of vocabularies and ontologies, thereby starting from a linked data approach (RDF graphs), aiming at representing building data on the web. Yet, a platform is needed that allows to manage the available linked building data, including 3D geometric visualisation, which is the topic of this article: a LBD server. The LBD server is a web-based application which allows users to upload building data and visualise their geometric representation in a graphical user interface, thereby also enabling to link to this data.
Automatic Modal Parameter Identification with Methods of Artificial Intelligence

The increasing use of operational and experimental modal analysis provides experienced and inexperienced users always face new challenges. Automated evaluation, as a tool for distinguishing between physical and mathematical poles and for removing uncertainties in the accuracy of results, is becoming increasingly important (Jenny Lau, et al.: “Automatic modal analysis: reality or myth?”). This work deals specifically with the description of extreme values and their automated selection in indicator functions. The developed method is based on methods of pattern recognition and artificial intelligence and does not require any further parameters and or expert knowledge for later execution. The functional values of the indicator functions are represented by a simple feature vector with small dimensions and classified by a “Support Vector Machine” (SVM). Since the characteristics describe the function course, they are independent of the measurement parameters, such as sampling rate or frequency spacing. To increase robustness, existing methods from the field of “computer vision” were adapted and new ideas developed. Due to the versatile applicability of the method, an enormous database is required for the learning process. Therefore, a method for the creation of a training basis with synthetic data was developed. This covers a very wide range of applications and special cases, such as modes that are close together or strongly damped. In addition, the influence of inaccuracies, such as sensor noise, was modelled.
Scientific libraries in the structure of Novosibirsk Scientific-Educational Complex

The study objective is to determine the potential of the city scientific libraries to support institutions of the scientific and educational complex with information. Using a wide range of statistical data, the authors describe the resource base development of scientific libraries in Novosibirsk including libraries of the Siberian Branch of the Russian Academy of Sciences and higher educational institutions. They show the influence of the scientific and educational space transformation on certain areas of scientific libraries activity. The article updates existing ideas on the component structure of the educational space interdependent subjects. Taking into account factors of a social nature affecting the structure of the scientific and educational environment of Novosibirsk, the authors characterize the scientific libraries potential, their documentary resources, and substantiate that accelerating the leading universities and scientific institutions integration motivates scientific libraries of different statuses to develop new models of their corporate interaction. Under the city scientific libraries disproportionately provision with information resources, it is advisable to form an adequate model for all participants interaction in the scientific and educational process, and create a system of informing about the resource capabilities of the entire conglomeration of city scientific libraries based on navigation tool application. Converging the resource base of the city scientific libraries should have a compensatory value for information support of scientific and educational activities.
Teaching English Through Literature-Based Instruction: An Integrated Study of Language and Literature

The study of language has been increasingly inseparable from the study of literature (Novianti, 2016).Therefore, it is suggested that working with literature in the classroom focus on integrating language and literature for more comprehensive and enjoyable learning activities.Literature-based instruction is one of the teaching language strategies by using literary works that are suitable for teaching English.In line with this, this study aims to explain the integrated study of language and literature in teaching English through literature-based instruction in EFL classroom.This is a qualitative case study conducted in an Indonesian Islamic Junior High School at Makassar, South Sulawesi, Indonesia.Data were obtained through interview, observation, and documentation techniques of data collection.The data gathered were analyzed qualitatively through Miles and Huberman stages of condensation, display, and drawing conclusion.The study result showed that the literature-based instruction in teaching English for EFL students in an Indonesian Islamic Junior High School is very necessary to be developed and practiced.This integrated study of language and literature is practically rich of activities due to the teaching purposes, namely (1) reading and literary work, (2) speaking and literary work, (3) listening and literary work, and (4) writing and literary work.Indeed, teaching English through literature-based instruction is an evidence that language and literature can be taught two in one formula as an integrated study to develop the students' ability in both language and literature skills.
Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications

Medical image analysis benefits Computer Aided Diagnosis (CADx). A fundamental analyzing approach is the classification of medical images, which serves for skin lesion diagnosis, diabetic retinopathy grading, and cancer classification on histological images. When learning these discriminative classifiers, we observe that the convolutional neural networks (CNNs) are vulnerable to distractor interference. This is due to the similar sample appearances from different categories (i.e., small inter-class distance). Existing attempts select distractors from input images by empirically estimating their potential effects to the classifier. The essences of how these distractors affect CNN classification are not known. In this paper, we explore distractors from the CNN feature space via proposing a neuron intrinsic learning method. We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space. The novel loss is combined with the original classification loss to update network parameters by back-propagation. Neuron intrinsic learning first explores distractors crucial to the deep classifier and then uses them to robustify CNN inherently. Extensive experiments on medical image benchmark datasets indicate that the proposed method performs favorably against the state-of-the-art approaches.
Viewing Style of Augmented Reality/Virtual Reality Broadcast Contents while Sharing a Virtual Experience

A conceptual space-sharing broadcasting service has been proposed, using augmented reality/virtual reality (AR/VR) with a head-mounted display. As proposed, virtual performers are displayed in their real-life sizes; the user experiences proximity to them. Family and friends living apart can also be displayed in this manner, and an individual can communicate with them in real time; this enables both the individual and their peer to enjoy the broadcast media together. In this study, we implemented this concept as a suitable style for daily use and confirmed the effect of the viewing experience. We developed a prototype of an environment for watching AR/VR mixed content along with a person in a distant place, which is expected to become a popular viewing style of future broadcast media. The individual is displayed as a live-action 3D point cloud image, and verbal and nonverbal communication with the individual are enabled. A demonstration showed that the system renders a sense of presence to a distant person and provides the feeling of sharing the same experience among all its users.