Reviewerly/exHarmony huggingface dataset:
eXeLearning como herramienta para la virtualización de la enseñanza: el diseño de Objetos de Aprendizaje para el estudio del paisaje urbano

The design and implementation of Learning Objects can contribute to the virtualization of teaching, enabling develop our materials or adapt them for a particular teaching situation. This fact becomes of special relevance in circumstances in which we must adapt face-to-face teaching to virtual teaching or under pedagogical approaches based on the flipped classroom. In this paper, we will describe an initiative in which, we have designed with eXeLearning a Learning Object for the study of the urban landscape. eXeLearning allows the creation of diverse, accessible, and friendly resources, allowing instructors to save time in preparing learning materials and facilitating their adaptation to different educational and social needs. We apply this initiative, developed thanks to a Project for Educational Innovation, within the degree of Geography and Spatial Planning of the UPV/EHU, but it could be adapted to other levels of education.
Comparative Analysis of Genetic Algorithm with Swarm Intelligence Algorithms

Traveling Salesman problem is the most recognized NP-hard problem for the researches in the field of computer science which focused on optimization. TSP finds the minimum travelling distance between given set of cities by traversing each of these cities only once except the starting city. This study discusses the salesman problem by presenting Particle Swarm Optimization Algorithm (PSO), Ant Colony Optimization algorithm (ACO) and Genetic Algorithm (GA) which intends to solve the problem. In this paper, these algorithms are applied on the benchmark TSP dataset Oliver 30. Also, the paper provides comparison between these algorithms and this comparison helps to choose the better algorithm.
Online Gruplarda İletişimsel Bellek: Aracılı ve Müşterek Bilgi

Online social networks enable the creation of groups within a wide variety of topics. Social network sites, like Facebook, offer options for users to create groups or to join existing groups for any topic they are interested in. The aim of this study is to investigate the function of the communicative memory, accepted in the literature as a notion providing continuity of communication, in online groups. In-depth interviews are conducted with seven participants who are members of the same Facebook group in the study in order to investigate and discuss the parameters of communicative memory, its role in-group communication and its effect on group solidarity in the online groups. The data obtained from the interviews are analyzed in the context of communicative memory. In the light of data and evaluations, it is concluded that each of the posts shared in Facebook groups is cumulatively generating their own collective knowledge; it can be said that communicative memory supports this commonness and provides the transmission of fragmented information.
G2R Crypto Wallet using Indian language Mnemonics

We present the design of a G2R Crypto Wallet here. BIP39 uses random mnemonics. The mnemonic library is added as a separate .bsv file. The bsv file contains word lists from varied languages - English, Japanese, Spanish, French, Italian, Portuguese, Russian etc. But it does not contain any Indian language mnemonics. We have created .bsv files for Hindi and Tamil language. In this paper we present how Indian language mnemonics are created and the design of a G2R crypto wallet. G2R crypto wallet uses a random function generator and a HMAC cocktail. This paper also justifies the choice of selection of SRFG and Cocktail for the design of G2R crypto wallet.
A Data Warehouse System for University Administration with UML Schema and Relational Decisive Approach

Data warehouse serves as a primary component in decision making systems by relying on multidimensional models. Multidimensional models provide a business-oriented view of data to decision makers for easy analysis and navigation. The main goal of this paper is to design a hybrid methodology depending on various multidimensional models. Intricate problems on classification require a machine learning technique called Decision tree to solve the issues in decision making. Standard algorithms for decision tree suffer from their inability in processing imprecise, meaning uncertain, incomplete and imperfect data. In this paper, a new approach for decision tree is developed using an integration of relational decisive rough set theory with improved ID3 algorithm. The proposed system illustrates how the methodology can be applied to the university environment. The performance of the system is evaluated and compared with conventional rough set methodologies. The designed model shows an improvement in the quality of conceptual schema and addresses the issues related to computational overhead.
Generic complexity of solving of equations in finite groups, semigroups and fields

Abstract The paper is devoted by investigation of generic complexity of the algorithmic problem of solving of systems of equations in finite groups, finite semigroups and finite fields. We show that if this problem is intractable in the worst case and P = BPP, then there is no polynomial strongly generic algorithm, which solves it. The first author is supported by Russian Science Foundation, grant 19-11-00209. The second author is supported by Russian Science Foundation, grant 18-71-10028.
Haze Identification and Classification Model for Haze Removal Techniques

In the modern age, several applications depend on the information collected from the images and its classification. However, due to the poor climatic conditions, the images captured by the camera are degraded in terms of contrast, brightness, color, and sharpness, which create hazy images. Therefore, several techniques have been suggested in the literature to restore the degraded image. Besides, poor climate conditions are not same all the time which leads the idea, that, if images are identified before applying haze removal technique, then it can save processing time which otherwise will be consumed for clear images as well. Therefore, in this article, the haze identification model is proposed to identify the hazy and non-hazy images which makes all haze removal systems more efficient. Moreover, proposed model also classifies the hazy images based on their densities which can be utilized for selection of configuration parameters of haze removal techniques. Finally, the performance of the proposed model is demonstrated and the valuable identification, as well as classification parameters, are computed and presented.
Construction and application of spatiotemporal information infrastructure and service standard system

Abstract Based on the current application of geospatial technology in China, this paper constructs the hierarchical structure and category definition of the framework of spatiotemporal information infrastructure and service standard system according to the actual requirements of spatiotemporal information infrastructure and service standards, and expounds its future application and development trend. In order to provide reference for the future development of spatiotemporal information industry.
The Formation of The Ability of University Students to Work in a Team Through Web Quest in Social Psychology

Purpose of the study: The purpose of the study was an experimental test of the results of the formation of the ability to work in a team through social-psychological web quests with university students. The subject of the research was pedagogical conditions of the formation of the ability of university students to work in a team through a web quest in social psychology. Methodology: The methodology of the research was represented as a competency-based approach Leading methods of the research were a pedagogical experiment and psychological testing. The method of diagnostics of motivational orientations in interpersonal communication, the method of diagnostics of communicative tolerance, the method for evaluating students' web quests were used. Principal Findings: Contains experimental data showing positive dynamics of the formation of the ability to work in a team with university students while doing social-psychological web quests. For the first time, the results of the analyses of the results of the formation of motivated orientations in interpersonal communication and communicative tolerance with the students of different high schools are represented.
Towards Artefact-based Requirements Engineering for Data-Centric Systems.

Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at run-time rendering such non-deterministic systems as complex. This complexity growth affects our understanding on needs and practices in Requirements Engineering (RE). There is, however, still little guidance on how to handle requirements for such systems effectively: What are, for example, typical quality requirements classes? What modelling concepts do we rely on or which levels of abstraction do we need to consider? In fact, how to integrate such concepts into approaches for a more traditional RE still needs profound investigations. In this research preview paper, we report on ongoing efforts to establish an artefact-based RE approach for the development of datacentric systems (DCSs). To this end, we sketch a DCS development process with the newly proposed requirements categories and data-centric artefacts and briefly report on an ongoing investigation of current RE challenges in industry developing data-centric systems.
Immersive Installations in Museum Spaces: Staging the Past

This chapter reflects on museums’ use of immersive media (IM) as a specific form of presentation. Augmented Reality and Virtual Reality have long since found their way into museums. Considering that the museum itself is effectively a stage on which objects are displayed to the public in a way intended to convey meaning, one can assume that immersive installations build on an already semiotically complex reality. By drawing on the concept of theatricality, as defined in cultural studies, as well as contributions made by micro-sociology, a conceptual framework is proposed to understand and analyze IM in the museum. Against this background, two theses are discussed: First, to create a meaningful experience, curators have to manage a specific form of perspectivity to integrate physical space, and the rules applied to it, into its augmentation. Second, like every form of media, IM must develop strategies to cope with technical imperfection by creating a sense of immediacy.
A Study of Machine Learning Inference Benchmarks

Machine learning (ML) is becoming a powerful tool for a variety of applications where artificial intelligence solutions are required. A ML benchmark is a standard suite to measure, evaluate and compare the performance and efficiency of ML systems. This study analyzes the benchmark results from two famous benchmarks MLMark and MLPerf to provide a basis of comparison between both benchmarks as well as to provide recommendations on computer architectures to utilize for ML inferencing. Lastly, special emphasis is placed on the performance of edge AI devices.
Curiosity Guided Fine-Tuning for Encoder-Decoder-Based Visual Forecasting

An encoder-decoder (Enc-Dec) model is one of the fundamental architectures in many computer vision applications. One desired property of a trained Enc-Dec model is to feasibly encode (and decode) diverse input patterns. Aiming to obtain such a model, in this paper, we propose a simple method called curiosity-guided fine-tuning (CurioFT), which puts more weight on uncommon input patterns without explicitly knowing their frequency. In an experiment, we evaluated CurioFT in a task of future frame generation with the CUHK Avenue dataset and found that it reduced the mean square error by 7.4% for anomalous scenes, 4.8% for common scenes, and 6.6% in total. Some other experiments with the UCSD dataset further supported the reasonability of the proposed method.
Fixation: A universal framework for experimental eye movement research✱

We propose a free and open-source framework for experimental eye movement research, whereby a researcher can record and visualize gaze data and export it for analysis using an offline web application without proprietary components or licensing restrictions. The framework is agnostic to the source of the raw gaze stream and can be used with any eye tracking platform by mapping data to a standard json-based format and streaming it in real time. We leverage web technologies to address data privacy concerns and demonstrate support for recording at 300 Hz and real-time visualization.
Hybrid medical image compression using the IKL transform with an efficient encoder

Medical images are generated in a huge number in the research centers and hospitals every day. Working with the medical images and maintain the storage needs an efficient fund and huge storage space. Retaining the quality of the medical image is also very essential. Image compression without losing its quality is the only term to achieve the desired task. Achieving the desired task using the integer Karhunen Loeve transform attains a quality output and also with less storage space. JPEG and JPEG 2000 are also challenging to the integer transform based compression. Resulting the compression quality in terms of peak signal noise ratio, compression ratio is attained. Proposed method of compression is compared with the other efficient algorithms. Thus this proposed method can be used efficiently for the medical image in order to store and retrieve in healthcare industry.
Towards a Toolbox for Mining QA-pairs and QAT-triplets from Conversational Data of Public Chats

Communities of various specialists are using public groups on platforms like Telegram or Slack to discuss specific domain-oriented topics, for instance, Python programming language, Clickhouse database management system or even film making peculiarities. Conversations in such chats often have a form of questions and answers: someone is looking for information, and someone is giving answers. Both sides working to create a community-driven knowledge source. In a group chat, several parallel discussions on different topics can be held simultaneously, which leads to mixing messages up between dialogues and makes it difficult to get individual dialogues from the chat. In this paper, we consider the problem of data preprocessing for the automatic formation of QA-pairs and QAT-triplets from dialogues of group QA-chats that may be used to exploit information and knowledge stored in conversations. Therefore, to deal with this problem we formulate a set of related tasks and consider approaches to solve them. In particular, we highlight two of the most important tasks: identification of the start of new discussions and classification of mixed-up messages to dialogues they belong to. We perform a comparative study of proposed methods on three large groups from Telegram messenger representing user communities that have variations in communication style and patterns, moderation aspects and topics. The experiments allow us to highlight the influence of these variations on the performance of the proposed methods for the tasks and find the best alternatives among them.
Advances in Text Steganography Theory and Research

There is an immense advancement in science and technology, and computing systems with the highest degree of security are the present hot topic; however, the domination of hackers and espionage in terms of disclosing the sensitive information are steadily increasing. This chapter presents a theoretical view and critical examination of the few text steganography methods in the contemporary world. It tells the direction in which research has developed over the past few years. Cryptography, the encipherment to a certain extent, protects the data by making it unreadable but not safe. Improvisation of the same can be done using another layer of protection that is steganography in which the secret embedded inside the cover text will not be revealed.
Correcting Corrupted Labels Using Mode Dropping of ACGAN

Machine learning often requires a large amount of training data, and the training data obtained from various sources is often of poor quality, such as a large number of corrupted labels. Researchers using machine learning often apply some data cleaning techniques to clean up corrupted data. There are two popular methods to clean corrupted data: one is to set manual cleaning rules, and the other is to use positive samples for machine learning or statistical methods. Our work proposes a data cleaning method based on ACGAN since it is difficult to manually formulate cleaning rules, and there are often no positive samples of training data too. Our work does not need to artificially add cleaning rules or positive samples, and subtly uses mode dropping of GAN to eliminate the impact of noisy labels on corrupted data so which can be converted to relatively clean synthetic training data. Mode dropping of ACGAN will naturally happens, which is originally a disadvantage that usually needs to be eliminated in GAN, we tom the disadvantage into advantage, ACGAN will ignore some non-subject features when generating data, so as to eliminate the impact of noisy labels. And we also apply our method to correct noisy labels on corrupted training data.
Role of Blockchain Enabled Transparency in Risk Management and Sustainability of the Complex Global Supply Chains

Lack of transparency in the global supply chains has emerged as a major risk for sustainability in the recent times. This is due to customer awareness about human rights and environmental issues and government legislations in the hosting countries to hold the organizations responsible for the actions of their globally scattered suppliers. This has caused the globalization organizations to make their supply chains transparent. Blockchain technology has been proposed as an efficient solution to not only make supply chains transparent, but also bring unprecedented changes to processes related to global supply chains and bring sustainable competitiveness for the organizations using it. This work finds the issues of supply chains for which global supply chains are using blockchain and what operational benefits they are getting. We also discuss why it is the future of supply chain management.
Speed Reading as a Psychological Problem

The demand for innovative learning methods is dictated not only by the speed of changes and updates of the knowledge system, but also by the enormous growth of data. The need to navigate the vast and dynamically changing field of information requires more sophisticated skills of working with it. Reading is still one of the main ways of gaining knowledge. Improving the reading skill allows not only to work with text information faster and more efficiently, but also to qualitatively change the intellectual abilities of a person by means of speed reading. This study presents a theoretical overview of the modern scientific understanding of speed reading, and examines the psychological aspects (cognitive, motivational-meaning-building) that have an impact on the effectiveness of speed reading learning process. Our aim is to develop an appropriate training program, which will take into account not only the capabilities and limitations of the visual and cognitive systems, but also motivational-meaning-building component, which largely determines the student’s involvement in the lesson and the level of results achieved. The implementation of this program in the educational process will allow to optimize the time and effort necessary to acquire a certain amount of educational knowledge, as well as to increase the motivation and personal interest of the individual in the process of education and self-education.
Joint Triplet Autoencoder for histopathological colon cancer nuclei retrieval

Deep learning has shown a great improvement in the performance of visual tasks. Image retrieval is the task of extracting the visually similar images from a database for a query image. The feature matching is performed to rank the images. Various hand-designed features have been derived in past to represent the images. Nowadays, the power of deep learning is being utilized for automatic feature learning from data in the field of biomedical image analysis. Autoencoder and Siamese networks are two deep learning models to learn the latent space (i.e., features or embedding). Autoencoder works based on the reconstruction of the image from latent space. Siamese network utilizes the triplets to learn the intra-class similarity and inter-class dissimilarity. Moreover, Autoencoder is unsupervised, whereas Siamese network is supervised. We propose a Joint Triplet Autoencoder Network (JTANet) by facilitating the triplet learning in autoencoder framework. A joint supervised learning for Siamese network and unsupervised learning for Autoencoder is performed. Moreover, the Encoder network of Autoencoder is shared with Siamese network and referred as the Siamcoder network. The features are extracted by using the trained Siamcoder network for retrieval purpose. The experiments are performed over Histopathological Routine Colon Cancer dataset. We have observed the promising performance using the proposed JTANet model against the Autoencoder and Siamese models for colon cancer nuclei retrieval in histopathological images.
Observations on the Security of COMET

Abstract This paper investigates the security of counter mode encryption with authentication tag (COMET), one of the 32 second-round candidates in National Institute of Standards and Technology’s lightweight cryptography standardization process, against differential cryptanalysis. CHAM-64/128 is a block cipher chosen as one of the underlying block ciphers in COMET for hardware-oriented applications, and a differential characteristic with a high probability for CHAM-64/128 is useful for forgery attacks on COMET. However, we find that the optimal $\mathbf{39}$-round differential characteristic for CHAM-64/128 proposed by Roh et al., which is the longest differential characteristic of CHAM-64/128, is invalid. Then, we propose a new method of distinguishing an $\mathbf{m}$-bit block cipher from an $\mathbf{m}$-bit random permutation using a differential characteristic with a probability not higher than $\mathbf{2^{-m}}$. Using our method, we use two $\mathbf{39}$-round differential characteristics with a probability of $\mathbf{2^{-64}}$ for CHAM-64/128 to distinguish $\mathbf{39}$-round-reduced CHAM-64/128 from a $\mathbf{64}$-bit random permutation, respectively. Furthermore, we refine the probabilities of two differentials with the same input and output differential masks as the two $\mathbf{39}$-round differential characteristics, respectively. Finally, we present the first forgery attacks on COMET with the two differentials without using weak keys. Our forgery attacks follow the nonce-misuse scenario. It should be noticed that this attack does not invalidate the security claims of the designers.