Reviewerly/exHarmony huggingface dataset:
Interactive redescription set mining and exploration

InterSet is a client-server web application that allows targeted and contextual redescription set exploration. The main drawback of this tool is that it uses fixed, precomputed sets of redescriptions to allow obtaining novel knowledge. In this work, we significantly extend the capabilities of InterSet by adding the possibility to create new redescriptions on predefined data. The main advantage of this is that the user can create new redescriptions in any step of exploration depending on the context and current research hypothesis, utilizing entity or attribute constraint-based redescription mining and enriching the existing set of redescriptions with newly obtained knowledge. This procedure further enhances the potential of forming and exploring new research hypotheses using large sets of redescriptions. The redescription mining ability of the InterSet tool is achieved by utilizing the CLUS-RM algorithm. Usefulness of the obtained environment for interactive redescription set creation, targeted and contextual exploration is demonstrated on real-world use case datasets.
Relativistic GAN using Receptive Field Block for Single Image Super-Resolution with improved Perceptual Quality

Generative adversarial networks (GAN) are proved to be extremely useful to solve the Single image super resolution(SISR) problem as they can recover the finer texture details even with large upsamplng factors. In this paper, we propose a deep network architecture using a relativistic generative adversarial network (V-SRGAN) with receptive field block (RFB) for image super-resolution having good perceptual quality. Our generator network uses multi-scale RFBs which are capable of extracting the coarse and finer features from the input low resolution image to recover the super resolved image with finer details and textures. It is initially trained on mean absolute error (MAE) followed with relativistic average GAN (RaGAN) loss for both discriminator and generator. Training based on RaGAN loss enables the network to map the low resolution images to more realistic high-resolution counterparts. The proposed network was able to attain better results in terms of PSNR and learned perceptual image patch similarity (LPIPS) metric in comparison with the other GAN based methods. https://github.com/hrishikeshps94/RGAN-with-Receptive-Field-Block-for-SISR
ELM based Ensemble of Classifiers for BGP Security against Network Anomalies

The Border Gateway Protocol (BGP) is an essential element of the Internet infrastructure, playing a crucial role in ensuring global connectivity and stability for smooth communication. Numerous techniques are created by the researchers to detect anomalies in the network for enhancing stability of the BGP network (BGP based environment). On the other hand, the dynamic nature and intricate structure of the network poses challenges in identifying attacks environment. As a result, applying a single classifier to classify them is a challenging task. In this paper a method based on ensemble learning is proposed where Extreme Learning Machine (ELM), K-Nearest Neighbor (KNN), and Naive Bayes (NB), classifiers are ensembled to detect the attacks in BGP based environment. The proposed approach is evaluated on Reseaux IP Europeens (RIPE) and British Columbia’s Advanced Network (BCNET) datasets and compared with other recent approaches. On investigating the results, it is found that the proposed approach is providing better performance on both datasets.
Multiclass Altitude and Rotation Invariant Object Detection Using Slicing Objectness Loss (SOL) For Images with Complex Background

The difficulty of detecting and monitoring diverse types of solar panels from various elevations and complex backgrounds has not been investigated. The varying orientation and complex backgrounds lead to false positives and erroneous classifications. However, by employing our technique, we could precisely distinguish between three diverse types of solar panels in various challenging backgrounds. We propose a slicing objectness loss specifically to address model learning issues, false positives, and oriented object localization on complex backgrounds. We based our work on popular existing neural network architectures and designed them to adapt to areal object detection for complex backgrounds. To our knowledge in the multi-solar panel, detection has not been conducted previously due to a lack of data availability. We have designed the neural network to process the full-size image and extract multi-Altitude local and global features thus our models are all altitude invariant and can detect shapes of arbitrary orientation. To demonstrate Slicing loss function produces superior results, we present a comparison study between pre and post-application SOL. We show clear evidence that our slicing approach and slicing objectness loss (SOL) has a significant effect on multi-solar panel detection WPV (water heater Photovoltaic), FPV (farm type photovoltaic), and SPV (Single Photovoltaic). We have also shown our approach and custom loss works for other complex multi-object detection e.g., identifying enclosures, water, and fuel tanks from different altitudes.
Building the Research Infrastructure module in the Ukrainian Research Information System (URIS)

In recent years, Ukraine has made significant progress in developing open science practices to ensure open access to research infrastructure. In 2019, in accordance with the program "On Approval of the Concept of the State Targeted Program for the Development of Research Infrastructures in Ukraine for the Period up to 2026" [1], the effectiveness of the program implementation is ensured by attracting targeted investments to increase the competitiveness of research infrastructures, coordinating their operation, collecting and analyzing data on such infrastructures, implementing the provisions of international legislation in the relevant field into national legislation to ensure the implementation of the Agreement on the Association of Research Infrastructures. In 2020, the Ministry of Education and Science of Ukraine published the National Research Infrastructure Roadmap, which outlines the strategic priorities for the development of research infrastructure in the country. The Roadmap included plans to create new research facilities, modernize existing infrastructure, and develop human capital in research. In 2021, the Government of Ukraine approved the Concept of the State Targeted Program for the Development of Research Infrastructures in Ukraine for the period of 2022-2026 [1]. The program aims to modernize the research infrastructure in Ukraine and support the development of innovative research [1]. The program is a significant step forward for the research infrastructure in Ukraine and has the potential to improve research capacity and stimulate innovation in the country. In 2022, the Government of Ukraine adopted the National Plan for Open Science, covering the period from 2023 to 2030. The aim of the plan was to bring Ukraine's research practices in line with the EU's open science policy [2]. The development of research infrastructure in Ukraine has been a priority for the government in recent years, as the country seeks to modernize its scientific potential and support innovation.
An Ego-Centric Interface for Human Intervention in Semi-Autonomous Navigation through Unknown Environments

Semi-autonomous navigation through unknown environments is an important area of research for robotics, with many potential applications in fields such as search and rescue, environmental monitoring, and exploration. However, existing methods rely on the robot to already have a semi-accurate map of the environment which cannot be assumed to be true in an unfamiliar environment. To address this, a new egocentric interface has been presented in this research paper, which provides a Robot Operating System (ROS)-based software utilizing the robot’s camera view. With this interface, a human operator can select a point visible in the robot’s camera view and provide it as an intermediate goal in the robot’s local environment. Using the robotic system’s inherent path-planning and navigation tools, a collision-free path is derived for the robot to follow. While the robot moves along the given path, a human operator may continue to provide intermediate goals and adjust its path based on recent observations. This process is simulated through the Gazebo simulator.
Ubiquent Narratives for the Design of Natural and Invisible Interfaces

The article proposes to explore the concepts of storytelling (Lupton, 2020 and Xavier, 2015) and ubiquity (Santaella, 2013 and Santos, 2021) applied to the design of natural and invisible interfaces (Benyon, 2011).With Natural Interface it is possible to interact with devices directly, without needing the help of a mouse or keyboard.To do so, it presents as a case study the project "Criatura de Luz" (2015), by Estúdio Guto Requena, carried out to compose the new facade of the WZ Hotel (São Paulo).The purpose of this research is to understand the role of the designer in the creation of interfaces that are not only interactive, but that begin to propose immersive and ubiquitous narratives in design projects and the method for this analysis will be the case study from the design perspective as storytelling proposed by Lupton (2020).The aim is to do exploratory research to provide greater familiarity with the problem.
Open-source responsive solar tracker performance evaluation: a comparative study

Due to the current energy crisis in Brazil and in the world, it is important to explore renewable energy sources, such as photovoltaic systems.Considering this, this research analyzes a responsive capture system called Girassol, which seeks better efficiency in energy production.Tests were performed on the mechanism to assess the advantages and disadvantages of using the solar tracker compared to fixed systems.The tests were carried out using the physical prototype and computational tools to compare the results.The methodology used was Design Science Research, which has to develop and improve affordable solutions, based on experiments and computational simulations.Compared to the physical prototype it was possible observe that there is a greater energy upgrade of the Girassol mainly in the initial and final moments of the daylight hours.
Emotional Involvement in Social-Network Communication: Reference Group and Gender Bias

The paper provides empirical evidence on the potential links between different types of verbal reactions to initial texts and comments on them. Besides, it includes the agreement with the positions of initial text authors and people commenting on initial texts within the framework of social network sites. The experimental design includes three sessions and involves a homogeneous group of subjects who should imagine themselves as participants of Internet discussion and comment on the initial text and comments following it. The primary research issue is how people verbally react to any social network message and whether reference group factor affects them when social interaction happens within Internet-communication. A self-evaluation questionnaire precedes the experimental study. Analysis of the data obtained during experimental research is gender-sensitive. The primary findings argue that subjects’ reactions to the content of the stimuli prevail over reactions to the form of stimuli presentation. There are almost no reactions revealing the subjects’ intentions. The research results support the gender bias hypothesis confirming that females are more likely to encourage people even if they do not share their point of view. Females also feel freer to express their reaction to the form of the stimulus message. Nonetheless, males, in general, react verbally more often than females.
Intercultural Communication Competence as a Basic Condition for the Successful Adaptation of Young Foreign Students to Russian Culture

The authors analyze the relevance of international students’ intercultural competence for their successful adaptation to Russian culture, in general, and for adaptation to the educational environment of the university, in particular. The topicality of the issue represents several factors, the most essential of them being the increase in the number of international students in Russian universities. It correlates with the strategic tasks set before the institution of higher education in Russia. In order to comprehend the levels of intercultural communication competence of international students, the authors utilize the self-assessment test of individual intercultural competence, developed by L. A. Arasaratnam and successfully applied in various foreign universities. The authors allow themselves to expand this test by including questions directly related to the educational environment of the university. Students from China, Gabon, South Africa, Congo, Iraq, and Egypt participate in the testing. The authors deliberately exclude the students from neighboring countries from the survey since continuous contacts between Commonwealth of Independent States countries create favorable conditions for forming intercultural communication competence in this student group. The analysis of the obtained results demonstrates that despite the similar training programs and methods of applied educational work, the level of intercultural competence differs among the students. Therefore, the authors conclude that it is necessary to develop unique programs for those groups of students whose level of intercultural competence is not high enough.
Experience of Using PBL and Gamification as an Active Methodology in Time of Pandemic in Ecuador

During the COVID 19 pandemic, the use of innovative and attractive strategies within educational environments has been demonstrated, where teaching is focused on the student, in the development of skills. These strategies conceive learning as a constructive and motivating process. To do this, a topic that generates great interest in the current educational scenario was analyzed: the active methodologies applied within the current situation experienced by the pandemic; which is eminent because through its use it is possible to continue with the teaching/learning process between teacher and student in difficult times such as those currently experienced by society in general. The objective is to know the experiences of teachers in the application of PBL and Gamification as strategies of active methodologies in the teaching process. The methodology is qualitative approach. The sample consisted of 15 teachers from the Sierra del Ecuador educational system. The survey technique was applied. In conclusion, it has been confirmed that the application of active methodologies encourages students to learn, in a practical way, useful and concrete knowledge that they will usually find in their daily context and curiosity about learning is rewarded.
Determinants of Cybercrime Victimization: Experiences and Multi-stage Recommendations from a Survey in Cameroon

Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and user's vulnerability to attacks catalyze their success. Several surveys have been conducted to determine the factors favoring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication Technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers and a theoretical framework has been provided with several stages that could be followed to contain cybercrime.
Construction of a Core Ontology of Endogenous Knowledge on Agricultural Techniques: OntoEndo

Several resources have been identified on endogenous agro-sylvo-pastoral techniques, but some of them remain unformalized data. In a context of climate change (CC), it is essential to take into account this endogenous knowledge on agricultural techniques as an adaptation measure. We propose in this paper to build an ontology of this endogenous agricultural knowledge. Our methodology identifies the terms on agricultural techniques that will be formalized and used to build the OntoEndo ontology. OntoEndo will allow the construction, co-construction and sharing of agricultural techniques for climate change adaptation.
To Build 3D Indoor Navigation Application for Museum Visitors

Museums are filled with abundance of rich history, culture and amazing architectural grandeur. But yet many people would much rather prefer to watch a documentary or read a book than to visit a museum. With the aim of building a 3D interactive application for museum, the visitors will get a 3D virtual tour just at the screen of their mobiles. The app will aim at creating 3D interactive map, which will allow anyone to get a personalized experience of the museum. This will not only increase the popularity of the museums but also help in circulation of knowledge as each and every person not just a visitor will be able to access the app and get a personalized tour of it. In this paper, we have created a prototype of the E3 block of Amity School of Engineering and Technology in order to show the vision we intent on creating in the form of application. For this, the use of Blender software and Unity Game Engine has been made.
Phishing Attack Detection Using Machine Learning

Another kind of online crime is phishing, in which the perpetrator attempts to steal sensitive information from an unsuspecting victim by luring them to a phony website that looks like the real thing. This is a major worry in many sectors, including e-commerce and financial management. There are a lot of moving parts and criteria involved in detecting phishing sites, making it a highly unpredictable and elemental problem. Some astute proactive strategies can be helpful, and powerful tools, such as fuzzy, neural system, and data mining methods, can be a successful mechanism in distinguishing phishing sites. This is due to the aforementioned, as well as the ambiguities in arranging sites due to the intelligent procedures programmers are using. One of the many machine learning-based techniques used to identify Phishing websites was Random Forest (RF), which we employed. Finally, we evaluated the classifier's accuracy and compared its results to others (Chatterjee and Namin in IEEE 43rd annual computer software and applications conference (COMPSAC), [4], Blum et al. in 3rd ACM workshop on Artificial intelligence and security, Chicago, Illinois, USA, pp 54–60, [6]).
Data-Driven Stability Analysis of a Chaotic Time-Delayed System

Systems with time-delayed chaotic dynamics are common in nature, from control theory to aeronautical propulsion. The overarching objective of this paper is to compute the stability properties of a chaotic dynamical system, which is time-delayed. The stability analysis is based only on data. We employ the echo state network (ESN), a type of recurrent neural network, and train it on timeseries of a prototypical time-delayed nonlinear thermoacoustic system. By running the trained ESN autonomously, we show that it can reproduce (i) the long-term statistics of the thermoacoustic system’s variables, (ii) the physical portion of the Lyapunov spectrum, and (iii) the statistics of the finite-time Lyapunov exponents. This work opens up the possibility to infer stability properties of time-delayed systems from experimental observations.
A Method of Social Context Enhanced User Preferences for Conversational Recommender Systems

Conversational recommender systems (CRS) can dynamically capture user fine-grained preference by directly asking whether a user likes an attribute or not. However, like traditional recommender systems, accurately comprehending users’ preferences remains a critical challenge for CRS to make effective conversation policy decisions. While there have been various efforts made to improve the performance of CRS, they have neglected the impact of the users’ social context, which has been proved to be valuable in modeling user preferences and enhancing the performance of recommender systems. In this paper, we propose a social-enhanced user preference estimation model (SocialCRS) to leverage the social context of users to better learn user embedding representation. Specifically, we construct a user-item-attribute heterogeneous graph and apply a graph convolution network (GCN) to learn the embeddings of users, items, and attributes. Another GCN is used on the user social context graph to learn the social embedding of users. To estimate better user preference, the attention mechanism is adopted to aggregate the embedding of the user’s friends. By aggregating these users’ embeddings, we obtain social-enhanced user preferences. Through extensive experiments on two public benchmark datasets in a multi-round conversational recommendation scenario, we demonstrate the effectiveness of our model, which significantly outperforms the state-of-the-art CRS methods.
Toxicity in Evolving Twitter Topics

Tracking the evolution of discussions on online social spaces is essential to assess populations’ main tendencies and concerns worldwide. This paper investigates the relationship between topic evolution and speech toxicity on Twitter. We construct a Dynamic Topic Evolution Model (DyTEM) based on a corpus of collected tweets. To build DyTEM, we leverage a combination of traditional static Topic Modelling approaches and sentence embeddings using sBERT, a state-of-the-art sentence transformer. The DyTEM is represented as a directed graph. Then, we propose a hashtag-based method to validate the consistency of the DyTEM and provide guidance for the hyperparameter selection. Our study identifies five evolutionary steps or Topic Transition Types: Topic Stagnation, Topic Merge, Topic Split, Topic Disappearance, and Topic Emergence. We utilize a speech toxicity classification model to analyze toxicity dynamics in topic evolution, comparing the Topic Transition Types in terms of their toxicity. Our results reveal a positive correlation between the popularity of a topic and its toxicity, with no statistically significant difference in the presence of inflammatory speech among the different transition types. These findings, along with the methods introduced in this paper, have broader implications for understanding and monitoring the impact of topic evolution on the online discourse, which can potentially inform interventions and policy-making in addressing toxic behavior in digital communities.