Reviewerly/exHarmony huggingface dataset:
Investigating the Effect of Shading on the Capacity Factor of Floating Photovoltaic Systems

In this study, remote sensing (RS) was used to determine the 20-year area and shoreline changes of Demirköprü Dam reservoir. Using a geographical information system (GIS) solar analysis tool, annual and monthly total global horizontal irradiance (GHI) values were calculated within the area of the reservoir’s shorelines based on 20-years of observations. The regional theoretical capacity factor (RTCF) proposed in this study was modelled using total annual GHI values. The water surface was divided into four regions using RTCFs 94.97%, 4.92%, 0.08%, and 0.02% of the total water surface area were classified as RTCF21, RTCF20, RTCF19, and RTCF18, respectively. The annual electrical energy potentials per unit for each RTCF were calculated. The novel method developed in this study for determining the optimum location of FPV SPPs to be installed on water surfaces reveals the importance of evaluating land topography and considering annual patterns of shading.
Comparative Analysis of Ensemble Learning and Non-Ensemble Machine Learning Algorithms for Phishing URL Detection

Phishing is a social engineering attack that has been perpetuated for long and is still a prominent attack with an attending high number of victims. Through phishing, attackers can gain easy access to sensitive information about a company or an individual. This research compares the import of features such as lexical features, Domain Named Based features, HTML Features, and tokenization of URLs in detecting phishing URLs. Experimental procedures were designed to compare the efficiency of the four categories of features used separately on three machine learning models (K-Nearest Neighbour, Decision Tree, Logistic Regression) and five ensemble learning classifiers (Random Forest, Bagging, Stacking, Ada Boost, Gradient Boost). Results obtained show higher accuracy for experiments done using URL tokenization with stacking classifier with accuracy scores of 96% and 99.3% respectively for the two datasets used. Future study would be based on more dataset with larger sample size to provide a basis for generalisation.
Describing UI Screenshots in Natural Language

Being able to describe any user interface (UI) screenshot in natural language can promote understanding of the main purpose of the UI, yet currently it cannot be accomplished with state-of-the-art captioning systems. We introduce XUI, a novel method inspired by the global precedence effect to create informative descriptions of UIs, starting with an overview and then providing fine-grained descriptions about the most salient elements. XUI builds upon computational models for topic classification, visual saliency prediction, and natural language generation (NLG). XUI provides descriptions with up to three different granularity levels that, together, describe what is in the interface and what the user can do with it. We found that XUI descriptions are highly readable, are perceived to accurately describe the UI, and score similarly to human-generated UI descriptions. XUI is available as open-source software.
Computer Game Elements and its Impact on Higher Education

This review paper analyses authors who used gamified e-learning systems in higher education. Their thoughts, problems and conclusions are accompanied and observed. Positive guidelines are provided for the inclusion of computer game elements in the education system. Review paper includes a comparison of the gamified and traditional education system with an incentive to conduct further research activities. The list of researchers is provided who had experiments on students in the higher education system. Duration of those experiments is shown as well as the number of used game elements for their gamified systems. The list of survey questions for teaching staff (N=98) is provided as well as their thoughts on gamified systems and its use. The conclusions are that it is necessary to pay attention to the visual appearance and environment in which teaching materials are presented and studied.
The Importance of Developing Preventive Techniques for SQL Injection Attacks

Many intentionally vulnerable web applications are circulating on the Internet that serve as a legal test ground for practicing SQL injection attacks. For demonstration purposes the attacks will target an Acunetix test web application created using PHP programming language and MySQL relational database. In the practical part, the execution of the attack itself largely depends on the database management system, so the displayed syntax is intended only for the MySQL database management system. Example of an automated attack will be executed on SQLmap in a Linux Kali virtualized environment. Security guidelines with a purpose of protecting databases are also discussed.
DHT-Based Blockchain Dual-Sharding Storage Extension Mechanism

The expansion of blockchain storage has become a major problem limiting the application of blockchain. From the perspective of improving the scalability of blockchain storage, a DHT (distributed hash table)-based blockchain dual-sharding storage extension mechanism (DBDSM) is proposed. The nodes in the network are divided into m DHT clusters. Each cluster includes n nodes, and stores 1/m of the transaction data, and the nodes within each cluster store part of data allocated to that cluster. In this way, node storage pressure is alleviated. Furthermore, a hybrid query mechanism has been designed to achieve efficient querying of transaction data, without changing the original state data query. Simulation results showed without changing the original state data query, that the storage space consumed by the nodes is only s/(m × n) of that used in the traditional method; when the number of faulty nodes in the cluster does not exceed s − 1, the integrity of blockchain data can still be ensured. For transaction data queries, the average number of hops was 1.99, greatly improving query efficiency in the sharded state.
AFMB-Net

With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes.
Needs of persons with disabilities for competences that expand employment opportunities in the conditions of society digitalisation

The article examines the needs of students and graduates of universities from among persons with disabilities in additional professional competencies necessary for successful employment in the context of the society digitalisation. The empirical base was the materials of a survey on a representative sample of 140 final year students and graduates with disabilities from the Sverdlovsk, Chelyabinsk and Kurgan regions. As a result, professional competencies were identified that expand their employment opportunities for people in this category: practical professional skills; professional knowledge in the subject area; digital competencies and information technology skills; the ability to work in the media space, including social networks; knowledge of interaction technologies, self-presentation and effective communication; the ability to tell a wide audience about their services, skills, about themselves as a specialist; the ability to promote themselves, their professional competencies and services. The authors of the article substantiate the need to obtain such competencies within the framework of additional professional education. It has been established under what conditions graduates and students with disabilities are ready to receive the additional professional education they need, how the assessments of these competencies differ in groups according to the formation of competencies, employment, disability, types of health disorders and gender. The results of this study indicate the importance of digital competencies for persons with disabilities in current conditions, they are significant for further study of this problem, the implementation of additional professional education programmes.
An Ontology for Architectural Heritage: Historical Figures and Organizations

The goal of the article is to present one of a total of seven ontologies in the project for the Semantic Modelling of the Bulgarian Pre- and Revival houses. The ontological representation of historical and contemporary figures and organizations concerning the residential revival architecture will be considered.
Mobile Presentation of the War History of the City of Niš

Contemporary visitors of a historical and tourist site expect access to quick information about different subjects according to their interests. Unfortunately, billboards and plates near monuments usually do not provide enough information that can satisfy the visitors’ needs. However, mobile devices such as tablets and smartphones with appropriate software solutions can help visitors get the information in an appropriate manner. In this paper, we present two ways of providing information such as a web application with the usage of QR codes, and a purposely developed application for mobile devices implemented by using augmented reality technologies. We compare both these solutions and discuss their advantages and possible drawbacks. The application of these two approaches of using information technologies in representing national historical heritage are illustrated by concrete examples of their implementation in the city of Niš. By using these applications, visitors are informed about the war history of Niš, a city that was occupied many times and by many conquerors in its millennia-long history.
Information Day: Research Infrastructure Services in the Humanities and Social Sciences

During the information day, the results of the work of the IMI-BAS team on the project CLaDA-BG, the Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies in Favor of the Bulgarian Language and Cultural Heritage, Part of the EU Infrastructures CLARIN and DARIAH, will be presented: the development of the Humanities and Social Sciences Data Storage, Retrieval and Curation Environment (CHCS-DSRCE) and its implementation in the Digital Library "Virtual Encyclopedia of Bulgarian Iconography" and for the needs of the "Ivan Vazov" Regional Library in Plovdiv and “Peyo Yavorov” Regional Library - Burgas.
Treewidth for Argumentation Frameworks with Collective Attacks

Abstract Argumentation is a key formalism to resolve conflicts in incomplete or inconsistent knowledge bases. Argumentation Frameworks (AFs) and extended versions thereof turned out to be a fruitful approach to reason in a flexible and intuitive setting. The addition of collective attacks, we refer to this class of frameworks as SETAFs, enriches the expressiveness and allows for compacter instantiations from knowledge bases, while maintaining the computational complexity of standard argumentation frameworks. This means, however, that standard reasoning tasks are intractable and worst-case runtimes for known standard algorithms can be exponential. In order to still obtain manageable runtimes, we exploit graph properties of these frameworks. In this paper, we initiate a parameterized complexity analysis of SETAFs in terms of the popular graph parameter treewidth. While treewidth is well studied in the context of AFs with their graph structure, it cannot be directly applied to the (directed) hypergraphs representing SETAFs. We thus introduce two generalizations of treewidth based on different graphs that can be associated with SETAFs, i.e., the primal graph and the incidence graph. We show that while some of these notions allow for parameterized tractability results, reasoning remains intractable for other notions, even if we fix the parameter to a small constant.
Composite Argumentation Systems with ML Components

Today AI systems are rarely made without Machine Learning (ML) and this inspires us to explore what aptly called composite argumentation systems with ML components. Concretely, against two theoretical backdrops of PABA (Probabilistic Assumption-based Argumentation) and DST (Dempster-Shafer Theory), we present a framework for such systems called c-PABA. It is argued that c-PABA lends itself to a development tool as well and to demonstrate we show that DST-based ML classifier combination and multi-source data fusion can be implemented as simple c-PABA frameworks.
An Argumentative Explanation of Machine Learning Outcomes1

The black box model used in Machine Learning is considered one of the major problems in the application of Artificial Intelligence techniques [1] as it makes machine decisions non-transparent and often incomprehensible even to experts or developers themselves.In this paper, we provide an argumentative interpretation of both the training process and the results predicted.The goal is to build a Bipolar Argumentation Framework (BAF) [2] showing the dialectical reasoning behind the assignment of a certain class to a given record.Since we make assumptions neither on the dataset nor on the algorithm used, the presented procedure can be applied to existing models without the need for further adjustments.To illustrate our proposal, we use the Titanic dataset from www.kaggle.com,which contains records relating to people involved in the Titanic disaster.We consider three categorical features, namely Survived (the class to predict, with value 1 if the person survived or 0, otherwise), Pclass (ticket class among 1, 2 and 3) and sex (0 for woman and 1 for man), and two numerical features: Age (passenger age, ranging from 0.17 to 76) and Fare (passenger fare with values from 0 to 512).In the following, we describe the step our procedure goes through in order to find an explanation for the class Survived=1.Dataset Clustering.In the first step, starting from the input dataset, we create a new clustered dataset in which numerical features are split into categories that group ranges of values to obtain a more appropriate and concise explanation.BAF Generation.Then we build a BAF based on the correlation matrix computed among the features.By construction, the obtained BAF only has symmetric relations.Breaking Complete Symmetry.Given the correlation matrix, we apply a procedure that removes symmetric edges from the BAF to establish a causal relationship between features.In particular, we use the conditional probability [3] computed for arguments which attack/support each other.We choose the minimum values possible that keep the graph connected.Computing Extensions.To identify the set of arguments which are more likely to be accepted, we compute the semi-stable extensions [4] of the previously obtained
Stability and Relevance in Incomplete Argumentation Frameworks

We explore the computational complexity of stability and relevance in incomplete argumentation frameworks (IAFs), abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. IAFs can be specified by, e.g., making uncertain arguments or attacks certain; the justification status of arguments in an IAF is determined on the basis of the certain arguments and attacks. An argument is stable if its justification status is the same in all specifications of the IAF. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? We redefine stability and define relevance for IAFs and study their complexity.
A Ranking Semantics for Abstract Argumentation Based on Serialisability

We revisit the foundations of ranking semantics for abstract argumentation frameworks by observing that most existing approaches are incompatible with classical extension-based semantics. In particular, most ranking semantics violate the principle of admissibility, meaning that admissible arguments are not necessarily better ranked than inadmissible arguments. We propose new postulates for capturing said compatibility with classical extension-based semantics and present a new ranking semantics that complies with these postulates. This ranking semantics is based on the recently proposed notion of serialisability that allows to rank arguments according to the number of conflicts needed to be solved in order to include that argument in an admissible set.
Generating Contrastive Snippets for Argument Search

In argument search, snippets provide an overview of the aspects discussed by the arguments retrieved for a queried controversial topic. Existing work has focused on generating snippets that are representative of an argument’s content while remaining argumentative. In this work, we argue that the snippets should also be contrastive, that is, they should highlight the aspects that make an argument unique in the context of others. Thereby, aspect diversity is increased and redundancy is reduced. We present and compare two snippet generation approaches that jointly optimize representativeness and contrastiveness. According to our experiments, both approaches have advantages, and one is able to generate representative yet sufficiently contrastive snippets.
Weakly Supervised Real-Time Object Detection Based on Salient Map Extraction and the Improved YOLOv5 Model

In order to improve the accuracy and processing speed of object detection in weakly supervised learning environment, a weakly supervised real-time object detection method based on saliency map extraction and improved YOLOv5 is proposed. For the case where only image-level annotations are available, class-specific saliency maps are generated from the backpropagation process using a VGG-16-based classification network. After obtaining the position information of the target in the image, the pseudobounding box of the target is generated, and the pseudobounding box is used as the ground-truth bounding box to optimize the real-time target detection network. An improved YOLOv5 model is proposed to transfer clear target features to deeper network layers by designing a jump connection operation, thereby solving the problem of feature ambiguity. At the same time, the convolutional attention mechanism module is introduced to solve the problem that the recognition accuracy is affected by invalid features. Experiments on the PASCAL VOC 2007+2012 datasets show that when only image-level annotations are available in the training data, the proposed method can effectively improve the processing speed and maintain a good target detection accuracy, realizing real-time object detection under weakly supervised conditions.
Hybrid Multiple Deep Learning Models to Boost Blocking Bug Prediction

&lt;p&gt;A blocking bug (BB) is a severe bug that could prevent other bugs from being fixed in time and cost more effort to repair itself in software maintenance. Hence, early detection of BBs is essential to save time and labor costs. However, BBs only occupy a small proportion of all bugs during software life cycle, making it difficult for developers to identify these blocking relationships. This study proposes a novel blocking bug prediction approach based on the hybrid deep learning model, a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). Our approach first extracts summaries and descriptions from bug reports to construct two classifiers, respectively. Second, our approach combines two classifiers into a hybrid model to predict the blocking relationship of each blocking bug pair. Finally, our approach produces a report of identified blocking bugs for developers. To investigate the effectiveness of proposed approach, we conducted an empirical study on bug reports of seven large-scale projects. The final experimental results illustrate that our approach can perform better than the recent state-of-the-art baselines. Precisely, the hybrid model can predict BB better with an average accuracy of 57.20%, and an improvement of 73.53% in terms of the F1-measure when compared to ELBlocker. Moreover, according to the bug report’s description, BB can be predicted well with an average accuracy of 49.16%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Click-Through Rate Prediction Algorithm Based on Modeling of Implicit High-Order Feature Importance

&lt;p&gt;Click-through rate (CTR) prediction plays a central role in online advertising and recommendation systems. In recent years, with the successful application of deep neural networks (DNNs) in many fields, researchers have integrated deep learning into CTR prediction algorithms to model implicit high-order features. However, most of these existing methods unify the weights of implicit higher-order features to predict user behaviors. The importance of such features of different dimensions for predicting user click behaviors are different. Base on this, we propose a prediction method that dynamically learns the importance of implicit high-order features. Specifically, we integrate the output features of deep and shallow components, and adaptively learn the weights of implicit high-order features from among all features through the designed attention network, which effectively capturing the deep interests of users. In addition, this framework has strong versatility and can be combined with shallow models such as Logistic Regression (LR) and Factorization Machines (FMs) to form different models and achieve optimal performance. The extended experiment is conducted on two large-scale datasets, AVAZU and SafeDrive, and the experimental results show that the performance of the proposed model is superior to that of existing baseline models.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Deep Learning-Based Text Emotional Analysis Framework for Yellow River Basin Tourism Culture

As an important carrier of cultural communication, tourism can play a positive role in promoting regional ecology and cultural heritage. Therefore, this paper takes tourist attractions in the Yellow River basin as the research object and constructs mining and comment sentiment analysis for tourism text information in the Yellow River basin that appears on social media platforms. Based on the theory of the social center network, the tourism culture network of the Yellow River basin based on tourists’ emotion analysis is constructed. In addition, based on the linear fusion algorithm of semantic orientation pointwise mutual information and word2vec, this paper constructs an emotion dictionary in the field of tourism review and proposes a set of comprehensive emotion calculation rules based on Chinese text expression structure. The experimental results of 32 scenic spots in the Yellow River basin show that the proposed algorithm can achieve better sentiment classification of tourism texts, broaden the scope of application of the domain sentiment dictionary construction method, and improve efficiency.