Reviewerly/exHarmony huggingface dataset:
Cultural Bat Optimization Algorithm: An Improved Variant of Bat Algorithm

This paper presents a novel variant of the BAT algorithm called the Cultural Bat Algorithm CABAT. The new variants were achieved using situational and normative knowledge inherent in the cultural algorithm. The idea of the Bat algorithm is inspired by the intelligent echolocation behaviors of microbats. These intelligent behaviors are codified as an optimization agent for solving global optimization problems. Although this algorithm has been widely accepted due to its robustness, simplicity, and flexibility, it still suffers from an imbalance between exploration and exploitation. This research developed improved variants of the original Bat algorithm using cultural evolution processes to address these challenges Four new variants of the Bat algorithm were eventually developed using cultural situational and normative knowledge. The performance of the new variants was tested against the standard Bat algorithm using eleven commonly used benchmark functions. An extensive comparative study was conducted using statistical metrics computed over thirty independent runs. Results showed that all the CABAT variants performed better than the original bat algorithm.
Questionnaire Approach for Assessing Software Engineering and Quality Assurance Practices

The industry-academia gap is one of the persistent challenges of Software Engineering education. Software development is a rapidly moving industry, and academia is not quick enough to adapt to the changing software engineering profession. To this end, this paper introduces a project that seeks to bridge the Software Engineering -related industry-academia gap in the Finnish region of South Savo. The project intends to bring together regional software engineering companies with teachers, developers, and students in higher education. The objective of such a community is to provide a platform for discussion and the development of collaborative models to improve education and interaction. As the first step towards building the community, we are building a better understanding of the software development companies in the region. In this paper, we describe our method of inquiry and early experiences for understanding companies’ type of business, software engineering activities, and attitude towards collaboration. The preliminary results show that our method of inquiry was perceived as useful for both industry and academia. While we were able to gather useful information about the industry practices and feedback for higher education about the skills graduates should possess, industry representatives considered the data gathering as an opportunity for self-reflection.
Look, Dude: How hyperpartisan and non-hyperpartisan speech differ in online commentary

Identifying the characteristics of hyperpartisan communication that make it so amenable to sharing is crucial to combating the spread of misinformation. We analyzed a corpus of hyperpartisan and non-hyperpartisan writing produced on internet forums and found that markers of spontaneous communication are strongly predictive of hyperpartisan speech, regardless of whether that speech is left- or right-leaning. The markers of spontaneous communication included swear words, discourse markers, local pronouns like I and you, and exclamation marks. This suggests that speakers in hyperpartisan online communities exploit linguistic resources, even those without overtly political or persuasive content, to engage readers through appeal to closeness and familiarity.
Selection Of Ad Blockers According To Their Type Of Installation

The paper describes the principles of interaction between ads and client browsers and focuses on the prevention of negative impacts. It discusses different types of ad blockers and explains their methods of blocking ads, which allow users to counter tracking and unsolicited advertising. It compares tools that in most cases manage to block third-party trackers and ads or replace ads with alternate images while browsing popular websites. An overview of known classifications of ad blockers is made and a new one is proposed, which distinguishes them according to the methods of their installation. Based on the newly proposed classification, the applicability of the different types of blockers in specific situational conditions is analyzed. Factors in the analysis include the hardware equipment, the degree of protection they provide, the degree of integration with the browser and the requirements for the IT knowledge and skills of the user.
NLP-based Typo Correction Model for Croatian Language

Spelling correction plays an important role when applied in complex NLP-based applications and pipelines. Many of the existing models and techniques are developed to support the English language as it is the richest language in terms of resources available for training such models. The good occasion is that few of the methodologies provide the opportunity to adapt to other, low-resource languages. In this paper, we explore the power of the Neuspell Toolkit for training an original spelling correction model for the Croatian language. The toolkit itself comprises ten different models, but for the purposes of our work, we use the leverage of pre-trained transformer networks due to their experimentally proven spelling correction efficiency in the English language. The comparison is performed over different pre-trained Subword BERT architectures, including BERT Multilingual, DistilBERT, and XLM-RoBERTa, due to their subword representation support for the Croatian language. Furthermore, the training is done as a sequence labeling task on a newly created parallel Croatian dataset where the noisy examples are synthetically generated, and the misspelled words are labeled with their correct version. Finally, the model is tested in-vivo as part of our originally developed speech-to-text model for the Croatian language.
Malicious script distribution pattern detection technique for image search websites

Recently, the number of cases of distributing malicious codes by exploiting homepages that provide an image search continues to increase, and malicious codes distributed through homepages are causing personal information infringement accidents and DDoS attacks. Due to the malware spread through web pages, privacy theft and infringement are getting serious and DoS attacks happen frequently. Distribution patterns of hidden malicious codes on the image search website were collected, and patterns of collected malicious codes and malicious scripts were analyzed. We have analyzed the malicious samples and derived some additional distribution patterns of web-based malware. Similar patterns are grouped together and a representative feature is then extracted from each group. Each category of the malicious samples contains malicious script codes and their variants. We have implemented a system to automatically detect malicious web sites using the malicious script patterns. The proposed malicious script pattern is expected to be available for the zero-day attacks.
Materials Science Training Programs for Students and Masters with a Specialization in Electric Engineering

The development of materials science largely determines the development of the production of new materials for all areas of human life: medicine, building industry, transport, etc. Materials science in the education system, especially in electric engineering, ranks high. This is due to both the development of directions in instrument manufacture in electric engineering (microelectromechanical systems (MEMS), nanoelectromechanical system (NEMS), ultracapacitors, etc.), and invention and development of new materials (e.g, graphene). Creation of new methods and equipment for obtaining materials and testing their characteristics is also of great importance. The paper considers the development of methods and programs for teaching materials science in electric engineering using information technology (digital education). The role of online education in teaching materials science is considered. The work was carried out at the department of electronics of Ryazan State Radio Engineering University and Ryazan Region Housing and Communal Services Training Center.
Comparative Analysis Between the Traditional and Nearest Centroid Methods in the K-Nearest Neighbor Algorithm

One of the most commonly used algorithms in machine learning is the method of the nearest neighbors. The key points in this algorithm are determining the parameter k or the number of nearest neighbors and how to select these neighbors. Whether this choice should be based on the distance between objects in the set or use modifications such as the nearest centroid method. This publication describes a study and comparison of the two types of KNN, conducted using 18 different public data sets that were selected to correspond to the data that a small business would have - small learning sets, heterogeneous data, data with anomalies, unbalanced data. A more unusual category of data has been added to the sets used - those that have the problem n>>p, ie. the number of attributes is many times greater than the number of objects. Experimental studies have shown that there are statistically significant improvements in the effectiveness of nearest centroid neighborhood classification in two of the training sets.
Optical flow estimation technique for hazy scenes

Hazy scenes limit the visibility and make the optical flow estimation task challenging due to low contrast images. Previous methods are not robust when applied to hazy image sequences because the two assumptions (brightness constancy and smoothness) break down due to the low contrast outdoor effects. Based on the pre-processing work on images, we applied an image dehazing method on image sequences first to handle haze and then estimated optical flow. The proposed method is based on a classical pyramidal technique. Results show that our method performs well on hazy scenes as compared to the other existing method.
New Method of Song Sentiment Classification Based on Neural Network

In order to classify song text information more effectively, this paper proposes a new neural network classification model based on emotion vector. On the basis of Word2vec, a double word-embedding word vector containing emotion vector is constructed, and the vector is used as the input of Long Short Term Memory (LSTM). LSTM neural network is trained to extract the features of song text and realize emotional classification of song text. The experimental results show that the method can better embody the emotional features of text, and has higher accuracy than the traditional LSTM text classification.
Web-based Library Information System at SMK PGRI Sukodadi Lamongan

The purpose of school library as a place to read is now less and less desirable by students to read books and search for scientific literature, this is because it is defeated by information media such as the internet which is easier to use in searching for various kinds of knowledge and reading sources. The impact of the information media is also experienced by the SMK PGRI Sukodadi Library which has a large collection of books that should be used by students to support learning activities but students are less interested in reading and borrowing books from the library in addition to having certain assignments from the teacher that require students to borrow books. from the library. Seeing the problems above, a website-based library information system design is made and the development of a library management information system at SMK PGRI Sukodadi Lamongan.
A Privacy Preservation Pipeline for Personally Identifiable Data in Images Using Convolutional and Transformer Architectures

Image and video data of people, shared voluntarily and involuntarily, is ubiquitous. There is an increased need for techniques that enable privacy protection via removal of personally identifiable information in such data, spurred by regulatory interest and increased social awareness of privacy implications. In this paper, we introduce a privacy preservation pipeline that enables de-identifying personal data in images and videos via replacement image synthesis while retaining data utility. We utilize the recently proposed convolutional VQGANs with autoregressive transformers to synthesize realistic and fully de-identified images of people that are then blended with the original scene. Experimental results show that the method provides strong de-identification while retaining the realism of the scene.
Accelerating Neural Network Training Process on Multi-Core Machine Using OpenMP

Modern machine learning algorithms when applied to some real-world data, such as social networks and web graphs, can be very time-consuming. Despite enormous researches were focusing on making their approaches more scalable, however, their proposed approaches are running sequentially which makes the training run time remain noticeably long. Thus, it is reasonable to split the research among multiple processes. This is where we believe parallel processing can help. In this paper, we develop an OpenMP-based approach for parallelizing neural networks on multi-core CPUs. The novelty of our approach is that it is more general as it mainly covers CPU-based parallel training implementation, we focus on accelerating the training phase of neural networks using OpenMP that divides the work among multiple threads to run in parallel. Our experimental evaluation of a binary classification problem run on the banknote authentication dataset which contains images of banknotes with a machine with 12 cores demonstrates a significant acceleration of the training process compared to related works. We outline some possible approaches for further research concerning parallel optimization of execution time in neural network processes.
Prediction of CNC Machine Tool Wear Stat Based on LSTM

In this paper, the tooth-shaped tool in CNC machine tools is taken as the research object, and the time series of tool vibration signal is estimated based on the deep learning algorithm LSTM, which verifies the effectiveness of using LSTM to predict the vibration signal. Firstly, the experimental system obtains the time series of tool vibration signals, and denoises the obtained experimental data by wavelet, and analyzes the frequency distribution diagram of the system. On this basis, SE complexity analysis of vibration signal shows that the greater the wear value, the greater the complexity of vibration signal. According to the results of wavelet transform, LSTM algorithm is adopted, and the accuracy rate reaches 99%, which provides a new idea for intelligent equipment operation and maintenance technology.
Self-optimizing neural network in the classification of real valued data

The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called self-optimizing neural networks (SONN). The proposed approach works on discretized data. Using a special procedure, we assign a feature vector to each element of the real-valued dataset. Later the feature vectors are analyzed, and decision patterns are created using so-called discriminants. We focus on how these discriminants are used and influence the final classifier prediction. Moreover, we also discuss the influence of the neighborhood topology. In the article, we use three different datasets with different properties. All results obtained by derived methods are compared with those obtained with the well-known support vector machine (SVM) approach. The results prove that the proposed solutions give better results than SVM. We can see that the information obtained from a training set is better generalized, and the final accuracy of the classifier is higher.
GenericConv: A Generic Model for Image Scene Classification Using Few-Shot Learning

Scene classification is one of the most complex tasks in computer-vision. The accuracy of scene classification is dependent on other subtasks such as object detection and object classification. Accurate results may be accomplished by employing object detection in scene classification since prior information about objects in the image will lead to an easier interpretation of the image content. Machine and transfer learning are widely employed in scene classification achieving optimal performance. Despite the promising performance of existing models in scene classification, there are still major issues. First, the training phase for the models necessitates a large amount of data, which is a difficult and time-consuming task. Furthermore, most models are reliant on data previously seen in the training set, resulting in ineffective models that can only identify samples that are similar to the training set. As a result, few-shot learning has been introduced. Although few attempts have been reported applying few-shot learning to scene classification, they resulted in perfect accuracy. Motivated by these findings, in this paper we implement a novel few-shot learning model—GenericConv—for scene classification that has been evaluated using benchmarked datasets: MiniSun, MiniPlaces, and MIT-Indoor 67 datasets. The experimental results show that the proposed model GenericConv outperforms the other benchmark models on the three datasets, achieving accuracies of 52.16 ± 0.015, 35.86 ± 0.014, and 37.26 ± 0.014 for five-shots on MiniSun, MiniPlaces, and MIT-Indoor 67 datasets, respectively.
Multi-class classification and modeling of the hospitalization status of COVID-19 patients

In recent times, the unprecedented surge in the Coronavirus disease 2019 (COVID-19) due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to several attempts at understanding and containing the outbreak of the pandemic as well as to ultimately eradicate it. Steps taken so far include encouraging the wearing of face masks and shields, municipality restrictions such as work-from-home orders, the development of vaccines by health research institutions among others. It is widely believed that the main mode of transmission of the virus is from human to human. In this paper, we present the multi-class classification and modeling of the hospitalization status of COVID-19 patients by using both machine learning and compartmental mathematical models focusing on critical factors like hospital stay-days (SDs) and admission type based on severity of illness. The classification of hospitalization status of COVID-19 patients is necessary in order to know priority cases and give them prompt attention. Two key machine learning algorithms-the decision tree and random forest, are deployed in our analyses. The Levenberg–Marquardt (L-M) algorithm was used for parameter estimation for the mathematical model. From our results, it is easy to identify high risk patients in order to optimize treatment plans that would lower cost of treatments, reduce the chances of others getting infected and assist logistics teams to optimally allocate hospital resources. Hospital administrations can also be supported in deciding the number of staff and visitors per patient per day in a facility.
Does Software Piracy Mitigate Poverty?: Evidence from Developing and Latin America Countries

This study analyzes the effect of usage of pirated software on poverty by using six prominent poverty indicators for the samples of developing and Latin America countries. The data utilized in models is out of balance and employ the period between 2003 and 2017. Our hypothesis asserts that increases in usage of pirated software diminish poverty in developing and Latin America countries. Firstly, univariate analyses are conducted, and the results of univariate analyses are demonstrated that there is a statistically significant opposite relationship between usage of pirated software and poverty in all six distinct poverty models. Secondly, three control variables (i.e., unemployment, health expenditure, and human capital) were included to our six poverty models to find out if the finding of univariate analyses retains its validity. The statistically significant reverse relationship between usage of pirated software and poverty remained the same after the inclusion of the three covariates. In other words, usage of pirated software maintains its negative significant effect on poverty in all models for both developing and Latin America countries samples. In consideration of the control variables, statistically significant negative coefficients were obtained for health expenditure and human capital whereas a positive coefficient was obtained for unemployment.
CampusTracer: A Privacy-Aware Contact Tracing Protocol for Campus Environments

Contact tracing has become a major weapon against fighting the spread of diseases like SARS-CoV-2. Unfortunately, the ability to inform potentially infected people always comes with a downside to privacy, as contacts traced could also be misused in other contexts. In this paper, we present CampusTracer, a novel contact tracing system specialized for university campus environments. Based on an in-depth analysis of existing contact tracing approaches and university-specific requirements, we elaborate a Privacy-by-Design solution to contact tracing that caters for most real-world requirements while preserving the privacy of its users in a best-possible way.
Optimization techniques for image representation in visual place recognition

In visual place recognition we aim to match a given query image from a query database with the most appropriate reference image from a reference database. One of the main issues is how to represent a place. Although an ordinary RGB representation can represent a place, various, either handcrafted or learned representations such as deep convolutional neural networks achieve better quantitative results. By using optimization techniques, both convex and non-convex, we can adapt a place representation such that it fits into the problem of visual place recognition. Therefore, in this paper we examine numerous optimization techniques and incorporate them in the context of our problem. Quantitatively, in terms of the area under a curve (AUC) measure, conducted experiments show how such optimized representation outperforms unoptimized one.
Team Building Elements and Their Importance According to STEAM Students

Students who choose the STEAM system type of education are required to have a talent for natural sciences and master computer skills. But also, they must master soft skills, for example coping skills and ways to cooperate with other people. During their academic years, and later in their careers, students will surely find themselves in situations where they will have to work and research as a part of a team. That requires teamwork skills, to manage the process of working within a group of people to achieve a goal. To make teamwork as efficient as possible, team leaders often incorporate team building as an activity that strengthens the team. Even though every team has its way of working, every team building contains elements important for its functioning. The research that was conducted on a sample of 87 students at the Algebra University College, in the academic year 2021/2022, intended to investigate the extent to which students recognize the importance of certain elements of teamwork, such as team building, team development, cohesion, and identifying team members’ roles; and how they can be applied in teamwork and other fields related to this growing phenomenon.