Reviewerly/exHarmony huggingface dataset:
An equilibrium honey badger algorithm with differential evolution strategy for cluster analysis

Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population’s diversity and the performance of global search, the differential evolution method is incorporated into algorithm’s initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm (HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm’s performance is assessed using the Wilcoxon rank sum test and Friedman’s test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems.
Digital technologies as an adaptive learning tool in higher education

Education in the modern society is in need for new and more efficient pedagogical technologies. The development of individual qualities becomes the most important mission of education. However, graduates of educational institutions for the most part do not have sufficiently formed personal qualities. It is necessary to create due conditions for the development of individual personality traits, such as: adaptation to life situations, acquiring knowledge independently, the ability to apply it in practice; being able to think critically, to identify problems and select the necessary solution rationally and competently; working competently with information; sociability, being able to work in a team. The relevance of the issue under investigation in the conditions of digital education becomes an important tool for extensive access to adaptive learning at higher educational establishments. The trends in adaptive learning systems will make it possible to transform the educational system, helping students to learn more efficiently. In order to achieve this goal, the following tasks need to be addressed: to make analysis of scholarly pedagogical literature focused on the adaptive learning model; to select and analyse the material for the realisation of the adaptive learning model.
Architecture of a Low-Cost Solution for ROVs to Improve Navigation and Data Collection

The documentation, conservation, and preservation of underwater archaeological sites is conventionally performed by divers in shallow waters. However, when dealing with significant depths, the sites can be inaccessible for humans, presenting significant challenges and risks, making these activities hazardous. Recently, the use of robotic platform such as remotely operated vehicles (ROVs) allows to overcome the difficulty of such a harsh environment. Nevertheless, professional-grade ROVs employed for this purpose are expensive and specifically designed with integrated advanced sensor systems for data acquisition and navigation. In the context of the MSCA-RISE H2020 Technological Consortium TO develop sustainability of underwater Cultural heritage (TECTONIC) project, a solution consisting of an optical payload and an acoustic localization system for low-cost ROV, was defined in order to support ROV pilots during documentation and monitoring activities. The project aims to preserve underwater cultural heritage (UCH) encompassing the selection of a commercially available ROV and the definition of a customized hardware/software (HW/SW) architecture. After market research, a suitable commercial off-the-shelf (COTS) low-cost ROV was defined and used as the foundation for integration. Mechanical, electric, and data interface modules were designed to seamlessly integrate and manage the sensor suite. The results of the integration demonstrate the potential of the chosen algorithms and the sensor suite for improved navigation and data collection in challenging underwater environments, where high-grade and expensive sensors are not available, thereby making more accessible the field of underwater survey missions.
Leveraging open data for reference services delivery in academic libraries

Purpose Librarians play a critical role in curating, organizing and facilitating access to open data (OD) resources, supporting research, learning and information dissemination. This study aims to explore the use of OD for reference services delivery in academic libraries. It is believed that the concept of OD in librarianship refers to the practice of making publicly available data freely accessible, usable and shareable by anyone. Design/methodology/approach This research examined how the utilization of OD may affect library services and operations. This paper used the systematic review of literature to answer the research questions; hence, it was a desk study. Findings Given that there has been recently a research shift in linking OD to library metadata, this study revealed some interesting findings. More specifically, it discovered that incorporating OD into reference services can provide several benefits and enhance the quality and relevance of the information provided to library users. In addition, this paper reported that OD enriches reference services by providing comprehensive, current and interdisciplinary information, supporting evidence-based research, enabling data visualization and analysis, fostering community engagement and promoting innovation. Originality/value This paper proposes that libraries that possess extensive collections are in a favorable position to embrace the shift toward becoming OD and open big data libraries. It is also believed that academic libraries, through reference services, meet the challenge of providing access to their institutional repositories by holding many sets of data in various formats and providing various interfaces, developing metadata systems for various data streams, supporting all ecosystems of software and data products for reformatting and reusing large and complex data, and support for data sets identifiers. By actively engaging with OD, librarians can play a vital role in facilitating access to information and promoting the use of open knowledge for academic pursuits.
Credit Card Fraud Detection Using Machine Learning

To make life better, many mechanisms in modern environment are carried out via the Internet. The economy is expanding yet on the other side, there is a lot of illegal and unauthorised activity carried throughout the country that is seriously hampering that progress. Scam instances, which mislead individuals while also causing economic losses, are just one of them. In realistic conditions, fraud involving credit cards surveillance is the main emphasis of this research. Contrary to earlier eras, the number of credit card scammers is drastically increasing right now. Criminals use various forms of innovation, fake documents, and deception to con others and take their cash. Therefore, it is extremely crucial to discover a solution to these frauds. As technology advances, it becomes harder to keep up with the behaviour and trends of illegal activities. Ai technology, machine learning, as well as other relevant data technology fields have advanced to the point that it is currently feasible to expedite this process and reduce the volume of labour-intensive effort needed in recognizing credit card scams. The user-submitted utilization of credit cards database might be collected initially, then using machine learning approach; it would be split into databases for testing and training purposes. This methodical technique could be utilized by researchers once they have evaluated both the larger information collection and the user-provided available data collection. Enhance the accuracy of the outcome statistics after that. Depending on its exactness and precision, a technology's efficiency is assessed. The results show that XG-Boost and Random Forest techniques have the greatest performance.
A Privacy Risk Analysis of Identity Federation Topologies in Single Sign-On (SSO) Web Domain

Single Sign-On (SSO) allows users to authenticate once and gain access to different web services. Thus, users do not need to provide their credential information again. Three main entities are involved in the SSO: the user, Identity Provider (IdP), and Service Provider (SP) or Relying Party (RP). These entities are linked through different relationships under various IdP-SP association models, mainly decentralized, explicit, federated, and bridge. Utilizing SSO under these models or their overlapping in a specific context raises various privacy risks for all entities involved. This paper identifies a set of privacy requirements and outlines the privacy risks for the involved entities in simple SSO scenario and topologies. With regard to topology, it is vital to consider a combination of different association models with respect to privacy requirements. To address the common privacy risks with a focus on user identities, attributes, and changing behavior, the present study proposes a fuzzification system to infer the user’s privacy risk during both the authentication and access processes under semi-honest and malicious models. Analyzing privacy risks is crucial in ensuring the web cloud environment’s security as it helps reduce the likelihood of attacks and identify potential vulnerabilities.
PERANCANGAN DASHBOARD PENGAWASAN AKTIVITAS TRUK PADA PT CIPTA NIAGA SEMESTA

PT. Mayora Indah Tbk. (Mayora) was founded in 1977 with the first factory located in Tangerang with the target market in Jakarta and its surroundings. Currently, Mayora needs a way to monitor the activities of distribution trucks or truck vendors, such as data on incoming and outgoing factory trucks, data on goods transported by trucks, and unloading goods from trucks to assess employee key performance indicators. Therefore the author proposes to design a dashboard for monitoring truck activity at PT Cipta Niaga Semesta using Pentaho and Grafana with the prototyping method and the nine-step Kimball method. Pentaho is a business intelligence software, while Grafana is a multiplatform open source analytics and interactive visualization web application. The feature that will be made is a dashboard that displays truck activity data with filter options to display the data you want to see.
Pelatihan Pembuatan Media Pembelajaran Bagi Guru Berbasis ICT dan VSDC Di MIS Al Abrar Kota Makassar

This study used the lecture method and questions and answers were given to convey the material to be given to partner MI teachers. 1) The lecture method aims to convey a series of theories and procedures for making literary teaching materials, and how the learning process can be carried out properly and effectively through Microteaching, 2) The discussion and question and answer method is used to make it easier for instructors or executors to determine strategies to be applied in mentoring. While the debriefing is conducted to check whether the participants have understood the ICT-based learning material provided by the instructor, besides that the question and answer session helps participants adapt the media to the subject to be taught. The assignment method is used to train teachers to make ICT-based learning media using VSDC and PPT and implement these media to students. 3) Practice and assignment methods are used to provide training to workshop participants to prepare and compile ICT-based learning media using VSDC and PPT. 4) The process of making ICT-based learning media is preparing a laptop or PC complete with an Internet network, then preparing a guidebook that can be used by teachers in running applications for making ICT-based media using VSDC and PPT. The first problem encountered in learning literature is 1) the teacher's inability to master literature, 2) the lack of teaching skills for teachers, 3) the lack of understanding of literature. Furthermore, the second problem in the field of technology is 1) not being able to package media properly, 2) have never attended training in making teaching media, 3) lack of motivation in improving IT skills. Given the problems mentioned above, the solutions offered are 1) training in making teaching materials for literature, 2) training for making learning media 3) training and mentoring for packaging literary learning media, 4) training and strengthening strategies in using media that have been made by the teacher , 5) Training on promoting works through websites/youtube. Through this media the teacher will be helped to explain how to read poetry properly according to what is shown by the audio-visual media. School is a place for seeking knowledge, but if the school is not able to make children excited, it is necessary to re- evaluate what is lacking in the learning process. While those who are assessed are still at the level of memorization ability. Therefore, this service is carried out to help solve problems that occur in learning in Madrasas.
EVENT: Real-time Video Feed Anomaly Detection for Enhanced Security in Autonomous Vehicles

Autonomous Vehicles have long leveraged Artilicial Intelligence to be capable of self-driving without the need for a human supervisor. To achieve self-driving autonomy, various sensors are installed onboard the vehicle in order to be able to perceive information from its surroundings. However, since autonomous vehicles’ capabilities rely heavily on sensor readings, various challenges arise in terms of security and privacy. Thus, it is of the essence to design methodologies able to detect anomalies caused by malicious threat actors or sensor malfunctions. This paper proposes an anomaly detection algorithm for autonomous vehicle camera sensors. By utilizing Recurrent Neural Networks in combination with Convolution operations, it is possible to obtain a sequence of images and reconstruct the next frame in real-time. By leveraging image similarity techniques such as Mean Squared Error and Structural Similarity Index, it is possible to compare the ground truth with the predicted image and draw conclusions about whether an anomaly is present. The experiments in real datasets captured from autonomous vehicles within the European-funded nIoVe project highlighted that the proposed framework is able to detect anomalies and malfunctions with high accuracy, clearly indicating the necessity of such algorithms to enhance the security of autonomous vehicles.
Projection Mapping on Building Facade Using Augmented Reality

Projection mapping and augmented reality are evolving in the tourism sector and helping tourists interpret and understand tourist sites. These experiential features of these two technologies provide an immersive experience to visitors while keeping them engaged within the places where they can relate to the structure. Projection mapping has replaced the sound and light which show at different heritage sites by enhancing the experience. The limitation of projection mapping is that tourists can experience it for a limited duration at night time only when the projected graphics are clearly visible on the building façade. Augmented reality is proved to be the best experiential technology for individual sightseeing experiences. Its easy access, availability, and ease of use are engaging more people towards this feature. In this paper, the author focuses on integrating these two technologies where visitors can experience the projection mapping with the help of augmented reality on their mobile devices. Here, in this paper, the residential building is documented, and the methodology is explained for documentation of the structure as well as to develop an app for android phones where it can detect the façade and show motion graphics on it.
Compositional clustering: Applications to multi-label object recognition and speaker identification

We consider a novel clustering task in which clusters can have compositional relationships, e.g., one cluster contains images of rectangles, one contains images of circles, and a third (compositional) cluster contains images with both objects. In contrast to hierarchical clustering in which a parent cluster represents the intersection of properties of the child clusters, our problem is about finding compositional clusters that represent the union of the properties of the constituent clusters. This task is motivated by recently developed few-shot learning and embedding models can distinguish the label sets, not just the individual labels, assigned to the examples. We propose three new algorithms -- Compositional Affinity Propagation (CAP), Compositional k-means (CKM), and Greedy Compositional Reassignment (GCR) -- that can partition examples into coherent groups and infer the compositional structure among them. We show promising results, compared to popular algorithms such as Gaussian mixtures, Fuzzy c-means, and Agglomerative Clustering, on the OmniGlot and LibriSpeech datasets. Our work has applications to open-world multi-label object recognition and speaker identification & diarization with simultaneous speech from multiple speakers.
Massive Natural Language Processing in Distributed Environment

Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires an extensive amount of training data sets to operate on. As such data sets grow, it becomes impossible to process them on a single machine, even using parallel computations. In this paper, the problem of distributing the NLP computations is taken into consideration. The Scalable Distributed Two-Layer Data Structures were used to organize the distributed environment. Stanford Core NLP library was used to perform accurate NLP computations. The experiments focused on the efficiency of the computation in the distributed environment and the accuracy of the predictions in relation to the scale of the distributions. The results clearly show that an increase in performance can be obtained without a significant decrease in accuracy.
A Strategy to Evaluate Test Time Evasion Attack Feasibility

New attacks against Computer Vision systems and other perceptive machine learning approaches are currently published in high frequency. Often the assumptions or limitations of these works are so strict that the attacks seem to have no practical relevance. On the other hand, recent reports show the effectiveness of attacks against cyber-physical systems (CPS). In particular, attacks on automotive systems demonstrate the safety-impact in real-world scenarios. We discuss the practical relevance of security threats for machine learning approaches in automotive use cases and we propose a strategy to evaluate the feasibility of such threats. This includes a method to potentially discover existing vulnerabilities and rate their exploitability in the use case.
KLASTERISASI KARAKTERISTIK WISATAWAN MANCANEGARA MENGGUNAKAN METODE K-MEANS CLUSTERING

Since the Covid-19 pandemic, Indonesian tourism has experienced a drastic decline. This decline can be seen in the number of foreign tourists visiting Indonesia. The number of foreign tourist arrivals in 2020 and 2021 is far less compared to 2019 before Covid-19 entered. As a result, the Indonesian economy also suffered. Regarding the recovery of Indonesian tourism after the pandemic has been slow down, this study aims to cluster foreign tourists visiting Indonesia based on the amount of their expenditures and length of stays using the K-means algorithm. Secondary data from National Statistics Bureau classified the origin of tourists were 86 countries. Applying k-means algorithm methods to cluster country of origin, result showed they were three clusters formed based on the attributes of visiting, i.e. length of stay in Indonesia and total amount of their expenditures. Each cluster consists of 14, 54 and 18 countries. The first cluster is characterized by countries that have high tourism spending; the second cluster is formed by countries with moderate tourism spending; and the third cluster is characterized by countries with low tourism spending. The accuracy of the three clusters in explaining the variance of tourist spending is 68.8 percent.
Optimal Methods for Convex Risk-Averse Distributed Optimization

This paper studies the communication complexity of convex risk-averse optimization over a network. The problem generalizes the well-studied risk-neutral finite-sum distributed optimization problem, and its importance stems from the need to handle risk in an uncertain environment. For algorithms in the literature, a gap exists in communication complexities for solving risk-averse and risk-neutral problems. We propose two distributed algorithms, namely the distributed risk-averse optimization (DRAO) method and the distributed risk-averse optimization with sliding (DRAO-S) method, to close the gap. Specifically, the DRAO method achieves optimal communication complexity by assuming a certain saddle point subproblem can be easily solved in the server node. The DRAO-S method removes the strong assumption by introducing a novel saddle point sliding subroutine which only requires the projection over the ambiguity set . We observe that the number of -projections performed by DRAO-S is optimal. Moreover, we develop matching lower complexity bounds to show the communication complexities of both DRAO and DRAO-S to be unimprovable. Numerical experiments are conducted to demonstrate the encouraging empirical performance of the DRAO-S method.
SoK: Machine Learning for Continuous Integration

Continuous Integration (CI) has become a well- established software development practice for automatically and continuously integrating code changes during software development. An increasing number of Machine Learning (ML) based approaches for automation of CI phases are being reported in the literature. It is timely and relevant to provide a Systemization of Knowledge (SoK) of ML-based approaches for CI phases. This paper reports an SoK of different aspects of the use of ML for CI. Our systematic analysis also highlights the deficiencies of the existing ML-based solutions that can be improved for advancing the state-of-the-art.
Internet of Things and Supply Chain Management: An Empirical Analysis of Nigeria Perspective

Information Tecnology (IT) developments is a paradigm shift in various domains, including supply chain management (SCM). This study examines internet of thing (IoT) and SCM; an empirical analysis of Nigeria perspective. The study examined IoT factors and its effect on SCM, with aspecific focus on Jumia Nigeria. The survey research design was used in this study. The study's sample size is comprised of 265 members of Jumia employees who were randomly selected with 253 valid responses retrieved. SmartPLS3 was used for the analysis, and hypotheses were tested using structural equation model at a significance threshold of 0.05 to see whether they were correct. Results demonstrated that IoT has a standardized beta value and P value of 0.948 and 0.000 on SCM which its r2 indicates 89.9% on SCM. In conclusion, IoT factors integration will lead to continuous improvement as it helps to enhanced its supply chain management.