Reviewerly/exHarmony huggingface dataset:
Multiple lives of the products: An investigation of products’ journey in freecycle community

In today's needs, it is not enough to imagine products who have only one owner in their entire lives.To create more sustainable futures, designers might increase their ability to imagine multiple lives for things.To enable it, scale is the matter of concern.By increasing the usage scale, and examining the exchange of second-hand products informs designers by imagining multiple scenarios related to things lifes.In this paper we focus on local freecycle groups on Facebook in the context of the second-hand product's circulation.In the field research, we identify significant usage cases of second-hand products that have multiple owners.We classify them under four sections, which are student house, permanent house, families with a baby, and repurposers according to their concerns, criteria and behaviors related to handed-over products.Finally, we present insights about users' expectations and concerns that has decisive role in determining the life cycle of the product.We propose thinking for larger usage scales through examples that we provide, guide designers and companies in terms of products' journeys in circulation.
Amphibious scales and anticipatory design

This paper considers dynamics between Anticipatory Design and relational ontological scales in imagining, articulating and shaping futures.This spans speculative, experimental and experiential engagement with imaginary futures for rethinking relations to the present and long-term sustainable ones.Such acts are situated as design futures literacies that encompass design fiction, extended choreography and arctic futurescaping.Drawing on three design fictive devices developed across two projects, a set of eight 'Amphibious Scales' we developed in the context of the Anthropocene.The scales are amphibious in their slipperiness and dynamic, and emergent status.Their genesis is given via accounts of the design fictive works centring on the persona of an octopus and scenarios on the Arctic Northern Sea Route.
Forecasting political parties and candidates for Indonesia’s presidential election in 2024 using twitter

The purpose of this study is to forecast the political parties and candidates running for president of Indonesia in 2024. Nvivo 12 software is used in this study's quantitative methodology for descriptive content analysis. Twitter users are the study's research subjects. The information was collected via Twitter Search, which targeted the term "Pilpres 2024." According to this analysis, Anies Baswedan, Prabowo, and Ganjar Pranowo will be the winning candidates. Political parties' geographic distribution reveals that the 2024 presidential election would see political tensions between parties with nationalist and religious ideologies.
Geospatial Information Construction and Usability Evaluation Using Nadir Image and Oblique Image

Recently, spatial information construction using drones has been employed in various fields such as aerial photogrammetry and ortho image production.Various types of drones for spatial information construction, such as fixed wing, rotary wing, and vertical takeoff and landing (VTOL) types, are being produced, and the field of application of drone photogrammetry is expanding as a result of intensive research.However, research on the construction and analysis of spatial information using oblique images is insufficient.In this study, geospatial information was constructed using a nadir image and an oblique image, and the utility of the oblique image was demonstrated through analysis.Accuracy was evaluated by comparing the accuracy of the ortho images obtained using the nadir image and oblique image with the Virtual Reference Station (VRS) survey result.As a result, accuracies of -0.024-0.017and -0.023-0.031cm were shown for the nadir image and oblique image, respectively, and the same accuracy as for the ground sample distance was confirmed.These results indicate that geospatial information constructed using oblique images can be employed for surveying, similarly to the results obtained using existing nadir images.Through additional research, it was possible to effectively perform 3D modeling of buildings using oblique images.The construction of geospatial information using an oblique image will be an effective way to create more realistic data.
Multi-Optimal Deep Learning Technique for Detection and Classification of Breast Cancer

Breast cancer is a prominent cause of death for women in both developing and underdeveloped countries. Patients can receive proper treatment if breast cancer is detected and classified early in its development. Histopathological and cytological images offer sufficient phenotypic information, indicating their importance in the diagnosis and treatment of breast cancer. Deep learning architectures are typically designed to be unique to problems and are implemented in isolation. Trainable parameters and large dataset are two of the most important challenges researcher face while training classifiers. To overcome these constraints, a model has been proposed for the classification of histopathology images and early detection of breast cancer. The rescaling, resizing, pixel intensities and balancing were performed on the dataset to improvise the model accuracy and the performance of the model was evaluated using precision, recall, and F1-score. The results suggested that the proposed model works effectively and produces high accuracy with minimal data pre-processing and feature-extraction techniques.
A New Leakage Resilient Symmetric Searchable Encryption Scheme for Phrase Search

Symmetric searchable encryption (SSE) schemes are preferred over asymmetric ones for their lower computational cost.Owing to the big data size of most of the cloud applications, SSE with keyword search often yields a large number of search results matching the search criterion, but only a small portion of them is of actual interest.This results in unnecessary increase of network traffic.A customized search against a phrase instead of keywords can yield more specific and relevant search results and can reduce the network traffic.This motivates the idea of phrase search in SSE.Most of the existing symmetric key searchable encryption schemes either do not support phrase search or have unwanted leakage associated with them.In this paper, we propose a symmetric key searchable encryption scheme for phrase search that minimizes the leakage of information from search pattern and access pattern.We propose a probabilistic trapdoor generation algorithm for phrase search and thereby prevent the leakage due to search pattern.In earlier SSE based schemes, an honest-butcurious server could always learn about the position of the sentences and keywords in the encrypted text after the search operation is performed.This is referred to as the leakage from access pattern.This may turn out to be a significant security concern owing to the prior knowledge of positions of certain sentences and keywords in certain documents.In this paper, we provide the access pattern secure encryption scheme such that, an honest-but-curious cloud server could not learn anything about the position of the phrase in the sentence even after the search.We implement a prototype of our scheme and validate it against commercial data and provide security and performance analysis to demonstrate its practicality.
5IRE’S COMPREHENSIVE PLATFORM FOR CBDC

It is sufficiently predictable that most paper currencies will disappear and digital currencies will become the mainstream. This is why the Central Bank Digital Currency (CBDC) has recently begun to attract attention. Blockchain technology bring unique advantages to a CBDC. A blockchain-based CBDC enables central banks to control the currency while protecting the privacy and independence of the CBDC’s use to the end users. The 5irechain based CBDC benefits from the innovative products and services that are being built across the open source blockchain ecosystem including zero-knowledge cryptography, non-custodial wallets, and decentralized finance. 5irechain ecosystem assists all of new financial models that will guarantee financial stability and inclusion, fraud control as well as greater efficiency and modernization of the monetary system. In this paper, we propose 5irechain based CBDC that enables communication between blockchains using Inter-Blockchain Communication (IBC) protocol to ensure interoperability. It can give different user privileges and privacypreserving.
Web 2.0 Technologies Use in ARCS Motivational Model-Based Online Learning on Student Performance in STIPAK Malang

Online learning which capitalizes on Web 2.0 technologies combined with ARCS Motivational model-based learning strategies (Attention, Relevance, Confidence, and Satisfaction) has effects on students' performance in STIPAK Malang.Web 2.0 technologies offer lots of advantages such as facilitating students in obtaining information, transmitting information rapidly that they may be information producers, and producing knowledge.Besides, Web 2.0 technologies contribute to creativity, support learning, and communication as well as the interaction between teachers and students or student-student interaction.ARCS Motivational model focuses on four things in learning that give rise to students' retained attention towards satisfaction.There was a strong relationship between the use of Web 2.0 technologies and ARCS motivational model-based learning, and student's performance in STIPAK Malang indicated at 0.717** with sig value 000<0.01.The effect revealed between variables was shown by the coefficient of determination (R) value of 51.4% meaning that the use of Web 2.0 technologies in the ARCS motivational model-based learning had a significant effect of 51.4% on student's performance whereas 48.6% was attributed to other factors.
Estimating the Optimal Number of Clusters from Subsets of Ensembles

This research estimates the optimal number of clusters in a dataset using a novel ensemble technique -a preferred alternative to relying on the output of a single clustering.Combining clusterings from different algorithms can lead to a more stable and robust solution, often unattainable by any single clustering solution.Technically, we created subsets of ensembles as possible estimates; and evaluated them using a quality metric to obtain the best subset.We tested our method on publicly available datasets of varying types, sources and clustering difficulty to establish the accuracy and performance of our approach against eight standard methods.Our method outperforms all the techniques in the number of clusters estimated correctly.Due to the exhaustive nature of the initial algorithm, it is slow as the number of ensembles or the solution space increases; hence, we have provided an updated version based on the single-digit difference of Gray code that runs in linear time in terms of the subset size.1 Gray code is the weighted code where only one-bit changes for every two consecutive members.
POTENSI PEMANFAATAN YOUTUBE DALAM PEMBELAJARAN MATEMATIKA

Information technology has a very important role in learning. YouTube is used as a teaching media to help overcome the difficulties of teaching and learning activities during the Covid-19 pandemic period. Providing sources of information in the form of materials and other additional information is very useful so students can understand the material optimally. The purpose of writing this article is to provide information so that it can be used as a source of readers about the potential for youtube utilization in mathematics learning. This research is a literature review that examines ideas, opinions, or findings contained in online articles and journals related to titles and research content so that it provides theoretical information. The results of this study concluded that the potential for youtube utilization in mathematics learning has a positive effect in increasing student learning interest and can motivate students to learn because information on YouTube is very diverse so that it will be useful in the current learning conditions.
Utilization of Chabot in an Educational System

Chabot are recently being utilized in a variety of online applications such as education, marketing, supporting systems, cultural heritage and industry from e-commerce to travel. It provides several benefits, such as availability, personalization, etc. Chabot is appeared in very large numbers at the start of the present decade. In a present day, the usage of Chabot is increasing day by day in large scale of the application that is providing better intelligence to the user. In fact, to speed up the assistance these systems are equipped with Chatbots which can interpret the user questions and provide the right answers, in a quick and proper manner. Hence, it is not a part of virtual assistants, but it can be utilized for governments as well as organizations on websites, applications and instant messaging platforms to develop the products, ideas or services. This paper presents the realization of a chatbot in the educational system. In addition, the authors present the historical development, types of chatbot, applications and their future in education sector is described.
Multi-Model Analysis of Mammograms

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis

The term “data-drift” refers to a difference between the data used to test and validate a model and the data used to deploy it in production. It is possible for data to drift for a variety of reasons. The track of time is an important consideration. Data mining procedures such as classification, clustering, and data stream mining are critical to information extraction and knowledge discovery because of the possibility for significant data type and dimensionality changes over time. The amount of research on mining and analyzing real-time streaming data has risen dramatically in the recent decade. As the name suggests, it’s a stream of data that originates from a number of sources. Analyzing information assets has taken on increased significance in the quest for real-time analytics fulfilment. Traditional mining methods are no longer effective since data is acting in a different way. Aside from storage and temporal constraints, data streams provide additional challenges because just a single pass of the data is required. The dynamic nature of data streams makes it difficult to run any mining method, such as classification, clustering, or indexing, in a single iteration of data. This research identifies concept drift in streaming data classification. For data classification techniques, a Labelled Classifier with Weighted Drift Trigger Model (LCWDTM) is proposed that provides categorization and the capacity to tackle concept drift difficulties. The proposed classifier efficiency is contrasted with the existing classifiers and the results represent that the proposed model in data drift detection is accurate and efficient.
Continuous improvement of information security management: an organisational learning perspective

This study explores ways to empower organisations to continuously improve their information security management (ISM). Drawing upon the dynamic capabilities approach, we investigated the mechanism wherein absorptive capacity has an effect. We found that absorptive capacity affects an organisation’s continuous improvement of ISM, with its effect mediated through an organisation’s adaptability to information security threats. In addition, the effect of absorptive capacity on adaptability is contingent upon the organisation’s competitive pressure, which enhances the mediating effect of adaptability. We tested our research model using survey data collected from 130 US-based managers familiar with information security management in their organisations. Theoretical and practical implications of the study are discussed.
Ukucha: a video game for teaching experimental psychology

The COVID-19 pandemic has forced educational institutions to find a way to meet educational needs, within these those for experimentation. This article implements a video game for teaching experimental psychology in a private university. The research is of an applied type and experimental design. For the development of the software, the SUM methodology was used for the development of the video game and the OOHDM methodology for the development of the web page according to the video game. Additionally, cutting-edge tools have been considered to be able to make a scalable and robust application such as the use of Unreal Engine 4 in its version 4.27 for the development of the video game, Blender in its version 3.0 for 3D modeling and character animation, Django as a framework Python for web development in conjunction with PostgreSQL for the elaboration of the database through the software architecture Model Vista Template (MVT).
Proposal for a Scientific Collaboration Recommender System Based on Soft-Computing Techniques

This article presents a proposal for the recommendation of scientific collaborations for researchers and research groups using the methodology for knowledge discovery in databases and an inference procedure based on fuzzy relations. Each phase of the methodology is presented, from information gathering to the generation of recommendations considering that a collaboration can have different motives and interests and for this purpose it is proposed to use two defined criteria: bibliometric affinity and semantic affinity. To validate the inference procedure, an experiment between research groups from two universities in different countries is presented.
Comparison of Support Vector Machine and K-Nearest Neighbors in Breast Cancer Classification

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.
Recent Developments in Big Data Warehouses

Big data is usually defined as data that is so big, that may arrive at a pace or with such a variety that makes it difficult to manage them with methods and tools. Social networks, the Internet of Things (IoT), smart city devices, and mobility data, among others, produce enormous volumes of data that must be analyzed for decision making.
An Overview of Feature Extraction Techniques in Content-Based Image Retrieval Systems

The collection of images has grown rapidly and continues to increase in future due to widespread use of internet. Content based Image Retrieval (CBIR) system aims at retrieval of images of relevance to the query image input by the user from an enormous image database by low-level feature (such as color, texture and shape) extraction from the image. Some common applications of CBIR and existing commercial systems employing CBIR have been highlighted. General algorithm for explaining the working of CBIR is presented. This article presents a review on methods for low level feature extraction like extracting color, texture and edge features. CBIR still faces some challenges like judgement of human perception of visual content, less appropriate selection of similarity measure, semantic gap and other factors.
Evaluation of the Italian Cytological Reporting System and Comparison with Histopathology with Respect to Indeterminate Thyroid Lesions

Introduction: The British system (RCPath-Thy1-5), The Bethesda System for Reporting Thyroid Cyto-Pathology (BSRTC) and The Italian Society of Anatomic Pathology and Cytology (SIAPEC) classification are the most practised thyroid reporting systems with high discriminative factor in classifying thyroid lesions on Fine Needle Aspiration Cytology (FNAC). Surprisingly, none of these classification systems are reliable, when the question is about ‘indeterminate for malignancy’ category. Aim: To find out a better cytological reporting system between Bethesda and Italian classification with respect to indeterminate lesions of thyroid. Materials and Methods: A cross-sectional retrospective study was undertaken in department of Cyto pathology at SDM College of medical sciences and hospital, Dharwad from year January 2015 to January 2018. Thyroid FNAC cases were collected from departmental archives. Among these cases, all FNAC slides for which thyroid surgery were done were reviewed by two cytopathologists and reporting was done using SIAPEC reporting system and BSRTC system. Among those cases (N=33), only indeterminate lesions were selected for the study. Fourteen cases were reported under TIR-3A and 19 cases were reported under TIR-3B. Histological outcomes and cyto-histopathological comparision was done using Chi-Square test. Risk Of Malignancy (ROM), sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) were determined. Results: ROM for TIR-3A lesions is 35.7% and for TIR-3B lesions is 58.89%. ROM for Atypia of Undetermined Significance/ Follicular Lesion of Undetermined Significance (AUS/FLUS) lesions is 71.4% and Follicular Neoplasm (FN)/Suspicious for FN category (FN/SFN) lesions are 42.3%. The study revealed very high sensitivity and specificity under Italian system when compared to Bethesda system in identifying malignant outcomes. ROM in Italian sub-classification was more than the anticipated levels. Conclusion: Current study concludes that SIAPEC reporting system is more satisfactory than BSRTC system while evaluating the indeterminate nodules for malignant outcomes. Our results indicate that cases classified solely on architectural atypia have almost 50% lower ROM when compared with cases with cytological atypia.