Reviewerly/exHarmony huggingface dataset:
Project ReShare: Building a Community-Owned Resource Sharing Platform

Project ReShare is a community-driven effort to build an open-source, highly scalable resource sharing platform that supports the research lifecycle from discovery through fulfillment. The project’s initial focus will be on designing tools to facilitate reciprocal borrowing agreements within library consortia, including a shared index, request management system, and workflows for unmediated borrowing. This paper introduces ReShare and describes how the project partners are working to improve the resource sharing experience for library users and staff. It provides background about the project, describes development goals and progress, and addresses future possibilities for ReShare beyond its initial release.
An application of ANFIS for Lung Diseases Early Detection System

Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier.
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption. Such meta-knowledge is often represented as a fixed distribution; this, however, may be too restrictive to capture various specific task information because the discriminative patterns in the data may change dramatically across tasks. In this work, we aim to equip the meta learner with the ability to model and produce task-specific meta-knowledge and, accordingly, present a localized meta-learning framework based on the PAC-Bayes theory. In particular, we propose a Local Coordinate Coding (LCC) based prior predictor that allows the meta learner to generate local meta-knowledge for specific tasks adaptively. We further develop a practical algorithm with deep neural network based on the bound. Empirical results on real-world datasets demonstrate the efficacy of the proposed method.
Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors

Crowd analysis is receiving much attention from research community due to its widespread importance in public safety and security.In order to automatically understand crowd dynamics, it is imperative to detect and segment crowd from the background.Crowd detection and segmentation serve as preprocessing step in most crowd analysis applications, for example, crowd tracking, behavior understanding and anomaly detection.Intuitively, the crowd regions can be extracted using background modeling or using motion cues.However, these model accumulate many false positives when the crowd is static.In this paper, we propose a novel framework that automatically detects and segments crowd by integrating appearance features from multiple sources.We evaluate our proposed framework using challenging images with varying crowd densities, camera viewpoints and pedestrian appearances.From qualitative analysis, we observe that the proposed framework work perform well by precisely segmenting crowd in complex scenes.
Open Government on Disaster Information Management in Regional Disaster Management Agency of South Sulawesi

This study is aimed to discuss open government in the context of disaster information management in Disaster Management Agency (BPBD) of South Sulawesi.The study employs qualitative method and data was collected by interview, document study and observation.The informants of the study are BPBD and NGO which work in the disaster management sector.The result of the study shows that the principles of open government have been implemented in the disaster information management in BPBD, though not covering all stages of information management.The principle of transparency, participation and collaboration have been visible in the stage of collecting disaster information.In contrast, those initial principles are not visible in the stage of classifying information.In the stage of documenting and information service, these are found to be the principle of collaboration.Generally, BPBD does not maintain information transparency via website like others but focuses more on utilizing more accessible and practical media for users and disaster information sources like radios, flags, and social media.The openness of BPBD in disaster information management is influenced by some factors such as: (1) sources of disaster information are provided by various parties beside government (2) BPBD eagers to collaborate with other agencies.Lastly, disaster management needs a wide variety of specific skills.
Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV. The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.
Development of Indonesian Language Learning Video Media Based on Multicultural Education for Establish Anti-Ethnic Collection at High School Levels in Singkawang City

The objectives of this research are (1) Development of video media for multicultural education-based Indonesian learning to build inter-ethnic harmony at the high school level in Singkawang City and (2) the feasibility of video media for Indonesian language learning based on multicultural education to build inter-ethnic harmony at the high school level in the city. Singkawang. This research is a development study using the 4-D model (Four-D developed by Thiagarajan (2011) which includes the stages of defining, designing, developing, and disseminating). This research was carried out in 1 phase with a duration of 1 year The results showed that (1) The stages of development of the development of Multicultural Education-Based Indonesian Language video media for Building Ethnic Harmony at the High School Level in Singkawang City adopted the step or stage of Thiagarajan (2011) which included the defining stage (define) , design, develop, and disseminate (2) The results of the assessment by media experts for all n scores are 4.1 with good category and suitable for use, then based on the assessment of media experts the mean of all aspects is 4.3 with a very good category so that the material is suitable for use.
An efficient differential evolution algorithm for task scheduling in heterogeneous cloud systems

Due to the ever-growing data and computing requirements of applications, it is very challenging for cloud scheduler to guarantee the optimal solution at a reasonable time. Although varieties of heuristics have been devised to solve the task scheduling problems in heterogeneous cloud systems, the results are still unsatisfactory, especially for large applications. Evolutionary algorithms outperform heuristics in terms of the quality of the solutions, however, they are often time-consuming and need lots of computing power. To address the above problems, this paper proposes an efficient differential evolution algorithm for task scheduling problems. This algorithm extends the canonical differential evolution in three aspects of hybrid initiation population, less greedy mutation and adaptive parameter adjustment. The results of the experiments indicate that our proposed algorithm consistently produces better solutions with smaller makespan and has the advantage of rapid convergence.
Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists

The article deals with application of mobile technologies for the formation of analytical competence of future specialists. It discusses the concepts of mobile learning and mobile technologies. Advantages of using mobile technologies within the learning process are presented. The use of mobile learning is one of the most effective and affordable ways of formation of students' analytical competence. Managing, strategic, informational, reflexive components within the structure of analytical competence are revealed in the article. The results of experimental work of formation of analytical competence of future specialists by means of mobile learning technologies are presented. The authors conclude that formation analytical competence of future specialists using mobile devices expands the conventional framework by using the capabilities of mobile platforms and expands the learning environment.
The Foundation of Distributed Ledger Technology for Supply Chain Management

Distributed ledger technology (DLT) appears to be one of the most promising technologies in the field of supply chain management (SCM).However, as the technology is still evolving, only limited empirical evidence has been analyzed, managers and scientific scholars alike seek to understand how DLT can help improving SCM.This study aims to shed light into the current DLT applications in SCM to identify the foundation of the technology for SCM and uncover what DLT brings to the table.It develops seven foundational characteristics of DLT in SCM that describe both the nature of DLT and its characteristics for SCM.The study reveals that DLTs are interorganizational information systems that are diverse in their realizations and enable modular platform ecosystems.Nowadays application in SCM build on steady data availability, selective transparency, high authenticity and a source of mutual trust.
Chemical Similarity Based on Map Edit Distance

Quantifying the similarity of compounds is a particularly important problem, as it is able to give rise to many new possibilities for understanding the relationship between molecules. Here we develop a novel algorithm for computing the Molecular similarity. Experimental results on a broad range of real world graphs and tasks show that the proposed similarity measure is very effective.
Consumers’ Need for Negative Product Information Before Reading Reviews

Negative product-related information is crucial to consumers in purchase decisions.Consumers perceive negative information stronger than positive, and next to a stronger perception, consumers also have a high demand for negative product aspects, as these show the problem areas of a product and can help avoid losses.But negative product-related information is not available in the product search process until the customer reads reviews at a very late phase of the decision process.Even though we know about a bias in perception of negative information, little is known about the exact need for negative product-related information during the search process.We examine the need for negative product-related information throughout the purchase-decision process for different product types.Insights about the need for negative product-related information can inform ecommerce platform providers how to design a better product search on their site.
Enhanced Recursive Residual Network for Single Image Super-Resolution

Single image super-resolution (SISR) based on deep learning methods has achieved great advance. Despite the great performance of these models, it is challenging to be applied to practical applications because of enormous parameters. In this paper, we propose an enhanced recursive residual network (ERRN) to address this problem. Specifically, based on residual networks, group convolution and recursive learning are adapted to reduce parameters. The results of evaluation on benchmark datasets show that the performance of ERRN is comparable with the state-of-art methods with much fewer parameters.
Correction to: Keyword Search Based Mashup Construction with Guaranteed Diversity

In the originally published version of chapter 31 the funding information in the acknowledgement section was incomplete. This has now been corrected.
Correction to: An efficient content based image retrieval using enhanced multi-trend structure descriptor

Figure 1 in the original article was incorrect. The correct Fig. 1 is shown below with the corrected part in red.
An Explorative Study of Creative Idea Generation Using Mobile Collaboration Platform

This study examines creative idea generation within a given task involving the use of a mobile collaboration platform (MCP).Drawing upon the theoretical perspectives on cognitive creativity, mobility, and selfselect communication mode, the study proposes a research model that explains how individuals generate creative ideas through MCP use in a group collaboration context.To validate our model, survey data was collected from individuals involved in a group collaboration-particularly when performing tasks that requiring creative solutions.The results of this study indicate that: 1) MCP use has a positive effect on the individual's creative idea generation; 2) both perceived MCP effectiveness and perceived freedom determine the individual's MCP use levels; and 3) the individual's personal innovativeness level in information technology (PIIT) has a conditional effect on the relationship between the perceived MCP freedom and MCP use.The significance of these findings stems from the fact that creative idea generation through group collaboration is a highly sought-after quality in organizational setting.Given the centrality of the mobile ecosystem in today's organizational task environment, this study presents both theoretical and practical contributions.
The training of masters of pedagogical education to the integrated teaching of the schoolchildren in mathematics and computer science

Problem and goal. The single subject area “Mathematics and Computer Science”, provided by existing standards, implies the need to introduce the integration of teaching these subjects in primary and secondary schools; the training for such teaching is one of the goals of the implementation of the master’s program “Mathematics in Primary Education”. Methodology. The implementation of the following factors improves the quality of training of the masters to the integrated teaching of the schoolchildren in mathematics and computer science: the training of the masters is based on the competencies that they assimilated in the stages of their previous training, in particular in undergraduate studies; the developed integrated content of the modular training, implemented in the system of specialized disciplines, allows to implement the systematic, fundamental and invariance training of the masters; the readiness of the masters to the integrated teaching the schoolchildren in mathematics and computer science includes the formed competencies in the selection of teaching content and the use of educational electronic resources; taking into account various factors of using information and communication technologies in the educational process, forming an idea of the role of informatization of education in society, its language, methods and means, and their quality control. Results. The practical activities of masters in creating the electronic courses for organizing online-learning were implemented, a system of computer tasks was created for the integrated teaching of the schoolchildren in mathematics and computer science. Conclusion. The training of masters to the integrated teaching of the schoolchildren in mathematics and computer science ensures their orientation in information and communication technologies and subsequent competent use of these technologies in their professional activities.
Crab Larvae Counter Using Image Processing

Very small larval size, high mortality rate of crab larvae, the absence of an accurate crab larva counter at affordable price, the density of larvae that are not known accurately, the ratio of feed that not in accordance with the number of larvae to damage water quality and cannibalism, water quality can not be adjusted to the density of distribution, and economically unprofitable both in terms of feed efficiency and buying and selling transactions. By knowing the number of crab larvae accurately the survival rate of crab larvae increases and is economically beneficial. In this study larvae counters were designed and developed. This tool uses digital imagery to detect and count the crab larvae zoea-1 phase. The accuracy of crab larva counter is 89,8%.
Automatic RoI dan Active Contour untuk Deteksi Penggunaan Helm pada Pengendara Sepeda Motor

Based on data from the Central Statistics Agency, motorcycle accidents are the most common accidents and many contribute to the death rate in traffic accidents. Some cases of deaths in motorcycle accidents are caused by riders not wearing helmets. Monitoring via CCTV video has been done but it takes a long time so we need another solution to be more effective. Some techniques have been carried out including detection of the use of helmets on motorcyclists by using digital image processing. Some previous studies on these cases experienced obstacles such as overlapping images in the identification process. This study aims to develop a detection method that focuses on the segmentation stage to produce a better segmentation image. The method used in this study is Automatic RoI and Active Contour at the segmentation stage which is then classified using the Multilayer Perceptron classifier. The results obtained give an accuracy value of 72.97%, a sensitivity of 76.19% and a specificity of 68.75%.
Model Interpretation and Explainability towards Creating Transparency in Prediction Models

Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability.We tackle the issue of model explainability in the context of prediction models.We analyze a dataset of loans from a credit card company using the following three steps: execute and compare four different prediction methods, apply the best known explainability techniques in the current literature to the model training sets to identify feature importance (FI) (static case), and finally to cross-check whether the FI set holds up under "what if" prediction scenarios for continuous and categorical variables (dynamic case).We found inconsistency in FI identification between the static and dynamic cases.We summarize the "state of the art" in model explainability and suggest further research to advance the field.
Automated reasoning framework for traceability management of system of systems

An important aspect in system of systems (SoS) is the realization of the capabilities in different systems that work together. Identifying and locating these capabilities are important to orchestrate the overall activities and hereby to achieve the overall goal of the SoS. System elements and capabilities in SoS however, are rarely stable and need to evolve in different ways and different times in accordance with the changing requirements. To manage the SoS and cope with its evolution it is necessary that the dependency links to the capabilities and the system elements can be easily traced. Several approaches have been proposed to model traceability and reason about these by extending a predetermined set of possible trace links with fixed semantics. However, for the context of SoS a fixed traceability model with fixed traceability semantics is limited to consider the various different and changing scenarios. In this article, we first present the different traceability requirements for managing traceability in the context of SoS. Subsequently, we present the metamodel and the corresponding domain specific language to support modeling traceability and traceability analysis approaches within the evolving SoS context. Further, we provide the tool support for automated reasoning of traceability of SoS capabilities and system elements. We illustrate and discuss the approach for the application to a smart city SoS.
Non-Exhaustive, Overlapping k-medoids for Document Clustering

Manual document categorization is time consuming, expensive, and difficult to manage for large collections.Unsupervised clustering algorithms perform well when documents belong to only one group.However, individual documents may be outliers or span multiple topics.This paper proposes a new clustering algorithm called non-exhaustive overlapping k-medoids inspired by k-medoids and non-exhaustive overlapping k-means.The proposed algorithm partitions a set of objects into k clusters based on pairwise similarity.Each object is assigned to zero, one, or many groups to emulate manual results.The algorithm uses dissimilarity instead of distance measures and applies to text and other abstract data.Neo-k-medoids is tested against manually tagged movie descriptions and Wikipedia comments.Initial results are primarily poor but show promise.Future research is described to improve the proposed algorithm and explore alternate evaluation measures.