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The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model can be applied to simulate hydrological processes. This model has been widely used in recent days because it is openly accessible to the public. The search result from this study identifies 180 cases discussing the model applications in Indonesian watersheds in peer-reviewed journal articles and conference papers. The model has been used in Indonesia since 2005, but the highest application is in 2020 with 42 articles. In the context of application distribution, Java Island ranks first place with 123 scientific publications. However, the HEC-HMS model is predominantly used to simulate the "Small" and "Very Small" watershed size categories. The availability of insufficient field data is huge obstacle for hydrological modeling in Indonesia. Furthermore, the model may be extended to address the issues in Indonesia's eastern watersheds. Moreover, the model also can be used to assess the impact of climate change on streamflow. [ABSTRACT FROM AUTHOR]

The paper describes digital strategies helping to boost the competitiveness of Russian as a foreign language. The authors argue that it is impossible to deny the universal popularity of the English language. Knowledge of English is important to learners of Russian as a foreign language since it functions as a social language facilitating their transition to multilingualism. Thus, to improve the competitiveness of Russian courses, it is beneficial to supplement them with instructions in English and modern online tools with English interfaces. The paper offers two scenarios of in-class activities for beginner and intermediate-to-advanced learners of Russian, featuring key strategies utilized for students from Kazakhstan and the People's Republic of China. The authors argue that the described activities are also useful for students from other countries. The scenarios are included in the common framework of the "Working for a Russian Digital Company" business game and enhance the competitive advantages of Russian studies programs expanding the limits of teaching language and culture in the field of online technologies and Java programming basics. [ABSTRACT FROM AUTHOR]

This paper is situated within the discussion of mega-urbanization, a particular urbanization process that entails a large-scale agglomeration. In this paper, our focus is on urbanization in Java, Indonesia's most dynamic region. We add to the literature by investigating the change and prediction of the land use/land cover (LULC) of mega-urbanization in Java. This research uses a vector machine approach to support the classification of land cover change dynamics, cellular automata-Markov (CA Markov), and the Klassen typology technique. This paper indicates that major metropolitan areas are still expanding in terms of built-up areas, generating a larger urban agglomeration. However, attention should be also given to the urbanization process outside existing metropolis' boundaries given that more than half of the built-up land coverage in Java is located in non-metropolitan areas. In terms of future direction, the projection results for 2032 show that the Conservative scenario can reduce and slow down the increase in built-up land on the island of Java. On the other hand, the Spatial Plan (RTRW) scenario facilitates a rapid increase in the LULC of built-up land from 2019. The urban spatial dynamics in Java raises challenges for urban and regional planning as the process is taking place across multiple administrative authorities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In image processing, computer vision algorithms are applied to regions bounded by closed contours. These contours are often irregular, poorly defined, and contain holes or unavailable areas inside. A common problem in computational geometry includes finding the k-sided polygon (k-gon) of maximum area or maximum perimeter inscribed within a contour. This paper presents a generic method to obtain user-defined polygons within a region. Users can specify the number k of sides of the polygon to obtain. Additionally, users can also decide whether the calculated polygon should be the largest in area or perimeter. This algorithm produces a polygon or set of polygons that can be used to segment an image, allowing only relevant areas to be processed. In a real-world application, the validity and versatility of the proposed method are demonstrated. In addition, the source code developed in Java and Python is available in a GitHub repository so that researchers can use it freely. [ABSTRACT FROM AUTHOR]

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test cases. However, existing automated tools are not mature enough to be widely used by software testing groups. This paper conducts an empirical study on the state-of-the-art automated tools for Java, i.e., EvoSuite, Randoop, JDoop, JTeXpert, T3, and Tardis. We design a test workflow to facilitate the process, which can automatically run tools for test generation, collect data, and evaluate various metrics. Furthermore, we conduct empirical analysis on these six tools and their related techniques from different aspects, i.e., code coverage, mutation score, test suite size, readability, and real fault detection ability. We discuss about the benefits and drawbacks of hybrid techniques based on experimental results. Besides, we introduce our experience in setting up and executing these tools, and summarize their usability and user-friendliness. Finally, we give some insights into automated tools in terms of test suite readability improvement, meaningful assertion generation, test suite reduction for random testing tools, and symbolic execution integration. [ABSTRACT FROM AUTHOR]

Artificial neural networks are an integral part of the curriculum in many undergraduate computer science programs. This paper introduces NNeduca, a simulation software system designed to teach undergraduate students the fundamental concepts of artificial neural networks. The system is written in Java and aims to provide an intuitive user interface, support for research and learning activities, and the ability to expand. This tool allows students to design and train a neural network with any architecture they desire and visualize some artificial neural network theoretical concepts such as definitions, topologies, training methods, and structure. An overview of educational software systems to aid in learning the fundamental concepts of neural networks was created based on a systematic literature review. A comparison of existing simulation systems and the developed NNeduca tool was performed in this paper. The evaluation was carried out using a newly developed model for assessing the quality of selected systems using established criteria formed as a result of a thorough examination of relevant methods of evaluating educational software. The usability evaluation results show that the NNeduca tool received the highest ratings, indicating that it can significantly improve the teaching process in artificial neural network courses. [ABSTRACT FROM AUTHOR]

Recent results in language engineering simplify the development of tool-supported executable domain-specific modeling languages (xDSMLs), including editing (e.g., completion and error checking) and execution analysis tools (e.g., debugging, monitoring and live modeling). However, such frameworks are currently limited to sequential execution traces and cannot handle execution traces resulting from an execution semantics with a concurrency model supporting parallelism or interleaving. This prevents the development of concurrency analysis tools, like debuggers supporting the exploration of model executions resulting from different interleavings. In this paper, we present a generic framework to integrate execution semantics with either implicit or explicit concurrency models, to explore the possible execution traces of conforming models, and to define strategies for helping in the exploration of the possible executions. This framework is complemented with a protocol to interact with the resulting executions and hence to build advanced concurrency analysis tools. The approach has been implemented within the GEMOC Studio. We demonstrate how to integrate two representative concurrent meta-programming approaches (MoCCML/Java and Henshin), which use different paradigms and underlying foundations to define an xDSML's concurrency model. We also demonstrate the ability to define an advanced concurrent omniscient debugger with the proposed protocol. The paper, thus, contributes key abstractions and an associated protocol for integrating concurrent meta-programming approaches in a language workbench, and dynamically exploring the possible executions of a model in the modeling workbench. [ABSTRACT FROM AUTHOR]

Objectives: Acupuncture therapy has been used for cognitive impairment-related diseases, however, there are still few studies on the overall trend of acupuncture therapy on cognitive impairment based on bibliometric analysis. The purpose of this study was to explore the research trend of the impact of acupuncture on cognitive impairment in the past 15 years, analyze the research trends and hotspots, and provide new ideas and theoretical basis for future research directions. Methods: From the Web of Science Core Collection (WoSCC), the relevant literature on the treatment of cognitive impairment with acupuncture from 2007 to 2022 was retrieved. Then, based on the CiteSpace and VOSviewer software of the Java platform, the cooperation between countries and institutions in this field, the co-citation of journals and documents, and the cooperation between authors and authors, etc. were analyzed. In addition, the co-occurrence and burst analysis of keywords are also carried out, and a visual knowledge map is drawn. Results: As of August 08, 2022, a total of 394 records related to the treatment of cognitive impairment with acupuncture were identified. The analysis results show: The number and rate of annual publications have steadily increased, with some fluctuations from year to year. The countries that contribute the most to this field are China and the USA. Among them, Beijing University of Chinese Medicine and Capital Medical University are tied for first place in terms of the number of published papers. Tao Jing is the most prolific author and the number one cited author. Conclusions: The number of publications on acupuncture for cognitive impairment is expected to increase rapidly in future research, suggesting a bright future for the field. Future research hotspots will focus on pain, injury, protocol, diagnosis, guidelines, etc. It is also necessary to strengthen cross-regional and cross-country cooperation among various academic groups. [ABSTRACT FROM AUTHOR]

Refactoring is a maintenance task that aims at enhancing the quality of a software's source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%. [ABSTRACT FROM AUTHOR]

Moving Target Defense (MTD) is a security technique for Software Defined Networks (SDN) to change the attack surface constantly. Although MTD is an effective technique, it makes the digital forensics procedure challenging due to high transitions in the system state. There is an ever-increasing requirement for SDN forensics due to the increasing number of cyberattacks and the adoption of SDN by large-scale cloud service providers, telecommunication operators, and internet service providers. In this paper, we have proposed a digital forensics scheme for MTD-based SDN to record every movement of the MTD for collecting attack-related evidence, especially the attacker (attack source), to augment the forensics investigation. The proposed technique consists of a three-level logging mechanism. The first one is the native logging technique of ONOS. The second is a Java-based logging application called "Java ONOS Logs Collector (JOLC)", developed to capture MTD-based SDN logs. Lastly, we utilized the Fluentd unified logging tool to dig out evidential data from MTD logs. The experimental testbed comprises an ONOS SDN controller, Mininet, and an event-based MTD application running over SDN using JSON FlowRule scripts on the ONOS controller while using sflow-rt to detect the level of attack/number of packets sent by the attacker. The native ONOS logging mechanism provides initial-level artifacts. The developed JOLC application creates separate files for ONOS and Mininet/host machine logs stored with the current timestamp. Fluentd generates a single file for the SDN controller, Mininet, and host machine logs, along with the flow rule entry into the SDN controller. Experimental results confirmed that our proposed multi-level forensics technique successfully collected all the relevant records. [ABSTRACT FROM AUTHOR]

Architecture-Driven Modernization (ADM) is a special kind of reengineering that employs models along the process. The main ADM metamodel is the Knowledge Discovery Metamodel (KDM), which is a platform-independent metamodel able to represent several views of a system. Although a lot of research is currently focused on the reverse engineering phase of ADM, little has been devoted to the forward engineering one, mainly on the generation of Platform-Specific Models (PSMs) from KDM. The forward engineering phase is essential because it belongs to the end of the horseshoe model, completing the reengineering process. Besides, the lack of research and the absence of tooling support in this phase hinder the industrial adoption of ADM. Therefore, in this paper, we present a process for creating Transformation Engines (TEs) capable of transforming KDM instances in a chosen PSM. We highlight two main contributions in this work. The first is a process that software engineers can follow for building TEs capable of generating PSM instances (e.g., Java model, Python model, etc.) from KDM instances. Having that on their hands, modernization engineers can then use generators for generating language-specific source code from the PSM. The second is delivering a specific TE called RUTE-K2J, which is able to generate Java models from KDM models. The transformation rules of RUTE-K2J have been tested considering sets of common code structures that normally appear when modernizing systems. The test cases have shown that in this version of RUTE the transformation rules are able to correctly generate 92% of the source code submitted to the transformation. [ABSTRACT FROM AUTHOR]

This paper re-examines the influence of urban development on the number of flood events in Java from 1970 to 2020. This paper aims to prove development policies in Indonesia in the last 50 years and to analyze the correlation between flood events, population growth, and urbanization on a broader regional scale. The Moran-I spatial autocorrelation analysis method is applied to measure its spatial autocorrelation between urbanization and flood events. The dynamic urbanization illustrated by spatial–temporal analysis from the 1970 to the 2020s using Landsat data; in this case, urbanization is measured using the parameters of population concentration and built-up area growth in urban areas of Java. In more detail, the results of hot-spot clusters show development from "lower-low" dispersion in the 70 s to areas with a majority of "higher-high" clustered in 2000–2020. It proves that the development burden in Java is in high category impact, so solutions for population distribution, land development, and spatial planning are needed to ensure environmental sustainability in Java. [ABSTRACT FROM AUTHOR]

The dish of Ketupat and Opor has played a significant role in the spread of Islamic preaching. This traditional dish has been used as a means of reaching the local population through their stomachs, incorporating Islamic teachings into local culinary practices. This paper aims to explore the philosophical implications of the spread of Islamic preaching through local culinary practices in Java Islands, with a focus on the dish of Ketupat and Opor. This is a qualitative method with library study, the instruments are observation and documentation. The paper draws on a combination of anthropological and philosophical approaches to explore the significance of Ketupat and Opor in the context of Islamic preaching in the Java Islands. Results found the dish has been used as a means of incorporating Islamic teachings into local culinary practices, creating a uniquely Javanese form of Islamic practice that resonates with the local population. In conclusion, the dish has become an important part of Javanese cultural and religious celebrations. The combination of Ketupat and Opor has become an important symbol of unity and togetherness and has helped to create a strong sense of community among Javanese Muslims. [ABSTRACT FROM AUTHOR]

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed 'most likely trajectory' and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub. [ABSTRACT FROM AUTHOR]

Indonesia is the country with the largest archipelago in the world, with a total of seventeen thousand islands. The term 'maritime nation' has actually been surging through stories of local wisdom (culture), but during the colonial period, the term lost its original significance and Indonesia came to be treated as if it were an agrarian country. This paper shows that Indonesian maritime society contains various inherited values of religiosity, including myths, legends, folklore, and oral traditions about marine cosmology. Folklore, myths, and legends have an important position in society, not only referring to cultural traditions but also containing religious or theological values that are closely related to the identity of the Indonesian nation. This paper studies the mythical figure of Nyi Roro Kidul who is considered the sea guardian. The legend surrounding the figure of Nyi Roro Kidul can serve as an important trope to develop a contextual Javanese eco-pneumatology [Holy Spirit] and help address some of the contemporary ecological issues. [ABSTRACT FROM AUTHOR]

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients' jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat's behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs. [ABSTRACT FROM AUTHOR]

The core activity of population-based cancer registries (PBCRs) is to gather information from all new cancer cases in a defined geographic area, in order to measure the magnitude of cancer burden and to provide a basis for cancer research. The Joint Research Centre-European Network of Cancer Registries Quality Check Software (JRC-ENCR QCS) is a Java standalone desktop application, under development since 2015, created to support PBCRs in the validation of the collected data. The JRC-ENCR QCS performs internal consistency checks on the cancer registry dataset, to detect impossible or unlikely codes or combination of codes, and is thereby an important tool to support the validation efforts by registries and improve data quality and European-wide harmonisation. The software package also includes the JRC CSV Data layout converter, a complementary tool for transforming PBCR incidence files into a format compatible with the JRC-ENCR QCS. This paper gives an overview of the JRC-ENCR QCS, describing the role of the software in processing data files submitted by PBCRs contributing to the European Cancer Information System (ECIS) as well as its functionalities. The development of the JRC-ENCR QCS is an evolving process, with regular updates implementing new and revised European and International recommendations and classifications. [ABSTRACT FROM AUTHOR]

The swine fever virus seriously affects pork production, and to improve pork production, pig breeding efficiency needs to be improved, and the detection of boar sperm activity is an important part of the pig breeding process. Traditional laboratory testing methods rely on bulky testing equipment, such as phase-contrast microscopes, high-speed cameras, and computers, which limit the testing scenarios. To solve the above problems, in this paper, a microfluidic chip was designed to simulate sperm in the oviduct with a channel thickness of 20 um, which can only accommodate sperm for two-dimensional movement. A miniature microscope system which can be used in combination with a smartphone is designed that is only the size of the palm of the hand and has a magnification of about 38 times. An intelligent diagnostic app was developed using Java language, which can automatically identify and track boar sperm with a recognition rate of 96.08% and an average tracking rate of 86%. The results show that the proposed smartphone-based hand-held platform can effectively replace the traditional microscope compound computer to diagnose sperm activity. In contrast, the platform is smaller, easier to use and is not limited by the usage scenarios. [ABSTRACT FROM AUTHOR]

Recommender systems can offer a fertile ground in e-learning software, since they can assist users by presenting them with learning material in which they can be more interested, based on their preferences. To this end, in this paper, we present a new method for a knowledge-graph-based, path-based recommender system for learning activities. The suggested approach makes better learning activity recommendations by using connections between people and/or products. By pre-defining meta-paths or automatically mining connective patterns, our method uses the student-learning activity graph to find path-level commonalities for learning activities. The path-based approach can provide an explanation for the result as well. Our methodology is used in an intelligent tutoring system with Java programming as the domain being taught. The system keeps track of user behavior and can recommend learning activities to students using a knowledge-graph-based recommender system. Numerous metadata, such as kind, complexity, and number of questions, are used to describe each activity. The system has been evaluated with promising results that highlight the effectiveness of the path-based recommendations for learning activities, while preserving the pedagogical affordance. [ABSTRACT FROM AUTHOR]

In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system. [ABSTRACT FROM AUTHOR]

Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out. [ABSTRACT FROM AUTHOR]

In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator for mutation step sizes outperformed the other distributions used. The aim of using the chosen Pareto optimal methods was to determine which method can give a better exploration–exploitation trade-off for the solution space to obtain the optimal or near-optimal solution. The best combination for that in terms of winning rate is the Cauchy distribution for mutation step sizes with method 3 of the Pareto optimal procedure. Moreover, different random number generators were evaluated and analyzed versus datasets in terms of NDCG@10 for testing data. It was found that the Levy generator is the best for both the MSLR and the MQ2007 datasets, while the Gaussian generator is the best for the MQ2008 dataset. Thus, random number generators clearly affect the performance of ES-Rank based on the dataset used. Furthermore, method 3 had the highest NDCG@10 for MQ2008 and MQ2007, while for the MSLR dataset, the highest NDCG@10 was achieved by method 2. Along with this paper, we provide a Java archive for reproducible research. [ABSTRACT FROM AUTHOR]

Web 3.0 is the basis on which the proposed metaverse, a seamless virtual world enabled by computers and interconnected devices, hopes to interact with its users, but beyond the high-level project overview of what Web 3.0 applications try to achieve, the implementation is still down to low-level coding details. This article aims to analyze the low-level implementations of key components of Web 3.0 using a variety of frameworks and tools as well as several JVM-based languages. This paper breaks down the low-level implementation of smart contracts and semantic web principles using three frameworks, Corda and Ethereum for smart contracts and Jeda for semantic web, using both Scala and Java as implementing languages all while highlighting differences and similarities between the frameworks used. [ABSTRACT FROM AUTHOR]

The presence of naturalized Alocasia cucullata (Lour.) G.Don (Araceae) in Java is reported for the first time in this paper. The species is an introduced ornamental plant native to India, Sri Lanka, and Indo-China and was only known in cultivation. In this study we collected samples from its naturalized populations in Sukabumi Regency (Cibadak Subdistrict) and Sumedang Regency (Jatinangor and Tanjungsari Subdistricts). The observed population grows along the roadside, coastal, ITB Jatinangor green space area, and palm oil plantation. In nature, A. cucullata may spread vegetatively either through root suckers, corms, and stem fragments. The description, distribution map, photographs, and a brief discussion are provided here. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the recent years, there are a lot of swarm-based stochastic optimization techniques i.e., metaheuristics were introduced. Most of these techniques were tested to solve the sets of theoretical functions. Some of them were enriched with practical tests where the common use cases are the mechanical engineering designs. On the other hand, the similar studies that utilized the optimization in power system are difficult to find. Moreover, the environmental issues become major considerations in engineering field. Based on this evidence, this paper constructs a new swarmbased optimization technique called stay-jump optimizer (SJO). The equal size swarm split is applied in the beginning of the process. Then, two directed searches toward the highest quality sub-swam members and two randomly selected higher quality sub swarm members are employed. The performance investigation is performed by employing SJO to find the optimal solution of 23 classic functions and the economic emission dispatch (EED) problem. The use-case for EED is the Java-Bali power grid system in Indonesia that consists of six steam power plants and two hydro-electric power plants. Five new optimization techniques including addax optimization algorithm (AOA), dollmaker optimization algorithm (DOA), giant armadillo optimization algorithm (GAO), zebra optimization algorithm (ZOA), and total interaction algorithm (TIA). The result shows that SJO is superior to its opponents as it is better than AOA, DOA, GAO, ZOA, and TIA in 17, 17, 16, 19, and 14 functions out of 23 functions respectively. SJO also becomes the best in the EED problem. [ABSTRACT FROM AUTHOR]

Code smells are poorly designed code structures indicating that the code may need to be refactored. Recognizing code smells in practice is complex, and researchers strive to develop automatic code smell detectors. An obstacle to developing these solutions is the datasets' limitations. Manually labeled datasets were collected to investigate the developers' perceptions of code smells. They are characterized by a high label disagreement that hurts the performance of Machine Learning (ML) models trained using them. Furthermore, all large, manually labeled datasets are developed for Java. We recently created a novel dataset for C# to alleviate these issues. This paper evaluates ML code smell detection approaches on our novel dataset. We consider two feature representations to train ML models: (1) code metrics and (2) CodeT5 embeddings. This study is the first to consider the CodeT5 state-of-the-art neural source code embedding for code smell detection in C#. To prove the effectiveness of ML, we consider multiple metrics-based heuristics as alternatives. In our experiments, the best-performing approach was the ML classifier trained on code metrics (F-measure of 0.87 for Long Method and 0.91 for Large Class detection). However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. Finally, our ML model surpassed less experienced annotators and nearly matched the most experienced annotator, suggesting it can assist less experienced developers under tight deadlines. To the best of our knowledge, this is the first study to compare the performance of automatic smell detectors against human performance. [ABSTRACT FROM AUTHOR]

Apart from good instructional design and delivery, effective intervention is another key to strengthen student academic performance. However, intervention has been recognized as a great challenge. Most instructors struggle to identify at-risk students, determine a proper intervention approach, trace and evaluate whether the intervention works. This process requires extensive effort and commitment, which is impractical especially for large classes with few instructors. This paper proposes a platform, namely i-Ntervene, that integrates Learning Management System (LMS) automatic code grader, and learning analytics features which can empower systematic learning intervention for large programming classes. The platform supports instructor-pace courses on both Virtual Learning Environment (VLE) and traditional classroom setting. The platform iteratively assesses student engagement levels through learning activity gaps. It also analyzes subject understanding from programming question practices to identify at-risk students and suggests aspects of intervention based on their lagging in these areas. Students' post-intervention data are traced and evaluated quantitatively to determine effective intervention approaches. This evaluation method aligns with the evidence-based research design. The developed i-Ntervene prototype was tested on a Java programming course with 253 first-year university students during the Covid-19 pandemic in VLE. The result was satisfactory, as the instructors were able to perform and evaluate 12 interventions throughout a semester. For this experimental course, the platform revealed that the approach of sending extrinsic motivation emails had more impact in promoting learning behavior compared to other types of messages. It also showed that providing tutorial sessions was not an effective approach to improving students' subject understanding in complex algorithmic topics. i-Ntervene allows instructors to flexibly trial potential interventions to discover the optimal approach for their course settings which should boost student's learning outcomes in long term. [ABSTRACT FROM AUTHOR]

Technical debt refers to decisions made during the design and development of software that postpone the resolution of technical problems or the enhancement of the software's features to a later date. If not properly managed, technical debt can put long-term software quality and maintainability at risk. Self-admitted technical debt is defined as the addition of specific comments to source code as a result of conscious and deliberate decisions to accumulate technical debt. In this paper, we will look at the presence of self-admitted technical debt in open-source blockchain projects, which are characterized by the use of a relatively novel technology and the need to generate trust. The self-admitted technical debt was analyzed using NLP techniques for the classification of comments extracted from the source code of ten projects chosen based on capitalization and popularity. The analysis of self-admitted technical debt in blockchain projects was compared with the results of previous non-blockchain open-source project analyses. The findings show that self-admitted design technical debt outnumbers requirement technical debt in blockchain projects. The analysis discovered that some projects had a low percentage of self-admitted technical debt in the comments but a high percentage of source code files with debt. In addition, self-admitted technical debt is on average more prevalent in blockchain projects and more equally distributed than in reference Java projects.If not managed, the relatively high presence of detected technical debt in blockchain projects could represent a threat to the needed trust between the blockchain system and the users. Blockchain projects development teams could benefit from self-admitted technical debt detection for targeted technical debt management. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since colonial times, substantial regional income disparities have been reported for Indonesia. However, in spite of a wide variety of available data and indicators, so far published data on Indonesian per capita GDP in colonial times are limited to macro-estimates for the entire archipelago or are confined either to Java or to the Outer Islands. In this paper we provide a first attempt to arrive at estimates of diverging income and living standards at a regional level. We implement the Geary and Stark method on a large body of data collected by the colonial government, to estimate GDP for ten macro-regions and five benchmark years between 1870 and 1930. Our findings, corrected for prices, confirm the image arising from the existing literature of major divergences within the Indonesian archipelago in general, and of a higher per capita GDP in most of the Outer Islands (all islands of the Indonesian archipelago except for Java and Madura) compared with Java in particular. This was definitely the case in 1870 and still the case in 1920, but the picture is less clear for 1930, which was the final year before Indonesia's commodity exports started to collapse. [ABSTRACT FROM AUTHOR]

Copyright of Walisongo: Jurnal Penelitian Sosial Keagamaan is the property of State Islamic University, Institute for Research & Community Services and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this paper is to design a research model and analyze the relationship between open innovation and cleaner production. The paper maps and characterizes the conditions of open innovation against cleaner production in Indonesian batik small and medium enterprise (SME), particularly in Java and Madura. The mapping process is executed by classifying the batik SME into four quadrants. The diagram is a quadrant in which there are four parts to distinguish each of the ability of batik SMEs in understanding and achieving cleaner production through open innovation. This research will obtain a new method or model that can be applied by organizations to achieve cleaner production through an open innovation. The data is obtained from 182 batik SMEs located in Laweyan, Madura and Lasem (in Java Island, Indonesia). Design/methodology/approach: One of the problems in batik SME is the waste management from the dyeing and wax removal process. In the first stages of this research, a number of initial models were elaborated as a reference, then the results of the elaboration became a new research model. The research model that has been produced is then tested using data from respondents. Based on the test results, the model can be stated valid or not. In this study, the model is valid after testing data from 182 respondents, because all outer loading for all indicators is above 0.7. The composite reliability and AVE values of all constructs were above 0.7 and 0.5. Based on the validated research model, the data is statistically processed by using the Structural Equation Modeling (SEM). By using the SEM method and statistical software SMART PLS 3.0this research can be supported to achieve the research objectives. Findings: Based on data testing and processing, open innovation climate could predict a sustained relationship to open innovation with an accuracy rate of 0.466 and influence rate of 0.427, whereas open innovation could predict a sustained relationship to cleaner production with an accuracy rate of 0.183 and influence rate of 0.324. The relationships between open innovation climate and open innovation; including open innovation toward cleaner production, are statistically significant because all prediction values and accuracy in the model have met the criteria for measurement parameters based on the value of R2, p value and T-statistics to be stated as a significant relationship. Research limitations/implications: This research provides an overview of the influence and importance of open innovation in creating an environmentally friendly production process in the context of cleaner production. Cleaner production on batik SMEs can be achieved through open innovation, both for inbound open innovation and outbound open innovation. Open innovation comprehensively provides support for batik SMEs in achieving cleaner production. Open innovation can be run well and optimally if it gets support from a conducive climate open innovation. Furthermore, the implementation of cleaner production could be a guideline for the owner to minimize the waste from batik SME production, both for natural and synthetic dyes. Some limitations in these study include the absence of influence from the existing stakeholders on batik SMEs on the implementation process of open innovation; the use of the cross-sectional approach that results in the unavailability of further analysis regarding the dynamics or improvements that occur in attaining cleaner production through open innovation; and finally providing no analysis of the differences in characteristics at each location of batik SMEs. Originality/value: The implementation of cleaner production model is considered as one of the new methods and references in conjunction with reducing the negative impact of waste toward the environment, particularly in the traditional textile industry which is limited in waste management capability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to identify variables that determine the differing levels of environmental quality on Java and other islands in Indonesia. Design/methodology/approach: Using a quantitative approach, secondary data were sourced from the Central Statistics Agency and the Ministry of Environment and Forestry. The data were obtained through the collection of documentation from 33 provinces in Indonesia. The analytical approach used was discriminant analysis. The research variables are Trade Openness, Foreign Direct Investment (FDI), industry, HDI and population growth. Findings: The variables that distinguish between the levels of environmental quality in Indonesian provinces on the island of Java and on other islands are Industry, HDI, FDI and population growth. The openness variable is not a differentiating variable for environmental quality. The most powerful variable as a differentiator of environmental quality on Java Island and on other islands is the Industry variable. Research limitations/implications: This study has not classified the quality of the environment based on the Ministry of Environment and Forestry's categories, namely, the very good, good, quite good, poor, very poor and dangerous. For this reason, further research is needed using multiple discriminant analysis (MDA). Practical implications: Industry is the variable that most strongly distinguishes between levels of environmental quality on Java and other island, while the industrial sector is the largest contributor to gross regional domestic product (GDRP). Government policy to develop green technology is mandatory so that there is no trade-off between industry and environmental quality. Originality/value: This study is able to identify the differentiating variables of environmental quality in two different groups, on Java and on the other islands of the Indonesian archipelago. [ABSTRACT FROM AUTHOR]

Background: Data redundancy (DR) and data privacy (DP) is a critical issue that increases storage and security problems in cloud environments. Data de-duplication (DD) is one of the efficient backup storage techniques to reduce DR. The main problem with using cloud computing (CC) is more storage, the cost of deployment and maintenance. Objective: To minimize this problem, High-performance Grade Byte Check and Fuzzy search Techniques (HP-GBC-FST) based DD is proposed in this paper. Methods: The HP-GBC-FST is based on the pre-process of data by comparing their first byte and categorizing the byte based on the first byte. After DD, encryption has been processed on data to improve the data security in the cloud environment and then encrypted data is stored in the cloud. This HP-GBC-FST recognizes DR at the block level, reducing the redundancy of data more effectively. Then, HP-GBC-FST is created to detect and eliminate duplicates, improve security and storage efficiency (SE), reduce DD time and computation cost (CPC) in the DD verification and auditing phase. Result: The experiment has been conducted in an Intel I5 system and 500GB, 1Tb memory space and implemented in the Java programming environment. The results of the experiment reveal that the HP-GBC-FST improved the DD ratio and security by 3.7 and 97%, respectively, and reduced the DD time and CPC by 87% and 84.4%, respectively, over the existing technique. Conclusion: It concluded that the HP-GBC-FST has greater improvement over DD data in the cloud. Finally, the performance analysis of the HP-GBC-FST achieves higher storage, both privacy and security attributes, and incurs minimal CPC, DD time compared with the state he art research. [ABSTRACT FROM AUTHOR]

There are many repair alternatives for resolving model inconsistencies, each involving one or more model changes. Enumerating them all could overwhelm the developer because the number of possible repairs can grow exponentially. To address this problem, this paper focuses on the immediate cause of an inconsistency. By focusing on the cause, we can generate a repair tree with a subset of repair actions focusing on fixing this cause. This strategy identifies model elements that must be repaired, as opposed to additional model elements that may or may not have to be repaired later. Furthermore, our approach can provide an ownership-based filter for filtering repairs that modify model elements not owned by a developer. This filtering can further reduce the repair possibilities, aiding the developer when choosing repairs to be performed. We evaluated our approach on 24 UML models and four Java systems, using 17 UML consistency rules and 14 Java consistency rules. The evaluation data contained 39,683 inconsistencies, showing our approach's usability as the repair trees sizes ranged from five to nine on average per model. Also, these repair trees were generated in 0.3 seconds on average, showing our approach's scalability. Based on the results, we discuss the correctness and minimalism with regard to the cause of the inconsistency. Lastly, we evaluated the filtering mechanism, showing that it is possible to further reduce the number of repairs generated by focusing on ownership. [ABSTRACT FROM AUTHOR]

This paper explores the temporalities and emotions of youth left-behind by migrant parents by using Jennifer Lois' temporal emotion work as an analytical lens to foreground youth's management of conflicting feelings by reworking particular experiences of time. We extend Lois' concepts of 'sequencing' (strategic ordering of emotions and time) and 'savouring' (intentional maximizing of specific times) to include a contextualized, gendered angle, while also engaging with the additional concept of 'supressing'. The work draws on qualitative interviews conducted in 2017 with left-behind youth from migrant households from rural migrant-sending villages in two districts in Java, Indonesia. By highlighting youth's shifting temporal emotions and how aspirations and experiences of left-behindness are affected, our research reveals gendered strategies of temporal emotion work. Young women enact 'sequencing' and 'savouring', aspiring to stay as a means of restorative, temporal-emotional justice for their families. Conversely, young men are more inclined to enact the 'suppressing' of emotions while aspiring for migration. Among a generation that has grown up in the wake of parental migration, most youth conform to traditional gendered scripts within an older culture of masculinized circular migration. [ABSTRACT FROM AUTHOR]

Current Internet of Things (IoT) systems concentrate on one domain, such as smart home, university, office, etc., while many entities such as humans and mobile phones are mobile entities, roaming between different IoT domains. Most of the research on context-aware middleware for IoT addresses a specific domain and limited kinds of applications. Realizing the ultimate intelligence and automation in IoT will need a multiple-domain system. The architectural design of a multiple-domain context-aware middleware for IoT and pervasive computing envisages serious challenges, including extensibility of the environment, need for distribution, resource limitation of mobile devices, mobility, and need for unique name allocation, which have not been fully addressed by related studies. This paper proposes the architectural design of a multiple-domain context-aware middleware (CAMID), which uses a distributed two-layer architecture to handle the above issues. CAMID has been developed in Java in two layers, including H-CAMID and L-CAMID. Scenario-based Architecture Analysis Method (SAAM) shows that CAMID satisfies the target quality attributes and outperforms previous multiple-domain solutions. Finally, an experimental evaluation of CAMID shows acceptable response time in a critical scenario. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to reframe the whistleblowing process by examining the individual and situational factors that have been overlooked by prior studies. Ethical climate, public service motivation (PSM), organisation identification and psychological safety are inquired. Design/methodology/approach: The present study sample was drawn from a population of Indonesian local governments located in east Java, Indonesia. Particularly, self-administered questionnaires were hand-distributed to the employees in the four local governments. Of 2,169 questionnaires distributed to the employees, 1,687 questionnaires were returned to the researcher. However, the researcher removed 33 returned questionnaires because of poor data quality, such as incomplete answers. Thus, only 1,654 questionnaires were analysed in this study. Findings: The findings support the idea of an ethical climate that can encourage the individual to blow the whistle. However, its effect is indirect. The predictive power of ethical climate on the individual's whistleblowing intentions depends on the meditating roles of PSM, psychological safety and organisation identification. Interestingly, the mediating effects of PSM, psychological safety and organisation identification are extremely acknowledged when individuals have an opportunity to choose internal or external disclosures. Originality/value: This study produces a different approach to understanding people's intentions to report any wrongdoings. This study is dissimilar from prior studies in terms of the theoretical paradigm and research design. Previous studies mostly used students as their experiments. In contrast, the current study recruited employees who work in local governments. This situation fundamentally affects the understanding of the impact of an ethical climate on the individual intention to blow the whistle. [ABSTRACT FROM AUTHOR]