Reviewerly/exHarmony huggingface dataset:
Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities

Motivation: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self-Admitted Technical Debt (SATD). Therefore, SATD indicates weak code that developers are aware of. Problem statement: Inspecting source code is time-consuming; automatically inspecting source code for its vulnerabilities is a crucial aspect of developing software. It helps practitioners reduce the time-consuming process and focus on vulnerable aspects of the source code. Proposal: Accurately identify and better understand the semantics of self-admitted technical debt (SATD) by leveraging NLP and NL-PL approaches to detect vulnerabilities and the related SATD. Finally, a CI/CD pipeline will be proposed to make the vulnerability discovery process easily accessible to practitioners.
Weakly supervised salient object detection via image category annotation

&lt;abstract&gt;&lt;p&gt;The rapid development of deep learning has made a great progress in salient object detection task. Fully supervised methods need a large number of pixel-level annotations. To avoid laborious and consuming annotation, weakly supervised methods consider low-cost annotations such as category, bounding-box, scribble, etc. Due to simple annotation and existing large-scale classification datasets, the category annotation based methods have received more attention while still suffering from inaccurate detection. In this work, we proposed one weakly supervised method with category annotation. First, we proposed one coarse object location network (COLN) to roughly locate the object of an image with category annotation. Second, we refined the coarse object location to generate pixel-level pseudo-labels and proposed one quality check strategy to select high quality pseudo labels. To this end, we studied COLN twice followed by refinement to obtain a pseudo-labels pair and calculated the consistency of pseudo-label pairs to select high quality labels. Third, we proposed one multi-decoder neural network (MDN) for saliency detection supervised by pseudo-label pairs. The loss of each decoder and between decoders are both considered. Last but not least, we proposed one pseudo-labels update strategy to iteratively optimize pseudo-labels and saliency detection models. Performance evaluation on four public datasets shows that our method outperforms other image category annotation based work.&lt;/p&gt;&lt;/abstract&gt;
Development of a UAV Test Stand

Performing acoustic measurements on a multicopter in a confined space, such as a small anechoic chamber, can be an extremely difficult and time-consuming process. Inherent risks to the vehicle, laboratory equipment, and technicians are present when a multicopter is operated. In addition, standard multicopter maneuvers are difficult to conduct due to spatial constraints, and obtaining near field acoustic measurements is dangerous due to the inherent dynamic variability that occurs during flight. A UAV test stand was developed to remove these limitations and allow for rapid and safe acoustic measurements in a controlled environment. The design, development, and initial measurements are discussed. The performance and acoustic response of the brushless electric motors and propellers are assessed to examine the effectiveness of the test stand. The contribution noise from the test stand and a measurement grid are evaluated, as well as the influence of stationary propeller testing. Lastly, the pros and cons of using a test stand for UAV measurements are discussed.
PENGARUH SIMULASI TERHADAP PENINGKATAN PENGETAHUAN DAN KETERAMPILAN APARATUR DINAS PEMADAM KEBAKARAN DAN PENYELAMATAN KABUPATEN SUMBAWA BARAT

The number of fire incidents in West Sumbawa Regency has increased significantly. In response to this problem, the study attempted to examine the issue of evaluating the effect of simulation training on improving the knowledge and skills of the Fire Service apparatus of West Sumbawa Regency. This study used a comparative causal research method involving 30 respondents out of 75 total research respondents and used a quantitative approach to measure the impact of simulation training on increasing the knowledge and skills of the apparatus. From the test results obtained, it has a Cronbach alpah value&gt; 0.804 from the R Tabe value of 0.600 so that the instrument can be declared reliable and has a very high influence on increasing the knowledge and skills of the Fire Apparatus. this supports the hypothesis that simulation training has a positive impact on the knowledge and skills of fire fighting apparatus. The implications of this research provide a basis for the West Sumbawa Regency Fire Service to consider the integration of simulation training as an effective strategy in improving the readiness and skills of the apparatus in dealing with fire and other emergency situations.
The Impact of Three-Step Interview towards Students’ Speaking Skill at the Eleventh Grade of Senior High School Adhyaksa 1 Jambi

This research was made to find out whether there is any good impact after the Three-Step Interview Method was applied to the students of the eleventh grade of Senior High School Adhyaksa 1 Jambi. This research used quantitative method, and true experimental design to approach the students with pretest-posttest control group design. Through simple random sampling technique, the researcher took 48 students as the sample of this research, with the total population of 140 students. XI MIPA 1 was chosen as the control class and XI MIPA 2 as the experimental class. The technique of distributing the data was speaking test where the students were asked to give their opinion using English through the given case, with pre-test, treatments, and post-test. This research used t test as the data analysis technique with Statistical Package for the Social Sciences (SPSS) for Windows version 21 program. The results that were found in this research are: (1) students’ speaking skill taught by using the Three-Step Interview method reached the mean of 20,00; (2) students’ speaking skill taught by using the normal group discussion method reached the mean of 19,21; (3) students’ speaking skill taught by using the Three-Step Interview method is higher than students’ speaking skill taught by using the normal group discussion method with a value of Sig. 0.000 &gt; 0.05. Therefore, there is a significant impact in using Three-Step Interview for the students’ speaking skill.
Single Sample Face Recognition Based on Identity-Attribute Disentanglement and Adversarial Feature Augmentation

To address the issue of facial variation interference, this paper proposes a novel approach for single sample face recognition. Inspired by human visual perception, we introduce an attribute disentanglement module to separate identity features from attribute features using canonical correlation analysis. Due to the lack of attribute labels in the single sample set, we utilize the attribute features of the generic set to construct the SOM attribute space. Then, we fine-tune the network by reducing the distance between the attribute features of single sample and the attribute space. Finally, we use feature adversarial augmentation module to generate more intra-class features and train more robust classifier. Experimental results on AR, LFW and FERET datasets show significant improvements in accuracy and generalization performance compared to other methods.
DECENTRALIZED AUTONOMOUS ORGANIZATIONS: UNLOCKING THE FULL POTENTIAL OF BLOCKCHAIN TECHNOLOGY FOR THE REAL PHYSICAL WORLD BY EXPLORING SELF-ORGANIZING AND SELF-REGULATING DECENTRALIZAD SYSTEMS BY APPLYING SMART CONTRACTS AND FIRST ATTEMPTS OF APPLYING AI

This paper explores the potential of DAOs (Decentralized Autonomous Organizations) built on blockchain technology, which are expected to revolutionize our computing and transaction infrastructuresThis paper will focus on the legal classification of DAOs, with an emphasis on the mechanisms of raising capital through ICOs and NFTs as alternative financing options for easier access to capital. The potential of linking DAOs and AI is also briefly addressed. Corporate law must keep pace with this rapid change, and the question arises whether it is "sufficiently flexible to make room for the new technical possibilities" and to cover completely "new forms of organization" based on software code that may be inadequately reflected in existing regulations. Overall, this paper highlights the potential of DAOs and their impact on the future of business models, organizational structures, and financing options.
Automated Security Policy Feasibility Assessment

Security policy feasibility assessment is to evaluate the ability of the implemented policy to resist threats, fix system vulnerabilities, etc., whether it is in the user’s acceptable range, and also a measure of security and cost balance. There has been outstanding progress in the current research on automated security policies, and the implementation and application of various security policies are progressing well, but we should pay more attention to security policy effectiveness, which better reflects the risks and the scope and degree of risks accepted by the existing system. However, in many security policy evaluations, there is basically no comprehensive evaluation of existing security policies, and more often the existing vulnerabilities and the degree of risk after being threatened are presented, without quantitative and qualitative feasibility assessment. Based on the existing security policy evaluation methods, a method for assessing the feasibility of automated security protection policies is introduced.
Hurricane: a Dataflow-oriented Data Service for Smart Cities Applications

The concept of Smart Cities has gained relevance, especially in the last decade, due to the availability of data associated with cities, e.g., car traffic, public transportation, crime data, etc. The purpose of using these data is to improve the services offered to the citizens. Most of these applications manipulate spatiotemporal data. These data are processed in a dataflow that starts with the collection, integration, and aggregation and ends with visualization. This way, specialized data services for smart city applications are most welcome. However, many of the existing data services in this context, are either specific to a particular application/domain or do not consider the entire data life cycle. In this article, we present Hurricane, a dataflow-oriented data service for smart city applications. Hurricane executes multiple dataflows to gather, pre-process, integrate, and public data. Hurricane was evaluated with an application in the area of public security and results reinforced the importance of this type of data service.
Tren Terkini dalam Dunia Pendidikan

The trend of Information Technology which is getting more rapid in the current era of globalization cannot be avoided anymore its impact on the world of education. This is because it has several advantages, including learning resources that are easier to find benefits in the world of education such as media and multimedia and e-learning, Blended learning, Ubiquitous Learning and others which have the benefit of increasing the motivation of students in learning activities. Thus, educators can determine the variation of learning as desired by students. In addition, there are obstacles to implementing Information Technology in education in Indonesia, namely the lack of availability of facilities and infrastructure. Information technology owned by educational institutions, in addition to development and procurement costs related to information technology infrastructure, has not been provided maximally by the government.To overcome these obstacles, it is necessary to have commitment and seriousness to apply Information Technology for education from the government and it is an important decision to answer and ensure the availability of information technology for our world of education so that we are no longer left behind with other countries.
Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems

The prediction of terrain elevation values is a key task when it comes to off-road dynamics and inertial data estimation. A reliable elevation map can help in the estimation of future vehicle states and thus extend the response time window for autonomous navigation and control. We trained a deep learning model that is able to successfully predict top-down terrain depth maps in an off-road setting using a lightweight monocular depth estimation network. The labels were generated using a custom preprocessing algorithm to aid single image depth model training. Unlike other elevation estimation algorithms, our work can predict terrain variation from a higher camera setting without the use of a multi-sensor system. The network is also shown to work outside of the training data domain. The code will be available at https://www.github.com/norbertmarko/terrain-depth.
Preliminary Results of Mapping Capabilities for Agile Software Team Formation

Context: Team formation is critical in agile software development, directly affecting project effectiveness and performance. Ensuring teams possess the necessary capabilities is vital for success. However, the specific capabilities used in practice for team composition remain unclear. Aims: Our objective is to understand the evolution, relevance, and practical application of capabilities used in agile team formation and gain real-world insights. Method: We conducted a comparative analysis, contrasting the software engineers’ capabilities identified in a prior review with those used in agile team formation based on recent studies. To gain insights into real-world practices, we interviewed a key practitioner from a software organization through a semi-structured interview. Results: The study highlights the most impactful capabilities for agile team formation, providing valuable guidance for practitioners and researchers to enhance software engineering practices. Conclusions: This research advances the understanding of essential attributes for successful agile team formation and offers valuable implications on underlying aspects and challenges faced in the practical application of these capabilities.
Multi-scale Feature Injection for Occluded 3D Human Pose and Shape Estimation

3D reconstruction has been applied to many research fields such as robots and computer vision with the fast development of technology. Despite significant progress, current 3D human pose and shape estimation methods still remain challenge to recovery 3D human mesh under occlusions. Previous works use a Iterative Error Feedback (IEF) loop to construct the regressor and often have disregarded information at occluded regions that make them difficult to handle occlusions. However, we argue that occluded regions have strong correlations with human body so that they can offer effective information for 3D human pose and shape estimation. To address this, we propose a multi-scale feature injection network MFINet, that utilizes the information at occluded regions as a secondary clews to enrich the image features in a coarse-to-fine manner. In MFInet, given the image feature at current scale, a Transformer-based module, called feature inject transformer module (FIM) is used to inject human feature into occluded region by considering their correlation. To this end, experiments show that our method is effective in both object and subject results on several benchmarks including Human3.6M, 3DPW, LSP and COCO.
Design and Implementation of Dual-Arm Robot Rubik’s Cube Solving System

The existing Rubik’s cube robots are developed based on specialized hardware structures, such as six axis and rolling robots, which have no other use except for cube solving. In the future, humanoid dual-arm robots will work for every family, including doing housework, tutoring homework, etc. In this paper, we design and implement a Rubik’s cube solving system based on the dual-arm robot named Baymax. The system consists of three nodes, including the visual perception node, the cube solving node and the motion control node. The visual perception node is to recognize the color sequence of the cube by using HSV color mode. The cube solving node uses the Kociemba algorithm to calculate the action sequence to restore the cube. The motion control node implements eighteen actions by using RRT-Connect algorithm. It executes the actions according to the sequence. Experiments show that the success rate of the system designed in this paper is 86%.
Real-time intersection vehicle turning movement counts from live UAV video stream using multiple object tracking

The intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.
DIGITALISASI DONGENG NUSANTARA SEBAGAI ALTERNATIF MEDIA PEMBELAJARAN ERA DIGITAL DI TINGKAT SEKOLAH DASAR

This study aimed to: Produce Digital Learning Media in Indonesian language learning, know the feasibility of Digital Learning Media as an alternative media used in learning Indonesian, knowing the opinions of C11 PGSD students as designers and users in the field. This research uses the research and development (R&amp;D) type. This development research is a research method used to produce a certain product and will also be tested for the effectiveness and attractiveness of the product. The model used in this study is the ADDIE model by Robert Maribe Branch. This model aims to produce new products through several stages of development which include the stages of analysis, design, development, implementation and evaluation. The subjects in this study were 35 students of the C11 PGSD Study Program. This study's methods of data collection were observation, interviews, and questionnaires. The research instruments were in the form of observation guides, interview guides, and questionnaires. The research results were the design of the Digital Nusantara Fairy Tale Media, which media experts had tested, material experts, and students as product users. The field states that Digital Fairy tales are suitable for use with 90% media expert test results, material expert test 85%, small scale test 80%, and large scale 85%. The results concluded that the Digital Nusantara Fairy Tale is feasible to be used as an alternative learning media at the elementary school level.
IMPLEMENTASI PEMBELAJARAN PENDIDIKAN JASMANI OLAHRAGA DAN KESEHATAN PADA MASA PANDEMI COVID-19

The general objectives of this study are to describe the implementation of PJOK learning for class IV B at SDN 1 Kenukut during the COVID-19 pandemic, to find out the Inhibiting and Supporting Factors in the PJOK learning process for class IV B at SDN 1 Kenukut during the COVID-19 pandemic, PJOK learning outcomes during the COVID-19 pandemic. The method used in this study is a qualitative method with a descriptive research form, while the subjects in this study are homeroom teachers, PJOK teachers who collect 2 people and class IV B students who return 25 people. The method used in this study is a purposive method. The sampling object is students in the PJOK learning process and class IV B teachers at SDN 1 Kenukut. Data collection techniques used are Non-Participant Observation Techniques, Semi-Structured Interview Techniques, and Documentation Techniques. While the data collection tools are Observation sheets, Interview sheets and Documentation sheets in the form of Absence, RPP, Assignments, and Student Learning Outcomes. Results Based on research at SDN 1 Kenukut, during the COVID-19 pandemic in the 2020/2021 school year, the learning process at SDN 1 Kenukut uses online and offline learning processes, online learning in question is learning using the whatsapp application where the teacher informs students to take assignments go to school and if you don't understand then you can ask the teacher via whatsapp, while offline learning is where students do assignments from home. As for the instrument in the teacher's assessment of student results, namely, the accuracy of student answers, timeliness and timeliness given.
Early Formalization of AI-tools Usage in Software Engineering in Europe: Study of 2023

This scientific article presents the results of a study focused on the current practices and future prospects of AI-tools usage, specifically large language models (LLMs), in software development (SD) processes within European IT companies. The Pan-European study covers 35 SD teams from all regions of Europe and consists of three sections: the first section explores the current adoption of AI-tools in software production, the second section addresses common challenges in LLMs implementation, and the third section provides a forecast of the tech future in AI-tools development for SD. The study reveals that AI-tools, particularly LLMs, have gained popularity and approbation in European IT companies for tasks related to software design and construction, coding, and software documentation. However, their usage for business and system analysis remains limited. Nevertheless, challenges such as resource constraints and organizational resistance are evident. The article also highlights the potential of AI-tools in the software development process, such as automating routine operations, speeding up work processes, and enhancing software product excellence. Moreover, the research examines the transformation of IT paradigms driven by AI-tools, leading to changes in the skill sets of software developers. Although the impact of LLMs on the software development industry is perceived as modest, experts anticipate significant changes in the next 10 years, including AI-tools integration into advanced IDEs, software project management systems, and product management tools. Ethical concerns about data ownership, information security and legal aspects of AI-tools usage are also discussed, with experts emphasizing the need for legal formalization and regulation in the AI domain. Overall, the study highlights the growing importance and potential of AI-tools in software development, as well as the need for careful consideration of challenges and ethical implications to fully leverage their benefits.