Reviewerly/exHarmony huggingface dataset:
Multi-Scale Convolutional Network for Knowledge Graph Completion

Knowledge graphs reflect various facts in the actual world through formal representations and are interconnected through topological structures. However, most knowledge graphs are incomplete. Knowledge graph completion infers unknown or implie <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> facts based on existing facts. ConvKB is a typical CNN-based model that captures the translation properties between embedding units of the same dimension through one-dimensional convolution. Since one-dimensional convolution can only receive restricted semantic information, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> e use multi-scale convolution for capturing feature interactions between units of different dimensions. To further increase the number of interactions, we employ circular convolution. In our research, we introduce a simple and effective embedding model, named MCNKC, for knowledge graph completion. We demonstrate that MCNKC achieves better results than ConvKB on FB15K-237 and WN18RR.
Progress of Application of Digital Image Correlation Method (DIC) in Civil Engineering

Digital Image Correlation Method (DIC), also known as Digital Speckle Correlation (DSC), is an optical measurement method that uses computer vision technology to identify and calculate the probability statistical correlation of randomly distributed speckle features on the surface of an object before and after deformation, in order to determine the displacement or deformation of the object. Digital image correlation method is a modern full field optical measurement technology with the characteristics of non-contact and non interference, which has been widely applied in fields such as aerospace, civil engineering measurement, and object deformation monitoring. This article elaborates on the development process of digital image related technologies and their applications in the field of civil engineering. This technology has a simpler operating process, can be applied to a wider range of usage environments, and achieves higher accuracy. Therefore, its application in the field of civil engineering is becoming increasingly common.
Improved Genetic Algorithm-Based Obstacle Avoidance Path Planning Method for Inspection Robots

The current conventional genetic algorithm is prone to the problem of local optimum due to the lack of adjustment of parameters when planning the path of inspection robots. In this regard, an improved genetic algorithm-based inspection robot obstacle avoidance path planning method is proposed. The working space type of the inspection robot is analyzed, and the scenario is modeled by combining the spatial interference weights. The traditional genetic algorithm is optimized by introducing variational operators, and finally the robot obstacle avoidance path planning algorithm is solved to obtain the optimal route. In the experiments, the planning performance of the proposed method is verified. The analysis of the experimental results shows that the path distance is small and has a more desirable path planning performance when the proposed method is used to plan the path of the inspection robot.
POSTER: Collaborative Authority-Based Searchable Encryption Using Access Control Encryption

In this poster, we propose a novel searchable encryption (SE) scheme that leverages access control encryption to enhance the security and flexibility of search operations. Our approach requires collaborative authority users to authorize data users for SE requests, ensuring that only authorized users can access the encrypted data. The proposed scheme utilizes keyword and collaborative authority user IDs as attributes in the access control encryption, allowing for the design of different search permissions tailored to specific use cases. We conduct a thorough correctness analysis to demonstrate the robustness of our approach.
Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System

In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system.
PENGARUH PIK-R ( PUSAT INFORMASI DAN KONSELING REMAJA) TERHADAP PERILAKU SISWA (ANGGOTA PIK-R) DALAM MENGKOMUNIKASIKAN PROGRAM GENRE ( GENERASI BERENCANA) (Studi Kasus : SMA N 5 Kota Bengkulu)

This study entitled Effect of PIK-R (Center for Information and Counseling Adolescents) on student behavior (member PIK-R) in communicating program genre (Generation Planning) at SMA N 5 Bengkulu City. The purpose of this study was to determine the effect of PIK-R (Center for Information and Counseling Adolescents) on student behavior (member PIK-R) in communicating program genre (Generation Plan). This study uses the theory of group communication. The variables in the study was PIK-R (Center for Information and Counseling Adolescents) as a variable X and the behavior of the students as a variable Y. This research was conducted in August 2015 by using the techniques of data collection questionnaire and literature study, to suspensions and measurement criteria based on the Likert scale scores 1-5. The sample in this research is the whole activity PIK-R in SMA N 5 Bengkulu city, the technique determination of sample that is in the census, respondents numbered 50 people and research methods are quantitative analyzed using simple linear regression analysis and assisted by SPSS version 17. This study used some components as a guide to answer the questions in the desired, these components include the PIK-R (X) and student behavior (Y). From result of respondents giving the results of the genre in the delivery of program material PIK-R activities significantly influence the behavior of students on the knowledge, attitudes and actions. Based on calculations of regression using SPSS version 17, the obtained results were significant influence Value Event PIK-R (X) of the student's behavior (Y) of 0001 &lt;alpha 0.05, which has the meaning that the activity PIK-R (X) effect significantly influence student behavior (Y). With the results of the hypothesis if the hypothesis significance value less than 0.05, then Ho is rejected and Ha accepted, and that means the H test results received because the significance value less than 0.05.
ANALISIS TINGKAT KEJENUHAN MAHASISWA PROGRAM STUDI TEKNIK INFORMATIKA PADA MATA KULIAH BAHASA INDONESIA

The purpose of this research is to know the level of boredom of student learning in Indonesian subjects at the Informatics Engineering Study Program, Indraprasta University, PGRI Jakarta during the pandemic. This study uses a descriptive qualitative technique where the sample used in this study amounted to 40 students from the regular morning class of RH and RI each 20 students using purposive sampling technique. The data from this study were taken using a student learning saturation questionnaire with a total of 40 questions. And refers to the saturation level indicator which is then processed with statistical descriptions. The results of the study showed that student learning saturation was known to 7 people (17.5%) had a low level of saturation, 15 people (37.5%) had a moderate level of saturation, while those who had a high saturation category were 18 people (45%). From the results of the calculation of the average value obtained a value of 36.8, it is included in the low category, so it can be concluded that the level of saturation of the students of the informatics engineering study program in the Indonesian language course is low.
PVM: Efficient Shadow Paging for Deploying Secure Containers in Cloud-native Environment

In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor.
Performance evaluation of facial landmark detection methods

The detection and localization of landmarks in human face images is an essential task in many computer vision applications. This task is challenging because face images can contain geometric modifications due to gesticulations and pose changes, and degradation caused by noise or nonuniform illumination. This work presents an exhaustive evaluation of several state-of-the-art facial landmark detection methods. The performance of each tested method is characterized in terms of reliability of landmark detection and accuracy of landmark localization. Computational results obtained in facial landmark recognition using images from well-known datasets are presented, discussed and compared in terms of objective measures.
First-order spectrum filtering in Fourier transform profilometry: a method comparison

Fourier Transform Profilometry (FTP) is a powerful 3D reconstruction method based on structured-light projection suitable for dynamic shape measurements. A main feature of FTP is that it works using a single fringe pattern. However, the quality of the 3D reconstruction largely depends on the accuracy of first-order spectrum filtering. This work compares some representative spectrum filtering methods in different simulated situations, highlighting advantages and drawbacks. This study provides a reference for the practical implementation of a FTP system.
Enhancing Internet Interference Complaint Resolution: A Case of Object-Oriented Systems Approach at Kominfo Kota Gorontalo

In order to address and manage complaints arising from disruptions to internet networks or services, especially among various agencies, the Gorontalo City Information and Encryption Communication Service has implemented an information system. This system facilitates the reception, processing, and resolution of complaints from these agencies within the city. Notably, observations reveal potential areas for enhancement within this system, including the lack of comprehensive complaint details upon successful submission by the complainant (client) and the absence of a ticketing system. This study is focused on examining the role of the Object-Oriented System Approach in resolving issues within Kominfo Kota Gorontalo. Employing a descriptive qualitative methodology, the research employed data collection methods such as observation, interviews, and literature review. The findings underscore the efficacy of the Object-Oriented Analysis &amp; Design (OOAD) in effectively addressing the identified challenges within the Gorontalo Office of Communication, Informatics, Statistics, and Encryption.
Three-dimensional object reconstruction using multi-ocular vision

Computation of three-dimensional information of an object from captured images is an important task in computer vision. The use of binocular vision for this task has been widely explored for years. However, the accuracy of three-dimensional reconstruction using binocular vision is conditioned by operating within a specific field of view. This work presents a three-dimensional reconstruction method based on multi-ocular vision. This method achieves higher accuracy in comparison with the conventional binocular approach. The performance of the proposed method is evaluated for three-dimensional reconstruction using images from an existing stereo dataset and a real laboratory experiment using four cameras.
Path Planning of UAV Navigation Mark Inspection Using a K-means Clustering ACA

With increasing speeds and application of artificial intelligence in the shipping industry, unmanned aerial vehicle (UAV) technology has been applied to navigation mark inspections to improve the inspection efficiency and safety. Aimed at the UAV path planning problem of navigation mark inspection, this paper proposes an improved K-means clustering ant colony algorithm (KCACA) to design the shortest route for UAV navigation mark inspections. First, the K-means algorithm and the UAV maximum flight distance were used to cluster the navigation marks, which were then split into several secondary clusters. Each cluster was regarded as an independent traveling salesman problem to be evaluated using the ant colony algorithm (ACA). Second, after optimizing the ACA pheromone update formula, the attenuation factor was gradually reduced according to the number of iterations. Experiments showed that the improved KCACA not only optimized the shortest path but also calculated the optimal path in a short time, improving the operation efficiency.
Impact of educational technology policies in higher education improvements, challenges and perspectives

The purpose of this research is to evaluate the impact of educational technology policies in the field of higher education, covering both the progress achieved and the challenges faced, as well as future perspectives. In order to carry out this study, a qualitative methodology has been selected based on the analysis of documents related to information and communication technology (ICT) policies and programs in the educational system, as well as on the research of innovative practices of ICT integration in the teaching and learning process. The results obtained show that, although ICTs have had a substantial impact on the transformation of higher education, significant challenges remain, such as the need to promote greater digital inclusion and to design and implement policies and programs more effectively. Regarding future perspectives, the importance of strengthening ICT-related policies and programs, as well as promoting innovative practices for the integration of these technologies in education, in order to achieve inclusive and high-quality education, is emphasized.
Project-based pedagogical inevitability and social media impact

Enforced pandemic online learning has raised the opportunities of quality learning and derived numerous learning methodologies with the intense influence of social media platforms. Project-Based Learning (PBL) is greatly poignant in engineering discipline with Social Media (SM) assistance. Cutting-edge technology and industry-tech innovation demands higher skill set apart from routine learning. Real-use cases in PBL enhance creativity, critical thinking and prepare learner to deal with challenges of workplace. Community and SM reinforced learning acts as a virtual teacher for successful PBL implementation. SM supported PBL provides lifelong learning lessons. SM assisted PBL is carried out for Machine Learning (ML) course using different use cases. Modalities, like open online course, SM, community support and PBL, show 18% students carrying higher impact and 81% students upgraded their skill in real-life problem solving. Study shows 100% students gained a skill in solving real-life problem and achieved all graduate aspects magnificently.
Formulation and fabrication of biofoam from rice waste as a substitute for styrofoam in food packaging using the thermopress technique

Styrofoam as food packaging has a bad impact on the environment and the human body because it is made of benzene compounds. The purpose of this research is to look for environmentally friendly and safe food packaging alternatives in the form of biofoam made from waste rice plants and tapioca flour as natural adhesives. The biofoam fabrication process as food packaging is done by mixing straw waste that has been mashed with tapioca solution until it is homogeneous and has a higher viscosity, and then printing using a tool. Thermopress uses a pressure of 9,801 MPa (100 kg) with a temperature of 120°C for 14 minutes of pressing. The variations used are the use of mesh 40, 60, and 80 for the level of roughness of rice straw that has been treated in a crusher. The results of Biofoam testing with the three mesh variations 40, 60, and 80 can decompose inside soil for 28 days, and the results of testing with a mesh of 40 found low water absorption resistance with a weight gain of 36%, and the heat resistance test experienced a weight gain of 1.681%, meanwhile, biofoam with a mesh of 60 has added weight after testing water absorption of 23%, and the heat resistance test has a weight gain of 1%. And on biofoam with mesh 80, there was a weight gain after testing water absorption of 15% and resistance to heat with an increase in weight of 1%.
Extensive Review of State-of-the-Art Classification Techniques for Cuneiform Symbol Imaging: Open Issues and Challenges

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancient clay tablets demands a substantial investment of time and persistent practice over a long period of time. As the fourth millennium came to a close, earlier recording methods gave way to the development of writing – the visual representation of spoken language. The first language to be transcribed in written form in Mesopotamia was Sumerian. Predominantly, the earliest tablets originate from the Uruk site in southern Mesopotamia, possibly marking its birthplace. Digitization cuneiform documents is imperative to boost research focused on the ancient Middle East. A few initiatives embarked upon this endeavor around the year 2000. Nonetheless, the digitization process is time-consuming due to the extensive volume of documents, and a dependable (semi) automatic methodology has yet to be developed. Given the antiquity of cuneiform script, recognizing cuneiform signs using real-world applications via two graph-based algorithms, each with complementary runtime characteristics, remains a manual procedure. Translating cuneiform proves to be a daunting task. Only in relatively recent times has grammar been established scientifically, while lexical challenges remain abundant and far from resolved. Furthermore, the majority of the Sumerian tablets have succumbed to the ravages of time, leaving behind only a handful of ancient depictions. Some of these old images have been preserved in a unique collection or in museums worldwide, allowing specialists to easily apply the sign detector to their cuneiform text studies. In this paper, we will discuss the categorization and analysis of clay tablets using a trained cuneiform model, employing artificial intelligence methodologies. Additionally, we will explore the methods employed, highlighting their strengths and weaknesses. Finally, we will propose potential directions for future research.
Support for Single Page Application Frameworks on FrameWeb

In the field of Web Engineering, many methods have been proposed to guide developers in designing and coding Web applications. The FrameWeb method is a model-driven approach that targets the development of systems that use certain kinds of frameworks in their architecture, proposing the use of models that incorporate concepts from these frameworks during design. Currently, the FrameWeb method does not consider SPA (Single Page Application) frameworks and, in recent years, they have gained a lot of popularity among developers. In this work, we propose to add support for SPA frameworks to FrameWeb. With our research, we have managed to update the FrameWeb meta-model so that its modeling language now supports SPA frameworks and their constructs. FrameWeb tools (graphical editor and code generator) also evolved to support the new elements. Experiments of modeling existing SPAs with this new version of FrameWeb, generating code from the models and comparing with the original, showed that, in average, around 69% of the HTML tags could be generated from the models. The support for SPA frameworks in FrameWeb allows developers to design and model their applications using constructs that relate to the frameworks used in practice, facilitating developer communication using the models and generating code to improve developer productivity.
eXplainable Artificial Intelligence in sentiment analysis of posts about Covid-19 vaccination on Twitter

Considering the impact of the use of Artificial Intelligence (AI) in the most diverse branches of society and the use of eXplicable Artificial Intelligence (XAI) to improve the interpretability of these intelligent models, this paper aims to analyze some existing XAI methods to verify their effectiveness. To this end, experiments were conducted with LIME, SHAP, and Eli5 solutions in a scenario of sentiment classifications in Twitter posts about the Covid-19 vaccination process in Brazil. Thus, it is observed that the tools provide relevant information about the aspects that interfere in the classification of tweets as favorable or not favorable to vaccination, which allows concluding that the methods bring the necessary transparency to confirm the AI decisions regarding the sentiments related to the vaccination process in Brazil.
English Lecturers' Difficulties in Teaching English for Specific Purposes (ESP) in Various Higher Education in Indonesia

&lt;p&gt;This research investigates the English lecturers’ difficulties in teaching English for Specific Purposes (ESP) in higher education. This research is descriptive qualitative. There are 58 English lecturers from various higher education involved in this research. The analysis shows that the most dominant English lecturers teach ESP in the 2nd semester. Besides, in the 1st, and 3rd semesters. English skills taught by English lecturers in ESP courses are speaking, writing, reading, speaking, vocabulary, and translation. Speaking skills is the difficult English skill taught by them in the ESP course. The most dominant English lecturers have been teaching ESP for more than 5 years. They state that teaching ESP requires more challenges than teaching EGP (English for General Purposes). In the implementation of teaching ESP, they face several difficulties including) 1) making syllabi and lesson plans. 2) making ESP course materials according to student needs. 3) selecting and using learning methods. 4) getting ESP textbooks related to student needs. 5) making/creating ESP textbooks related to student needs. 6) difference in students' English proficiency levels. 7) difference in the level of student's motivation to learn English. 8) different levels of students' awareness of the importance of ESP. 9) the institution’s policy related to the ESP course curriculum has not been clearly stated. 10) institutional policies related to facility policies are not adequate. 11) the number of lectures/credits, and the number of semesters in the ESP course are not as expected. 12) compensation (income) in teaching ESP courses is not expected.&lt;/p&gt;