Reviewerly/exHarmony huggingface dataset:
Application of Information Systems at PT Angkasa Pura II Silangit Airport

PT Angkasa Pura II (Persero) is one of the State-owned Enterprises engaged in the business of airport services and services linked to the air city. The purpose of the research is to analyze the accounting data system at PT. Angkasa Pura II Silangit. This research uses descriptive research methods. From the results of this research, it can be seen that the accounting data system presented by the industry is stored and recorded in SAP. Strandar is used to view code in processing and inputting every transaction contained in industrial activities. PT. Angkasa Pura already has a very complete accounting data system, this system can be connected to all components of central and branch activities. In manual use, this system has been designed very well but must be checked and memorized in use with SAP that has been available. Employees cannot use the system carelessly and the code must also be able to be adjusted properly.
Research Review on Blockchain Adaptation to VAT

Blockchain technology may provide a solution for the tax industry. It is a distributed ledger that makes it possible to trade anything of value securely, openly, and with less threat of fraud. People might find it simpler to pay their taxes, and governments might find it easier to close the tax gap. But getting from theory to practice is a long journey that also presents challenges, which needed to be discussed. This paper reviewed previous research specifically on the adaptation of blockchain to Value-Added System (VAT). Apart from the theoretical framework and proposed prototype, the majority of research used a qualitative approach, according to the findings. The decentralized network was mostly used as the blockchain structure and design for VAT, with a private block. The consensus that’s mainly used among the articles is PoA, PoA, and PBFT.
Pelatihan Konten Kreator Media Pembelajaran Online Di SMK Yasiha Gubug

The Covid-19 pandemic has brought significant changes in various aspects of life, including the education sector. One of the noticeable changes is the shift to online learning. However, the problem faced by teachers in this online learning process is the lack of skills in selecting relevant learning media that suits the condition and needs of the students. Therefore, the aim of this community service program is to provide an understanding of the concept of online learning media and to introduce and improve the teachers' ability in using the online learning support system through training and mentoring in creating online learning media content, particularly through the YouTube platform, for teachers at SMK Yasiha Gubug. The method used in this community service program was an informative approach and training mentoring. The results of this program showed that the motivation, confidence, understanding of online learning media, and teachers' skills in using technology to create online learning media have improved.
Penyusunan Sistem Pengelolaan Kearsipan Digital Berbasis Microsoft Access Pada Bagian Sertifikasi PT Insurin Balikpapan

In the archive management system in a company, it is necessary to renew the archive management so that the records can be managed properly. The problem in this study is that the archive management system at the PT INSURIN Balikpapan Certification section can still be said to be less than optimal, due to the lack of knowledge about archive management and also a lack of awareness of the importance of archives. This study aims to develop a digital archive management system that can facilitate the search for archival data that is safe, fast, accurate, and more efficient. In this study, the type of research used was descriptive research with a qualitative approach using descriptive data analysis. The data collection techniques used are observation, interviews, and documentation, and use of seven other stages in the preparation of the system, namely the planning stage, the implementation stage, the testing stage, the analysis stage, the feedback stage, the repair stage, and the validation stage. The data analysis technique is through data reduction, data presentation, and conclusion drawing. Based on the research conducted, it can be concluded that the preparation of the application program that has been compiled can be used in archival management activities at the PT INSURIN Certification section to replace the previous system because it is considered effective but still requires the addition of other menus, especially in terms of archive search. It still needs development with the addition of other menus considering the many tools offered in the Microsoft Access application so that the system becomes more innovative, as well as renewal of archival equipment in the form of a roller-type scanner so that the process of scanning reports can be more practical and effective.
Towards a Scalable Proof Engine: A Performant Prototype Rewriting Primitive for Coq

Abstract We address the challenges of scaling verification efforts to match the increasing complexity and size of systems. We propose a research agenda aimed at building a performant proof engine by studying the asymptotic performance of proof engines and redesigning their building blocks. As a case study, we explore equational rewriting and introduce a novel prototype proof engine building block for rewriting in Coq, utilizing proof by reflection for enhanced performance. Our prototype implementation can significantly improve the development of verified compilers, as demonstrated in a case study with the Fiat Cryptography toolchain. The resulting extracted command-line compiler is about 1000× faster while featuring simpler compiler-specific proofs. This work lays some foundation for scaling verification efforts and contributes to the broader goal of developing a proof engine with good asymptotic performance, ultimately aimed at enabling the verification of larger and more complex systems.
Tool use moves the peri-personal space from the hand to the tip of the tool

In this study, we used a visual target detection task to investigate three hypotheses about how the peri-personal space is extended after tool-use training: Addition, Extension, and Projection hypotheses. We compared the target detection performance before and after tool-use training. In both conditions, the participants held a hockey stick-like tool in their hands during the detection task. Furthermore, we added the no-tool-holding condition to the experimental design. In the no-tool-holding condition, a peri-hand space advantage in the visual target detection task was observed. When the participants held the tool with their hands, this peri-hand space advantage was lost. Furthermore, there was no peri-tool space advantage before tool training. After tool training, the peri-tool space advantage was observed. However, after tool training, the advantage of the peri-hand space was not observed. This result suggested that the peri-hand advantage was reduced by simply holding the tool because the participants lost the functionality of their hands. Furthermore, tool-use training improved detection performance only in the peri-tool space. Thus, these results supported the projection hypothesis that the peri-personal space advantage would move from the body to the functional part of the tool.
ANALISIS MYLOFT DALAM MEMENUHI KEBUTUHAN INFORMASI PEMUSTAKA UPI

The Covid-19 pandemic has forced every activity to adapt to new habits, including the library. To overcome this, the UPI Library provides online services in the form of e-resources that can be accessed using the MyLOFT application. This study aims to determine how librarian services, service quality, and the role of e-resources services at MyLOFT are in terms of ease of access and in meeting the information needs of users. This research method uses a descriptive quantitative method. The sample of this study was active UPI students aged 18-22 years. The results showed that socialization and guidelines helped users in facilitating access to e-resources on MyLOFT, the quality of the service also had a pretty good appearance that made it easier for users to access, this service has also played quite a role in meeting the information needs of users at UPI. Although it is sufficient to meet the information needs of users, this service must continue to improve its services and continue to strive to provide the best service, so that the e-resources service on MyLOFT can be increasingly recognized and able to meet the information needs of users.
Effective Probabilistic Feature for Adaptive Learning Model to Accelerate Approximate Nearest Neighbor Search

Abstract The quantization-based approaches are the effective methods for solving the problems of approximate nearest neighbor search. However, many quantization-based approaches usually employ an uniformed number of nProbes as termination condition to performing search process for each query. This will result in extra time consumption due to the fact that the search for many queries is terminated rapidly before completely retrieve the points contained in its nProbes nearest subspace. To this end, we propose to train an uniformed learning prediction model using effective input features generated by locality sensitive hashing, such that adaptively predicts the termination condition for each query to speedup the query process. Many experiments show that our proposal is superior to other state-of-the-art methods.
Lossless HDR Image Compression by Modulo Encoding

In High Dynamic Range Images (HDR), the illumination intensity levels vary over a very wide range and hence the storage space required is very large compared to normal Low Dynamic Range images. Transmission and distribution of HDR images and videos demand a large bandwidth coupled with higher communication overheads. Hence, image compression is invariably adopted for HDR images/videos so that they can be used efficiently in communication-intensive scenarios such as video conferencing and mobile applications. In this paper, a new method of lossless HDR image compression is presented that uses modulo encoding and subsequent reconstruction.
Penggunaan Media Pembelajaran Powtoon untuk Meningkatkan Hasil Belajar Menggambar Komik Siswa Kelas VIII.5 MTs Negeri 3 Padang

This study aims to determine the increase in student learning outcomes in the material for drawing comics using powtoon learning media in class VIII.5 students of MTsN 3 Padang. The type of research used in this research is classroom action research (CAR). This research was carried out in two cycles, each cycle having two meetings. Methods and data collection tools in this study are in the form of observation, tests, and documentation. Data analysis in this study was by t-test using SPSS version 26. The results obtained in this study indicate that using powtoon learning media can improve learning outcomes in arts and culture in comic drawing materials for class VIII.5 MTsN 3 Padang. In the pre-cycle the average value obtained was 73.28 and then continued by carrying out cycle II the average student learning outcomes increased by 86.21. Thus it can be concluded that the use of powtoon learning media can improve the learning outcomes of drawing comics for class VIII.5 students of MTsN 3 Padang.
A Study on the Technology Utilization and systematic Management Plan of Offset Program

This study identifies problems with the Offset quantitative performance management methodology proposed by Kim Ki-hwan and Lee Sang-bok (2022) in 2022 to quantitatively evaluate the performance of R&D technologies obtained through Offset. In addition, domestic defense and private sector rating methodology management institutions are selected as advisory institutions to verify the validity and fair evaluation of major technical achievements obtained through compromise offset, and the characteristics and advantages of each institution are analyzed. Finally, to systematically database and manage the results created, a ‘computer management system construction plan’ is proposed by deriving essential elements of the computer management system and analyzing the system needs.
Sistem Penerimaan Dan Pengelolaan Administrasi Keuangan Pendidikan Di Kober TK Islamic Centre Berbasis WEB

Administrative financial education is an important component in the field of education to obtain educational facilities, including in Kober TK Islamic Center. Recording of acceptance and management of school financial administration at the Kober TK Islamic Center using handwriting or has not been computerized, which can result in a lot of bookkeeping, writing errors, and delays in financial reporting. In addition to the long recording process, data search also takes a long time, which can make it difficult for administrative staff to process acceptance and manage financial administration. Based on the problems in the Kober TK Islamic Center, it is necessary to build an information system that can assist in recording and managing financial administration. This information system creates using the MySQL database, the PHP (Hypertext Preprocessor) programming language, and Sublime Text for the text editor. the test server uses XAMPP, and the data used is obtained from Kober TK Islamic Center. The expected results of this final project are to assist employees in recording acceptance and managing financial administration efficiently. Then, for this project, it is hoped that it can also assist in the acceptance and management of financial administration in the Kober TK Islamic Center.
Online Voting System Using Blockchain Technology

Abstract: An online voting system that utilizes blockchain technology is a promising solution for conducting elections in a secure and transparent manner. The decentralized and distributed nature of the blockchain ledger ensures that every vote is recorded accurately and cannot be tampered with or altered without detection. This is because every node on the blockchain network has a copy of the ledger, making it virtually impossible to manipulate election results. One of the biggest advantages of blockchainbased voting systems is that they allow voters to cast their ballots from anywhere, as long as they have an internet connection. This makes voting more accessible and convenient for people who might have difficulty getting to a polling station. Additionally, blockchain technology allows for the verification of voter identity and prevents double voting, ensuring that every vote is legitimate and counted accurately. Another benefit of blockchain-based voting systems is their resistance to cyber attacks. The decentralized nature of the blockchain network makes it extremely difficult for hackers to compromise the system, as they would need to gain control of a majority of the nodes on the network. Additionally, the transparent nature of blockchain-based voting systems makes them more trustworthy and helps to prevent voter fraud.
Gesture Recognition based Virtual Mouse and Keyboard

Abstract: The field of computer vision has advanced significantly, enabling computers to identify their users using simple programs based on image processing. This technology has been widely used in various day-to-day applications, such as face recognition, color detection, and autonomous driving. This research project aims to use computer vision to develop an optical mouse and keyboard that can be operated through hand movements. The computer camera will capture images of different hand gestures made by the user, and the mouse pointer or cursor on the computer screen will move accordingly. Different hand gestures can be used to execute right and left-clicks. Similarly, the keyboard functions can be performed using different hand actions, such as using a finger to select an alphabet and a four-digit swipe left or right. The virtual mouse and keyboard can be used wirelessly or externally, and the only hardware required for the project is a webcam.
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

Conformer-based models have become the dominant end-to-end architecture for speech processing tasks. With the objective of enhancing the conformer architecture for efficient training and inference, we carefully redesigned Conformer with a novel downsampling schema. The proposed model, named Fast Conformer(FC), is 2.8x faster than the original Conformer, supports scaling to Billion parameters without any changes to the core architecture and also achieves state-of-the-art accuracy on Automatic Speech Recognition benchmarks. To enable transcription of long-form speech up to 11 hours, we replaced global attention with limited context attention post-training, while also improving accuracy through fine-tuning with the addition of a global token. Fast Conformer, when combined with a Transformer decoder also outperforms the original Conformer in accuracy and in speed for Speech Translation and Spoken Language Understanding.
A Multi-modal Approach to Single-modal Visual Place Classification

Visual place classification from a first-person-view monocular RGB image is a fundamental problem in long-term robot navigation. A difficulty arises from the fact that RGB image classifiers are often vulnerable to spatial and appearance changes and degrade due to domain shifts, such as seasonal, weather, and lighting differences. To address this issue, multi-sensor fusion approaches combining RGB and depth (D) (e.g., LIDAR, radar, stereo) have gained popularity in recent years. Inspired by these efforts in multimodal RGB-D fusion, we explore the use of pseudo-depth measurements from recently-developed techniques of ``domain invariant" monocular depth estimation as an additional pseudo depth modality, by reformulating the single-modal RGB image classification task as a pseudo multi-modal RGB-D classification problem. Specifically, a practical, fully self-supervised framework for training, appropriately processing, fusing, and classifying these two modalities, RGB and pseudo-D, is described. Experiments on challenging cross-domain scenarios using public NCLT datasets validate effectiveness of the proposed framework.
Accessible Instruction-Following Agent

Humans can collaborate and complete tasks based on visual signals and instruction from the environment. Training such a robot is difficult especially due to the understanding of the instruction and the complicated environment. Previous instruction-following agents are biased to English-centric corpus, making it unrealizable to be applied to users that use multiple languages or even low-resource languages. Nevertheless, the instruction-following agents are pre-trained in a mode that assumes the user can observe the environment, which limits its accessibility. In this work, we're trying to generalize the success of instruction-following agents to non-English languages with little corpus resources, and improve its intractability and accessibility. We introduce UVLN (Universal Vision-Language Navigation), a novel machine-translation instructional augmented framework for cross-lingual vision-language navigation, with a novel composition of state-of-the-art large language model (GPT3) with the image caption model (BLIP). We first collect a multilanguage vision-language navigation dataset via machine translation. Then we extend the standard VLN training objectives to a multilingual setting via a cross-lingual language encoder. The alignment between different languages is captured through a shared vision and action context via a cross-modal transformer, which encodes the inputs of language instruction, visual observation, and action decision sequences. To improve the intractability, we connect our agent with the large language model that informs the situation and current state to the user and also explains the action decisions. Experiments over Room Across Room Dataset prove the effectiveness of our approach. And the qualitative results show the promising intractability and accessibility of our instruction-following agent.
ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion

Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, typically by adjusting the prediction thresholds using manually annotated examples. In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation. Our new method ACTC finds good per-relation thresholds efficiently based on a limited set of annotated tuples. Additionally to a few annotated tuples, ACTC also leverages unlabeled tuples by estimating their correctness with Logistic Regression or Gaussian Process classifiers. We also experiment with different methods for selecting candidate tuples for annotation: density-based and random selection. Experiments with five scoring models and an oracle annotator show an improvement of 7% points when using ACTC in the challenging setting with an annotation budget of only 10 tuples, and an average improvement of 4% points over different budgets.
Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement.
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing

Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, especially in real-time video processing applications. Such applications exhibit high inter- and intra-frame redundancy, allowing further improvement. This paper proposes a similarity-aware training methodology that exploits data redundancy in video frames for efficient processing. Our approach introduces a per-layer regularization that enhances computation reuse by increasing the similarity of weights during training. We validate our methodology on two critical real-time applications, lane detection and scene parsing. We observe an average compression ratio of approximately 50% and a speedup of \sim 1.5x for different models while maintaining the same accuracy.