Reviewerly/exHarmony huggingface dataset:
Self-Knowledge Distillation from Target-Embedding AutoEncoder for Multi-Label Classification

Target-Embedding Autoencoder (TEA) has been successfully utilized in Multi-Label Classification (MLC), where each instance is associated with multiple labels. However, most existing TEA-based approaches mainly focus on the latent space alignment in their encoding phase, ignoring the output bias induced by overfitting in the training process. To address this issue, we provide a new approach named Self-Knowledge Distillation from TEA (SKDTEA) by removing the latent space alignment of TEA-based solutions with self-knowledge distillation in a simple yet effective manner. Unlike conventional self-knowledge distillation in multi-class learning, our SKDTEA leverages self-knowledge distillation by fully exploring the relationship between label smoothing and knowledge distillation. Specifically, an auxiliary module of SKDTEA is designed for ground-truth targets reconstruction, which outputs the recovered outputs as knowledge in a learned multi-label smoothing manner. The whole distillation process provides an efficient regularization to alleviate the overfitting issue in the training process. As far as we know, we are the first attempt to introduce the self-knowledge distillation into TEA-based approaches for the MLC. Experimental results demonstrate our proposed method achieves significant superiority over the well-established approaches in MLC.
Vibro-vestibular Wheelchair for Vehicle Riding Experience in Immersive Virtual Reality

In this study, we modified a wheelchair by attaching two direct current motors to drive each wheel and provide whole-body vibration and vestibular sensation. This motion simulator can be used to enhance the vehicle riding experience in immersive virtual reality or telepresence. We examined herein the effectiveness of the translational motion feedback from the vestibular wheelchair when presenting recorded images with spatial motion from the stereoscopic cameras on the head-mounted display (HMD). The results of the intensity comparison with the magnitude estimation method showed that the perception of vestibular acceleration by the wheelchair was stronger than that of visually presented acceleration in the HMD. Moreover, approximately 25% intensity of the visual acceleration is appropriate for suppressing virtual reality sickness. The questionnaire results revealed that adding vestibular acceleration to the visual acceleration increases the sense of presence, regardless of the vestibular sensation intensity.
Encoder-Decoder Language Model for Khmer Handwritten Text Recognition in Historical Documents

Correcting spelling errors in texts extracted from Khmer palm leaf manuscripts by handwritten text recognition (HTR) systems can be very challenging. A Khmer Language Model developed in this study aims to facilitate the task mentioned above. The proposed model utilizes long short-term memory (LSTM) modules applicable for improving the performance of text recognition which is to predict a sequence of characters as output. The architecture of the language model is based on an encoder-decoder mechanism which is composed of two parts: an encoder to capture the context of the input erroneous word and a decoder to decode and predict the correctly spelt output word. Experimental evaluations are conducted on a text corpus consisting of Khmer words extracted from Sleuk-Rith set.
Drones-aided Asset Maintenance in Hospitals

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.
Label Enhanced Event Detection with Collective Knowledge and Heterogeneous Graph

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages.
Integrating Philanthropy System in Indonesia Using Service-Oriented Architecture

In Indonesia, the most extensive philanthropy is related to zakat (obligation for Moslem to help poor people). The potential for zakat that continues to grow is not accompanied by the realization of zakat collection in the field. Based on the actual data on zakat collection, the zakat potential in 2015 is predicted to reach Rp. 286 trillion, in realization, only reached Rp. 3.7 trillion or less than 1.3 percent of its potential. One of the problems is the lack of system and institutional arrangements for zakat, limited synergy, integration, and cooperation in managing zakat nationally. The purpose of this research is to build integrated media by providing web services as an effort to increase synergy and cooperation between zakat institutions. The web service to be built uses a Service Oriented Architecture (SOA). In contrast to traditional point-to-point, which requires similar technology between provider and receiver, SOA is independent of any particular technology or protocol. The results of this research are web services that can be called with different protocols and message formats.
Semantic similarity of Indonesian sentences using natural language processing and cosine similarity

Natural language processing is a commonly used method for processing text input. The text is widely used in many ways, such as determining hoaxes, concluding sentences, sentiment analysis, and measuring similarity. Many other studies in English have carried out measurements of similarity between two sentences. However, there are still few studies that use Indonesian sentences. This study proposes an approach to assess the similarity between two Indonesian sentences. The motivation of this research is to prove that the proposed approach can be applied. Similarity assessment uses a combination of natural language processing and cosine similarity. Natural language processing begins with POS tagging, tokenization, stemming, and similarity measurement between words using WordNet and Wu Palmer. The results showed that the accuracy value is 86,67%. Therefore, this study can be used as a preliminary process in natural language processing with more complex objectives.
Early Warning System Design for Flood Disasters Using the IoT-Based Fuzzy Logic Control Method

Indonesia is one of the developing countries that are vulnerable due to various natural disasters, especially flooding is a form of natural phenomenon that occurs due to high rainfall intensity where there is excess water that is not accommodated by the water channel network in an area. Therefore, an early warning system is needed using the fuzzy logic control method as a decision maker to cope with disasters so that the anticipation of residents in dealing with disasters will increase. From the results of research conducted on the HCSR04 sensor for water level readings with an average percent error of ±0.50%, the Rain gauge sensor for rainfall readings with an average percent error of ±0.73%, and the Turbidity sensor with an average - Average percent error is ±12.2%. In testing this tool using parameters when the water level is 0–40 cm, the rainfall intensity is 0-100mm and the water turbidity reads 0–200 NTU it will display safe conditions. When the water level is 41–80 cm, the rainfall intensity is 101-300mm and the water turbidity reads 201–467 NTU, it will display an alert condition and give a warning with the siren on three times and also display a notification on the blynk application. And when the water level is 81–100 cm, the rainfall intensity is 301-500mm and the turbidity of the water reads 468–700 NTU, it will display a dangerous condition and give a warning with the siren on continuously and also display a notification on the blynk application.
Coordination of a Multi Robot System for Pick and Place Using Reinforcement Learning

Recent advances in deep reinforcement learning are enabling the creation and use of powerful agent systems in complex areas such as multi-robot coordination. These show great promise to help solve many of the difficult challenges of rapidly growing domains such as smart manufacturing. In this paper we describe our ongoing work on the use of single agent deep reinforcement learning to optimise coordination in a multi-robot pick and place (PnP) system. We describe the implementation of the DQN agent as well as a bespoke multi robot PnP simulator, implemented as an OpenAI Gym environment. We present our initial results and outline future work.
Digitizing Farmers' Land Data Collection Systems in Indonesia with the Use of Tani Millenial Apps

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.
HTransE: Hybrid Translation-based Embedding for Knowledge Graphs

Basically, a Knowledge Graph (KG) is a graph variant that represents data via triplets comprising a head, a tail, and a relation. Realistically, most KGs are compiled either manually or semi-automatically, and this usually results in a significant loss of vital information with respect to the KG. Thus, this problem of incompleteness is common to virtually all KGs; and it is formally defined as Knowledge Graph Completion (KGC) problem. In this paper, we have explored learning the representations of a KGs with regard to its entities and relations for the purpose of any predicting missing link(s). In that regard, this paper proposes a hybrid variant, composed of TransE and SimplE models, for solving KGC problems. On one hand, the TransE model depicts a relation as the translation from the source entity (head) to the target entity (tail) within an embedding space. In TransE, the head and tail entities are derived from the same embedding-generation class, which results in a low prediction score. Also, the TransE model is not able to capture symmetric relationships as well as one-to-many relationships. On the other hand, the SimplE model is based on Canonical Polyadic (CP) decomposition. SimplE enhances CP via the addition of the inverse relation, while the head entity and tail entity are derived from different embedding-generation classes which are interdependent. Hence, we employed the principle of inverse-relation embedding (from the SimplE model) onto the native TransE model so as to yield a new hybrid resultant: HTransE. Therefore, HTransE boasts of efficiency as well as improved prediction scores. Efficiently, HTransE converges much quicker in comparison to TransE. In other words, HTransE converges at approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n/2$</tex> iterations where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> denotes the iterations required to fully train TransE. Our results outperform the native TransE approach with a significant difference. Also, HTransE outperforms several state-of-the-art models on different datasets.
Evaluation of Elearning Using the Human Organization Technology (HOT) Model

In its implementation, e-learning requires development to make it easier for users, in this case, lecturers and students, to use e-learning in the online learning process. So that the use of the Human Organization Technology (HOT) evaluation model Fit model is a complete model and most appropriate to the conditions of the problems in this study. The main focus of research is directed at the relationship between the human (human) aspect as a user consisting of system use and user satisfaction, the organizational part consisting of organizational structure and environment, technology aspect consisting of system quality, information quality, and service quality, the results of this study show that the relationship between the Organization (ICT Unit) as an e-learning service provider has interacted quite well with users in the context of developing e-learning systems. It is necessary to develop further the relationship between the Organization (ICT Unit) and e-learning users (students and lecturers).
Clustering by peak merging

Clustering by fast search-and-find of density peaks (CFSFDP) is a recently developed density-based clustering method that is being widely used as it can effectively detect isolated high-density regions. However, it often fails to identify true cluster structures from data owing to its intrinsic assumption that a cluster has a unique and high-density center, because a single cluster can contain several peaks. We call this the “multi-peak problem”. To overcome this, we propose a peak merging method for clustering. In the proposed algorithm, a valley and its local density are defined to identify the intersection between two adjoined peaks. These are used to construct directed and connected subgraphs, using which we merge multiple peaks if needed. Unlike CFSFDP and its variants, the proposed method is capable of identifying highly complex shaped clusters with no interpretation of a decision graph. Numerical experiments based on synthetic and real datasets demonstrate that our method outperforms the benchmarking methods.
Attractive Learning Media for Introduction to Popular Fruits Using Computer Vision

Early Childhood Education (ECE) starts from the age of 1 year to 4 years, this program helps children to prepare for school. Because children have a short range of concentration power so that children's attention will be easily distracted by other things that attract their attention. Learning media usually use books, cards (flash cards) or items made of paper and cause students to get bored quickly, so alternative learning media are needed to increase student learning motivation. Another method is the use of computer vision as an attractive learning media for early childhood. Fruit recognition is a learning material that is easy to apply to early childhood. The stages in this research consist of a collection of popular fruit datasets, converting RGB color space to HSV color space, segmentation process, feature extraction and classification of popular fruits using the KNN method. Parameters used for fruit recognition based on color and shape features. The results of the system accuracy in recognizing popular fruit types are 100% for the value of K is 1 for both blue images and saturation images.
Development Of A Chatbot For The Online Application Telegram Chat With An Approach To The Emotion Classification Text Using The Indobert-Lite Method

The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, text-based communication usually results in misunderstandings resulting from the absence of feeling intonation and emotions in the text. This study aims to create a chatbot that can detect emotions text to be entered into online chat applications. This study used a pre-trained model specifically trained from a collection of Indonesian-language datasets, namely IndoBERT-lite. The dataset used to train the model is a collection of Indonesian tweets totaling 4,403 which have been labeled with 5 classes of emotions, namely love, happy, anger, sadness, and fear. The hyperparameters used in this study to train the model were 5 epochs, batch size 16, learning rate 0.000003, and adam optimizer. Based on the test results with the parameters already mentioned, the accuracy, F1 score, recall, and precision values were obtained in the training set of 89%, 89%, 89%, and 90%, while the validation set obtained 70%, 71%, 70%, and 72%.
SUMSHINE: Scalable Unsupervised Multi-Source Heterogeneous Information Network Embeddings

Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing HIN representation learning methods can effectively learn the semantic and structural features in the network, little awareness was given to the distribution discrepancy of subgraphs within a single HIN. However, we find that ignoring such distribution discrepancy among subgraphs from multiple sources would hinder the effectiveness of graph embedding learning algorithms. This motivates us to propose SUMSHINE (Scalable Unsupervised Multi-Source Heterogeneous Information Network Embedding) — a scalable unsupervised framework to align the embedding distributions among multiple sources of an HIN. Experimental results on real-world datasets in a variety of downstream tasks validate the performance of our method over the state-of-the-art heterogeneous information network embedding algorithms.
Modify Linear Congruent Generator Algorithms Using Inverse Elements of Modulo Multiplication for Randomizing Exams

Linear Congruent Generator randomization results are pretty easy to predict. In addition, improper parameter selection in LGC will make the LCG period predictable, and randomization results will repeat quickly. To overcome the weakness of LCG, researchers have developed LCG and hybrid variants, such as CLCG, dual CLCD, RSA hybrid, Hybrid ECC and Fibonacci hybrid. Although LCG variants and combinations produce better random results, the computational level is more complex than LCG. In this study, we propose a modification of LCG by adding an inverse element to the multiplication mod m. Like CLCG, this research is an LCG which is continued by calculating the inverse element of the multiplication mod m. The results showed that the resulting modified LCG worked well, and the randomization effect was more random than the original LCG.
Progressive Feature Upgrade in Semi-supervised Learning on Tabular Domain

Recent semi-supervised and self-supervised methods have shown great success in the image and text domains by utilizing augmentation techniques. Despite such success, it is not easy to transfer this success to a tabular domain. The common transformations from image and language are not easily adaptable to tabular data containing different data types (continuous and categorical data). There are a few semi-supervised works on the tabular domain that have focused on proposing new augmentation techniques for tabular data. These approaches may have shown some improvement in datasets with low-cardinality in categorical data. However, the fundamental challenges have not been tackled. The proposed methods either do not apply to datasets with high-cardinality or do not use an efficient encoding of categorical data. We propose using conditional probability representation and an efficient progressively feature upgrading framework to effectively learn representations for tabular data in semi-supervised applications. The extensive experiments show the superior performance of the proposed framework and the potential application in semi-supervised settings.
Application of Design Thinking in the Creation of UI/UX on E-learning Websites

The emergence of the e-learning culture has taken the world by storm, and its trend has only increased with the COVID-19 pandemic. Being confined at home, students and educators are forced to transition to online learning no matter their familiarity or skill level with technology. One such issue is when a website is not designed effectively, making it needlessly complicated. On the other hand, taking away the complexity of a site may cause it to lack the information presented. To overcome this, an e-learning website should be designed using a design thinking approach that centers around creative problem solving to better innovate a solution. This paper aims to delve deeper into how the design thinking approach may improve an e-learning website's design. The results of the research that we have done show that the application of design thinking in our e-learning has the highest score in the navigation aspect and is also user-friendly, with a score of 8.1 (out of 10). While the lowest score is in the use of color with a score of 6.9 (out of 10), from these results, we need to improve in aspect use of color. This proves that the application of design thinking in e-learning is necessary to facilitate and assist users in using the application