Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Lifecycle Approach towards a Secure Building Information Modelling (BIM) Workflow

Data safety concerns are critical during the whole lifecycle of any built asset from the design stage until destruction. Information security in Building Information Modeling (BIM) enabled projects represents a key factor for the whole process success. Unsuitable distribution of sensitive information may cause financial loss, lack of trust and even physical security threats. This paper would propose a novel solution to secure BIM workflow through a Blockchain technology-based lifecycle approach. The aim in this solution is to integrate a blockchain-based lifecycle management platform with BIM software. The interactions and system transactions between the two entities will be managed by distributed ledger technology and smart contracts. This will ensure workflow's security by strengthening the transparency, traceability and securing information exchange.
Time Series Anomaly Detection for Smart Grids via Multiple Self-Supervised Tasks Learning

Smart grids have achieved tremendous success in energy infrastructure and also play an vital role in our daily lives. As an important auxiliary service, anomaly detection in smart grids, e.g., detecting abnormal activities and anomalous events from the electricity data, is very crucial to enhance the reliability and practicability for smart grids. In this paper, we mainly focus on the anomaly detection for time series data in smart grids. Although lots of anomaly detection approaches have been developed for time series data, most of them can not capture the mutual anomalous patterns dynamically among different sensors. In other words, dynamic correlations between different time series are always ignored, which consequently results in high false positive rates. To tackle the problem, we propose a self-supervised framework consisting of multiple tasks, i.e., inter-sample comtrastive task, intra-sample prediction task and reconstruction task, without using groundtruth labels. Technically, in the first task, we construct contrastive pairs from different samples (inter-sample pairs) and propose to use the multi-headed attention model to capture the dynamic mutual anomalous patterns. Furthermore, the prediction task with multiple fully connected (MLP) layers stacked with dropouts is proposed to capture the temporal intra-sample trends in time series data. In the final task, we use Gate Recurrent Unit (GRU) based autoencoders to compute the reconstruction error. The final loss is combined with inter-sample contrastive loss, reconstruction loss and intra-sample prediction loss. Compared with several baselines on a real-world dataset, the experimental results demonstrate that our proposed approach can effectively detect anomalies and significantly outperforms the state-of-the-art.
Chronic Kidney Disease Severity Identification Using Template Matching Feature Selection Statistics Based

The number of patients with Chronic Kidney Disease is currently increasing in this decade. This needs to be a concern, especially in determining the severity level from the start. Chronic Kidney Disease consists of 5 grades from Grade 1 to Grade 5, where Grade 1 indicates the mildest condition and Grade 5 is the most severe condition. Determining the severity of this is very important because on the basis of this level of severity can be determined the next step of healing. This study aims to propose a way to identify Chronic Kidney Disease severity using a template matching feature selection statistics based. This research stage is the stage of data initialization, statistical analysis, and the trial process using eucledian distance. The experiments carried out were divided into 4 parts, namely using 26 parameters (based on mean data) and using 28 parameters (based on mode data). The results show that the accuracy value obtained is 76.88%. The results of the analysis show that the age parameter is not a significant parameter to cause Chronic Kidney Disease, meaning that this disease can occur in all age groups.
Augmented Reality Design Using the ADDIE Model as An Introduction to Kindergarten Interior Interactive Elements

In today's era, technology continues to experience rapid development. Quite a lot of technology applications are found in education that can support the learning process to increase interaction in the process, one of which can be applied is kindergarten. Kindergarten is an institution where children can grow and develop behavior and thinking skills. High interaction during the learning process can make children feel bored and can increase children's productivity. This interaction can be obtained through AR technology, which is a technology that combines virtual objects with things as if they were real. This study uses the ADDIE method, namely Analysis, Design, Development, Implementation, and Evaluations, because it is considered more rational and easier to understand. The trial results show that the longer the marker tracking test is, the farther away the marker is. However, the tracking speed will increase as the distance between the marker and the camera narrows. It takes about 0.018 seconds on average to detect a marker. All identified markers display 3D objects with exact matches at a distance of 20cm to 80cm, and test tilt angles from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15^{\circ}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$45^{\circ}$</tex> . This application helps kindergarten instructors with its knowledge and 3D interactive Augmented Reality.
Implementation of The K-means Clustering for The Public Health Center Data

The Public Health Center is one of the first-level healthcare facilities that have an essential role in the national health system. 186 Public Health Centers in East Kalimantan Province are spread over ten districts/cities. However, many people have not chosen health services at the Public Health Center because many are considered inadequate in terms of human resources and infrastructure. This study aims to cluster the eligibility conditions of Public Health Centers based on human resources and infrastructure owned. Cluster analysis using the K-Means Clustering method is presented in a geographic information system. According to the results of applying the K-Means Clustering method, it is known that in East Kalimantan Province, there are 42 health centers with inadequate conditions, 73 health centers with quite decent conditions, and 71 health centers with proper conditions. The results of the clustering evaluation obtained a Silhouette Coefficient value of 0.07096, and a Davies Bouldin Index of 0.934. The geographic information system built includes a website-based mapping of the feasibility of the Public Health Center in East Kalimantan.
Optimization of Random Forest with Genetic Algorithm for Determination of Assessment

The Government of the Republic of Indonesia has issued a self-study regulation in which there are many parameters to determine the graduation status of MBMK participating students. Choosing the graduation of MBKM learning is not only based on rules from managers, universities, faculties, departments, or students. The most fundamental problem is that there are no standard rules regarding graduation status and graduation parameters in MBKM learning. Based on these problems, this study applies the random forest method optimized with genetic algorithms to classify students' graduation status accurately and quickly. After testing, the average accuracy with the random forest method optimized by the genetic algorithm is 88.58%, the average precision is 77.34%, the recall average is 57.68%, and the f1-score is 66.54 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , with the parameter size population of 21, 3 iterations, crossover rate value 0.8, mutation rate value 0.4, and several features 4. The evaluation results of accuracy, precision, recall, and f1-score obtained in this study show that the genetic algorithm can find optimal parameters. The random forest result in higher accuracy, precision, recall, and f1 scores
A Clustering Based Priority Driven Sampling Technique for Imbalance Data Classification

Classification of Imbalance data is one of t he most vital tasks in the field of machine learning because most of the real-life datasets available have an imbalanced distribution of class labels. The effect of imbalanced data is severe where the predictive model trained on the imbalanced data faces some unprecedented problems like overfitting where t he model gets biased towards the majority target class. Many techniques have been proposed over time to deal with the imbalanced distribution caused by problems like oversampling and undersampling where oversampling isn't able to match the performance acquired by the undersampling method. One such baseline method is clustering the majority of data into multiple clusters and then randomly sampling some of the redundant data but we believe that randomly sampling the data sample might open the loophole to losing informative data samples. So, in this work, we would like to propose two clustering-based priority sampling methods which manage to boost the performance of the predictive model compared to the clustering-based random sampling techniques.
The Development of A Medical Chatbot Using The SVM Algorithm

Technology development has rapidly increased in every division, especially in healthcare. Hospital management started to improve by incorporating technological tools and systems in this era. With the system prepared from the hospital, patient data can be saved and prepared systematically to be used as a queue line of appointments. It could be improved by using a chatbot which increases the efficiency of healthcare services, supported by natural language processing (NLP). The support vector machine (SVM) method is used as an optimal classifier that learns the classification hyperplane in a space map that has the maximal distance (margin) to the training examples. The SVM will predict the suggested specialist based on the given symptom and comorbidities by the users.
The New Progress and Methods of Privacy Protection on Medical and Health Big Data

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of “Internet plus medical and health” has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected.
Students Experience Testing in the Implementation of the “Gather Town” Meeting Platform as an Alternative Learning Media other than Zoom Cloud Meeting Application

The COVID-19 pandemic has limited the mobility of everyone in the world. Education is one of the most affected sectors because education systems have been done face to face. Most educational institutions switch to online learning by using online meeting platforms. We discovered an online meeting platform called “Gather town” which looks more attractive in increasing learning motivation and may be an alternative solution for online education. This paper aims to test student experience using “Gather Town” as a new learning medium and compare the effectiveness with Zoom Meeting Application. We compare the User Experience of both applications by letting respondents try the application and ask for feedback from our questionnaire. The result from the questionnaire shows that Gather Town has excellent potential as an alternative new learning media.
Classification of Rice Leaf Diseases Based on Texture and Leaf Colour

Agriculture is a sector that contributes greatly to the Indonesian economy. The role of the agricultural sector in economic development in Indonesia is as a producer of food. The high demand for rice as a staple food in the community requires farmers to be able to produce rice of good quality and in large quantities to meet the needs of the community. One of the factors that affect the quality of rice plants is the attack of pests and diseases. Farmers have difficulty in identifying pests and diseases in rice plants due to limited knowledge. Improper handling of rice plants that are attacked by pests and diseases will result in decreased yields and farmers suffer losses. The problems that occur require a solution so that by designing a modeling of identification of pests and diseases it can be fast and accurate based on the texture and color of the leaves. Disease identification consisted of brown spot and leaf blight using rice leaf imagery using GLCM and K-NN. The results of the application of GLCM feature extraction and classification using the K-NN method are very good with an accuracy rate of 89%.
An Efficient Storage Scheme for Sustainable Development Goals Data Over Distributed Knowledge Graph Stores

The achievement of the Sustainable Development Goals (SDGs) is important in order to ensure a world worth living in for future generations. Digitization and the plethora of data available for analysis offer new opportunities to support and monitor the achievement of the SDGs. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of data, as intended by this work. In this paper, we propose dimensionality reduction methods to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. In particular, our work proposes and experimentally corroborates the use of Hilbert Space Filling Curves (HSFCs) to efficiently store real SDG data with reduced retrieval times and preservation of their semantic closeness. First, algorithm is theoretically founded and explained and an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our algorithm. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.
Generalization of Public Key Cryptosystem Based on Singular Matrix Using Ring of Integer Modulo

In this research, we modify the cryptosystem from a ring over an integer to an integer modulo m. In the previous research, the researcher used a singular matrix over integers so that the plaintext and ciphertext spaces became relatively large. For this reason, in this study, we limit the ring over the integer to the ring over integer mod m. We provide proposed modifications and simulations of key generation protocol, encryption and description. The results show that the proposed public key cryptosystem can work well and remain safe from mathematical attacks that hackers may carry out. The use of modulo m can also limit the plaintext and ciphertext space. In addition, the concept of modulo m makes the message more random and difficult to guess.
Object Detection on Bottles Using the YOLO Algorithm

Object recognition is a tool that is often used in today's digital era. Object recognition can identify an object. However, we cannot identify every single object unless the object has been tagged and studied by the machine. Our goal in this research is to create a program that can detect bottles with the YOLOv3 and COCO datasets and a simple architectural model that can be easily practiced. In this research, we will use YOLO, and the dataset is taken, or the objects that can be identified are only objects in the COCO dataset. Then we do object recognition of the bottles that we collect ourselves as a real case data test. We found that YOLOv3 is better at detecting objects than YOLOv2 with the same dataset.
Multi-Sentence Matching via Exploiting List-level Semantics Expansion

Sentence semantic matching plays a fundamental role in natural language understanding and is widely used in various tasks. Previous methods are mainly based on the One-to-One single pair matching process, while in many real application scenarios, such as user query matching in FAQ, we need to match multiple sentences with the specified query sentence. The reality is that the multi-sentence matching problem is largely unexplored. Although to some extent, we can use multiple One-to-One pair matching on the multi-sentence matching problem, the exploitation of list-level candidate semantics is still limited, which leads to inferior performance. To that end, in this paper, we present a focused study on the novel multi-sentence matching problem. To begin with, we propose a Multi-Sentence Matching Network (MSMN) with a meticulously designed Fusion Module and Contrastive Matching Module to deeply exploit the list-level candidate semantics expansion for the multi-sentence matching task. Then, we construct two multi-sentence matching datasets, i.e., mQuora and mReceipt, to bridge the gap of benchmark data in this area. Finally, we conduct extensive experiments on two datasets. The experimental results clearly demonstrate the effectiveness of our method.
Sentiment Analysis of Government Policy Regarding PPKM on Twitter Using LSTM

The Policy of PPKM Covid from the government has become a popular topic to be discussed among the public, especially on Twitter. Due to the many responses or opinions about the PPKM that has been implemented by the government in Indonesia. Sentiment Analysis is the basis for research on the issue of Indonesian PPKM by using a deep learning model, namely LSTM. The data collection of tweets is obtained through crawling the data of Twitter API using the ‘snscrape’ module with the keyword “PPKM COVID” and the target data is 15,001 tweets. The data is processed and divided into two parts become 80% training data, 20% testing data and using the GRU, BiLSTM and RNN comparison models. Accuracy performance obtained from the four models include LSTM 90%, GRU 89%, BiLSTM 90% and RNN 85%. The comparison of the best accuracy results is obtained from the LSTM and BilSTM models. Furthermore, the result of sentiment obtained a high percentage for negative sentiment with a total percentage of 54.6%, while the positive sentiment had a percentage of 37.0% and neutral sentiment is 8.5%.
BSG-Net: A Blind Super-resolution Guided Network for Improving Ultrasound Image Segmentation

Ultrasound image is an essential basis for clinicians to diagnose and make treatment plans. Due to the limitation of imaging mechanism, ultrasound images are usually accompanied with speckles and noise, resulting in a adrop in image quality. However, image quality is a key factor for lesion region segmentation and pathological analysis. Therefore, suppressing speckles, artifacts and noise is of great significance for diagnosing. In this paper, we propose a blind super-resolution guided network(BSG-Net) for improving ultrasound image segmentation. Our BSG-Net consists of a novel self-supervised cycle blind super-resolution generative adversarial network(CycleBSR-GAN) for ultrasound image reconstruction and a W-shape network(W-Net) for lesion segmentation. To avoid redundant textures and to ensure the cycle-consistency between the ground truth and the generated image, we adopt the structure of Cycle-GAN. Our CycleBSR-GAN consists of two generators and two attention based discriminators, which aims to enhance ultrasound image quality in terms of both numerical criteria and visual results. The W-Net comprises a multi-scale input layer, EfficientNetB3 encoder blocks, atrous spatial pyramid pooling(ASPP) blocks, attention module and a our proposed parallel atrous convolution(PAC) block. Because of its powerful feature extraction capability, we use the W-Net as our low to high resolution generator and also perform the segmentation task. In order to simulate the characteristics of uneven grayscale, speckles and noise in real-world ultrasound images, we use a random multiple combination degradation strategy(RMCDS) to consturct training sample pairs. The experimental results have shown that our BSG-Net paired with RMCDS achieved state-of-the-art(SOTA) performance on the TNUI-2021 datasets.
Endpoint-Performance-Monitoring for a better End-User Experience

In an increasingly diverse and complex digital world a key challenge for companies is to maximize the productivity and motivation of their office workers. Thus, the task to measure, analyze and optimize the experience that these employees have with their digital devices becomes more and more important to ensure the competitiveness as well as the attractiveness of a company. In this paper end-user experience (EUE) includes measurable aspects such as boot-times, performance of tools and stability and availability of systems and software. In particular, for the IT administration, continuously optimizing the end-user experience is a considerable challenge. Our vision is to efficiently measure and quantify end-user experience and to automate the optimization of the infrastructure in order to support IT administrators. This paper shows an idea and a first concept for realization. A first step in measuring and evaluating end-user experience is to identify anomalies on endpoints. An endpoint can be any IT device used by the end-user. This paper presents a first implementation and evaluation of anomaly detection in IT infrastructures. First, the data collected on the endpoints is examined using a principal component analysis. Then, the data is analyzed for outliers using a neural network. For the implementation in this paper, an autoencoder is used. The evaluation of the results shows that an automated assessment of endpoint telemetry data using machine learning is possible. In summary, it is possible to detect anomalies in IT infrastructures using autoencoders. The anomalies in turn have an impact on the current or future end-user experience. In this way, autoencoder can be used in the future to improve the end-user experience of employees.
Crypto-Egalitarian Life

Abstract In this article I explore the fundamental tension in the world of Bitcoin between ‘maximalists’, who see Bitcoin as a tool for the promotion of a moral revolution, and ‘traders’, who approach Bitcoin pragmatically as a financial tool. Based on ethnography of a crypto gold rush that took place in the Bitcoin Embassy in Tel Aviv, I argue that, despite heuristic distinctions, both of these attitudes advance egalitarian tendencies. While maximalists offer a sense of belonging to a close-knit community of equals, traders promote the nominal equality of all value-making strategies in an open financial environment. I use the terms ‘ideational’ and ‘materialist’ to characterize these two modes of practice, which realize contemporary visions of egalitarian life in different forms.