Reviewerly/exHarmony huggingface dataset:
Contemporary Generation: Artificial Intelligence Contribution to Manufacturing

The manufacturing industries’ main purpose is transforming raw materials into finished goods, usually on a large industrial scale. The creation and trading of quality manufacturing items could expose challenges that could put a great deal of strain on companies. Delivering a product that needs to meet global high-quality standards, requires: navigating through often a confusing logistical supply chain; customer driven time-to-market deadlines and maybe, if it is a new product, companies will have to cope effectively in terms of cost, design and time required to fulfil this unfamiliar project. This paper highlights the positive influence AI (Artificial Intelligence) has on the manufacturing processes. Contemporary manufacturing industries are characterized by a comparatively low level of IT spending and tend to be at the forefront in the use of IoT (Internet of Things). Predicting and reducing failures can yield significant cost savings in contemporary generation of goods. This paper presents the research on predictive maintenance in AI with the use of Unsupervised Learning (UL) algorithm, related to manufacturing industries. This is done to show how the implementation of innovative technologies is beneficial in terms of reduction in human error and the need for human availability andassistance. All of which leads to the lowering of costs by automating specific parts of the industrial processes and tasks, in this case, predictive maintenance. Innovative technology is common in the present generation, but what is still lacking is the right use when it comes to businesses and specific needs.
Real Time Web-based Facemask Detection

The widespread spread of the Covid-19 virus in 2020–2021 is very worrying for all people around the world, coupled with the spread of a new variant of the Covid-19 virus, which is more aggressive and easily transmitted, causing public unrest about when this pandemic will end. The policy of using masks to reduce the spread of the virus has been made to minimize the spread. But even if there is a policy, there are still people who don't want to wear masks. Therefore, a mask detection system is needed to help differentiate whether someone uses a mask or not by displaying alerts in a form of web application. This research was conducted using several data augmentation techniques to increase the variation of the data to be used before training the algorithm model using the Convolutional Neural Network (CNN) algorithm with MobileNetV2 and VGG19 architectures. Both models are then evaluated where the architecture with the best performance will be implemented in the form of a web application. The accuracy of both models was compared, with the result of MobileNetV2 being 99% accurate and VGG19 being 98%. MobileNetV2 as the model that has the best accuracy value will be implemented in the form of a web application using the Haar Feature-Based Cascade to detect masks. The web application will be publicly accessed local at Universitas Multimedia Nusantara.
On a Comprehensive Implementation and Performance Evaluation Framework of Fast Algorithms for Exact String Matching

String pairing is a highly significant issue in the broader area of data preparation, making string matching a classic as well as an existing problem in computer science study domains. Although data is remembered in numerous other ways in computer programming, language remains the primary medium of information transmission. A sequence method compares a given string to a well-built word or "Content". There seem to be various sequential ways for dealing with the sequence match problem, some of which are often used in practice. This paper will describe Java implementations of five well-known sequence methodologies, proposing a unique testing platform: Horspool, Brute Force, Rabin Karp, Boyer Moore and Knuth Morris Pratt. Because such methods might have a major influence on availability when utilized in various sequence match applications, this study will compare the computing time and efficiency of the methods using a unique scenario by developing a novel integrated framework in Java for string matching algorithms comparison.
Educational Question Classification with Pre-trained Language Models

This paper presents a study on classifying questions into 10 categories based on cognitive science: verification, disjunction, concept, extent, example, comparison, cause, consequence, procedural, and judgmental. These types are more related and useful for a n educational environment. The source of the question dataset is real questions asked by users on Reddit and Yahoo Answer forums. As far as we know, this study is the first to compare several pre-training models for classifying questions of these types and provide a detailed analysis of the dataset. Specifically, we used BERT for feature extraction followed by classification with Random Forest, and performed fine-tuning on BERT, XLNet, and Roberta. The experimental results show that the model with the fine-tuning pre-trained language model achieved 0.8367 accuracies and 0.8116 F1-score. Moreover, evaluation on top-2 prediction produced by the best model, Roberta, shows significant improvement in accuracy to 0.9496. Detailed error and data analysis show some issues in the dataset, opening the possibility of improving dataset quality.
Energy Efficiency in Buildings Using Multivariate Extreme Gradient Boosting

Humans need energy. Energy is often used in our daily activities, from helping with work and household chores to lighting our homes and on the street. Waste of light energy often occurs in households and large buildings. Lots of people do wasteful things like turning on all the lights in the room to make it look bright when they're not needed. A lot of research has recently been done on concerns about wasting energy and its long-term negative effects on the environment. A previous study in 2012 by Tsanas and Xifara used statistical machine learning to assess the energy efficiency of buildings. Their research focuses on calculating results individually rather than calculating all results directly. Other studies were also conducted using Multivariate Random Forest, resulting in a fairly good performance. In this study using the Multivariate Extreme Gradient Boosting method. The concept of this method is the same as the Extreme Gradient Boosting method, but is suitable for multivariate data types. The best results were obtained with MSE 0.537471 and RMSE 0.733124 733124 with gamma parameters 0.1, n-estimator 200 and a combination of training and testing data 90:10 by testing the data without normalization. Based on this test, it proves that Multivariate Extreme Gradient Boosting can produce better performance than previous studies.
The Evaluation on Acceptance of the Use of Social Media in the Implementation of Blended Learning in Private Higher Education in Indonesia

Distance learning using the blended learning method is encouraged to be implemented in higher education. This is deemed suitable to be implemented in teaching and learning activities in the current conditions that can optimize the integration of oral communication as in face-to-face learning in class and written communication through online learning, through the use of social media. However, in practice, the actual use of blended learning by the faculty is at a minimal level. Therefore, it is not known for sure how is the acceptance of its use by higher education in general. In this case, the researcher is interested in measuring its acceptance. The purpose of this research is to determine the level of acceptance of the use of social media and the implementation of blended learning in higher education. This research uses a model developed by comparing, combining, and adapting the technology readiness model, technology acceptance model, information literacy variable, truth perception variable and trust perception variable with a quantitative approach and distributing questionnaires to obtain data from respondents. The distribution of the questionnaires was carried out by collecting samples using a purposive sampling technique. Furthermore, the data obtained were analyzed using Ms. Exel 2016 with the PLS-SEM approach using SmartPLS 3.0. This study reveals that 42% of the data stated that the percentage of university acceptance of the use of social media in the implementation of blended learning was more than 75% and 84% stated that higher education was ready to implement it.
Development of Signer Portal for Digital Products by Using Iterative Model at PT RST

The COVID-19 pandemic has affected the way people work, forcing people to reduce physical contact with other people. So, legalizing documents by adding a signature and stamp in PT RST now requires a new method. Electronic documents with the legalization using digital products become legal for use. Digital products for legalization include electronic stamps, signatures and seal stamps. Adding a digital product to a document is done on a web application that provides document legalization services. Currently adding digital products to web applications is only for one or two products, for example electronic stamps, so to be able to add 3 products we have to use 3 different web applications. This method makes the process of adding digital products done in many stages. Therefore, it is necessary to create a web application portal that combines all services such as e-stamp, e-sign and e-seal stamp in one application. The web-based portal is developed using agile software development methods. The development process is done iteratively in 3 iterations. The result of this research is a web-based portal that can provide services for adding e-materai, e-stamp and e-sign for oneself, other people and collaborate with several people. Furthermore, user satisfaction was measured with the SUS questionnaire and the usability measurement result was 80.3. This score is in the “Acceptable” acceptability range, acceptable with efficient use, easy to understand, and has a simple user interface. The signer portal development process for PT RST can be a reference for other industries to implement similar products in their organization, both in terms of technology and business processes.
LSTM and ARIMA for Forecasting COVID-19 Positive and Mortality Cases in DKI Jakarta and West Java

The spread of COVID-19 in Indonesia is still classified as a pandemic until October 31, 2022. Even though the endemic has been enforced in several nations worldwide. However, the fact that people's mobility is increasing means that this condition can increase the number of new cases of COVID-19. The Indonesian government remains vigilant about any decisions that will be taken to maintain the stability of the country's health sector, economy, and population mobility. First, The purpose of this our research is to forecast of daily positive confirmed and daily mortality for the next 13 days using COVID-19 epidemiological data in Indonesia, i.e. DKI Jakarta and West Java. Second, the forecasting model uses a deep learning approach, i.e. LSTM and ARIMA. furthermore, The LSTM method and ARIMA modeling results are compared based on their respective to regions. Finally, The LSTM method has good model performance and the ability to forecast COVID-19 cases based on RMSE and MAPE.
Automating the Extraction and Activation of Web Service Credibility Measures - A Framework Design

Web service credibility measurement techniques provide a professional and experiential assessment of web services to the user community. Unfortunately, existing web service credibility measures are limited to implementation by only web service stakeholders. Thus, they are usually limited to registered users or customers and not open to public voting and acquaintance. Existing techniques only allow internal user evaluation at the web service levels by using pre-specified inputs limited to specialized questionnaires or checklists to evaluate their services. In this paper, we propose a design model for automating crowd input incorporation in credibility measures of web services regardless of whether these services allow feedback from the user or not. Our design facilitates web service evaluation to every internet user openly at the web browser level, not only at the web service level. This model also allows the dissemination of credibility measures to the public through available and easy-access channels. This work will enable web authors, stakeholders, and innovators to claim or vote on authorship or ownership of information and impact their claims on the digital world. It also allows encapsulating and exchanging user experience and claims into credibility measures of web objects and facilitates them to internet users.
Mitigating Security Threats of Bitcoin Network by Reducing Message Broadcasts During Transaction Dissemination

Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. The transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.
Parallelizing Multi-Keys RSA Encryption Algorithm Using OpenMP

The RSA algorithm is an asymmetric encryption algorithm used to ensure the confidentiality and integrity of data as it travels across networks. Security has grown in importance over time, resulting into more data requiring encryption. Parallelization represents an ideal solution to speed up the encryption and decryption processes. An advance implementation of RSA using parallelization concept leads to improve security and performance. In this paper, we represent a parallelized version of Multi-Keys RSA algorithm implemented using OpenMP library. Furthermore, we provide parallel implementation of Multi-Keys RSA under both static and dynamic scheduling with different chunk sizes, and our experimental results show that static scheduling is more optimum for RSA cryptography as compared to dynamic. As a final result, we have achieved an average speed up of 4.4 and efficiency of 0.7.
JUX - A Cloud Hosted Learning Management System Based on OpenedX

Higher education across the world during the COVID pandemic changes its knowledge delivery mode from on-campus studies to off-campus studies, i.e. E-Learning. The e-education provider must be competent in order to create a robust learning environment that can handle the difficulties facing teachers, students, and system administrators at this rapid pace of change. The system administrator needs to improve the network connectivity, bandwidth etc. for providing seamless connectivity for E-Learning alongside their campus network services. The challenge of providing smooth services for e-learning is sometimes hurdled the other network services for the campus and therefore the management and administrator suggest deploying the e-learning services on the cloud and setting apart the campus network services. This will solve the problem of available network limits can face by the institute due to the limited amount of hardware and bandwidth issues. Furthermore, the cloud deployment reduces the capital as well as the recurring cost of running the services. This paper will focus to address the problem defined above and providing Amazon Web Services (AWS) based cost-effective cloud architecture for OpenedX based learning solutions. This study is expected to demonstrate a technological solution for the process of implementing a cloud-based LMS.
Reducing the Speckle-Induced Measurement Uncertainty in Laser Triangulation Sensors

Laser triangulation sensors are one of the most commonly used optical sensors in dimensional metrology and quality control. This article presents a method for simulating the laser speckle-induced measurement uncertainty, representing a major performance limitation of these sensors, as well as the effects of speckle reduction mechanisms based on spatio-temporal averaging. To investigate the relation between triangulation angle and the resulting speckle-induced uncertainty, simulations are performed for three sensor geometries, revealing that a larger angle results not only in a smaller measurement range but also in a reduced influence of laser speckle and a smaller uncertainty. A parameter study on a speckle-reducing moving diffuser mechanism, integrated into the optical sensor path, investigates the achievable improvement for various combinations of motion frequency and amplitude. The accuracy of the simulation results is validated by measurements conducted with an experimental setup, demonstrating good agreement between the measured and simulated uncertainty values with and without the speckle reduction mechanism. It is shown that the diffuser is capable of reducing the resulting speckle-induced measurement uncertainty by up to 63%.
An Artificial Intelligence System for Automatic Recognition of Punches in Fourteenth-Century Panel Painting

In Late-Medieval panel paintings from the Tuscan area, mechanical tools called punches were used to impress repeated motifs on gold foils to create decorative patterns. Such patterns can be used as clues to objectively support the attribution of the paintings, as proposed by art historian Erling S. Skaug in his decades-long study on punches. We investigate the feasibility of employing automatic pattern recognition techniques for accelerating the process of classification of punches by experts working in the field. We propose a system composed of (a) a Convolutional Neural Network for categorizing a punch contained in a frame, and (b) an additional component for uncertainty estimation, aimed at recognizing possible Out-of-Distribution (OOD) samples. After collecting a set of 14th century panel paintings from Tuscany, we train a Convolutional Neural Network which achieves very high test-set accuracy. As far as the uncertainty estimation is concerned, we experiment with two techniques, OpenGAN and II-loss, both exhibiting very positive results. The former seems to work better on specific data extracted from images of panel paintings, while the latter showcases a more consistent behavior when considering additional OOD data obtained randomly. These outcomes indicate that an application of our system in support of experts is feasible, although we subsequently show that additional experiments on larger datasets might be required.
Learning From Guided Play: Improving Exploration for Adversarial Imitation Learning With Simple Auxiliary Tasks

Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naïve approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.
A Smart Metro Passenger Detector Based on Two Mode MetroNexts

Accurate metro passenger detection is considered a fundamental technique for the smart metro station. The major challenge is false positives (FPs) when hunting for passengers in metro carriages and stations. This article provides an in-depth analysis to reveal the reasons for this problem in the two-stage convolutional neural network (CNN)-based detector and proposes a novel two-mode refined proposals algorithm to address the problem, which includes cascade mode and intrinsic information mode, or CRP and IRP algorithms for short. Aided by the two-mode refined Proposals algorithm, this article designs two small but fast and accurate pedestrian detectors: MetroNext-CRP and MetroNext-IRP to meet the application requirements of different tasks. Based on various challenging benchmark datasets and two metro scene datasets, the experimental results have demonstrated that the two-mode refined proposals algorithm is effective and can improve pedestrian detection accuracy by removing the FPs. Compared with the existing state-of-the-art detection networks, MetroNext-CRP achieves competitive detection results with acceptable computational cost and MetroNext-IRP demonstrates better detection accuracy with fast inference speed and without extra memory consumption, thus providing a practical solution for pedestrian detection tasks on various hardware platforms, particularly tailored to edge devices. Finally, the two-mode MetroNexts are deployed in a metro’s onboard computer, and the experiments in real-life campus and metro scenes further demonstrate the feasibility of the two detectors, especially the field experiments in metro scenes strongly support that they can be served as a smart instrument to provide accurate passenger position information to metro operators in complicated and realistic metro scenes.
Hummingbird: Dynamic Path Validation With Hidden Equal-Probability Sampling

Path validation has already been incrementally deployed in the Internet architecture. It secures packet forwarding by enabling end hosts to negotiate specific forwarding paths and enforcing on-path routers to prove their forwarding behaviors along these paths. Most existing path validation solutions target static paths, paying less attention to fully dynamic paths that support flexible routing. In this paper, we present Hummingbird as the first validation solution over fully dynamic paths. It features a hidden equal-probability sampling technique. Gaining efficiency via routers probabilistically sampling packets to validate, we craft the sampling probability such that each router validates a similar amount of packets given an unknown path length. We further hide the state of whether a packet has been sampled and validated using a lightweight, non-cryptographic scheme. This prevents attackers from differentiating and selectively mis-forwarding packets. We validate security and efficiency of Hummingbird through both theoretical proof and experimental evaluation.