Reviewerly/exHarmony huggingface dataset:
Perbandingan Relational Database dan Non-Relational Database dalam Pengembangan Smart Tourism

The smart tourism application that is built cannot be separated from the role of the database. The more data stored in a database system, it will affect the speed of access of the information system that uses it. This study will test the performance of query response time between two types of databases with 92 data records of tourist attractions, 106 restaurants and 59 lodging data. Tests were carried out on Data Manipulation Language (DML), aggregate function testing, and logical operator testing. The result of this study is that Non-relational databases are proven to be superior in the DML testing process (insert, select, update, and delete) than Relational Databases. For the process of testing logical operators (AND, OR, and NOT) Non-relational databases are proven to be superior to Relational Databases. In the MIN aggregate function test the results are the same, while the MAX Relational Database aggregate function test is superior to the Non-relational database.
Fuzzy multi-agent assistance system for elderly care based on user engagement

An intelligent system intended to provide assistance to an elderly user in their home is presented. This multi-agent system is designed to monitor the interaction between the user and other agents, and regulate itself based on the estimated level of mental engagement of the user in order to contribute to help them maintain adequate levels of awareness and interaction with their home environment. The agents are devices programmed to preserve the security, comfort and health of the user, who is also conceptualized as one of the agents in the system. The user engagement level is estimated through fuzzy inference, that is, by means of a set of fuzzy membership functions and rules applied over the interaction of the user with different categories of agents. The system incorporates several dozen agents and is tested using two different datasets of real interactions between users and devices in their home environments. A uniform parameter configuration yielded very positive results for diverse scenarios and subsequently the parameters of the system were automatically optimized to show that improved performance, in the form of a more efficient system regulation and increased certainty in adjustment estimation, is obtained.
Verifying Privacy-Preserving Financing Orders on a Consortium Blockchain Based on zk-SNARKs

Due to its efficiency, low overhead, and high scalability, consortium blockchain has been deeply applied in various fields of society. Order financing is one of the scenarios of applying consortium blockchain. Since data on the consortium blockchain is available to the blockchain members, information of a financing order written directly to the blockchain will leak the commercial privacy of the purchaser and supplier. Therefore, the financing order data should be encrypted when published as a transaction on the consortium blockchain. However, the investor needs to verify the financing order data on a consortium blockchain before loaning money to the supplier. It is tricky to efficiently satisfy the verifiability of encrypted financing order data on the consortium blockchain. This work proposes VmppOrder, a verifiable model for privacy-preserving financing orders on a consortium blockchain based on zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs). By the supplier publishing zero-knowledge proofs generated from the financing order, the investor can verify the encrypted financing order published on the consortium blockchain without decrypting it. We elaborate on the specific construction of VmppOrder and analyze the security of the constructed circuit with zero-knowledge proof. We implement a prototype of the model on Hyperledger Fabric based on Libsnark and conduct comprehensive experiments to evaluate its performance. Our experimental results validate the efficiency of the proposed model. Its order proof generation takes about 6.31 seconds, the order verification takes only 2.58 milliseconds, and the transaction processing speed is about 660 transactions per second on a moderately equipped machine.
Chatbots: Their Uses and Impact in the Hospitality Sector

This study deals with the AI influence and impact on the hospitality industry, mainly focusing on hotel chains. The customer satisfaction parameters are measured by the integration of technology and automated systems in the service delivery experience. The main focus is on the use of chatbots in the hospitality industry and how useful it is in customer satisfaction. The obstacle for this artificial intelligence integration is the lack of awareness among both the service providers and customers. The acceptance of virtual assistants rather than having human meetings in different segments of the customer’s is more. Younger people are more likely and excited to use virtual assistants like Siri and Alexa, but most of the customers have more hesitant toward having the technological integration or intervention in their service encounter they are more comfortable with personnel assistance due to the lack of awareness toward technology.
The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision avoidance purposes

This paper proposes a new navigation control scheme for a three-wheel OMnidirectional Robot (OMR), taking into account the dynamic constraints with the aim of collision avoidance in dynamic environments. The proposed control strategy is inherently a multi-modal adaptive-Reinforcing model-based controller (MAR-MPC), which can be regarded as a judicious synergy of multi-modal MPC (MMPC) and Q-Learning (QL) to optimal navigation. This method takes advantage of a larger convergence area, compared to MMPC and MPC, and presents near-optimal MPC performance. The navigation scheme utilizes an online stochastic observer, namely, a Kalman filter, to estimate the future state of the robot. This paper formulates a general collision avoidance navigation problem in a constrained linear convex cone structure to make it real-time implementable. Furthermore, the proposed multi-modal path planning algorithm reduces the required prediction horizon and consequently affects computational cost. To evaluate the performance of the proposed navigation strategy, two static and two dynamic environment instances are simulated, and the results are compared with four algorithms, namely, Exploring Random Tree (RRT), Potential Field (PF), MPC, and MMPC. Results indicate the superior performance of the proposed navigation method in aspects of time and distance costs and collision avoidance criterion. Moreover, the proposed algorithm provides the feasibility and stability guarantee and a larger convergence region.
An Efficient Multi-Layer Perceptron Neural Network-Based Breast Cancer Prediction

Cancer is the most deadly disease for human beings across the world due to the adoption of new food habits and pollution. Breast cancer is becoming a common disease for women. After years of research a better analysis and possessing a higher prediction of any kind of cancer, it is still a very imperative contribution in healthcare. In the literature, several attempts were made deploying machine learning methods yielding a significant prediction of cancer. The goal of this study is on machine learning techniques that can enable prediction of breast cancer from the attributes with more accuracy. In this study, Wisconsin Breast Cancer (WBC) dataset has been used to compute machine and deep learning algorithm. This research focuses on logistic regression, decision tree, and random forest and a novel deep learning adoption, multi-layer perceptron. The performances of these algorithms have been correlated with each other using accuracy percentage. Also, sensitivity and specificity are computed as evaluation metrics. The multi-layer perceptron (MLP) gives 97% accuracy.
The virtual reality as a flexible resource to improve engineering education

The coronavirus pandemic has changed the way education is delivered, and because of this problem, challenges and new needs have arisen that have led us to adapt the way we teach and evaluate. In this context, much work has been done to bring students learning experiences in new modalities to safeguard the well-being of all its participants. Virtual reality in engineering education plays an important role because students can acquire the concepts, knowledge, skills, and competencies more completely through immersive, multi-sensory, and credible environments arising as a flexible resource to improve engineering education in a post covid era.The use of virtual reality in the teaching-learning process is an innovation in teaching didactics and constitutes a very important opportunity to guarantee quality in the training of future engineering students. In parallel, the technological advances brought about by the industrial revolution that is taking place have increased the levels of complexity in terms of cost, product design, innovation, implementation, performance evaluation, to name a few.
Design of Quadrature Mirror Filter using Grasshopper Optimization Algorithm

Nature-inspired algorithms have been recently identified as efficient methods of solving complex computational problems. The application of these algorithms is being extended also towards solving the problems of signal processing in the digital diaspora. The Grasshopper Optimization Algorithm (GOA) is a new development of a nature-inspired algorithm with meta-heuristic features. Quadrature Mirror Filters are very important and useful for multi-rate signal processing. This paper renders an alternate method of designing a quadrature mirror filter bank using the GOA. The performance of this design algorithm is demonstrated and simulation results are compared with existing methods.
A Distributed Control Architecture for Resource-constrained Autonomous Systems

The use of autonomous systems is burgeoning in the world today for many applications in many fields from scientific, industrial, to military. At the same time, advances in semiconductor technology have enabled ever smaller, complex and dedicated microprocessors. This work details a control system architecture that takes advantage of these advances for use in resource-constrained autonomous systems. The architecture consists of a real time hardware controller, a guidance and navigation computer, and an edge TPU for machine learning inferences. While the latter two processors are commercially available, a dedicated, modular real time controller is not. Therefore we present an open source design for a real time controller that is intended to be adapted to many types of autonomous systems. We present three different vehicle platforms that implement this control system including a ground vehicle, a surface vessel, and a quadcopter. Finally, we present results using this control architecture on a few key topics of interest in autonomous systems. The first is a novel spatial estimation algorithm called partitioned ordinary kriging that is designed for resource-constrained systems and can be used for path finding during mapping missions. The second result pertains to a sensor calibration utilizing sensor data collected from the real time controller and performed on the navigation computer. Finally, we demonstrate using the edge TPU (tensor processing unit) for object detection using an onboard camera and an object detection algorithm using machine learning.
A taxonomy of learning outcomes developed for Moscow Electronic School (a case study of individual topics in the Russian language)

The paper examines the issue of designing planned learning outcomes in the process of teaching Russian to school students. This problem became especially relevant after the adoption of the Federal State Educational Standards for primary, basic, and secondary general education. These standards define requirements for personal, meta-subject, and subject-related learning outcomes. Teachers had to respond to the need of specifying planned learning outcomes for an academic year, a topic, an academic period, a lesson taking into consideration the requirements of the system-activity approach as well as different levels of achieving these outcomes by different students. The paper analyses foreign and domestic experience in developing taxonomies for educational purposes and presents the taxonomy of planned learning outcomes developed for Moscow Electronic School. The research is based on theoretical and empirical methods. As a result, planned learning outcomes for the topics included in the course in Russian studied in the fifth form were defined. The obtained results were uploaded to the platform of Moscow Electronic School. Three types of learning outcomes were identified: reproductive, productive, and reflexive. Additionally, the paper describes the possibilities of using the «Planned learning outcomes» service of Moscow Electronic School by teachers. Planned learning outcomes are a methodological tool in teachers’ work. They help to specify lesson objectives, select lesson content, choose the forms of organising students’ activity aimed at producing subject and meta-subject outcomes.
Innovative System Design for Remote Air Traffic Control Simulation Training

Conventional ATC simulation training is practice-based training that aims to teach students hands-on experiences before handling real traffic situations in a stressful environment. However, COVID 19 has profoundly affected society and higher education in Australia. It brings enormous challenges to delivering this hands-on practical face-to-face training for students in a typical way. Hence, remote ATC simulation training is emerging in aviation education during COVID. This paper will present an innovative system design for remote ATC simulation training at an Australian university. Firstly, infrastructure upgrading of hardware and software from an existing stand-alone system to a remote network is discussed in detail with consideration of costs, cyber security, system compatibility with the university-wide network; Secondly, preliminary experiment results about the quality of remote ATC simulation training is present by compared with traditional face-to-face training to validate the whole system design; Last but not least, the enhancement of the current remote training system beyond COVID-19 regarding reliability and capacity is listed for future development. This foundation paper will support an understanding of how digital technology can transform STEM practical education during and beyond the COVID-19 pandemic, thereby providing an excellent example for the rest of the world by its leading role in developing advanced ATC training systems for application at a global level.
Single-shot phase retrieval for aspheric surface testing based on the transport of intensity equation and a prism-mirror module

Transport of intensity equation (TIE) relates the longitudinal intensity derivative of propagating light wave to the transverse phase gradient. The phase is retrieved by measuring the intensity of propagating light wave at two or more longitudinally displaced planes by moving the detector and then solving the TIE. We present a single-shot transport of intensity equation-based phase retrieval using a prism-mirror based module to capture the two defocused intensity images simultaneously. The results obtained by single-shot transport of intensity equation-based phase retrieval have been compared with interferometry and profilometer-based measurements. We highlight an important aspect of the TIE-based approach that this formulation decouples the intensity and the unwrapped phase of the unknown field. Intensity and phase are individually low-bandwidth functions when compared to the complex field associated with the unknown wavefront. When compared to interferometry, the TIE-based approach therefore inherently has many reduced sampling requirements. For a given array sensor, TIE can therefore measure large phase deviations for which an interferometer would produce very high density fringes that are impossible to sample as per the Nyquist criterion. The prism-mirror module shown in this work is easy to fabricate, align, and can be easily integrated with an experimental setup. The applications of the proposed method for the wavefront measurement of spherical and aspheric lenses are demonstrated.
Fermionic Correlation Functions from Randomized Measurements in Programmable Atomic Quantum Devices

We provide an efficient randomized measurement protocol to estimate two- and four-point fermionic correlations in ultracold atom experiments. Our approach is based on combining random atomic beam splitter operations, which can be realized with programmable optical landscapes, with high-resolution imaging systems such as quantum gas microscopes. We illustrate our results in the context of the variational quantum eigensolver algorithm for solving quantum chemistry problems.
Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimised and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multi-layer Perceptron) error correction function is defined; with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch; and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed and the practical application value of the system is highlighted.
Problem Compilation for Multi-Agent Path Finding: a Survey

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community. The task in the standard MAPF is to find discrete paths through which agents can navigate from their starting positions to individual goal positions. The combination of two additional requirements makes the problem computationally challenging: agents must not collide with each other and the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include dedicated search-based methods, and compilation-based methods that reduce a MAPF instance to an instance in a different formalism, for which an efficient solver exists. In this survey, we summarize major compilation-based solvers for MAPF using CSP, SAT, and MILP formalisms. We explain the core ideas of the solvers in a simplified and unified way while preserving the merit making them more accessible for a wider audience.
Human activity recognition based on fusing inertial sensors with an optical receiver

Human activity recognition (HAR) gained great interest in today’s research activities, especially in regard to demographic change. Especially when complex activities have to be recognized, HAR systems often rely on multiple sensors necessary to be worn by the user. In this work, we propose a novel approach for combining a segmented optical receiver with a single IMU device. By fusing IMU related real world experimental data with precise optical simulations of a segmented optical receiver we can not only determine the activity of the user, including complex movements like walk-up and walk-down, but can also determine the user´s location.
Multi-domain adaptation for named entity recognition with multi-aspect relevance learning

Domain adaptation for named entity recognition (NER) has achieved remarkable results in recent years. However, most previous domain adaptation methods mainly focus on a single source domain without considering all available annotation data. When domain shift between the target and the single source domain is too large, performance would drastically decline. In addition, samples from different domains may have diverse degrees of relevance to the target domain, which requires further consideration. To tackle these problems, we propose a multi-domain adaptation model with multi-aspect relevance learning for NER. Specifically, a multi-aspect relevance learning method is proposed to promote the effect of multi-domain adaptation in NER. Meanwhile, a novel strategy called BERT-based cross-domain pre-training is applied to reduce the dependence on large-scale domain related data. We conduct experiments on five target domains, and the experimental results have achieved a new state-of-the-art performance in terms of all metrics, which proves the effectiveness of our model.
Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network

Garbage is a unique problem in Indonesia. From ordinary waste to limited emergency plastic waste, Indonesia is the second-largest source of plastic waste in the world. Separate collection and disposal of waste is one way to reduce the waste generated by society and industry in Indonesia. Sorting out the types of waste is the first step before the recycling process. In the field of Computer Vision research, it is difficult to see the type and form of waste with a camera, therefore this study aims to overcome this problem by using Deep Learning technology which is expected to be implemented in the whole of Indonesia starting from some of the largest waste-producing cities. Deep Learning is a computer (AI) technique for learning like a human - with experiments being a Part of Machine Learning that can be used to classify images. The method used in this study uses the Convolutional Neural Network (CNN) method which can be used to detect and recognize objects in an image, which can be used to create an automatic waste classification system. Broadly speaking, CNN utilizes the convolution process by moving a convolution kernel (filter) of a certain size to an image, the computer gets new representative information from the results of multiplying that part of the image with the filter used. The test results show that the CNN method can classify inorganic waste with accuracy. 96% and organic waste with an accuracy of 62%.
Sibylvariant Transformations for Robust Text Classification

The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label.In this work, we propose the notion of sibylvariance (SIB) to describe the broader set of transforms that relax the labelpreserving constraint, knowably vary the expected class, and lead to significantly more diverse input distributions.We offer a unified framework to organize all data transformations, including two types of SIB: (1) Transmutations convert one discrete kind into another, (2) Mixture Mutations blend two or more classes together.To explore the role of sibylvariance within NLP, we implemented 41 text transformations, including several novel techniques like Concept2Sentence and SentMix.Sibylvariance also enables a unique form of adaptive training that generates new input mixtures for the most confused class pairs, challenging the learner to differentiate with greater nuance.Our experiments on six benchmark datasets strongly support the efficacy of sibylvariance for generalization performance, defect detection, and adversarial robustness.