Reviewerly/exHarmony huggingface dataset:
RoadNote: Automated Road Closure Detection using Urban Sensing

Maps and navigation applications are essential tools in the modern era, especially for smartphone users. Navigation apps not only guide us on the correct path to the destination but also serve to find convenience and provide connectivity by sharing locations, travel status, and expert guidance. Map applications offer real-time updates which rely on crowdsourcing data from users, historical data, and advanced prediction algorithms. However, due to the dynamic nature of the urban environment, navigational apps fail to provide unscheduled road closure information. This study investigates erroneous situations and found 23 incidences where maps fail to navigate the closure information. We propose ROADNote, an automated system that accommodates urban sensors and provides closures update to users. ROADNote provides real-time traffic conditions by automated detections using future-generation commodity sensors. We built a prototype of ROADNote; after that, we conducted experiments to get real-time road-closer information by visual sensors (i.e., drone, camera). ROADNote facilitates to reduce of average travel time by 3.48 minutes and distance by more than 300 meters.
Autonomous Low Power IoT System Architecture for Cybersecurity Monitoring

Network security morning (NSM) is essential for any cybersecurity system, where the average cost of a cyber attack is $1.1 million. No matter how secure a system, it will eventually fail without proper and continuous monitoring. No wonder that the cybersecurity market is expected to grow up to $170.4 billion in 2022. However, the majority of legacy industries do not invest in NSM implementation until it is too late due to the initial and operation costs and static unutilized resources. Thus, this paper proposes a novel dynamic Internet of things (IoT) architecture for an industrial NSM that features a low installation and operation cost, low power consumption, intelligent organization behavior, and environmentally friendly operation. As a case study, the system is implemented in a mid-range oil a gas manufacturing facility in the southern states with more than 300 machines and servers over three remote locations and a production plant that features a challenging atmosphere condition. The proposed system successfully shows a significant saving (>65%) in power consumption, acquires one-tenth of the installation cost, develops an intelligent operation expert system tool as well as saves the environment from more than 500mg of CO2 pollution per hour, promoting green IoT systems.
Dynamic Data Source Selection: A Case of Weather Stations for IoT Applications

Quality data is crucial to dependable, trustworthy data analytics and data-driven decision-making in IoT-based applications. Data pre-processing and data cleaning are the only considered strategies for data quality assurance. Whilst these have provided usable results, within IoT, a problem still exists. Data quality issues within IoT proliferate throughout all the stages of the data cycle, with the majority at the source. This work proposes a solution to achieve two main objectives; 1) improving data quality of IoT streams by using alternative sources. 2) demonstrate that improved data quality results in improved application performance. Firstly, results from two experimental settings based on weather data from a real-world deployment show that by seeking alternative sources, data quality improved by 15.6% and 0.2% for completeness and accuracy, respectively. Secondly, two distinct scenarios show that by improving data quality, the model's RMSE improved by an average of 2 and 2.5 from 4 and 17.5, respectively.
Recent Trends on Artificial Intelligence-Enabled Internet of Things Platform and Standard Technologies

Artificial intelligence (AI), Internet of Things (IoT), and cloud computing are regarded as the core technologies of a variety of state-of-the-art technologies having disruptive innovation that are intended to lead the 4th Industrial Revolution. A common point shared by the services that utilise the three technologies, such as smart city, autonomous vehicles, unmanned aerial vehicles, and smart factory, is that they are all services related to data. That is, data lie at the heart of future technologies. However, there are still challenges in the development of efficient services and technologies due to the absence of standards for data collected in a variety of domains. Therefore, in this study, we examine the latest trends in the development of international standards for the management of data in support of AI services and technologies led by oneM2M, which is a global initiative for the development of international standards for IoT and cloud platforms. More specifically, the trends include the development of common functions in support of AI technology, standard technologies for the support of data licence management, support for data compliance with privacy protection regulations, and semantic functions for metadata management in support of AI/machine learning.
Analysis of GST Revenue Collection in Uttarakhand

Dr. Jeevan Chandra Upadhyay, Manoj Kumar Pandey
Reconociendo contextos: escenarios de aprendizaje apoyados en tecnología

The project, ICT-supported learning scenarios: Design and contextualization of pedagogical strategies for available learning environments, led by RILPE - Ibero-American Network of Leadership and Educational Practices - seeks to generate methodological strategies that enable the effective use of ICT-enriched learning environments available in educational institutions. However, the contexts are very diverse, so a collaborative work is proposed, first to recognize and characterize the technological scenarios that are configured today in educational institutions and second, to analyze their current use and suggest others through active methodologies. This article describes the description of ICT contexts and scenarios, the phases developed, the methodologies proposed, the resources used, the information collected and analyzed and the results, the product of an online questionnaire filled out by 618 teachers from more than six countries and analyzed by researchers attached to RILPE, Aulas Amigas, a company dedicated to the training of teachers in educational innovation. The project seeks to contribute to the introduction of technologies in educational processes, opening possibilities for future research.
POLICY IMPORT TAX INCENTIVE UTILISATION DURING THE COVID-19 PANDEMIC

The objective of a policy will be achieved if the policy targets are well-defined. However, it often happens that the policy targets do not utilise the public policies given optimally. Therefore, it is essential to predict the targeted group that will utilise the policy provided so that the policy implementation can be more efficient. This study uses the Data Mining method as a policy decision support tool to analyse the best predictive model of the PPh 22 Import Tax Incentive utilisation during the COVID-19 Pandemic. The predictive model can be practical and valuable for Indonesia's tax authority and policy analysis knowledge. This study uses administrative data of 43.547 taxpayers who are already utilising and not utilising the tax incentive, combined with the data mining method. The results showed that with the Random Forest algorithm, the utilisation of the tax incentive could be predicted with an accuracy above 94%. Furthermore, The Total Sales Value, The Export Value, and The Bonded Zone Category are the variables that mostly predict the utilisation of the tax incentive. Therefore, tax authorities should be able to utilize data mining as a tool to be able to implement tax incentive policies more efficiently and accurately.
Biometric based Mobile Voting Application

With a population of more than one billion, India is the largest democracy in the world. Its electorate is more than 668 million people and includes 543 congressional districts. Each Indian citizen above 18 years of age is entitled to vote in elections to choose their leaders. The traditional method of voting involved paper ballot voting and at present Electronic Voting Machines (EVM) are employed. The goal of the mobile voting system research is to create a software that makes voting simple in all elections. This system aims to increase election security by using a fingerprint recognition algorithm for voter identity authentication. This allows all eligible voters to participate in the election from any place. There are some circumstances like COVID epidemic where a physical election is not feasible, and this model overcomes it. It helps to provide reliable, timely, and accurate election results. Voter ID, Aadhar ID and biometric system (fingerprint) are used by the website for user login.
Relationship Between Academic Performance, Socio-Academic Background, and Student Perception of Flipped Learning in University Education

This study aimed to Â explore the potential relationship between academic performance, socio-academic background, and student perception of the Flipped Learning (FL) pedagogical strategy over a five-year period in a health related subject. Â The goal was to identify the actual benefits of FL in university education. A total of 385 students participated in this longitudinal study, with 77.7% having prior experience with FL. It was observed that students with a higher grade in secondary education and with economic benefits approved in a higher percentage, while students from private schools presented a higher failure rate in the same subject. In addition, a direct association between positive perception and approval was observed in higher courses, but not in freshmen years , where the use of FL did not have the expected effects. By comparing student performance, academic background, socioeconomic conditions, and perception, it was concluded that FL is effective Â for higher-level students but not for thoseÂ in initial courses. These findings prompt reflection on the significance of context in implementing pedagogical innovations and highlight the need for further research in university education.
Digital Watermarking

The paper introduces the digital watermarking technology which is a data hiding technique that embeds a message into a multimedia work such as an image or text or other digital object. The proposed technique has several important applications; the majorly important is the digital copyrights protection. The digital watermarking system as any other data hiding technique has its own requirements that make the digital watermark strong as possible. Technologies of digital watermarking are mainly classified depending on their domain to spatial domain watermarks, Watermarks belonging to frequency (transform) domain and wavelet domain watermarks. The digital watermarks suffer from different types of attacks that include either state-of-the-art watermarking attacks or watermark estimation attacks. The recovery from these attacks requires strong detection techniques; the digital watermark agent provides a professional solution for these attacks.
Spam Detection Using Machine Learning

Deep learning has emerged as a powerful technique for spam detection due to its ability to automatically learn relevant features from raw data. This abstract presents an overview of deep learning approaches for spam detection and highlights their effectiveness in combating the ever-evolving landscape of spam. The challenges of spam detection, includes the use of sophisticated techniques by spammers to evade traditional rule-based filters. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in addressing these challenges by automatically extracting high-level features from spam messages. The Spam Detection Using Machine Learning in this paper employs the Deep Learning model Bi-GRU to detect the spam messages. The advantages of this approach, is that it has the ability to handle large-scale datasets and their potential for transfer learning across different spam detection tasks. It highlights the role of deep learning in enhancing feature representation, model generalization, and the overall accuracy of spam detection systems
An Access Control Framework using Blockchain on Medical Data

Cloud is a computing model that provides sharing and supports ubiquitous on-demand access computing, providing new data processing and services for many industries, significantly reducing user computing and storage costs, and improving ease of use. With the development of cloud-scale and intensification, cloud security has become an essential issue in the field of cloud computing. Access control is one of the critical security technologies for protecting sensitive data stored in the cloud by enterprises and individuals. Since the centralized access control mechanism is adopted in the cloud, the sensitive data in the cloud are easy to be tampered with or leaked by hackers or cloud internal managers. To address this issue, we propose a blockchain- based access control framework with privacy protection called AuthPrivacyChain. Firstly, we use the account address of the node in blockchain as the identity, and at the same time, redefine the access control permission of data for the cloud, which is encrypted and stored in blockchain. After that, we design processes of access control, authorization, and authorization revocation in AuthPrivacyChain. Finally, we implement AuthPrivacyChain based on enterprise operation system (EOS), and the results show that AuthPrivacyChain can not only prevent hackers and administrators from illegally accessing resources, but also protect authorized privacy.
A topic-aware classifier based on a hybrid quantum-classical model

Abstract In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhancing classification tasks’ accuracy and providing an exponentially large quantum state space in which complex linguistic structures can be efficiently embedded. In this work, a QNLP model is used to determine if two sentences are related to the same topic or not. By comparing our QNLP model to a classical tensor network-based one, our model improved training accuracy by up to 45% and validation accuracy by 35%, respectively. The QNLP model convergence is also studied when varying: first, the problem size, second, parametrized quantum circuits used for model’s training, and last, the backend quantum simulator noise model. The experimental results show that strongly entangled ansatz designs result in fastest model convergence.
R Workshops for Researchers: A Successful Partnership Between a Library and a Statistical Consulting Laboratory

Colorado State University Libraries has partnered with the university’s Franklin A. Graybill Statistics and Data Science Laboratory to jointly offer workshops teaching R coding for academic research, while employing Statistics graduate students as instructors. This partnership has capitalized on the strengths of both units and has provided an opportunity to build campus relationships. The development, assessment, challenges, benefits, and evolution of this fruitful and ongoing collaboration are described with the intent that this information may assist in the creation or enhancement of services at other institutions.
Building music with Lego bricks and Raspberry Pi

Abstract In this paper, a system to build music in an intuitive and accessible way, with Lego bricks, is presented. The system makes use of the new powerful and cheap possibilities that technology offers for making old things in a new way. The Raspberry Pi is used to control the system and run the necessary algorithms, customized Lego bricks are used for building melodies, custom electronic designs, software pieces and 3D printed parts complete the items employed. The system designed is modular, it allows creating melodies with chords and percussion or just melodies or perform as a beatbox or a melody box. The main interaction with the system is made using Lego-type building blocks. Tests have demonstrated its versatility and ease of use, as well as its usefulness in music learning for both children and adults.
Smart Remainder SoS And Emergency Detection Device

The lack of a dedicated device to remind chronic patients when to take their medications, the difficulty in quickly pressing an SOS button, and most importantly the delay in communication between the time of an accident and contact with care takers or hospitals were the three factors that served as the inspiration for the project. A study found that prompt treatment could preventable most 85% of deaths brought on by minor and major heart attacks, strokes, and shortness of breath. Therefore, a visible SOS button would be adequate A side from that, data demonstrate that it is detrimental to one's health to take prescriptions as prescribed and to take the incorrect medication at the incorrect time. In addition, News18 reports that each year 36,579 persons pass away in motor vehicle collisions, 36,213 in two-wheeler collisions, 31,997 in truck and lorry collisions, and nearly 45,000 in incidents involving other modes of transportation. A delay in bringing the patient to the hospital accounts for over 76percent of deaths in each vehicle. Guardians were unable to reach the injured in half of the cases, or the victim was not helped to get to the hospital in time. In such a scenario, a device or technology that detect
Contributions of remote collaborative sketching to the design of infovis in public health

This paper presents contributions from using remote collaborative sketching to infovis for public health. The results come from an interdisciplinary study that involved designers, health researchers, public managers and computer scientists in the production of sketches generating alternatives for creating valuable graphics and dashboards. One of the creative stages was the remote collaborative workshops in which design alternatives were proposed based on sketches. The sketches were developed with the support of videoconferencing meetings, virtual boards, and graphic tablets. From this practice, we were able to include as contributions to the creative process in infovis: remote collaboration expansion; agile and iterative cycles improvement; interface design preview; data modeling supporting; addressing graphic literacy issues; creative process documentation; participant experience improvement; and additional information incorporation.
4自由度球面パラレルリンク機構を有する脚部の機構解析

We have designed and built a leg named “MELEW-2LR” with a 4-DoF spherical parallel link mechanism, in order to develop a highly maneuverable 4-legged wheeled robot with active wheels mounted on the tip of the leg-type robot. This paper describes the mechanical analysis of the 4-DoF parallel link mechanism of the MELEW-2LR. In this paper, the leg mechanism of MELEW-2LR is examined and the features and DoF analysis of the designed parallel link mechanism are reported. The derivation of analytical solutions for the forward and inverse kinematics of MELEW-2LR and the range of motion of MELEW-2LR are also presented. Finally, the validity of inverse kinematics of MELEW-2LR is confirmed by conducting motion experiments in which the translational motion of the tip of its leg in the forward-backward, left-right, and vertical directions and the rotational motion of its yaw angle are controlled by inverse kinematics.
Event-triggered nonlinear information fusion preview control of a two-degree-of-freedom helicopter system

This research presents a novel nonlinear information fusion preview control (NIFPC) method for the flight control problem of two-degree-of freedom (2-DOF) helicopter system. First, according to the information fusion optimal estimation methodology, the system performance index and the system dynamic equation are converted to several information equations about the control input, and then the control input is directly derived by fusing these information equations via the centralized information fusion optimal estimation. In the limited bandwidth, transmitting a large number of sampled data through the network will increase the computational and communication burden of the system. In order to overcome this difficulty, event triggering mechanism is introduced. The asymptotic convergence of system performance index is guaranteed by Lyapunov stability theory. Numerical simulation and experiment results have been provided to demonstrate the effectiveness of the proposed control scheme.
Design of Log Collection Architecture Based on Cloud Native Technology

With the continuous expansion of the Internet business, the enterprise technology architecture has been transformed into a large and medium-sized platform model. The emergence of the middle stage avoids the contradiction between the fast pace of innovation and change in the front stage and the slow pace of stable development and change in the back stage, and maximizes the efficiency of cooperation between the front stage and the back stage. The core technology of the mid-platform architecture is Kubernetes, which can be used to automatically deploy, expand and manage the open source system of container applications. It combines the containers that make up the application into logical units for easy monitoring and management. This paper takes the design of Kubernetes cluster log collection architecture as the starting point, and studies and designs a central platform architecture based on cloud native technology, which is of great significance for improving the efficiency of enterprise research and development and realizing digital and intelligent transformation.
Hybrid Algorithm for Item Collaborative Filtering Based on Matrix Factorization

The traditional collaborative filtering algorithm obtains K nearest-neighbor items, which may have the problem of too little similarity among nearest-neighbor items, making the accuracy of rating prediction low. And because in the actual situation, users have very little data on movie ratings, it is less likely for them to rate two movies at the same time, leading to an overestimation of similarity in the comparison. Not only that, users' interest in movies usually changes with time, which affects the prediction of users' ratings of items. To address the above-mentioned problems, this paper proposes a hybrid algorithm for collaborative filtering of movies based on matrix factorization. Firstly, matrix factorization is used to fill the vacant part of the user-item rating matrix, which is incorporated into the K nearest neighbors of the collaborative filtering algorithm, effectively alleviating the problem of low similarity between nearest neighbor items and improving the accuracy of predicted ratings. Secondly, a common rating weight for rating differentiation is added to calculate the similarity between movies to correct the bias of the similarity calculation. Finally, a temporal weighting function is introduced in predicting ratings, thus reducing the influence of temporal factors on user preferences and further improving the accuracy of recommendations. Experiments on the Movielens dataset show that the algorithm has significantly improved over traditional collaborative filtering algorithms.