Reviewerly/exHarmony huggingface dataset:
Music Within Limits

The creation of music relies heavily on, and is tightly intertwined with, mass production and industrialisation, making it virtually inseparable from many of the fundamental issues that make up the climate crisis today. Music (and the arts more generally) has the potential to play a role in influencing behaviour and effecting structural change by showcasing the issues at play and raising awareness around the magnitude of the crisis. However, digital music making contains an embedded political ecology and problematic history which must be assessed and acknowledged in order to effectively leverage this power. Therefore this paper will situate digital music within a cultural history of industrialisation, with all of the ecology contained therein. Building upon this, a set of approaches will be laid out that draw from established ICT4S concepts of limits theory and heuristic design, combined with artistic practice, proposing potential ways forward that will encourage digital music making to move towards a sustainable future and position musical tools to challenge societal norms.
Consistent Extended Kalman Filter Design for Maneuvering Target Tracking and Its Application on Hand Position Tracking

This paper proposes the consistent extended Kalman filter (CEKF) for the maneuvering target tracking (MTT) with nonlinear uncertain dynamics, and applies it on hand position tracking. The general model of the MTT system is presented with unmodeled dynamics in terms of nonlinear unknown function of states. The CEKF is proposed to ensure that the bounds of the estimation error’s covariance matrix are available through the filter algorithm. As a result, the corresponding accuracy of the filter approach can be achieved online. Furthermore, a CEKF-based MTT algorithm is constructed via the tuning law of the critical parameter matrix [Formula: see text]. Finally, the effectiveness of CEKF is verified by MTT numerical simulations and hand tracking experiments under different maneuvers. Specifically, two indices are employed to compare the CEKF with extended Kalman filter (EKF): the mean square errors (MSEs) and the bounded percentage, i.e. the percentage of the range where the estimation error is enclosed by the bound calculated by algorithms. All MSEs of CEKF are smaller than those of EKF, where the worst MSEs of CEKF and EKF are 0.14 and 4.18 in the simulation, as well as 0.11 and 0.59 in the experiments, respectively; all bounded percentages of CEKF are larger than those of EKF, where the worst average bounded percentages of CEKF and EKF are 87.86% and 14.58%, as well as 97.41% and 41.79% in the experiments, respectively.
Classification of Traffic Event Tweets in Portuguese Language Using Deep Learning

In urban mobility scope, one of the main current challenges is fast and localized information gathering for decision making. In this sense, social media can be an information extraction source for several tasks, such as traffic control. However, these data have to be well classified in order to ensure that only relevant information is used. Particularly in Portuguese-speaking countries, there are not many works with this approach. Thus, we designed a methodology to identify traffic information from social media using modern techniques for classification. In addition, we generated a dataset, by extracting and manually labeling tweets in Portuguese language, and used deep learning techniques to classify these messages with respect to the traffic domain. Finally, we perform experiments employing different combinations of deep learning architectures for representation and classification, obtaining results of more than 95% on average for accuracy and precision metrics.
A Survey of Traffic Obfuscation Technology for Smart Home

With the proliferation of smart home, the research on the attack and protection methods of smart home privacy has gradually increased. Research has shown that adversaries can infer users' privacy information by analyzing smart home traffic traces. To address this problem, we investigated and summarized the existing smart home traffic obfuscation schemes. Firstly, the current situation that smart home privacy is easy to be leaked through traffic is explained. The necessity of smart home privacy data protection and challenges faced are expounded. Then, the smart home traffic obfuscation technologies are classified and summarized, including data packet filling, traffic shaping, false traffic injection, user simulation and adversarial learning. The limitations and application scenarios are analyzed. Finally, the paper holds the view that the smart home traffic obfuscation technology based on user simulation and adversarial learning is the direction with great development potential.
Applying Artificial Intelligence (Ai) To Enhance Teaching Quality At FPT High School

In recent times, AI technology is gradually becoming ubiquitous in our daily lives. Not only AI is the applications in the fields of banking, finance, healthcare, and business, but also its important role in the field of education. Education is always the top priority in each country, how is AI applied in FPT high school - Vietnam? As a group specializing in digital transformation, FPT has successfully applied AI in many fields, most notably education. FPT high school system has pioneered the application of AI in teaching activities. Specifically, the online education system VioEdu; application “virtual teacher”; AI software to help improve the quality of teaching and learning English; online exam. These applications help FPT education system integrate into the world's digital transformation trend, improving the quality of teaching throughout the system.
Construction of Evaluation Index System for College Students' Text Programming Computational Thinking

research-article Share on Construction of Evaluation Index System for College Students' Text Programming Computational Thinking Authors: Wei Zhang Central China Normal University, Central China Normal University, China Central China Normal University, Central China Normal University, ChinaView Profile , Jihan Wang Central China Normal University, Central China Normal University, China Central China Normal University, Central China Normal University, ChinaView Profile , Xinyao Zeng Central China Normal University, Central China Normal University, China Central China Normal University, Central China Normal University, ChinaView Profile Authors Info & Claims ICMET '22: Proceedings of the 4th International Conference on Modern Educational TechnologyMay 2022 Pages 38–43https://doi.org/10.1145/3543407.3543414Published:20 July 2022Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Applying Some Software to Create Documentaries for Historical Events in the Topic “Vietnam - Cambodia War (1975-1979)” at FPT Education

Currently, the digital transformation revolution is developing, the application of information technology plays an indispensable part in all fields including education. Education organization FPT requires promoting the application of information technology in education and training at all levels and consider information technology as the most effective support tool to innotivate teaching methods in all subjects, especially teaching war history. This article introduces the activities: Applying some software to create documentaries for historical events in the topic “Vietnam - Cambodia war (1975-1979)” at FPT education organization. Because the characteristics of historical events are quite boring for learners, the teacher has made use of some software such as Moho Software; Scratch 1.4; Flip Boom Doodle; iClone, to create animated movies. The use of this method contributes to an increase in enriching visual aids in teaching thematic topics of war history in Vietnam - Cambodia (1975-1979). The results of the case study show that the application of animation software to better serve the thematic teaching of the Vietnam - Cambodia war (1975-1979) has brought better results for the students and aroused the interests of learners in studying war history at FPT education.
Pandemic agility

Since the world was taken over by the Coronavirus pandemic, professionals from all areas have had to adapt to the changes imposed by health measures, in a sudden process, which points to permanent changes in the forms of consumption and work. In the present study, we aimed to document the main practical challenges faced by agile software development teams in this adaptation process. We conducted an interview based qualitative study, using techniques from Grounded Theory, and collected data on the experience of five agile software development teams, in three different countries. Besides bringing a rich description of the journeys of the studied teams, our results highlighted four main challenges in the adaptation processes: (1) the full digitization of agile ceremonies, (2) a loss of project awareness, (3) the administration of work-life balance, and (4) the self-management transition in less mature teams. Overall, the participants reported increased levels of perceived productivity, and we share along practices and recommendations that may be useful for other teams facing similar challenges.
Design of electronic shelf label and bluetooth gateway based on electrophoretic display technology

Shelf labels have always been an essential role in supermarkets and warehouses, but with the expansion of business scale, the complex management problems of ordinary price labels have also become prominent. In recent years, electronic shelf labels have been widely used, but most of them use 433M wireless communication or WiFi communication. This paper presents a shelf electronic label developed using Bluetooth protocol, which uses the ink screen module based on electrophoretic display technology as the label display. The complete electronic shelf label system includes three parts: server, Bluetooth gateway and electronic shelf label. Server communicates with Bluetooth gateway using MQTT protocol. This paper introduces the design and implementation of electronic label and Bluetooth gateway.
A boosting based approach to handle imbalanced data

Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure. In the proposed algorithm misclassified examples are not given equal weights. The proposed algorithm selects useful samples from the majority class and creates synthetic samples from the minority class to indirectly change the update weights. We designed experiments using ten datasets from various domains and different evaluation metrics that show improved prediction performance on the minority class.
Job Title Prediction from Tweets Using Word Embedding and Deep Neural Networks

The more social media take its place in our lives; the more critical their analysis becomes and the more researchers’ attention is drawn to it. Studies contain various topics such as sentiment analysis, trend prediction, bot detection, Etc. Here, for the first time, we propose a novel method to predict the job title of social media users. Twitter, a popular social media, is our target social media. We introduce a dataset consisting of 1314 samples, including users’ tweets and bios. The user’s job title is found using Wikipedia crawling. The challenge of multiple job titles per user is handled using a semantic word embedding and clustering method. Then, a job prediction method is introduced based on a deep neural network and TF-IDF word embedding. We also use hashtags and emojis in the tweets for job prediction. Results show that the job title of users in Twitter could be well predicted with 54% accuracy in nine categories.
Privacy Preserving for Faces in Images based on Chaotic Maps and Elliptic Curve Cryptography

The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as medical information, official correspondence or governmental and military documents saved and transmitted in the form of images over public networks. Cryptography is a solution to protect confidential images by encrypting before transmission over unsecure channels. This paper will focus on selective image encryption algorithm to improve and enhance the security of transmission. Selective encryption is a solution to decrease the consumed time for encryption, which reduces the encrypted regions as small as possible. Hence, a hybrid cryptosystem is proposed based on the combination of ECC and chaotic maps that detects the face(s) in an image and encrypt the selected regions. This scheme will encrypt around five percent of the whole. The results of security analysis demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
Intelligent Filter of L<sub>1</sub> Adaptive Controller Using ANFIS System Optimized with Genetic Algorithm

$\mathcal{L}_{1}$ adaptive controller is known for ensuring fast adaptation with the optimal transient performance for input and output signals using a low-pass filter with adjustable gain in the feedback loop. During choosing a filter gain, the main criteria are the tradeoff between the performance, robustness and fast adaptation. There are several methods with different complexities for determining bounds or initial values of filter gain, such values may require the use of many iterations of trial and error implemented in the controller. In these methods, the specified gain is kept constant, which leads to non-optimal performance in adaptation speed and robustness. In this paper, a new approach based on an adaptive-neural-fuzzy inference system (ANFIS) optimized with a genetic algorithm is presented to continuously determine this gain and optimal performance according to the error rate. The objective function optimized by the genetic algorithm is also determined in terms of position tracking error, speed tracking error, and control signal. To evaluate the performance, the proposed method is implemented on a nonlinear feedback system in the presence of unmatched uncertainties. The simulation results show that the controller performance improves more than the constant filter efficiency in this method.
A New Unsupervised Feature Learning Method for Object Recognition using Prior-Knowledge Data

In this paper we first propose a Self-Organized Incremental Gaussian mixture based clustering mechanism (we named it as SOIGmm). The recognition system runs this clustering mechanism on some local features, which have been extracted, from some prior unlabeled data to create a strong visual codebook. In addition, a wavelet transform is applied for extracting color-based features. The second objective is considering the spatial importance of the different parts of each image into the representation. The saliency map is extracted and used which has a high role in the representation of each image. Furthermore, in the classifier, we used pyramid matching to find similarity between images and used averaging to combine the kernels on the classifier. The experimental results show that the proposed method could achieve excellent performance on object recognition both for one shot and multi-shot training data sets.
Clustering of Fuzzy Data Based on Particle Swarm Optimization

In this paper, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find the centers of the fuzzy cluster. Whatever the centers of the fuzzy cluster have more points from the corresponding cluster, clustering accuracy increases. To compare triangular fuzzy numbers, a similarity criterion based on the intersection area of the fuzzy numbers is used. The performance of the suggested clustering method has been experimented on both fuzzy benchmark and artificial datasets. The experiential results represent that the suggested clustering method can cluster triangular fuzzy datasets well. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.
Entanglement-Assisted Classical-Quantum Multiple Access Wiretap Channel: One-shot Achievable Rate Region

In this paper, the problem of secure communication over an entanglement-assisted classical-quantum multiple access channel is studied under one-shot setting. The main employed approach to this end, is combination of simultaneous decoding and position-based decoding techniques while a recently introduced approach is used to calculate leaked information.
Novel Power Management Technique for Energy Efficient Cloud System Applications using a Java Agent

The increasing growth in cloud service has also increased the power usage required to have excellent functionality and stable cloud services. Globally, there has been a massive growth in Internet activity and Internet users, which significantly impacts the cloud network’s power consumption rate. Therefore, having fundamental expertise in balancing the trade-off between cloud system performance and energy efficiency is the key to leveraging the cloud network’s alarming high power consumption rate. In this work, a mobile agent will be used to regulate the activities in data centre networks to minimise the energy usage rate at each runtime. Mobile agent technology is a powerful tool compatible with the distributive and adaptive cloud architectural design platform with a novel solution to its power usage excesses. Agents are empowered to switch-off switches/links that are inactive. Java agent was used to schedule and consolidate the servers and network activities. Our approach enhanced cloud resource utilisation and drastically increased power efficiency by reducing servers’ idle time.
Using the Blockchain as Notary Service for a Local Energy Market

Smart energy supply systems are increasingly in the spotlight to fight climate change. Decentralized local energy markets are one concept of these systems. However, these decentralized markets need a verification mechanism to secure the trading, for example through a notary service. Blockchain is a modem technology that enables the creation of audit-proof applications. In this paper, we analyze how well blockchain can be used as a notary service in a local energy market. We will provide a classification of notary services link them to different data storage methods and demonstrate their application in a local energy market.
Development of an Online Course for «Web Programming» discipline with OLAT

This paper describes the technologies for organizing online courses, provides a brief analysis of technologies for organizing online courses. The paper describes the technology for creating an online course «Web programming» using OLAT.The course was developed at web application OLAT - Online Learning and Training. The course consists of theoretical resources and practical elements, allowing the author to create own course, set the time frame for the course, indicate the certain measure and evaluate the student’s work according to the passed course. The article shows the technology of online course implementation with OLAT. These technologies allow you to automatically form an individual learning path based on the analysis of students’ knowledge.
Bayes-optimal Solution for Environmental Prediction Using Human Sensitivity

From the theoretical viewpoint of statistical physics, we investigate the dynamical properties for predicting environment utilizing human sensitivity of a sensor network for a small-scale system with an air conditioner. We analyse how environmental variables evolve in such a small room by a dynamical procedure in accordance with Bayesian inference with expected a posterior estimation and statistical physics with the Master equation and mean-field theory. We clarify that the temperature-humidity index (THI) smoothly approaches to the optimum value due to the use of the Bayes-optimal solution and that both temperature and relative humidity is close to the original variables along a curve on the temperature-humidity plane. Then, we discover that the THI also approximates the optimum value by a method without using a term that improves the optimum THI, although the convergence time is longer than that of the Bayes-optimal solution.
A Novel Feature Selection Method Based on Ambiguity Distribution

Feature selection is one of the key processes in pattern classification. For the task of feature selection, an important problem is the evaluation of the classification capability or clarity of different feature spaces, which is related to their uncertainty. In the framework of Dempster-Shafer evidence theory (DST), the Basic Belief Assignment (BBA) can well describe the uncertainty. In this paper, Generative Adversarial Network (GAN) is used to generate BBAs in each feature space, and a novel feature selection approach based on ambiguity distribution is proposed, which uses the skewness and variance of the BBAs’ ambiguity values on each feature space. Experimental results of pattern classification on various UCI datasets show that our new proposed method is rational and can effectively improve the accuracy of pattern classification.
Deep Reinforcement Learning for Control Design of Quantum Gates

This paper investigates quantum gate control problems using the deep reinforcement learning algorithm, i.e., a model-free machine learning method. We implement the twin delayed deep deterministic policy gradient (TD3) algorithm to search for piece-wise constant control pulses for quantum gates through the trail interaction with the quantum system. Simulation results on four typical gates, including three one-qubit gates and a two-qubit CNOT gate, demonstrate that deep reinforcement learning exhibits improved performance for quantum gate control tasks. By setting punishment for steps in the reward function, DRL can automatically find a shorter control sequence than the traditional gradient-based algorithm (e.g., GRAPE algorithm) and the evolutionary algorithm (e.g., DE algorithm) while maintaining high control precision.
Realization of Long Short-Term Memory Networks on Quantum Circuits

As one of the well-known methods to solve natural language processing (NLP) problems, the long short-term memory neural network (LSTM) has been designed to implement on quantum computers. However, existing method requires the number of qubits is equal to the dimension of the word vectors, and may result in a surge of the qubit number. To address this problem, we propose a duplication-free quantum long short-term memory neural network (DQLSTM). Specifically, our DQLSTM adopts the amplitude encoding method to store the classical information, and further can reduce the requirement for the number of qubits. Our numerical results show that our DQLSTM has similar effectiveness to the classical counterpart in a Chinese sentiment analysis task, and outperforms several previous proposals in the same task.