Reviewerly/exHarmony huggingface dataset:
Smart Cities Using Machine Learning and Intelligent Applications (ITALIC)

The goal of smart cities is to properly manage to expand urbanization, Reduce energy usage, Enhance the economic and quality of life of the locals while also preserving the environment conditions, and improve people's ability to use and adapt modern technology used in information and communication efficiently (ICT). Information and communication technology (ICT) is central to the concept of smart cities because it facilitates the development of policies, decision-making, implementation, and, eventually, the provision of useful services. This review's main objective is to examine how machine learning, deep reinforcement learning (DRL), and artificial intelligence are advancing smart cities. The previous strategies are effectively utilized to provide the best policies possible for a number of challenging issues relating to smart cities. The uses of the earlier methods are thoroughly discussed in this survey. in intelligent transportation systems (ITSs), Cybersecurity, as well as smart grid energy efficiency (SGs), ensuring the best 5G and beyond 5G (B5G) networking service, as well as a smart city with a smart health system, by effectively using unmanned aerial vehicles (UAVs). Finally, we list a number of the above-mentioned research problems and potential future directions approaches can be extremely helpful in bringing the idea of a smart city to life.
Enhanced visual multi-modal fusion framework for dense video captioning

Abstract Dense video captioning is a machine translation task that aims to localize events from full video and describe them separately. Human observers who parse the video are also impressed by frames containing a high amount of information and duplication, but video often appears unrelated frames in the subject. However, existing works have largely ignored these details. To fully incorporate human visual perception into the process of understanding video, we propose an enhanced visual multi-modal fusion framework (Evmff), which utilizes the captions of video keyframes to improve dense video captioning performance. We first extract video keyframes through time stamps and then apply the recently proposed image captioning method DLCT to obtain a temporally aligned caption of the keyframe. Evmff fuses the textual information of speech, the caption of image keyframes, video features, and audio features, applying transformer architecture to convert the data into text descriptions. The performance of our model is verified using the ActivityNet Captions dataset with four different indicators, Bleu@N, METEOR, Rouge_L, and CIDEr-D. Ablation experiments show that employing the video keyframe description as the input of the multi-modal model compensates for the deficiency in visual information understanding. Our code will be released.
Private Data Protection in Social Networks Based on Blockchain

With the rapid development of big data and social networks, user data in social networks are facing huge risks of privacy leakage. It is urgent to establish a complete and effective method for protecting private data in social networks. Based on the problem of information leakage in social networks, classifies user privacy data, and constructs different privacy data protection schemes through blockchain time stamp recording data storage, hash function anonymous operation of data, asymmetric encryption and digital signature of sending information. The blockchain-based privacy data protection method in social networks can effectively solve the privacy leakage problem in social networks, and provide a reference for the research in the field of information security and social network security. This paper designs a new blockchain-based privacy data protection scheme for different privacy disclosure categories, which provides a new solution to the current privacy disclosure problem in social networks. However, the existing methods will consume a lot of computational power in the process of information interaction. The subsequent research will optimize the computational power of blockchain and try to build a better blockchain social network privacy data protection system.
Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words

Word embedding systems such as Word2Vec and GloVe are well-known in deep learning approaches to NLP. This is largely due to their ability to capture semantic relationships between words. In this work we investigated their usefulness in capturing rhythmic similarity of words instead. The results show that vectors these embeddings assign to rhyming words are more similar to each other, compared to the other words. It is also revealed that GloVe performs relatively better than Word2Vec in this regard. We also proposed a first of its kind metric for quantifying rhythmic similarity of a pair of words.
Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classifiedimage patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLoV3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted throughbounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things).The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology.
Graph Filters for Signal Processing and Machine Learning on Graphs

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article provides a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations at the intersections of signal processing, machine learning, and application domains.
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, or playing a song. These two directions have been studied separately due to their different purposes. However, how smoothly transitioning from social chatting to task-oriented dialogues is important for triggering business opportunities, and there is no public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches.
Design and FPGA Implementation of Numerically Controlled Oscillators with ROM Look-Up Table Structure

A design of numerically controlled oscillators based on Read-Only Memory locate form schematic is proposed. The working principle of NCO numerical control oscillator is introduced, and its implementation method in FPGA is analyzed in detail. Using Quartus II software to write Verilog HDL program, the NCO design is completed, and Modelsim software is called to simulate it. Finally, the performance index of NC oscillator is simply analyzed, the key parameters such as signal-to-noise ratio and spurious degree are tested, and its performance is simulated by MATLAB software. The results show that the design scheme is feasible.
An adaptive wavelet shrinkage based accumulative frame differencing model for motion segmentation

Motion segmentation in real-world scenes is a fundamental component in computer vision. There exists a variety of motion recognition algorithms, each with varying degrees of accuracy and computational complexity. The most widely used techniques, in the case of static cameras, are those based on frame difference. Those methods have a significant weakness when it comes to detect slow moving objects. Therefore, we introduce in this paper a novel approach that aims to improve motion segmentation by proposing an accumulative wavelet based frame differencing technique. Moreover, in the proposed approach we exploit a combination of several techniques to efficiently enhance the quality of motion segmentation results. The approach's performance on real-world video sequences shows that comparing frames using the 2D wavelet transform increases motion segmentation quality.
“In-Math” as a Website-Based e-Learning Media in the Endemic Era

This research aims to develop a website-based e-learning for social arithmetic for Grade VII of junior high school, named “In Math”. This study refers to the Borg and Gall model with five stages. The first, the need for analysis for teachers and students. The second, planning and developing the initial product. The third, validation from material and language experts and media experts, and revising the product. The fourth, small group testing, while the fifth stage is large group testing on teachers and seventh grade students in a junior high school in Jakarta, Indonesia. Based on this research, the percentage of the product feasibility according to the material and language experts is 85.42%, 83.39% for media experts, 75% for the small group tested on teachers 75%, 86.67% for the evaluation of 21 students, 95.2% for the large group tested of teachers, and 86.25% for the evaluation of 41 students. The average percentage of the product feasibility at each stage reached 85.32% and is categorized as very feasible. Thus, the “In Math” website-based e-learning is feasible to use.
Modern training strategies used in e-learning

The study focuses on the role of e-learning in the training process against the background of the digitalization of education.We aim to analyze the impact of elearning on the school performance of the students.Starting from a comparative analysis between traditional learning and e-learning, we focus on the training strategies that capitalize on the new information and communication technologies.In the experimental part of the research, we will start from the suggestions offered by the students of the Faculty of Sciences, the University of Craiova (undergraduate cycle, year II) with the purpose to improve online instruction.In this regard, we will use a questionnaire.The designed training model, based on the proposals of the students, will include current training methods, modern instruments, leading to better results in the final evaluation.This aspect will be verified in the final part of the pedagogical experiment, in which we will follow the results of the exam and we will analyze the feedback provided by the students concerning the implementation of their proposals.
Depth Continuous Super-Resolution with Local Implicit Guidance Function

Depth super-resolution (SR) is an effective approach to compensate the resolution gap between the relative low resolution of current depth sensing devices and the increasing demands of high-resolution content in many nowadays applications and future scenarios. However, most convolutional neural networks (CNNs) for depth SR are designed for particular integer SR ratios ( e.g. 2x, 4x, or 8x) or their combinations, which limits its practical applications that usually demands fractional SR ratios. This paper proposes a depth map continuous SR (DCSR) framework that is able to achieve resolution adaptation at arbitrary SR ratios. Specifically, we proposed an encoder with a dual-stream architecture. Using depth map and the guided color image as input, each stream interacts with the other by a series of adaptive resize blocks (ARBs) to retain essential details and resize stabilization across features of various resolutions. In the decoder, depth information at arbitrary missing locations is fitted as the weighted prediction of neural implicit functions (NIFs) from latent codes containing relative coordinate, target pixel size, and fused depth-color features. Extensive experiments demonstrate that the proposed DCSR method provides high quality depth SR results at arbitrary ratios, which is a promising merit for practical applications. Code will be released upon the publication of this work.
Classification of Finger Movements Using EMG Signals with PSO SVM Algorithm

Electromyography (EMG) is a signal produced by human muscles when they contract or relax. This signal is widely used as a controller, for example, to control a robotic arm. This study aims to identify the pattern of finger movement in the form of finger movement using a bracelet-shaped device that has eight EMG sensors. This tool is placed on the lower right hand of a subject to get a signal from the EMG. This study uses the support vector machine (SVM) algorithm combined with the particle swarm optimization (PSO) method. For pattern recognition, the properties of the signal in the time domain are used. From this system, the success of pattern recognition is between 68% to 86%.
Correlation of Students’ Reading Interest, Vocabulary Mastery and Their Reading Comprehension

The study is aimed to find out The Correlation of Students’ Reading Interest, Vocabulary Mastery and Their Reading Comprehension at Tenth Grade of SMA Paradigma. The method of the research used quantitative research. The population was 175 students and 40 students as a sample. The result showed that (1) There is no positive significant correlation between students’ reading interest and their reading comprehension at SMA Paradigma Kabupaten Tangerang in Academic Year 2020/2021. With the value t_count &lt; t_table (1,238 &lt; 1,687). (2) There is positive significant correlation between students’ vocabulary mastery and their reading comprehesion at tenth grade of SMA Paradigma Kabupaten Tangerang in academic year 2020/2021. The result of the variable students’ vocabulary mastery is 2,473 which means t_count &gt; t_table (2,473 &gt; 1,687). (3) There is a positive simultaneous correlation of students’ reading interest, vocabulary mastery and their reading comprehension at tenth grade of SMA Paradigma Kabupaten Tangerang in academic year 2020/2021. The result of students’ reading interest and vocabulary mastery on their reading comprehension with a significance value of 0,005 &lt; 0,05 it can be concluded that the greater the interest in reading, the greater their vocabulary mastery, and automatically students will have a good reading comprehension.
A Mobile Augmented Reality Application for Automotive Sparepart Installation

Automotive exterior spare parts retailers frequently sell exterior spare parts for a range of cars via direct sales transactions and online media. A major issue has come up for consumers who purchase products online must install spare parts independently, with limited instructions that make it difficult for customers to install the replacement components. This is demonstrated by nearly 50% of online consumers complaining to the seller about installation errors, indicating the need for a solution to these issues. Augmented reality was used as a solution to educate how to install automotive exterior spare parts. In this work, the Spatial Tracking technology is utilized to project objects from a viewpoint in order to send data digitally and show the 3D model to the object's surrounds in real time. This study included as samples rear-view mirrors, headlights, stop lamps, and fog lamps. According to the study's findings, The user acceptance test was carried out by online consumers who buy spare parts at the store, and the results of the average percentage score of the entire questionnaire by more than 53% agree and strongly agree that the use of augmented reality can ease the spare-part installation bought online, the results of testing the AVAR application by the user are successful and well accepted.
Hermits with Duden, tipex and an Olivetti Studio 45

This little article summarizes the changes in translators’ work from the 1990s as a tribute to the Revista Tradumàtica on its 20th anniversary. No surprises: text processing, the Internet as a source for information, the use of CAT tools and neural MT, and changes in the market and labor relations stand out as the prominent features that have changed (or not) in the last 30 years.
Four States of the Personality of Composer Iannis Xenakis and their Extensions in the Contemporary Digital Environment

The research addresses some digital implementations in music-specificprogram ming environments generated by Xenakis' musical concepts, capitalizing on influences of the composer's artistic thinking. Starting from the identification of four states of his personality, I present and analyze selected software that bears the imprint of the composer's ideas or influence and techniques that were at the basis of the conception of the composition I.X.@100 for soprano and tape.
Life cycle models, principles and methodologies of software development

The development of software security in today's IT companies is considered in this article. The discussion focuses on the software life cycle, its models used to create a product, as well as the main principles and methodologies of software development, their key advantages and disadvantages. The optimum areas of using methodologies to achieve quality standards are investigated.
Application of a digital twin for research and optimization of local hybrid power complexes with res-based generation

The paper shows the effectiveness of using a universal verified digital twin to study and develop recommendations for choosing the optimal parameters and operating modes of hybrid energy complexes at all stages of the life cycle, which have a heterogeneous composition of generating plants and a high dependence on natural uncertain factors. The proposed methods and the results obtained can be used in solving design problems to justify the parameters of consumer energy supply systems in isolated and hard-to-reach energy regions, as well as in nodes of distributed energy systems
Hybrid quantum ResNet for car classification and its hyperparameter optimization

Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.
Pemanfaatan Media Gambar Animasi berbasis PowerPoint untuk Meningkatkan Kemampuan Membaca Permulaan Siswa Sekolah Dasar

This classroom action research aims to examine the portraits of early reading learning, the application of early reading learning by using PowerPoint animation media, and the use of this media in improving students' early reading skills at one of elementary school at Wawonii Tenggara, Southeast Sulawesi. The results of qualitative analysis showed that the form of learning to early reading at the school was conducted in a simple way i.e. by writing letters of the alphabet on the blackboard and pronouncing the letters aloud together. The activities of teachers and students in learning have shown a good increase from cycle to cycle. Teachers find it easier to manage classes, direct students, and focus students on learning, meanwhile students seem more confident to ask questions and make presentations in front of the class. The use of PowerPoint animated image media has been proven to be able to help students improve their beginning reading skills.Keywords: Beginning reading; powerpoint animation media; elementary school