Reviewerly/exHarmony huggingface dataset:
Gesture controlled stand-up wheelchair with enhanced traits

This paper is based on gesture-controlled stand-up wheelchair with enhanced traits, which provides instrumental aid for ill, senior citizens or physically challenged people so that they can move around conveniently. People with disabilities confront many challenges in day-to-day life. To prevent and avoid such circumstances, this system can be used. The system is accelerated by Arduino Microcontroller LilyPad, RF module and BO motor. The RF module plays a vital role in the interaction between the Rx/Tx. A person can move the wheelchair by using hand gestures at a certain angle in right, left, and forward-backward directions. Similarly, the patient can use the stand-up feature and return to its original position by using head gestures. This system is based on a prototype which also has an emergency feature in case of an emergency. When the patient presses the send button, the message will be sent to the caretaker through GSM module. This switch is really useful when the person is all alone and needs help.
Quantized Distributed Online Kernel Learning

In this paper we propose a communication-efficient kernel-based learning method by means of random-feature approximation and quantization. The proposed algorithm is named quantized distributed online kernel learning (QDOKL). We theoretically prove that QDOKL over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> time slots can achieve an optimal sublinear regret <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{O}(\sqrt{N})$</tex> , provided that a quantization level scales with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sqrt{N}$</tex> . Our analysis implies that every node in the network can learn a common function having a diminishing gap from the best function in hindsight. We verify our theoretical result via numerical tests with real datasets on online regression tasks. Also, it is demonstrated that QDOKL can achieve the almost same accuracy as the unquantized counterpart while having a lower communication overhead.
Implementation of a high-performance portable real-time dehazing system using the DCP algorithm

This document presents a portable, high performance system for real-time image dehazing using the DCP algorithm. The software runs on a CPU-GPU heterogeneous environment in a development platform from NVIDIA with support for CUDA. Three different window sizes for the DCP algorithm were explored. These low values allow a high throughput and reduce the halo artifacts but results in low-luminosity frames which negatively impacts quality. To solve this, a gamma correction was introduced as a final stage. The coefficient for this filter can vary and is a value which can be manually modified with feedback from the users, depending on their preferences. The picture resolution utilized was <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1280\times 720$</tex> pixels, which the system was able to process at a frame rate of 40 fps using a window size of 3. The image capture can come either from an USB camera or from a video file. The output can be store as a file or it can be wirelessly transmitted to a mobile device using the RTP protocol.
A = 440

Abstract This chapter discusses the music theory and aural skills practiced daily by an important and influential segment of the public: the session musicians, engineers, songwriters, and producers in the recording studios and publishing houses of Nashville’s Music Row. Through interviews with leading engineers and studio musicians, the chapter reveals that particular kinds of music theoretical knowledge and aural skills are valued in these contexts. Efficiency and accuracy are prized during recording sessions, and there are high expectations for the fluid and immediate application of practical knowledge and skills to writing, recording, producing, and performing music. While some in these situations have had formal, academic training in music theory, that is not true of everyone. Some terminology from academic music theory is valuable, but there is also the need for additional terminology and systems in order to develop a common language for all participants. This chapter provides detailed information about an important aspect of this common language, the Nashville Number System, a musical shorthand developed within the studios of Music Row that now has currency among musicians around the world, bringing music theory to an ever-expanding public.
Agility in Software Development and Project Value

Agile Development Methods, considered as an alternative to the traditional plan-based methods, have received much attention since their inception. These practices have evolved and developed over time, culminating in 2001 with the Agile Manifesto. Since that time, preferred methodologies, implementations, and best practices have continued to evolve with a focus on doing what works best for the individual company or project. However, the concept of agility in software development has remained quite nebulous, lacking in clarity particularly about its underlying dimensions. In this research the authors conceive agility in terms of four distinct dimensions. Drawing from the theoretical perspective of holographic organization, they develop a model explaining how each of these underlying dimensions of agility contributes to project value in software teams. The authors test the model using survey data collected from industry practitioners and discuss findings.
Design And Implementation of Video Learning Platform Based on B / S Architecture

With the further popularization of computer technology, communication technology and network technology, students can't live or study without the help of network. Video learning platform is such a benchmark. It has a large number of data resource databases, breaks the boundaries of traditional teaching, reveals the development direction of modern education, and makes the learning environment more diversified and enriched. The system is deeply rooted in the background and significance of today's online education, integrates the practical problems existing in the immature video learning platform in the past, analyzes the specific needs of current teachers and students for the learning platform, grasps the development trend of the future education industry from offline to online, and realizes the effective management of a series of categories such as member courses in online learning. It makes use of the advantages of the computer industry It integrates with models, concepts and methods, adopts MVC three-tier pattern design, based on B / S architecture and Struts + Spring + Hibernate framework design, MySQL and eclipse in parallel, using mature Dao mode to access MySQL database, and comprehensively realizing a complete set of video learning system.
Requirements of a Common Data Environment (CDE). Study Case of VIRCORE

Nowadays, it is unquestionable to consider BIM when dealing with a project in the AEC (Architecture, Engineering and Construction) sector. BIM is more and more present in the construction industry due to the technological evolution and the challenges that appear every day. The use of BIM provides advantages such as: reliable and accurate information, simple and instant communication, updated register of actions, error reduction, teams connection, etc. BIM methodology involves collaboration between different disciplines and people, allowing them to have controlled access to the project data depending on their role. From this collaboration between stakeholders arises the need for a Common Data Environment (CDE). This collaborative platform makes it possible to work on the different parts and disciplines of a project on a distinct federated model at the same time along its entire life cycle. The main goal of this article is to establish the minimum requirements for a CDE, as well as to describe the functionalities of the collaborative platform VIRCORE. This article discusses its potential, advantages and versatility.
Learning adversarial point-wise domain alignment for stereo matching

The state-of-the-art stereo matching models trained on synthetic datasets have difficulty in generalizing to real-world datasets. One major reason is that illumination and texture in the real world are hard to be simulated, resulting in big differences between synthetic and real-world data. In this study, instead of narrowing the image-level appearance difference, we focus on aligning both data domains in feature space in an unsupervised manner and propose an end-to-end domain alignment stereo network (DAStereo). A domain alignment module (DAM) is introduced by learning a point-wise linear transformation. We demonstrate that DAM can maintain sufficient alignment capacity with fewer parameters than the globally nonlinear mapping. To explicitly promote the point-wise domain alignment, adversarial learning is further introduced using a cost volume discriminator in a hybrid training manner. Experimental results show that DAStereo outperforms the state-of-the-art unsupervised and adaptive methods and even achieves comparable performance to some supervised methods.
CLSDRL:A routing optimization method for traffic feature extraction

In order to solve the impact of the temporal and spatial characteristics of traffic on network routing optimization, this paper proposes convolution long-short memory neural network deep reinforcement learning (CLSDRL) model for routing optimization. The CLSDRL model consists of deep deterministic policy gradients (DDPG) deep couple with convolution neural network (CNN) and long-short memory neural network (LSTM). After extracting the spatial and temporal characteristics of network traffic with CNN and LSTM, routing decisions are made with DDPG algorithm. Experiments are conducted under different load intensities, and the network performance is evaluated by the average network delay and packet loss rate, experimental results show that this method can improve significantly network performance.
Trends in Terminology

The purpose of this chapter is to analyze and review trends in digital terminology. The chapter begins by examining the origins of computerization in the United States during the 19th and 20th centuries. Next, the chapter examines the key concepts punch cards and computer science. The chapter then discusses how the term computer science is misleading. This is followed by a discussion of how information technology became the most popular term in the US. The chapter then switches focus to Europe and discusses France's promotion of informatique as well as Europe's switch from informatics to ICT. Next, the chapter considers how the internet has given rise to terms like e-commerce. The chapter concludes by considering the transition from ICT to digital informing and informing technology.
OUPIP

This article aims to propose an ontological user model called OUPIP (Ontology-Based User Profile for Impairment Person), that extends existing ontologies to help designers and developers to adapt applications and devices according to the user's profile, disability and dynamic context. Besides, the approach has been applied in a typical real-life scenario in which personalized services are provided to impairment person through a mobile phone.
3SHACC: Three stages hybrid agglomerative constrained clustering

Traditionally within the unsupervised learning paradigm, hierarchical and partitional clustering techniques have been shown to produce better results when provided with partial information, leading to a renewed attention towards this topic. Constrained clustering is a semi-supervised learning problem that combines classic clustering techniques with background knowledge given in the form of a set of constraints. In this paper, we propose to incorporate constraints into the clustering process in three phases: the first phase is devoted to quantify constraint relevance and to learn a metric matrix according to such relevance, a second phase computing similarities between instances by means of the reconstruction coefficient and pairwise distances, and a third stage performing agglomerative hierarchical clustering with a reward-style stepped affinity function favoring merges satisfying the higher possible number of constraints. Experimental results, supported by Bayesian statistical testing, show a consistent improvement in favor of our proposal over previous approaches to the constrained clustering problem.
Experiment-driven improvements in Human-in-the-loop Machine Learning Annotation via significance-based A/B testing

We present an end-to-end experimentation framework to improve the human annotation of data sets used in the training process of Machine Learning models. It covers the instrumentation of the annotation tool, the aggregation of metrics that highlight usage patterns and hypothesis-testing tools that enable the comparison of experimental groups, to decide whether improvements in the annotation process significantly impact the overall results. We show the potential of the protocol using two real-life annotation use cases.
Substantive Foundations of Future Mathematics Teachers’ Training for Mathematical Literacy Formation in Primary School Students

The study aims to identify the content of students’ mathematical literacy in order to determine the substantive foundations of future mathematics teachers’ methodological training. The paper considers the notion of mathematical literacy in the context of functional literacy formation in primary school students. The author analyses the structure of mathematical literacy and research papers addressing the formation of the content of its individual components. The work shows that in Russian studies, there is no unified approach to determining the set of actions that need to be taught to schoolchildren when they operate at the stage of formalisation of a situation presented in a natural language and interpretation of the result obtained in the process of solving practice-oriented tasks. Having analysed the toolkit of the international PISA study and papers by Russian researchers, the author has compiled a list of actions targeted training of which will prepare future mathematics teachers for the formation of students’ skills in building a mathematical model and interpreting the results obtained in the process of solving practice-oriented tasks. Scientific novelty of the study lies in providing rationale for the need to include in the content of future mathematics teachers’ methodological training special actions underlying the stages of formalisation of a situation presented in a natural language and interpretation of the results obtained in the course of solving practice-oriented tasks with the purpose of forming students’ mathematical literacy. As a result, the relationship between certain specific actions necessary for the implementation of the stage of formalisation of a situation presented in a natural language and communicative, educational and informational skills that determine primary school students’ functional literacy has been shown.
Digital Forensics in Internet of Things

As IoT technology becomes an integral part of everyday life, enhancing productivity for businesses through automation, it should come as no surprise that attackers would seek to exploit these systems and the services they provide for profit.
Multi-sensor Bearings-only Target Tracking Using Two-stage Multiple Hypothesis Tracking

The problem of multi-sensor bearings-only target tracking is addressed in this work. A practical challenge in this problem stems from the data association in dense clutter. In order to avoid the increase of computational complexity with the increase of the number of sensors and clutter density, a two-stage multiple hypothesis tracking (MHT) algorithm is proposed. First, the first stage MHT is performed at each local sensor, and the measurements with effective data association are sent to the fusion center. Second, the measurements from different sensors are combined and augmented to form new measurement vectors, which are then converted to Cartesian coordinates by iterated least squares (ILS) estimator. Therefore, in the second stage MHT, the Kalman filter can be used to update the target track. The performance of the two-stage MHT algorithm is illustrated with an example.
Research on the Control Method of Unmanned Helicopter Under the Background of Artificial Intelligence

A synopsis of a multidisciplinary research initiative focused on critical strategies for the genuine independent flight of tiny, vertical flying take-off (VTOL) unmanned aerial vehicles (UAVs). The research activities are the flight testbed, a simulation and test environment, and integrated components for onboard navigation, perception, design, and control. The necessity to create an unmanned helicopter system in different new civil applications cannot be overlooked. A highly reliable model may be used in the design, analysis, and implementation. The helicopter is fitted with a reference system for flight test data measurement and recording attitude heading reference system (AHRS) and the accompanying data storage modules. Recently, artificial intelligence-based deep learning (DL) has demonstrated excellent outcomes for a wide range of robotic activities in the areas of perception, planning, location, and management. Its remarkable skills to learn from complex data obtained in actual surroundings make it appropriate for many autonomous robotic applications. At the same time, UHS is currently widely utilized in various civil tasks in security, cinematography, disaster assistance, package delivery, or warehouse management (Unmanned Helicopter System). This paper conducted detailed work on current applications and the most significant advances and their performance and limits for the DL-UHS method. Furthermore, the essential strategies for deep learning are explained in depth — finally, discussing the principal hurdles of applying deep learning for UHS solutions. The proposed DL-UHS enhance outcome to evaluate the control strategies for the unmanned helicopter to achieve the low signal to noise error ratio of 31.3%, the error rate of 33.6%, the high-performance ratio of 91.4%, enhance accurate path planning 97.5%, prediction ratio of 96.3%, less trajectory cost ratio of 17.8% and increased safety tracking rate 93.6% when compared to other popular methods.
Dance Guiding Art Innovation Framework Based on Video Action Recognition Technology

For dance, it can not only improve people's aesthetic ability and self-quality, but also benefit people's physical and mental health and physical fitness. Therefore, in the process of college dance teaching, teachers should increase the importance of dance teaching, formulate a set of teaching plans that are in line with the development of students according to the actual situation of students, and continuously cultivate students' dance awareness. The main research is to use image preprocessing, 3DZernike moments, building codebooks and support vector machines (SVM) to recognize human actions in action videos. Simulation test results show that this method can effectively identify KTH and A-go-go dance video databases.
Detect Darknet URL Based on Artificial Neural Network

Darknet is a network that transmits data on the Internet through anonymous network technology and protects the relationship between the two sides of communication from being leaked. Because the IP addresses of both sides of the communication cannot be traced on the darknet, the identity of the user cannot be determined. The darknet is used by criminals to engage in criminal activities. This paper studies the URL address of the darknet, proposes an algorithm for darknet URL recognition using artificial neural network. The algorithm transforms URL into a fixed length vector, and then uses it as a part of the input data of artificial neural network for learning and classification. Experiments show that the proposed algorithm has high accuracy, can accurately identify the darknet URL through multiple iterations under different attribute accuracy. Experimental results show that the proposed algorithm can achieve 99.3% detection accuracy.
Humanitarian engineering approach for re-designing pedestrian traffic inside the lecture building during the new normal COVID-19 pandemic

Abstract COVID-19 has brought a massive impact on people’s daily activities, office workers have implemented “Working from Home” (WFH), an elementary school to high school students have implemented “Study from Home” (SFH), as well as Campus academic communities’ activities’ implementing Learning from Home. This circumstance has changed daily human behavior for a year. In 2021, upon the completion of a global vaccination program, the situation is followed by a change in the pattern of daily living activities towards “a new normal”, in which direct human interaction is reinstated while still fulfilling health protocols, one of the actions is social distancing. This study examined the detection of pedestrian traffic for the academic community of the Department of Urban and Regional Planning (DURP), Brawijaya University and predicted certain crowds’ spots in the DURP building, using Computer Vision and the integration of Agent-Based Model and Geographical Information Systems Indoor. Furthermore, alternative designs for pedestrians were carried out in the DURP building to prevent the occurrences of crowds in certain spots. The results obtained are that on the 1st Floor of the DURP building, the West and East entrance paths have high traffic, so it is necessary to arrange for the opening of the Southside door as an alternative for pedestrian access. Likewise, for the 2 nd Floor, the opening of the south gate contributes to minimizing crowd spots. All in all, the blended learning process through face-to-face learning at students grades 1 and 2, then online learning at students grades 3 and 4 might minimize the potential Covid-19 transmission facing a new normal higher education learning process.
Synthetic Data Augmentation of Cycling Sport Training Datasets

Planning sport sessions automatically is becoming a very important aspect of improving an athlete’s fitness. So far, many Artificial Intelligence methods have been proposed for planning sport training sessions. These methods depend largely on test data, where Machine Learning models are built, yet evaluated later. However, one of the biggest concerns of Machine Learning is dealing with data that are not present in the training dataset, but are unavoidable for predicting the further improvements. This also represents a bottleneck in the domain of Sport Training, where algorithms can hardly predict the future training sessions that are compiled with the attributes of features presented in a training dataset. Usually, this results in an under-trained trainee. In this paper we look on this problem, and propose a novel method for synthetic data augmentation applied on the original dataset.