Reviewerly/exHarmony huggingface dataset:
Interacting Propeller Thrust for Navigating Omni-Directional Remotely Operated Vehicle

Abstract The role of underwater vehicles in surveying, observation, search and rescue activities are increasing along with the development of the underwater positioning technology, such as sonar technology, underwater modem, etc. These technology help to develop the navigation algorithms, such intelligent system are developed in operating the vehicle autonomously or controlled. The Remotely Operated Vehicle (ROV) is equipped four fixed horizontal propellers installed at 45-degree angle to axis and one vertical propeller. The propellers are controlled simultaneously to drive the vehicle in multi-direction. Mapping movement strategy of interacting propeller were studied to control the vehicle’s movement and increase the of ROV performance.
Real-Time Textureless-Region Tolerant High-Resolution Depth Estimation System

This study presents a real-time depth estimation hardware system aiming to provide high-resolution depth data and to eliminate its noise on textureless regions without causing any interference problem by utilizing artificial pattern projection. The system generates up to 2K resolution depth data and reaches up to 256 disparity range which are configurable by the end-user owing to its parameterized design. It is capable of streaming depth data with 21 frames per second (fps) with 2K resolution and 128 pixel disparity range, and its throughput performance changes depending on configuration of the output resolution and the disparity range.
Dynamic Sensor Orientation Unit for the Intelligent Mobility Cane

Numerous studies have been conducted to improve the features of the "smart" canes. However, there is insufficient research performed to develop the guidelines to create a usable smart cane. In this paper, a Dynamic Sensor Orientation Unit (DSOU) is introduced to maintain a detection distance irrespective of the angle of the cane. The paper also aims to outline guidelines for placement of the Obstacle Detection System (ODS) and the sensor angle to be used in our Intelligent Mobility Cane (IMC) prototypes. A usability test with 11 people who use a white cane for daily navigation was conducted to study the users' preference and behavior with a fixed sensor angle IMC. The analysis revealed the angle of the sensor is dependent on the participant's height and how users hold the canes. The results suggest requirements for sensor angle orientation and outline guidelines for ODS designs on future IMCs.
Study on a New Method of Checking the Actual UV Photon Number of Substation Electric Apparatus

As an effective approach of testing corona discharge, UV imaging technology is non-contact, sensitive and intuitive, thus with a bright application prospect in the fault detection of electric apparatus. Currently, the strength of corona discharge is usually represented by the photon number output by UV imager, but in field service, it is difficult to accurately determine and quantitatively control the measuring distance. As a result, the different detection results of photon number are less comparable. This paper first deduces the relationship between the image height of substation electric apparatus and the actual number of UV photons by establishing a model for actual UV photons calculating, and then obtains the statistical results of UV photons of insulators and grading rings in time of different measuring distances and image heights by conducting the field UV corona test. Finally, a new method of checking the actual photons of UV corona is proposed according to the proposed model and field test data. According to the research results, the checking of actual photon number after taking the image height of electric apparatus into account may effectively eliminate the impact of measuring distance during field UV detection.
Learning Representational Invariance Instead of Categorization

The current most accurate models of image object categorization are deep neural networks trained on large labeled data sets. Minimizing a classification loss between the predictions of the network and the true labels has proven an effective way to learn discriminative functions of the object classes. However, recent studies have suggested that such models learn highly discriminative features that are not aligned with visual perception and might be at the root of adversarial vulnerability. Here, we propose to replace the classification loss with the joint optimization of invariance to identity-preserving transformations of images (data augmentation invariance), and the invariance to objects of the same category (class-wise invariance). We hypothesize that optimizing these invariance objectives might yield features more aligned with visual perception, more robust to adversarial perturbations, while still suitable for accurate object categorization.
Towards Creating a Speech Corpus for Polish Using Public Domain Audiobooks

The goal of automatic speech recognition (ASR) is to convert spoken language into text. To deliver high-quality speech recognition, large training data sets, called speech corpora, are required. Preparation of such resources is time-consuming and usually involves many people. In this paper, an attempt to generate a Polish speech corpus automatically, based on existing public domain audiobook recordings and book texts, is presented. The described system for automatic generation of speech corpora was designed and then implemented with the Kaldi toolkit and additional scripts. Several experiments were conducted to prove the usefulness of the obtained corpus in speech recognition and to emphasize the importance of the amount of training data.
Client analysis and interface design of mobile meteorological service in low altitude airspace

Low altitude airspace mobile weather service is based on the short service of meteorological services in China's rapidly developing low altitude navigation industry. Through the analysis of the weather information data acquisition status and the analysis of the main meteorological service software products in the market, it is concluded that professional low altitude weather service software products are needed to meet the demand of low altitude aircraft operation. The software interface design of low altitude airspace mobile meteorological service demand function is analysed, and the Android technology is used to design and implement the client interface of meteorological services.
Intelligent Techniques for Prediction of Engineering Colleges After XII

Today, students are very confused while selecting colleges based on their ranking after XII standard exam. If students are willing to go for engineering, then they are interested to know the name of colleges on the basis of their merit. The particular college depends on several factors. More and more colleges are interested in mapping students' other features such as extra-curricular activities and financial background, so that they can provide better platforms to sharpen their skills. Thus, this paper proposes an intelligent technique to provide students a platform that will help them to match the colleges based on their academics and extra-curricular qualifications. A fuzzy inference and weighted fuzzy decision tree are used to calculate the score of each student based on the multiple factors of the student where results are shown to be promising.
Development of a New System for Handwriting Information Collection

The useable information of most Pen User Interfaces mainly consists of pen point trajectories and pressure. However, the handwriting is created dynamically, meanwhile, velocity, acceleration and force are changing during the handwriting generating. Actually when the pen point does relative motion to the paper, the motion between pen point and paper contain three-dimension force vector, the position of pen points and velocities, etc. Pen User Interface could not only acquire trajectories of pen points, but also obtain the contact force and the force direction, researchers could extract more abundant personalized features. Based on the innovative force information acquisition ie Force-Pen, this study designs a new handwriting information collection system which is offered by the Force-Pen.
Real-Time Hand Pose Estimation Using Depth Camera

In recent years, we have witnessed a steady growth of the research in real-time 3D hand pose estimation with depth cameras, since this technology plays an important role in various human–computer interaction applications. In this chapter, we first review existing techniques and systems for real-time 3D hand pose estimation. Then, we will discuss two point-set-based methods for 3D hand pose estimation from depth images: (1) point-set-based holistic regression method that directly regresses holistic 3D hand pose; (2) point-set-based point-wise regression method that generates dense outputs for robust 3D hand pose estimation. Extensive experiments are conducted to evaluate the effectiveness of these two methods. We will also discuss the limitations and advantages of the proposed methods.
An Advanced Algorithm for Perfect Image Selection Based on Quality Matrix

Almost every human being is dependent on all the information received from the environment, so to improve the quality of visual information, various image enhancement methods are used. It means that enhancement methods are very useful to improve a bad quality image into an improved image that is liked by the viewer. We have tried to prepare a powerful algorithm to compare various image enhancement techniques on the basis of various quality parameters and tell their performance. All the codes have been executed using MATLAB code and assembled on MATLAB R2017b software gizmo.
Application of Anomaly Detection Methods in the Housing and Utility Infrastructure Data

Efficient and timely fault detection is a significant problem due to the intensifying use of modern technological solutions in machine condition monitoring. This work is carried out as part of a project that is aimed at development of software solutions for a housing and utility condition monitoring system. An experimental setup was designed and assembled for the study of basic housing infrastructure elements operating modes. The setup includes electric pumps, power transformers, ventilation and air conditioning systems (HVAC), heaters and electric boilers. Every element is equipped with various sensors. Sensor readings were gathered, processed and analyzed. This dataset was used to fit statistical and probabilistic models such as linear regression and Hidden Markov model in order to classify regular and faulty operating modes of equipment. Nine classes of equipment malfunction were modeled, these models are intended to be used as a theoretical basis for the design of industrial housing and utility condition monitoring systems.
User Requirements Elicitation

This chapter identifies why the collection of end user requirements is such a defining and important stage of any serious game development project. It outlines requirements elicitation techniques and discusses their strengths, weaknesses and where they are useful. It will then discuss how to analyse the data that has been obtained. Finally, it will describe the processes and lessons learned along the various stages of end user requirements gathering using a concrete case example.
Active Sampling Based on MMD for Model Adaptation

In this paper, we demonstrate a method for transfer learning with minimal supervised information. Recently, researchers have proposed various algorithms to solve transfer learning problems, especially the unsupervised domain adaptation problem. They mainly focus on how to learn a good common representation and use it directly for downstream task. Unfortunately, they ignore the fact that this representation may not capture target-specific feature for target task well. In order to solve this problem, this paper attempts to capture target-specific feature by utilizing labeled data in target domain. Now it’s a challenge that how to seek as little supervised information as possible to achieve good results. To overcome this challenge, we actively select instances for training and model adaptation based on MMD method. In this process, we try to label some valuable target data to capture target-specific feature and fine-tune the classifier networks. We choose a batch of data in target domain far from common representation space and having maximum entropy. The first requirement is helpful to learn a good representation for target domain and the second requirement tries to improve the classifier performance. Finally, we experiment with our method on several datasets which shows significant improvement and competitive advantage against common methods.
Statistical machine learning

Abstract This chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.
Improving Service Performance in Oversubscribed IaaS Cloud

Cloud customers tend always to overestimate their resource requirements and thus, they utilize only a portion of the allocated resource which gives an opportunity for cloud providers to oversubscribe their resources. Oversubscription is a powerful technique that leverages unused resources which improves the profit of cloud providers while minimizing cost for customers. However, the benefits of this technique are without inherent risks: it increases the possibility of overload. This article proposes an autonomous architecture that uses memory oversubscription to maximize resources utilization rate. This architecture uses live migration of VMs as well as network memory as two strategies to mitigate overload generated by oversubscription.
New single‐trace side‐channel attacks on a specific class of Elgamal cryptosystem

The so-called attack is one of the most important order-2 element attacks, as it requires a non-adaptive chosen ciphertext which is considered as a more realistic attack model compared to adaptive chosen ciphertext scenario. To protect the implementation against attack, several literatures propose the simplest solution, i.e. ‘block the special message ’. In this study, the authors conduct an in-depth research on the attack based on the SMA and Montgomery ladder (ML) algorithms. They show that despite the unaccepted ciphertext countermeasure, other types of attacks are applicable to specific classes of Elgamal cryptosystems. They propose new chosen-message power-analysis attacks with order-4 elements which utilise a chosen ciphertext c such that where p is the prime number used as a modulus in Elgamal. Such a ciphertext can be found simply when . They demonstrate that ML and SMA algorithms are subjected to the new -type attack by utilising a different ciphertext. They implement the proposed attacks on the TARGET Board of the ChipWhisperer CW1173 and the proposed experiments validate the feasibility and effectiveness of the attacks by using only a single power trace.
Motion Estimation Approach for UAV Controls Using Bidirectional Two-Layer LSTMs

With the widespread use of unmanned aerial vehicles (UAVs), there is an increasing demand for the development of their control technology. The key interaction technology between humans and UAVs needs to focus on the human body language, which comprises rich interactive information, as it is the most natural, intuitive, and easy to master approach of interpersonal communication for humans. Therefore, the research on human motion estimation for UAV control is of considerable practical significance. Recently, deep learning has made breakthroughs in speech, image recognition and, other fields, and has crushed the performance of traditional methods in many fields. However, in the field of human motion estimation, deep learning has been progressing slowly. To overcome the limitations of the traditional methods and explore the application of deep learning methods in the field of motion estimation, this study proposes a method to estimate human arm motion using deep learning networks. We proposed a bidirectional two-layer LSTM fusion network to estimate the forearms' motion according to the hand position measured by HTC Vive. The performance was verified using a real data set. The average Euclidean distance similarity can reach up to 56%. In comparison with the traditional methods, the proposed method demonstrated wider applicability and better performance.
CyberCentric: Increasing SME and Citizen Resilience Against Cyberattacks

This chapter showcases the realistic cybersecurity scenario simulation platform CyberCentric. The objective of CyberCentric is to reach out to small- and medium-sized enterprises (SMEs) in a number of defined areas to provide them with the capability to share information on cybersecurity-related issues and help them experience the impact of cyberattacks as well as means to mitigate against them. CyberCentric’s overall goal is to provide SMEs and citizens with protective knowledge to improve their cyber resilience. This chapter provides an overview of the game and its components from a user perspective.
Compact Frequency Memory for Reinforcement Learning with Hidden States

Memory-based reinforcement learning approaches keep track of past experiences of the agent in environments with hidden states. This may require extensive use of memory that limits the practice of these methods in a real-life problem. The motivation behind this study is the observation that less frequent transitions provide more reliable information about the current state of the agent in ambiguous environments. In this work, a selective memory approach based on the frequencies of transitions is proposed to avoid keeping the transitions which are unrelated to the agent’s current state. Experiments show that the usage of a compact and selective memory may improve and speed up the learning process.
Analyses of the effectiveness of management of national educational systems by the example of the experience of leading countries

The article presents the results of a study of the effectiveness of management of national educational systems by the example of the North American, European and Scandinavian models of higher education as the most successful in accordance with the data of leading world rankings. The study took place in two stages. At the first stage, the main factors affecting the efficiency and competitiveness of the educational system were ranked, such as: high government spending on the education system, continuous increase in accessibility of higher education for the population, ensuring high quality educational services, export orientation, and others. At the second stage of the study, a system of indicators was formed to evaluate the effectiveness of managing national educational systems, which are presented by the example of the leading countries - representatives of the North American, European and Scandinavian models of higher education (the USA, the UK and Sweden). In addition to quantitative indicators, such as the number of students or government spending on education, which may have a rather large variation of values, since they are related to objective parameters: the total population of a country or occupied territories. We also identified quality indicators: mobility ratios, enrollment in higher education, etc.
Assessing and Improving Process Monitorability

In Chap. 5, we presented a method to easily derive from a BPMN collaboration diagram the E-GSM process models and the criteria to map smart objects to artifacts.
Neural Machine Translation on Myanmar Language

This work covers neural machine translation from Myanmar (Burmese) to English languages. Rule-based syllable breaking approach is used for Myanmar sentences as pre-processing stage. Recurrent neural network Encoder-Decoder architecture with attention mechanism is implemented to evaluate BLEU score on Myanmar English translation results. Batch size 64 can give better BLEU score than other batch sizes from our experimental results. However, increasing training epoch doesn’t have much effect on BLEU score.
Analysis of No-Reference IQA Metrics and Their Verification for Image Databases

This paper is devoted to assessing effectiveness of verification of no-reference quality metrics. The analysis of the impact of image visual quality on accuracy of verification of quality metrics is carried out. The problem of estimating metrics adequacy for images with almost unnoticeable distortions is shown. Modifications of rank correlation coefficients are proposed to address this problem. The results are presented for several good particular quality metrics as well as the designed neural network based combined visual quality metric.
How to guarantee the results of investigations with limited statistical information

Abstract What kind of problems do researchers usually have after investigations connected with different physical processes (e.g., temperature, deterioration, pressure, vibration, and so forth)? In many mathematical papers, textbooks and reference books it is suggested that 340-360 statistical data are needed to obtain the right conclusion by experimentation [1-3]. In these cases, much money (and time, days and even months) will be required. Usually, researchers try to restrict the amount of statistical data to 3, 5, 10 or 30 data points. But in this case, they do not have enough information to determine the trustworthiness of their deductions. Our task is the following: to solve this problem and demonstrate the fact that if there are even only three points of observation, we can still obtain a convincing response/result. It is rather difficult to overestimate the significance of this achievement in practice.
Digital dawn

Academic libraries had already undergone many changes, including several phases of automation before the launch of the World Wide Web in 1989, but the development of easily accessible online information resources would later challenge the professional identity of librarians and value of libraries. Before directly addressing the problem of electronic resources for libraries, it is helpful and necessary to explore an important intermediate step, the development and demise of CD-ROM services in academic libraries. Whatever the many shortcomings of this technology and associated business models, the very first documented electronic resources librarians were in fact CD-ROM specialists. Understanding and adapting to the challenges of CD-ROM helped libraries begin to adapt to the new online frontier.
Noise Reduction in Network Embedding

Network Embedding aims to learn latent representations and effectively preserves structure of network and information of vertices. Recently, networks with rich side information such as vertex’s label and links between vertices have attracted significant interest due to its wide applications such as node classification and link prediction. It’s well known that, in real world applications, network always contains mislabeled vertices and edges, which will cause the embedding preserves mistake information. However, current semi-supervised graph embedding algorithms assume the vertex label is ground-truth. Manually relabel all mislabeled vertices is always inapplicable, therefore, how to effective reduce noise so as to maximize the graph analysis task performance is extremely important. In this paper, we focus on reducing label noise ratio in dataset to obtain more reasonable embedding. We proposed two methods for any semi-supervised network embedding algorithm to tackle it: first approach uses a model to identify potential noise vertices and correct them, second approach uses two voting strategy to precisely relabel vertex. To the best of our knowledge, we are the first to tackle this issue in network embedding. Our experiments are conducted on three public data sets.