Reviewerly/exHarmony huggingface dataset:
Copyright Protection Method for Vector Map Data

In this paper, we propose a copyright protection method for vector cartographic data, mostly used in geographic information systems. The method is based on the following key ideas: firstly, the watermark image is embedded into the vector map by cyclically shifting the vertex list of each polygonal object, and secondly, the watermark image is used as a secondary carrier for the watermark, presented in the form of a bit sequence. A new sequence detection algorithm providing high robustness against map cropping is proposed. The experimental study on the information capacity of the watermark image is performed. The experimental study on the method robustness against vector data transformations is performed. It is shown that the proposed method is robust against all possible transformations of the vector map and, thus, completely suits for copyright protection. The quality analysis of the proposed method is performed. It is shown that the obtained method for vector map data protection satisfies all the necessary requirements for watermarking methods, thereby demonstrating a clear advantage over the known ones.
An Approach of Security Model to Mitigate Risk of Cyberattacks on Public Institutions in Ecuador

Security problems due to cyberattacks were analyzed, checked security models designed to protect and preserve databases of information from these institutions, using all the parameters of information security. It is planted propose a prototype of a security model to mitigate the risks of cyberattacks on public institutions to define the organizational structure and security levels in order to adopt security protocols. A security model was proposed, and a general network topology that helped meet the objective. The deductive method was used in the investigation to verify the current trends of security models and to protect the information available on the ministerial entity. The prototype of a security model was established to mitigate the risks of cyberattacks. It is concluded that a security strategy are necessary data through a correct, reliable and highly available route that offers a higher level of reliability. The content of this plan was presented and made available to all members of the public institution, especially those who are actively involved in safety procedures of information.
Design of Data Mining System Based on Cloud Computing

In order to improve the performance and speed of data storage, this paper designs a data mining system based on cloud computing. The basic structure, operation mode and programming principle of the cloud computing platform Spark are described in detail. The cloud computing platform Spark realizes the parallel design of decision tree C4.5 algorithm and K-medoids clustering algorithm, which greatly improves the operation speed, convergence speed and result stability of the algorithm. The experimental results show that the data mining system designed in this paper has faster operation speed and better classification efficiency when analyzing and processing massive data.
Automated Cells Counting for Leukaemia and Malaria Detection Based on RGB and HSV Colour Spaces Analysis

There are various types of diseases which are originated from the blood, for example leukaemia, malaria and anaemia. Leukaemia is a cancer which starts in blood forming tissues usually the bone marrow. On the other hand, malaria is transmitted through the bite of infected mosquito that carrying the Plasmodium parasite. Haematologists needs to perform the WBCs count in order to determine if a person has leukaemia and parasite count to check for the malaria density. However, the conventional procedure is very vulnerable due to human error and large time consumption. As a solution, this study proposes automated cells counting for leukaemia and malaria detection by analyzing the best colour component of RGB and HSV colour spaces. To obtain the cells counting result, there are several image processing steps to be implemented; (1) image acquisition by capturing the leukaemia blood samples using a computerized Leica DLMA 1200 digital microscope, (2) colour conversion from RGB to single colour component of RGB and HSV, (3) image segmentation using Otsu thresholding, (4) removing of unwanted regions and, (5) cells counting process. Overall, segmentation using green component of RGB colour space has proven to be the best in segmenting leukaemia images with 83.84% while saturation component of HSV colour space hold the highest accuracy for malaria images with 89.87%. Conclusively, this research is expected to help improving the detection phase of malaria and leukaemia diseases by overcome problems that been identify in this research.
Topic Identification for Semantic Grouping based on Hidden Markov Model

Topic identification is a research initiative, that aims at techniques for the easy extraction of required news events. Unstructured news data is in the form of Natural Language, which is a challenge to identify the topics of the same events. It has structural and lexical ambiguity which makes it difficult to correlate and extract the information. In our work to make Information Extraction in news events efficient, an approach is proposed where Parts of Speech (POS) Tagging and Named Entity Recognition (NER) is used. Python library spacy uses Hidden Markov Model (HMM) and Conditional Random Fields (CRF) to identify the 16 NER tags. Accuracy of 16 tags is also discussed. In this way, in future, two or more events can be correlated and can get to know information related to specific news events.
Anti-Fake Technology of Commodity by Using QR Code

QRcodes are often used in commodities trade, one of its applications is covering credible authentication information to avoid customer buying forged goods. This paper presents an applicable scheme which proposes an idea on how the QRcode data interacts with a customer and the interaction between qrcodes and the server. RSA and DES were used to encrypt qrcode to ensure the security of data. Besides, a scheme which enables decrypting qrcode and storing keys is presented and textbooks are served as client-server in this research. By comparing the effect to the security of qrcode when using DES or RSA only, it shows that in this way the cost of forging goods is significantly increased, which makes the rapid anti-fake identification possible.
Mutational puissance assisted neuroevolution

Artificial Neural Networks (ANN) are often trained using back-propagation, wherein the interconnection weights are determined based on the error gradient. ANNs have also been evolved using neuroevolutionary techniques. The weights in such ANNs are updated randomly by Gaussian mutation. Either all, or a random number of weights are modified, and the performance of the resulting ANN is determined. This paper describes a novel mutation strategy that enhances the performance of random selection by augmenting a dedicated mutational puissance to every weight within the ANN. These puissances are either replenished or consumed based on whether the performance of the ANN improved or deteriorated. Mutational puissance thus, tends to preserve, in some form, the information on how well the associated weight contributed to the performance of the ANN. We have embedded this technique in a robot to enable it to evolve ANN-based controllers for specific tasks, on-the-fly. The experiments and results portrayed in the paper conclusively endorse the efficacy of guiding mutation using the concept of puissance.
Exponential Cipher Based on Residue Number System for the Security of Text Document

Residue number system which was inspired by the Chinese remainder theorem provides a method for using groups of smaller numbers to represent large integers. Although cryptographic algorithms based on radix or decimal arithmetic have their advantages, direct sum of smaller rings properties-based algorithms offers better benefits. In this paper, the application of RNS number systems successfully utilized in text security for exponential cipher. RNS-based cipher systems exhibit properties that increase computation speed, reduce the time complexity, and make the system immune to known plain text attacks and algebraic attacks. In addition to this, the characteristics of exponential stream cipher systems, the key generation, and residue number system-based encryption and decryption are discussed in this paper.
Simulation Studies of the Hybrid Human-Fuzzy Controller for Path Tracking of an Autonomous Vehicle

Human intelligence and experience help them in making a decision and recognize a pattern. This ability enables the driver to take action even in an unexpected situation. The hybrid integration between human intelligence/experience and machine controller able to improve the autonomous vehicle path tracking capability. The path tracking capability is the main concern of the autonomous vehicle. The Fuzzy developed from the experiment’s data. The experiments (human navigation experiments) used to gather the appropriate data from humans while controlling the buggy car. Data then use to develop the membership functions for inputs and output of the Fuzzy controller. The simulation uses to study the performance of the Fuzzy controller. The recorded path tracking error from the simulations for the right and left turn maneuver is 9 m and 7.5 m, respectively.
Real-Time Interactive Online 3D Graphical User Interface (GUI) Technical Implementation and Usability Test for Architectural Technical Teaching

The development of a real-time GUI (graphical user interface) is considered to be an important factor in the future success and growth of the Internet for products and services towards entrepreneurship. However, there is lack of technical implementation and usability test for real-time interactive online 3D GUI for architectural technical teaching in terms of software and development of GUI, which is the aim of this study that covers the use of physical algorithms, programming, graphical programming, and conventional and traditional experimental design. These work resulted in the development of a complete process road map for implementation and testing, highlighting the many variables that can affect the success or failure of the GUI development process. Through the design of the online real-time 3D interactive GUI, there are five novel discoveries in key software platforms, which are (1) optimizing file download time, (2) optimizing baking to maximize visual quality, (3) optimizing the overall texture size of the GPU, (4) optimizing the relationship between the number of surfaces, and (5) optimizing the file size and understanding the impact of copied surfaces and textures. The results of the technical section lead to the conclusion that real-time 3D Interactive online GUIs are possible and may lead to a new 3D internet browsing experience in the future.
Gen_braille: Development of a Braille Pattern Printing Method for Parametric 3D CAD Modeling

The aim of this research is to establish a method that visually impaired people can use for shaping three-dimensional (3D) objects by themselves using programmable computer-aided design (CAD). In parametric CAD, variable definition and substitution are devised in a source code of the programmable CAD. If we prepare 3D data of assistive technologies (ATs) with the source code of the parametric CAD and also prepare an intuitive user interface for variable substitutions, we can realize a do-it-yourself AT useful for many visually impaired people. Thus, it is effective to place braille on the surface of a modeled object and use the 3D model as an AT for the visually impaired people. In this research, a CAD module called gen_braille was created for the OpenJSCAD software. When an arbitrary braille character string is provided to this model, it outputs corresponding 3D dot patterns. The aim of this research is to realize a braille generation system that can be used for parametric CAD; therefore, a required area calculation function was implemented. The author was able to produce an output of 32 square plates having sides of 2 cm each that displayed a single braille character. Debris, which could be misread, had adhered to 25 print results. The mean of the amount of the adhered debris was 1.08 (SD = 0.93). The debris could be removed with an ordinary post-printing process called manual removal. To prove that gen_braille could be used in parametric CAD, the tactile braille ruler creation system was made. By changing the value of the unit variable, it was possible to output a ruler with a freely chosen unit system (metric or inch).
Depth and Colour Perception in Real and Virtual Robot Cells in the Context of Occupational Safety and Health

Investigations into comparisons of real and virtual environments serve multiple purposes. Results may improve development, implementation and application of virtual environments, inform about potentials for positive transfer of effects and facilitate decision making about appropriate virtual reality techniques in specific situations on human factors and ergonomics in human-system interaction. Absolute and relative estimations of object sizes, colours and greyscales were differentially investigated in virtual and real robot cells. Analyses revealed differences for environments (e.g. size, colour) and in some cases even for specific characteristics under investigation (e.g. robot cell size, 10% greyscales). Differential results on human task performance in virtual environments with potential technical constraints are crucial for safety and health reasons and because they provide a basis for human behaviour consequences and work system design decisions and requirements for transfer research. Results support informed decision making about virtual reality techniques and media selection in OSH applications on training and work systems design.
DCT Image Compression Implemented on Raspberry Pi to Compress Image Captured by CMOS Image Sensor

The purpose of compression is to reduce the amount of data at the same time maintain the quality of image and signal for the other purpose. Discrete Cosine Transform (DCT) is a family of image compression where the raw image is transformed to the other domain to produce smaller size of data. DCT transform has low computational complexity and fast processing algorithm. In this project, DCT transform will be implemented using PI camera and Raspberry Pi SBC development board running on an ARM based processor. The raspberry Pi board has an advantage of image processing implementation due to the existing software development tool offered a rich feature for image processing such as OPENCV. The result of applying DCT compression algorithm on images with six compression rate level which are 10, 20, 50, 100, 170 and 200. The best performance can be achieved with compression rate level 200. However, on reducing the quality level of compression rate, the error measurements start becoming worse until a point is reached, where the perceptual difference from the original image can be easily noted.
Arms and Hands Segmentation for Egocentric Perspective Based on PSPNet and Deeplab

First person videos and games are the central paradigms of camera positioning when using Head Mounted Displays (HMDs). In these situations, the user’s hands and arms play a fundamental role in self-presence feeling and interface. While their visual image is trivial in Augmented Reality devices or when using depth cameras attached to the HMDs, their rendering is not trivial to be solved with regular HMD, such as those based on smartphone devices. This work proposes the usage of semantic image segmentation with Fully Convolutional Networks for detaching user’s hands and arms from a raw image, captured by regular cameras, positioned as a First Person visual schema. We first create a training dataset composed by 4041 images and a validation dataset composed of 322 images, both of them receive labels for an arm and no-arm pixels, focused on the egocentric view. Then, based on two important architectures related to semantic segmentation - PSPNet and Deeplab - we propose a specific calibration for the particular scenario composed of hands and arms, captured from an HMD perspective. Our results show that PSPNet has better detail segmentation while Deeplab achieves best inference time performance. Training with our egocentric dataset generates better arm segmentation than using images in different and more general perspectives.
Proposed Pairs Selection Framework

This chapter proposes a new framework for pairs selection aiming to answer this work’s first research question (introduced in Chap. 1): “Can Unsupervised Learning find more promising pairs?”. It starts by describing the problems associated with the most commonly used approaches to select pairs, and how they may guide us in the quest for a novel approach. Next, a framework composed of 3 stages, (i) dimensionality reduction, (ii) Unsupervised Learning and (iii) pairs selection, is introduced. Each stage is described separately. First, a useful tool for reducing data dimensionality, Principal Component Analysis, is explored. Then, the pursuit of the most suitable Unsupervised Learning algorithm for clustering is followed in detail. Finally, the criteria adopted to select potentially profitable pairs from the clusters previously formed are outlined. In the end, a summary diagram connecting the different concepts introduced throughout the chapter is presented, for consolidation purposes.
User-Centric AR Sceneized Gesture Interaction Design

With the rapid development of AR technology, the interaction between humans and computers has become increasingly complex and frequent. However, many interactive technologies in AR currently do not have a very perfect interaction mode, and they are facing huge challenges in terms of design and technical implementation, including that AR gesture interaction methods have not yet been established. There is no universal gesture vocabulary in currently developed AR applications. Identifying appropriate gestures for aerial interaction is an important design decision based on criteria such as ease of learning, metaphors, memory, subjective fatigue, and effort [1]. It must be designed and confirmed in the early stages of system development, and will seriously affect each aerial application project development process as well as the intended user of the user experience (UX) [2]. Thanks to user-centric and user-defined role-playing method, this paper set up a suitable car simulation scenarios, allowing users to define the 3D space matches the information exchange system under AR design environment based on their habits and cultural backgrounds, in particular, It is a demanding gesture during the tour guide and proposes a mental model of gesture preference.
A multiresolution mixture generative adversarial network for video super-resolution

Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the visual perception quality and coherence in videos, this paper proposes a multiresolution mixture generative adversarial network for video super-resolution (MRMVSR). We propose a multiresolution mixture network (MRMNet) as the generative network that can simultaneously generate multiresolution feature maps. In MRMNet, the high-resolution (HR) feature maps can continuously extract information from low-resolution (LR) feature maps to supplement information. In addition, we propose a residual fluctuation loss function for video super-resolution. The residual fluctuation loss function is used to reduce the overall residual fluctuation on SR and HR video frames to avoid a scenario where local differences are too large. Experimental results on the public benchmark dataset show that our method outperforms the state-of-the-art methods for the majority of the test sets.
Single-Frame-Based Deep View Synchronization for Unsynchronized Multicamera Surveillance

Multicamera surveillance has been an active research topic for understanding and modeling scenes. Compared to a single camera, multicameras provide larger field-of-view and more object cues, and the related applications are multiview counting, multiview tracking, 3-D pose estimation or 3-D reconstruction, and so on. It is usually assumed that the cameras are all temporally synchronized when designing models for these multicamera-based tasks. However, this assumption is not always valid, especially for multicamera systems with network transmission delay and low frame rates due to limited network bandwidth, resulting in desynchronization of the captured frames across cameras. To handle the issue of unsynchronized multicameras, in this article, we propose a synchronization model that works in conjunction with existing deep neural network (DNN)-based multiview models, thus avoiding the redesign of the whole model. We consider two variants of the model, based on where in the pipeline the synchronization occurs, scene-level synchronization and camera-level synchronization. The view synchronization step and the task-specific view fusion and prediction step are unified in the same framework and trained in an end-to-end fashion. Our view synchronization models are applied to different DNNs-based multicamera vision tasks under the unsynchronized setting, including multiview counting and 3-D pose estimation, and achieve good performance compared to baselines.
Interactive Mixed Reality Cooking Assistant for Unskilled Operating Scenario

As the further development of virtual reality technology, mixed reality (MR) promotes the innovation of human-computer interaction. It can integrate virtual information (objects, pictures, videos, etc.) into the real environment, and directly interact with people. It can greatly improve the efficiency of information reception, reduce the cost of understanding and learning, and improve the fluency and accuracy of operational tasks. In the kitchen scene, when the novices cook according to the recipe, they usually do not perform well in memory and learning, which causes many problems in the cooking process. Therefore, we propose an interactive intelligent cooking system design, which focuses on the five kinds of information that novices need most help in the cooking process, and subverts the traditional recipe with MR presentation. The experiment also shows that the intelligent cooking system can make up for the guidance to novice which is easy to be ignored by traditional recipes, and can effectively improve the user experience of novices.
“Trust Me, You Will Need It”: Cybersecurity as Extracurricular Subject at Estonian Schools

Discovering new talents in IT and cybersecurity is increasingly vital in the information society. The traditional approach has been to start professional profiling when the youth reach grade 10 (age 16–17) – many young people find a topic to focus on in the secondary school and finally form a profession with the 3rd-level studies (trade school or university). Yet, this may be an outdated way, as the leaders of tomorrow will need competencies in more than one field, cyber skills being likely one of them. The initial results of the Estonian best-practice: Competition CyberCracker School round for 7–12th graders suggests that the method works in several aspects. First, the number of talented youth is quite large in comparison with population numbers - the mass screening in the first rounds allows find also those who would not enter any competitions (for various reasons). Second, while the gender gap still exists (interviews with the participants reveal that the number of girls seeing themselves as future cybersecurity specialists is still small compared to boys), the 2/2 quota does not reflect ‘equality on paper only’ - the actual enthusiasm does not differ (once the latter have actually reached the final round). Overall, the positive trend is clearly visible - just in a year (2018/2019), the number of participants went up from 1000 to 2344 and the number of participating schools from 47 to 65.