Reviewerly/exHarmony huggingface dataset:
A Novel Algorithm of Real-time Aerial Image Mosaic

Aerial image mosaic is to stitch the images taken by UAV (Unmanned aerial vehicles) from different positions into a panoramic image, which has attracted significant interest in enemy reconnaissance, terrain detection and environmental monitoring. Aiming at the problem of low process speed in the traditional algorithm, this paper proposes a real-time aerial image mosaic algorithm. First, we take advantage of ORB feature for feature extraction, and then KNN (K-Nearest Neighbor) algorithm based on Hash table is applied for fast feature matching. In order to make the algorithm more robust, we use RANSAC (Random Sampling Consistency) method to remove mismatched feature points in the process of estimating the transformation matrix. In the stage of image registration, we propose a new coordinates unification method based on the background image, which greatly reduces the amount of computation. Finally, we present a method to find the overlapped areas of images effectively and the distance weighted fusion algorithm is used in the overlapped areas to eliminate seams between images. Experiments show that the image mosaic algorithm proposed in this paper has both real-time performance and good stitching quality.
Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks

Training Deep neural networks (DNNs) on noisy labeled datasets is a challenging problem, because learning on mislabeled examples deteriorates the performance of the network. As the ground truth availability is limited with real-world noisy datasets, previous papers created synthetic noisy datasets by randomly modifying the labels of training examples of clean datasets. However, no final conclusions can be derived by just using this random noise, since it excludes feature-dependent noise. Thus, it is imperative to generate feature-dependent noisy datasets that additionally provide ground truth. Therefore, we propose an intuitive approach to creating feature-dependent noisy datasets by utilizing the training predictions of DNNs on clean datasets that also retain true label information. We refer to these datasets as "Pseudo Noisy datasets". We conduct several experiments to establish that Pseudo noisy datasets resemble feature-dependent noisy datasets across different conditions. We further randomly generate synthetic noisy datasets with the same noise distribution as that of Pseudo noise (referred as "Randomized Noise") to empirically show that i) learning is easier with feature-dependent label noise compared to random noise, ii) irrespective of noise distribution, Pseudo noisy datasets mimic feature-dependent label noise and iii) current training methods are not generalizable to feature-dependent label noise. Therefore, we believe that Pseudo noisy datasets will be quite helpful to study and develop robust training methods.
Deep Fisher Vector Coding For Whole Slide Image Classification

Adopting machine learning methods for histological sections is a challenging task given the generated huge size of whole slide images (WSIs) especially using high power resolution. In this paper we propose a novel WSI classification method which efficiently predicts a WSI's label. The proposed method considers each WSI as a population of patches and computes a statistic by having some samples from the population. This statistic can be computed efficiently, and our test time on a WSI is about one tenth of that of the existing methods. Moreover, our pooling strategy on the WSI is more general than that of previous works. Further, the assumptions of our method are quite general, and therefore, it is applicable to any WSI classification task. The experiments show that the performance of our method is competitive in two different tasks, while, unlike some of the competing methods, it does not consider any prior clinical knowledge about the label to be predicted.
Digital Image Falsification Detection Based on Dichromatic Model

Nowadays, digital images may be simply changed by victimization superior computers, subtle photo-editing, tricks package, etc. These changes will impact picture validity, from law, politics, the media, and industry. Detecting falsification of digital images is one of the most critical analytical practices in the present time. Altering the digital image will, in general, disrupt those underlying vision-based regularities that occur due to environment, lens, and sensor, and this initiative introduces new forensic image related vision-based research method for color lighting.
MobileNet-based Neural Image Caption Model in Title Generation for Product's Images

In e-commerce, it is not hard to find a product title that doesn't match with the product image, or even is totally not relevant. A machine learning model can be employed to generate a suitable title product given a product image. In this paper, we describe the development of neural network model to address the aforementioned issue. We use Neural Image Caption (NIC) that is originally for general image captioning as our baseline model. We also describe the development of our own dataset to build the model, the modifications of NIC for product title generation, and the performance of the proposed model, which is measured by using BLEU score. The proposed model modifies the baseline by changing the CNN encoder with a fine-tuned MobileNetV2 and changing the inference technique to greedy search. We then provide performance evaluations for these modifications with respect to the baseline model. We also compare the model performance on the proposed preprocessed data and the raw (original) data. Training using the preprocessed dataset yields a model with a better performance than using raw data, both in higher BLEU score and faster inference time. Our modified model with fine-tuning MobileNetV2 has BLEU-1 score of 58.2, while the baseline model which uses GoogLeNet without fine-tuning has BLEU-1 score of 55.5. Furthermore, changing inference technique to greedy search results a faster inference time (0.1 seconds) compared to the baseline model that uses beam search with beam size of 20 (27.661 seconds). Our model is then implementable for the chosen problem domain, e-commerce product title generation, which needs fast inference time without sacrificing model performance.
Priorities and development perspectives of pedagogical specialities and environmental training

The increasing of the teachers’ professionalism is the priority and the necessary condition of the modernization the educational system in the Moscow region. The state educational organizations of secondary vocational and higher education solve this task through the connection with the local authorities of the Moscow Region educational municipalities. Method of theoretical analysis and systematization of scientific ideas; study and analysis of regulatory document were used. The conducted research allowed substantiating the priorities and prospects for the pedagogical specialties development in colleges through the necessity to implement the fundamental goals of continuing pedagogical education. The activity of the Moscow region pedagogical colleges focused on the next key points: the increasing of the raising the profile of the teaching profession; the increasing of the number of students, participating in the professional competitions; the development and integration of the digital tools into the educational process; the increasing of the percentage of graduates ‘ employment and retention in the profession; motivation to obtain higher education; training of persons with disabilities; introduction of the electronic resources’ system in the training process in the context of a pandemic, restrictive measures and the use of distance education opportunities.
Protection of critical information in external data stores using the dissection-separation method

Abstract Today, due to the large spread of cloud technologies, more and more individuals and businesses use their services. It is common practice in the field of cloud technologies to store in-formation (data) from one service provider in one physical and geographical location. At the same time, we must remember that the information passes through third parties whose intentions are not always known. Therefore, valuable critical information is usually encrypted before it is sent to the cloud on the user’s side. To do this, you can use both simple tools (for example, creating password-protected archives) and more advanced tools (software for creating encrypted partitions). However, in addition to normal encryption, you can use the fact that there are many unrelated repositories. Such methods include, for example, the dissection-separation method, which is discussed in this paper.
METHODS TO MOTIVATE REMOTE TEACHERS IN A PANDEMIC ENVIRONMENT

The article examines the factors that affect the success of university remote teachers in a pandemic environment, the importance of maintaining the work motivation of teachers to ensure high performance e-learning, and the need for joint efforts by teachers and university administration to prevent professional burnout and maintain a sense of belonging to the team.
Lifestyle Authentication Using a Correlation Between Activity and GPS/Wi-Fi Data

In recent years, lifestyle authentication, which combines multiple personal behavioral data for authentication, has been proposed as a new authentication method in addition to traditional knowledge-based authentication, possession-based authentication, and biometrics-based authentication. In previous research on lifestyle authentication, authentication scores of each authentication element were often calculated independently and used for the final authentication, ignoring the correlation between each element. It was also often difficult to apply lifestyle authentication methods in the real world because they required a large amount of preliminary data. In this paper, we propose a new method that solves these problems by using the correlation between GPS/Wi-Fi data from smartphones and activity data (activity types that are inferred from the metabolic equivalent of task (MET)) from activity trackers. We applied our method to the data collected in the MITHRA project, which is a proof-of-concept experiment of lifestyle authentication. As a result, we achieved an equal error rate (EER) of 0.087 and 0.130 when ideal data were obtained and not obtained, respectively.
Developing HDR Tone Mapping Operators Based on Uniform Colour Spaces

To perform tone mapping from high dynamic range images to standard dynamic range, scene luminance is typically used. The lightness or brightness from uniform colour spaces (UCSs) are hardly used. In this study, the role of the UCSs was evaluated by transforming the HDR radiance map to a particular colour space, followed by tone mapping of brightness, lightness or intensity channel of that colour space. Four UCSs, CIELAB, CIECAM02, IPT and recently developed Jzazbz were used in tone mapping. The image was first decomposed into large scale variation base layer and detail layer. The tone curve was applied on the base layer while detail layer was enhanced by local adaptation. The author’s newly developed method to evaluate TMOs was used to develop new TMOs by minimizing the CIELAB(2:1) colour difference between the TMO and the reference images.
Copelia: Wearable Augmented Reality Application for Training and Maintenance of Reclosers

This short paper describes the study case of Copelia, an on-going project for a major Brazilian electricity company. The application employs augmented reality for training and maintenance. It runs on an HMT-1 smart glass and features automatic detection of reclosers and guided instructions for maintenance operations. This allows generalist electricians to perform tasks that would otherwise be performed exclusively by other skilled professionals. Preliminary results show that Copelia’s features are desirable for this kind of application and they can indeed be useful for both maintenance and training. However, users are not familiar with the technology and resisted the voice control interaction paradigm. The team identified problems related to functionality and user experience which may apply to similar applications. Future works include user testing to assess whether the improvements to the user interface reflect in a broader acceptance of the application.
Piece Matching Optimization Based on Improved Genetic Strategy

Information Piece matching is a link in the system of shredded document reconstruction, and it plays a important role in the entire system. At present, although some methods have been proposed in research on it, these methods still have problems such as low matching accuracy, falling into local optimality easily and so on. Especially for actual document pieces, the results of existing methods are not good. In view of this, this paper starts from the perspective of combinatorial optimization, deeply analyzes the solution strategy of piece matching, and proposes a piece matching method based on improved genetic strategy. Through comparative experiments, the method is proved to realize the piece matching optimization in a global scope effectively.
Influence of anomalous truth values on logical inference in V<sup>TF</sup>-logics as a basis for verification of rule-based systems

Abstract The paper is devoted to the problem of expert systems knowledge bases verification. The methodological basis of verification is logics with vector semantics in the V TF -logics form. The knowledge model is a rule-based system. The issues of algorithmization of contradictions and other problems detection are considered in the paper. Algorithmization is based on the characteristic features of in V TF -logics inference. It is shown that in the formalism under consideration, the anomalous truth of a small premise generates the same conclusion (a large premise is considered strictly true). Anomalies such as falsity, uncertainty, and contradiction are considered. The problems of reducing the computational complexity of algorithms are considered.
Ingeniería de Software aplicada al método global de análisis estructural para el aprendizaje del Español, Tének e Inglés en educación básica

The global method of structural analysis is characterized in that from the first moment the information units with a complete meaning are presented to the child. The global method is to apply to the teaching of reading and writing the same process that is followed in children to teach them to speak. The child has a visual memory, recognizes words, sentences and phrases, with this, establishes relationships between words and discovers the identical elements in the image of two different words. The writtenword is the drawing of and image that evokes each idea. The prototype model of software engineering when applying the analysis technique, determines the functional requirements of the global method and transforms it into a software product for teaching Spanish, Tenek and English in first grade primary school children. the region of Ciudad Valles, San Luis Potosí. The prototype methodology has the following stages: communication, rapid plan, modeling, design, prototype construction and development. The use of technology combined with the global teaching method will achieve in children the meaningful and constructive learning of their mother tongue (tének), the Spanish language and a foreign language.
Ant Colony Optimization for Traveling Salesman Problem with Modified Pheromone Update Formula

Traveling Salesman Problem is a combinatorial problem from which various other problems have been derived in the real-world application. It is a well-known NP-complete problem. Its instances are used in various fields around the globe. There have been various optimization techniques that are used to solve this problem. The Ant Colony Optimization (ACO) is an optimization method that is very useful in solving various artificial intelligence problems and obtaining the optimized solution. There have been methods proposed after its introduction in 1991. When using the traditional ACO pheromone update formula on the large dataset of Traveling Salesman Problem, one might get an optimal solution at the cost of a great amount of time. In this paper, we have proposed a modification in the basic Ant Colony Optimization pheromone update formula for discovering the optimized solution for the Traveling Salesman Problem using the probability from the pheromone value from succeeding nodes. This updated formula also helps in reducing the time to obtain the optimal solution as compared to the traditional formula.
Phase-Based Estimation of the Harmonic Signal Frequency

This paper examines estimating the frequency of the information signal based on the analysis of the probability characteristics of the phase fluctuations when a mix of signal and narrowband noise is observed. The new estimation algorithm is described while the shortcomings of the commonly applied methods are specified. The performance of the proposed approach is illustrated by a number of examples. In particular, it is demonstrated that the application of the introduced procedure allows high accuracy measurements of the carrier frequency of the non-modulated, the multilevel phase and quadrature keyed signals.
Challenges of Algeria’s Economic Development: A Youth Entrepreneurship Perspective

This chapter develops a conceptual framework of economic development of Algeria with a particular focus on youth entrepreneurship. Drawing on the opportunity-based approach of Drucker (1985) and the impact analysis theory (IAT) advocated by Murphy and Marvel (2008), Lenjo (2015), and Dontigney (2018), this proposed conceptual framework theoretically allows the integration of different perspectives including economic development of a country and youth entrepreneurship. In principle, the opportunity-based theory posits that entrepreneurship represents a response to a stimulus, through a business venture, where an unemployed person exploits opportunities engendered by social, technological, and cultural changes in the environment. The IAT is a paradigm that deconstructs the causes and effects of a phenomenon (i.e. youth entrepreneurship) to afford greater understanding and appreciation of the research agenda, while providing an indicative assessment of the overall impact of the issue (OECD, 2014). The authors discuss the core barriers (venture capital, foreign direct investments, bureaucratic obstacles, gender discrimination, and soft skills) to entrepreneurship in Algeria, Furthermore, the authors assess the different economic forces in Algeria that appear to be promoting or sustaining entrepreneurship, particularly in young people. These forces have a tendency to be connected to a broader possibility of economic development. Further analysis is required that demonstrates the potential upsides of promoting youth entrepreneurship. Thus, the authors attempt to answer a key question: What can be done to engage the youth in small business entrepreneurship across Algeria and thus implement a sustainable solution to the country’s employment problem?
Meta-context Transformers for Domain-Specific Response Generation

Transformer-based models, such as GPT-2, have revolutionized the landscape of dialogue generation by capturing the long-range structures through language modeling. Though these models have exhibited excellent language coherence, they often lack relevance and terms when used for domain-specific response generation. In this paper, we present DSRNet (Domain Specific Response Network), a transformer-based model for dialogue response generation by reinforcing domain-specific attributes. In particular, we extract meta attributes from context and joinly model with the dialogue context utterances for better attention over domain-specific keyterms and relevance. We study the use of DSRNet in a multi-turn multi-interlocutor environment for domain-specific response generation. In our experiments, we evaluate DSRNet on Ubuntu dialogue datasets, which are mainly composed of various technical domain related dialogues for IT domain issue resolutions and also on CamRest676 dataset, which contains restaurant domain conversations. We observe that the responses produced by our model carry higher relevance due to the presence of domain-specific key attributes that exhibit better overlap with the attributes of the context. Our analysis shows that the performance improvement is mostly due to the infusion of key terms along with dialogues which result in better attention over domain-relevant terms.
Robust Graph Neural Networks Against Adversarial Attacks via Jointly Adversarial Training

Graph neural networks (GNNs) are powerful tools for analyzing graph-structured data. However, recent studies have shown that GNNs are vulnerable to small but intentional perturbations of input features and graph structures in the node classification task. Existing researches focus on enhancing the robustness of GNNs for a single type of perturbation such as graph structure perturbation or node feature perturbation. An ideal graph neural networks model should be able to resist the two kinds of perturbations. For this purpose, we propose a new adversarial training method for graph-structured data named Graph Jointly Adversarial Training (GJAT) which incorporates Graph Structure Adversarial Training (GSAT) and Graph Feature Adversarial Training (GFAT) two components and can resist perturbations from the topological structure and node attribute. Extensive experimental results demonstrate that our proposed method combining two kinds of adversarial training strategies can effectively improve the robustness of graph convolutional networks (GCNs) which is an important subset of GNNs.
Optimal Path Planning of UGS

Nowadays, unmanned ground systems (UGS) have broad prospects in many fields, involving transportation, military, industrial, agricultural, and space exploration, etc., which has a deep impact on the national economy, national defense security, and social progress. The chapter focuses on the path planning problem for UGSs.
The Minutovka – a Word Typing Web Game for Obtaining Typos to Create an Error Corpus

The paper describes the web application of a simple typing game, which we designed and implemented mainly to obtain typos and create an error corpus. We created the game for the Slovak language, as we lacked such data. However, it could be used for languages other than Slovak. We called it Minutovka (in English “One minute game”) because we set the maximum duration of the game time to one minute. During this time, a player must rewrite the words from the randomly displayed sentences as quickly as possible and with as few errors as possible. On the one hand, the player is motivated to get the highest score and beat other players in the daily, weekly, or overall rankings, while improving his typing on the keyboard. On the other hand, our motivation was to obtain data on how users write and especially what typos and errors they make during the transcription of the text. In this paper, we describe this game from the design to the implementation, including issues that have occurred during the operation of the game. We also designed fraud protection and implemented it with the proposed hacker score. At the end, we evaluate and visualize all the obtained data, which we store in the document-based database MongoDB.
Decision Pathways Leading to the Successful Digital Transformation of Learning

The journey to modernizing learning and development (L&D) in an institution involves many decisions, with each choice creating a unique path for that particular organization. Many paths may lead to successful results, though each will achieve those outcomes in a distinctive way. One path may, for example, rely heavily on a particular technology, while another is more engaging for learners, while another fits better within the budget of the institution. The key is to recognize that all decisions - even some minor ones - will influence successful outcomes at the end. This chapter provides a guide to help make more informed choices among alternatives along the way toward the successful digital transformation of learning.