Reviewerly/exHarmony huggingface dataset:
Enhancement Algorithm for Sandstorm Images

In order to reduce the impact of the increasingly frequent sandstorm weather on computer vision-related applications, we propose an enhancement algorithm for sandstorm images. First, we achieve white balance through the Shades of Gray (SoG) algorithm optimized by blue channel compensation. Then, we convert the corrected image into Lab color space. Next, using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the brightness part (L component) to improve the contrast of the sandstorm images. Finally, the L component is further Laplace sharpened to enhance the details. The experimental results show that the proposed method can effectively restore the fade characteristics of sandstorm-degraded images and improve the clarity of the images. In addition, our experiments also prove that our algorithm can significantly improve the feature point extraction results of images captured during sandstorm weather conditions to ameliorate computer vision applications during sandstorm weather.
Development of Fast Exponentiation Algorithm «To Center and Back»

In the present paper the exponentiation algorithm “To Center and Back” based on the idea of the additive chains exponentiation method is developed. The created by authors algorithm allows to reduce the calculation time and to improve the performance of conventional and cryptographic algorithms, as pre-quantum and quantum, and then post-quantum, in which it is necessary to use the fast exponentiation algorithm.
Vehicle-NnA Cnn Based Local Vehicle Detection Classifier

In this technological era, computer vision accomplishes superior performance and it introduced to the Convolutional neural network (CNN) which is acquainted for recognize an object. In addition to object detection and classification are considered difficult tasks in computer vision. From the ancient times vehicle is the only communication media to move from one place to another place. In this automation period local vehicles are decreasing day by day. Because these type of vehicles motion are slow. We want to preserve our culture with the help of vehicle-NN model. We presented our own CNN model name Vehicle-NN which will be a convenient effect on the vehicle sector of Bangladesh. We compared our CNN model with other pre-trained models like MobileNet, VGG16, InceptionV3. This proposed model plays an effective role in the future automation vehicle identification sector. Besides we want to hold on to our traditional roots and preserving our local culture. Our work approach is novel for the detection of local vehicles in Bangladesh. In this experiment, we have used 7 classes (Bus, CNG, Leguna, Pickup, Rickshaw, Thelagari, Van). We trained our model with our own dataset and our CNN model acquired accuracy is 96&#x0025;.
Evaluation of Deep Learning Models for Smoking Recognition with Smartwatch and Smartphone Sensors

Smartwatches and smartphones are extensively used in human activity recognition, particularly for step counting and daily sports applications, thanks to the motion sensors integrated into these devices. Machine learning algorithms are often utilized to process sensor data and classify the activities. There are many studies that explore the use of traditional classification algorithms in activity recognition, however, recently, deep learning approaches are also receiving attention. In this paper, we use a dataset that particularly consists of smoking-related activities and explores the recognition performance of three deep learning architectures, namely Long-Short Term Memory (LSTM)}, Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). We evaluate their performances according to different hyperparameters, different sensor types and device types. The results show that the performance of LSTM is much higher than that of CNN and RNN. Moreover, the use of magnetometer and gyroscope together with accelerometer data improves the performance. Use of data from smartphone sensors also enhances the performance results and the final accuracy with the best parameter combinations is observed to be 98%.
Affective Color Fields

In this art project, we create Affective Color Fields: an interactive artifact that takes in a user's narrative of their emotional experiences and dynamically transforms it into Rothkoesque color fields through emotion classification. Inspired by Mark Rothko's abstract depiction of human emotions and Merleau-Ponty's phenomenological inquiry, we wish to establish an intimate relationship between interactive art and the subject by employing user's own interpretation and framing of life events. Through the performative and improvisational art-making process, users can playfully appropriate our artifact for a rich and personal aesthetic experience.
Gatekeeper Security Check System Using VGG

Today, the world has become a place of uncertainty. With digitalizing the work architecture, there is high need for advancing the physical security of the organization too. The main reason behind this research is the issue of centralization of data inside the office within servers, physical files, etc. Ultimately the motive of an organization is protection from potential data theft and destruction and also security of their employees. The security guards play an important role in this, but human beings make mistakes. Thus, digitizing this system is the only way to keep up the level. This system leverages the image recognition technique to process every individual and filter the intruders from entering inside the organization. The CCTV camera propagates the real-time video streaming for processing to the face recognition engine and compares with existing models of regular persons (registered persons). For the research, we have used ID card detection for an additional layer of security.
THE VAKHTANGOV SCHOOL: CREATION OF TEACHING METHODS

The article presents information about the faithful disciple of Evgeny Vakhtangov, Boris Zakhava. For 50 years, Boris Zakhava continuously led the Shchukin Theater Institute. He was, perhaps, the only one of Vakhtangov’s students who, having come to the Studio from the first days of its foundation, remained faithful to the theatrical principles of Evgeny Vakhtangov. Also, the article examines the period of creation of Zakhava’s method of teaching acting at the Shchukin Theater Institute. A significant role in the formation of Zakhava as a theorist and teacher was played by his teacher, Evgeny Vakhtangov, a student of Stanislavski. Vakhtangov mastered the method of Stanislavski’s work with an actor and became the head of the Mansurov Studio, continued to search for new theatrical techniques, influencing the formation of Zakhava’s creative personality. In his practice, Vakhtangov moved away from the realism of the early Moscow Art Theater and created his direction, which he called fantastic realism. Thanks to his pedagogical talent, many wonderful actors appeared, who were able to achieve the independence of the Studio and organise not only the theatre named after their teacher but also to preserve the theatre school based on the principles of the work of Evgeny Vakhtangov. The name of Zakhava is not often heard in the theatre environment at present; however, his contribution to the preservation of the Vakhtangov Theater and the Shchukin Theater Institute is fundamental. The educational system within the walls of the Shchukin Theater Institute was created based on the practical works of Stanislavsky (first year) and the theoretical and practical works of Zakhava, who preserved in his memory the precepts of his teacher, Evgeny Vakhtangov. Work on creating a methodology for acting was actively going on in the 1930s. For several years, Zakhava was engaged in developing the education system, engaging Vakhtangov’s direct students in pedagogical activities at the school. And as a result, a unified educational system with specific goals and objectives, as well as a set of exercises for each semester was created. Zakhava contributed to the creation of one of the best theatre educational institutions in the country. His activities were entirely directed into pedagogy to help students master the Vakhtangov method of education.
Analysis of User Generated Content Based on a Recommender System and Augmented Reality

Recommender systems have demonstrated to be very useful in various research areas such as education, e-government, e-commerce, and collaborative and entertainment applications. These systems are based on a set of preferences that aim to help users make decisions by offering different items or services that might interest them. However, by using traditional search approaches, the user often obtains results that do not match the desired interests. Thus, a new search approach is required to use semantic-based retrieval techniques to generate conceptually close results to user preferences. In this paper, a methodology to retrieve information about user preferences based on a recommender system and augmented reality is proposed. As a case study, an Android mobile application was implemented, considering augmented reality to recommend multiplex cinemas that are generated from the genres of movies preferred by users and their geographical location at the time of the search.
Practice-Oriented Real-time Person Occurrence Search System

Face recognition is a potential technology to realize Person Occurrence Search (POS) application which retrieves all occurrences of a target person over multiple cameras. From the industry perspective, such a POS application requires a practice-oriented system that can respond to search requests in seconds, return search results nearly without false positives, and handle the variations of face angles and illumination in camera views. In this paper, we demonstrate a real-time person occurrence search system that adopts person re-identification for person occurrence tracking to achieve extremely low false positives. Our proposed system performs face detection and face clustering in an online manner to drastically reduce the response time of search requests from users. To retrieve person occurrence count and duration quickly, we design a process so-called Logical Occurrences that utilizes the maximum interval of detected time of faces to efficiently compute the occurrence count. Such a process can reduce the online computational complexity from O(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(M) by pre-computing elapsed time during the online face clustering. The proposed system is evaluated on a real data set which contains about 1 million of detected faces for search. In the experiments, our system responds to search requests within 2 seconds on average, and achieves 99.9% precision of search results over more than 200 actual search requests.
Digital Pathology and Tissue Image Analysis

The age of digitalization has arrived in light microscopy. While the current practice of toxicologic pathology remains largely based on the light microscopic evaluation of stained tissue sections, it is no longer limited to it. Thanks to the availability of whole-slide imaging (WSI), histopathologic evaluation can now be accomplished via a computer screen. Substituting a computer monitor for a traditional microscope may at first seem to be a minor change. However, the digitization of glass slides has opened up a host of new options to interrogate tissue sections. These include geographically remote viewing of slides for more convenient and expedient pathology peer review, simplification of obtaining rapid and even real-time consultations with specific subject matter experts, formation of pathology working groups with global membership, building digital workflows that link WSI to associated metadata stored in laboratory information management systems, integrating other relevant data (e.g., in vivo data), enhancing teaching and training capabilities, and applying image analysis algorithms to whole-slide images to extract data previously not easily accessible or quantified. While WSI has been commercially available for over 20 years and is being rapidly adopted by the global pathology community, regulatory guidance on the use of digital pathology and related topics in toxicologic pathology is still evolving and currently incomplete. This chapter introduces digital pathology, including WSI and tissue image analysis, and explores potential future use in nonclinical toxicology. Additional related topics such as stereology and alternative imaging modalities are briefly introduced. General principles for consideration in use of WSI in regulated studies are included. A list of relevant references provides additional scope for the interested reader.
An Intelligent Question Answering Method Combining Knowledge Graph and Corpus

Traditional question answering methods are basically divided into the retrieval of KG (Knowledge Graph) and the QA (Question Answering) corpus. The purpose of this paper is to combine both types of methods to devise a quick and comprehensive QA method. In terms of question identifying, a method of question classification label and answer template are defined. The data used to fabricate the KG comes from the books. During processing the text data, Bi-LSTM-CRF combined with the BIO text labeling method is used to carry out named entity recognition. The relationships and attributes of entities are identified using parameter sharing through the previously defined tags and dictionaries. The KG will be stored and retrieved in the graph database. Moreover, the QA corpus comes from three sources: discussion issues, tutor-student interaction, and crowdsourcing platform. It focuses on solving multi-attribute multi-relationship problems that are not included in the KG. QA corpus will output the highest-ranking answer by comparing the semantic similarity of the questions. The final answer output is given both from KG and QA corpus.
Penerapan LKPD Berbasis Android Untuk Meningkatkan Kualitas Pembelajaran Sejarah

The outbreak of the Covid-19 virus pandemic has an impact on the world of education, namely the process of learning and teaching activities carried out online. Schools are not allowed to carry out face-to-face teaching and learning activities. Especially class XII, which must focus on preparing for the final exam at school. The alternative provided is that teaching and learning activities are carried out online using learning technology such as Google Scholar, Zoom Meeting and Google Meet. However, the use of learning technology faces obstacles including internet networks and internet quotas. Therefore, the teacher seeks other alternatives that students can use to learn online and work on practice questions. Another alternative is to use student worksheets based on android applications that can be used anywhere without an internet network. The student worksheet application was developed for class XII history subjects. History subject requires many sources and information related to historical material. The use of the internet network and internet quota is only when downloading the application on the student's android gadget. With the student worksheets based on the android application, class XII students can access historical materials and work on practice questions available in the application along with answer keys.
Robust Single-Step Adversarial Training with Regularizer

High cost of training time caused by multi-step adversarial example generation is a major challenge in adversarial training. Previous methods try to reduce the computational burden of adversarial training using single-step adversarial example generation schemes, which can effectively improve the efficiency but also introduce the problem of “catastrophic overfitting”, where the robust accuracy against Fast Gradient Sign Method (FGSM) can achieve nearby 100% whereas the robust accuracy against Projected Gradient Descent (PGD) suddenly drops to 0% over a single epoch. To address this issue, we focus on single-step adversarial training scheme in this paper and propose a novel Fast Gradient Sign Method with PGD Regularization (FGSMPR) to boost the efficiency of adversarial training without catastrophic overfitting. Our core observation is that single-step adversarial training can not simultaneously learn robust internal representations of FGSM and PGD adversarial examples. Therefore, we design a PGD regularization term to encourage similar embeddings of FGSM and PGD adversarial examples. The experiments demonstrate that our proposed method can train a robust deep network for \(L_{\infty }\)-perturbations with FGSM adversarial training and reduce the gap to multi-step adversarial training.
Conceptual neuron-like models for living systems description

It is shown that the conceptual neuron-like models proposed on the basis of the availabel scientific paradigms can be used to reduce the dimension of descriptions for a vast amount of experimental data on the behavior of living systems. The division of conceptual models into groups is determined by the choice of areas of operational activity in the hierarchical architecture and by different stages of development of the studied cognitive systems. The areas of application of homogeneous neuron-like systems, elementary cognitive systems, variants of hierarchical architectures from cognitive systems, and developing cognitive systems are considered. An integral version of the classification of the modes of perception and social behavior of living systems is presented. Priority application areas of the development of conceptual neuron-like models are considered. This review of the main conceptual models contains elements of the descriptions taken from the previous works of the author. The importance of developing a consistent version of the integral view on the currently used neuron-like models compels the author to revise the most significant issues and encourage the interested researchers to widely discuss them.
An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.
The Structures of the Fault-Tolerant Automation and Computing Devices Based on the Boolean Complement

The paper suggests new structures of fault-tolerant digital devices that have reduced indicators of technical implementation complexity compared to the known structure based on triple modular redundancy. Structures are implemented not on the basis of introducing exact copies of the source object, but using the Boolean complement principle, which implies the use of a special check block that calculates the Boolean functions. Its complexity in practice may be significantly less than the complexity of the source object, which makes it possible to synthesize simpler fault-tolerant devices than when using the principles of triple modular redundancy. The paper considered two structures implemented on the basis of the Boolean complement principle–a structure with duplication of check blocks (duplication of complements), as well as a structure based on Boolean complement with the control of calculations by the main object by parity. The authors showed the advantages and disadvantages of the new structures. We presents the results of experiments with check combinational circuits, which demonstrates the effectiveness of the proposed structures.
Generating Adversarial Remote Sensing Images via Pan-Sharpening Technique

Pan-sharpening is one of the most commonly used techniques in remote sensing, which fuses panchromatic (PAN) and multispectral (MS) images to obtain both the high spectral and high spatial resolution images. Due to these advantages, researchers usually apply object detectors on these pan-sharpened images to achieve reliable detection results. However, recent studies have shown that deep learning-based object detection methods are vulnerable to adversarial examples, i.e., adding imperceptible noises on clean images can fool well-trained deep neural networks. It is interesting to combine the pan-sharpening technique and adversarial examples to attack object detectors in remote sensing. In this paper, we propose a method to generate adversarial pan-sharpened images. We utilize a generative network to generate the pan-sharpened images, and then propose the shape loss and label loss to perform the attack task. To guarantee the quality of pan-sharpened images, a perceptual loss is utilized to balance spectral preserving and attacking performance. The proposed method is applied to attack two object detectors: Faster R-CNN and Feature Pyramid Networks (FPN). Experimental results on GaoFen-1 satellite images demonstrate that the proposed method can generate effective adversarial images. The mAP of Faster R-CNN with VGG16 drops significantly from 0.870 to 0.014.
Advancing Human Adoption of Technology

As technology advances, humans will need to advance with it. This paper begins by analyzing how humans adopt technology at a societal level. From there, I discuss some of the methods with which we can increase technology’s ability to be adopted: mainly pertaining to education and increasing usability of technology. These methods will help bring us to a future paradigm of technology with has great benefits-that of ubiquitous computing. Finally, I discuss some ethical implications of technological adoption and address them in a contemporary sense.
Detection Continuity Loss for Video Object Detection

The video object detection is one of the key challenges in the field of object detection. The detection accuracy and real-time performance must be considered, and requirement for detection continuity is also put forward. This article proposes a detection continuity loss (DCL) to solve the problem of poor detection continuity in the video object detection. The proposed loss function is aimed at three different situations of poor detection continuity, and corresponding loss functions are designed respectively. DCL can be used in combination with the loss function of existing video object detection algorithms. Experimental results show that the use of DCL to optimize the loss function of the existing algorithms improve the detection accuracy performance.