Reviewerly/exHarmony huggingface dataset:
Information Hiding Mechanism Based on Generative Adversarial Networks

With the rapid development of artificial intelligence and the Internet of Things technology, the demand for concealment of data and information is increasingly strong, and information hiding technology plays a key role in information security. At present, there are some problems in the generative steganography mechanism based on deep learning, such as low quality of generative steganography and weak anti-steganographic analysis ability. In view of the above problem, this paper proposes a Realness-DCGAN based information hiding mechanism, this mechanism will secret information mapping division and the noise vector generator, according to the noise generated vector containing the secret image by using fidelity distribution as a standard of judging output, constraint generator to generate a higher quality of the secret image, finally using an extractor to extract the secret information. Experiments show that compared with the traditional steganography mechanism based on deep learning, the information hiding mechanism proposed in this paper by embedding RealnessGAN into the traditional DCGAN can significantly improve the quality of generated images containing secrets, and improve the accuracy and security of information extraction.
Influence of Speckle on Micro Form Measurement by a Laser Triangulation Sensor

A laser displacement sensor based on the triangulation method is capable of high-speed measurement, has a long working distance, and has a wide measurement range, so it can be used for distance measurement, vibration measurement, positioning measurement, form measurement, etc. However, speckles are generated in the image spot because the laser, which is coherent light, is used as the light source. It is known that the center of gravity of the image spot changes due to this speckle pattern, resulting in a change in displacement. The displacement fluctuation due to speckle is considered to be related to the NA of the light receiving lens, the triangulation angle of the receiving lens, the wavelength of the light source, and the surface textures of the irradiation spot. In this study, we varied conditions of these elements and measured micro form. As a result, it was found that the displacement fluctuation decreased with increasing NA and triangulation angle. Regarding the wavelength of the light source, it was possible to reduce the displacement fluctuation by shortening the wavelength. It was also found that the displacement fluctuation differs depending on the submicro topograpy of the irradiation spot. These results are important in designing the laser triangulation sensor.
Data Hiding Enhancement in Arabic Text

Recently, information security has become a very important topic for researchers as well as military and government officials. For secure communication, it is necessary to develop novel ways to hide information, for this purpose, steganography is usually used to send secret information to their destination using different techniques. The aim of this article is to provide a new text steganography method. Hidden information in text files is difficult to discover as text data has low redundancy in comparison to other mediums of steganography. Hence, we use an Arabic text to hide secret information using a combination of Unicode character's zero-width-character (ZWC), zero-width-joiner (ZWJ) and pseudo-space (PS) in the proposed algorithm. The experimental results show increasing in hidden data capacity per word in comparison to the recently proposed algorithms. The major advantage of our proposed algorithm over previous researches is the high visual similarity in both cover and stego-text that can reduce the attention of intruders.
DNN based approaches for Segmentation of Handwritten Gujarati Text

In this fast-evolving world, documents in numerous regional languages are finding a prominent place on the internet. Segmenting and detecting words is the first and necessary stepping stone in a text recognition task. Hence this work is a preliminary step in exploring the efficiency of various deep learning techniques like Efficient Det, Yolo, and Faster R-CNN in segmenting and detecting Gujarati handwritten words from scanned documents collected manually and annotated using Labeling tool. We have also experimented with some conventional method, which serves as a pre-processing step in document annotation as the manual annotation is very prolix, monotonous, and time occupying. The results obtained from the collected Gujarati data by performing various state-of-art methods are encouraging. EfficientDet Neural Network architecture renders better performance than other deep learning techniques like YOLO, and Faster RCNN experimented with the same dataset.
ICT as Blending Methodology: A Case Study in a Bilingual School in Castilla la Mancha

The use of Information and Communication Technologies (ICT) is increasingly affecting every aspect of our daily lives, including the educational field where new approaches and methodologies are being developed. CLIL is currently one of the most popular educational approaches in Europe as it combines specifically created online course materials with more traditional teaching strategies. The case study introduced in this paper involves a charter school in Castilla-La Mancha called La Merced where CLIL was introduced in 2013. In the academic year 2018-2019, the school was taken over by an international educational network named Red Educativa Arenales, which decided to modernize the school by introducing highly innovative education teaching strategies related to ICT methodology. The initial stage of the modernization plan consists of equipping every classroom with digital devices such as laptops and tablets, as physical textbooks have been substituted by an online learning platform. Most school stakeholders agree that this first year has proved to be highly satisfactory regarding students’ motivation and academic achievement. Drawing on my experience as an English teacher in La Merced since 2011, in this paper I compare the classroom atmosphere and the academic achievements of my students until 2018, when more traditional methods were in use, to last year’s textbook-free pilot program. I will focus on my own grade, namely 3rd level of secondary education class (age 15-16) whose exam results have remarkably improved since the implementation of ICT blending methodology.
Development of Regular Polygons Learning Support Robot using Elementary School Arithmetic and Programming Thinking

In the new guidelines to be implemented from 2020, the programming education is introduced from the elementary school stage. But, on implementation of the programming education, it is said that there are various problems. The purpose of this research is to develop a robot that draws an arithmetic [5th grader] regular polygon in elementary school without using visual programming. Specifically, by inserting three blocks into the top of the robot, some regular polygons can be written. These three blocks set the size of the interior angles of some regular polygons, the number of repetitions, and the length of the sides. In order to evaluate the robot, we showed a video of the robot in action and several questionnaires were conducted to the elementary school teachers.
Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System

Virtual reality (VR) has been one of the most important topics in the field of multimedia signals and systems for approximately 10 years [...]
Network Slimming with Augmented Sparse Training and Optimized Pruning

Previous works use a similar process to prune channels: train, prune, fine-tune. In this paper, we treat channel pruning as a method of network architecture search. Specifically, we limit the search space by adding some conditions on it, and after searching, we only reserve the architecture of the network and train it from scratch. We train the model with augmented sparsity to get a higher ratio of pruning. During pruning, we add a protect threshold to prevent the pruned model from being disconnection. Our process of channel pruning is as follows: train with sparsity, prune, train from scratch. we verified the effectiveness of our method on several models, including VGGNet, ResNet and DenseNet on various datasets. Otherwise, we test our method on different architectures of ResNet and analyze the results on both models.
“Want to come play with me?” Outlier subgroup discovery on spatio‐temporal interactions

Abstract Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio‐temporal data from the real‐time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio‐temporal interaction data, focusing on two areas: exceptional subgroups and spatio‐temporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery , combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.
Experience in using distance technologies under online learning conditions

The pandemic situation has revealed some problems in education, which should be solved by educational entities. One of such problems is unreadiness of teachers to organize online classes. More complex aspect is organization of feedback. Some platforms used for online learning demonstrated their incompetence for widescreen education. Not all teachers managed to create comfortable learning digital space for students. This article analyzes the existing situation in vocational training due to transition to distance learning. Some scientists believe that the educational community evidenced revolutionary shift in the pedagogic paradigm. The opinion of other scientists is that today there are no preconditions for changing classic paradigm in pedagogy and only temporary modifications occurring under pandemic conditions should be considered. This article is devoted not only to identification of problems of organization of educational process in distance format but also to proposing solutions to the highlighted problems, verified by the authors, since the circumstances faced by all teachers forced to accelerate transition to digitalization of educational environment. The educational route is now arranged with consideration for digital experience acquired during the pandemic. Due to the experience orientation the article attracts interest not only of university teachers, forced to modify on the go their pedagogical style and to adapt it to the context of current situation, but also of school teachers facing similar problem and locked in forced professional isolation.
Hardware/Algorithm Codesign for Adversarially Robust Deep Learning

This article describes DeepFense, a framework to make deep learning models automatically and efficiently realizable on constrained devices. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana.
Self-Learning pLSA Model for Abnormal Behavior Detection in Crowded Scenes

Abnormal behavior detection is now a widely concerned research field, especially for crowded scenes. However, most traditional unsupervised approaches often suffered from the problem when the normal events in the scenario with large visual variety. This paper proposes a self-learning probabilistic Latent Semantic Analysis, which aims at taking full advantage of the high-level abnormal information to solve problems. We select the informative observations to construct the “reference events” from the training sets as a high-level guidance cue. Specifically, the training set is randomly divided into two separate subsets. One is used to learn this model, which is defined as the initialization sequence of “reference events”. The other aims to update this model and the the infrequent samples are chosen into the “reference events”. Finally, we define anomalies using events that are least similar to “reference events”. The experimental result demonstrates that the proposed model can detect anomalies accurately and robustly in the real-world crowd environment.
Optimization of Face Retrieval and Real Time Face Recognition Systems Using Heuristic Indexing

Retrieving fast and accurate information is the key factor in real time biometric systems. The indexed data provides fast access to the pool of closest matching patterns in the database or the input given to the real-time systems. This paper aims to demonstrate the impact of heuristic indexing in realtime face recognition and on face retrieval systems using face skin color and complexion. This paper also presents the comparison of face recognition techniques with and without heuristic indexing and their impact on realtime performance. Proposed method works on both the indexed and non-indexed database. The survey shows that indexing not only improves retrieval speed but also rejects the wrong samples to be inspected, speeding up the overall system. We have proposed a novel heuristic indexing algorithm which locates key pixels on a face, calculates Skin Complexion Value (SCV) and their tolerance range, reduces search space and finally performs classification. The proposed approach yields significant improvement in terms of speed and reduction in False Acceptance Ratio (FAR).
TECHNOLOGIES FOR DEVELOPING DECISION SUPPORT SYSTEMS FOR THE DIAGNOSIS OF BLOOD DISORDERS USING CONVOLUTIONAL NEURAL NETWORKS

In this study, we analyzed technologies for obtaining, processing, segmentation, and transmitting of microphotographs for subsequent recognition. We selected the most promising machine learning algorithms optimal for the processing of medical images, investigated the technologies of analyzing medical texts, studied the aspects of using the Watson neural network for analyzing the semantics of medical images, as well as the aspect of using the unified medical language UMLS for the needs of syndromic diagnostics for the evaluation of medical texts from medical histories in natural language. We also developed an interface for receiving, processing, segmenting, and transmitting microphotographs to artificial neural networks and an interface for the primary accepting and processing of microphotographs based on the OMERO medical image processing platform. To send data online, a demo script for jupiter was prepared. An interface for transmitting medical texts to the medical text semantics recognition system was also developed. The IBM Watson Annotator for Clinical Data was used to perform preliminary analysis of medical texts. We created a database of medical images of the bone marrow for neural network training. We made 3,500 color microphotographs (600 × 400 pixels) of bone marrow smears at a resolution of × 600 (light microscopy; hematoxylin and eosin staining). We performed marking of 11 types of bone marrow cells. We created a database of medical texts (167 patients, 40,000 words) to prepare a neural network. The database was stripped of all personal identifiers.
Data Augmentation and Deep Learning Modeling Methods on Edge-Device-Based Sign Language Recognition

In this paper, methods of realizing sign language recognition (SLR) on mobile edge devices - AutoML and Transfer Learning - are recommended and compared. Their performance in SLR is compared in this article in terms of model size, speed, and accuracy. To perform a more reliable comparative analysis, we first built and applied data augmentation to obtain a benchmark dataset based on Chinese Sign Language (CSL). With this dataset, models were then trained and deployed to mobile edge devices for real-time testing. It is found that data augmentation can effectively improve the diversity of datasets and improve the robustness of the trained model. The results of the model comparison show that AutoML is more dominant in accuracy, and Transfer Learning is more suitable for low-latency applications. In addition, AutoML excels at classifying macroscopically homogeneous images, such as gestures, despite its low training speed. On the other hand, the training process of Transfer Learning is speedy, but low accuracy remains to be its problem. These conclusions are of guiding significance to the lightweight mobile application implementation of SLR.
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis

Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy images are normally hazy, lack of focus, or uneven illumination. Thus, these could difficult the diagnosis process for doctors. In this paper, we propose MIINet, a novel image-to-image translation network for improving quality of medical images by unsupervised translating low-quality images to the high-quality clean version. Our MIINet is not only capable of generating high-resolution clean images, but also preserving the attributes of original images, making the diagnostic more favorable for doctors. Experiments on dehazing 100 practical throat images show that our MIINet largely improves the mean doctor opinion score (MDOS), which assesses the quality and the reproducibility of the images from the baseline of 2.36 to 4.11, while dehazed images by CycleGAN got lower score of 3.83. The MIINet is confirmed by three physicians to be satisfying in supporting throat disease diagnostic from original low-quality images.
PRACTICAL IMPLEMENTATION OF THE BYOD CONCEPT IN HIGHER EDUCATIONAL INSTITUTIONS

The emerging range of personal mobile devices, due to their advanced technical characteristics, has made it possible to widely use them at enterprises, offices or educational institutions almost in any place. This has led to the introduction of a new concept of BYOD (Bring Your Own Device), when a personal device (phone, tablet) is used for work or study. One of the serious problems encountered by teachers on the way of implementing BYOD into the educational process is the insufficient level of information competence and the lack of educational manuals for such classes. The aim of the research is to investigate the relevance of implementing BYOD approach in teaching Computer Engineering students and to consider the possibilities of using mobile devices for didactic purposes directly in the classroom and in distance learning. Within BYOD, the use of learning support mobile systems in the learning process gives its participants access to training materials, services and models adapted for the use in a mobile environment. The teachers effectively manage the learning process, in particular, focus the students’ attention on the sources of information that give the most correct and complete answers within the relevant disciplines. The research presents learning outcomes of two groups: control, with the traditional method of teaching and the equipment provided by the educational institution, and experimental, when students use their own mobile devices (tablets and smartphones) in the educational process, which gives a possibility to learn in any place convenient for a student and remotely send progress reports. The results showed that the students of the experimental group outperformed their groupmates from the control group in academic progress. The implementation of BYOD elements has led to the increase in students’ motivation and interest in their own learning. The main positive aspects of using BYOD technology in the learning process have been identified, such as convenience and flexibility, reducing the expenses of educational institutions, simplifying work with the network services, interactivity of learning, and applying multimedia format in the presentation of educational material during lectures, laboratory and practical classes, effectiveness of work with information and the presentation of results.
Formation of social and information competence of adolescents by means of case technology

Preconditions of the presented research are the modern concepts of digital competences. Success of a modern person mainly depends on how well he is familiar with the skills of digital technologies. Significant influence of digital technologies on everyday life is obvious in education sphere. Continuous life-long-learning, interactive studies and textbooks, access to investigations and learning materials are the peculiar features of 21st century students. In this regard, the analysis of social and information competence is an important demand of modern society. Despite the fact that nearly each family at present has a computer, the use of Internet by children raises fears of parents and teachers. This can be explained as follows: Internet can be used for games and not for learning; in addition, an adolescent may be a victim of cyberbullying. This work is aimed at verification of the hypothesis that formation of social and information competence of adolescents is a predictor of socially efficient behavior in virtual environment. The obtained results demonstrated positive variation of the considered indicators. Despite the fact that more precise conclusion should be based on more expanded samplings, in general, the hypothesis of opportunities of this trend has been partially verified.
Multi-level lecture video classification using text content

Recent interest in e-learning and distance education services has significantly increased the amount of lecture video data in public and institutional repositories. In their current forms, users can browse in these collections using meta-data-based search queries such as course name, description, instructor and syllabus. However, lecture video entries have rich contents, including image, text and speech, which can not be easily represented by meta-data annotations. Therefore, there is an emerging need to develop tools that will automatically annotate lecture videos to facilitate more targeted search. A simple way to realize this is to classify lectures into known categories. With this objective, this paper presents a method for classifying videos based on extracted text content in several semantic levels. The method is based on Bidirectional Long-Short Term Memory (Bi-LSTM) applied on word embedding vectors of text content extracted by Optical Character Recognition (OCR). This approach can outperform conventional machine learning models and provide a useful solution for automatic lecture video annotation to support online education.
Archive of Professor in Theology Archpriest Pavel Svetlov as Unique Written Monument of the Russian Orthodoxy of the First Decades of the XX Century

The purpose of the study is to determine peculiarities of archival heritage of Professor in Theology Archpriest P. Ya. Svetlov. The article considers his archive as a unique written monument of the Russian Orthodox thought of the first decades of the XX century. It is found that the main content of this archive is typewritten and handwritten Collection of Works, the structure of which is the focus of the article. Scien-tific novelty of the study lies in the fact that it sheds fresh light on biography and archive of Father P. Svetlov and at the same time on the issues of the Russian Orthodox theology and intellectual history of Russia. As a result, new sources, never published before and unknown even to specialists, are for the first time introduced into scientific use.