Reviewerly/exHarmony huggingface dataset:
NATIONAL PROGRAM "BUILDING A SCHOOL STEM ENVIRONMENT", CONDITION, CHALLENGES AND PROSPECTS

In 2020, the Ministry of Education and Science of the Republic of Bulgaria launched its national program Building a school STEM through which to stimulate the creation of new school centers - an integrated set of specially created and equipped learning spaces with a focus on learning and applying competencies in the field of natural sciences in the state and municipal schools in the country. Each school center will include a change in the following elements: physical environment (improvement of the interior architecture and furnishing of existing spaces), technologies, learning content, teaching methods and management of the educational process. The program is aimed at schools with innovative practices and those with the potential to develop innovations in science, digital technology, engineering thinking and mathematics (STEM).The objects of our study are the changes in the state, challenges, and prospects of Bulgarian schools after the launch of this program.The subject of our study are the schools that have filed an application under the national program Building a school STEM and the results of the completed evaluation of a project proposal.The schools participating in the projects are categorized and analyzed in the context of the Bulgarian educational system and conclusions are made about their activity and interest in the development of this type of school centers.The results of the evaluation of the submitted project proposal under national program Building a school STEM are analyzed. The projects which are approved for financial and organizational support by the Ministry of Education and Science are categorized in the context of the Bulgarian education system and also relevant conclusions are made.The article analyzes the institutional support implemented through the national program Building a school STEM in the context of the efforts of Bulgarian schools which already introduce new and innovative practices, as well as those with a potential for STEM innovations.The conclusions outline the prospects and challenges for the digital transformation of the educational process with a focus on the study and application of competencies.Acknowledgments: The work is funded by project 80-10-165-24.04.2020
Path Planning for Tractor-Trailer System

Tractor-trailer systems are now widely used in various fields, such as factory transportation, farming, geographic exploration, etc. It consists of a tractor and one or more trailers, and the tractor mainly determines the direction and speed of system movement, it’s a typical nonholonomic constrained and underactuated system. This chapter focuses on path planning problems of tractor-trailer systems.
GAN-based Data Augmentation for Semantic Segmentation in Multiple Weathers

Datasets play an important role in determining the features that deep neural networks can acquire, but they can also contain unintended biases when constructing datasets. The BDD100K dataset, famous for its semantic segmentation task, was collected to include traffic scenes for multiple weather conditions. However, due to differences in frequency of occurrence, there is a bias in the number of data for each weather condition. Therefore, the segmentation network trained by BDD100K has poor recognition performance in some weather conditions. Semantic segmentation is an urgent issue because it is expected to be applied to traffic scene recognition systems. In this paper, we aim to improve the performance of semantic segmentation by designing a method that generates images of desired weather conditions and uses them for data augmentation. In our experiments, we first show that the image generation method we have developed produces images of a quality that can be used for data augmentation. Next, we examine the effect of data augmentation on the semantic segmentation task. As a result, compared to baseline, the mean intersection over union (mIoU) improved by about 15% in wet weather, about 9% at night, and about 7% overall.
A Synonym-Based Bi-LSTM Model for Machine Reading Comprehension

Machine reading comprehension (MRC) makes the system understand the passage and predicts the answer for the given query. The focus of natural language processing (NLP) is to make the system understand the natural language as it gives the ability for the system to interact with the user in terms of texts. MRC is the extension of question answering task which aims to predict the answer for the given question. In recent days, a deep neural network dominates the machine reading comprehension task. Many attention-based machine reading comprehension aims to find the interaction between the contexts of a given passage and query. In this paper, the synonym-based bidirectional model with attention mechanism is discussed for enhancing the performance of MRC system. Our model is experimented in 20 QA bAbI task dataset and produced the promising results.
Study for the Processing of the Chinese Verb-Noun Anomalous Collocations with Metaphors

The processing of Verb-Noun Anomalous Collocations is a hot issue of Chinese information processing. The Verb-Noun Anomalous Collocations with metaphorical is due to verbs or nouns with the characteristics of conceptual metaphor. This paper build a set of special word-description system based on the Generation Lexicon theory in order to restore Anomalous Collocations which caused by metaphor and to interprets their combination mechanism and the generation process. Then, by replacing the certain words, we reduce the Anomalous Collocations to the conventional collocations. Experiments show that this approach is effective in dealing with the Verb-Noun Anomalous Collocations with metaphorical.
Reality Augmented Nightmares or Experiences of Fear

Virtual reality- and augmented reality-supported games can be used to appeal to all tastes and audiences, especially for people who seek adventure, adrenaline, and fear and who want to take a break from their daily life with action. The element of fear, which has been used frequently in every branch of art, has been indispensable in the field of entertainment and art, although it is far from the functioning of this feeling. When we look at the purpose of fear, it is an unconscious sensation that allows the person to take action against a situation that threatens or endangers the life of the person. The person is afraid of danger and develops a reaction to the situation. Fear within the boundaries of entertainment and art, although parallel to this situation, is a controlled and safe form of true fear. Fear of man in the face of art or game area creates emotional satisfaction because it will not cause a life threat. For this reason, films and games created, especially in the genre of fear, are indispensable for the masses.
Target Text Location for Vehicle Inspection Based on Template Matching

Nowadays, vehicle inspection has an urgent demand for automatic target information recognition technology. Target text location is the prerequisite for target text recognition and has a great influence on recognition. Experimental results show that deep learning algorithms, such as faster RCNN, are not effective on such problems to some extent. Therefore, this paper adopted the method of template matching, took the vehicle license application form as the research subject, realized the target text location according to the relative location between the target texts in the image, and achieved satisfactory results. The application of this method on the driving license proves that it is a target text location technology that can be applied to document images with relatively fixed layout.
Arabic Reading Comprehension Benchmarks Created Semiautomatically

Reading comprehension is the task of answering questions from paragraphs; it is also considered a subtask of question-answering systems. Although Arabic language is a language spoken by more than 330 million native speakers, it lacks the required resources, which are needed by the Arabic reading comprehension task to serve as a benchmark dataset. The goal of this work is to present the phases of creating Arabic reading comprehension benchmark dataset semiautomatically. The phases include; data collection, manual check, Google search, document retrieval, and paragraph retrieval. The paper also conducts a thorough evaluation for the created datasets.
Motion discovery through change discovery filter matrix derived from the discrete cosine transform

The motion discovery algorithm through a change discovery filter matrix which is derived from the distinct Cosine Transform. Currently, a Fourier reorganization strategy gives efficient outcomes for motion detection. The major drawback of the motion detection was mentioned to be its calculation cost. The developers again retained about the issue and attained two orders of magnitude speedy than the earlier algorithm with good working. The rushing capability of the projected algorithm is about 800 frameworks per second for Video Graphics Array resolution portraits on customer hardware with the use of static matrix multiplication alone. The property applied for change detection filter matrix is named as symmetric property. Along with this, the algorithm is basically strong to the unexpected variation of brightness as a result of it operations depending on edge information. The developers examined and mentioned that the algorithm projected in this paper authenticate with confronting datasets which are having athletic and unexpected brightness variations.
Analysis of Variability in Calculated Performance Loss Rates of Large-Scale PV Plants

The performance loss rate (PLR) is a key parameter in predicting and measuring the lifetime production from a PV plant. In this study, PLR is calculated by using historical on-site performance and meteorological time-series data from common, existing sensors and instrumentation at large-scale PV plants and the RdTools software package. Results show that current analysis techniques are not precise enough to identify performance improvement opportunities at any single plant but can enable insights at an aggregated fleet-level. The high variability in the results over time combined with the correlation among subarrays within a plant suggest that factors in addition to module degradation, such as soiling and un-normalized weather, have a significant impact. In addition, the reported confidence intervals are likely an underestimate of uncertainty due to the non-independence of daily calculated loss rates. This work examines PLR results to gain insight on how the analysis precision might be improved to better identify underperformance at the plant level and, subsequently, enable performance remediation opportunities. Also, a method to quantitatively validate loss rate analysis techniques using synthetically generated performance data is proposed.
Combined Convolutional Neural Network for Highly Compressed Images Denoising

Many methods for denoising additive white Gaussian images have been developed, such as the use of non-local mean filters (NLF) and deep convolutional neural networks (CNN). However, these denoising methods still have many limitations on compressed images such as JPEG2000 compression. Based on quantization of noisy wavelet coefficients, JPEG2000 may lead to very specific visual artifacts. This compressed image's noise distribution model is highly spatially correlated and very different from the noise distribution model in additive Gaussian white noise images. In this paper, we propose a convolutional neural network structure combined with nonlocal filter. At first a convolutional neural network have been trained by using highly compressed noisy images to obtain a specific noise model estimation and this noise model estimation is used for the residual neural network. Secondly, it based on non-proximity average filtering, where a similar block selection method is modified to find block artifacts in the compressed image and then do denoising. Finally, combining these two methods can get a clear image output. The evaluation results of this method on the grayscale image dataset are better than the latest technology. Contribution- We produced a noise distribution CNN model that can predict the noise of highly compressed images with complex noise distribution, and combine CNN and Nonlocal mean filters to obtain good denoising results.
Sentiment Analysis of Emoticon Based Neuro Fuzzy System

The widespread Virtual societal space has become an integral part of human life, in which opinion generated by others plays a significant role in one's actions. Tweets as a point of the source have emerged as a way of expressing users' views on a particular subject. Historically sentiment analysis of tweets focused on polarized views, but in the real world, the opinion on a topic usually quite diverse. Therefore, using the classical classification methods cannot adequately fit the human sentiment, and we require a robust way that can match the broad scale of opinion-making. Present work describes an approach that can fit the opinion-making scale of human sentiment by using a fuzzy neuro system to train the model for opinion-making. Emoticon used in tweets provides an additional layer with classical textual analysis to incorporate complex emotions that can be easily conveyed by emoticons. The scored sentiment is then classified using various machine learning algorithms to measure its accuracy.
Statistical filtering algorithms based on the maximum cross section method for stochastic systems with regime switching

Abstract New statistical algorithms are formed to estimate the state vector of stochastic systems with regime switching. These algorithms are based on the particle method, i.e., on numerical methods for solving stochastic differential equations, methods for modeling conditional Markov processes with a finite set of states, and methods for calculating weights. An important part of proposed algorithms is the use of the maximum cross section method and its modification, which allows the exact simulation of the regime switching process. This paper continues the authors’ research in the field of statistical methods and algorithms of the analysis and filtering for continuous-time stochastic systems.
Design and implementation of partition Operating System based on ARINC653 standard

Abstract The ARINC series of standards are mainly used to define how to use the computing resources of avionics. The ARINC 653 standard applies to the service interface of the spacecraft’s integrated electronic operating system to the upper-layer application software. It isolates the application software from the underlying processor architecture and meets the requirements of the on-board real-time system with good scalability. This article will follow the ARINC 653 standard, design and implement a partition operating system, providing basic system static configuration capabilities, space partition capabilities, time sharing capabilities, inter-zone communication capabilities, and security monitoring capabilities
The Digital Educational Model

The declaration of the sanitary emergency of COVID-19 changed the way that multiple sectors operate. In education, in addition to taking care of the health and safety of the community, continuity and academic quality of programs must be ensured. This transition gave rise to a new digital educational model to offer a quality distance university experience. The objective of this chapter is to present a framework to assess the quality of a digital educational model, and to present the findings of an implementation of this model that resulted from the transformation of a medical program amid the health emergency of COVID-19.
Flooding, landslides impact Washington coastal communities

Steady rain fell along Washington state’s beaches Tuesday. The winter weather pushed rivers to run high, flooding roadways and causing landslide to impact travel and large waves are blamed for killing one person in Pacific Beach earlier this week.
A Study on Evidences Stored in Android Smartphones

Smartphones have become a necessity of modern people and have been used for various purposes beyond simple call and text transmission and reception. Therefore, various personal information has been stored in smart phones, which has become an important evidence of digital forensics. In this paper, we study the logs of messenger and web browser in Android smartphone which is widely used.
Fading with the Time

Nowadays, most Internet applications are generally faced with various traffic monitoring and content monitoring threats. To protect business secrets and personal privacy, anonymous communication technology has attracted widespread attention. Anonymous communication technology not only provides protection of confidentiality, integrity and availability, but also hides communication relationships and routing information through onion routing and other technologies. Although anonymous communication networks can provide better security and confidentiality, the control plane of most anonymous communication networks still lacks adequate protection mechanisms. For example, the Tor network needs to explicitly download control information from a directory server. This method of obtaining control information exists security risks. This paper proposes a method for obtaining control information of anonymous network based on asynchronous communication. We use the public storage service of the Internet as a transit platform to exchange control information between the user terminal and the control center of the anonymous communication network, and design an automatic destruction mechanism to make the buffered control information on the public platform will not be obtained by third parties. This method is suitable for most anonymous communication networks and can effectively improve the protection of the control plane of anonymous communication networks.
A Framework for Mobile Personalized-Based Recommender System Using Social Tag Clustering Approach

Whereas the social tag clustering approach has been widely used in various recommender systems, recent work has rarely shown the utilization of the tag clustering in the development of mobile recommender systems. In this study, we explore the work in mobile recommendation systems and propose a framework for developing a personalized-based recommendation system for mobile application, utilizing a social tag clustering approach. Additionally, we also design the interface on mobile and conduct an experiment with three fundamental pipelines. The results indicated that the similarity measure affects the recommendation results. This contribution, the proposed framework using a social tag clustering approach, will add the novelty to the research communities and be an alternative for developing future mobile recommendation systems.
A Neural Machine Translation Approach for Translating Malay Parliament Hansard to English Text

Parliament Hansard is one of the most precious texts made available to the public. In Malaysia, the parliament Hansard records the debate and discussions in Malay language. Topic modelling, sentiment analysis, relation extractions, trend prediction or temporal analyses are frequently applied on parliament Hansard to discover interesting patterns. However, most of the matured tools for such processing tasks work on English text. As such, before the Malaysian parliament Hansard can be further processed, it is essential to translate the Malay text into English. Several machine translation approaches have been surveyed in this paper. From the literature review, neural machine translation, particularly the Transformer Model has been proven to provide promising results in translating different languages. In this paper, we present our implementation of neural machine translation for Malay to English text. The experimental design shows that with a good set of parallel corpus and minimal fine-tuning, neural MT can achieve as high as 35.42 in BLEU score.
Text Summarization and Dimensionality Reduction using Learning Approach

Expect to the exponential increase of records on the web, a client needs everyone the associated data in one volume without any trouble. These manage by Automatic text summarization. It is a manner that recognizes the major details of the total relevant documents to present a succinct summary. The proper text summarization and dimensionality reduction (TSDR) of summarized text can lead to a notable reduction in accessing time for the input elements. The proposed method produces the summarization task by dimensionality reduction using learning methodology. The line is beginning pre-processed to tokenize the orders and produce stemming medicines. Then descent-based text summarization includes choosing determinations of great value without the rank of the report based on the word including sentence characteristics and planted them collectively to produce a report. The preliminary returns confirm that this organization has better execution than different existing organizations in document summarization.
Autonomous Amphibious Vehicle for Monitoring and Collecting Marine Debris

Our chief objective is to prototype a vehicle that can collect garbage floating on the surface of a water body (lake, pond, river, sea, or ocean) as well as the shorelines and riverbeds, without any major human assistance or more than one person being involved. The collection process of the garbage has automated, i.e. the vehicle will detect the garbage and will make it move accordingly to be picked up by the collection system. The recognition part is done using a deep learning model that detects a selective class of objects. After identifying the trash, it will place itself in line with the object. For the mobility of the boat, twin screw propellers have been used, as shown in Fig. 1, to facilitate smooth movement on land as well as in water. A separate compartment is allocated in the vehicle where all the collected trash is transferred via a conveyor system. An alternative remote control is used for controlling the boat. Since the DL model is not 100% accurate, we have kept remote control as an option that is required in the initial stage of the movement and for the shift between different terrains.