Reviewerly/exHarmony huggingface dataset:
Deep Learning Algorithm for Video Saliency Object Detection using 3D DWT with Set Partition Integer Hierarchical Tree List

In this work detection of salient objects in image and video sequences with higher accuracy, faster processing speed and reduced computation complexity is designed using deep learning algorithm.3 Dimensional (3D) Discrete Wavelet Transform is combined with Set Partition Integer Hierarchical Tree List (SPIHTL) encoding methods for identifying selfsimilarity coefficients of the salient object to be detected.Deep learning algorithm with seven layers is proposed in this invention that processes the quantized wavelet sub bands obtained after 2-level 3D inverse DWT reconstruction process detects the optimum number of features representing salient objects in the input data.An apparatus for evaluation of proposed method for salient object detection is designed in this invention that reconstructs the image from the features.Evaluation metrics are identified for measuring the performance of the proposed methods in this invention and are compared with traditional methods.The measure of PSNR and MSE for 120 different data sets of various dimensions and orientations demonstrates an improvement of 12%-18% in PSNR measurement as compared with existing methods.
Remarks on J-hyperideals and their expansion

The aim of this research work is to define and characterize a new class of hyperideals in a Krasner (m,n)-hyperring that we call n-ary J-hyperideals. Also, we study the concept of n-ary delta-J-hyperideals as an expansion of n-ary J-hyperideals. Finally, we extend the notion of n-ary delta-J-hyperideals to (k,n)-absorbing delta-J-hyperideals.
ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation

Co-occurrent visual pattern makes aggregating contextual information a common paradigm to enhance the pixel representation for semantic image segmentation. The existing approaches focus on modeling the context from the perspective of the whole image, i.e., aggregating the image-level contextual information. Despite impressive, these methods weaken the significance of the pixel representations of the same category, i.e., the semantic-level contextual information. To address this, this paper proposes to augment the pixel representations by aggregating the image-level and semantic-level contextual information, respectively. First, an image-level context module is designed to capture the contextual information for each pixel in the whole image. Second, we aggregate the representations of the same category for each pixel where the category regions are learned under the supervision of the ground-truth segmentation. Third, we compute the similarities between each pixel representation and the image-level contextual information, the semantic-level contextual information, respectively. At last, a pixel representation is augmented by weighted aggregating both the image-level contextual information and the semantic-level contextual information with the similarities as the weights. Integrating the image-level and semantic-level context allows this paper to report state-of-the-art accuracy on four benchmarks, i.e., ADE20K, LIP, COCOStuff and Cityscapes.
Active teaching methods as a resource for effective training and education of primary school students

The article discusses the main resources for the effective education of primary schoolchildren that contribute to successful adaptation to school, the development of the personality of a primary schoolchild - such as: active teaching methods, positive personal characteristics of the teacher, positive personality characteristics of the student, cognitive activity, favorable psychological climate in the classroom; it is proved that active teaching methods actualize such resources as cognitive activity of primary school children, favorable psychological climate in the classroom, positive personality characteristics of a teacher and a student; it is described the main groups of active teaching methods; some active teaching methods developed by the best teachers of elementary schools in Russia are given as an example.
Color Marker Search System using a Pan-Tilt-Zoom Camera for Intelligent Worker Assistance

In this study, we developed an autonomous color marker search system that uses a single pan-tilt-zoom camera to detect the 3D locations of multiple markers placed in a field. The system was designed to scan for multiple markers over a sensing range within 40 m in a field divided into 855 marker search grids. In the experiments, our system was able to robustly measure marker 3D positions within a sensing range of approximately 30 m, and with a root mean square error of less than 0.5 m, without large height differences between markers and camera, and without marker deformation, with the maximum marker search time required to scan the entire field of approximately 7 min.
International Economic Sanctions and Income Mobility: Evidence from a Natural Experiment

This paper, for the first time, studies the impact of international economic sanctions on income mobility. The United Nations Security Council imposed a series of sanctions on Iran from the end of 2006 to 2015 and then lifted them to some extent in 2016. We employ 20 household surveys of income and expenditures collected by the Statistical Center of Iran from 1999 through 2018 to analyze the impact of the sanctions on intra-generational income mobility. We use various empirical methods to see how mobility varies by the imposition/removal of sanctions. All methods show that the sanctions affect all income groups adversely in one or two years. With the removal of sanctions in 2016, all people experience upward mobility, although they do not return to their pre-sanctions status.
The Influence of Experience on Gazing Patterns during Endovascular Treatment: Eye-Tracking Study

In various fields, differences in eye-gazing patterns during tasks between experts and novices have been evaluated. The aim of this study was to investigate gazing patterns during neuro-endovascular treatment using an eye-tracking device and assess whether gazing patterns depend on the physician's experience or skill.Seven physicians performed coil embolization for a cerebral aneurysm in a silicone vessel model under biplane X-ray fluoroscopy, and their gazing patterns were recorded using an eye-tracking device. The subjects were divided into three groups according to experience, highly experienced (Expert) group, intermediately experienced (Trainee) group, and less experienced (Novice) group. The duration of fixation on the anterior-posterior (AP) view screen, lateral (LR) view, and out-of-screen were compared between each group.During microcatheter navigation, the Expert and Trainee groups spent a long time on fixation to AP, while the Novice group split their attention between each location. In coil insertion, the Expert group gazed at both the AP and the LR views with more saccades between screens. In contrast, the Trainee group spent most of their time only on the AP view screen and the Novice group spent longer out-of-screen.An eye-tracking device can detect different gazing patterns among physicians with several experiences and skill levels of neuroendovascular treatment. Learning the gazing patterns of experts using eye tracking may be a good educational tool for novices and trainees.
Designing LEEF (Learning English Easy and Fun) as Learning Media for the Seventh Grade of Junior High School

This research aims at designing a learning media which named LEEF (Learning English Easy and Fun) as English learning media for seventh grade students of Junior High School. This learning media consists of some materials and exercises which is appropriate and feasible to be studied for students. All materials in this media are based on junior high school syllabus of K13 curriculum. The research method used in this research is Research and Development. This development used ADDIE model which have several steps, those are; 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. The result of this research shows that from the media validation result got 4.00 score it was in “fair and worth to use” category, and the material validation got 3.86 score. The students’ responses of try out result score 4.26 were in “fair and worth to use” category. By having those result, this LEEF (Learning English Easy and Fun) is feasible to be used as a learning media for seventh grade students of junior high school. Furthermore this research can be used as reference to other researchers in developing English learning media in different level of students.&#x0D;
Optimization of Online Learning and Teaching Learning Activities on LMS (Learning Management System) Based Incentives Using Decision Support Systems

Based on data obtained from the E-Learning Bureau of University of Pembangunan Panca Budi, only about 20% of lecturers want to use LMS or e-Learning portal in the teaching and learning process. Some lecturers use other platforms that they find easier and some use email, WhatsApp Group and flash drives in data transfer or assign assignments to students on the other side. Universities are required to use an online learning platform that can be synchronized with the SPADA DIKTI page so that checking online teaching activities can be monitored or reported periodically and easily. This research will focus on assessing lecturers' activities on online learning at LMS using a Decision Support System where the criteria used are criteria that must be met in the study program accreditation form item. The goal is in addition to increasing the activities of lecturers through incentive giving also directly assisting the study program in the fulfillment of study program form documents. The results of this research will be a reference for the leadership in making policies related to the amount of incentive that will be given as well as other provisions that can stimulate lecturers in increasing teaching and learning activities in LMS or e-Learning portals. Keywords: e-Learning, Learning Management System, Decision Support System, System Additive Weight, Online Learning System, Online Learning.
5G Traffic Prediction with Time Series Analysis

In todays day and age, a mobile phone has become a basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary to accurately predict the user traffic in cellular networks, so as to improve the performance in terms of resource allocation and utilisation. By leveraging the power of machine learning and identifying its usefulness in the field of cellular networks we try to achieve three main objectives classification of the application generating the traffic, prediction of packet arrival intensity and burst occurrence. The design of the prediction and classification system is done using Long Short Term Memory model. The LSTM predictor developed in this experiment would return the number of uplink packets and also estimate the probability of burst occurrence in the specified future time interval. For the purpose of classification, the regression layer in our LSTM prediction model is replaced by a softmax classifier which is used to classify the application generating the cellular traffic into one of the four applications including surfing, video calling, voice calling, and video streaming.
Performing Information Extraction and Ontology Development for Mission Engineering Applications

The development and evaluation of Mission Engineering Threads (METs) require an understanding of the operational context for which a system-of-systems (SoS) will be employed as well as an assessment that the performance of a complex SoS is effective and safe. The information describing the design and performance parameters of the systems within the SoS is distributed in many different physical locations, and is represented in a variety of formats, both structured and unstructured. Because of this dynamic on the structuring of the data to include differing ontology frameworks, it is necessary to develop a framework and toolset to handle the automated extraction of information from disparate information sources. In addition, this extracted information needs to be categorized properly into defined data types as represented in the specific MET to correctly capture the appropriate context of the mission scenario. Semantic technique solutions will be researched, analysed, and applied as a means to infer new facts from existing facts and data. These techniques are particularly powerful when the amount of data and/or the relationships and constraints among data are too cumbersome and complex for human understanding and reasoning. Using the characteristics of wine as an example, we present our framework to show how it enables rapid and contextually relevant extraction, and represents complex information in a user-friendly format.
The Implementation of The Pjbl Method Assisted by Padlet on Environmental Pollution Material on Student Science Literature

The study aimed to 1) determine the effect of learning using the PjBL model assisted by Padlet on environmental pollution material on student literacy, and 2) find out the student responses toward the learning using the PjBL model assisted by Padlet on environmental pollution material on students scientific literacy. The research used a quasi-experimental method with a Pretest-Posttest control group design. The sample selection techniques used Purpose Sampling, both Experiment and control class. Data collection techniques were questionnaires, documentation, and tests. Data analysis used the Independent Sample T-Test and the coefficient of determination. The finding showed that the treatment of the research subject was the class. In the experimental class, the pretest score was 71.79 and the posttest score was 75.94. Meanwhile, in the control class, the pretest score was 60.27 and the posttest score was 66.53. The N-Gain value was 0.35 in the Experiment class and 0.31 in the control class, so it was in the medium category. In short, the implementation of the PjBl model assisted by Padlet on environmental pollution material affects students' scientific literacy by 4.6%.
Remote Laboratory in Control Process during COVID-19 Pandemic Situation

&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;Today we are witnessing an unprecedented pandemic called the "COVID-19 pandemic" which has created the greatest disruption to education systems in human history. It has caused the closure of schools, institutions and other learning spaces, causing profound changes in all aspects of our lives. Policies of social distancing and restriction of movement have significantly disrupted traditional educational practices. Faced with this problem, it is time to innovate and implement an alternative education system and assessment strategies. The work presented in this paper responds to this problem and aims to provide a practical work environment which allows engineers to complete their basic training with the use, control and handling of measuring instruments often used in the real world. This environment will therefore be accessible online and makes it possible to control the process.&lt;/span&gt;&lt;/p&gt;
Introduction to Signal Processing and Machine Learning

This chapter introduces this book and presents a brief overview of some key aspects and terms related to signal processing and machine learning. Though intended for non-specialists, it includes some technical details and introductory background information required for the rest of the chapters. The chapter starts with the basic definitions and concepts of signal processing, stressing on its applications in the modern era. The challenges related to traditional digital signal processing techniques as well as the immediate global need to deal with enormous data are emphasized, elaborating on the need to use machine learning algorithms and SPML (signal processing and machine learning) techniques which is the latest advancement in this field. Several basic concepts of signal classification, distance metrics, discrete signal representation, sampling, quantization, change of basis, and the importance of the analysis of time and frequency domains are discussed to review the fundamentals of signal processing. The chapter proceeds with a discussion on the benefits of distance-based signal classification, nearest neighbor classifier, and Hilbert space. Some basic terminologies of machine learning are also covered. The chapter then concludes by highlighting the benefits of SPML and how signal processing links seamlessly with machine learning to develop essential solutions to real-world problems.
USING THE ASSESSMENT SYSTEM AND MODERN DIGITAL TECHNOLOGIES IN THE PROCESS OF TEACHING ENGLISH

The predominance of English in the media, particularly on the internet, have been responsible for driving change in language education policy and there is a global trend towards introducing English language teaching into the system of education.The introduction of web 2.0 network services in language education is one of the promising areas of informatization of the educational process, which helps to foster a stable communication competence in the students and design formative assessment under the guidance of an experienced teacher. Modern digital technologies with the transition to a more communicative approach offer new forms of assessments that were not available to us even ten years ago. Students have the opportunity to record videos while interacting in groups or even working on a monologue or story. Students often practice uploading podcasts and audio files and creating diary and wiki entries.The article deals with the benefits of digital technology during teaching English, which opens new perspectives on the assessment. The research describes the stages of the organization of assessment in the classroom, emphasizes the importance of feedback on testing, examines some factors that affect the teaching of English in terms of its assessment, gives an overview of the benefits of using washback effect and lists the problems associated with the use of information and communication technologies. The author explores the potential of using formative and summative assessments as well as the feedback to improve students' academic achievement and progress. Digital technology and assessment help us to achieve some of our goals: improved motivation, autonomy or closer cooperation between students, and so on. In addition, feedback can even be provided by students themselves, which contributes to the formation of self-assessment. Consequently, it becomes obvious what should be changed in the course on the basis of the assessment of students’ progress during the learning cycle.There is a lack of professionalism in the use of some information and communication technologies, which negatively affects the effectiveness of assessment systems of English.&#x0D; Keywords: formative assessments, summative assessments, information and communication technologies, digital technologies, feedback, self-assessment, motivation, self-reflection, English.
Scaling Graph-based Deep Learning models to larger networks

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an unprecedented capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous solutions based on Machine Learning (ML), GNN enables to produce accurate predictions even in other networks unseen during the training phase. Nowadays, GNN is a hot topic in the Machine Learning field and, as such, we are witnessing great efforts to leverage its potential in many different fields (e.g., chemistry, physics, social networks). In this context, the Graph Neural Networking challenge 2021 brings a practical limitation of existing GNN-based solutions for networking: the lack of generalization to larger networks. This paper approaches the scalability problem by presenting a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.
Physical Context and Timing Aware Sequence Generating GANs

Generative Adversarial Networks (GANs) have shown remarkable successes in generating realistic images and interpolating changes between images. Existing models, however, do not take into account physical contexts behind images in generating the images, which may cause unrealistic changes. Furthermore, it is difficult to generate the changes at a specific timing and they often do not match with actual changes. This paper proposes a novel GAN, named Physical Context and Timing aware sequence generating GANs (PCTGAN), that generates an image in a sequence at a specific timing between two images with considering physical contexts behind them. Our method consists of three components: an encoder, a generator, and a discriminator. The encoder estimates latent vectors from the beginning and ending images, their timings, and a target timing. The generator generates images and the physical contexts at the beginning, ending, and target timing from the corresponding latent vectors. The discriminator discriminates whether the generated images and contexts are real or not. In the experiments, PCTGAN is applied to a data set of sequential changes of shapes in die forging processes. We show that both timing and physical contexts are effective in generating sequential images.
WebAssembly enables low latency interoperable augmented and virtual reality software

There is a clear difference in runtime performance between native applications that use augmented/virtual reality (AR/VR) device-specific hardware and comparable web-based implementations. Here we show that WebAssembly (Wasm) offers a promising developer solution that can bring near-native low latency performance to web-based applications, enabling hardware-agnostic interoperability at scale through portable bytecode that runs on any WiFi or cellular data network-enabled AR/VR device. Many software application areas have begun to realize Wasm's potential as a key enabling technology, but it has yet to establish a robust presence in the AR/VR domain. When considering the limitations of current web-based AR/VR development technologies such as WebXR, which provides an existing application programming interface (API) that enables AR/VR capabilities for web-based programs, Wasm can resolve critical issues faced with just-in-time (JIT) compilation, slow run-times, large file sizes and big data, among other challenges. Existing applications using Wasm-based WebXR are sparse but growing, and the potential for porting native applications to use this emerging framework will benefit the web-based AR/VR application space and bring it closer to its native counterparts in terms of performance. Taken together, this kind of standardized "write-once-deploy-everywhere" software framework for AR/VR applications has the potential to consolidate user experiences across different head-mounted displays and other compatible hardware devices to ultimately create an interoperable AR/VR ecosystem.
Competencias TIC de los maestros directivos de centros educativos públicos

This research was carried out with the objective of diagnosing the ICT competencies of school principals of public schools in the Educational District 09D08 of Guayaquil. A basic research was carried out from a qualitative study, based on a literature review and data collection through a field study where 44 managers were surveyed through two questionnaires designed to identify their level of integration of technological skills and the level of knowledge and usability of technological tools. The results show that not all managers are familiar with search engines and the web, need strategies for the publication of digital content and the design of virtual learning environments and the use of technological tools to help students develop critical thinking and build meaningful learning.