Reviewerly/exHarmony huggingface dataset:
An Investigation on Performance of Attention Deep Neural Networks in Rapid Object Recognition

Inspite of the huge success of Deep Neural Networks in object recognition, there are still situations in which they cannot reach human performance. In this work, the performance of an attention Deep Neural Network which is cued by important pixel of objects is addressed. First, the effect of color on accuracy of classification is evaluated. Then the network performance is compared with humans by using a set of images from different levels of revelation of important pixels. The results indicate that color information enhances the recognition of objects and there is a correspondence in accuracy of classification as well as correlation in decisions between human and attention networks at middle and low levels of important pixel revelation respectively.
Automated Machine Learning for Short-essay Answer Grading

Natural language processing coupled with machine learning has been employed to great effect in the realm of essay and short-answer essay scoring. However, these models typically require a strong understanding of machine learning concepts along with domain knowledge specific to the dataset in order to achieve optimal results. An emerging trend in machine learning is that of automated machine learning. Our project seeks to demonstrate the effectiveness of an automated machine learning system designed to score a critical thinking assessment with no machine learning experience or domain knowledge required. We will show that an ensemble of deep and non-deep machine learners, designed and tuned automatically, can outperform human scorers on this task.
Unified Extensible Framework For Sentiment Analysis Based On Opinion Disambiguation

Sentiment Disambiguation is a vital component of any sentiment analysis or opinion analysis of the Data Mining domain. It plays a decisive feature to distinguish sentiment polarity of different text granularity. Another important point is to process the fine-grained large data sentiment analysis based on sentiment disambiguation lexicon to improve the accuracy of sentiment prediction. This system uses the Statistical Natural Language Processing computational techniques for the sentiment analysis. The existing system is based on a customized fuzzy method approach with two-stage training to deal with text ambiguity. The existing system classifies three types of cyberhate speeches: race, disability, and sexual orientation. In the existing system, the prediction accuracy has decreased when large data set contains huge diversity in the text. The existing system cannot achieve the efficiency of removing the ambiguity when there is intersectionality among a wide variety of cyberhate speech. The proposed system implements the process to determine the equivocal text into various categories toward Sentimental Disambiguation which narrows down the meaning of the words. The proposed system combines the sentiment label data and sentiment contrast data between focus keywords and context words into words vector representation learning model. The accuracy of the proposed prediction model will be very high even though the large diversity in the input text has fuzziness, ambiguity, and vagueness.
Networking of Strategic Partnerships in Higher Education: Prerequisites and Readiness of Universities

The priority task of the modern education system development is the organization of a comprehensive partnership, which, among other things, implies the development of network interactions between the university and various economic entities. In this regard, the need to apply new forms of partnerships is actualized, in which openness and readiness of partners for equal, effective and multilateral interaction is in the first place. However, at present, not all economic entities are ready to implement such a form of partner relations. The article examines the tendency of networking in higher education and the level of their development from the perspective of openness. In this work, we tested the methodology for a university readiness evaluation to form and develop a strategic partnership based on the network approach using the example of leading universities of the Russian Federation.
USING FLASH CARDS TO IMPROVE STUDENTS’ VOCABULARY

Vocabulary is one of an important thing in English learning that cannot be delivered by people. For get a good ability in listening, speaking, reading and writing, people should be mastering a vocabulary. That is the reason why vocabulary is crucial in language learning and when the researcher establish that vocabulary mastery of Junior High School are less. Then, teaching vocabulary using flashcard chosen as the theme of this classroom action research. The sample of this research was seven grade of junior high school in Katapang. The researcher presented the result and the analysis of data collected that consist of cycle 1 and cycle 2. Each cycle of the research consist of planning, acting, observing and reflecting activities. In the research, there are some steps before cycle 1 and cycle 2, there are observation and pre-test. The last steps after cycle 1 and cycle 2 are post-test. In the pre-test the researcher used the convensional method. Most of students not active and enthusiasm in the lesson in cycle 1.But, when the researcher used flashcard, the student more active and interest. It could be seen in mean score of the post-test in cycle 1, that is 79.50. After that, in th cycle 2, the mean score was 87.50. It was higher that pre-test. Finally, from the researcher explanation concluded based on the qualitative data that consist of pre-test until post-test, that flashcard could advance the students vocabulary.
Didactic Model to Form Process Modeling Skills When Teaching to Solve Economic Tasks

The article discusses the possibility of organizing educational process in institutions of basic and secondary general education using the means, techniques and technologies promoting the formation of pupils’ culture of thinking in the process of teaching Mathematics. It is shown that this objective involves modeling a real-life economic process and necessary presupposes the formation of cognitive actions. The content of a didactic model for forming process modeling skills is revealed, an example of a mathematical model to solve an economic task is provided.
SJSU Research Guides: Online Library Resources and Services: Resources for Faculty

This guide contains information about the library resources and services that are available during the COVID-19 closure.
3D Video Spatiotemporal Multiple Description Coding Considering Region of Interest

3D video applications are being more favourable for observers as their requirements to receive and display 3D videos become more available recently. Therefore the demand for more processing power and bandwidth is increasing to stream and display 3D multimedia services in either the wired or wireless networks. Since channel failure has been always as an integral part of communication between a receiver and transmitters, a robust method of video streaming is always a hot topic for researchers. To make robustness against failure more stronger, it needs to increase redundancies however it destroys the coding and compressing efficiency. Therefore there is a trade-off problem between the coding efficiency and robustness of the stream. Among different methods of reliable video streaming, this paper introduces a new reliable 3D video streaming using hybrid multiple description coding. The proposed multiple description coding creates 3D video descriptions identifying interesting objects of the scene. To this end, a map for the region of interest is extracted from the depth map image first with a not complex algorithm compared to the available machine learning algorithm. Having realized region of interest, the proposed hybrid multiple description coding algorithm creates the descriptions for the color video using the advantages of both spatial and temporal multiple description coding methods; To this end, a non-identical decimation method concerning the identified objects assigns more bandwidth to those objects; second, background quality is improved with the temporal information. This way, first, the proposed method provides better visual performance as the human eye is more sensitive to objects than it is to pixels; second, the background is reconstructed with higher quality as it usually has a low movement and temporal information is a better choice to estimate the lost information. The objective test results verify the fact that the proposed method provides an improved performance than previous methods.
Usaha-Usaha Mahasiswa Dalam Memanfaatkan Waktu Luang Untuk Menambah Pendapatan Pada Mahasiswa Fakultas Keguruan Dan Ilmu Pendidikan Universitas Syiah Kuala

This study entitled Student Efforts in Utilizing Spare Time To Increase Revenue At Students Faculty of Teacher Training and Education University of Syiah Kuala. This research raises about the efforts undertaken by students in the use of leisure time. The purpose of this study is to find out what are the efforts of students in utilizing spare time to increase income on FKIP Unsyiah students. The subject of this research is all students of FKIP Unsyiah who do business as many as 18 students, the object of this research is student effort in utilizing spare time to increase the income of FKIP Unsyiah students. The type of research used is descriptive research with qualitative approach. Data collection techniques in this research is library research and field research in the form of observation and interview. Data processing techniques in this study presents all research results obtained into the paper. From the result of data analysis, it can be concluded that the student's effort in using spare time to increase the income of FKIP Unsyiah students is the effort of sewing, distro, olshop, selling package card and pulse, makeup, making cake, bucket flower, brown bucket and photo wood. In doing this research many have limitation that is limitation of energy, time, and cost. Therefore the researcher is looking forward to constructive suggestions.
RANCANGAN SISTEM INFORMASI KONVERSI MATA KULIAH MAHASISWA PINDAHAN DAN LANJUTAN (STUDI KASUS DI STMIK LOMBOK)

Lombok Institute of Information and Computer Management (STMIK) is one of the colleges in Central Lombok. In Higher Education there is a set of subjects given to participants in a period of education called the curriculum. Because the curriculum in each tertiary institution is different, a system that can equalize or convert courses is needed. Especially in supporting all lecture activities which in Higher Education must have a conversion system for equalizing subjects along with the number of effective semester credits takes up to two days, the head of study program is required to be able to produce or which is often referred to as a conversion term, both for advanced students and transfer students. The process of converting courses has not resulted in a decision that is a fast and appropriate decision for transfer student lectures. Based on the description above, the author intends to make a design entitled "The Design of Information Systems Conversion for Transfer and Advanced Students (Case Study of STMIK Lombok)".
Multimode squeezed light and coupled squeezed vacuum (Conference Presentation)

Multipartite entanglement is a key resource for various quantum information tasks. Here, we present multiple schemes for multimode entanglement and squeezing via nonlinear optical processes. We define a new "coupled three-mode squeezed vacuum" state. Non-intuitive behaviors arise in intensity squeezing between two of the three output modes due to the coupling. We also show that this state can be genuinely tripartite entangled, and extend the work to a four-mode output system.
Design and Development Paper Printout System for Gardu Tol Otomatis (GTO) based on Mechanical Approach

The existence of Gardu Toll Otomatis (GTO), automatic toll payment, becomes a general model for every toll payment in Indonesia. Almost toll payment models in Indonesia have a paper printout as received which is often wasted. This condition happens since not all the driver needs a paper proof of the transaction. Therefore, the wasted paper will occur everywhere for 24 h. Furthermore, in this work the design of printout system using a more efficient printout model with rotation feeding method has developed printout system. In this work, the rotation mechanism system is applied through product design engineering through Industry 4.0. This system is made by removing the printout paper that remains present to the driver who needs it, but not wasted because of the driver who does not need it. There are three important steps as follows; in the first step, the printout paper appears in a special tray which is easy to reach and takes for those who need it. In the second step, the paper will remain in the tray for the driver who did not pick it up and final step, the tray with paper printout will rotate mechanically back into the GTO machine to be destroyed by a paper shredder. The experiment of the paper printout system has been carried out. The results show that the proposed approach produces a better accuracy and efficiency. Experimental results show the testing accuracy of 90 %. The system confirms the effectiveness of the proposed paper printout system for the GTO.&#x0D; Keywords: digital technology; digital transformation; electronic banking; online payment; electronic wallet
Optical neural networks: from integrated photonics to free-space solutions (Conference Presentation)

Here we discuss two different approaches to realize optical neural networks (NN). Part ‘A’ focuses on integrated-photonics solution to realize a perceptron using (i) optoelectronic components to perform the MAC operation and (ii) a more novel concept of an all-optical nonlinearity for thresholding and thermo-optical memory for zero-power synaptic weighting. The latter is unique since the processor’s delay simply depends on the time-of-flight of the photon through the NN, which can be in the picosecond range enabling applications in real-time signal processing. Part ‘B’ discusses our recent process of 4f-based Fourier-optical filtering towards convolution neural networks (CNN); this approach uses massive parallelism (10^6 channels) of digital-mirror-display (DMD) technology. Here the computational expensive convolution is simple realized as a multiplication in the Fourier domain via a lens. Our generation (Gen) 1 system shows 250 TMAC/s process capability at 8-bit resolution.
Twitter topic Analysis using Multi-Tweet Sequential Summarization for Sentimental Data

The rise of social media has generated tremendous interest and changes among Internet users today. Data from these social media sites can be used for a number of purposes, like prediction, marketing or sentimental analysis. Twitter is a social networking service on which users post and interact with a message called TWEETS. The millions of tweets received every year could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a tedious task to take up. The current Analytics tools and models used that are available in the market, but are not sufficient to manage big data. Therefore, we have utilized Hadoop for intelligent analysis and storage of big data. In this proposed work, we did sentiment analysis on tweets in Hadoop environment.
BIBLIOGRAPHIC SUPPORT FOR LIBRARY AND INFORMATION SCIENCE AND PRACTICE

The provision of bibliographic resources for individual branches of science and / or practical activity is traditional and now it is one of the most developed areas of bibliography. The library and information sphere, including library, bibliographic and scientific information activities and the scientific disciplines that study them needs (library and information sciences) it as well. These areas are characterized by the proximity and significant concurrency of goals, objects, processes, methods, and results of activities. At present, their further integration, transformation of traditional libraries (first of all, scientific ones) into library and information centers is underway. The bibliography of library and information science and practice includes: 1) bibliography of literature on history, theory, methodology, organization, methodology and technology of these activities, 2) bibliography of publications and electronic resources of library and information institutions; for bibliography as such bibliography of bibliography is also of particular importance, 3) bio-bibliography of branch leaders (polybiobibliographic and monobibiographic (personal) resources). Since the 1940s the Russian State Library is the main center of the current bibliographic registration of literature on librarianship and bibliography (until 2015 – the release of current indexes, since 1989 – the formation of a thematic abstract bibliographic database). The library prepared many printed retrospective indexes and lists on the problems of library and bibliographic science and practice, which creation not only its bibliographic, but also research, methodical and production structural units took active part in. In the XXI century, the Library activities in the field of retrospective branch bibliography are limited to the creation of a series of indexes “Bibliography. Bibliographic science”, reflecting the scientifically significant array of native bibliographic literature in Russian since 1959. The main attention is paid to bibliographic registration of publications and electronic resources of the Library and about it, as well as works of its staff. Electronic catalogs and bibliographic indexes are being created that take into account the products of libraries of the Russian Federation, as well as countries – the former Soviet republics. Polybiobibliographic and personal indexes are being prepared.
3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The proposed model is capable of modeling spatio-spectral dependency while preserving the flexibility towards HSIs with arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over state-of-the-arts under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.
Сomputer tools for data collection in the student-sociologist's workshop

The article is devoted to the collection and processing of sociological data using mobile and office computers in a workshop of sociological students. The relevance of these issues is due to the fact that new applied information competencies should be formed for the student in addition to his ability to work with questionnaires on paper, work on mobile and office personal computers in local and global computer networks with office programs and Internet technologies at present with mass the spread of computer and mobile technologies, the growth of the functionality of mobile personal computing devices, the increase in the number of developed services about online-survey, in the learning process, a sociologist student. These competencies are associated with the ability of sociological students to prepare and conduct case studies, use modern methods of collecting and preliminary processing of case data using mobile and office computers, apply statistical analysis programs for these data, and prepare final reports. The article shows how the authors solve this problem, relying on service programs for online questioning of Russian and foreign companies, as well as using their two developed programs. These programs run on Windows and provide a solution to the problems of data collection when filling out questionnaires on mobile and office computers. They are registered by the Federal Service for Intellectual Property of the Russian Federation and for several years have been used in the workshop of sociological students on personal computers. The article describes an example of the use of these programs in the preparation and conduct of a large-scale case study on the topic "Self-assessment by students of their level of computer literacy." The purpose of the study is to analyze the methods, technologies, tools and results of a large-scale questionnaire on the topic "Self-assessment by students of their level of computer literacy", on the example of which these competencies can be formed. During the study, the method of interviewing students according to developed questionnaires and a correlation analysis of the collected data and the results of computer testing were used. When writing an article, a comparative analysis of modern online questionnaire service programs of Russian and foreign companies was performed. In addition, two authoring programs were used, running Windows and providing a solution to the problems of data collection when filling out questionnaires on mobile and office computers. These programs are registered by the Federal Service for Intellectual Property of the Russian Federation and for several years have been used in the workshop of sociological students on personal computers.
Deep Distributional Temporal Difference Learning for Game Playing

Temporal difference learning is considered one of the most successful methods in reinforcement learning. Recent developments in deep learning have opened up a new world of opportunities. In this project, we compare classic scalar temporal difference learning with three new distributional algorithms for playing the game of 5-in-a-row using deep neural networks: distributional temporal difference learning with constant learning rate, and two distributional temporal difference algorithms with adaptive learning rate. All these algorithms are applicable to any two-player deterministic zero sum game and can probably be successfully generalized to other settings. As it turned out, all algorithms performed well and developed strong strategies. The algorithms implementing the adaptive methods learned more quickly in the beginning, but in the long run, they were outperformed by the algorithms using constant learning rate which, without any prior knowledge, learned to play the game at a very high level after 200 000 games of self play. (Less)
RAPID: Real-time Analytics Platform for Interactive Data Mining

Twitter is a popular social networking site that generates a large volume and variety of tweets, thus a key challenge is to filter and track relevant tweets and identify the main topics discussed in real-time. For this purpose, we developed the Real-time Analytics Platform for Interactive Data mining (RAPID) system, which provides an effective data collection mechanism through query expansion, numerous analysis and visualization capabilities for understanding user interactions, tweeting behaviours, discussion topics, and other social patterns. Code related to this paper is available at: https://youtu.be/1APLeLT_t8w.
Joseph Krauskopf Memorial Library: Help Desk and Technology: Working Remotely

Delaware Valley University technology and help desk services. Working remotely support page