Reviewerly/exHarmony huggingface dataset:
Error Analysis in Korean EFL Learners’ Writing: A Case Study of Elementary Level Learners

This study investigated the types of errors in the essay writing of college students at the elementary level. A total of sixty-four data were collected from thirty-five college students at the elementary level who took a general English course. Dulay et al. (1982)’s surface strategy taxonomy was adopted for the analysis and the students’ errors were categorized into four parts, such as omission, addition, misformation, and misordering. Results showed that the frequency of errors from misformation was the highest (51%), followed by omission (32.95%), misordering (10.71%), and addition (4.76%). In more detail, tense errors were revealed most frequently, followed by word choice. In particular, errors in the use of articles and prepositions occurred in various forms, which are construed as a result of a language system that does not exist in the mother tongue. The students in this study committed various grammatical errors at the sentence level as well as showed a lack of general knowledge about writing. It suggests that second language learners should be taught not only linguistic knowledge but also the strategy to improve writing skills.
Analysis of a Lecturer’s Directive Speech Acts to Student in Online Pragmatic Studies

This research aims to describe the form and function of the lecturer's directive speech actions to Class A students in the 2019 batch participating in online learning in pragmatic studies. This research employed descriptive qualitative methods of observable words said by speakers (lecturers) to speech partners (students). The data source was the lecturer's remarks to students during online learning via zoom). The data collection techniques employed listening, note-taking, screen recording, and screenshots. The researcher discovered six types of directive speech acts including (1) a request form; (2) in the form of a lecturer's command; (3) A question; (4) a sort of prohibition; (5) An invitation in the style of Lecturer; and (6) As a demand.. Four directive speech acts functions are (1) competitive (competing) functions; (2) convivial (fun); 3) collaborative function (collaborating); and (4) conflict function (contrary) as the lecturer said: If no one answers who wants to present their work, I will remove them from the zoom room”.
Implementation of Travel Chatbot using NLP and Python

This can be described as software where people can use this for chat using artificial intelligence. These works very active in such a way that where user gets a quick response for the queries they have. This helps customers to purchase products and provide better service. Computer is here used for the communication purpose using internet.
Analysis on the Path of Local Universities’ Intervention in the Sustainable Development of Poor Rural Areas in the Era of Post-Poverty Alleviation: Taking Sichuan University of Science and Engineering as an Example

China has entered the era of post-poverty alleviation, which effectively links poverty alleviation achievements with rural revitalization strategies. In the era of post-poverty alleviation, in order to effectively promote the sustainable development of poor rural areas, local colleges and universities should actively nurture the self-development skills of the needy. Local colleges and universities should guide the transformation of ideas and concepts, scientifically optimize the allocation of poverty alleviation resources, improve the interest linkage mechanism between colleges and universities, strengthen the investigation and analysis of poverty alleviation projects, improve the convergence of assistance and practice, actively create a good spiritual and cultural environment, uphold the mission of helping cadres, attach importance to the support for the development of characteristic industries, and lastly, promote the convergence of rural revitalization strategies.
Quantum relaxed row and column iteration methods based on block-encoding

Iteration method is commonly used in solving linear systems of equations. We present quantum algorithms for the relaxed row and column iteration methods by constructing unitary matrices in the iterative processes, which generalize row and column iteration methods to solve linear systems on a quantum computer. Comparing with the conventional row and column iteration methods, the convergence accelerates when appropriate parameters are chosen. Once the quantum states are efficiently prepared, the complexity of our relaxed row and column methods is improved exponentially and is linear with the number of the iteration steps. In addition, phase estimations and Hamiltonian simulations are not required in these algorithms.
RADAR and Camera Sensor Data Fusion

As demand for vehicle automation has expanded in the last few years, it has become imperative to more precisely recognize the position and speed of surrounding vehicles. Object detection has been recognized as an important feature of the advanced driving assistance system (ADAS). This also ensures the safety of vehicles and prevents accidents caused by the negligence of humans. Object detection with sensor data fusion has proved to be very effective. Obstacles can be detected and labeled with the help of RADAR, LIDAR, and camera. Every sensor has advantages and limitations. Limitations of one sensor can be overcome by another sensor. Sensors such as LIDAR, RADAR, and camera are used together in order to obtain optimum results contributing to better object detection in autonomous systems. The paper describes the fusion of data acquired by the two sensors RADAR (AWR1642BOOST) and a two-dimensional camera (LOGITECH C170). RADAR can achieve better results in distance calculation than camera, whereas camera can achieve better results in angle compared to RADAR. Similarly, RADAR works efficiently in poor weather conditions and lighting, whereas camera may not provide accurate results. The data acquired by both the sensors are fused in order to obtain better object detection and ensure accurate calculation of parameters of the object detected. Region of interest detection and Haar Cascade algorithms are implemented to get satisfactory results and has been implemented in real time.
On Using Blockchains for Beyond Visual Line of Sight (BVLOS) Drones Operation: An Architectural Study

Beyond Visual Line of Sight operation enables drones to surpass the limits imposed by the reach and constraints of the eyes of their operator. It extends their range and, as such, productivity and profitability. Drones operating BVLOS include a variety of highly sensitive assets and information that could be subject to unintentional or intentional security vulnerabilities. As a solution, blockchain-based services could enable secure and trustworthy exchange and storage of related data. They also allow for traceability of exchanges and synchronization with other nodes in the network. In this context, we develop simulation models to evaluate different blockchain-based solutions before implementing them in real systems. However, most of these blockchain-based approaches focus on the network and the protocol aspects of drone systems. Few studies focus on the architectural level of on-chip compute platforms of drones. Based on this observation, the contribution of this paper is twofold : (1) a generic blockchain-based service architecture for on-chip compute platforms of drones in a simulated environment, and (2) an illustration of the proposed generic architecture in a simulated environment for authentication.
A Bayesian Spatial Contamination Model

In environmental research, it is often the case that to cluster observations into environmentally polluted and natural groups is an important issue. The Bayesian contamination model which adopts a multivariate mixture regression model has been developed in that it aims to cluster observations and estimate the average amount of pollution. However, because the Bayesian contamination model does not take spatial correlations between observations into consideration, a Bayesian spatial contamination model is proposed. A simulation study was conducted showing that the proposed model has an advantage over the Bayesian contamination model in terms of biases and RMSE of estimators of the logistic regression parameters. We applied the proposed model into environmental data and confirmed the improvement on the model fit. Also, the clustering was reasonably performed from the environmental perspective, which was coherent with the fact that the underground water flows from the southwest side to the northeast side. This model is expected to be utilized effectively to monitor the quality of a ground or groundwater and capture the heterogeneity in it which is suspected of environmental pollution especially when the interested site consists of areas with strong spatial dependency.
CONTINUING EDUCATION OF TEACHERS THROUGH INTER-SCHOOL MODELS OF SELF-EDUCATION

Thanks to various federal projects and initiatives, innovative approaches to the design and implementation of continuing education programmes for teachers have emerged. Lifelong learning models are innovative. The proposed inter-school model of teacher self-study is individual-networked, it corresponds to the trends of modern education, contributes to the formation and development of professional competence of teachers in accordance with modern requirements.
Parallel histogram

This chapter introduces the parallel histogram computation pattern and the concept of atomic operations. It shows that atomic operations to the same location are serialized and that their throughput is determined by their latency. It further introduces four important optimization techniques: thread coarsening–based interleaved data partitioning for improved memory coalescing, caching for reduced latency and improved throughput of atomic operations, privatization for reduced contention, and aggregation for reduced contention.
Visualization of Subordinate Circumstantial Sentences for Concession in the Context of Syntax Training in 8th Grade (Project-Research Model)

The article shares experience from the application of a model for project and research activities related to the acquisition of knowledge of complex compound with subordinate circumstantial sentence for concession – through the obtained additional information about subordinate circumstantial sentences for concession students upgrade their knowledge of syntax. The research focuses on the visualization of the acquired new knowledge by the students as a result of their active learning, which includes independent search, selection, interpretation and attractive presentation of the studied information. This series of consecutive actions of the student directly corresponds to the role of the teacher – this is where the skills of the teacher-innovator become apparent – he not only prepares an interesting project topic for work, but also correctly selects and distributes learning tasks, taking into account students' abilities, provoking their interest. Thus, having received support and guidance, students are highly motivated and willing to work on the project assignment.
Constrained clustering and multiple kernel learning without pairwise constraint relaxation

Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performance. These pairwise constraints, which come in the form of must-link and cannot-link pairs, arise naturally in many applications and are intuitive for users to provide. However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances. We introduce a new constrained clustering algorithm that jointly clusters data and learns a kernel in accordance with the available pairwise constraints. To generalize well, our method is designed to maximize constraint satisfaction without relaxing pairwise constraints to a continuous domain where they inform distances. We show that the proposed method outperforms existing approaches on a large number of diverse publicly available datasets, and we discuss how our method can scale to handling large data.
OCR on English Handwritten Text

Image Processing is a vital tool when one is dealing with several images and wishes to perform several complex actions on the same. With advances in technologies, one can now compress, manipulate, extract required information etc. One such application of Image Processing is detecting handwritten text and converting it into a digital text format. The main objective is to bridge the gap between the actual paper and the digital world and in doing so, one can operate on the digital data much faster as compared to the actual data. The detection of handwritten text via optical Character Recognition.
Romanian Coins Recognition and Sum Counting System From Image Using TensorFlow and Keras

The daily necessity for automation in simple and repetitive jobs, as well as in industry, has resulted in rapid technological advancement. Several organizations choose to combine robotic tools for physical jobs, as well as Artificial Intelligence (AI), Machine Learning (ML), and Neural Network (NN) -based applications for optimizing the flow of work, minimizing human error, and executing chores faster, safer, and more reliably. Simple tasks that require a little time every day can be done automatically by creating a simple AI-based app, conserving us a lot of time in the long run. Because time equals money, a technology that saves time may be extremely important and necessary for many people and businesses. Counting money, particularly coins, is a simple chore that causes many firms to waste several minutes each day and that may be conveniently completed by an application. Even though many physical machines can do this task, there are several drawbacks: they can be expensive if the device's accuracy is great, they are huge, not portable, and are viewed as a waste of money for small enterprises. Such counting machines are simply replaced with an application that recognizes the value of the coins and counts the exact value. A coin-counting application can be useful for calculating the total number of coins inside a deposit account from home, or for calculating coins at the end of a shift in commerce. The objective of this work is to create an application that calculates the total worth of Romanian coins from an input image. The developed application is divided into two components: coin recognition and coin classification. The end application should be able to detect currency in an image and distinguish between coins worth 10 Bani and coins worth 50 Bani.
Direct regression scene text detection with accuracy scoring

A large variation in size and aspect ratio is a common and important characteristic of scene text. Existing scene text detection methods are still inadequate in dealing with large variations in size and aspect ratio. There are two main deficiencies that limit the performance of these methods: the range of the effective receptive field of output features is not wide enough for large variations in size and aspect ratio of text, and the text confidence score of candidate boxes is irrelevant to the size, orientation and location of candidate boxes. We propose a direct regression method of scene text detection, which improves the feature extraction network to enlarge the range of the effective receptive field of output features, and generates a regression accuracy score instead of a text confidence score to represent the regression accuracy of candidate boxes. In the proposed method, we use the Aggregated Feature Pyramid module to generate multi-scale feature maps which contain both high-level and low-level information and have a wide range of effective receptive fields. We also propose a Regression Accuracy Scoring module to calculate the regression accuracy score of each candidate box and keep the one with the highest regression accuracy score for each text instance as the final result. Experiments on datasets ICDAR2015, ICDAR2017 and COCO-Text demonstrate that the proposed method has a comparable performance with state-of-the-art methods while it is more efficient. Meanwhile, we provide abundant ablation experiments to verify the effectiveness of the modules in our proposed method.
PB1956: EVALUATION OF AN INNOVATIVE NEW METHOD FOR QUANTITATION OF PLASMA CELLS ON CD138 IMMUNOHISTOCHEMISTRY

Background: The estimation of plasma cells in bone marrow biopsies using CD138 immunohistochemistry, as proposed by the World Health Organization (WHO), has become popular over the last few years. The determination of the plasma cell percentage plays a vital role in categorising patients into different diagnostic and prognostic groups at diagnosis and evaluating response to treatment during follow-up, which guides management protocols. There is currently no reference method (gold standard) for the estimation of plasma cells. The most commonly used method involves scanning of slides and expression of plasma cell numbers as a percentage of all nucleated cells (overview estimate). Aims: To compare the frequently used CD138 immunohistochemistry- based method of plasma cell quantitation, to a proposed new method, using interobserver and intraobserver concordance parameters. Methods: Archival CD138 immunohistochemically stained slides made from paraffin- embedded bone marrow biopsies of 33 patients with a confirmed diagnosis of multiple myeloma were used. Light microscopic examination was performed using low magnification lenses (10×) for both the overview estimation method (method A) and the new method (method B), and high magnification lenses (50×), for method B only. For method B, reviewers selected three areas with low, intermediate and high plasma cell densities using 10× lenses. Using a well- defined technique, the 50× lens was then used to count plasma cells as a percentage of all nucleated cells. After blinded relabelling of all the slides, the nine reviewers repeated the plasma cell quantitation using both methods. The plasma cell counts were obtained, and the review times were recorded. Results: Overall intraobserver concordance was comparable for method A (concordance correlation coefficient (CCC)=0.840) and method B (CCC=0.733). Interobserver concordance for method A (intraclass correlation coefficient (ICC)=0.793 and 0.713) and method B (ICC=0.657 and 0.658) indicated high similarity between reviewers. Method A showed poor interobserver concordance (ICC=0.105) at low plasma cell densities. Summary/Conclusion: The new method is comparable to the frequently used overview estimation method in terms of intraobserver and interobserver concordance, and cost. The new method has superior interobserver concordance at low plasma cell densities. The new method appears more amenable to digital scanning and analysis.
Intellectual Property (IP) Protection for Deep Learning and Federated Learning Models

This talk focuses on end-to-end protection of the present and emerging Deep Learning (DL) and Federated Learning (FL) models. On the one hand, DL and FL models are usually trained by allocating significant computational resources to process massive training data. The built models are therefore considered as the owner's IP and need to be protected. On the other hand, malicious attackers may take advantage of the models for illegal usages. IP protection needs to be considered during the design and training of the DL models before the owners make their models publicly available. The tremendous parameter space of DL models allows them to learn hidden features automatically.
Outsourced Encrypted Private and Secured Data Storage on Dynamic Server Using AES Algorithm

Clinical imaging is vital for clinical assessment, and the delicate idea of clinical pictures requires thorough security and sequestration results to be set up. In the paper, we propose a protected and successful plan to find the specific closest neighbor over deciphered clinical pictures. Not at all like most extreme being plans, our plan can acquire the specific closest neighbor as opposed to a rough outcome. Our proposed conspire clearly meets the security conditions. It safeguards the mystery and sequestration of information as well as the stoner's feedback question while contemporaneously concealing information access designs. Our plan is intended to safeguard the classification of all subsidiary clinical pictures. To safeguard inquiry sequestration, the data set and question should be made an interpretation of prior to moving to the pall garçon. Proposed Homomorphic calculation is utilized to encode information pictures. The trendy framework is Advanced Encryption Standard framework (AES). There are various kinds of AES that can be utilized yet the best is AES-128. Thus, the finish of this study is to configuration picture cryptographic activity utilizing the AES-128 framework. Cycle of plan tasks with this framework is through a few phases, comparable as interaction of encryption, decoding, urgent age and testing of the styles utilized. The assaults test is given by editing, obscuring, and upgrading the ciphertext picture. To lessen the storage facility issue in Cloud we've settle the picture and train into various square and get put away, so storage facility issue get helped. The proposed plot necessities to lessen the computation cost on the end-stoner however much as could be expected.
Entropy as an Intellectual Detector for the Researcher

As a result of the economic and structural-legal transformations of Russian science, an internal conflict is forming in the mind of an individual researcher between the creative and the practical. These two principles are organically presented in everyone’s consciousness, but now they also manifest themselves in teams of scientists in the form of diametrically opposed basic aspirations of researchers, priorities of choice, basic values, interests and style of work, which is revealed in sociological and scientometric analysis. Attempts of Russian science and technology policy subjects to shift the boundary between these principles in the minds of every Russian scientist toward applied problems and economic innovation is, as the history of our country shows, an achievable but dangerous undertaking. The choice – to engage in applied development or fundamental science – should probably be given to the researcher, but not by breaking his interests and priorities directly at his workplace in a historically established scientific institution. For implementation of the state task it is necessary to create an alternative in one or another organizational form – the creation of technological laboratories, innovation centers, scientific and educational complexes, etc. There is a need fora modern branch of applied science in the country. In this case, the complex personal choice may well be realized and implemented by the researcher himself without external destructive administrative pressure.
Convolution

This chapter presents convolution as an important parallel computation pattern. While convolution is used in many applications such as computer vision and video processing, it also represents a general pattern that forms the basis of many parallel algorithms. We start with the concept of convolution. We then present a basic parallel convolution algorithm whose execution speed is limited by DRAM bandwidth for accessing both the input and mask elements. We then introduce the constant memory and a simple modification to the kernel and host code to practically eliminate all the DRAM accesses. This is followed by an input tiling kernel that eliminates most of the DRAM accesses for the input elements. We show that the code can be simplified with data caching in more recent devices. We then move into a two-dimensional convolution kernel along with an analysis of the effectiveness of tiling as a function of tile sizes for one- and two-dimensional convolution.