Reviewerly/exHarmony huggingface dataset:
The Openings and Closings in Student-to-Professor E-mails of Korean as a Foreign Language Students and KNS Students

ABSTRACT E-mail is one of the most popular communication methods between students and instructors in a college setting, and it is necessary for a Korean as a Foreign Language (KFL) learner to be able to communicate through e-mail politely and appropriately in Korean. Previous research (e.g., Ko, 2018; Shim, 2013; Yoon &amp; Lee, 2011) has pointed out that KFL learners were not able to use appropriate politeness strategies in writing e-mails, revealing their incompetencies in pragmatics. In this paper, we examine e-mails written by KFL and Korean Native Speakers (KNS) students, focusing on their strategies in the openings and closings. The data showed that KFL students' e-mails generally focus on the meaning of the message and do not follow a format that is appropriate between a student and an instructor in written Korean communication. This study suggests that raising students' awareness on intercultural differences between their L1 and Korean, and learning about pragmatic features, will help them to communicate through e-mail more successfully.
AStudent Engagement Measurement System via Passive WiFi Monitoring

The ubiquity of mobile devices and wireless infrastructures in university classrooms have aided student learning experience in recent years. Among other things, using such technology, the students can follow along lecture slides and search through key concepts from the Internet in real time in order to better comprehend the instruction material. While aiding learning experiences, these devices can also be used for non-academic Internet browsing, which is a major source of distraction, and can negatively impact the overall quality of education. This work attempts to measure spatiotemporal usage of mobile devices during lectures by passively monitoring WiFi traffic generated inside classrooms. Such measured data can be used to determine student engagement in real-time. Using temporal patterns of classroom generated WiFi traffic, the proposed system can generate automatic measurements of student engagement in real time, and for longer durations in various time scales. This ability enables instructors to make short-term and long-term amendments to their lectures that are aimed at increasing or maintaining student engagement throughout the lecture. This work demonstrates the practicality and feasibility of a low-cost WiFi traffic monitoring system that can be used as an instructional tool to measure, analyze, and improve student engagement, thus improving overall quality of education in university classroom settings.
A Study on Machine Learning based Conversational Agents and Designing Techniques

Chatbots are a computer program that was created to imitate the human during a conversation. In this technological era, humans were replaced by machines for performing most of the work. So chatbots were developed to mimic the conversation a human does with another person. The work a chatbot does ranges from answering simple queries to acting as personal assistant to the boss. There are different kinds of chatbots developed to cater to the needs of the people in different domain. The methodology of creating them also varies depending on their type. In this paper, the various types of chatbots and techniques such as Machine Learning, deep learning and natural language processing used for designing them were discussed in detail.
Computational cognitive modeling of human calibration and validity response scoring for the Graduate Record Examinations (GRE).

Most research on skill acquisition and retention focuses on the individual being tested. Yet sometimes another person is responsible for evaluating the individual’s performance. Here, we study the acquisition and retention of rater skill using data for the Graduate Record Examinations (GRE). Our work is based on the idea that response scoring, like other cognitive skills, will gradually improve with amount of practice, and decline with elapsed time since that practice occurred. These classic findings are the focus of a computational cognitive model called the Predictive Performance Equation (PPE). However, the generalizability of these findings to response scoring and the applicability of PPE to that domain have not yet been demonstrated. To address this issue, we leveraged a naturalistic dataset containing rating performance from over 23,000 sessions. Our analyses provide empirical support for PPE and establish a basis for using a model like PPE to personalize rater training requirements.
Bayesian Randomly Wired Neural Network with Variational Inference for Image Recognition

The architecture of a neural network (NN) plays a significant role in its performance. Recently, automating the process of choosing the architecture has received huge attention. While neural architecture search (NAS) methods mainly concern on searching, the searching space is constrained and designed manually. Stochastic network generators will loosen the constraint as well as automating the process. In this work an uncertainty quantification (UQ) for randomly wired neural networks (RWNN) is investigated. The classical Watts-Strogatz (WS) random graph is utilized as the random generator and a bayes by backprop algorithm is introduced to measure the epistemic and aleatoric uncertainties of RWNN for image recognition tasks. The RandAlexNet architecture is proposed and the algorithm is applied on. We test the algorithm on FashionMNIST, CIFAR10 and MNIST datasets and the good results achieved illustrate the effectiveness of our proposed method.
Understanding Traffic Safety Culture in Washington Using Twitter Mining

Traffic safety culture has emerged as an important factor in support of acceptance of existing traffic safety policy and programs and as a contextual variable to define high-risk behaviors of drivers. However, it is challenging to understand people’s beliefs and attitudes that collectively influence traffic safety-related behaviors. The increasing popularity of social media platforms like Facebook, Twitter, blogs, and online forums have generated massive data that can be collected and used for intensive analysis of people’s perspectives on a certain topic. In this paper, we attempted to understand people’s perception of traffic safety in the state of Washington. To this end, we collected traffic safety-related tweet data over the past four years based on the generation of a set of keywords. The collected tweet data was then cleaned and reprocessed by the NLP (natural language processing) technique and following that, the LIWC (linguistic inquiry and word count) sentiment analysis procedure was applied to measure the public’s beliefs and attitudes towards importance of traffic safety, possibility of zero fatalities, usefulness of traffic law enforcement, and six types of high-risk behaviors including impairment driving, speeding, distracted driving, unrestrained vehicle occupants, teenage drivers, and older drivers. The present study that capitalizes on social media mining overcomes the limitations of the traditional survey method that is time-consuming and expensive. The generated information from this study is expected to facilitate uncovering of and development of solutions to overcome the barriers to prevention of fatal traffic accidents in Washington.
Ultimate Pólya Gamma Samplers–Efficient MCMC for Possibly Imbalanced Binary and Categorical Data

Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Pólya-Gamma random variables for a range of commonly encountered logistic regression models. From these latent variable representations, new Gibbs sampling algorithms for binary, binomial, and multinomial logit models are derived. All models allow for a conditionally Gaussian likelihood representation, rendering extensions to more complex modeling frameworks such as state space models straightforward. However, sampling efficiency may still be an issue in these data augmentation based estimation frameworks. To counteract this, novel marginal data augmentation strategies are developed and discussed in detail. The merits of our approach are illustrated through extensive simulations and real data applications.
Implementation of "Magma" and "Kuznyechik" ciphers using HIP

In this paper, we present an implementation of Russian cryptographic standards using HIP, an open-source technology that allows writing a C++ code, which can be compiled for AMD and NVIDIA GPUs. The details of effective CPU-GPU data copy are considered. The 8 times performance gain over the CPU AVX version is reached.
MiteS: Software-based Microarchitectural Attacks and Countermeasures in networked AP SoC Platforms

The impact of microarchitectural attacks in Personal Computers (PCs) can be further adapted to and observed in internetworked All Programmable System-on-Chip (AP SoC) platforms. This effort involves the access control or execution of Intellectual Property cores in the FPGA of an AP SoC Victim internetworked with an AP SoC Attacker via Internet Protocol (IP). Three conceptions of attacks were implemented: buffer overflow attack at the stack, return-oriented programming attack, and command-injection-based attack for dynamic reconfiguration in the FPGA. Indeed, a specific preventive countermeasure for each attack is proposed. The functionality of the countermeasures mainly comprises adapted words addition (stack protection) for the first and second attacks and multiple encryption for the third attack. In conclusion, the recommended countermeasures are realizable to counteract the implemented attacks.
Hierarchical Extraction Algorithm of Video Summary Based on Multi-feature Similarity

In view of the complex sources and diverse formats of network video data, the existing video summary extraction algorithms based on content and clustering methods have a large amount of computations; accuracy and reliability are difficult to meet the actual needs. A video summary hierarchical extraction method based on video features is proposed. Hierarchical video shot segmentation uses multi-feature similarity algorithm. The global features are extracted for the keyframes generated by each segmented video clip, and the threshold is determined according to the actual needs to generate the final video summary. The experimental results on public datasets such as VSUMM show that the method effectively reduces the computational complexity, avoids the problem of feature weight allocation, adaptively generates video summary, and retains the temporal information. The average accuracy and recall rate are 0.84 and 0.89, respectively, which are superior to the traditional similar methods.
Neural Machine Translation with Soft Reordering Knowledge

The Transformer architecture has been widely used in sequence to sequence tasks since it was proposed. However, it only adds the representations of absolute positions to its inputs to make use of the order information of the sequence. It lacks explicit structures to exploit the reordering knowledge of words. In this paper, we propose a simple but effective method to incorporate the reordering knowledge into the Transformer translation system. The reordering knowledge of each word is obtained by an additional reordering-aware attention sublayer based on its semantic and contextual information. The proposed approach can be easily integrated into the existing framework of the Transformer. Experimental results on two public translation tasks demonstrate that our proposed method can achieve significant translation improvements over the basic Transformer model and also outperforms the existing competitive systems.
Mutual Transformation of Geographical and Magnetic Coordinates by Rotations of Corresponding Rectangular Coordinates

By the method of rotations of the geographic coordinate system, formulas for the interrelation of geographic and simple magnetic coordinates are obtained. A complete set of six formulas is presented for the mutual recalculation of geographical and magnetic coordinates. The relationship between geographic and magnetic coordinates was obtained by the means of formulas for transforming three-dimensional rectangular coordinates between positions corresponding to a geographic and magnetic coordinate system, respectively.
Webrarian

This chapter presents the current trends of Library on the web and the importance of the Librarian in the web-library as a webrarian (web-librarian). In the modern technological era, Ranganathan's five laws of the library may be extended to the web using information and communication technologies (ICTs). When the fundamental laws of library science are applied to the web, then the professional responsibilities and duties of a librarian will be enhanced in order to effectively and efficiently manage the web and serve the web users. Realizing the differences between the web and the library? An idea of the joint venture has been the practicalities of creating another platform for the both and the initiative is a library on the web which is well known as a web-library. Mostly libraries have their websites to organize information and resources and provide access to users for the collections of quality resources. The role of librarians is to adopt the Web technologies into the profession of librarianship. Librarians have been induced by technological developments to become more efficient as but not limited to the role of organizers, indexers, abstractors, archivers, data brokers, and webrarians.
A Neural Framework for English-Hindi Cross-Lingual Natural Language Inference

Recognizing Textual Entailment (RTE) between two pieces of texts is a very crucial problem in Natural Language Processing (NLP), and it adds further challenges when involving two different languages, i.e. in cross-lingual scenario. The paucity of a large volume of datasets for this problem has become the key bottleneck of nourishing research in this line. In this paper, we provide a deep neural framework for cross-lingual textual entailment involving English and Hindi. As there are no large dataset available for this task, we first create this by translating the premises and hypotheses pairs of Stanford Natural Language Inference (SNLI) ( https://nlp.stanford.edu/projects/snli/ ) dataset into Hindi. We develop a Bidirectional Encoder Representations for Transformers (BERT) based baseline on this newly created dataset. We perform experiments in both mono-lingual and cross-lingual settings. For the mono-lingual setting, we obtain the accuracy scores of 83% and 72% for English and Hindi languages, respectively. In the cross-lingual setting, we obtain the accuracy scores of 69% and 72% for English-Hindi and Hindi-English language pairs, respectively. We hope this dataset can serve as valuable resource for research and evaluation of Cross Lingual Textual Entailment (CLTE) models.
Challenges Toward Blockchain and Renewable Energy Linked to IoT: A Survey

The Internet of things in general linked and focused with artificial intelligence, real-time analytics, machine learning, and sensors along with cloud storage. It may be associated with creating new thoughts and prospects along with its merits for businesses in current and new market. In the networked structure of IoT, the blockchain may be associated to monitor the relevant records linked with smart devices. As a matter of fact, the blockchain may be defined as the technology to chain as well as link scalability, privacy, and reliability concerns associated with things in virtual platforms. In practical situation, the applications linked to Internet of things in blockchain being decentralized may be more useful and may eradicate the associated complexities. Also, it may be more faithful toward centralized, brokered communication models with the challenges to secure IoT deployments. Being associated with the cloud architecture, sometimes, it may be very sensitive during storage of large amount of data in the cloud. Considering the blockchain applications in IoT, it may be observed that the automobile industries mostly may be associated with blockchain as it may be considered as a part-intensive industry. The centralized supply chain and trust-based distribution may be the recent innovation to achieve new technologies and may be automatically updated to maintain the records. In this context, it may also be associated with multi-layered and may involve toward device authentication and verification of transactions. As being observed, the storage associated with blockchain may be quite expensive and the amounts of IoT data sometimes may be increased continuously. As it is seen the incremental changes of energy systems to accumulate renewability, the flexibilities in such case must be maintained toward regeneration as well as storage allocation in energy systems. In many situations, the transformational changes are being observed linked to distributed energy sources with smart applications. Therefore, in this work, it has been thought of to include inter-serviced managed system to generalize the solution which is also treated as distributed peer-to-peer file system employed to manage file systems with heterogeneity.
Improved method of deblocking filter based on convolutional neural network in VVC

With the rapid development of 5G and the Internet of Things, video has gained broad application space as a carrier of information, and video codec technology can reduce video storage space, save transmission bandwidth, and provide the possibility for the promotion of video applications. Versatile Video Coding (VVC), the latest generation of video coding standard today, still uses block-based coding, which inevitably introduces coding artifacts, and deblocking filters can effectively reduce artifacts and improve the quality of compressed video. The deblocking filter in VVC still adopts the method based on empirical threshold when making the filtering decision, but the actual video scene changes variously, and it is obviously impossible to obtain the optimal filtering effect with a fixed threshold. The decision of the filtering mode is essentially a classification problem. Existing research shows that the convolutional neural network (CNN) has a strong ability in classification tasks, and its feature extraction and nonlinear fitting ability can greatly improve the learning ability of the target, so as to obtain better classification accuracy. Therefore, we improves the deblocking filter in VVC based on CNN. Experimental results show that, compared with the original filtering method of VVC, our method can better improve the quality of compressed video.
An extractive streaming system to reduce monitoring traffic

Generally, a monitoring system has tradeoff between network fee and monitoring precision. If the system user wants to monitor a something target from a remote office precisely, the network fee becomes higher because of taking lots of monitoring packets (i.e., video frame). Here, I propose an extractive streaming system to address this problem. The proposed system is installed for both the video sender and receiver. The sender system extracts the pre-determined target area (e.g., license plate) with high quality from a video frame, and sends both roughly compressed entire video frame (e.g., encoded H.264) and the extract area to the receiver in parallel. Subsequently, the receiver system overlays the extracted area on the entire video frame. Through these process, the user can monitor a target area (i.e., focused area) precisely with low network fee. I evaluated the proposed system and investigated the extent to which additional latency and communication traffic was incurred by the proposed system and conducted that the system results in reduced network traffic with decreased latency.
Constructing Uyghur Named Entity Recognition System Using Neural Machine Translation Tag Projection

Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data. Constructing a well-annotated named entity corpus manually is very time-consuming and labor-intensive. Most existing methods based on the parallel corpus combined with the word alignment tools. However, word alignment methods introduce alignment errors inevitably. In this paper, we address this problem by a named entity tag transfer method based on the common neural machine translation. The proposed method marks the entity boundaries in Chinese sentence and translates the sentences to Uyghur by neural machine translation system, hope that neural machine translation will align the source and target entity by the self-attention mechanism. The experimental results show that the Uyghur named entity recognition system trained by the constructed corpus achieve good performance on the test set, with 73.80% F1 score (3.79% improvement by baseline).
Research work of schoolchildren related to the use of computer modeling in problems of nuclear physics

The article presents the experience of organizing research activities of schoolchildren at the Institute of Nuclear Physics and the Faculty of Physics of Moscow State University. The subject matter of the works is connected with different sections of nuclear physics, the study of which is especially important in secondary school. In the process of preparing for their implementation schoolchildren listen to a short course devoted to computer methods in physics. They then develop programmes to simulate various nuclear phenomena. A laboratory experiment is then conducted to check and correct the results of the simulation. The article presents two examples of works, the authors of which have successfully performed at various scientific and practical conferences for schoolchildren. The first one is related to the absorption of gamma radiation and the second to the study of cosmic radiation background. It is especially important to note that these works were conducted with pupils of Lyceum № 87 of Nizhny Novgorod in a partly remote format. This suggests that such work can be successfully performed by schoolchildren living in other cities and not having the opportunity to visit the laboratory frequently.
Image feature hierarchical index method for large image

Aiming at the hierarchical index method of large image features, the hierarchical index structure of large image is established by using the global position constraint information of large image. This method combines LSH algorithm and KD tree algorithm based on location information. It can control the space complexity of LSH algorithm and the time complexity of KD tree algorithm in high-dimensional feature retrieval. It can speed up the search speed and improve the accuracy of finding the nearest neighbor features, so as to obtain the best comprehensive search performance compared with LSH algorithm and KD tree algorithm. This method mainly includes three steps: extracting thumbnail to build twolayer pyramid image index structure, using LSH algorithm to search rough position for thumbnail, and using KD tree algorithm based on position information to search feature points near neighbor for original image.