Reviewerly/exHarmony huggingface dataset:
The Dual Function of Q-Marker in Korean: Evidence from Unselected Embedded Questions

This study investigates the distribution of unselected embedded questions (EQs).Cross-linguistically, with the exception of certain restricted adverbial uses, EQs tend to appear with question-embedding predicates.However, Kim & Tomioka (2014) noted that EQs in Korean/Japanese are in wider distribution compared to English.Comparing Korean/Japanese with Chinese/English, this study shows that the difference between these two groups regarding the availability of EQs lies in the overt realization of an interrogative complementizer or Q-marker.Korean/Japanese deploys an overt form of Q-marker that can convert/ grammaticalize into an adverbializer; specifically, it has a dual function, primarily as a Q-marker and secondarily as an adverbializer.Contrarily, Chinese/English does not, which restricts the use of EQs in these two languages.Along this line of analysis, the study also suggests that indefinites in Korean derived from wh-phrases plus Q-markers are fed by the same strategy of deriving unselected EQs.
SimDet: Cross Similarity Attention for One-shot Object Detection

Object detection based on the convolutional neural network requires a large of datasets for training to achieve good results. However, it is labor-intensive or unrealistic to prepare such high-quality training data in most industrial applications. Recently, one-shot object detection task was proposed aiming to tackle this challenging problem by using only one sample for reference. In this work, a new framework named SimDet based on Faster R-CNN has been proposed for one-shot object detection. Specifically, query and target image features are extracted through a Siamese network, and target features are enhanced in where has high similarity with query feature. In order to solve the problem of RPN learning difficulty with one sample, we propose a new module of Cross Similarity Module, which focuses more on the difference between query and support rather than the feature itself. Furthermore, we design a similarity loss for learning the cross similarity between target feature and query feature. Finally, the extensive experiments show that our model achieves state-of-the-art performance on PASCAL VOC under one-shot setting of detecting objects from both seen and novel classes and on MS COCO from seen classes.
Few-Shot Learning for Time Series Data Generation Based on Distribution Calibration

Insufficient training data often makes the learning model prone to overfitting and bias in the selection of the sample leads to obtaining the wrong distribution. For this reason, few-shot learning has gained widespread attention as a challenging endeavor. Current work in few-shot learning is focused on developing stronger models, but these models does not have good generalization capabilities. In this paper, Our approach is find a similar base class with sufficient data for class with few-shot samples, then use statistical information to calibrate the distribution of class with few-shot samples. Time series are characterized by variability within the variance at each point in time and by overall statistical regularity and periodicity. So time series are extremely suitable for our approach. This approach do not require complex models and additional parameters. Our approach generate data that better match the actual distribution of the data. Validated with 9 time series data sets, the data generation for five samples led to some improvement in the classification accuracy. Moreover, it is found that this approach is not only applicable to the case of small data size, but also the classification effect is improved if the method of this paper is applied on the basis of sufficient data size.
Utilization of Question Categories in Multi-Document Machine Reading Comprehension

Multi-document machine reading comprehension has become a hot topic in natural language processing due to its more realistic setting and wider applications. However, how to effectively exploit the information of multiple documents and the question is still a challenge. In this paper, we propose a new end-to-end reading comprehension model with the utilization of question categories. To compress the search space of the answer and pinpoint it more precisely, we make the best use of the question and its category to predict the length of the answer. To better evaluate the importance of each document and give a more suitable score, we integrate the question category into multi-step reasoning based document extraction. Besides, we propose a new question classification model based on keyword extraction to get the question categories. The experimental results show that our method outperforms the baselines on the English MS MARCO dataset and the Chinese DuReader dataset.
HybridSkipList: A Case Study of Designing Distributed Data Structure with Hybrid RDMA

RDMA (Remote Direct Memory Access) delivers both high performance and OS kernel bypass thus triggers the revolutions of the modern data center. Especially, compared with traditional Ethernet, by exploiting memory semantics operations, RDMA-based data structure has shown great potential for high scalability and CPU utilization reduction. SkipList is an elegant sorted index data structure, and yet presents low throughput upon memory semantics RDMA due to the heavy remote access.By using SkipList as a case study, we revisit the current design paradigm of RDMA such as operation type, transport type, and caching. Accordingly, we propose a one-sided/two-sided hybrid paradigm to gain high scalability and at the same time achieve high throughput based on lock-based concurrent SkipList. To reduce the number of expensive network round trips, a client-sided cache is presented. With an in-depth analysis of the design choices, we implement HybridSkipList and deploy at an eight-machine RDMA cluster. The evaluations show it can outperform two baselines by 4.41× and 3.45× respectively.
Algorithms for contactless scanning of book monuments

The article is devoted to the questions of cultural heritage preservation by creating the digital collection of book monuments. The original documents are monuments of book culture and their dilapidated state requires careful handling, splitting of documents for scanning is extremely undesirable. The market does not present the equipment for contactless scanning of books without embroidering, therefore an algorithm that allows digitalizing book monuments in a contactless way has been developed. The technique has been constructed using an algorithm based on the projection of the light grid on the object scanned. The authors propose a sequence of actions consisting of image processing and comparing the results between two images. The first snapshot determines the initial parameters of the grid; the second snapshot determines the actual distortion of the test snapshot. Subsequent mathematical processing allows getting scanned images without absence of geometric distortions of the scanned page due to the system of using the two-dimensional array of corrections. The application of the system has been modeled on the example of «The legend of the destruction of Siberian cities of Tara and Tyumen by the lesser Tatars / / Collection of moral stories, words, lives and other articles [hand.]». The evaluation parameters of the simulation result have been the following: text distinctness, absence of geometric distortions, color quality, uniformity of document scanning quality within a single book, etc., as checked and recognized as high by the experts. The experience described opens possibilities of book monuments digitization using the new algorithm. The development of the system is aimed at expanding the database of objects of material culture to be digitized, perfecting the software, improving the quality of digital images, as well as the capabilities of image recognition and search for the document itself and information it contains.
Modeling Multimodal Interactions and Feedback for Embodied Geovisualization

Modern wearable devices, especially those aimed at enabling Augmented and Virtual Reality, permit an increasing number of ways to translate data about us and our surroundings into sensory impressions. This is especially useful for geospatial data – data that already has a location in the environment. In the same way that a modern weather map translates simulation results into red, blue, and green pixels, we could now, for example, translate air quality data in a user’s vicinity into an audio rhythm, creating a sort of virtual Geiger counter not limited to ambient ionizing radiation. However, there are currently few conceptual tools that help with designing these kinds of multisensory representations. This poster shows preliminary work on a type of diagram specifically aimed at modelling the interactions and feedback loops between these interactive, embodied visualizations and the human body.
The Educational Effects of the Empathy Circulation Dialogue Education Model

This study aimed to analyze the educational effectiveness of the “empathy circulation dialog education model,” developed to enhance empathetic dialog capabilities. It is based on empathetic listening, non-violent dialog, and dialogic listening in terms of content related to the detailed functions that learners will perform in individual procedures. This study conducted a survey of students to determine the educational effectiveness of the model. According to the survey, participants effectively expressed their needs, empathized with the other person’s situation, and resolved conflicts in a sound manner using nonviolent dialog, empathic listening, and dialogic listening. This study is meaningful in that it conducted a level of class design that could be implemented in actual educational institutions and verified its effectiveness. However, it is limited in that there has been a change in the place of experiment, as it was conducted both online and offline. Further, it has been difficult for learners to engage in dialog practice by internalizing their conversation methods due to the short experimental period.
Study on Application of Zero-Velocity Update Technology to Tracked Vehicle Inertial Navigation

Tracked vehicle navigation technology provides an important guarantee for military and civilian tracked vehicles to perform their tasks. In this paper, the zero-velocity update technology (ZUPT) is used to optimize the inertial navigation algorithm of the crawler and improve the navigation accuracy based on the motion constraints of the crawler. Through uphill & downhill experiment, it is verified that the optimized zero-velocity detection algorithm has better detection effect. The closed-loop experiment proves that the ZUPT can effectively suppress the navigation errors of the tracked vehicle, and combined with the zero-angular-rate update technology (ZARU) can achieve higher navigation accuracy.
Comments on Test Methods of Encryption and Decryption Speeds of Image Cryptosystems

It is easy to design an image encryption scheme with good security but slow encryption speed. To effectively measure the encryption and decryption speeds of image cryptosystems, some effective methods are suggested to evaluate their processing speeds. Using MATLAB, C#, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{C}++$</tex> and Wolfram languages, two image cryptosystems, an AES based system and a unified system, are implemented to compare their processing speeds. Then some remarks for the comparative analysis of encryption and decryption speeds are given. This work provides reference measures for relatively fairly comparing the processing speeds of various image encryption systems, and suggests that the encryption and decryption speeds of image cryptosystem based on AES be used as the benchmark for evaluating the processing speeds of image cryptosystems based on chaos.
Research on the safe and stable operation of power grid after the renewable energy connected

The traditional automatic power generation control (AGC) responds to the output meter caused by the ultra-short-term forecast error of new energy power generation When planning deviations, the output adjustment distribution of each AGC unit has not been optimized and safety verified. To solve this Problem, this chapter proposes an asymmetric linear response strategy for the AGC link to deal with the deviation of the new energy power generation plan Based on the robust optimization theory, the unit combination considering this strategy and its solution are proposed accordingly Methods: According to the characteristics of the new energy generation forecast error interval, the adjustment coefficients in the asymmetric linear response strategy are fully optimized and verified. The calculation example shows that the proposed scheme can make full use of the output adjustment space of each controllable unit, Under the premise of not violating operating safety constraints, economically respond to new energy power generation forecast errors / output plan deviations.
A Novel Elman Network Based INS/GPS Fusion Filter to Enhance Tracking Accuracy in UAVs

The development of a highly accurate position tracking technique for extremely nonlinear dynamic systems such as unmanned aerial vehicles (UAV) using integrated global positioning system (GPS) and inertial navigation system (INS) is a challenging problem. During GPS outages, the existing systems experience considerable errors. In this paper, we propose a novel fusion algorithm based on an extended Kalman filter (EKF) - Elman neural network (ENN) that is capable of enhancing the tracking accuracy during GPS outages. The proposed technique relates the INS outputs from the sensors to the GPS position increment. ENN corrects the system during GPS outages by predicting the pseudo GPS position. The time information is also considered to obtain precise estimates and also to reduce the computational complexity. Simulation studies are performed on a UAV trajectory using low-cost MEMS-INS sensors to test the robustness of the algorithm under extreme varieties. The proposed system shows a reduction in root mean square error (RMSE) of the estimated position compared to the existing back propagation neural network (BPNN) model.
Unbounded Capacity Associative Memory for Real-valued Pattern Storage and Recall

We describe an unbounded capacity Associative Memory which effectively stores and retrieves unrestricted real-valued data/patterns with fidelity. This Associative Memory arises from a gradient system of a (differentiable scalar) energy function, and thus can directly be incorporated within existing layers of computational Deep Learning (DL) frameworks. The design effort also describes two options for key pattern retrieval.
Methodology for Random Number Generation Based on FPGA

Random numbers are playing an increasingly important role in the modern information technology field. The methods for generating random numbers are mainly divided into two categories: analog and digital methods. Due to their portability and simple structure, digital methods have been widely used in communication and other fields. The digital method based on M-sequence is the mainstream method for generating random numbers. The existing M-sequence methods for generating random numbers have disadvantages such as insufficient period and insufficient bit width. In this paper, we propose a random number generation method based on FPGA hardware, which adds a multi-tap structure to M-sequence and synthesizes multiple parallel M-sequence outputs into a new random number generator. It implements a random number generator with arbitrary bit width and ultra-long period. Finally, we analyzed 512 k output sample data of the FPGA chip xcku040 used to implement this design. The experimental results verify the feasibility and superiority of the method.
Decoupling Sparsity and Smoothness in Dirichlet Belief Networks

The Dirichlet Belief Network (DirBN) has been proposed as a promising deep generative model that uses Dirichlet distributions to form layer-wise connections and thereby construct a multi-stochastic layered deep architecture. However, the DirBN cannot simultaneously achieve both sparsity, whereby the generated latent distributions place weights on a subset of components, and smoothness, which requires that the posterior distribution should not be dominated by the data. To address this limitation we introduce the sparse and smooth Dirichlet Belief Network (ssDirBN) which can achieve both sparsity and smoothness simultaneously, thereby increasing modelling flexibility over the DirBN. This gain is achieved by introducing binary variables to indicate whether each entity’s latent distribution at each layer uses a particular component. As a result, each latent distribution may use only a subset of components in each layer, and smoothness is enforced on this subset. Extra efforts on modifying the models are also made to fix the issues which is caused by introducing these binary variables. Extensive experimental results on real-world data show significant performance improvements of ssDirBN over state-of-the-art models in terms of both enhanced model predictions and reduced model complexity.
The Use of Instagram in Creating Brand Equity of Nosh Kitchen

Online branding activities can save time and reach all levels of society. Nosh Kitchen, a restaurant brand, is active in providing information and branding activities about its restaurant to its consumers through social media. This research aimed to understand the use of Instagram to create Nosh Kitchen's brand equity and find the obstacles and solutions for Nosh Kitchen in creating a strategy to build brand equity through Instagram. This research used a descriptive qualitative method through data collection with observation, in-depth interviews, and documentation. Besides, this research used the analytic data reduction technique of the Miles and Huberman model. The data analysis consists of data reduction, data presentation, and drawing conclusions and verification. The results indicated that Nosh Kitchen had gained its brand awareness on its formative efforts on Instagram. Moreover, Nosh Kitchen has created brand equity efforts by increasing its brand awareness, so that Nosh Kitchen needs to develop a better Instagram social media strategy.
Semi-Hard Margin Support Vector Machines for Personal Authentication with an Aerial Signature Motion

The classifiers for personal authentication needs to be robust against spoofing attacks. In this paper, we propose semi-hard margin support vector machines (SH-SVMs) for personal authentication problems with an aerial signature motion. To be robust against spoofing attacks, SH-SVMs are trained so that false acceptance rate (FAR) of training data is zero. There are three types of SH-SVMs as follows: (1) standard SHSVM based on L2-SVM, (2) semi-hard twin SVM (SH-TWSVM) based on TWSVM, (3) semi-hard support vector data description (SH-SVDD) based SVDD. In computer experiments, we compare our methods with the conventional methods, using the aerial signature dataset and show effectiveness of SH-SVMs.
Automatic Development of Knowledge Graph Based on NLTK and Sentence Analysis

In this paper, our group came up with a new method that automatically creates knowledge graphs from the natural language. It combines different technologies, such as parse of the sentence, Resources Description Framework (RDF), and NoSQL database. For sentences analysis part, we used NLTK and Stanford coreNLP to get parse trees from natural language sentences. Then extract RDF triplets from parse trees, which can be used to create knowledge graphs. Test and compare them on Visual Genome datasets from Stanford University because they include many images with different regions and their descriptions. Finally, we achieved 68%, 91%, and 92% accuracy in predicting subject, predicate, and object, respectively. The result shows that it’s possible to apply for it on the automatic development of knowledge graphs.
Free Speech and Antisemitism: Collin v. Smith Today

The Skokie-based Collin v. Smith litigation resulted in our law's most significant constitutional response to antisemitic hate speech. The Skokie case opinions shed light on how antisemitism was thought of at the time and place in question. More importantly, how we choose now to understand the Collin v. Smith cases tells us much about how we conceive of antisemitism and of antisemitic injury today. The argument herein is that our understanding of freedom of speech, and of its value and limits, has significantly evolved over the decades since Collin v. Smith. Relatedly, our collective understanding of the harms and injuries inflicted by antisemitic speech has, at the deepest level, been significantly changing as well. In both of these respects, the Collin v. Smith litigation has only increased in importance over time.
Investigating learning outcome based on a model of English teaching of Indonesian elementary students

This research aim of this study is to develop a model of teaching English for Indonesian elementary students. This developmental research consisted of five key procedures: analysis of need, model design, development, implementation, and evaluation. In the model development, two treatments were conducted to test the model. In the evaluation, a pre-test and post-test were administered to see the effectiveness of the model and the instructional materials in enhancing the English achievement of the students. The subject consisted of three school headmasters, three English teachers, and the fourth graders of three elementary schools in Makassar city. The teaching model consisting of four main stages: pre-activity, presentation, practice, and evaluation considers three main aspect: the language skill, language performance, and pedagogic aspects. The result indicates that the English teaching model is successful in enhancing the students’ achievement in English and learning interest. In its form, the teaching model is flexible; it helps teacher write instructional materials, write lesson plans, teach, and analyze the teaching and learning process. The standards in thefieldsupportthe model and the teaching of students.
Monolingual Sentence Similarity Measurement using Siamese Neural Networks for Sinhala and Tamil Languages

Sentence similarity is useful in many Natural Language Processing tasks such as plagiarism checking and paraphrasing. So far, only conventional unsupervised sentence similarity measurement techniques (knowledge-based, corpus-based, string similarity-based, and hybrid) have been used to measure sentence similarity for Tamil and Sinhala languages. In this paper, we present a Deep Learning technique to measure sentence similarity for these two languages, which makes use of a Siamese Neural Network that consists of two Long Short-Term Memory (LSTM) networks, and neural word embeddings as the input representation. This approach achieved a 3.07% higher Pearson correlation coefficient for the dataset of 2500 Tamil sentence pairs, and a 3.61% higher Pearson correlation for the dataset of 5000 Sinhala sentence pairs over the conventional unsupervised sentence similarity measurement techniques.