Reviewerly/exHarmony huggingface dataset:
Japanese Particle Error Correction employing Classification Model

We present a grammatical error correction system for Japanese particles based on the classification method. We define a confusion set of the particles for detection of particle errors and prediction of the correct word. Our method can handle not only substitutions but also insertions and deletions. For building the training data, we used two datasets: a large amount of native language data and corrected learners' sentences. That is, we did not require a parallel corpus of learners. We show the results for Japanese particle error correction on the NAIST Goyo corpus, evaluated by the MaxMatch (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) score. In addition, we analyze the effect of percentage changes in deletion labels while building the training data and analyze the prediction probability threshold at correction. Our best model achieved 46.4 F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> .
Exploiting Affordances in Monitoring a Smart Home

The Internet of Things (IoT), where internet connections are extended to every-day objects, enables collaboration, monitoring, and remote control. An IoT can make a home smart at least in the sense that those in it can be monitored. The question arises of how much and what kind of data are needed to monitor a person as they move about a smart home. This paper focuses on affordances, manifest possibilities for action provided by the environment (e.g., the appearance of a doorknob). Our thesis is that, since the same things are affordances for the monitored and monitoring persons, a little data goes a long way in monitoring a subject's interaction with affordances. This paper describes a scaled-down model of a floor in a smart home equipped with only passive infrared (PIR) motion sensors where nevertheless the data allows high-level interpretation of the subject's activities on that floor.
Into the Weeds: High-volume Weeding at the Texas Tech University Health Sciences Center

As print usage continues to shrink while the demand for student space grows, many academic libraries have felt the pressure to weed more aggressively. While not a new trend, much of the discussion surrounding weeding draws on the implicit assumption of some occasional weeding as collection maintenance. This article describes an aggressive weeding project at Texas Tech University Health Sciences Center, which had previously adhered to a no-weeding policy. It outlines the library’s development of selection criteria and methods where none had previously existed as well as the challenges encountered and the outcomes achieved.
End-to-End Deep Background Subtraction based on Encoder–Decoder Network

Background subtraction or change detection aims to segment the moving object from the background, and it has become a vital task in many video analysis and surveillance systems. There are different approaches were proposed to solve this problem effectively. In the past, all background subtraction methods use low-level handcraft features such as raw color space or local pattern. Recently, there are many background subtraction methods based on a convolutional neural network that have demonstrated astonishing results. Therefore, in this paper, we introduce an end-to-end convolutional neural network for background subtraction. The network is based on the idea of encoder-decoder deep neural network. In the encoder part, deep features from target frame and reference frame are extracted and compared to measure the difference. Then the decoder converts these features from an encoder to into segmentation map with fine detail. The experimental results tested on CDNet 2014 dataset show that the proposed structure archives the state-of-the-art performance.
Python Implementation of Temperature and Rainfall Multi-sensor Standard Controller Algorithm

Temperature and rainfall elements are widely concerned in meteorological observations. They are widely used in various mode calculations. The accuracy of data collection directly affects the quality of output results of mode calculations. The appearance of temperature and rainfall multi-sensor standard controller improves the accuracy and stability of temperature and rainfall data collection. However, there is still a long way to improve the algorithm for temperature and rainfall multi-sensor. The research methods and forms provided in this paper can help researchers quickly verify and improve the algorithm. It has a catalytic effect on accelerating the maturity of technology. The data of each node is easy to communication with other data processing software. The code logic is clear and easy to change. It can also be used as a learning aid for the internal algorithm of temperature and rainfall multi-sensor standard controller.
New orogenic type gold occurrences in the Uyanga ore knot (Central Mongolia)

The Khangay-Khentey belt is located in central Mongolia (Central Asian Orogenic Belt). The Uyanga ore knot district of the Khangay metallogenic zone are hosted by the lower-middle Devonian volcanogenic-sedimentary Erdenetsogt formation. The new Burgetei, Ult and Senjit gold occurrences were studied. The rocks of the Erdenetsogt formation have an irregular gold content: 0.96 g/t Au is determined in quartz vein (BG-7/16), Au content is highest up to 3.5 g/t in the quartzite-jasper (Ult-7/16 and Ult-9/16) cut by quartz veins in the Ult occurrence. The Senjit occurrence represents Au-Hg-Sb epizonal level of orogenic gold deposits structure with highest Hgand Sb content up to 8.5 ppm and 39 ppm respectively. The Au content of arsenic pyrite of the Burgetei and Ult is below the detection limit by electron microprobe analysis. The Au content of arsenopyrite of the Ult occurrence is highest (up to 238 ppm). The ore-mineral assemblages in the new gold occurrences reflect the differences between three explored sites, formed in the course of fluid evolution during the water-rock interaction. Variable concentrations of indicative elements (As, Te, Sb, Hg) and their ratios confirm this fact.
A Simple Gaussian Kernel Classifier with Automated Hyperparameter Tuning

This paper establishes a fitting method for a kernel logistic regression model that uses generalized Gaussian kernel and its parameter optimization method. Kernel logistic regression is a classification model that uses kernel methods effectively. This is one of the methods to construct an effective nonlinear system with a reproducing kernel Hilbert space (RKHS) induced from positive semi-definite kernels. Most classifiers that are combined with Gaussian kernel functions generally assume uncorrelatedness within the feature vectors. Thus, the Gaussian kernel consists of only two parameters (namely, mean and precision). In this paper, we propose a model using a generalized Gaussian kernel represented flexibly in each dimension of feature vector. In addition, the parameters of kernel are fully data-driven. For the fitting of proposed model, an ℓ1-regularization is introduced to supress the number of support vectors. A numerical experiment showed that the classification performance of the proposed model is almost the same as RBF-SVM even though the proposed model has a small number of support vectors.
Method of Recognition MODE-S Signal Format by the Preamble and Its FPGA Realization

To ensure flight safety, all aircraft and unmanned aerial vehicles are equipped with transponders. Using a similar navigation system, usually operating in MODE-S mode, you can get the most detailed set of information about the type of aircraft, its purpose, speed and altitude, etc. It is extremely important to accurately determine the preamble of the signal on the air, since both the detection of the signal itself and the correctness of the decoding carried out in the future depend on this. The article proposes an efficient algorithm that allows to detect preambles with high accuracy even in the presence of interference and distortion. The use of integer computing provides high-speed device and ease of implementation of architecture on the FPGA. During the tests, the algorithm showed higher performance compared to well-known counterparts.
Transfer Entropy Quantifies Information Leakage

The cybersecurity world faces multiple hardware level, information leakage threats. System complexity has led to new attack vectors. Cache bank malicious operations, Rowhammer, Spectre, Meltdown, and Foreshadow attacks are all hardware level information leakage attacks. These attacks take advantage of correlated hardware level operations. These attacks take place at a level below the protection provided by a separation kernel. This paper investigates modeling information leakage using transfer entropy. This paper presents a brief introduction to information leakage attacks, an introduction to transfer entropy, and concludes with a transfer entropy - information leakage model. The authors hope this paper will inspire future transfer entropy and cyber-systems information leakage research.
Research on Optimization Strategy of Regional Haze Governance Based on Synergy Effect——A Case of Central Plains City Group

Abstract As an atmospheric pollution, haze governance involves economic restructuring, energy change, environmental science, laws and regulations and other aspects, is an important measure to improve people’s livelihood and optimize the mode of economic development. Through the analysis of the causes of haze and based on the synergistic effect, taking the Central China urban agglomeration as an example, from the perspective of public management, this paper puts forward the optimization strategy of regional haze co-governance, by constructing government departments 、 different levels 、 inter-regional intergovernmental coordination mechanisms, mechanisms for communication between local government and enterprises, citizen and local government、 enterprise’s supervision mechanism, scientific research institutions and government、 enterprise’s decision-making staff and technical support mechanism, to form a multi-subject, multi-pronged, seamless haze governance structure, which will prevent and control the tragedy of public sharing, promote the governance of public goods such as the atmosphere, break the administrative limitations of the administrative region, crack the decline of the Central Plains, and the development collapse of central region. It is of great significance to avoid the ecological overload and atmospheric pollution of the Central Plains urban agglomeration and to build a beautiful Central Plains.
A Linear Time Algorithm for Finding Tandem Repeat in DNA Sequences

Tandem repeats cause several genetic diseases in humans and play an essential role in DNA fingerprinting for paternity analysis, forensic investigations, tracing the evolution of populations, and disease diagnosis. However, the process of tandem repeat detection in DNA sequences takes time and requires significant computational memory. To improve the efficiency of this process, we present an algorithm that runs in linear time toward finding tandem repeats in DNA sequences with the Longest Previous non-overlapping Factor (LPnF) table and use the suffix tray data structure.
Algorithm Design of Dynamic Question Answering System Based on Knowledge Base

This paper presents an ongoing study of the structure and operation of a dynamic question answering system. The main result of this paper is to use architecture to master more intelligent answers. This system is designed for the natural language process in the open domain of knowledge base of HowNet on the basis of knowledge base. Natural language processing can get better results. At the same time, the system has the ability of learning through the update of database. The purpose of this system is to improve the search speed and search the answers accurately. This paper improves the algorithm from three aspects. In text clustering, the new ant tree algorithm is used to replace the original k-means algorithm to accelerate the clustering. When decomposing keywords, we combine classic algorithm and knowledge base of HowNet to understand natural language with high precision. When we do the answer extraction, we should consider the necessary factors to improve the old algorithm. By testing the prototype of the dynamic question answering system, the improved algorithm is proved to be effective.
TRACKED ROBOT FOR CLEANING OF PIPE

Urgency of the research. Service robot is as device which is currently used for standard works as cutting the grass or vacuum cleaning and also teleoperation robot which is remote controlled by human for special tasks as exploring, bomb removing, rescue action etc. Tendency is substitute human in dangerous or monotonous works via using of robots. Target setting. Tracked robot is designed for cleaning of pipe, which has inner pipe wall covered by sediments. The typical example is chimney, where carbon particles cover the pipe wall. Carbon particles can start burning process with result of total damaging of chimney and also building. Pipe robot can be used as practical aid for cleaning and inspection of pipes. Actual scientific researches and issues analysis. Other similar task is repairing of damaged inner pipe wall. Robot which repair pipe from inside pipe saves the costs for site excavation works. Uninvestigated parts of general matters defining. The questions of the design of pipe repairing robots are uninvestigated, because the next research will be focused to this. The research objective. In-pipe robot is as device for locomotion inside pipe with aim to make inspection or cleaning of inner surface of pipe wall. Tracked robot is designed because of better properties as overcoming of problematic places inside pipe and also lower normal force between the tracks and inner pipe wall. The statement of basic materials. Tracked segments are pressed to inner pipe wall and normal force is controlled by controller on the base of measurement of normal force. Cleaning brush module is connected to robot for dirties removing. CCD camera for inspection can be also connected to robot. Conclusions. The cleaning robot is important device for service of pipe systems as prevention of pipe damage of other negative phenomena. Contribution of this robot is significant, because it saves money and time.
Online Video Anomaly Detection Methodology With Highly Descriptive Feature Sets

This paper presents a novel methodology for online video anomaly detection. The proposed algorithm divides each video sequence into non-overlapping cuboids, and assigns a state-of-the-art feature vector to each of them. The incoming patterns in testing phase will be then evaluated based on their similarity to the learned patterns. The first achievement of the proposed method is to introduce and apply highly descriptive features and build a histogram of vertical component of optical flow for different regions of the scene. Since the vertical component of optical flow contains both information of magnitude and orientation, it can be considered as an abstract feature rather than using magnitude and orientation, separately. As a result, the dimension of feature vector decreases which leads to reduce the complexity of entire system. The entropy of vertical component is also considered, and hence the differences in velocity and direction of the movements will be monitored. Finally, an efficient technique for anomaly detection search is presented that makes the proposed algorithm an applicable candidate for online performance. The simulation results on UCSD and UMN data sets confirm that the proposed methodology achieves high performance results in case of accuracy and total processing time compared with counterpart approaches.
A Salp Swarm Algorithm to Improve Power Production of Wind Plant

Currently, the main problem of wind plant power production is definitely the control system of a wind generator that is not able to cope with the impact of turbulence and thus weakens complete power output. In this paper a Salp Swarm Algorithm (SSA) is proposed as a data-driven method to improve the controller variable and thus optimize the complete power production of the wind plant. The SSA is among of the meta-heuristic technique and imitates the salps chain’s swarm movement depending on the food placement. The model used in this study originates from Denmark’s actual Horns Rev wind plant. The analysis result demonstrates the SSA generates significantly better total wind power production as opposed to the Spiral Dynamic Algorithm (SDA) and the Particle Swarm Optimization (PSO) technique.
An Adaptive Pooling Model for Aesthetic Quality Assessment Based on Convolutional Neural Network

It is a requirement to find well-photographed and attractive photos from a large number of images, but image aesthetic quality assessment (IAQA) is challenging because of the subjectivity of aesthetic criteria. In this paper, we propose an adaptive pooling model for IAQA based on convolutional neural network (CAP) which consists of three parts: feature extraction, feature adaptive pooling, and obtaining quality scores. Through experimental comparison, we choose ResNet50 network as the image feature extraction network. In order to reduce the fixed input size limit, we introduce Spatial Pyramid Pooling (SPP) in model. The final score is weighted summation of the score distribution. The experimental results show that the proposed model achieves the state-of-the-art performance.
Virtual Reality for Learning: Assessment of Awareness and Preference in Emerging Regions

In this research, the awareness and the preference of using Virtual Reality (VR) as a medium of Learning in the Emerging Regions in South Asia such as Sri Lanka, was assessed with a randomly selected audience of 250. Currently VR is one of the fastest growing industries globally and application of Virtual Reality in Education and Learning plays a significant role in its growth. Yet in emerging regions such as Sri Lanka, the uses of such technologies are relatively less. The experiment was conducted with 250 volunteers provided with a questionnaire before and after experiencing the Virtual Learning Environment (VLE). Historic scenery of Chola Dynasty was created with a Virtual tutor where the user was given the complete freedom to navigate in the historic scenery and learn from the virtual tutor. The scenery was designed and developed by considering Human Computer Interaction (HCI) heuristics, Hand-Gestures and realistic audio material in order to provide the learner a realistic and an immersive experience. The results showed that the majority of the school students show a major interest in using VR as a medium of learning and the second highest to show such an interest within the sample were the undergraduates. Even though the majority of the student and undergraduate community have not experienced VR before, this being their first encounter with a VR based application showed positive results. Also having various hand-gestures for the virtual characters used for tutoring showed to be very much effective as demonstrated by the results of the experiment.
Using Convolution and Sequence-discriminative Training to Improving Children Speech Recognition

The conclusion that ASR for children's speech is especially difficult compared to adult was given by the robotics community from recent works. Challenges on Children's speech recognition mainly due to the increased variability in acoustic and linguistic correlates depending on a young age. This work focused on the recognition of oral English spoken by Chinese children aging six to twelve. Experiments were conducted on: (1) Speaker Normalization algorithms, including Cepstral Mean and Variance Normalization (CMVN) and Vocal Tract Length Normalization (VTLN) techniques; (2) Acoustic models adapting techniques, such as Maximum Likelihood Linear Transform (MLLT) and Speaker Adaptive Training (SAT) based on Constrained MLLR; (3) Different acoustic models, GMM-HMM, DNN-HMM, CNN-DNN; (4) Training criterion, with frame-level training such as Cross entropy (CE), and sequence-discriminative training (SDT) such as MMI, MPE and sMBR were conducted in this paper. The results included: (1) with the increase of age, the variability of children's pronunciation decreased significantly; (2) the convolution on the frequency axis has a great performance contribution (34.72%) to the variability of children over the baseline system.
COLLECTION AND ANALYSIS OF INFORMATION FOR MAKING MANAGEMENT DECISIONS ON THE DEVELOPMENT AND PROMOTION OF INNOVATIVE EDUCATIONAL PROGRAMS OF THE UNIVERSITY

The article is devoted to the study of the issues of making managerial decisions in the University for the development and promotion of innovative educational programs in the educational services market (ROE) based on the results of data collection and analysis.Two approaches are proposed: studying the data belonging to the University, and searching for the necessary data in the external environment.The article describes the sources of data necessary for making decisions about the promotion of innovative educational programs of the University, the format of working with data sources, sources for registers of information systems, catalogs of websites Ключевые словаинновационная образовательная программа, цифровизация, цифровые следы, управленческое решение
Hardware Implementation of Minimum Filter based on 2-D Cumulative Histogram

Recently, advance technology such as automated driving or surveillance requires the clear scene of input image or video, therein lies the reason for the growing importance of haze removal algorithm. In order to remove fog, atmospheric light estimation is required. Since white objects can be mistaken as fog, minimum filter is needed to prevent this problem by reducing the effect of these objects in the image's local patches. This paper proposes hardware design of minimum filter based on cumulative histogram. The synthesis is done using a SoC evaluation board.
A Grid based Approach for Handwritten Text Segmentation

The hand-written text is subject to inconsistency and variability. To achieve the accuracy and consistency in recognizing handwritten characters, the present work discusses an efficient approach to segment the handwritten text into individual characters. In order to achieve this, the writing space is divided into three different zones and then the zones are subdivided tiny square shape grids, which are distributed evenly. After this, we analyze to find the presence of handwritten character in a selected grid by performing horizontal scanning method. Moreover, we propose an approach of segmenting the character set of a single line text by vertical scanning and curve pattern analysis on the touching zones of the selective grids. The efficacy of the approach has proven through rigorous testing on hand-written texts of both professional as well as non-professional users. The testing set consists of 106 text samples including both single line text phrases and short sentences. The proposed approach achieves around 100% accuracy for word level text segmentation on the handwritten texts of both professional and non-professional users. In addition, the average character level segmentation accuracy on texts written by professional and non-professional users are found to be 98.4% and 96.2% respectively. Thus, the average character level segmentation accuracy is improved by 97.3%.
Graph-Based Tracking with Uncertain ID Measurement Associations

While multiple-hypothesis tracking is a leading paradigm for multi-sensor multi-target tracking, it is not effective in settings with disparate sensors that include high-rate kinematic data and low-rate identity data. Recent work has led to an effective graph-based approach to this challenge. This paper introduces two further advances: a generalization that allows for multiple (indistinguishable) objects of each type, and a scalable, time-based framework for hypothesis resolution. We illustrate promising performance results for multi-target track maintenance scenarios.