Reviewerly/exHarmony huggingface dataset:
Query Complexity of k-NN based Mode Estimation

Motivated by the mode estimation problem of an unknown multivariate probability density function, we study the problem of identifying the point with the minimum k-th nearest neighbor distance for a given dataset of n points. We study the case where the pairwise distances are apriori unknown, but we have access to an oracle which we can query to get noisy information about the distance between any pair of points. For two natural oracle models, we design a sequential learning algorithm, based on the idea of confidence intervals, which adaptively decides which queries to send to the oracle and is able to correctly solve the problem with high probability. We derive instance-dependent upper bounds on the query complexity of our proposed scheme and also demonstrate significant improvement over the performance of other baselines via extensive numerical evaluations.
Integration of Fuzzy and LSTM in Three-Way Decisions

Recently, a three-way decision concept is studied to handle the uncertainty and incompleteness in the given attribute set based on acceptance, rejection, and uncertain regions. A three-way decision is a vital method in solving the problem of uncertainty in opinion mining models. There are models developed to solve the problem of uncertainty under three-way decision theory. However, with the classification performance of existing models, we further investigate the formulation of three-way regions using fuzzy concepts and Long Short Term Memory (LSTM). To demonstrate the effectiveness of the method, experiments were conducted using a movie review and an ebook review datasets. The experimental results show that the proposed three-way framework is effective for dealing with uncertainties in opinions.
Emergence of Web Collective Intelligence and Its Impact on Technology Foresight

Network community plays an important role in obtaining information, expanding communication, attracting attention and providing services. The continuous development of network technology boosts deep-level interaction and coordination among expert users, and remarkable web collective intelligence emerges in the process. It is an urgent research problem to explore the connotation, characteristic and external influence of web collective intelligence in order to better understand and utilize it. With network community as the environment, expert users as the subject, and web comments as the carrier, web collective intelligence has a broad and profound impact on the research of technology foresight.
Research and Application of Mobile App for Digital Oil and Gas Reservoir in Changqing Oilfield

With the rapid development of mobile communication technology and Internet technology, the scale of enterprise mobile application market has shown explosive growth. Mobile application has become a new trend of enterprise information system development with its characteristics of portability and immediacy. However, the scientific research information system of Changqing Oilfield still adopts the traditional application mode which can only be used by fixed location, fixed computer and fixed network. There is no formed mobile deployment system, which cannot meet the urgent needs of more and more users to obtain data in real time. Mobile application efficient development, large capacity data mobile display, mobile data security protection facing challenges. In view of the above difficulties, based on cross platform development, large capacity unstructured data compression, digital watermark encryption and other technologies, this paper proposes a mobile application architecture of scientific research information system for oilfield enterprises. The deployment and application of oil field shows that the development efficiency of mobile application is increased by 100% and the compression rate of unstructured data is increased by 99%. At the same time, the data security in mobile application is greatly improved, so that scientific research and management personnel can obtain production data and research results at anytime and anywhere in the non-office environment such as scheme discussion and business trip, which effectively improves the efficiency of enterprise scientific research. In oil field company It has good application value.
Clustering Analysis using an Unsupervised Machine Learning Method

Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.
Cross Domain Transfer for Sketch-based Clothing Retrieval

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. In the huge commodity database, it remains a long-standing unsolved problem for users to find the interested products quickly. Because the sketch contains more content than the text, and the way to get is more convenient than the exemplar image. In this work, We propose a sketch-based clothing retrieval model. It implements cross-domain retrieval and is used to search for specific clothing. Because most of the existing clothing datasets are only composed of photos, it is difficult to obtain dataset which composed of sketch-photo pairs. Thus, we contribute a clothing dataset, including 34142 sketch-photo pairs. Whether our model in our dataset or other dataset has achieved compelling performance.
Research on Path Planning of Mobile Robot Based on Deep Reinforcement Learning

Reinforcement learning has been widely used in the path planning of mobile robots, and has gradually become the main technology in path planning research. However, in the process of using reinforcement learning, there are problems such as “dimensionality disaster”, slow convergence, and poor generalization. In order to solve these problems, this paper proposes a new learning framework of mobile robots called deep Q learning with experience replay and heuristic knowledge or HDQN in short. In this system, there is a new reward function defined. Then, heuristic knowledge can be designed, which includes knowledge about the target and avoiding obstacles. Finally, experiments can be executed to verify the effect of the HDQN algorithm.
Moving Object Inpainting using Deep Learning

Video painting is designed to fill video holes with plausible material. Despite the enormous success of deep-neural networks for image paint, owing to the additional time factor, it is difficult to apply these approaches to the video domain. In this job, we introduce a new, profound, video painting learning algorithm. Screen painting is a video procedure that completes corrupt or incomplete areas. The need for the maintenance of time consistency as well as the need to keep video painting in comparison to picture painting poses additional difficulties. The network is qualified to mask and adhere to the relevant contents into reference frames Inpaint the void in the mark framework. The D-network proposed similarly comprises a network for arrangement framing matrices toward permit the net toward gather information for robust after reserved settings aimed at the purpose of calculating affinity matrices. In comparison to the sophisticated picture painting algorithm our system makes videos even more semierect and temporally smooth. The Deep Network proposed approach is near-real-time and competitive.
Multi-level feature extraction network for person re-identification

In the task of Person re-identification (reID), the range of motion of pedestrians often spans multiple camera areas, and their motion direction and behavior cannot be constrained, and irrelevant people or objects in different scenes will also obtain target pedestrian information for us Cause interference. At the same time, the surveillance system also has many characteristics such as a fixed shooting angle of a single camera, different angles between different cameras, and low image resolution. These characteristics make the task of Person re-identification difficult. This paper proposes a Multi-level Feature Extraction Network (MFEN) based on SEResNet-50. Extracting richer and more diverse pedestrian features from poor-quality images will effectively improve the re-identification ability of the network, and MFEN can obtain Multistage key features in the image through the Feature Re-extraction Method (FRM) proposed in this paper. Experiments show that compared with AANet-50, MFEN has 3.85% /0.71% improvements of mAP/ Rank-1 on the Market1501 dataset, and 2.74% /1.28% improvements of mAP/ Rank-1 on the DukeMTMC-reID dataset.
Andragogical Approach and Problems of Its Application in Professional Education

It is shown that professional education is received by adults with psychological and social maturity, life and professional experience, therefore, in the process of their training; an andragogical approach should be applied. The basic principles of the andragogical approach (independent learning, joint activities, reliance on the student's experience, and an individual approach to learning) have been determined, which allow purposefully and effectively apply this approach in vocational education based on the methods, techniques and means of teaching adults that correspond to the selected principles. It was revealed that the majority of university teachers do not use or rarely use the andragogical approach in their professional activities, and to solve this problem, advanced training courses, classes of methodological associations of teachers are needed, which will purposefully study the application of the andragogical approach in vocational education.
Distributed Maximum Correntropy Kalman Filter with Consensus Strategies

In wireless sensor networks, finding a consensus in which the system works collectively is a fundamental problem of distributed estimation. Meanwhile, non-Gaussian problems arise in many real scenarios, such as target tracking and digital communications. The recently proposed distributed maximum correntropy Kalman filter (D-MCKF) has performed better than the conventional distributed Kalman filter (DKF) in non-Gaussian environments. In the DKF and D-MCKF, the nodes with more neighbors can achieve better performance than those with fewer neighbors. The differences among different nodes may weaken the network. In this paper, the consensus strategies are incorporated into non-Gaussian KFs to mitigate state differences among different nodes. We firstly incorporate the average consensus into the D-MCKF, and secondly propose a new weighted consensus strategy based on the correntropy cost function, where the estimated state and observations of neighbors are involved. Simulations show that the proposed algorithm mitigates state differences among different nodes.
Exploratory data analyses: describing our data

In analyzing data, our first step is to explore our data and see what may exist in our variables. This step of exploratory data analysis (EDA) opens up a world of possibilities that provide us a foundation for decision-making. These analyses vary depending on the type of variable we are working with. For categorical data, we focus on summarizing the proportions for each category. For quantitative data, we find summaries for the center, spread, and shape of the data. Even further, our analyses can help us understand relationships between our variables, particularly through both graphical and mathematical summaries such as plots and correlations.
Cooperative Target Observation using Density-based Clustering with Self-tuning and a New Grid Environment

This paper describes and evaluates a Mean-Shift-based (MS) approach to an instance of the Cooperative Target Observation (CTO) problem domain. A performance comparison is presented with a k-means-based approach to the baseline implementation published to the CTO problem. Inspired by the idea of modeling the problem for urban centers in which the movement of targets is restricted to the streets and roads, we also evaluate the effect of the movement of the targets being restricted to a rectangular grid on the relative performance of the algorithms. We conclude that the MS-based approach is superior to the k-means-based approach and that the target motion restricted to a grid improves both algorithms' performance but does not change its relative positions.
“Touch a Paper” System Design for Reading Utilizing Physical Touch

Every environment and tool in our lives has been increasingly digitized: watches, cameras, books, magazines, and other stationery goods. Most basic necessities are integrated into a smartphone. The digitization of our live has greatly contributed to the increased convenience. Yet, there are also lost attractions due to digitalization. For instance, people choose analog records for the pursuit of the sound quality and its design, rather than convenience. There are still so many unwelcoming the digitization of living environments and tools. A typical example is "books". While in recent years e-books are rapidly becoming widespread, many people choose "paper books" to enjoy reading. Part of the reason is assumed that reading books is not just about absorbing the information written in them, but it is about enjoying a sense of touch when turning the pages. In this research, we focus on "a sense of physical touch", the feeling of actually turning the pages of a paper book. We also design software and devices that provide users with a "sense of physical touch", often lost when reading an e-book on a smartphone.
Statistical Approach for Mining Frequent Item Sets

Current paper submits an altogether novel method to identify Frequent Itemset which applies approach of statistics further based on association rule mining which otherwise a commonly applied method in transactional database to trace high frequency itemset in data mining arena. The algorithm designs frequency distribution table, Algorithm applies the formulas of class intervals to design frequency distribution table. It calculates the frequency if item sets present in the database. It functions in two phases. Phase 1 is termed as filtering and debulking phase. Phase 2 is termed as application step alongside comparison step among class interval techniques. Threshold values are calculated within the algorithm to find frequent item sets. Threshold numeric is subjected on to the formed table showing frequency distribution and simultaneously products listed are also selected. It generates pairs by applying combination technique. Database is the alone requirement as input. Current paper therefore addresses the worth of identifying and searching frequent item sets in huge transactional database.
Multiplier-less Lifting-based Photo Core Transform for JPEG XR Ultrasound Image Compression

The new standard of the JPEG community, called the JPEG XR, carries the features of its earlier standards, namely the JPEG Baseline and the JPEG 2000. JPEG Baseline and JPEG 2000 have been in extensive use since their introduction; and have pre-defined tools in various platforms, such as MATLAB, for simulation and testing purposes. However, the same doesn't apply to the latest JPEG XR standard and thus creates a barrier in the ease of use for the global community. JPEG XR employs photo core transform for still-image compression, and in this paper, we propose a novel software implantation of the forward and the inverse photo core transform, PCT and IPCT respectively. The implementations are lifting-based operations that can be made multiplier-less for hardware simplicity.
Usability Testing for Student Academic Information System in State Polytechnic of Creative Media

Abstract SIAM (Student Academic Information System) is an academic services information system that has been implemented at the State Polytechnic of Creative Media (Polimedia). It’s been two months SIAM has been integrated using Single Sign On (SSO). After we implemented SIAM new version we received a lot of complaints every day. In this research, the authors conducted a usability test on the SIAM new version in order to determine the effectiveness and performance of the application. Usability test components include effectiveness, efficiency, and satisfaction. The average usability 70% indicates that the SIAM new version has a good usability performance.
Is This Really Relevant? A Guide to Best Practice Gaze-based Relevance Prediction Research

As eye tracking is becoming feasible on commodity devices, it provides a powerful tool for inferring users’ perceived relevance of objects. Yet the prediction quality depends on multiple parameters that have to be considered when designing the prediction model. In this paper, we review approaches to predict relevance from gaze with regard to five design issues: 1) extracting features, 2) defining the algorithm, 3) setting a prediction scope, 4) eliminating visual distractors, and 5) evaluating the system. The insights may serve as a guide to establish best practices for the design and evaluation of relevance prediction models, thus allowing for better comparability of future work. We further discuss promising fields of application that will drive future research on gaze-based relevance prediction.
Knowledge Acquisition from Natural Language with Treebank Semantics and $$\mathcal{F}\textsc {lora}$$-2

Knowledge acquisition in this paper concerns converting raw natural language input into database entries. This is achieved by linking two systems: Treebank Semantics and $$\mathcal{F}\textsc {lora}$$ -2. Treebank Semantics (Butler 2021) is an implemented grammar system that converts parsed constituency trees from a treebank parser into logic based representations that capture sentence and discourse dependencies. Further postprocessing produces content for $$\mathcal{F}\textsc {lora}$$ -2, “a sophisticated object-based knowledge representation and reasoning system” (Kifer et al. 2020). A running example illustrates capabilities and use of the combined systems.
50‐2: LED‐Based Next Generation Immersive Virtual Reality

Currently, head mounted displays are the most widely used variant of Virtual Reality (VR) systems. While offering reasonable quality at moderate prices, their inherent limitation is that they isolate users from their surroundings, especially other persons in the room. Immersive VR systems with large fixed screens, such as CAVEs and Powerwalls, remain the preferable technology when maximum quality and unencumbered interpersonal communication is required. However, the state‐of‐the‐art of this technology is limited to a single users' view, with the others only seeing a more or less distorted image. Moreover, they are mostly using on rear projection, with its inherent drawbacks on brightness, contrast, and space requirements. In this paper we propose a novel multi‐viewer immersive VR system based on LED display technology, which will overcome these limitations, leading to a new quality of co‐located collaborative VR.
Continuously Bulk Loading over Range Partitioned Tables for Large Scale Historical Data

To support efficiently and continuously loading large scale historical data into a distributed data management system (DDMS), it needs to balance the bulk workload across machines. The fundamental problem is to estimate the time used to merge currently loaded data (defined as incremental data) into previously loaded data (defined as baseline data) for each partition, referred to as partition merge. In this work, we present a learning-based framework, referred to as LeaBalancer, to balance the merge loads across cluster nodes. In the situation where the system is scheduled to have regular bulk loading tasks, LeaBalancer can learn to predict the partition merge time from the merge logs generated by previous bulk loadings. Nevertheless, it is still difficult to balance the bulk workload only using a single plan phase because of inaccurate merge time prediction or other in-progress heavy workloads during the bulk loading. To resolve this problem, we design a multi-round balancing strategy, and at the beginning of each round LeaBalancer carefully chooses partitions for migration according to the remaining merge loads in each node. Experimental results show that LeaBalancer can adaptively perform load balance under various settings.
Design and Implementation of Operation and Maintenance Support System of SDH Optical Fiber Transmission Network

SDH optical fiber transmission is widely used in various information transmission networks. It transmits a variety of information. According to the actual operation and maintenance requirements, the business management module of SDH optical fiber transmission network operation and maintenance support system is designed to realize the classified and targeted business management. The business records are information-based and scientific. Using the equipment performance data obtained from the northbound interface, the key performance indicators of the optical fiber transmission network can be extracted and displayed. It can give early warning of equipment failure and provide data support for network performance analysis and troubleshooting. Business management and device performance data are friendly displayed by the popular open source visualization tool based on JavaScript. The application of the system can improve the operation and maintenance efficiency of SDH optical fiber transmission network.
역량기반 교양교육은 교양인을 길러낼 수 있을까

The purpose of this study was to explore the concept of competency-based liberal education and discuss the issues related to how competency-based liberal education can impact on the nature of liberal education. The results of the study are summarized as follows.First, competency-based liberal education can lead to the neglect of theoretical or academic knowledge by making people think that they can replace liberal arts with arts of competence. Second, competency-based liberal education is inherently based on competencies that involve elements of performance(or behavior) and constantly decomposes competencies into aspects of (detailed) competencies or skills. Therefore, it could undermine the nature of liberal education by promoting behaviorism and reductionist approaches. Third, competence has been related to job performance, and it brings about job orientation by trying to derive competence based on recent industrial or social needs, so it subverts the nature of liberal education. Fourth, it commits category errors by indiscriminately involving moral aspects such as friendship and caring that are difficult to include in traditional liberal education.After all, current competency-based liberal education could certainly have a negative impact on the nature or identity of liberal education.