Reviewerly/exHarmony huggingface dataset:
Complete Coverage Path Planning for Industrial Mobile Robots

Due to the increase in labor costs and the popularization of intelligent factories, there are more and more mobile robots replacing human resources in large places such as factories, shopping malls, and hospitals. However, these places have a large number of obstacles composed of special materials such as glass and grids. The map constructed by the existing technology is flawed, so using the traditional complete coverage algorithm in the obstacle area will also be planned out of the path, burying security risks. Aiming at the problems in the existing methods, this paper proposes a complete coverage path planning algorithm for custom regions based on the idea of the cellular decomposition method. Convex quadrilaterals are selected as a subregion. The baseline offset method is used to calculate the waypoints. The greedy algorithm is used to calculate the connection cost of all sub-regions. The lowest-cost cleaning sequence and the order of waypoints in the sub-regions are selected, and all waypoints are summarized. The target points are published based on the ROS (Robot Operating System) navigation stack to complete the complete coverage path planning. This method effectively standardizes the operation area of the robot, optimizes the number of turns in the cell, and reduces the time cost. The mobile robot experiments in the factory show that the method has high security and coverage.
An Anomaly Detection Framework Based on Data Center Operation and Maintenance Data

Data centers need to monitor various metrics of their different application platforms and applications in real-time. As the system architectures and application services of different application platforms within them become more complex, the requirements for their anomaly detection capabilities are higher. Therefore, this paper proposes an anomaly detection framework based on data center operation and maintenance data. The framework in this paper consists of three parts, including operation and maintenance data cleaning, data feature extraction, and model routing. It is used to select the appropriate model through model routing based on the indicators such as stability and periodicity obtained from data feature extraction of each application platform. At the same time, in order to enhance the expansion capability of the detection algorithm, a cloud-ground hybrid framework is used and a module for algorithm model management is designed to facilitate interaction with the cloud. After testing on SWAT and WADI datasets, the anomaly detection algorithm with the addition of model routing in the framework has good accuracy and recall performance compared to a single anomaly algorithm model, showing advantages in the task of identifying anomalies.
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation

Lack of texture often causes ambiguity in matching, and handling this issue is an important challenge in optical flow estimation. Some methods insert stacked transformer modules that allow the network to use global information of cost volume for estimation. But the global information aggregation often incurs serious memory and time costs during training and inference, which hinders model deployment. We draw inspiration from the traditional local region constraint and design the local similarity aggregation (LSA) and the shifted local similarity aggregation (SLSA). The aggregation for cost volume is implemented with lightweight modules that act on the feature maps. Experiments on the final pass of Sintel show the lower cost required for our approach while maintaining competitive performance.
Comparison of military artificial intelligence development systems between the Korean and US military

본 연구는 "군사용 인공지능 개발이 세계적인 화두로 등장한 상황에서 한국군의 군사용 인공지능 개발체계는 타 국가에 비해 효과적인가?"라는 질문에서 출발하였다. 이를 위해 세계 각국의 인공지능 및 자율무기체계 개발에 대한 정책 등을 고찰하고 군사용 인공지능 기술 개발이 기존에 전통적인 소프트웨어 기술개발과 어떠한 차이점과 특징이 있는지 분석하였으며, 이를 기반으로 한국군과 미군간의 군사용 인공지능 개발체계를 비교하였다. 비교의 평가요소요소로는 ① 데이터관리 및 활용 인프라 구축 ② 소요군-개발자 협업 ③ 산학연과 협력을 선정하고 비교분석을 실시하였다. 그 결과 한국군의 군사용 인공지능의 신속한 적용을 위해 필요한 세 가지 개선사항(① 소요군내 첨단기술 R&D 수행조직 신설 ② 방위사업법 등 제도 개선 ③ 국방과학기술 개발간 전투실험을 통한 소요군 R&D 역량 확보)을 제시하였다. The purpose of this paper is to identify whether ROK"s defense AI model developing system has enough competitiveness to that of other countries. The rapid development of 4th generation technology such AI, 5G, Robot etc. increases nations interests in application of these technologies in military area. Especially military advanced countries - USA, China, EU - expand their investment in developing new AI technology applicable to defense area on the belif that AI could change aspect of future warfare. Method and system of increasing AI capability are different from these of developing general software in terms of the neccesary of huge amount of data for machine learning process and the requirement of close cooperation between user and developer. This paper suggests ways of improving ROK"s defense AI capability through comparison between the defense AI developing and applying system of U.S. and Republic of Korea.
Facebook Reactions at the Beginning of COVID-19 Lockdown

The COVID pandemic led to the closure of educational institutions worldwide. In Mexico, the education system went into lockdown on March 16, 2021. With people unable to interact in the physical world, virtual spaces provided alternatives where emojis and other graphical representations were used to convey emotions. This research aimed to investigate the Facebook reactions of four generations (Baby boomers, Generation X, Millennials, and Post-millennials) over three months: April, May, and June 2020. A mixed-method approach focused on a survey following an exploratory design was used to collect data. The data showed that participants had several reasons to click on emojis on Facebook during the COVID- 19 lockdown. Baby boomers used emojis the most and relied on Facebook to share information about the pandemic. While more work is necessary to understand the emoji phenomenon deeply, data revealed that the cave paintings we left on Facebook walls at the beginning of the year 2020 reflect the impact of COVID- 19 had on our lives.
Robust Visual Slam in Dynamic Environment Based on Moving Detection and Segmentation

It is difficult to achieve robustness and high accuracy in a dynamic environment for Visual Simultaneous Localization and Mapping (SLAM) because of the moving objects. In this study, we proposed a method to promote robustness and high accuracy in Visual SLAM system. Sparse optical flow and epipolar geometric constraint are combined to conduct moving detection by judging whether a priori dynamic object is in motion. Semantic segmentation is combined with moving detection to perform dynamic keypoints removal by removing dynamic objects. The dynamic objects removal method was integrated into ORBSLAM2 to fulfil robust and accurate localization and mapping. Compared with ORB-SLAM2, the experiments on TUM datasets show that our proposed system can significantly reduce the pose estimation error and increase robustness in dynamic environments. In contrast to other similar SLAM methods, our results were more accurate. The dense semantic map built using our method is much closer to the groundtruth of the dynamic environment than the map built using ORB-SLAM.
Development of Seismic Intensity Maps Using Low-cost Micro-electro-mechanical Systems Seismic Network

The Korea Meteorological Administration (KMA), SK Telecom, and Kyungpook National University collaborated to develop the low-cost IoT seismometer (LCIS) that embeds a micro-electromechanical systems accelerometer and LTE communication sensor.Currently, approximately 7,000 stations (the average density is 0.07/km 2 ) with LCIS devices are operating in real-time on the Korean Peninsula, which is about 25 times higher density than the national seismic network with high performance seismometers operated by KMA.The majority of LCISs are installed inside buildings so that adjustment is applied to convert to the free-surface equivalent vibration.Researchers derived seismic intensity maps using only LCIS network data, which showed very similar distributions to the maps from the high-performance network.This validates the usefulness of the LCIS network for Modified Mercalli intensity (MMI) map generation and earthquake early warning system.
A Novel Spam Classification System for E-Mail Using a Gradient Fuzzy Guideline-Based Spam Classifier (GFGSC)

Spam messages have increased dramatically in recent years even as the number of email clients has grown. Email has already become a valuable way of communicating because it saves time and effort. However, numerous emails contain unwelcome content known as spam as a result of social platforms and advertisements. Despite the fact that many techniques have already been created for spam mails categorization, none of them achieves 100 percent efficiency in analyzing spam messages. So, in this research, we propose a novel Gradient Fuzzy Guideline-based Spam Classifier (GFGSC) for classifying the spam e-mails as spam or non-spam. This research uses four types of datasets and these datasets are pre-processed using normalization. Then the set of data can be extracted using Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) techniques. The aspects are selected using Information Gain (IG) and Chi-Square (ChS) techniques. And the GFGSC classifier can be used for classifying the data as spam or non-spam with better effectiveness. Finally, the performances are examined and these metrics are matched with the existing approaches. The results are obtained using the MATLAB tool.
dExCount: A decentralized cross-chain discount web app for Token Sale

As the crypto market develops, new projects emerge with blockchains and tokens aimed at achieving specific goals. Some of them aim to outperform Ethereum by providing developers with improved scalability, low to no fees, and other benefits. Others are designed to be used only in decentralized applications such as online casinos or cryptocurrency loan services. This incredible variety of options eventually leads to the need to exchange one cryptocurrency for another, just as we would exchange dollars, euros, and yen. In the market, there are numerous ways to exchange cryptocurrencies. There are numerous applications and blockchain platforms that facilitate the exchange of cryptocurrencies from one token to another. However, there are numerous complications throughout the process. In some platforms, you have to write long lines of code in order to swap tokens or the transaction fee in some platforms is very high which makes it difficult for a token owner to swap the tokens and also earn profit with it. Therefore to solve this problem, we have come up with a web application ‘‘dExCount’’ that helps the token seller to sell their tokens at a discounted price by creating a discount pool without writing any code. This system blockchain platform will help cryptocurrency owners to increase the brand value of the tokens by hosting the tokens on the platform for sale at a discounted rate. The token owner can give detailed information about the token like the social media links, website, and various other information about the tokens.
A Feasible Compression Scheme for Bangla Natural Text

The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a useful method for compressing Bangla natural text called the 5 Bit Compression Scheme for Bangla Text(5BCS), which uses a 5-bit encoding method to change the 16-bit Bangla letters into a 5-bit format. This technique provides an encoding algorithm which representing the 16-bit Bangla characters to 5 bits by utilizing a query table. The query table contains 4 sets which comprises all the Bangla characters as well as transliterations and special symbols. An m-series technique is used for logically determining index of a given characters combination while translating characters into 5 bits. By utilizing the 5BCS, a natural text can be compressed by a maximum of 84%. The algorithm for decompress the compressed data to translate the original data is also detailed in this paper. Additionally, a comparison section is developed to compare our 5BCS technique with the widely used Huffman and LZW technique. The experimental result of the comparison looks very much optimistic.
Image Security Enhancement on Cloud Storage using AES Algorithm

Image Security is playing a significant role in the field of communication system and Internet. Cryptography is initially developed to protect the interaction between two parties known as sender and receiver in the presence of malicious third parties called as attackers by using the process known as encryption. The given algorithm transforms the plain image into cipher image if the same key is used. The encryption is done by using various algorithm, transformation and many more techniques to secure the image. This article presents a novel image encryption technique. This AES algorithm is incredibly reliable and helps to maintain images safe from malicious attack. The sender has an image, and this image is split into several segments. After the encryption and decryption, the receiver merges those images. In the proposed system, keys will generate manually. This research paper presents an effective method for encrypting & decrypting data on the cloud. Through this method, users can store data securely in the cloud without being damaged or corrupted. This system has an idea about authentication. Receivers' digital signature is stored on the cloud to decrypt the image by generating digital signature that match with the stored one. If both signatures match, then it can be user verified and authenticated.
Scenario Aware Recommendation Algorithm Based on School Enterprise Cooperation

School enterprise cooperation is an important way for enterprises to obtain external innovation resources, which is of great significance for enterprises to realize industrial technology upgrading and improve competitiveness. However, in the process of promoting school enterprise cooperation, it is not easy for technology brokers to match appropriate University experts for enterprises. The large group of experts is the main reason for the problems. At present, there is a platform in the market to provide services matching experts, but its functions can not fully meet the needs of technology brokers to promote school enterprise cooperation in some scenarios. The main idea of this paper is to use the system to understand the past history and current situation, and to predict the future situation. We have implemented the algorithm on our own research platform. Users can explore different scenarios by changing parameters (such as user preferences, interests and other factors). In addition, we also provide an easy-to-use interface that allows users to simulate various scenarios using real data in the system.
English Intelligent Translation Software Framework Based on Deep Learning Algorithm

With the rapid development of the information age, the traditional living habits and behavior patterns of human society are gradually broken, and new things are constantly seeping into daily life. Since the 21st century, the popularity of smart phones and the comprehensive coverage of network services have enabled most regions of the world to enter the era of national mobile Internet. Driven by the rapid development of information technology and Internet technology, the integration of mobile phones and APP has brought about significant changes in social situations and disruptive changes in daily life. The main purpose of this paper is to study the construction of English intelligent translation software framework based on deep learning algorithm. Design and development of text analysis, backend server and mobile software for an English learning system. The main modules included setting up an English web server, importing raw data from the learning materials, setting up an Open fire chat server and database connection, establishing communication between MT and the web server, logical display of the learning material data, English listening and reading exercises, and communication between friends. Finally, it was found that the variability in response time did not increase significantly with the number of users, but showed a very moderate upward trend. The test results show that the system structure, code logic and code optimisation meet the requirements of the proposed project and satisfy the users' requirements.
Interior Design Color Transfer Algorithm Based on Topologically Integrated Information

Due to the strong regional nature of interior design, the color in the interior scene is rich though, and the color difference between each scene area is large, so we propose the color transfer algorithm for interior design based on topological comprehensive information, and apply the color transfer algorithm to divide the design image area in interior design, and carry out area segmentation based on the topological information of the design image, and then determine the image topological structure area matching. This paper compares the number of regions and matching running time of image topological region matching between the color transfer algorithm based on lαβ color space and the color transfer algorithm based on image segmentation, and the results show that the color transfer algorithm based on image segmentation can reduce the image noise and obtain more topological region matching numbers, so as to get better color transfer effect.
VR Popular Science Education and Training System on Account of 3D Engine

With the rapid development of society and economy, the number of people who sincerely and actively participate in popular science education is increasing every year. However, the effect of traditional education mode still cannot reach the expected standard under the condition of high investment in space, capital and human resources. Based on 3D engine technology, this paper innovates the methods of site scene, training and quality assessment of traditional education mode, removes the unfavorable factors of traditional education system and adds the favorable factors of education system to create a new VR popular science education and training system. This method creates a new system with virtual scene construction, scientific teaching and research program, efficient learning environment and one-stop teaching service. This system has the advantages of low investment and high availability of resources. The practical exploration of 3D VR popular science education is expected to provide theoretical support and practical guidance for 3D VR popular science education exploration from the construction of 3D VR popular science education design framework to specific teaching design and teaching practice research. This paper studies the relevant professional theories and knowledge of VR popular science education and training system based on 3D engine, introduces some concepts and contents of VR popular science education and training system based on 3D engine, and analyzes and studies the VR popular science education and training system. After data testing, the effect was analyzed, and the test results showed that the 3D engine-based VR popular science education and training system achieved 80.85% in image rendering.
A Study on the Efficiency Analysis of Logistics IT Companies

Recently, the focus of the logistics and supply chain management has been on the developments and application of newly emerged technologies, such as Artificial Intelligence and Blockchain. They changed the whole landscapes of how logistics should be done in the field. Considering the changes around the industry, this study has conducted DEA analysis on major logistics IT companies that were chosen by the reliable sources of publication. Main discoveries are pretty different from previous studies. First, static analysis such as CCR, BCC, super efficiency finds that foreign companies and those who have special business sectors show high level of efficiency. As for Malmquist analysis, it was shown that between 2019 and 2021, there was no evidence of positive externalities. Thus, it is necessary for these companies to minimize the inputs and take time and wait to maximize their potential when the technologies are fully applied and utilized in the field.
Accident Prediction and Analysis using Machine Learning models

Road safety is a major concern due to the rapidly growing number of automobiles. Every year, 1.2 million individuals die in road accidents According to statistics, between 2017 and 2020, traffic accidents in Tamil Nadu resulted in the deaths of almost 22,000 individuals. According to the “Accidental Deaths & Suicides in India-2021” report released by the National Crime Records Bureau (NCRB) shows Tamil Nadu at second in the number of fatalities on roads caused by negligence. The State came in second to Uttar Pradesh, which recorded 18,972 fatalities in 18,228 accidents, with 15,384 fatalities in 14,747 occurrences. The proposed work is used to predict the severity of an accident at a place and time using few machine learning algorithms namely decision tree, random forest, logistic regression, and decision tree hyper parameter tuning. Various parameters are found, such as the speed limit, age, weather, vehicle type, light conditions, and day of the week had been used to train the model. Upon inferring the results, we found that random forest algorithm showed the highest accuracy of these algorithms, with a score of 88.89%, followed by logistic regression (86.23%), decision tree hyper parameter tuning (85.74%), and decision tree (66.67%), respectively. The major challenges faced in this project was collecting proper samples for training the models and modifying it as per the needs of the proposed model. This model will be crucial for traffic planning and management and will aid in the future reduction of many traffic accidents.
AGENT BASED APPROACH FOR TASK OFFLOADING IN EDGE COMPUTING

Due to limited resource capacity in the edge network and a high volume of tasks offloaded to edge servers, edge resources may be unable to provide the required capacity for serving all tasks. As a result, some tasks should be moved to the cloud, which may cause additional delays. This may lead to dissatisfaction among users of the transferred tasks. In this paper, a new agent-based approach to decision-making is presented about which tasks should be transferred to the cloud and which ones should be served locally. This approach tries to pair tasks with resources such that a paired resource is the most preferred resource by the user or task among all available resources. We demonstrate that reaching a Nash Equilibrium point can satisfy the aforementioned condition. A game-theoretic analysis is included to demonstrate that the presented approach increases the average utility of the user and their level of satisfaction.