Reviewerly/exHarmony huggingface dataset:
Search-Based Path Planning Algorithm for Autonomous Parking:Multi-Heuristic Hybrid A*

This paper proposed a novel method for autonomous parking. Autonomous parking has received a lot of attention because of its convenience, but due to the complex environment and the non-holonomic constraints of vehicle, it is difficult to get a collision-free and feasible path in a short time. To solve this problem, this paper introduced a novel algorithm called Multi-Heuristic Hybrid A* (MHHA*) which incorporates the characteristic of Multi-Heuristic A* and Hybrid A*. So it could provide the guarantee for completeness, the avoidance of local minimum and sub-optimality, and generate a feasible path in a short time. And this paper also proposed a new collision check method based on coordinate transformation which could improve the computational efficiency. The performance of the proposed method was compared with Hybrid A* in simulation experiments and its superiority has been proved.
Segmentation-guided Domain Adaptation for Efficient Depth Completion

Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks. A major problem for LiDAR-based depth completion is the inefficient utilization of convolutions due to the lack of coherent information as provided by the sparse nature of uncorrelated LiDAR point clouds, which often leads to complex and resource-demanding networks. The problem is reinforced by the expensive aquisition of depth data for supervised training. In this work, we propose an efficient depth completion model based on a vgg05-like CNN architecture and propose a semi-supervised domain adaptation approach to transfer knowledge from synthetic to real world data to improve data-efficiency and reduce the need for a large database. In order to boost spatial coherence, we guide the learning process using segmentations as additional source of information. The efficiency and accuracy of our approach is evaluated on the KITTI dataset. Our approach improves on previous efficient and low parameter state of the art approaches while having a noticeably lower computational footprint.
CHANGEOVER PROCESSING FOR ROW-WOOD USING POSITION AND POSTURE ESTIMATION SYSTEM

In this paper, image features are used to realize the repositioning of logs in between processing. The position-posture estimation by image features has some problems such as missing materials and textures. To solve the problem of position-posture estimation, we employed Affine-SIFT and segmentation matching. By adopting these two methods, it is possible to estimate the position and orientation regardless of some missing materials and textures. In order to verify the effectiveness of our method, a wall and a corner of a traditional wooden building were created. By creating them, the effectiveness of our method is confirmed.
Robust denoising-oriented point cloud plane fitting method based on a combined model of improved Cook’s distance and WTLS

Purpose In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold. Design/methodology/approach This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation. Findings (a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced. Originality/value The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.
Intellectual Virtues in Education: Digital Challenges

The study discusses the issue arising at the intersection of the virtue epistemology, the philosophy of education, and the contemporary philosophy of technology. Education turns out to be a field of application of the virtue epistemology, and attention to digital technologies gives it an expanded character. From an epistemological point of view, it is obvious that intellectual virtues have advantages in the field of education, concretizing its goals, justifying the acquisition of knowledge, endowing educational practices with value meaning. However, from a pedagogical point of view, there are doubts on the possibility of including intellectual virtues in specific educational strategies. The hypothesis is that it is precisely because of the interaction of digital technologies and intellectual virtues that the latter can become a distinct concern in education. In the first section, we analyze the intellectual virtues in relation to education and explicate the issue. In the second, we clarify the approach of the virtue epistemology to the education by including digital technologies in these practices. We demonstrate that the action of digital technologies provokes the “breaking” of intellectual virtues, which makes it necessary and concrete to move towards them. We relate the understanding of such provocative action with not only instrumental, but also social character of technologies. As a result, we expand the virtue epistemology, which does not consider this aspect, by referring to the critical theory of technology and offer the basis for improving specific educational strategies.
The construction and data analysis on Beijing Putonghua Shuiping Ceshi database

This paper presents an approach to construct Beijing PSC (Putonghua Shuiping Ceshi) Database by updating and refining the data fields of the original database of Beijing PSC 2020. Study shows that the newly constructed database has met the research needs. By analyzing the data, we find that the overall level of Putonghua in Beijing is high, and it has an overwhelming advantage at the higher levels; Moreover, as the main part of the PSC, college students are the main source of “Level F” candidates, most of whom are from the Northwest and North China. Based on our findings, we suggest that, first, more Putonghua training and testing centers in colleges and universities be established; second, the funding for Putonghua scientific research be increased; third, language intelligence technology in PSC be enhanced.
Remote sensing object detection based on lightweight YOLO-v4

The remote sensing object detection algorithms based on deep learning have high detection performances, but the network structures are too complexity to meet the real-time processing requirements in on-board remote sensing object detection. In order to solve this problem, we proposed a lightweight YOLO-v4 network, which is 76% smaller than the original YOLO-v4. As for the decrease of lightweight network’s accuracy, we adopted the general instance distillation algorithm, which used the original YOLO-v4 network as the teacher network and whose detection accuracy achieved 2.1% mAP gain.
Research on fast fusion method of power grid cloud data based on knowledge graph

The development of the physical information fusion technology, the explosive growth of data, it marks the power system has entered the era of big data, thus increasing the information platform of power system, dealing with the difficulty of data storage, management and analysis. And existing physical hardware and technical ability is difficult to adapt to the demand of the smart grid, large data analysis and control, so based on the knowledge map design grid cloud data fusion method is fast. The first grid cloud data collection and processing, the second grid cloud data integration framework is designed, based on the knowledge map design grid cloud data fusion algorithm quickly, so as to realize the fast integration grid cloud data. The experimental results show that the proposed fast data fusion method has good fusion effect and has certain application value.
Graph Anomaly Detection with Unsupervised GNNs

Graph-based anomaly detection finds numerous applications in the real-world. Thus, there exists extensive literature on the topic that has recently shifted toward deep detection models due to advances in deep learning and graph neural networks (GNNs). A vast majority of prior work focuses on detecting node/edge/subgraph anomalies within a single graph, with much less work on graph-level anomaly detection in a graph database. This work aims to fill two gaps in the literature: We (1) design GLAM, an end-to-end graph-level anomaly detection model based on GNNs, and (2) focus on unsupervised model selection, which is notoriously hard due to lack of any labels, yet especially critical for deep NN based models with a long list of hyper-parameters. Further, we propose a new pooling strategy for graph-level embedding, called MMD-pooling, that is geared toward detecting distribution anomalies which has not been considered before. Through extensive experiments on 15 real-world datasets, we show that (i) GLAM outperforms node-level and two-stage (i.e. not end-to-end) baselines, and (ii) model selection picks a significantly more effective model than expectation (i.e. average) -- without using any labels -- among candidates with otherwise large variation in performance.
Cloud Task Scheduling using Static Time Quantum Approach

Cloud Computing is the delivery of various services via internet namely hardware, software, servers, databases, network and data storage.Cloud Computing is the technology that allows users to access resources through Internet.Storing data on data cloud is growing more popular among individuals and businesses who need bulk storage space.In our daily life Cloud Computing plays a vital role.Cloud Computing is differentiated from conventional computing by its Scalability, Accessibility, Reliability, and On-demand pay-as-you-go services.At present Cloud Computing technology is used by Google, Amazon, Yahoo.In Cloud Computing Process Scheduling is an important aspect.Minimizing Waiting Time (WT), Minimizing Turnaround Time (TAT), Minimizing Response Time (RT) and Improves Resource Utilization are the important parameter for Task Scheduling.As an outcome various strategies have been suggested and implemented in order to improve scheduling performance.Because of its simplicity and fairness Round Robin (RR) is treated as the best scheduling algorithm.Our proposed algorithm named Enhanced Round Robin Algorithm for Cloud Scheduling (ERACS).The proposed algorithm reduces Average Waiting Time (AWT), Average Turnaround Time (ATAT), Average Response Time (ART) and Number of Context Switches (NCS).Comparative Analysis is being done with existing algorithms and get better result of our prosed algorithm in terms of AWT (28.2),ATAT (44.2),ART (24.2) and NCS (7).
A vehicle re-identification method fused with multi-scale and multi-granularity features

Vehicle re-identification is to recognize the same vehicle through vehicle images from different perspectives shot by multiple cameras. At present, the most of vehicle re-identification methods uses convolutional neural network to extract image features but lack the subtle feature discrimination ability. Therefore, we propose a novel vehicle re-identification methods called MSMG Net (Multi-Scale and Multi-Granularity) to solve this problem. We use ResNet-50 as the backbone network, and we design the multi-scale method to extract features of multi-granularity, which is from convolutional layer 3 to convolutional layer 5. For convolutional layer 3, we make the feature map to be divide global feature and local feature of three parts which dimension is 256. For convolutional layer 4, we make the feature map as layer 3 but has only one difference is two parts of local feature. For convolutional layer 5, we extract the global feature from global pooling which dimension is 256. Finally, we fuse these above eight features and get the 2048-dimension to compute the triplet loss and cross-entropy loss. Experiments on the Veri-776 dataset show that the proposed method achieves better performance, and good retrieval results have been achieved on the mAP(74.67%) and Rank-1(94.87%) evaluation index.
Global RTK Positioning in Graphical State Space

This paper proposes a new method for RTK post-processing. Different from the traditional forward-backward Kalman filter, in our method, the whole system equation is built on a graphical state space model and solved by factor graph optimization. The position solution provided by the forward Kalman filter is used as the linearization points of the graphical state space model. Constant variables, such as double-difference ambiguity, will exist as constants in the graphical state space model, not as time-series variables. It is shown by experiment results that factor graph optimization with a graphical state space model is more effective than Kalman filter with a traditional discrete-time state space model for RTK post-processing problem.
Localization of Human Organs with VR Tracking System and Machine Learning Techniques for Medical Purpose

Virtual reality and machine learning technologies have become focal points for research and development for medical studies in recent years. However, previous studies do not typically use virtual reality and machine learning in tandem. In this study, we propose a framework utilizing both virtual reality and machine learning to predict the localization of human organs in real-time. The HTC Vive Pro virtual reality system, while used originally for entertainment, is a viable, low-cost option for studies requiring precise measurements. Data collected by the virtual reality system is used as inputs for machine learning models for predictions of human organ localization in real-time. Further, data enhancement methods, such as data normalization and extreme event split, are leveraged to improve machine learning model performance. According to our experimental results, the gradient boosting regressor model performs accurately for almost every direction for either of the two tracker configurations, i.e., linear and triangular. The extreme event split can also improve machine learning performance, especially with rotational data. Overall, this framework is promising to be used as the localization basis for other surgical procedures, as well as other human organs.
Infinitely growing configurations in Emil Post's tag system problem

Emil Post's tag system problem is the question of whether or not a tag system $\{N=3, P(0)=00, P(1)=1101\}$ has a configuration, simulation of which will never halt or end up in a loop. For the past decades, there were several attempts to find an answer to this question, including a recent study by Wolfram (2021), during which the first $2^{84}$ initial configurations were checked. This paper presents a family of configurations of this type in a form of strings $a^{n} b c^{m}$, that evolve to $a^{n+1} b c^{m+1}$ after a finite amount of steps. The proof of this behavior for all non-negative $n$ and $m$ is described further in a paper as a finite verification procedure, which is computationally bounded by 20000 iterations of tag. All corresponding code can be found at https://github.com/nikitakurilenko/post-tag-infinitely-growing-configurations.
Analisis Kebutuhan Perangkat Lunak Sistem Informasi Back Office

Perumdam Among Tirto Kota Batu is a public drinking water company located in Batu City, East Java which runs its bu­si­ness in the field of clean water distribution for the majority of the citizen of Batu City. Perumdam Among Tirto Kota Batu still runs it business manually and has not fully utilized the information system. The development of the information system is need­ed by the company to be able to increase its productivity. Therefore, it is necessary to analyze the software requirements for the development of information systems that will be used by Perumdam Among Tirto Kota Batu to running its business, espe­cial­ly by the back office. In this research, the results of software requirements analysis for Perumdam Among Tirto Kota Batu back office information system are compiled to Software Requirements Specification (SRS) document using SRS outline that stand­arized by ISO/IEC/IEEE 29148:2018. The research carried out resulted in seven SRS documents made for seven divisions in Customer Relation and Marketing Departement and also Engineering Departement, that is Customer Service and Water Meter Di­vision, Equipment Service and SR Standardization Division, Information Technology Development and Marketing Division, Engi­neering Planning Division, Non Revenue Water and Supervision Division, Production, Transmition, and Laboratory Di­vi­sion, and Distribution Division. Each SRS document consist of five part that is Introduction, References, Requirements, Ve­ri­fi­ca­tion, and Appendices as standarize by ISO/IEC/IEEE 29148:2018.
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data

Machine learning practitioners often have access to a spectrum of data: labeled data for the target task (which is often limited), unlabeled data, and auxiliary data, the many available labeled datasets for other tasks. We describe TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers. The key components of TAGLETS are: (1) auxiliary data organized according to a knowledge graph, (2) modules encapsulating different methods for exploiting auxiliary and unlabeled data, and (3) a distillation stage in which the ensembled modules are combined into a servable model. We compare TAGLETS with state-of-the-art transfer learning and semi-supervised learning methods on four image classification tasks. Our study covers a range of settings, varying the amount of labeled data and the semantic relatedness of the auxiliary data to the target task. We find that the intelligent incorporation of auxiliary and unlabeled data into multiple learning techniques enables TAGLETS to match-and most often significantly surpass-these alternatives. TAGLETS is available as an open-source system at github.com/BatsResearch/taglets.
Mobile-Based Green Office Management System Dashboard (GOMASH) for Sustainable Organization

Green is now known for being environmentally friendly and energy-efficient, while sustainability aids in conserving and preserving natural resources and also the environment. Although the higher education institutes (HEI) in Malaysia play a critical part in providing more sustainable place, by introducing green campus initiatives to provide a greener environment, however, the university staffs’ high involvement in maintaining sustainability and green practices in the office side is still questionable. Therefore, this project is developed mainly to propose a green office management system dashboard (GOMASH) as an initiative to practice green inside faculty as well as to analyze the paper usage and reduce the total amount of paper wastage inside the office. Furthermore, GOMASH also provides paper limit suggestion to reduce the paper wastage in the office with a goal of sustainability. Agile methodology is adopted as the software development life cycle (SDLC) of GOMASH where the process iteratively caters the requirements from the users which makes way for improvement of the system by using a deductive approach with a general idea that GOMASH helps develop green awareness among users to reduce wastage of paper. This paper provides a conceptual and empirical study on the amount of paper wastage in faculty that is observed in the beginning of the development and the data collected from the application is then analyzed and visualized in a dashboard. All modules have been tested using software test description (STD) to make sure GOMASH is validated and verified and found appropriate to be implemented in order to improve green office management practice for sustainable organization.
Features of training specialists of financial and economic direction in virtual educational space

The article identifies the main possibilities of the virtual educational space in the training of specialists in the financial and economic direction.Analyzed the information technology tools that are used in the process of this training.We have defined the peculiarities of training of future financial and economic specialists.They consist of two types: a persona sign system and a persona person.It lays down the core professional competencies of future economist: encyclopedic knowledge, a high level of information culture, understanding of the essence of economics and finance, system thinking, simultaneous operation of multiple systems, etc.; personal and professional qualities such as overall level of development, ability to handle large amounts of information, commitment and others.We have figure out that virtual educational space is able to provide all the necessary personal and professional competencies of future specialists in financial and economic area.It has been found that information technology facilities are used both in theory and practice in the process of training.Key information technology consists of computer learning systems, multimedia training courses, business computer games, video-conferences.The main opportunities of virtual education that are aimed at students' research activities are as follows: increasing of creative activities, analyzing of large amount of information, variability of learning content.
Detecting Unintended Social Bias in Toxic Language Datasets

With the rise of online hate speech, automatic detection of Hate Speech, Offensive texts as a natural language processing task is getting popular. However, very little research has been done to detect unintended social bias from these toxic language datasets. This paper introduces a new dataset ToxicBias curated from the existing dataset of Kaggle competition named "Jigsaw Unintended Bias in Toxicity Classification". We aim to detect social biases, their categories, and targeted groups. The dataset contains instances annotated for five different bias categories, viz., gender, race/ethnicity, religion, political, and LGBTQ. We train transformer-based models using our curated datasets and report baseline performance for bias identification, target generation, and bias implications. Model biases and their mitigation are also discussed in detail. Our study motivates a systematic extraction of social bias data from toxic language datasets. All the codes and dataset used for experiments in this work are publicly available
Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.