Reviewerly/exHarmony huggingface dataset:
Research on athletics Image Classification and Application Based on Feature Extraction Algorithm

research-article Research on athletics Image Classification and Application Based on Feature Extraction Algorithm Author: Jun Zhao Wuhan Railway Vocational College of Technology, China Wuhan Railway Vocational College of Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 811–816https://doi.org/10.1145/3544109.3544359Published:18 July 2022Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Image acquisition and analysis method based on image segmentation in sports technology analysis

research-article Image acquisition and analysis method based on image segmentation in sports technology analysis Author: Jun Zhao Wuhan Railway Vocational College of Technology, China Wuhan Railway Vocational College of Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 822–825https://doi.org/10.1145/3544109.3544361Published:18 July 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Soft-Decision Weighted CNN for Specific Emitter Identification in Aeronautical Monitoring System

Specific Emitter Identification (SEI) is a hot topic in the field of aeronautical communication such as aviation safety. In this paper, we proposed a soft-decision weighted CNN model to make SEI in the field of special aeronautical monitoring system, i.g. Automatic Dependent Surveillance-Broadcast (ADS-B). Specifically, with the combination of statistical analysis, machine learning and neural network, the proposed model consists of statistical judgment, message clustering and network classification via CNN. The problem is that there are one hundred aircraft signals to be identify with limited data. Thus, we first calculate the soft decision probability and establish the radio frequency fingerprint (RFF) library via statistical analysis. Then, the K-Means is used to cluster the difference types of message and utilize the space characteristics of the aircraft such as speed, position and so on. At last, CNN is introduced to the model with the input of each RFF to make the classify for each cluster. We make the final identification by combining the soft decision probability and CNN classification result. The numerical result demonstrates that the proposed model can achieve performance with ADS-B data and the precision of identification is more than 90%.
Discrimination of News Political Bias Based on Heterogeneous Graph Neural Network

The polarization of western political ideology is becoming more and more serious, and it is difficult for news articles to keep objective justice. Their contents are often biased towards a particular political party to guide the trend of public opinion. Therefore, judging the political inclination of news texts is of great significance to national election prediction and public opinion control. The existing modeling methods based on news content mostly rely on the semantic information of news. The combination of various element features and structural information in the news is insufficient. This paper proposes a political bias discrimination method of news based on a heterogeneous neural network, with multiple information related to prejudice in the news as the nodes of a heterogeneous network. By enriching the representation of nodes through a heterogeneous graph neural network and using the fused node features to distinguish the political news bias. The experimental results show that our model can achieve 84.30% accuracy and 83.34% Macro F1 value in news political bias classification. Compared with the baseline model Bert +CNN with the best experimental results, the accuracy and Macro F1 are improved by 1.92% and 0.4%, respectively.
Using Referential Language Games for Task-oriented Ontology Alignment

Ontology Alignment (OA) is generally performed by requesting two parties to provide their complete knowledge to a third party that suggests potential schema alignments. This might however not always be possible or helpful, as for example, when two organisations want to query each other’s knowledge, and none of them is willing to share their schema due to information privacy considerations. This Ph.D. explores how to allow multi-agent communication in cases where agents operate using different ontologies that cannot be fully exposed or shared. Our preliminary experiments focus on the case where agents’ knowledge is describing a common set of entities and has the form of Knowledge Graphs (KGs). The suggested methodology is based on the grounded naming game, where agents are forced to develop their own language in order to refer to corresponding schema concepts of different ontologies. This way, agents that use different ontologies can still communicate successfully for a task at hand, without revealing any private information. We have performed some proof of concept experiments applying our suggested method on artificial cases and we are on the process of extending our methodology so that it can be applied in real-world KGs.
Continuous Defect Prediction in CI/CD Pipelines: A Machine Learning-Based Framework

Recent advances in information technology has led to an increasing number of applications to be developed and maintained daily by product teams. Ensuring that a software application works as expected and that it is absent of bugs requires a lot of time and resources. Thanks to the recent adoption of DevOps methodologies, it is often the case where code commits and application builds are centralized and standardized. Thanks to this new approach, it is now possible to retrieve log and build data to ease the development and management operations of product teams. However, even if such approaches include code control to detect unit or integration errors, they do not check for the presence of logical bugs that can raise after code builds. For such reasons in this work we propose a framework for continuous defect prediction based on machine learning algorithms trained on a publicly available dataset. The framework is composed of a machine learning model for detecting the presence of logical bugs in code on the basis of the available data generated by DevOps tools and a dashboard to monitor the software projects status. We also describe the serverless architecture we designed for hosting the aforementioned framework.
Differential Invariants for SE(2)-Equivariant Networks

Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e.g. rotated due to different camera orientations, or scaled due to perspective. The knowledge of such symmetries in data coupled with equivariance of neural networks can improve their generalization to new samples. Differential invariants are equivariant operators computed from the partial derivatives of a function. In this paper we use differential invariants to define equivariant operators that form the layers of an equivariant neural network. Specifically, we derive invariants of the Special Euclidean Group SE(2), composed of rotations and translations, and apply them to construct a SE(2)-equivariant network, called SE(2) Differential Invariants Network (SE2DINNet). The network is subsequently tested in classification tasks which require a degree of equivariance or invariance to rotations. The results compare positively with the state-of-the-art, even though the proposed SE2DINNet has far less parameters than the compared models.
Hand Gesture Recognition based Virtual Mouse using CNN

Advancements in the field of computer have been a crucial point in the history of mankind.It has proved to be beneficial in many countless fields.Input and output devices which are of utmost importance have also undergone numerous changes.Human Computer Interface (HCI) is an important part of computer systems.Mouse which is one of the input devices is constantly evolving giving rise to various alternatives like touchscreens.But still traditional mouse is prevalent.In this paper, we have developed a virtual mouse which can potentially replace traditional mouse.It detects hand in the live feed, recognizes hand gestures and Convolutional Neural Network (CNN) classifies them and accordingly appropriate mouse operation is done.Hand gestures which make up a crucial part of HCI are employed in this system.
Automatic universal taxonomies for multi-domain semantic segmentation

Training semantic segmentation models on multiple datasets has sparked a lot of recent interest in the computer vision community. This interest has been motivated by expensive annotations and a desire to achieve proficiency across multiple visual domains. However, established datasets have mutually incompatible labels which disrupt principled inference in the wild. We address this issue by automatic construction of universal taxonomies through iterative dataset integration. Our method detects subset-superset relationships between dataset-specific labels, and supports learning of sub-class logits by treating super-classes as partial labels. We present experiments on collections of standard datasets and demonstrate competitive generalization performance with respect to previous work.
International Chinese Students’Perception of Korean as a Medium of Instruction in a Korean University: Focusing on the Case of W University

This study explores international Chinese students’ experiences of Korean as a Medium of Instruction (KMI) course in a Korean university. This study focuses on the KMI course that allows students to simultaneously learn Korean academic language and content knowledge. Thus, this study examines the effectiveness of the KMI course for Chinese international students and proposes effective teaching methods that will enable international students to perform academic studies in the future successfully. This study is conducted at the graduate school of W University in Korea. The KMI method is applied to two graduate courses with 15 Chinese international students during the 2021 Fall semester. By using mixed-method, the survey is conducted to determine the perception and satisfaction of the international Chinese students’ experiences in the KMI course related to content knowledge, cognitive ability, and multicultural understanding. Among survey participants, interview applicants are selected and conducted in-depth interviews. The results as shown below. First, Chinese international students develop various content knowledge through the KMI course. Second, Chinese international students can improve their Korean language ability through the KMI course. Third, bilingual KMI courses are more effective in learning Korean vocabulary and grammar than monolingual KMI courses. Third, Chinese international students understand Korean culture and university life through the KMI course. Fourth, Chinese international students are generally satisfied with the KMI course. However, Chinese international students face a challenge that quickly improves their Korean academic ability through the KMI. The conclusions as shown below. First, the KMI course positively affects the content knowledge of Chinese international students. Second, the KMI course has a positive effect on improving the Korean ability of Chinese international students. Third, the KMI course positively affects international students’ understanding of multiculturalism. However, the KMI course may not dramatically impact international students’ academic Korean language skills in a short period.&#x0D;
Effects of Six Neural Machine Translators of the Two Business Major Text Types

This study investigates the effects of six major neural machine translators (NMTs) rendering four texts—E-C and C-E directions for the informative and the vocative. By comparing the errors and inadequacies, I found that western-produced NMTs are better at translating E-C and C-E informative texts, though they cannot render culture-loaded words. Baidu, Youdao, and WeChat are similarly good, yet sometimes with confusing blunders. Overall, QQ is the least satisfactory one. For highly vocative E-C texts, all the machine translators are fiascoes since they can only translate the surface meaning, sometimes even confusingly. For C-E vocative texts, DeepL and Google can produce acceptable sentences but retain every piece of information; Chinese-produced NMTs are better at dealing with culture-loaded words. This paper may provide some translation tips for Chinese researchers, the latest trends in NMT for teachers and researchers of the English language, and some insights for technical personnel.
Defogging Target Detection based on YOLOv4 and Deblurganv2

Aiming at the problem of low detection accuracy of existing object detection in fog and haze weather, a defogging object detection method based on DeblurGANv2 and YOLOv4 is proposed. In this method, the image enhancement algorithm in the generation countermeasure network DeblurGanv2 is added to the preprocessing module of YOLOv4 to preprocess the foggy image and retain the high-quality texture and color information of the image, At the same time, the lightweight neural network ShuffleNet V2 is used to replace the CSPDarkNet53 network used for backbone feature extraction in YOLOv4 to improve the speed of model mark detection. The experimental results show that the proposed method can reduce the problems of large color difference and fog residue, and the map value in the rest data set reaches 86.56%, It has achieved good results in the actual defogging target test.
Image Classification using Deep Learning and Tensorflow

Abstract: The image classification is one of the most classical problem of image processing. This research paper about image classification by using deep neural network(DNN) or also known as Deep learning by using framework Tensorflow. Python is used as a programming language because it comes together with Tensorflow framework. Image Classification nowdays is used to narrow the gap between the computer vision and human vision so that the images can be identify by the machine in the same way as human can do. It handle the assigning task for image class. So we are proposing a system called Image Classification using Deep Learning that classifies given images using classifiers such as Neural Network. The system will be built using Python as a programming language and Tensorflow to create neural networks. Keywords: Image classification, Deep Learning, Tensorflow
Optimized Resource Allocation on Virtualized Non-Uniform I/O Architectures

Nowadays, virtualization is a central element in data centers as it allows sharing server resources among multiple users across virtual machines (VM). These servers often follow a Non-Uniform Memory Access (NUMA) architecture, consisting of independent nodes with their own cache hierarchies and I/O controllers. In this work, we investigate the impact of such an architecture on network access. As network devices are typically connected to one particular NUMA node, this leads to a situation where device access on one node is faster than another. This phenomenon is called Non-Uniform I/O Access (NUIOA). This non-uniformity impacts the performance of I/O applications that are not executed on the correct NUMA node. In this paper, we are interested in NUIOA effects in virtualized environments. Our contribution in this work is twofold: 1) we thoroughly study the impact of NUIOA on application performance in VMs, and 2) we propose a resource allocation strategy for VMs that reduces the impact of NUIOA. We implemented our allocation strategy on the Xen hypervisor and carried out evaluations with well-known benchmarks to validate our strategy. The obtained results show that with our NUIOA allocation scheme, we can improve the performance of application in VMs by up to 20 % compared to common allocation strategies.
Research on Construction of Network Attack and Defense Virtual Simulation Training Platform

The virtual simulation of training platform network attack and defense in a network teaching management environment integrates the construction, display, management, sharing, exchange, and service of experimental resources. With the Internet as the carrier and cloud computing, big data, the Internet of things, virtual reality technology is used to assist students' cognition and understanding of the content of the experimental project. The construction of the platform realizes high-risk network attack and defense experiments, invisible principle experiments, large-scale comprehensive experiments, and so on. By constructing specific situations and scenes, students explore through self-experiments, use simulation technology to set technical problems in the scenes, master relevant knowledge points in the experiments, and improve practical teaching.
Actual Experiments of Sailing Robot Avoiding Obstacle

Currently, autonomous driving on sailing vehicles attracting attentions. In our previous study, a set of control rules was proposed to reach the windward target point using the position information obtained at intervals of several seconds for a sailing vehicle on the water, which is more difficult to stop and steer than on land and is more likely to sway leeward. In this study, in addition to this previous study, we conduct and analyze actual experiments using two methods that apply an obstacle avoidance method proposed previously on land to sailing vehicle on the water. Method 1 aims at the middle direction between the avoidance direction and the target point direction, and Method 2 steers in the direction opposite to the obstacle. We conducted actual experiments of obstacle avoidance using a sailing robot. We confirmed that obstacle avoidance was possible with either of the two proposed methods, and we clarified the characteristics of the methods by analysis. In the experiments, the wind blew almost orthogonal to the two-point reciprocating course with the midpoint as the obstacle, so the obstacle and the target point were located upwind from the direction of travel in all three times of meet with the obstacle. In this situation, Method 1 steered upwind to soften the Leeway effect and prevent the path from bulging downwind. In Method 2, the time to avoid obstacles was shortened by turning the rudder in the leeward direction.
Safety and Security of the Car-Sharing System

In the SECREDAS project, the car-sharing system embodies one of the laboratory demonstrators (pilots). The system consists of an embedded device for car access, a backend server, and a mobile app. In addition, the system can be easily adapted to an access control system in general, such as car fleet management. Our team designed, implemented, and validated the system in a laboratory environment, therefore TRL (Technology Readiness Level) 4 was achieved. The implementation was also supported by design patterns developed in the project. Moreover, we addressed the security, safety, and privacy issues. This article reports the progress of work and the results achieved. Key aspects such as Identity management, Representative State Transfer (REST) API (Application Programming Interface), threat assessment and key transfer methods are also described in more detail. In this work, the symmetric rent token (car key) is heavily used; however, other techniques are presented.
A Novel Method of Multi-sensor Information Fusion Based on Comprehensive Conflict Measurement

The Internet of things expands the ability of human beings to perceive the surrounding environment, bringing a great challenge to the multi-sensor data processing. Evidence theory, one of the most effective processing technologies, is commonly employed in the multi-sensor information fusion. However, many counter-intuitive results of multi-sensor data fusion may be obtained when fused evidence is highly conflicting. In this study, a new comprehensive method for calculating the entropy of each evidence is proposed, with the goal of improving information volume measurement. In addition, a conflict measure method of multi-sensor evidence is introduced, which can calculate the weighted average evidence, by synthesizing vector space and evidence distribution. Finally, the pre-processed body of evidences have been merged based on the evidence theory. The proposed multi-sensor fusion approach based on comprehensive conflict measurement produces a more credible fusion outcome compared to other approaches, according to experimental results.
Data Platform Solutions

Abstract Private and public organizations hoard troves of data yet remain unable to unlock its full business potential. Data exchange platforms powered by adapted technology and driven by data exchange strategies act as catalysts to develop data ecosystems and data spaces, accelerate data circulation, and liberate its value. Data spaces are powerful business, innovation, and societal enablers, whose growth and success rely on their ability to foster and develop trust. Data exchange platforms contribute a lot to building trust as they provide the required tools and automation to data acquirers, data providers, and data exchange services providers to operate at scale within secure and compliant environments. New and upcoming European regulatory frameworks also contribute to raising trust as they foster a harmonized data ecosystem across member states and define the rules of engagement between businesses, governments, and individuals engaged in data sharing and exchange. Additionally, data exchange environments must provide traceability at all levels of the data transactions, which is particularly needed in increasingly complex data ecosystems. Finally, in order to provide the flexibility required to answer the needs of complex, distributed, and heterogeneous environments, different models of data exchange governance are necessary.