Reviewerly/exHarmony huggingface dataset:
A Study on the Intellectual Property Rights and Design Protection Laws

As the economy and industry develop and the importance and value of intellectual property increase due to the advancement of science and technology, intellectual property rights are becoming a major competitiveness of the country. Accordingly, advanced countries are trying to prepare transparent legal systems and policies to protect and secure intellectual property rights. These fair legal systems and policies are to eliminate corrupt elements and factors in the field of intellectual property rights. Intellectual property is the mental product of human intellectual creative activities and has property value, and intellectual property rights that are legally recognized and protected are called intellectual property rights. Intellectual property rights can be largely divided into industrial property rights and copyrights, and new technology fields that have recently emerged and are not classified by existing classification methods are classified as new intellectual property rights. The Design Protection Act protects only the design involved in the movement of goods because it aims to contribute to industrial development as one of the types of intellectual property rights. Designs that can be protected by the Design Protection Act are products that can be traded independently and produced repeatedly. As such, it is limited to the design protection of industrial concepts based on commodities, so it is necessary to expand the concept of design in a comprehensive sense including socio-cultural concepts. Recently, non-examinationism, which is made in an effort to protect unregistered designs and shorten the design review period, has been expanding worldwide. This study examined the concepts and types of intellectual property rights and design protection laws, and examples of domestic and foreign design protection systems. The case countries were Korea, the United States, the EU, Japan, and China, which account for about 90% of the world's design applications and make up the world's top five patent office partners, IP5. and design protection laws and compares design protection laws with the principal national definitions and requirements for the design to be protected, as well as its implications.ions.
A multi-objective optimization method for efficiency and fairness in P2P electricity trading model

Recently, there has been a gradual shift from thermal energy to renewable energy, prompted by Feed-in Tariff (FIT) policy. However, the sustainability of FIT has come into question due to a lack of adequate funding. Then, the Peer-to-peer (P2P) energy system has attracted attention as an economically viable solution. This study proposes a method to enhance the efficiency and fairness of the entire electricity market by optimizing transaction partners and volumes among general households. Efficiency refers to the reduction of wasteful resources in the market, while fairness aims to prevent monopolistic practices. To achieve this, we conduct simulation experiments employing single-objective and multi-objective optimization methods for transaction matching. The results are then compared and verified to identify the most effective approach.
A Low-Complexity Patch Segmentation in the V-PCC Encoder

The video-based point cloud compression(V-PCC) is the compression standard of the dynamic dense point cloud established by ISO/IEC MPEG-I in 2021. It is designed to achieve significant data size reduction for the dynamic dense point cloud. However, the patch generation process in the V-PCC encoding is computationally very complex. To solve this problem, we propose a fast algorithm for patch partitioning using a look-up table instead of the k-d tree search. Experimental results show the reduction of the computational complexity of the patch segmentation process by over 40% while achieving the same encoding results as the original method.
A VR-Based Squat Self-Study System with Superimposing Motion on Model and Post-Checking Viewing from All Angles

This article proposes a VR-based self-study system, which helps beginners and novice users of strength training understand and reproduce correct squat motions. During his/her training, the system visually provides real-time superimposed his/her motion on a model. Also for the post-check of the training, the system visually the superimposed motion from all angles with analyzed information. Through both the features of the system installed on lightweight VR smart glasses connected with a PC, the system is expected to motivate users improve his/her training form continuously.
A Development of Coin Return Teaching Material to be Controlled by Embedded Systems Designed in a Student Experiment

Practical experiments are important in embedded system education at universities. In recent years, students have lacked hands-on experience in manufacturing, so when they develop a device control system using microcomputer boards and logic circuits, they say that they can control the device by simply checking the operation with switches and LEDs. It tends to be hard to feel. In this research, we developed a coin return teaching material as a control target teaching material that operates like a real machine by connecting embedded systems and logic circuits developed by students.
Monitoring in Function-as-a-Service Platforms

Functions deployed in FaaS platforms need to be monitored regarding resource consumption, errors, and application-specific metrics. Platforms like Google Cloud Functions or Azure Functions have dashboards and Web APIs that expose information about the execution of functions. However, the current approach collects data of general metrics about the function as an all and imposes a vendor-specific way for monitoring events. To the best of our knowledge, fine-grained function metrics are not available in FaaS platforms, e.g., the time that takes to execute only part of the function’s code.This work aims to explore how to build a system to provide fine-grained monitoring to FaaS platforms for developers. In this paper we present an architecture for this system, introduce the metrics library we are developing for FaaS platforms, and discuss some challenges.
Technologies of learning and knowledge for the interconnected education of students of the University of Guayaquil

The study arises from the challenges imposed by society to an interconnected education, for which a more efficient and innovative learning is required; its objective was to determine the form of use and learning generated by learning and knowledge technologies used for the interconnected education of students at the University of Guayaquil. As a methodology, a quantitative development was proposed, with field modality, descriptive type, a questionnaire of twenty items and Likert scale was used. The sample was chosen with stratified sampling, it was made up of 461 students. As results, it was found that the apprentices use diverse technological resources; however, it is necessary to promote the use of social networks as a resource to enhance learning; enthusiasm was almost always fostered; however, these learners must propose actions that lead to the construction of knowledge. In the adaptive learning activities, a lack was evidenced in the application of the diversity of evaluations and in the flexibility of the content. The study of the dimension: generated learning allowed to observe that the apprentices do not have the possibility of a rhizomatic learning, only sometimes they have the possibility of a nomadic learning; and, they almost never have the possibility of carrying out their educational process through open learning ecosystems. In conclusion, the form of use and learning generated by learning and knowledge technologies in the students of the University of Guayaquil are not adequate to the demands of an interconnected education.
A Method for Filling Signal in Live TV Key Based on Image Comparison and Keying Technology

In order to ensure the safety of key filling signal production and broadcast, studios or broadcast control platforms often use the system design method of main and backup key filling signal generators. Some television stations also use both main and auxiliary personnel to review the content. However, these two methods consider that when the main equipment malfunctions, the backup equipment can continue to design and broadcast subtitles or graphic packaging, Alternatively, relying on manual pre audit methods cannot solve the problem of reducing the error rate of key filling signal content and how to save it. This article proposes a method for broadcasting live TV keyfill signals based on image comparison and keying technology, which only processes the keyfill signals that have already made mistakes and does not affect the original video signal source or other keyfill signals. Practice has proven that the system established using this method can achieve automation of key filling signal broadcasting, greatly reducing the probability of error in key filling signal content, and can shorten the broadcasting time of incorrect key filling signal content by more than 83%.
Depth Spatiotemporal Information Fusion Moving Target Tracking Algorithm Based on CUDA Library

Mobile object tracking is a computer vision technology that detects, recognizes, and tracks moving objects in video sequences. It is widely used in various fields, such as intelligent security, traffic safety, unmanned aerial vehicle reconnaissance, sports events broadcasting, and wildlife protection. Although mobile object tracking technology has become increasingly mature in practical applications, many performance defects still exist under complex conditions. For example, targets can be occluded by other objects, or may temporarily disappear during motion, increasing the difficulty of association. Occlusion is, therefore, one of the primary challenges faced by current mobile object tracking technology. To address this problem, this paper proposes a CUDA-based motion object tracking algorithm called Deep Space Information Fusion (DSIF), which combines the cluster difference threshold method with template matching to improve the accuracy of mobile object detection. After detecting motion events using the difference threshold method, the method divides events into clusters and uses distance mean and matching templates to redefine motion events to filter noise and interference elements in order to improve the detection accuracy. The algorithm tracks objects based on feature information and temporal-spatial information changes between frames. By using the CUDA library, this algorithm significantly improves computational speed, making it possible to apply it to embedded devices. Experimental results show that the algorithm can lock onto and track multiple different objects in real time.
Radar Based Gesture Recognition in Embedded Systems

Gesture recognition has played a significant role in human-computer interaction. Millimeter-wave radar sensor is a class of necessary sensor for human gesture recognition in an indoor condition due to its advantages of private protection, low hardware cost. However, for gesture recognition application, it is difficult for us to meet the requirements of high accuracy and real-time simultaneously in embedded system. Unlike other conventional method which increase the cost of hardware of embedded system to improve detection accuracy, in this paper, we proposed a radar-based gesture recognition in low power consumption and low-cost embedded system by using 24 GHz millimeter-wave radar with 1 transmitting antenna and 2 receiving antenna channels. Besides, 2D trajectory information on XY coordination and lightweight deep neural network have been used to distinct ten air-writing numbers. As a result, this system could reconstruct the air-writing symbols and recognize numeric character 0-9 with 95.2% accuracy in real time.
Tilt-Rotor Vertical Takeoff and Landing UAV Transitional Flight Optimization

The aim of this paper is to optimize the transition path for a tilt-rotor VTOL unmanned aerial vehicle (UAV). This work proposes a transition strategy based on the trajectory optimization method. This is a reference maneuver enabling the vehicle to perform transition safely and comfortably by minimizing the cost of pitch rate and altitude change. The 3-DOF longitudinal aerodynamic model is developed as a dynamic constraint. The transition path optimization is then represented by nonlinear programming and solved by the direct numerical optimization method to obtain the reference path of the flight speed and title angle of the front rotor. Simulation results are presented to verify the effectiveness of the proposed method.
Location Privacy Protection of IoV based on Blockchain and K-anonymity Technology

The exposure of the real location of vehicles in the positioning service may lead to the disclosure of the important information of users. There are few comprehensive studies on the privacy security and delay of the vehicle positioning service, and less consideration is given to the combination of satellite network, UAV network and ground network to ensure vehicle positioning service More reliable, longer lasting and more flexible. We consider the application of anonymous privacy protection technology to reduce the leakage risk of real vehicle location and the deployment delay, and proposes the location privacy protection architecture of the Internet of Vehicles under the Space-Air-Ground Integrated Network (SAGIN), roadside service units (RSU) act as ground receiving devices. We propose a fast clustering K-anonymity privacy-preserving algorithm to reduce the time complexity of the K-anonymity algorithm while the probability of the real vehicle location being found does not exceed 1/K, and proposing evaluation-rating combined trust model to solve the problems of non-trust of the requesting vehicle and the cooperative vehicle during the construction of the anonymous area of the K-anonymity algorithm. Applying blockchain technology to store vehicle location privacy data, distributed ledger and fast Byzantine fault tolerance consensus mechanisms ensure that stored data cannot be tampered with while speeding up deployment. Simulation experiments indicate that our scheme is better than some existing studies in terms of time delay and safety, and can identify malicious vehicles with fast speed and high accuracy.
NExtGCN: Modeling Node Importance of Graph Convolution Network by Neighbor Excitation for Recommendation

To alleviate information overload, the recommender system is pushing personalized contents to users and improving the efficiency of information distribution. Graph Convolution Networks (GCNs), which can better gather structured information and becomes a new state-of-the-art for collaborative filtering. Many current works on GCNs tend to be easier to train and have better generalization ability like LightGCN. However, they care less about the importance of nodes. In this work, we propose a new model named NExtGCN (Neighbor Exci tation Graph Convolutional Network), which models the node importance of GCN by neighbor excitation. The NExtGCN can learn the importance of nodes via the global and local excitation layer which is inspired by the Squeeze-Excitation network. Furthermore, we propose a neighbor excitation layer that can fully utilize graph structure and make this model practical to large-scale datasets. Extensive experimental results on four real-world datasets have shown the effectiveness and robustness of the proposed model. Especially on the Amazon-Books dataset, our NExtGCN has improved by 10.95%, 49.36%, and 26.8% in Recall@20, MRR@20, and NDCG@20 compared to LightGCN. We also provide source code ( https://github.com/clemaph/NExtGCN.git ) to reproduce the experimental results (This job is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province, the item number is KYCX22_3071).
Knowledge Injection for Aspect-Based Sentiment Classification

Since the increase of Web reviews of products and services, Aspect-Based Sentiment Classification (ABSC) has become more important to determine the sentiment of online opinions. Useful information extracted from these reviews can then be used by companies themselves, but can also be applicable by consumers. In the recent literature on ABSC, hybrid methods, which combine knowledge-based and machine learning approaches, are becoming more popular as well. However, in this work, instead of following a two-step procedure, we attempt to improve the model accuracy by proposing to directly inject the information from a domain ontology in a state-of-the-art neural network model, more precisely LCR-Rot-hop++. Furthermore, by using soft-positioning and visible matrices we aim to prevent that the injected knowledge hinders the semantics of the original sentences. To evaluate the accuracy of our model, LCR-Rot-hop-ont++, we use the standard SemEval 2015 and SemEval 2016 datasets for ABSC. We conclude that knowledge injection in the neural network is effective for sentiment classification, especially if the amount of labeled data is limited.
Next POIs Prediction for Group Recommendations: Influence-Based Deep Learning Model

A significant amount of data are significantly available due to the dramatic proliferation of location-based social networks. This has led to the development of location-based recommendation tools that assist users in discovering attractive Points-of-Interest (POIs). Next POIs recommendation is of great importance for not only individuals but also group of users since group activities have become an integral part of our daily life. However, most existing methods make recommendations through aggregating individual predictive results rather than considering the collective features that govern user preferences made within a group. This insufficiency can directly affect the completeness and semantic accuracy of group features. For this reason, we propose a novel approach which accommodates both individual preferences and group decisions in a joint model. More specifically, based on influencing users in a group, we devise a hybrid deep architecture model built with graph convolution networks and attention mechanism to extract connections between group and personal preferences and then capture the impact of each user on the group decision-making, respectively. We conduct extensive experiments to evaluate the performance of our model on two well-known real large-scale datasets, namely, Gowalla and Foursquare.
An Efficient Embedding Framework for Uncertain Attribute Graph

Graph data with uncertain connections between entities is commonly represented using uncertain graphs. This paper tackles the challenge of graph embedding within such uncertain attribute graphs. Current graph embedding techniques are typically oriented towards deterministic graphs, or uncertain graphs that lack attribute data. Furthermore, the majority of studies on uncertain graph learning simply adapt conventional algorithms for deterministic graphs to handle uncertainty, leading to compromised computational efficiency. To address these issues, we introduce an optimized embedding framework UAGE for uncertain attribute graphs. In UAGE, nodes are represented within a Gaussian distribution space to learn node attributes. We also propose a Probability Similarity Value (PSV) to manage relationship uncertainty and ensure that nodes with higher-order similar structures are located more closely in the latent space. Real-world dataset experiments confirm that UAGE surpasses contemporary methods in performance for downstream tasks.
A Proposed Decision Matrix on Student Engagement for Engineering Online Learning Using Factor Analysis and Analytical Hierarchy Process

There were many attempts to establish online learning in the education system that solely conducted the traditional face-to-face way of teaching and learning over the course of time.The expeditious development of the digital age made online learning emerge along with difficulties in dealing with engagement and misbehavior that is unique to the online environment.The study aimed to propose a decision matrix with respect to laboratory, lecture, assessment, and examination that will support and enhance the student engagement of students in an online learning set-up.Additionally, the study also identified the common misbehaviors that students portrayed in an online set-up.The target respondents of the study are Engineering students and instructors across Metro Manila.Data collection was done via survey questionnaire for the students which were analyzed through Exploratory Factor Analysis (EFA).Moreover, an expert interview was conducted for the instructors to complete the Analytical Hierarchy Process (AHP) which was utilized to determine the most preferred component per criterion; laboratory prefers Collaboration with Peers, lecture prefers Student Support, assessment prefers Accessibility to Course Materials, and examination prefers Student Support.Common misbehaviors of Engineering students were also found to be present and common to most of the respondents.
Towards Ensemble-Based Imbalanced Text Classification Using Metric Learning

This paper reports s series of ensemble approaches for imbalance text classification. All the approaches utilize a metric learning technique for obtaining better representations of texts to train weak classifiers. Each approach deals with the class imbalance problem with an undersampling-based ensemble approach, because metric learning techniques also suffer from this problem. In this paper, four ensemble approaches (namely, MLBagging, MLBoosting, MLStacking, and MLBoostacking) are proposed, three of which are corresponding to ensemble frameworks (namely, bagging, boosting, and stacking), and the other is a combination of boosting and stacking. MLBagging, MLBoosting, and MLStacking train metric learners on the individual undersampled dataset and combine them, while MLBoostacking trains metric learners in a step-by-step manner; that is, a metric learner learns a feature transformation so that failed-to-classify samples in the previous step should be correctly classified. The experimental evaluation on three imbalanced text classification datasets (namely, unfair statement classification in terms of service, hate speech detection in a forum, and hate speech tweet detection) shows that the proposed approaches lift classification performance from BERT-based approaches, by improving the representations of texts through metric learning.
Network visualization techniques for story charting : The case of a book in Portuguese

Visualization techniques have been widely used to analyze various data types, including text. This paper proposes an approach to analyze a controversial text in Portuguese by applying graph visualization techniques. Specifically, we use a story charting technique that transforms the text into a graph. Each node represents a character or main entities, and each edge represents the interactions between characters. We also present several visualization techniques to gain insights into the story’s structure, relationships between the characters, the most important events, and how some key terms are used throughout the book. By using this approach, we can effectively reveal complex patterns and relationships that may not be easily discernible from reading the text. Finally, we discuss the potential applications of our technique in Literary Studies and other fields.