Reviewerly/exHarmony huggingface dataset:
Feature Alignment and Contrastive Learning for Dense Instance Segmentation

Instance segmentation is an important but challenging computer vision task. Existing approaches struggle to recognize independent objects in dense scenarios where objects are tightly in contact. In this paper, we propose a segmentation network called FAC-Mask. On the one hand, it incorporates a feature alignment module, which facilitates the network to effectively integrate multi-scale information. On the other hand, we design a contrastive learning module to assist the segmentation head in feature representation learning, by learning common features among similar instances and distinguishing differences between non-similar instances. We conduct experiments on the Cityspace dataset, our model improves the detection and segmentation accuracy by +2.4% and +2.8% when fused with RefineMask. The results have demonstrated the efficacy of our method.
Exploring Art in the Digital Era: Creating and Deploying an Immersive Virtual Gallery Experience

Art galleries play a vital role in preserving and promoting artistic heritage, but the current state of art galleries in Indonesia, particularly in attracting the younger generation, raises concerns. The general public, especially the younger generation, has shown the lack of interest in visiting art galleries, particularly in Indonesia. In light of these challenges, it becomes imperative to explore alternative approaches to engage the younger generation in the realm of art. Developing a virtual gallery platform that offers immersive and interactive experiences can help bridge the gap between the digital preferences of the younger generation and the traditional art world. By leveraging technology, this platform can provide a convenient and accessible medium for the younger generation to explore, appreciate, and interact with art, that can trigger their interest in the artistic heritage of Indonesia. This research explores the current state of art galleries in Indonesia and highlights the lack of interest among the younger generation in visiting physical art galleries. To counter this trend, a virtual gallery platform is developed to engage the younger generation and stimulate their interest in art. The platform offers an immersive experience, replicating the gallery environment virtually. Through software testing and initial user testing, the platform’s performance and usability are evaluated. The findings present the potential of this platform in revitalizing the younger generation’s interest in art. This web-based platform provides accessibility and convenience, allowing users to explore and appreciate art from anywhere.
Chinese Event Extraction Method based on Abstract Meaning Representation Graph

Most of Chinese event extraction methods mainly focus on syntactic information and pay scant attention to semantic information. To make full use of semantic information, we take event extraction as a sequence labeling task and propose a Chinese event extraction method based on abstract meaning representation to improve the accuracy and recall rate of event extraction tasks. The model generates abstract meaning representation graph for input sentences, and the graph attention mechanism is used to capture semantic relationships, extract event argument elements and event triggers, and improve the prediction ability of the model. Compared with the baseline models, the accuracy rate, recall rate and F1-score of ACE2005 Chinese set is significant increased, especially in argument classification, with 11.4%, 13.4%, and 12.5% respectively, which verified the effectiveness of our method.
Length and sentiment analysis of reviews about top-level video game genres on the steam platform

During game development, a common uncertainty revolves around the emotional reactions players might exhibit when experiencing newly released games. Understanding emotions and experiences of video game players is complex. As a first step, players can share their experiences of video games by writing reviews. By investigating these reviews, the emotions, experiences, concerns, and opinions of players can be understood. The goal of this study is to provide a deeper insight for video game developers to understand the players' emotional reactions regarding the top-level genres. Besides, as subgoals, it can be observed whether a connection exists between word numbers and review type (positive/negative); whether there is a difference between the type of emotions in the case of different genres; or whether the emotional valence changes during writing a review. In this paper, overall 35, 983, 481 reviews of 11 top-level video game genres are studied using natural language processing methods with the NRC Emotion Lexicon in the statistical program package R. The results show that people write negative reviews earlier than positive ones and no correlation exists between the time at review and their word number. A connection can also be observed between word numbers and whether a review is positive or negative: the median review length is 40 words in case of negative reviews, while it is 19 words in case of positive ones. Regarding emotions, the emotional valence as well as sentiments’ number and percentage differ between the top-level genres. Each has its unique emotional valence and evokes various emotions.
GIS-Based Disaster Management using Support Vector Machine Model for Hazard Level Classification in Disaster Areas

Due to the regular occurrence of catastrophes in Indonesia, the Indonesian SAR (Search and Rescue) team is essential in assisting with search and rescue activities for disaster victims, which frequently result in fatalities. However, they frequently encounter significant difficulties when determining the potential level of risks in the area. Apart from prioritizing the safety and security of victims, the SAR team’s safety is also important before they can effectively save others. In this study, we propose a system that can classify the level of hazard in a disaster area based on environmental parameters. Utilized a customized dataset with information on temperature, humidity, carbon dioxide (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ), dust level, air pressure, and hydrogen sulfide (H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> S), this system optimized the kernel function and data splitting using the SVM (Support Vector Machine) algorithm. As an outcome of this modeling, the degree of environmental hazard is divided into three categories: safe, moderate, and dangerous. With a 90% data training and 10% data testing distribution, the classification model training test results the highest accuracy. Among the four evaluated kernels, the Sigmoid kernel emerged as the top performer in terms of various model performance metrics, such as accuracy, precision, recall, fl-score, and confusion matrix. The results of the hazard level classification are also mapped on a GIS website to allow the SAR team to monitor and update the risk evaluation, and respond appropriately according to changing environmental conditions.
Evolutionary Perspective on Human Cognitive Architecture in Cognitive Load Theory: a Dynamic, Emerging Principle Approach

Abstract Adopting an evolutionary approach to substantiate major characteristics of human cognitive architecture has been one of the major recent developments in cognitive load theory. According to this approach, human cognitive architecture is a natural information processing system which can be described by five general principles. This paper attempts to (1) identify the scope of applicability of these principles in natural information processing systems of different levels of complexity, (2) reconcile the coexistence of implicit (primary) and controlled (secondary) processes within the same human cognitive architecture, and (3) incorporate motivational factors into the evolutionary approach to human cognitive architecture. The paper suggests two principal modifications to the traditional formulation of the evolutionary approach. Firstly, natural information processing systems are viewed as dynamically evolving systems with new principles added with increasing levels of complexity of the systems. Secondly, a new (the explicit intention to learn) principle is added at the level of human cognition. This sixth principle is expected to address (1) the emergence of controlled mechanisms dealing with biologically secondary information as expressed by conscious processing in working memory and (2) the role of learner motivation in such processes from an evolutionary perspective. The paper concludes with discussion of theoretical and practical instructional implications of the proposed modifications.
A Scholar Case Study of IT Education Knowledge Through Practice for Development in a Post-Conflict Region Kosovo

The technological advancements have enormously changed the way humans live today. According to literature studies, countries that have placed a strong emphasis on the IT revolution could benefit from the rewards of the industrialization era. The study's conclusions have drawn attention to Kosovo's lack of computer applications and IT integration in its educational system. The facts highlighted that Kosovo faces a lack of incorporations of computer application and IT in their educational system. There exists a significant disparity between the public and private educational institutes in this nation in terms of emphasis on IT in their educational system. It has been observed that more emphasis is being given in private sector educational institutes on IT incorporation in these schools and associated curriculums. The survey research has been conducted in person in schools and universities of Kosovo.
Hierarchical encoder-decoder architecture for carrier airwake prediction using attention in frequency domain

The carrier airwake is the result of ocean air flowing across the stern of a moving aircraft carrier. This airwake has a huge impact on how accurately a carrier-based aircraft can land. If it is possible to make accurate predictions for the future carrier airwake, the predicted results will be used to aid the landing process and ensure a more precise landing. In this study, the properties of the carrier airwake are first analyzed to provide a basis for the subsequent design of the neural network. Then we propose a hierarchical encoder-decoder neural network with an attention mechanism in the frequency domain to simultaneously predict the carrier airwake components in all directions. Finally, the full comprehensive experiments show that our model for predicting the carrier airwake is accurate.
Maximum correntropy Kalman filter algorithm with two-step random delay and packet loss

Due to the limited width of the communication channel, data delay and packet loss occur at the receiver when receiving measurement data. The number of delay steps is random, but the probability of delay occurrence decreases as the number of steps increases. Therefore, in this paper, we use a binary Bernoulli distribution to model measurement data with known delay probability, and redesign the Kalman filter(KF) and Maximum correntropy Kalman filter (MCKF) algorithms to accommodate a system with two steps of random delay and packet loss. We compare the improved KF and MCKF algorithms under Gaussian noise and non-Gaussian noise systems, and the experimental results show that the improved KF and MCKF algorithms possess high estimation accuracy under Gaussian noise and non-Gaussian noise systems, respectively, when the systems undergoe two-step random delay and packet loss.
Impact of Artificial Intelligence on the Criteria of Originality in Copyright Law

With recent technological advances in the field of artificial intelligence, new questions arise regarding the determination of copyright in works created using artificial intelligence technologies. This article examines the criterion of originality of a work in the legal systems of different countries and its importance for providing legal protection by copyright law to objects created with the use of artificial intelligence. Originality acts as a subjective criterion for determining the possibility of legal protection of a work as an object of copyright. Attention is focused on the lack of a single approach to establishing the originality of the work in the characterized countries. In particular, in the EU, the originality of a work is reduced to the personal creation of the author, in the USA — to the minimal creative contribution of the person-author, in United Kingdom — to its creation by the work and skills of a person, his or her efforts, in Ukraine — it is reduced to the criterion by which the work is characterized, which is the result of the author’s own intellectual creative activity, as well as a reflection of his or her creative decisions made during the creation of such a work. It is noted that all countries at the current stage are focusing their attention on the development of new legislation in the field of copyright regulation of objects created using artificial technologies, which is due to the desire for innovation and the introduction of modern digital technologies and artificial intelligence. That is why in the law-making process it is important to ensure that legal norms are flexible and correspond to changes in the technological environment, allowing to promote innovation and cultural development. Overall, it makes a valuable contribution to understanding the contemporary challenges of changing technologies and the impact of artificial intelligence on copyright law. It emphasizes the need for a balance between protecting the rights of creators and ensuring access to new technologies and innovations for the general benefit of society. Legal challenges related to determining authorship of works generated by artificial intelligence are discussed. The importance of further scientific research in this area is noted.
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

Existing industrial visual anomaly detection models can be broadly categorized into two paradigms, i.e., the paradigm based on image reconstruction and the paradigm based on implicit feature modeling. While both of them have demonstrated great performance in detecting low-level structural anomalies, their limited adjustability and ability to detect high-level logical anomalies hinder their broader use in industrial visual inspection. To solve the above issues, this paper proposes a novel paradigm, that is, Component-aware Anomaly Detection (ComAD), which can simultaneously achieve adjustable and logical anomaly detection. Specifically, it segments the original image into diverse components, enabling model adjustability based on components’ significance and long-range modeling capabilities based on components’ metrological characteristics. We demonstrate that the proposed paradigm can be realized by a lightweight and nearly training-free framework. ComAD is evaluated on real-world scenarios to show its adjustability and logical anomaly detection capabilities. It achieves state-of-the-art performance on the challenging MVTec LOCO AD and CAD-SD benchmarks. Code is available at: https://github.com/liutongkun/ComAD.
An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification

Cross-domain sentiment analysis (CDSA) aims to learn transferable knowledge from the source domain to facilitate the sentiment polarity classification on the target domain of lacking labeled data. Currently, two types of unsupervised domain adaptation (UDA) methods are widely used in CDSA tasks. One employs the domain adversarial strategy to extract domain-invariant features, and the other utilizes the distance metric strategy to reduce domain distribution discrepancy. However, the fine-grained domain-specific information related to categories aligned between domains is not preserved, which suppresses the performance of target-domain classification. To overcome the mentioned problem, a unified Domain Adversarial Category-wise Alignment Network (DACAN) was proposed in this paper. An integrated network was constructed with progressive multi-level feature learning. Specifically, a feature extraction module was constructed with parameter sharing between two domains at low-level text feature extraction layers. The domain adversarial module was added to enable shared knowledge transfer by extracting domain-invariant information and by updating the shared parameters at the feature extraction layers. A category-wise alignment module was built to achieve local distribution alignment at the high dimension-level semantic layers guided by fine-grained category structure information. Meanwhile, joint constraint was established with domain-invariant constraint based on domain adversarial, and domain-consistency constraint based on category-wise alignment. Comprehensive experiments were conducted on two standard Amazon review datasets. The results show that DACAN outperforms other state-of-the-art UDA methods by 0.7% and 1.1% on the two- and three-category CDSA tasks, respectively. Also, better performance results are achieved with a synergistic UDA scheme compared with a single UDA scheme.
Unstructured Small Target Obstacle Detection Technology on Lunar Surface Driven by Neural Network

Unstructured pavement features during lunar patrol pose a great threat to the safety of the rover, rocks and pits and other small targets may cause the rover to overturn or wheel trap, and at present, these small targets in the weak texture of the lunar environment has the problem of small number of pixels, small gradient resulting in difficult detection, this paper proposes a lightweight recognition network model based on variant yolo, first collect lunar surface environment images to make custom datasets, including data screening, annotation, calibration and image enhancement. Finally, the three improved algorithms based on attention mechanism and benchmark algorithms are experimentally verified on the target detection dataset of the star surface environment and the outdoor environment, and finally the YOLOv5-ECA algorithm with the highest mAP value is selected as the object detection algorithm in this paper, which can achieve obstacle detection of real-time rapid response to obstacle detection in the promising spaceborne computing power environment in the future.
An Analysis of Important Factors Affecting the Success of Blockchain Smart Contract Security Vulnerability Scanning Tools

Since the proposal and conception of smart contracts in blockchain-based platforms, the ever-growing use of smart contracts also comes with increased vulnerability exploits, leading to attacks causing significant losses. Various improvements and implementations have been made to strengthen smart contract security, including developing scanning tools to detect vulnerabilities in smart contracts, both open-source and commercial. These tools were met with varying degrees of success, with some thriving well while others have since stagnated. In this paper, we conducted case studies on six of these tools, three of which are highly regarded and still used today, while the other three have either limited success or have been deprecated. We identified three short-term (6 months or less) and five medium-term (6 to 24 months) factors affecting the tools’ successes. Based on our analysis, we outlined a framework with these factors for potential developers to consider for increasing the success of their projects.
Physics driven deep learning based simulation of side scan sonar images

We propose a novel method that leverages the combination of Blender and K-Wave software to generate a realistic acoustic dataset for side-scan sonar images. Side scan sonar (SSS) images have numerous applications in the underwater environment. However, the challenges and high cost of acquiring SSS images in real experiments and the lack of benchmark open-source datasets encourage the researchers to consider the generation of simulated SSS image datasets. There have been several attempts in the literature to combine open-source computer graphics design software such as Blender with generative adversarial networks (GANs) to generate synthetic SSS images. However, the speckles generated in all such approaches have been unrealistic; hence, the DL approaches trained for object detection employing such data are bound to fail. In the proposed method, we first generate acoustic images from Blender. Then, the CycleGAN is applied for style transfer to generate realistic acoustic images based on the speckle generated using k-Wave. The K-wave, Blender, and cycleGAN are used to generate a closed set of SSS images. We then leverage ConSinGAN to generate varying resolution high-quality sonar images from a closed set of SSS images. As per our knowledge, this is the first attempt that uses k-wave in conjunction with Blender and CycleGAN to generate SSS images. The proposed method is cost-effective and efficient alternative for acquiring large SSS datasets.
A Novel Face Recognition Model Based on Feature Registration

With the rapid development of information construction, identity identification has become an important factor affecting the economic and social security in the era of big data. Face recognition is the core research direction in the field of identity authentication and image processing security. In recent years, the research of face recognition mainly focuses on how to eliminate the influence of environmental factors and facial occlusion. A face detection model based on feature registration is proposed, which uses Gaussian filter processing to realize image noise reduction, and introduces the improved Prewitt operator edge detection to realize binarization. Through the gray level threshold separation and screening of specific areas, on the basis of position determination, location of face organs and parameters were analyzed, and the face recognition model was constructed, and puts forward the improved Harris corner feature extraction methods in combination with Normalized Cross Correlation face matching normalization algorithm. The experimental effect showed that the experimental accuracy reached 90.13%, and time efficiency was increased by 6.67%.
The Research of Using STEM-6E Teaching Model in AR Technology Learning: System Development and Evaluation

In recent years, the application of AR/VR/MR in various fields has gradually risen, and the popularization of 5G has driven the vigorous development of AR-related products in various application fields. This study mainly proposes an AR learning system that combines STEAM-6E teaching model to enable learners to understand the application of cross-domain technology in the process of “learning by doing”, and also understand the current technical principles of AR and the main applications in the industry. This system is mainly divided into four thematic learning units. The design of each unit uses STEAM-6E as a scaffold for learning and implementation to guide students from cognition, understanding, verification to implementation step by step to understand AR technology. The learning content conforms to the core literacy and learning goals of mathematics, natural sciences, arts and technology fields in the 108 curriculum for elementary, junior high and high schools in Taiwan, and has academic contributions and practical promotion value for K-12 science literacy education.