Reviewerly/exHarmony huggingface dataset:
From Composited to Real-world: Transformer-based Natural Image Matting

The task of image matting is an active research area in computer vision, and various trimap-free methods have been proposed to improve its performance. However, these methods do not consider the gap between composited and real-world images, resulting in limited generalization ability. To address this issue, we propose a domain alignment (DA) module that consists of local region-wise alignment (LRA) and global harmonious alignment (GHA). The LRA aligns the most diverse pixels in the transparent regions of the foreground between composited and real images. On the other hand, the GHA aligns the global image harmonization for both composited and real images, which helps the network choose the appropriate semantics for real harmonious images. Additionally, we design a transformer-based network with dynamic attention pruning (DAP) mechanism to accurately locate domain-sensitive regions, allowing the DA module to work more effectively. Furthermore, we introduce a new dataset, the Real-world Matting Dataset (RM-1k), to advance the real-world matting task. Our proposed method is evaluated on two composited benchmarks (Composite-1k and Distinctions-646) and two real-world datasets (AIM-500 and RM-1k), and the results show that our method achieves robust performance on both composited and real-world images.
Topological Conditions and Solutions for Repairing Argumentation Frameworks

This paper discusses how to make an argumentation framework (AF) with no stable extensions into one with a stable extension by adding a new argument, which we call ‘repair’. We remove the restrictions that were put on the target AFs in our previous work, and show a simple condition for an arbitrary AF to have no stable extensions. Then, we refine the conditions that an AF should satisfy to be repaired and identify the position where a new argument is added. We also discuss other possible repair types. The judgments are simple, easy to intuitively understand by virtue of the usage of topological features.
Fuzzy Labeling Semantics for Quantitative Argumentation

Evaluating argument strength in quantitative argumentation systems has received increasing attention in the field of abstract argumentation. The concept of acceptability degree is widely adopted in gradual semantics, however, it may not be sufficient in many practical applications. In this paper, we provide a novel quantitative method called fuzzy labeling for fuzzy argumentation systems, in which a triple of acceptability, rejectability, and undecidability degrees is used to evaluate argument strength. Such a setting sheds new light on defining argument strength and provides a deeper understanding of the status of arguments. More specifically, we investigate the postulates of fuzzy labeling, which present the rationality requirements for semantics concerning the acceptability, rejectability, and undecidability degrees. We then propose a class of fuzzy labeling semantics conforming to the above postulates and investigate the relations between fuzzy labeling semantics and existing work in the literature.
Analytical Approach for Data Encryption Standard Algorithm

Although it was first developed and studied in the late 1970s and early 1977s, the Data Encryption Standard (DES) algorithm has grown in popularity. There are two causes for this occurrence. First, the DES algorithm's complex mathematical structure allows it to serve as the theoretical foundation for a wide variety of applications. Second, the encryption technique works quite well in practice for a variety of applications when implemented correctly. In this paper, we undertake a thorough and practical review of the theoretical aspects of this sort of encryption algorithm and demonstrate how they have been implemented by executing multiple encryption configurations.&#x0D;
An Axiomatic Account of a Fully Abstract Game Semantics for General References

We present an analysis of the game semantics of general references introduced by Abramsky, Honda and McCusker which exposes the algebraic structure of the model. Using the notion of sequoidal category, we give a coalgebraic definition of the denotational semantics of storage cells of arbitrary type. We identify further conditions on the model which allow an axiomatic presentation of the proof that finite elements of the model are definable by programs, in the style of Abramsky’s Axioms for Definability.
Semi-supervised Video Object Segmentation Via an Edge Attention Gated Graph Convolutional Network

Video object segmentation (VOS) exhibits heavy occlusions, large deformation, and severe motion blur. While many remarkable convolutional neural networks are devoted to the VOS task, they often mis-identify background noise as the target or output coarse object boundaries, due to the failure of mining detail information and high-order correlations of pixels within the whole video. In this work, we propose an edge attention gated graph convolutional network (GCN) for VOS. The seed point initialization and graph construction stages construct a spatio-temporal graph of the video by exploring the spatial intra-frame correlation and the temporal inter-frame correlation of superpixels. The node classification stage identifies foreground superpixels by using an edge attention gated GCN which mines higher-order correlations between superpixels and propagates features among different nodes. The segmentation optimization stage optimizes the classification of foreground superpixels and reduces segmentation errors by using a global appearance model which captures the long-term stable feature of objects. In summary, the key contribution of our framework is twofold: (a) the spatio-temporal graph representation can propagate the seed points of the first frame to subsequent frames and facilitate our framework for the semi-supervised VOS task; and (b) the edge attention gated GCN can learn the importance of each node with respect to both the neighboring nodes and the whole task with a small number of layers. Experiments on Davis 2016 and Davis 2017 datasets show that our framework achieves the excellent performance with only small training samples (45 video sequences).
Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently.
Using Nominal Group Technique to Explore Publication Challenges and the Usefulness of AI-Based Writing Technologies: Insights From Indonesian Scholars

In the current academia landscape, academics, particularly those from non-native English speaking (NNES) countries, face formidable challenges in gaining publication in reputable international journals. The dominance of English as the language of scientific dissemination, coupled with limited resources and research infrastructure, hinders NNES academics from meeting the standards of high-impact journals. This issue is particularly pronounced in Indonesia, where most scholars face the additional challenge of conducting research in Bahasa Indonesia and writing manuscripts in English. The purposes of this study were to elicit the obstacles encountered by Indonesian scholars in gaining international publication, and the extent to which the advancement of AI-based writing technologies can provide solution. The study is expected to provide valuable insights into their experiences and contributing to a better understanding of the publishing landscape in Indonesia. The study utilized the Nominal Group Technique (NGT) to capture the opinions of four Indonesian academics pursuing their doctoral degrees overseas. The findings highlight the need for user-friendly, comprehensive technology solutions, including Artificial Intelligence (AI)-based software and applications, to assist academics in English academic writing in the Indonesian context. Despite the limitations of current digital writing assistants, they are recommended as useful aids in overcoming language barriers. Access to human consultants was unanimously considered crucial. Initiatives such as advocating for multilingualism and providing language editing services, as well as the availability of online resources and support, are important steps towards addressing disparities in academic publishing. The findings of this research have the potential to inform policies and initiatives aimed at supporting and promoting Indonesian academics in their pursuit of reputable international publications.
A systematic review on video encryption algorithms: A future research

&lt;p&gt;Video Encryption is widely used in many real-time applications today. Despite numerous video encryption techniques that are available today, the challenges such as time and space complexity, real time latency, scalability and vulnerability towards few attacks still exist in the research domain, however few algorithms have achieved acceptable computational complexity, but in such cases vulnerability to certain attacks (differential, statistical, plain text attack, and cipher text attacks) are still a threat to secure video transmission. To the best of our knowledge, a comprehensive but detailed systematic literature review on video encryption is needed for the researchers in the scientific community. This paper, therefore, presents a systematic literature review of 30 scientific documents extracted from platforms like scopus and web of science. The paper, comprehensively addresses, various techniques of video encryption and encoding, different evaluation parameters to testify the performance of the algorithms and discusses the challenges of the existing video encryption algorithms. After careful investigation, it has been observed the approaches which involve encryption of the video data post the same has been encoded is the most efficient and scalable approach towards video encryption. In addition, it also implies that in near future, the proposed algorithms, must be evaluated based on the various categorization of parameters illustrated in this paper.&lt;/p&gt;
Novetly in 3D CartPole Domain

This chapter expands on the underlying theory and applies it to a 3D CartPole domain. We introduce a Weibull Open World control-agent (WOW-agent) that uses Extreme Value Theory (EVT) to convert dissimilarity to probability of novelty. While novelty is something sufficiently dissimilar to the past experience, that does not mean the WOW-agent cannot reason about the novelty. While there have been many books addressing out-of-distribution detection, novel class discovery, and open-world learning, not all novelty is out-of-distribution data or a novel class.
A Unifying Framework for Novelty

“What is novel?” is an important AI research question that informs the design of agents tolerant to novel inputs. Is a noticeable change in the world that does not impact an agent’s task performance a novelty? How about a change that impacts performance but is not directly perceptible? If the world has not changed, but the agent senses a random error that produces an input that leads to an unexpected state, is that novel.
An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

Virtual Machine (VM) allocation are the crucial problems because cloud computing enables the rapid growth of data centres and compute centres. Power consumption and network expenses have increased as cloud computing becomes more and more prevalent. System instability may result from repeated requests for computing resources. One of the most important and difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical machines in cloud data centres. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. The proposed energy efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation to maximize the load balancing. System resources including CPU, storage, and memory are optimized using EEVMPSO. This research article suggests energy-aware virtual machine migration using the Particle Swarm Optimization Algorithm for dynamic VMs placement, energy efficient cloud data centres as a solution to this issue. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34% respectively, it demonstrated the improvements in the energy service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.
A Synthetic Dataset for Learning Optical Flow in Underwater Environment

Computing a reliable estimate of optical flow is a fundamental step in many computer vision applications, mainly based on the analysis of the motion of objects in the scene. Therefore, in recent years, many research applications have led to numerous solutions and methodologies for its estimation. In recent literature, several approaches based on deep neural networks have been proposed for the estimation of optical flow, performing with high accuracy when trained on large labeled datasets. Since the difficulty of obtaining optical flow ground truths from real-world image sequences, many efforts have been devoted to producing large synthetic datasets for effectively training such models. Nevertheless, being able to obtain an accurate estimate of the flow in real-life scenarios is still a great challenge due to the complexity of the environments. The problem is even more complex if an underwater environment is considered, due to sudden changes in lighting, water turbidity, movements of the background, particles, and other objects. In this perspective, our work presents a synthetic dataset of underwater scenes, endowed with optical flow labels, to demonstrate the benefits of training a specific deep neural model for estimating optical flow in the considered environment. To this aim, an experimental comparison between a general-purpose deep neural model and the same model trained specifically with the new proposed dataset has confirmed an increase in accuracy of the final estimation.
A Study on the Improvement of the Emergency Preparedness Organization of Local Government

In the event of a crisis, an emergency preparedness organization that oversees and coordinates emergency preparedness tasks from basic local governments to the central government plays a very important role. However, related laws and regulations are mainly composed of exhibitions, and emergency preparedness personnel often serve as disaster, safety management, and civil defense, and lack of expertise due to the perception that they are non-preferred positions and frequent job transfers. By expanding the scope of emergency preparedness work in peacetime in preparation for diversifying North Korean threats and national crises and emergencies, the effective operation of emergency preparedness organizations should be guaranteed. The person in charge of emergency preparedness of basic local governments should be reorganized, and professionalism should be improved through regular job training. In addition, it is necessary to ensure conditions for integrated crisis management during wartime and peacetime by revising laws and regulations such as the Emergency Preparedness Resource Management Act and the Integrated Defense Act.
Overcoming Limitations In Chat GPT And Wolfram Alpha With Peter Chew Rule

This article discusses the limitations of Chat GPT and Wolfram Alpha specifically in terms of their restricted knowledge and their inability to answer some Solution of Triangle questions. ChatGPT's knowledge is confined to data up until 2021. However, these limitations can be addressed by incorporating future knowledge, Peter Chew Rule for Solution of Triangle into Education 4.0 Calculator. This can be effective in let student interest in using technology while learning mathematics especially when analogous covid- 19 issues arise in the future.By utilizing Peter Chew Rule into the Education 4.0 calculator, significant advancements can be achieved. This innovative tool allows us for finding a non included angle directly when given 2 sides and an included angle. The integration of Education 4.0 provides a promising solution to the existing limitations in ChatGPT and Wolfram Alpha.By programming the Education 4.0 calculator with Peter Chew Rule and Method, these systems can enhance their capabilities and offer simple solution , more comprehensive and up-to-date answers to all triangle problem. This approach demonstrates the potential of leveraging advanced mathematical concepts to overcome the limitations posed by restricted knowledge in AI systems like ChatGPT and Wolfram Alpha.By incorporating Peter Chew Rule and Method for Solution of triangle and leveraging advanced mathematical tools, AI systems can expand their capabilities and offer simple solution, more accurate and comprehensive responses in the domain of Solution of Triangle.
Design and exploration of a virtual reality-based human-computer interaction system

First, data gloves were made. The STM32 microcontroller was selected for the data glove to collect hand motion pose data from the MPU6050 sensor, and the data was transferred to the Unity3D engine via a wireless Bluetooth module. The experiments show that the data glove can provide data to the system stably and the system does not lag. The HMM-based dynamic gesture recognition method is proposed. The hidden Markov model is studied in depth, and the gesture interaction recognition model is established by adding noise and normalization to the data generated by the data glove. The proposed algorithm has a correct recognition rate of more than 90% for all the eight defined gestures. Finally, This paper is based on virtual reality even though the research and integration of human-computer interaction systems has made some progress and breakthroughs in the new field of virtual reality.
A study on multi-strategy improvement algorithm for chimpanzee optimization by fusing differential variation and random variation

To address the problems that the chimpanzee optimization algorithm is prone to fall into local optimum and convergence speed is too slow. This paper proposes a multi-strategy improvement of the chimpanzee optimization algorithm by fusing differential variation and random variation (DRChOA). Firstly, an adaptive weighting factor is introduced to balance the global search and local exploitation ability of the algorithm, secondly, the differential variation strategy is introduced to facilitate the exchange of information among individuals for population position updating, and finally, the random variation strategy is used to enhance the ability of the algorithm to jump out of the local optimum.
Research on multi-view stereo based on depth estimation

In deep learning-based multi-view stereo methods, the accuracy of feature extraction and the effectiveness of cost volume generation directly affect the reconstruction accuracy. To address the issues of feature extraction and depth map accuracy in current methods, this paper proposes an improved 3D reconstruction network based on a multi-scale cost volume. Firstly, in order to obtain more accurate feature information and improve feature matching, an attention module is incorporated into the feature pyramid extraction network to output three different scale feature maps, and depth estimation is performed using a coarse-to-fine cascade structure. Secondly, to alleviate the problem of over-smoothed edges caused by the regularization process due to the large receptive field and depth map upsampling, the refined upsampled depth map is guided by the feature maps of the next stage with more detailed information, thus enhancing the edge detail information of the initial depth map. Experiments on the DTU dataset show that the proposed method achieves an accuracy of 0.323mm and a completeness of 0.361mm, with a 6.2% improvement in reconstruction completeness compared to the original model, effectively improving the 3D reconstruction performance and verifying the effectiveness of the proposed method.
A Proposed Architecture for Quality based Decision Making Approach for Software Re-engineering

Software re-engineering is the modification of a software system after it has been reverse engineered, typically to add new functionality or fix errors. Software re-engineering involves a set of activities aimed at reorganizing a legacy system into a new target system that conforms to quality constraints. This paper explores the reasons behind the failure of software re-engineering. This study has analyzed that some of the most common factors for failure of software re-engineering are misestimated costs and quality inefficiencies. As far as the quality factor is concerned, first the basic tool architecture is analyzed which is used by most of the tools for reverse engineering, reorganization and re-engineering. In this research study, a quality evaluation factor as well as parameters for quality evaluation are added to that particular architecture, so that customers and maintainers can analyze whether they achieve the desired goals for re-engineering. Ifthe target is achieved then the finished product is obtained again with quality and within budget, otherwise more attention should be given to the quality evaluation process.