Reviewerly/exHarmony huggingface dataset:
Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises converting histopathological images from Red Green Blue (RGB) to Chrominance of Blue and Chrominance of Red (YCBCR), utilizing a wavelet transform to extract texture information, and classifying the images with Extreme Gradient Boosting (XGBOOST). Furthermore, SMOTE has been used for resampling as the dataset has imbalanced samples. The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27% on the BreakHis 1.0 40X dataset, 98.95% on the BreakHis 1.0 100X dataset, 98.92% on the BreakHis 1.0 200X dataset, 98.78% on the BreakHis 1.0 400X dataset, and 98.80% on the combined dataset. The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.
A Text Image Watermarking Algorithm Based on Image Enhancement

Digital watermarking technology is adequate for copyright protection and content authentication. There needs to be more research on the watermarking algorithm after printing and scanning. Aiming at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark, an anti-print scanning watermarking algorithm suitable for text images is proposed. This algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform (DWT)-Singular Value Decomposition (SVD) to embed the watermark. Experiments show that the average Normalized Correlation (NC) of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group (JPEG) compression, JPEG2000 compression, and print scanning is above 0.93. Especially, the average NC of the watermark extracted after print scanning attacks is greater than 0.964, and the average Bit Error Ratio (BER) is 5.15%. This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.
Where is all the research software? An analysis of software in UK academic repositories

This research examines the prevalence of research software as independent records of output within UK academic institutional repositories (IRs). There has been a steep decline in numbers of research software submissions to the UK’s Research Excellence Framework from 2008 to 2021, but there has been no investigation into whether and how the official academic IRs have affected the low return rates. In what we believe to be the first such census of its kind, we queried the 182 online repositories of 157 UK universities. Our findings show that the prevalence of software within UK Academic IRs is incredibly low. Fewer than 28% contain software as recognised academic output. Of greater concern, we found that over 63% of repositories do not currently record software as a type of research output and that several Universities appeared to have removed software as a defined type from default settings of their repository. We also explored potential correlations, such as being a member of the Russell group, but found no correlation between these metadata and prevalence of records of software. Finally, we discuss the implications of these findings with regards to the lack of recognition of software as a discrete research output in institutions, despite the opposite being mandated by funders, and we make recommendations for changes in policies and operating procedures.
MFPWTN: a multi-frequency parallel wavelet transform network for remote sensing image super-resolution

How to fully capture high-frequency information is an important issue in the remote sensing image super-resolution (SR) task. Most of the existing convolutional neural network based methods usually apply the attention mechanism to capture the high-frequency information. However, it is often insufficient since the remote sensing images usually contain more high-frequency information than natural images. Recently, some studies try to transform the original image into the wavelet domain to capture more high-frequency information. However, we observe that these methods usually apply similar network structures to learn different wavelet components, which will be difficult to fully capture the different features. To solve this issue, we propose a method named multi-frequency parallel wavelet transform network (MFPWTN) for remote sensing image SR. Specifically, we initially design two different network structures to reconstruct the high-frequency and low-frequency wavelet components, which can fully capture the characteristics of different frequencies. Subsequently, we introduce a high-frequency fusion module to enhance the information transmission among different high-frequency wavelet components. In addition, we employ the dilated convolution to establish the network structure for reconstructing the low-frequency wavelet component, which allows us to capture different receptive fields by using relatively few parameters. The experimental results on two public remote sensing datasets, UCMerced-LandUse and NWPU-RESISC45, show that the proposed MFPWTN can get superior performance over many existing state-of-the-art algorithms.
IMPROVING CHILDREN'S MORALS AND INTEREST IN LEARNING THROUGH TUTORING AND OUT BOUND MEDIA

The quality of human resources is essentially determined by education. Through tutoring and outbound activities, it is proven that children's education is not only obtained at school; this activity is intended to be able to provide learning for children to be able to have good character traits. The activity method uses a theoretical approach and a practical approach. With the PAR (Participatory Action Research) approach, the service team views that the existence of tutoring and outbound activities for children is an important asset. The results of this development for children in outbound have a direct impact on the formation of morals, character and recreation in students, so as to improve the quality of education and skills. student. Tutoring and outbound is one of the learning methods that is a breakthrough so that children can learn comfortably and feel happy. This is considered important as an effort so that children grow up to be active individuals.
Owl Eye: An AI-Driven Visual Testing Tool

Visual testing is a software testing technique that checks the visual aspects of a Graphical User Interface (GUI). It helps identify visual defects that other GUI testing techniques, such as functional and performance tests, may not detect. While functional testing verifies the correct behavior of applications, it is not effective in catching visual issues. This paper introduces an AI-driven visual tester, a novel approach to visual testing that enhances the detection of visual defects in application interfaces. The tester can effectively test any platform, using screenshots as input. The process begins with object detection in the screenshots, employing two methods: traditional image processing and deep learning. The detected objects are then transformed into a tree data structure for efficient analysis. Object matching is utilized to correlate objects between the reference and updated images. Finally, change analysis and classification are performed to identify and categorize changes in each object, such as translation, scaling, color change, or object removal.
M2UNet: MetaFormer Multi-Scale Upsampling Network for Polyp Segmentation

Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\overline{(}\mathrm{M}^{2}\text{UNet})$</tex> for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods.
Entity-Relation Distribution-Aware Negative Sampling for Knowledge Graph Embedding

Knowledge Graph Embedding (KGE) is a powerful technique for mining knowledge from knowledge graphs. Negative sampling plays a critical role in KGE training and significantly impacts the performance of KGE models. Negative sampling methods typically preserve a pair of Entity-Relation (ER) in each positive triple and replace the other entity with negative entities selected randomly from the entity set to create a consistent number of negative samples. However, the distribution of ER pairs is often long-tailed, making it problematic to assign the same number of negative samples to each ER pair, which is overlooked in most related works. This paper investigates the impact of assigning the same number of negative samples to ER pairs during training and demonstrates that this approach impedes the training from reaching the optimal solution in the negative sampling loss function and undermines the objective of the trained model. To address this issue, we propose a novel ER distribution-aware negative sampling method that can adaptively assign a varying number of negative samples to each ER pair based on its distribution characteristics. Furthermore, our proposed method also mitigates the issue of introducing false negative samples commonly found in many negative sampling methods. Our approach is founded on theoretical analysis and practical considerations and can be applied to most KGE models. We validate the effectiveness of our proposed method by testing it on conventional KGE and Neural Network-based KGE models. Our experimental results outperform most state-of-the-art negative sampling methods.
Hardware-Aware Evolutionary Approaches to Deep Neural Networks

This chapter gives an overview of evolutionary algorithm (EA) based methods applied to the design of efficient implementations of deep neural networks (DNN). We introduce various acceleration hardware platforms for DNNs developed especially for energy-efficient computing in edge devices. In addition to evolutionary optimization of their particular components or settings, we will describe neural architecture search (NAS)Neural Architecture Search (NAS) methods adopted to directly design highly optimized DNN architectures for a given hardware platform. Techniques that co-optimize hardware platforms and neural network architecture to maximize the accuracy-energy trade-offs will be emphasized. Case studies will primarily be devoted to NASNeural Architecture Search (NAS) for image classificationImage classification. Finally, the open challenges of this popular research area will be discussed.
SeatmateVR: Proxemic Cues for Close Bystander-Awareness in Virtual Reality

Prior research explored ways to alert virtual reality users of bystanders entering the play area from afar. However, in confined social settings like sharing a couch with seatmates, bystanders' proxemic cues, such as distance, are limited during interruptions, posing challenges for proxemic-aware systems. To address this, we investigated three visualizations, using a 2D animoji, a fully-rendered avatar, and their combination, to gradually share bystanders' orientation and location during interruptions. In a user study (N=22), participants played virtual reality games while responding to questions from their seatmates. We found that the avatar preserved game experiences yet did not support the fast identification of seatmates as the animoji did. Instead, users preferred the mixed visualization, where they found the seatmate's orientation cues instantly in their view and were gradually guided to the person's actual location. We discuss implications for fine-grained proxemic-aware virtual reality systems to support interaction in constrained social spaces.
Greenhouse Horticulture Automation with Crops Protection by using Arduino

Agriculture significantly contributes to economic growth, generating employment opportunity also simulating the small-scale agriculture experiences. However, unforeseeable weather patterns, natural disasters, and unwelcome intruders are significant threat which brings severe financial losses for the owner. To overcome these challenges, this study aims to develop IoT-based automation greenhouse integrated with intrusion detection and prevention system. Automation greenhouse provides optimal environmental conditions for crop growth and enhances agricultural productivity, additionally inclusion of intrusion detection and prevention practices could detect and give immediate responses towards intrudes approaches. The automation greenhouse with intrusion detection and prevention with IoT-based provides the real-time monitoring and control as instant intrusion notification will be send to user through mobile application remotely. Thus, IoT-based automation greenhouse invention provides the sustainable environment condition for crop growth and reducing crop losses from treats.
Efficient Cloud Workflow Scheduling with Inverted Ant Colony Optimization Algorithm

Cloud computing has risen as a prominent paradigm, offering users on-demand access to computing resources and services via the Internet. In cloud environments, workflow scheduling plays a vital role in optimizing resource utilization, reducing execution time, and minimizing overall costs. As workflows comprise interdependent tasks that need to be assigned to Virtual Machines (VMs), the complexity of the scheduling problem increases in proportion to workflow size and VM availability. Due to its NP-hard nature, finding an optimal scheduling solution for workflows remains a challenging task. To address this problem, researchers have turned to metaheuristic approaches, which have shown promise in finding near-optimal solutions for complex combinatorial optimization problems. This paper proposes a novel metaheuristic algorithm called Inverted Ant Colony Optimization (IACO) for workflow scheduling in cloud environments. IACO is a variation of the traditional ACO algorithm, where the updated pheromone has an inverted influence on the path chosen by the ants. By leveraging the complementary nature of these two algorithms, our proposed algorithm aims to achieve superior workflow scheduling performance regarding total execution time and cost, surpassing existing approaches.
Instance Segmentation Method based on R2SC-Yolact++

To address the problems of missed detection, segmentation error and poor target edge segmentation in the instance segmentation model, a R2SC-Yolact++ instance segmentation approach based on the improved Yolact++ is proposed. Firstly, the backbone network adopts Res2Net which introduces spatial attention mechanism (SAM) to improve the problem of segmentation error by better extracting feature information; then, high-quality masks are obtained by fusing the detail information of the shallow feature P2 as the input to the prototype mask branch; finally, the problem of missed detection was solved by introducing Cluster-NMS in order to improve the accuracy of the detection boxes. In order to illustrate the effectiveness of the improved model, experiments were conducted on two publicly available datasets, the COCO and CVPPP datasets. The experimental results show that the accuracy on the COCO dataset is 1.1% higher than the original model. And the accuracy on the CVPPP dataset is 1.7% better than before the improvement, which is better than other mainstream instance segmentation algorithms such as Mask RCNN. Finally, the improved model is applied to the insulator dataset, which can segment the shed of insulator accurately.
QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

Cloud computing has become a viable option for many organizations due to its flexibility and scalability in providing virtualized resources via the Internet. It offers the possibility of hosting pervasive applications in the consumer, scientific, and business domains utilizing a pay-as-you-go model. This makes cloud computing a cost-effective solution for businesses as it eliminates the need for large investments in hardware and software infrastructure. Furthermore, cloud computing enables organizations to quickly and easily scale their services to meet the demands of their customers. Resource allocation is a major challenge in cloud computing. It is known as the NP-hard problem and can be solved using meth-heuristic algorithms. This study optimizes resource allocation using the Particle Swarm Optimization (PSO) algorithm and fuzzy logic system developed under the proposed time and cost models in the cloud computing environment. Receiving, processing, and waiting time are included in the time model. The cost model incorporates processing and receiving costs. Two experiments demonstrate the performance of the proposed algorithm. The simulation results demonstrate the potential of our mechanism, demonstrating improved performance over previous approaches in aspects such as providers' total income, users' total revenue, resource utilization, and energy consumption.
Hybrid Syntax Dependency with Lexicon and Logistic Regression for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is a fine-grained form of SA that greatly benefits customers and the real world. ABSA of customer reviews has become a trendy topic because of the profuse information that is shared through these reviews. While SA also known as opinion mining helps to find opinion, ABSA greatly impact business world by converting these reviews to finer form with aspects and opinion or sentiment. These review words are interwoven internally, which depends on the semantics besides syntax, and sometimes there are long dependencies. Recently, the hybrid methods for ABSA are popular, but most of them merely considered if the syntax and long dependency exist, thus missing the inclusion of multi and infrequent aspects. In addition, in most literature, sentiment classification is shown directly without calculating the sentiment scores in ABSA. To this effect, this paper proposes a hybrid with syntax dependency and the lexicon for aspect, sentiment extraction, and polarity classification by Logistic Regression (LR) classifier to overcome the issues in ABSA. The proposed method is able to address the challenges of ABSA in a number of ways. First, it is able to extract multi-word and infrequent aspects by using syntactic dependency information. Second, it is able to calculate sentiment scores, which provides a more nuanced understanding of the overall sentiment expressed towards an aspect. Third, it is able to capture long dependencies between words by using syntactic dependency and semantic information. The proposed hybrid model outperformed the other methods by an average of 8-10 percent with the standard public dataset in terms of accuracy.
Optimizing I/O Performance through Effective vCPU Scheduling Interference Management

Virtual machines (VMs) heavily rely on virtual CPUs (vCPUs) scheduling to achieve efficient I/O performance. The vCPU scheduling interference can cause inconsistent scheduling latency and degraded I/O performance, potentially compromising the services provided by affected VMs. Existing solutions have limitations, such as inefficiency in diagnosing interference issues or imposing undesired side effects on cloud systems. To address these challenges, we present Otter, a holistic technique for optimizing I/O performance in the presence of vCPU scheduling interference. Otter employs innovative methods to enhance interference diagnosis efficiency. First, we propose lightweight methods to measure the dynamic changes in scheduling latencies for co-running vCPUs, ensuring both flexibility and accuracy. Second, we propose fine-grained quantification methods to timely determine the interference, with low false positive and false negative rates. Third, we identify interference patterns that aid in analyzing the root causes of interference and preventing similar issues from recurring. Otter has been operational for one year in the production cloud at the National Supercomputing Center (Wuxi). It diagnoses and helps fix more than 470 vCPU scheduling interference-related issues, resulting in a 19.6% improvement in cloud service I/O performance with negligible overhead in production.
Moving Horizon Estimation for Stochastic Descriptor Systems With Inaccurate Noise Covariance Matrices

In this article, the problem of state estimation is investigated for stochastic descriptor linear systems with inaccurate process and measurement noise covariance matrices. Under the assumption that the noise and its covariance matrix, respectively, obey the Gaussian distribution and the inverse Wishart distribution, a novel adaptive moving horizon estimator is designed for descriptor systems based on the variational Bayesian inference method. By iteratively updating their approximate posterior probability distributions, the states of descriptor systems are estimated when noise covariance matrices are inaccurate. Finally, the feasibility and effectiveness of the proposed method are verified by experiments performed on the prototype of a dynamic point-the-bit rotary steerable drilling tool.
Developing Items to Measure the Assessment Literacy of ESL Teachers

This study aims to develop a reliable instrument measuring the assessment literacy of ESL teachers through Exploratory Factor Analysis based on five constructs. A survey was administered to 200 secondary school ESL teachers. The study revealed that only 78 items with a cut-off point of above .60 from the factor loading are retained. Cronbach’s Alpha showed that the items retained are reliable. This study developed and validated an instrument to measure ESL teachers’ assessment literacy in the Malaysian context. An assessment literacy framework can be developed based on this research which will benefit the education field in Malaysia.