Reviewerly/exHarmony huggingface dataset:
Deep Learning Method Based for Breast Cancer Classification

The most prevalent cancer in women worldwide and one of the main factors in cancer-related mortality is breast cancer. Extensive research efforts have been dedicated to early detection, diagnosis, and treatment of breast cancer to reduce mortality rates. This research aims to achieve accurate diagnosis of breast cancer and classify breast cancer using deep learning method. The study proposes deep learning techniques with Adam's optimization and two hidden layers for breast cancer classification. Addressing challenges such as data instability and overfitting during deep learning training, the research focuses on updating network weights. The experiments examine two hidden layers and varying learning rates to enhance classification accuracy. The datasets utilized in the experiments include the WBCD dataset (Original), the WDBC dataset (Diagnostics), and the Coimbra dataset. Additionally, the proposed scheme's accuracy is compared against existing benchmarks for breast cancer detection. The experimental findings show that the suggested scheme outperforms other benchmarks, achieving an impressive 96% accuracy in breast cancer classification.
Image Enhancement of Cardiac MR motion Image for High-Quality Segmentation using combined Fuzzy Pooling Layer in Convolutional Neural Networks

Deep Learning frameworks have proven significant advance in the segmentation of objects in images. However, for segmenting an object from the image, the image's quality is the principle concern. The movement of organs in body will result artefacts in Cardiac Magnetic Resonance(CMR) image. Ilkay Oksuz et al. suggested a detection and correction algorithm for Cardiac Magnetic Resonance motion artefacts. The image artefacts caused by the movement of organs are detected and corrected by Convolutional Neural Networks(CNN) and Convolutional Recurrent Neural Network(CRNN). IN CNN pooling is a significant operation, in this paper, we propose a combined fuzzy pooling layer in the CNN to boost the classification accuracy to improve reconstruction, where max pooling and fuzzy pooling are merged.
Incentive-Driven Pricing Game for Multi-Edge Service Providers towards Optimal Profits

The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further progress. Without proper pricing guidance, service providers tend to formulate pricing strategies solely based on their own interests, which frequently hinders the maximization of overall market benefits. This problem is even more challenging in edge computing scenarios as different Edge Service Providers (ESPs) are located in distributed regions and influenced by multiple factors, making it difficult to formulate a single pricing model. This paper proposes a multi-participant stochastic game model to formalize the multi-edge service pricing problem. An incentive mechanism based on Pareto improvement is then proposed to drive the game to the Pareto optimal direction with optimal profits. Finally, an improved PSO algorithm is proposed to solve the game model and analyze the equilibrium states under different evolutionary mechanisms. Experimental results indicate that the proposed pricing incentive mechanism can promote a more effective and reasonable pricing allocation, avoiding 21.6% anarchism loss of the overall profits, while showcasing the effectiveness of our algorithm in solving the game.
PUVAR:Minimize Idle Resource SLO Violations by Uncertainty-Aware Scheduling in Cloud Platforms

Nowadays, idle resource makes up a non-negligible fraction of datacenter capacity in mainstream cloud platforms. Cloud platforms offer idle resource with low service level objectives (SLOs) at low prices to attract cost-sensitive users. Despite their fault tolerance, these users still want some SLO guarantees for idle resource. Cloud platforms have started to provide statistical SLO for idle resource, but the violations of statistical SLOs have not been modeled and minimized. In this paper, we propose PUVAR, a scheduling policy based on prediction+optimization, to explicitly model and minimize the statistical SLO violation for idle resource in cloud platforms. The design of PUVAR possesses two major innovations: (1) explicitly quantify prediction uncertainty of idle resource capacity and iteratively reduce its impact on scheduling decisions; (2) treat the SLO of idle resources as a soft constraint and minimize its violation by two-stage scheduling of regular requests and idle requests. We provide a theoretical convergence rate for the parameter optimization of PUVAR. Promising results of ablation analysis by comparison with popular baseline algorithms on the trace of a real cloud platform indicate that PUVAR can significantly reduce the SLO violation of idle resource with little additional cost on the current scheduling of regular requests.
Dynamic Noise-based Semantic Related Distance Weights with Attention Network for Aspect Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA), is a method of sentiment analysis that focuses on identifying aspects/features in text and classifying their sentiment. Recently, many deep learning-based methods have achieved some success. However, they often focus solely on the relationship between aspect entities and sentiment words, overlooking the inherent fuzziness and dynamics of natural language. To tackle this issue, we propose a method that calculates semantic relevance weight based on dynamic noise, and combine it with an attention network to form our model (DNRA). Experimental results on restaurants dataset show that by our approach, accuracy is improved by 0.70%, reaching 83.83% compared to the baseline model. Meanwhile, we obtained the highest F1 scores compared to the baseline models on three datasets: laptops, restaurants, and Twitter, achieving scores of 73.33%, 76.59%, and 75.22%, respectively. In addition, we conducted a series of experiments to explore how different levels of noise affect performance on various datasets.
Intelligent Evaluation Model of Fetal Health based on PCA and Stacking Algorithm

Cardiotocograph is the most commonly used fetal health diagnosis technology. Because of subjective factors and low classification accuracy in traditional manual analysis of Cardiotocograph data, this paper proposes an improved Stacking classification model based on Principal Component Analysis dimension reduction. Firstly, based on the classification effect of learners and the differences among different learners, K-Nearest Neighbor, Gradient Boosting Decision Tree and Support Vector Machines are selected as base learners, and Logistic Regression is used as meta-learner, to form a two-layer Stacking model. In addition, Principal Component Analysis is used to reduce the dimension of high-dimensional data to remove noise and unimportant features. The results show that the Recall and Precision of this model reach 99.3%, which can effectively assist doctors in diagnosis and realize rapid and accurate diagnosis and prediction of fetal health.
Blurred Portrait Processing Technology for Public Security Criminal Based on MATLAB

Criminal digital image processing technology is becoming increasingly important in public security operations, especially in the identification of characters in surveillance videos and the analysis of behavior and actions in on-site videos, where blurry portraits in videos are particularly important. The paper mainly focuses on how to restore facial features of blurred human figures in video surveillance. Two methods, inverse filtering and Wiener filtering, are used to process blurred human figures, ultimately achieving the expected results. This provides assistance in identifying the same case as a major and difficult case.
HAICO: A Monitoring and Transaction Chatbot for Distributed Analytics Workflows

The fast-growing complexity of decentralized infrastructures outpaces end-users from acquiring the required knowledge to operate them. A possible remedy for this ever-aggravating problem might be the integration of natural-language-driven interfaces that allow end-users to interact with those systems. This work focuses on a Distributed Analytics (DA) use case, which includes the challenge above. We propose a DA-tailored and module-based monitoring and transaction bot called HAICO, which assists users in keeping track of their activities. Our user study (n=50) shows that our approach indeed contributes to increased transparency and accessibility of digital assets in these ecosystems. The results show that integrating automated agents can lower the barriers for non-technology-versed users to foster efficiency and adoption time.
Analysis of Social Media Data Using Deep Learning and NLP Method for potential use as Natural Disaster Management in Indonesia

During the last decade, the use of social media has exploded. One of the internet’s most popular and accessible social media sites is Twitter in which people can have micro-blog to post their views on any subject, called a tweet. Indonesia becomes one of country which has the most active user of social media. On the other side, Indonesia has experienced a great deal of natural disasters because it is located in a pacific ring of fire. It is needed effective disaster management used in Indonesia, especially with the help of social media data. Big data analysis obtained during turbulent and unorganized emergency situations is the perfect fit for effective management. During a disaster, it is vital to manage the proper decision to help people affected with their needs. This research investigated some methods used in social media analysis and then apply deep learning model for supervised tasks which classify the related and unrelated disasters on tweet datasets and Latent Dirichlet Allocation (LDA) for unsupervised learning which extracts the details category on the disaster Twitter data. The LSTM model architecture obtained a higher score accuracy than CNN. Moreover, the LDA topic modeling obtained potential results on the details topic from the datasets which could describe useful information to help disaster management.
Research on improved YOLOv5 face mask recognition algorithm based on

Accurately detecting whether a person is wearing a mask or not is important for preventing the spread of infectious diseases. In this paper, we propose a research method based on the improved YOLOv5 face mask recognition, which combines SE, CA and CBAMc3 attention mechanisms with YOLOv5 model for face mask recognition problem, and compare the effects of different attention mechanisms on recognition accuracy. The experimental results show that combining the SE attention mechanism with YOLOv5 can significantly improve the face mask recognition accuracy, and the mAP of the model is improved by 1.3%, while the CA and CBAMc3 mechanisms are less effective on the accuracy improvement. This study explores the effectiveness of different attention mechanisms in face mask recognition, and provides new ideas and methods for research in this field.
Evolving Graph Contrastive Learning for Socially-aware Recommendation

Social recommendations play a crucial role in providing personalized services to users by leveraging social relationships and user sessions. Despite recent advancements, it still faces challenges in dealing with social inconsistency and the loss of critical semantic information in user-service interactions. To overcome these problems, an Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users’ fresh interests. Specifically, the graph structure features on user-service interactions and the correlations between users and different sequences are extracted by the graph contrastive learning module. Then, social consistency sampling based on the graph convolutional network is adopted to filter out noise information effectively. Finally, time-sliced representations on the dual side (user, service) are integrated to capture users’ evolving interests by employing gated recurrent units. Comprehensive experiments on three datasets demonstrate the proposed model consistently outperforms the representative baseline methods in various evaluation metrics. EGCLSR facilitates the recommendation of services that fulfill instant requirements within dynamically evolving user interests.
Fed4ReID: Federated Learning with Data Augmentation for Person Re-identification Service in Edge Computing

Federated learning is a new distributed privacy-preserving learning paradigm which perfectly meets the requirements of many large service systems such as banking, healthcare, and smart city. Meanwhile, person re-identification, as a technology to associate the images of the same person from different data sources, has been widely used in many smart services such as smart logistics, smart surveillance, and many public searching and rescue missions. Therefore, it is a promising solution to use federated learning for person re-identification to improve the model accuracy while protecting the data privacy. However, the common problem of non-independent and identically distributed (Non-IID) data with heterogeneous clients in federated learning often causes undesirable model accuracy. To address such a problem, in this paper, we propose a novel strategy named federated learning with data augmentation for person re-identification (Fed4ReID). Specifically, to alleviate the impact of Non-IID data, we utilise a pre-trained DCGAN (Deep Convolutional Generative Adversarial Network) model for data augmentation at each edge servers. Experiments on public datasets show that our proposed strategy can outperform baseline method in general accuracy.
Generating Scenario-Centric TAP Rules for Smart Homes by Mining Historical Event Logs

Trigger-Action Programming (TAP) is a popular way of creating smart home automation applications. It can orchestrate IoT devices to fulfill user intents and make users’ daily lives more convenient. However, users’ daily lives usually have many complex scenarios that must be accomplished through several actions. The existing approaches cannot handle such situations as they mainly focus on creating simple TAP rules with a single action. This paper proposes SGen, an approach to automatically generate scenario-centric TAP rules by mining historical event traces. We first define two types of scenarios according to the characters of user activities. Accordingly, SGen identifies correlated and periodic events and uses them to synthesize scenario-centric TAP rules bottom-up without requiring all events of a potential scenario to happen at the exact moment and in the same order every time. Afterward, SGen ranks and recommends rules by prioritizing the candidates based on their diversity and significance. Finally, we evaluate SGen with two real-world datasets. The experimental results confirm that the generated scenario-centric TAP rules can match user scenarios and are more efficient in fulfilling user intents than simple rules.
Research on contrast enhancement algorithm of crop image based on improved camera response

Aiming at the problems of poor image quality, low contrast and difficult to identify crop maturity in crop images taken under rainy weather, a contrast enhancement algorithm for crop images based on improved camera response model is proposed. By maximizing the estimated exposure rate with the help of local information entropy, the camera response model was established to generate exposure sequence, and the multi-exposure fusion technology was used to enhance the contrast of crops taken under dim light. The simulation results show that the proposed method enhances the contrast of the local brightness unsaturated region of the crop image, and solves the problems of over-enhancement and color distortion.
Depth map cavity repair algorithm based on RGB image

The 3D reconstruction process often requires high-precision depth images. However, based on the limitations of the current depth sensor’s own equipment and the influence of shootingobjects, environment and other factors, the obtained depth map often has cavities and noise. Aiming at the problem that the traditional depth map cavity repair algorithm can not take into account the cavity filling and object edge preservation, and can noteffectively use the RGB image information obtained synchronously, a cavity repair algorithm based on RGB image is proposed. The algorithm divides the cavities in the depth map into edge parts and non-edge parts by obtaining the edge information in the RGB image, and then uses different methods to repair the two parts of the cavities:For non-edge cavities, because the neighborhood depth value changes continuously, the neighborhood depth value is directly used for repair; for the cavity at the edge, due to the discontinuous change of the neighborhood depth value, the depthvalue corresponding to the pointwith the largest distance centerweight in the neighborhood is selected to repair the cavity by using the pixel points corresponding to the cavity in the RGB image and its neighborhood. The experimental results show that the algorithm makes full use of the RGB image information, which can accurately fill the depth map cavitys and effectively avoid the edge blur of the object.
A Vehicle Tracking Algorithm Based on Extended Kalman Filtering

This paper is based on the actual application scenario of vehicles driving on the road. In order to better capture the apparent characteristics of vehicles, this paper proposes a target tracking algorithm based on improved Kalman filter in the tracking stage. The extended Kalman filter is used to track the vehicle. The state of the target is predicted and updated, and the predicted target is improved according to the linear model of the Kalman filter to further optimize the accuracy and precision of the vehicle target during the tracking process. The experimental results show that both accuracy and precise have been significantly improved, while identity switch has also decreased, thereby reducing the missed detection rate and false detection rate to a certain extent.
Blockchain Research in Indonesia: A Bibliometric Analysis

In the last decade, blockchain technology has been widely regarded as a disruptive technology that will drastically transform business processes in all sectors. Currently, its implementation is mainly in the private sector, especially in fintech businesses. However, in the near future, blockchain will potentially disrupt and transform all business processes, private or public, which involve intermediation by some third party. To maximize the benefits gained and at the same time mitigate the negative ramifications of the disruption, the Government needs to establish policies on the implementation of blockchain technology in Indonesia. These policies must be supported by extensive research on various aspects of blockchain implementation. This research aims to gain insights into the state, progress, and structure of blockchain research in Indonesia with bibliometric analysis. The relevant articles are collected from Scopus database for performance analysis and science mapping using the Biblioshiny software tool. The result showed that the contributions and collaborations of universities, government, and industries as the Triple Helix in blockchain research in Indonesia are still relatively small. The conceptual structure of research in this area is still evolving. The thematic analysis shows that research on the implementation of blockchain in Indonesia is mainly focused on smart contract, supply chain, security, education, and artificial intelligence. Research concerning government policies on the implementation and adoption of blockchain in e-government systems is still lacking.
Designing a Cybersecurity Risk Assessment Framework for Local Government Web-Based Applications

Administrators of e-government (SPBE) applications must prepare to manage cybersecurity risks to limit the impact of the growing number of cyber attacks on the government sector. The government has established regulations regarding SPBE risk management guidelines that need to be implemented by central and local governments. However, X District Government experienced difficulties, especially in risk assessment activities, when implementing these guidelines in the context of cyber security risks for applications. This research aims to design a risk assessment framework that is suitable for use in the context of cybersecurity risks in applications by considering the limitation of X District Government. The designed framework was tested on application Z belonging to the X District Government. Several approaches are used to carry out risk assessments, including CVSS, CAPEC, and the use of asset sensitivity forms to determine risk sensitivity. The test was performed successfully and obtained 13 high-risk categories, 7 medium-risk categories, and 1 low-risk category.
Facial landmark detection based on improved YOLOv5 and edge computing

Facial landmark detection is one of the important research algorithms in the field of image processing. In order to meet the need of lightweight facial landmark detection, this paper proposes a fast facial landmark detection algorithm based on the improved YOLOv5n model. The model adds an attention mechanism to YOLOv5n, and the local information characterization ability is enhanced. The model uses a camera to capture color pictures for input to the neural network; outputs face marker frames as well as keypoints information of eyes, ears and noses, and deploys the final model to a Jetson Nano edge computing board. Experimental results show: the proposed algorithm outperforms other similar algorithms in facial landmark detection, with recall and accuracy rates of 0.656 and 0.843, and faster detection speed, which can meet the requirements for real-time facial landmark detection on edge devices.
Implementation of Faster R-CNN with Colour and Blur Augmentation For Differentiate Cloves From Debris

Identification of cloves for debris separation (leaves and twigs) is very important in improving the quality of clove processing in the industry. This research develops a computer vision-based clove and debris object detection system using the Faster R-CNN algorithm with the ResNet50 architecture as an object detection model. The dataset consists of images of cloves and debris consisting of leaves and twigs, sometimes involved in weighing the cloves. This research utilizes the process of color augmentation and blurring to manage images during training as a contribution to this research. The testing phase was carried out using several schemes, namely comparing the detection results from testing using Faster RCNN without treatment and Faster R-CNN using color augmentation and blur. The experimental results show that the developed model can detect objects with better accuracy than the Faster R-CNN model without treatment, with mAP values of 87% and 97%, respectively. In addition, this study also evaluates the resulting model by comparing the test results with differences in speed and density resulting from data collection using a conveyor belt. The best accuracy of 96% is obtained at a minor density with a speed of 60 rpm.