Reviewerly/exHarmony huggingface dataset:
An overview of the application and development of data integrity verification techniques

With the development of computer technology to date, there is a large amount of data to be transmitted, processed, analyzed and stored, so the issue of data security is a very important aspect. Data integrity verification is an important part of operating system security or cloud server security, data integrity is the property that data has not been tampered with, damaged or lost during transmission and storage, data integrity that is compromised may result in data integrity, if compromised, can lead to data unavailability, which can lead to larger security issues. Therefore, to ensure the security of an operating system or cloud server, effective techniques and methods must be adopted in the operating system to protect data integrity. There are various data integrity verification techniques available, such as digital signature algorithms (DSA), Merkle hash tree and blockchain-based decentralization data storage and verification techniques. In this paper, current data integrity verification techniques are analyzed and presented.
Automatic left ventricle segmentation via <scp>edge‐shape</scp> feature‐based fully convolutional neural network

Abstract Left ventricle (LV) segmentation is essential to identify the cardiac functions for treating cardiovascular disorders. Cardiovascular magnetic resonance (CMRI) imaging is a non‐invasive technique for diagnosing cardiovascular diseases. CMRI is widely used to assess the functional integrity of the left and right ventricles for detecting changes in myocardial structure. Clinical parameters of the LV are often retrieved from CMRI scans, such as LV volumes, and ejection fraction. Moreover, manually segmenting cardiac diseases and evaluating such functions is time‐consuming and difficult for medical professionals. Deep learning networks require a lot of time, cost, and knowledge. To overcome this issue, a novel Edge and Shape feature‐based Fully Convolutional Neural Network (ES‐FCN) has been proposed for automatic LV segmentation. The ES‐FCN model segments MRI images based on edge maps instead of using gray‐scale images, which accelerates the performance of the FCN. The fuzzy‐based canny edge detection algorithm leverages fuzzy logic to detect structural changes in the LV and generate binary images. Additionally, binary‐valued kernels are used for convolution operations, where the binary values are influenced by biases derived from edge map shape descriptors. In ES‐FCN, bias values are learned from ground truth segmentation. The proposed ES‐FCN model achieves the Jaccard indices of 0.9484 ± 0.0188 for the ACDC dataset and 0.9476 ± 0.0237 for the LVSC dataset, and the dice index of 0.9319 ± 0.0188 for the ACDC dataset and 0.9314 ± 0.0237 for LVSC dataset respectively. The experimental results also reveal that the proposed ES‐FCN model is faster and requires minimal resources compared to state‐of‐the‐art models.
Energy Autonomy Scenario for the Island of Mykonos in Greece with Solar Power Plants

Mykonos is one of the fastest growing Greek islands, and it attracts hundreds of thousands of tourists, mainly in the summer months. A consequence of this rapid development is an ever-increasing energy demand. Solar energy exploitation is the topic of this study, and the aim is to provide an energy autonomous solution for Mykonos that covers its electricity needs for the whole year from photovoltaic plants. Data from the Copernicus Atmosphere Monitoring Service and Eumetsat’s support to nowcasting and very short-range forecasting were combined with Radiative Transfer Models and the Photovoltaic Geographical Information System in order to quantify the island’s expected electricity production and the effect of atmospheric parameters, and this enabled an energy adequacy scenario to be formed and financial analysis to be conducted. The aim of the method proposed in this study is to highlight the opportunity for energy transition for Mykonos as well as the rest of Cyclades islands, and to thereby provide viable and sustainable development.
Hierarchical feature learning network for salient object detection

How to effectively use multi-level features is the key to improve the performance of salient object detection (SOD) model. Many existing methods use the same or similar learning strategies to deal with multi-level features, while there are relatively few studies on using different learning strategies to deal with multi-level features. In addition, how to fully fuse multi-level features is an important process to obtain accurate saliency map, A novel hierarchical feature learning network (HFLNet) is proposed to realize salient object detection. The whole detection process can be divided into three stages. Firstly, ResNet-50 is used as the backbone network to extract multi-level features; Then, multi-level features are processed by residual denoising module, interaction module and adjacent pyramid module respectively; After obtaining the further learned multi-level features, a cross fusion module is proposed to progressively fuse the multi-level features from top to bottom and bottom to top, and finally fuse the results of the above two paths to obtain the final prediction results. During the training of network model, a hybrid loss function is applied to assist the training. The proposed method is compared with nine related methods on four public datasets. The experimental results show that the method not only improves the four important quantitative performance metrics, especially the MAE, but also can predict more complete saliency maps.
LSTM network implementation on news sentiment analysis

Begin the News is a kind of text using natural language and are highly publicized for new agencies are experts in collecting information, organize materials and identifying false facts. News titles are mostly used for state the facts and express emotion indirectly, while it is important for many organizations to analyze and collect the instant data of news sentiment and their spreading. In this research, the dataset is from Kaggle that contains more than 50,000 samples with the news titles and their sentiment score. Word embedding is used for word tokenizing and long short-term memory (LSTM) algorithm is used on the classification task. A binary classifier that can divide the samples into positive news and negative news reach an accuracy of 87.25%, and a triple classifier that can divide the samples into positive news, negative news and neutral news have 81.38% accuracy after tuning the hyperparameters. Both of the outcome is better than other methods in comparison. LSTM can be a reliable method to use for semi-supervised news title sentiment analysis.
ArrayNet: A Combined Seismic Phase Classification and Back-Azimuth Regression Neural Network for Array Processing Pipelines

ABSTRACT Array processing is an integral part of automatic seismic event detection pipelines for measuring apparent velocity and backazimuth of seismic arrivals. Both quantities are usually measured under the plane-wave assumption, and are essential to classify the phase type and to determine the direction toward the event epicenter. However, structural inhomogeneities can lead to deviations from the plane-wave model, which must be taken into account for phase classification and back-azimuth estimation. We suggest a combined classification and regression neural network, which we call ArrayNet, to determine the phase type and backazimuth directly from the arrival-time differences between all combinations of stations of a given seismic array without assuming a plane-wave model. ArrayNet is trained using regional P- and S-wave arrivals of over 30,000 seismic events from reviewed regional bulletins in northern Europe from the past three decades. ArrayNet models are generated and trained for each of the ARCES, FINES, and SPITS seismic arrays. We observe excellent performance for the seismic phase classification (up to 99% accuracy), and the derived back-azimuth residuals are significantly improved in comparison with traditional array processing results using the plane-wave assumption. The SPITS array in Svalbard exhibits particular issues when it comes to array processing in the form of high apparent seismic velocities and a multitude of frost quake signals inside the array, and we show how our new approach better handles these obstacles. Furthermore, we demonstrate the performance of ArrayNet on 20 months of continuous phase detections from the ARCES array and investigate the results for a selection of regional seismic events of interest. Our results demonstrate that automatic event detection at seismic arrays can be further enhanced using a machine learning approach that takes advantage of the unique array data recorded at these stations.
Towards a Robotic Intervention for On-Land Archaeological Fieldwork in Prehistoric Sites

Archaeological activities lead to ancient artifacts discoveries and vestiges. Some excavation operations are both difficult and repetitive: industry 4.0 concepts such as artificial intelligence (AI) and advanced robotics are exploited in the production manufacturing processes to increase performance and could help automate some stages of excavation. This paper deals with the integration of these concepts in the archaeological domain to address specific and tedious tasks. Indeed, archaeological sites are mostly difficult to access places such as open sites and caves, thus making excavation even more challenging, hence the need for robots. The Archaeological Cobotic Explorer (A.C.E.), presented in this paper is a robot that could work alongside humans during archeological surveys. It would be a precise and untiring workforce, capable of locating and retrieving artifacts underground. This project aims to describe the appropriate industry 4.0 concepts that could be exploited in this particular domain and to create a machine, (A.C.E.), built with commercially available materials or replaceable 3D-printed pieces, capable of automating specific stages of the excavation process. Advanced computer aided design and functional prototypes were built and are presented in this paper.
Construction of comprehensive application platform for intelligent teaching of photoshop course under cloud platform

Under the traditional teaching mode, there are some problems in the teaching practice of Photoshop computer-aided design course, such as lack of teaching resources, single teaching method and fixed curriculum, which restrict the effectiveness of teaching. In this regard, this paper puts forward a set of construction scheme of intelligent teaching comprehensive application platform to realize the network, digital and intelligent transformation of Photoshop course teaching mode and improve the training quality of information professionals in the new period. The whole system is B/S architecture, relying on the "cloud-network-end" functional service system, organically combining theoretical knowledge teaching with practical skills training, forming a new form of integrated teaching. Practice has proved that the system has strengthened the application support of multimedia digital teaching resources, and improved the application efficiency of teaching content and training materials by relying on LFM recommendation algorithm model, and realized the teaching evaluation based on standard deviation algorithm, which played a positive role in improving the efficiency and quality of classroom teaching.
F-ALBERT: A Distilled Model from a Two-Time Distillation System for Reduced Computational Complexity in ALBERT Model

Recently, language models based on the Transformer architecture have been predominantly used in AI natural language processing. These models, which have been proven to perform better with more parameters, have led to a significant increase in model size and computational load. ALBERT solves this problem by significantly reducing the number of parameters it retains by repeatedly reusing parameters. Although ALBERT significantly reduces the parameters it maintains, it requires a computational load similar to the original language model due to the reuse process. In this study, we develop a distillation system that decreases the number of times the ALBERT model reuses parameters and progressively reduces the parameters being reused. We propose a representation in this distillation system that can effectively distill the knowledge of the original model and develop a new architecture with reduced computation. Through this system, F-ALBERT, which had about half the computational load compared to the ALBERT model, restored about 98% of the performance of the original model on the GLUE benchmark.
FL4C^2: a trustworthy intelligent cloud computing for private machine learning

The cloud operating system plays a crucial role in supporting machine learning tasks by offering powerful computing capabilities and abundant resources. This enables the implementation of various complex applications, including health monitoring and noise pollution assessment. However, this paradigm involves centralized data storage and processing on a central server, which raises significant data security concerns. To tackle these security issues, we propose a state-ofthe- art framework called FL4C^2, which amalgamates federated learning (FL) and cloud computing (CC) eliminating their inherent drawbacks. Specifically, FL4C^2 allows users to collaboratively learn a shared model by solely submitting model parameters to the server, which ensures that user data remains localized and private, thereby preventing any potential data leakage. Moreover, FL4C^2 incorporates an encryption parameter transmission mechanism, which safeguards against indirect data leakage resulting from sharing the model parameters. Simultaneously, we employ an anonymity strategy to generate verifiable anonymous identities, which enhances identity privacy protection. Extensive experimental results based on the KunPeng operating system demonstrate that FL4C^2 achieves superior performance while effectively preserving user privacy.
Bounds on Performance for Recovery of Corrupted Labels in Supervised Learning: A Finite Query-Testing Approach

Label corruption leads to a significant challenge in supervised learning, particularly in deep neural networks. This paper considers recovering a small corrupted subset of data samples which are typically caused by non-expert sources, such as automatic classifiers. Our aim is to recover the corrupted data samples by exploiting a finite query-testing system as an additional expert. The task involves identifying the corrupted data samples with minimal expert queries and finding them to their true label values. The proposed query-testing system uses a random selection of a subset of data samples and utilizes finite field operations to construct combined responses. In this paper, we demonstrate an information-theoretic lower bound on the minimum number of queries required for recovering corrupted labels. The lower bound can be represented as a function of joint entropy with an imbalanced rate of data samples and mislabeled probability. In addition, we find an upper bound on the error probability using maximum a posteriori decoding.
Lightweight small target detection algorithm based on YOLOv5

To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTG-YOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further light weighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhanced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy.
Research on illegal access behavior control of heterogeneous cloud resources based on lifecycle characteristics

The current conventional illegal access behavior security control strategy for heterogeneous cloud resources mainly realizes the access control of resources by conceiving the digital identity of access units, which leads to the low security of access control due to the low encryption of heterogeneous cloud resources. In this regard, the heterogeneous cloud resource illegal access behavior security control strategy based on life cycle characteristics is proposed. By analyzing the user access history behavior data, the trust value of user access operation is calculated. And the access control policy is constructed based on the encryption of user's operation behavior as well as resources by using a two-layer encryption algorithm. In the experiment, the designed illegal access behavior security control policy is tested for security type. The final result can prove that when using the proposed method for access control of heterogeneous cloud resources, the speed of intercepting illegal users is faster, and when facing various types of attacks, this method has a low success rate and high access control security.
Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network

Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to the characteristics of remote sensing images and have achieved good performance, most of them still use horizontal bounding boxes, which struggle to accurately mark targets with multiple angles and dense arrangements in remote sensing images. We propose an oriented bounding box optical remote sensing image object detection method based on an enhanced feature pyramid, and add an attention module to suppress background noise. To begin with, we incorporate an angle prediction module that accurately locates the detection target. Subsequently, we design an enhanced feature pyramid network, utilizing deformable convolutions and feature fusion modules to enhance the feature information of rotating targets and improve the expressive capacity of features at all levels. The proposed algorithm in this paper performs well on the public DOTA dataset and HRSC2016 dataset, compared with other object detection methods, and the detection accuracy AP values of most object categories are improved by at least three percentage points. The results show that our method can accurately locate densely arranged and dynamically oriented targets, significantly reducing the risk of missing detections, and achieving higher levels of target detection accuracy.
Projected Augmented Reality to Display Medical Information Directly on a Patient Skin

A patient’s internal anatomy can be difficult to visualize when viewed on a monitor, head-mounted display, or even when looking at an actual patient. Combining medical images (CT, MRI, US, PET) with a physical model helps recover missing anatomical context and improves situational awareness. This chapter describes an augmented reality system capable of projecting medical image information directly onto curved targets such as the human body or a mannequin. The motion of the targets and the projector are tracked using a motion capture system so that the images are adjusted in real time to match the anatomy changes in position and orientation. The augmented information can be displayed using volume rendering for realistic visualization of the internal anatomy and 3D models from segmented images. Calibration is performed on the projector and the tracking system to obtain an accurate, common coordinate system and correct visual distortions created by the fact that the projected screen (human body) is no longer a plane. The system is easily extendable to other display technology and has many potential applications, including medical education, surgical planning, and laparoscopic surgery.
Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

During the Multi-Object Tracking (MOT) process, the effectiveness of Re-Identification (Re-ID) is typically contingent upon the performance of detection. In particular, detection errors may result in the blurring of Re-ID features, thereby diminishing the efficacy of Re-ID. In particular, Re-ID exhibits poor robustness in intricate scenarios such as distance and scale alterations, occlusion, and crowding. Meanwhile, the frequent occurrence of object occlusion can result in the interruption of object tracking, thereby impacting the tracking efficiency. To address these issues, a novel MOT method approach that leverages Re-ID enhancement and association correction is proposed. Through an unanchored, one-stage backbone network with CenterNet as the detector, the object position is detected by predicting the heatmap of the object center point. A constraint on the Predicted Distance Difference Loss (PDDL) is then designed for the response value of the predicted heatmap, effectively enhancing the detection accuracy and reduce the detection bias. To address the issue of Re-ID reliance on detection accuracy and enhance its robustness, an Effective Feature Graph Convolutional Neural network (EFGCN) is proposed. This approach expands learnable effective features by constructing a graph model that enhances them through a GCN model of heatmap true value locations and surrounding response value locations. Additionally, a target trajectory Association Correction Module (ACM) is introduced in the association phase to handle interruptions and trajectory fragments caused by occlusion. By improving data association for tracking judgment when dealing with occluded targets and considering the relationship between trajectory fragmentation and the detection of low-scoring targets, attempts are made to continue with tracking occluded targets to form complete object associations. The proposed method is evaluated on the MOT16 and MOT17 datasets and compared with other algorithms. The results demonstrate that it is highly effective.
The Calibration Process and Setting of Image Brightness to Achieve Optimum Strain Measurement Accuracy Using Stereo-Camera Digital Image Correlation

Combining the drilling method with the digital image correlation (DIC) method is becoming more common to speed up the measurement and evaluate the strains relieved at several locations. However, to obtain the most accurate results, it is necessary to be aware of the influence of possible aspects that could adversely affect the results of the strain/stress analysis carried out using DIC. The paper describes several analyses to assess the influence of the 3D DIC system’s calibration procedure for strain/stress analysis of the specimen with a hole loaded with four different levels of tensile force. In addition, the paper also deals with the analysis of the influence of the image brightness, which was modified by changing the exposure time of the cameras. Based on the results of strain/stress analyses performed on small areas (approx. 25 × 25 mm) of a specimen with a hole by a stereo-camera DIC system, it can be concluded that both analysed factors can negatively influence the results. The most accurate results are ensured using the calibration target of very high manufacturing precision sized similarly to the field of view observed in correlation mode. The optimal image brightness is adjusted when the mean grey value of the image is from the range of 56 up to 171 with as evenly distributed image point intensities as possible.