Reviewerly/exHarmony huggingface dataset:
How do you solve a problem like concordance? A study of radiologists’ clinical annotations for mammographic AI training

This preliminary study investigates the magnitude of concordance, affecting factors and restrictions when radiologists' make annotations on mammographic images. Annotated data is key to the development of artificial intelligence (AI) tools and errors from annotations can reduce the accuracy of these tool. Two highly experienced radiologists (&gt;20 years’ experience) provided annotations as rectangular regions of interest to mark the location of lesions when they read 856 mammographic images with known cancer signs. Mammographic images were resized to same resolution of 1664 &times; 768 pixels using bilinear interpolation. We calculated Lin’s concordance correlation coefficient (CCC) between the coordinates in x-axis and y-axis of the 4 corners of the overlapped annotations. The two overlapped annotations in different views (cranio-caudal (CC) and medio-lateral oblique (MLO)) were evaluated for agreement between radiologists. The values of Lin’s CCC were classified in four interpretation levels: the ‘almost perfect’, ‘substantial’, ‘moderate’ and ‘poor’ according to McBride's guide (2015). The results demonstrated ‘almost perfect’, ‘substantial’, ‘moderate’ and ‘poor’ concordance in 50.1%, 29.8%, 9.5% and 10.6% of the total overlapped annotations in the MLO view, with 93.1%, 5.6%, 0.3% and 1.0% of the total overlapped annotations in the CC view, respectively. Overall, the radiologists demonstrated stronger concordance when annotating the CC view compared to the MLO. Breast density (BD) also affected the concordance of the radiologists’ annotations with a decrease in the strength of concordance agreement between breast density classifications, from 0-50% BD = higher concordance to 50-100% BD = lower concordance. Our annotation investigation has implications for AI, where delineation of lesions is often the starting point for training data.
Using transformers to multi-label long legal documents

This paper describes experiments to find an efficient method to multi-label official legal documents using Transformers. Transformers are popular and powerful ML models because they are pre-trained on large amounts of data. A significant drawback is that most Transformers cannot process documents longer than 512 tokens. We try to tackle this issue by proposing a new method to multi-label long documents – summarizing long documents first before multi-labeling with the bert-base-cased Transformer. This summarization method is compared with two existing methods: truncating documents after the first 512 tokens and Longformer. The methods are evaluated on the F1 score, and the results show that the Longformer performs the best. The summarization method and truncating method seem to output almost equal F1 scores. Although the summarization method did not perform well on the dataset used in this research, it could be promising for datasets with more structured documents.
Multi-source dynamic time series data processing method based on comprehensive weighted fusion

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data.
Research and implementation of blockchain-based performance management platform for process manufacturing

The current performance management systems in process manufacturing are built on a centralized architecture. In the event of a single point of failure in the system, the entire system network is easily exposed to attackers. Secondly, as the amount of data increases, centralized storage will also increase the burden on the system, and opaque information will also lead to trust problems within the enterprise. In response to the above problems, this paper designs a performance management platform based on blockchain, which aims to provide a secure and reliable service platform for enterprises, which has the characteristics of access control and data transparency, traceability and non-tampering. This paper gives the overall framework, hierarchical structure, and system architecture design scheme, and designs a smart contract that automates data acquisition and calculation. Finally, this paper uses blockchain technology to build a performance management system in process manufacturing, and verifies the feasibility of the system by testing the throughput of the system.
Anomaly detection for steam turbine based on dual-attention autoencoder

Anomaly detection plays an essential role in routine operation and maintenance of steam turbine. Due to vague parameter correlation, various working condition, and nonstationary industry process, there still remain challenges in building representation models to detect abnormal working condition accurately and sensitively. To alleviate this problem, this paper proposes an autoencoder(AE) approach with dual-attention mechanism, including parameter attention and temporal attention, to learn data distribution under the normal working condition. In dual-attention mechanism, parameter attention mechanism exerts varying weight to parameters referring to their current significance, and temporal attention mechanism improves information integration by utilizing the hidden state of gated recurrent unit(GRU) cells. Based on the output of AE, box-cox transformation and three-sigma rule of thumb determine the dynamic threshold. The proposed approach is evaluated through experiments on steam turbine from a real case. The experiment result demonstrates it outperforms the state-of-the-art baselines for advanced alarm time and better stability.
Identification and evaluation method of bridge abnormal state based on cloud model

In view of the uncertainty existing in the current bridge technical state evaluation process, it is suggested to introduce anomaly identification in the evaluation process. Combined with the cloud model theory and the ensemble empirical mode decomposition method, a cloud model-based bridge abnormal state evaluation method is proposed. Firstly, the original monitoring signal is decomposed by EEMD(Ensemble Empirical Mode Decomposition) to obtain multiple IMF(Intrinsic Mode Function ) components, and the mutual information method is used to select the IMF components. Finally, based on the cloud similarity distance between the clouds generated by IMF, a new cloud is synthesized, and the final generated cloud is used to evaluate the operation status of the bridge. The research shows that this evaluation method can effectively evaluate the operation state of bridge quality engineering, and can effectively evaluate the reliability of the structure. The process is more objective and the results are more comprehensive.
A GABP based method to improved software defect prediction

Considering the uneven distribution of software defect datasets, the performance of traditional SDP (software defect prediction) model is relatively low, which cannot predict the defects well and fix them in time, resulting in the low reliability of the system. This paper proposed an improved SDP method based on GABP, which helps to allocate test resources reasonably, ensures the quality of software and improves the reliability of the system. Firstly, combining with the traditional BP neural network, SMOTE and TOMEK are applied to select the better data preprocessing method and obtain the initial network architecture; Secondly, GA is employed to optimize the network architecture obtained previously to build the final predictor. Experiments on five real datasets demonstrate that the proposed method can improve the prediction performance and show its effectiveness.
Research on classroom evaluation system under the mode of educational informationization

ICT in education has become a hot word in the field of education in recent years, which is the inevitable trend of the times and the result of the country's efforts to develop ICT in education. Education informatization is a strategic support made by the country at the top-level design level, and an indispensable and important measure to promote China's modernization. Classroom management, especially students' classroom management, has a great pressure on teachers' management, which often requires teachers to spend more time and energy to maintain normal classroom order. The introduction of information classroom management tools into students' classrooms is an inevitable requirement for improving management efficiency and teaching quality. Therefore, this research is based on the information classroom learning and explores the effectiveness of the application strategy of information classroom management tools in classroom management.
Test equipment diagnosis based on fault tree and Bayesian network

To ensure the high safety operation of test equipment and improve the diagnosis accuracy, this paper proposes a fusion fault diagnosis method based on the fault tree (FT) and Bayesian network (BN) model. To achieve a more comprehensive and in-depth diagnosis, and adapt to the characteristics of less fault data of the test equipment, the advantages of the FT and BN model are fully demonstrated to yield a combination results of qualitative and quantitative. Finally, the correctness and validity of the proposed model are verified by using a typical test equipment model.
Research on ship informational support management system

Ship is a kind of complex system. It faces a larger number of integrated logistics support works and its operational readiness and mission reliability are affected by support system. In order to improve the support efficiency, this paper studies a ship informational support management system based on interactive electronic technical manual and equipment support management technologies. It introduces the system composition, function and interface of the ship informational support management system.
Relation extraction for wind tunnel maintenance based on BiGRU and multi-head attention

In the process of wind tunnel maintenance and support, fault knowledge was usually stored in an unstructured form which was not easy to be efficiently reused. Therefore, it is necessary to investigate the knowledge extraction technology for wind tunnel fault text. As a key link of knowledge extraction, relation extraction had aroused wide attention. However, there were no reported methods for fault knowledge relation extraction. In this paper, we propose multi-head-attention-based bidirectional gated recurrent networks to extract relationships from text-based wind tunnel fault knowledge. In a case study, the validity of the proposed method is verified by real wind tunnel fault corpus.
An Effective Model for Smartphone Based Pothole Classification and Admin Alerting System

This research analyses and compares machine learning and deep learning models for classifying pothole images and recording exact GPS coordinates using smartphones' built-in sensors. This deep learning classification algorithm uses convolutional neural networks (CNN) built with the TensorFlow library. Five distinct deep learning and machine learning models were trained and compared (SVM, Random Forest, Decision trees, KNN, InceptionResnetV2, InceptionV3). The most accurate model has a 91 percent accuracy rate. According to the results of this study, the suggested approach is reliable and inexpensive.
zxCAPTCHA: New Security-Enhanced CAPTCHA

Automated attacks using CNN (Convolutional Neural Network), ML (Machine Learning), and DNN (Deep Neural Network have been successful in bypassing traditional CAPTCHAs. However, Deep Learning techniques, adversarial examples and style neural transfer, have been shown to be particularly effective in protecting CAPTCHAs. In this study, the authors proposed zxCAPTCHA, a new CAPTCHA that combines cognitive-based, image-based, and text-based CAPTCHA characteristics with Deep Learning techniques to improve security. Extensive evaluations were conducted to assess the improvement of the CAPTCHA security. The experiment shows that zxCAPTCHA considerably enhances the security while maintaining comparable usability. We also demonstrate the effectiveness of combining cognitive techniques and Deep Learning to improve CAPTCHA security.
Chinese Finger Sign Language Recognition Method with ResNet Transfer Learning

Sign language is one of the most effective ways to help hearing-impaired people to communicate with other people. Although deep learning methods have been used in recognition, there are still problems with finger sign language recognition. The major issue is that the gradient approach usually fails, or the obtained recognition accuracy is not high when the depth is increasing. We thus propose a Chinese finger sign language recognition method based on ResNet and Adam optimizer together with additional image processing techniques to gain higher accuracy. We then compare our recognition results to other convolutional neural network models which are widely used deep learning techniques for recognition. Even though we have a small size of the dataset, our proposed deep learning method for finger sign recognition still gives a higher recognition rate. Also, our prototypical method provides the capability to be applied to other recognition tasks of different gestures or objects from image datasets.
Reliable Block chain-Based Digital System of Voting

The creation of an electronic voting system formally satisfies the long-term requirements of lawmakers. In the field of information technology, distributed ledger technology has become a fascinating new development. As a result, several apps have offered advantages for economic sharing as a result of the technological growth of Blockchain. The goal of this paper is to outline the legal and technological restrictions placed on the use of the blockchain as a service, as well as to show the building of an electronic voting system. In the article, another well-liked blockchain system that provides technology as a service is examined. Due to its high security, consistency, geographical variety, auditing, and decentralized computing platform of privacy, blockchain technology has grown in relevance in the industrial era [1]. as well as openness.Blockchain technology has been extensively employed in the voting system, financial systems, commercial sectors, healthcare, and IoT.
ShEnc: A Versatile Secure Multi-Party Data Sharing Framework

Secure data sharing via public Internet or local networks is absolutely vital for people today. Confidential information is stored as a file in most settings and shared via intermediate systems, including email, file hosting service, and portable devices. Cyberattacks (malicious) and human errors (non-malicious) are potential threats in these intermediate systems which may result in information leakage, impersonation, and repudiability. This is also true for communication networks. This paper presents a new method and system, called ShEnc, for end-to-end (E2E) secure multi-party data sharing. E2E encryption provides secure transmission of data from one end to the other while the intermediate systems may not be especially trustworthy. The system depends neither on prior secret sharing nor a dedicated server, secure communication channel, and special devices. Instead, we utilize the public key encryption: RSA and ECC. That is, only the public keys of the participants are disseminated beforehand, and robust confidentiality of shared data and authenticity of the sender are ensured. Furthermore, the system introduces a unique file format, enabling multi-party data sharing with a single file. The results of performance evaluation revealed that the overhead of the encrypted file size is about 2+n KB for RSA and 1+0.3n KB for ECC for the number of participants n. The processing time is less than one second under the condition where sharing 128 MiB file with 16 participants and 4 MiB file with 100 participants.
User Recognition Via Facial Parameters With Occlusion Using CNN

The prime essence of occlusion based face detection has been derived across the world with a simple agenda to tackle the rise of frauds that security reinforcement or surveillance techniques provide concrete solution with bio metric database. Though the prime solution is by bio metric authentication via smart phones through facial recognition of users; still the misuse is a challenge as the user’s face is often partially covered or occluded. Therefore, face detection or rather authentication even when occluded has become very important to prevent cyber frauds. Traditional approaches in terms of facial bio metric authentication typically have comprised of image processing or machine learning steps: feature extraction, pooling, segmentation, flattening and recognition. The researchers of this paper propose a facial occlusion detection network utilizing Convolutional Neural Networks (CNN). The result showed that accuracy in our developed model is in the range of 88 to 91 percent which in competence with the reported algorithms till occlusion of face in terms of percentage is restricted up to 50 percent of the focus area and 40 percent of the complete face.
CHESS AI: Machine learning and Minimax based Chess Engine

Designing Chess Engine has been a main focus of research for a long time. The paper employs a novel combination approach of Machine learning based estimator with artificial intelligence (AI) to build chess AI. The Minimax Algorithm is a decision theory-based technique implemented for reducing the load on the chess engine’s hardware. Also, Alpha-Beta Pruning algorithm is implemented to eliminate any nodes in the search tree that aren’t essential and hence makes the AI efficient. Trained estimators achieved a high accuracy of 96.77% for calculating the probability of ‘good move’. A variable depth of search tree based on the number of legal moves has also been employed for the minimax algorithm.
Cloud Computing Challenges in Security Applications – A Review

Cloud computing platforms provide an advanced computer system that allows organizations and individuals to operate a wide range of tasks such as using online storage capacity, implementing business applications, developing customised computer software, and creating a "realistic" communication network. Because of numerous security and privacy concerns, cloud technology has struggled to gain popularity among many large and established organizations. This research discusses the security challenges of cloud computing networks, beginning with a description of cloud computing and its various types. Furthermore, it provides a brief explanation of the most common cloud computing service challenges.
An Exploration on Competent Video Processing Architectures

Video processing is a specific type of signal processing that frequently uses video filters and video files or video streams as both the input and output signals. In real-time applications like Bio-medical applications, and assist device applications, they use image enhancement methods and edge detection methods to extract data from the image or videos. According to the research, an effort has been done in constructing effective mathematical models with functional validity using MATLAB. But the data extracted/available is no potential in making a comment on design constraints like logic delay, Logic power Consumption, and area required to develop, while taking the model to the physical implementation, into a Signal /Video processing architecture in the form of IC. Hence it is essential to initiate development methods, to fill the gaps in the complete design, from the problem statement to the physically available one that can be used in the straight application. Initially, the IIR filter architectures are developed by using Xilinx System Generator (XSG) and quality metrics are calculated for a frame extracted from video w r t. reference frame. An HDL description of the same design would then follow. Then, it is implemented on an FPGA to calculate the design constraints and assess the most effective video processing architecture.
Resolving Attached USBs: Analysis of Windows 11 Artifacts

The Windows Operating System is known for its convenience which tends to breed more and more user information in form of Artifacts. Artifacts are important repository of potential evidence while conducting any computer investigation. This paper thus provides an analysis of artifacts that will bring out the inside of USB devices attached or mounted on a particular system. Moreover, this paper will parse the data out into usable information and helps to interpret the findings of forensic examiner. In this work, Image file of the suspected system has been exported from the system for further analysis to bring out relevant information.
A Detailed Study on Adversarial Attacks and Defense Mechanisms on Various Deep Learning Models

With the increased computational efficiency, Deep Neural Network gained more importance in the area of medical diagnosis. Nowadays many researchers have noticed the security concerns of various deep neural network models used for the clinical applications. However an efficient model misbehaves frequently when it confronted with intentionally modified data samples, called adversarial examples. These adversarial examples generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Thus, various adversarial attacks and defense methods certainly stand out from both AI and security networks and have turned into a hot exploration point lately. Adversarial attacks can be expected in various applications of deep learning model especially in healthcare area for disease prediction or classification. It should be properly handled with effective defensive mechanisms or else it may be a great threat to human life. This literature work will help to notice various adversarial attacks and defensive mechanisms. In the field of clinical analysis, this paper gives a detailed research on adversarial approaches on deep neural networks. This paper starts with the speculative establishments, various techniques, and utilization of adversarial attacking strategies. The contributions by the various researchers for the defensive mechanisms against adversarial attacks were also discussed. A few open issues and difficulties are accordingly discussed about, which might incite further exploration endeavors.