Reviewerly/exHarmony huggingface dataset:
Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Due to complicated transfer paths and strong background noise interference, the fault pattern information deeply hides in common features of the vibration signal at the engine surface. In this study, the refined composite multiscale fuzzy entropy (RCMFE) used to measure the irregularity and self-similarity of time series is proposed to quantify the feature of various fault patterns. Followed by RCMFE, the features dug out are recognized by a parameter-adaptive support vector machine based on the firefly algorithm (FASVM). After putting forward the diagnosing schematics, the RCMFE-FASVM is applied to a fault diagnosis case of a diesel engine on a test rig. A comparative analysis of the four methods to extract features and the four methods to recognize fault patterns are conducted. Results indicate the proposed method has superior recognition performance and can effectively identify the working states of the diesel engine, contrasting the existing methods. Under the small samples and features task of identifying the working states of a diesel engine, the recognition rate of the proposed method with more stability can reach 98.2%, which is larger than other methods. Given the superior performance of the proposed method, the number of input features and training samples should vary from 8 to 20 and from 35 to 50.
Certification of Gaussian Boson Sampling via graphs feature vectors and kernels

Abstract Gaussian Boson Sampling (GBS) is a non-universal model for quantum computing inspired by the original formulation of the Boson Sampling (BS) problem. Nowadays, it represents a paradigmatic quantum platform to reach the quantum advantage regime in a specific computational model. Indeed, thanks to the implementation in photonics-based processors, the latest GBS experiments have reached a level of complexity where the quantum apparatus has solved the task faster than currently up-to-date classical strategies. In addition, recent studies have identified possible applications beyond the inherent sampling task. In particular, a direct connection between photon counting of a genuine GBS device and the number of perfect matchings in a graph has been established. In this work, we propose to exploit such a connection to benchmark GBS experiments. We interpret the properties of the feature vectors of the graph encoded in the device as a signature of correct sampling from the true input state. Within this framework, two approaches are presented. The first method exploits the distributions of graph feature vectors and classification via neural networks. The second approach investigates the distributions of graph kernels. Our results provide a novel approach to the actual need for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.
Modality-correlation embedding model for breast tumor diagnosis with mammography and ultrasound images

The fusion of mammography and ultrasound images helps to improve tumor classification accuracy. However, traditional fusion models ignore the correlation between these two modalities, resulting in limited performance improvement. To address this problem, a modality-correlation embedding model was proposed for breast tumor diagnosis by combining mammography and ultrasound imaging. By jointly optimizing the correlation between mammography and ultrasound and classification loss of individual modalities, two mappings are learned to project mammography and ultrasound from their original feature spaces into a common label space. A novel modality-correlation term is introduced to maintain the pairwise closeness of multimodal data in the common label space. Contrary to previous studies that did not consider the correlation between multimodal data, the proposed term can exploit the learned correlation information in the fusion process, which guarantees the consistency of the diagnosis results of multimodal images from the same patient. The proposed method was evaluated on our dataset, which contained ultrasound and mammography images from 73 patients. The area under the ROC curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 95.83, 95, 91.67, 95.83, 95.83, and 88.89%, respectively. The experimental results also demonstrate that the proposed method outperforms traditional fusion methods.
A novel CapsNet neural network based on MobileNetV2 structure for robot image classification

Image classification indicates that it classifies the images into a certain category according to the information in the image. Therefore, extracting image feature information is an important research content in image classification. Traditional image classification mainly uses machine learning methods to extract features. With the continuous development of deep learning, various deep learning algorithms are gradually applied to image classification. However, traditional deep learning-based image classification methods have low classification efficiency and long convergence time. The training networks are prone to over-fitting. In this paper, we present a novel CapsNet neural network based on the MobileNetV2 structure for robot image classification. Aiming at the problem that the lightweight network will sacrifice classification accuracy, the MobileNetV2 is taken as the base network architecture. CapsNet is improved by optimizing the dynamic routing algorithm to generate the feature graph. The attention module is introduced to increase the weight of the saliency feature graph learned by the convolutional layer to improve its classification accuracy. The parallel input of spatial information and channel information reduces the computation and complexity of network. Finally, the experiments are carried out in CIFAR-100 dataset. The results show that the proposed model is superior to other robot image classification models in terms of classification accuracy and robustness.
The Establishment of College Student Employment Guidance System Integrating Artificial Intelligence and Civic Education

A logical and appropriate job advising system for college students is the only way to address the current dire work scenario for recent graduates in a society that is becoming more and more competitive. The process of building new career advice systems for college students is still fraught with many major issues. Existing problems are manifested in the lack of a perfect employment guidance system for college students and a lack of effective ideological and political education, which leads to the inability of college students to establish a correct employment concept. At the same time, the recommendation method used in the current college student employment guidance system is not reasonable enough and only considers students’ one-way preference for employment units. Then, the history of one user’s employment is likely to lead to unreliable negative samples, which affects the recommendation performance. In addition, the current employment recommendation system cannot fully integrate the service function, management function, education function, and research function of the employment guidance system. In this paper, we address the aforementioned issues and propose an unique career counseling system for college students by combining algorithmic recommendation with ideological and political education. The system adaptively aggregates the preferences and needs of students and employers and fully integrates several functions of the student career competence and career guidance system. The experimental results show that the establishment of a college students’ employment guidance system integrating artificial intelligence (AI) and civic education not only has better interpretability compared with the traditional employment guidance system but also has a higher area under curve (AUC). Furthermore, we also discuss the importance of ideological and political education and referral systems in the work of the career guidance system through ablation experiments. The results show that the AUC values of integrating ideological and political education and recommender systems were higher than those of integrating only ideological and political education or recommender systems. Therefore, establishing an employment guidance system for college students that integrates AI and civic education can effectively guide students’ employment. At the same time, the college student employment guidance system proposed in this paper can offer employment guidance courses in colleges and universities, help graduates to solve their doubts, and make graduates adjust their psychological state in the process of job hunting.
Event Related Brain Responses Reveal the Impact of Spatial Augmented Reality Predictive Cues on Mental Effort

This article presents the results from a Spatial Augmented Reality (SAR) study which evaluated the cognitive cost of several predictive cues. Participants performed a validated procedural button pressing task, where the predictive cue annotations guided them to the upcoming task. While existing research has evaluated predictive cues based on their performance and self-rated mental effort, actual cognitive cost has yet to be investigated. To measure the user's brain activity, this study utilized electroencephalogram (EEG) recordings. Cognitive load was evaluated by measuring brain responses for a secondary auditory oddball task, with reduced brain responses to oddball tones expected when cognitive load in the primary task is highest. A simple monitor n-back task and procedural task comparing monitor versus SAR were conducted, followed by a version of the procedural task comparing the SAR predictive cues. Results from the brain responses were able to distinguish between performance enhancing cues with a high and low cognitive load. Electrical brain responses also revealed that having an arc or arrow guide towards the upcoming task required the least amount of mental effort.
Providing scalability and privacy for smart contract in the healthcare system

As the healthcare industry evolved, it became an integral participant in providing services. In recent years, remote observation services have grown increasingly important for patients. In cases where patients are not physically present in clinics or hospitals, healthcare providers produce observations systems that diagnose and prescribe treatment. In/on patients' bodies, sensors are installed. Data can be exchanged wirelessly between healthcare systems. As a result of the implementation of blockchain-based characteristics (e.g. distributed ledger, decentralized storage, and authentication), healthcare systems can be enhanced. Accordingly, smart contracts are applied for the achievement of more authentication and sharing of records, and requirements for participants automatically. As the number of users or other components increases, blockchains and smart contracts should be able to provide quality service. Technically, smart contracts using blockchains have several challenges that are important, such as scalability, enhanced security, and optimal performance. There is still a question about the scalability of blockchain as there is a significant amount of computational power needed to scale up with a high number of participants in healthcare systems. In this paper, Hierarchical State Machines (HSMs) and ZoKrates are used to lower the consumption gas and the scalability of blockchain with smart contracts.
Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China

This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe2O3, etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection.
A dataset of images of public streetlights with operational monitoring using computer vision techniques

A dataset of street light images is presented. Our dataset consists of ∼350 k images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in the UK. Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb. Each node collects an image at hourly intervals for 24 h every day. The data collection spans for a period of six months. Each image taken is logged as a single entry in the dataset along with the Global Positioning System (GPS) coordinates of the lamppost. All entries in the dataset have been post-processed and labelled based on the operation of the lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be used to train deep neural networks and generate pre-trained models providing feature representations for smart city CCTV applications, smart weather detection algorithms, or street infrastructure monitoring. The dataset can be found at 10.5281/zenodo.6046758.
Cryptanalysis of a Semi-Quantum Bi-Signature Scheme Based on W States

Recently, Zhao et al. proposed a semi-quantum bi-signature (SQBS) scheme based on W states with two quantum signers and just one classical verifier. In this study, we highlight three security issues with Zhao et al.’s SQBS scheme. In Zhao et al.’s SQBS protocol, an insider attacker can perform an impersonation attack in the verification phase and an impersonation attack in the signature phase to capture the private key. In addition, an eavesdropper can perform a man-in-the-middle attack to obtain all of the signer’s secret information. All of the above three attacks can pass the eavesdropping check. Without considering these security issues, the SQBS protocol could fail to ensure the signer’s secret information.
Using Paper Texture for Choosing a Suitable Algorithm for Scanned Document Image Binarization

The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off.
How Do Deep-Learning Framework Versions Affect the Reproducibility of Neural Network Models?

In the last decade, industry’s demand for deep learning (DL) has increased due to its high performance in complex scenarios. Due to the DL method’s complexity, experts and non-experts rely on blackbox software packages such as Tensorflow and Pytorch. The frameworks are constantly improving, and new versions are released frequently. As a natural process in software development, the released versions contain improvements/changes in the methods and their implementation. Moreover, versions may be bug-polluted, leading to the model performance decreasing or stopping the model from working. The aforementioned changes in implementation can lead to variance in obtained results. This work investigates the effect of implementation changes in different major releases of these frameworks on the model performance. We perform our study using a variety of standard datasets. Our study shows that users should consider that changing the framework version can affect the model performance. Moreover, they should consider the possibility of a bug-polluted version before starting to debug source code that had an excellent performance before a version change. This also shows the importance of using virtual environments, such as Docker, when delivering a software product to clients.
A Comprehensive Study of Spatiotemporal Feature Learning for Social Medial Popularity Prediction

For accurately predicting the popularity of social media, the multi-modal approach was usually adopted to have promising performance. However, the popularity is highly correlated to its identity (i.e., user ID). Inappropriate data splitting could result in lower generalizability in real-world applications. Specifically, we observed that the training and testing datasets are partitioned on a specific timestamp, whereas some users were registered after the timestamp, implying that the partial identities could be missing in the testing phase. It turns the social media prediction (SMP) tasks temporally irrelevant, making the temporal-related feature useless due to missing identities. Therefore, we form the SMP task as an identity-preserving time-series task to observe the popularity scores for the specific identity. In addition, more valuable and essential features could be explored. In this paper, by reformulating the SMP tasks and integrating the multi-modal feature aggregation to the base learner for better performance, how identity-preserving is an essential property for SMP tasks is discussed. We firstly explore the impact of the temporal features with/without identity information in the conventional SMP tasks. Moreover, we reformulate the SMP task in the time-series data-splitting and evaluate the temporal features' importance. Comprehensive experiments are conducted to deliver the suggestions for the SMP tasks and offer the corresponding solutions for effectively predicting the popularity scores.
Construction of Virtual Marketing Interactive Platform for Digital Twin Innovation and Entrepreneurship Based on Blockchain

With the rapid development and application of internet technology, people are gradually entering a state of digital life. However, it is difficult for digital assets to be circulated with a unified standard protocol. Blockchain has the characteristics of decentralization, trustworthiness, and immutability, which can be used to solve the above problems. Although virtual marketing technology shows great potential, as a form of organization that is difficult to manage, there is still a lack of experience in the form factors and operation mechanism of a virtual business model. Therefore, combined with the realistic background, this paper built a digital twin innovation and entrepreneurship virtual marketing interactive platform based on blockchain. The article analyzed the data structure of the blockchain and completed the construction of the platform operation mode. It studied the encryption and decryption process of cryptography and expanded the ciphertext space, then the blockchain is encrypted and decrypted again, and finally, a chi-square analysis is performed. After building the platform, it conducted market research and analyzed the main operating indicators of the A platform on the market. Then, it compared the uncertain string preprocessing enumeration length change and the index occupied space between the platform constructed in this paper and the platform in the literature. The comparison results have shown that the index proposed in this paper is 320 MB in 8 iterative calculations, while the bibliographic index starts at 10000 MB and keeps increasing during the iterative calculation process. The two are not in the same order of magnitude, illustrating the superiority of the platform in this paper.
Application and Effect Evaluation of Nursing Risk Management in Nursing of Cardiovascular Medicine

Cardiovascular medicine patients have complex conditions and rapid progress. They will cause a variety of complications during the illness and are difficult to care for. In addition, the above departments have many treatment tools, complex nursing points, and nursing risks. Scientific risk management should be carried out to avoid the occurrence of adverse events. Existing studies incorporating nursing risk management into cardiovascular health care are incomplete. This paper aims to explore the analysis and research methods of nursing cardiovascular medicine applications and effects evaluation based on nursing risk management and deep learning. Through the observation and experiment of grouping 100 cardiovascular medicine patients in a hospital, the total satisfaction degree of the experimental group reached 90%, and the control group was only 48%, which is quite different in comparison. The factors affecting the occurrence of nursing risk have the characteristics of multiplicity, instability, and uncertainty. Therefore, in order to improve the nursing effect and the rescue rate, it is imperative to strengthen the nursing risk management and, at the same time, reduce the patient’s physical pain and nursing risk.
Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor

Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time.