Reviewerly/exHarmony huggingface dataset:
An Abstract Syntax Tree based static fuzzing mutation for vulnerability evolution analysis

Zero-day vulnerabilities are highly destructive and sudden. However, traditional static and dynamic testing methods cannot efficiently detect them. In this paper, a static fuzzy mutation method for program code is studied. This method can improve the efficiency of mutation sample generation according to the vulnerability evolution law, thus promoting the development of zero-day vulnerability detection methods based on deep learning techniques. A static fuzzy mutation method based on the Abstract Syntax Tree (AST) is proposed. Under the guidance of software vulnerability evolution law, potential evolution paths that threaten program security are detected, and mutation samples containing vulnerabilities are generated at the syntax tree level based on the paths. To verify the effectiveness of static fuzzy mutation based on ASTs, this paper starts with Concurrent Use After Free (CUAF) homologous vulnerability. It uses multi-threaded programs to perform vulnerability feature statement insertion processing to infer the optimal mutation operator execution sequence corresponding to CUAF vulnerabilities triggered by data competition. The Linux kernel code is used to verify whether it can effectively reduce the number of invalid mutation samples. In this paper, we filter the code fragments in the Linux kernel public code containing CUAF vulnerability fix commits and perform static fuzzy mutation on the fix versions of the vulnerabilities to reproduce the vulnerabilities of this type triggered by these code fragments on the timeline. We compare the process with the execution of the random mutation operator in traditional detection methods horizontally and improve the efficiency by 42.4% on average. The static fuzzy mutation based on the AST is effective in stages. When this method is explored in more vulnerability-type evolution laws, it is expected to promote the development of the zero-day vulnerability active detection technology framework.
A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection

Data augmentation is an important procedure in deep learning. GAN-based data augmentation can be utilized in many domains. For instance, in the credit card fraud domain, the imbalanced dataset problem is a major one as the number of credit card fraud cases is in the minority compared to legal payments. On the other hand, generative techniques are considered effective ways to rebalance the imbalanced class issue, as these techniques balance both minority and majority classes before the training. In a more recent period, Generative Adversarial Networks (GANs) are considered one of the most popular data generative techniques as they are used in big data settings. This research aims to present a survey on data augmentation using various GAN variants in the credit card fraud detection domain. In this survey, we offer a comprehensive summary of several peer-reviewed research papers on GAN synthetic generation techniques for fraud detection in the financial sector. In addition, this survey includes various solutions proposed by different researchers to balance imbalanced classes. In the end, this work concludes by pointing out the limitations of the most recent research articles and future research issues, and proposes solutions to address these problems.
A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework

Artificial intelligence (AI) technologies have seen strong development. Many applications now use AI to diagnose breast cancer. However, most new research has only been conducted in centralized learning (CL) environments, which entails the risk of privacy breaches. Moreover, the accurate identification and localization of lesions and tumor prediction using AI technologies is expected to increase patients’ likelihood of survival. To address these difficulties, we developed a federated learning (FL) facility that extracts features from participating environments rather than a CL facility. This study’s novel contributions include (i) the application of transfer learning to extract data features from the region of interest (ROI) in an image, which aims to enable careful pre-processing and data enhancement for data training purposes; (ii) the use of synthetic minority oversampling technique (SMOTE) to process data, which aims to more uniformly classify data and improve diagnostic prediction performance for diseases; (iii) the application of FeAvg-CNN + MobileNet in an FL framework to ensure customer privacy and personal security; and (iv) the presentation of experimental results from different deep learning, transfer learning and FL models with balanced and imbalanced mammography datasets, which demonstrate that our solution leads to much higher classification performance than other approaches and is viable for use in AI healthcare applications.
Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.
A cross-channel color image encryption algorithm using two-dimensional hyperchaotic map

This paper innovatively proposes a high-sensitive cross-channel color image encryption algorithm (SFHM-IEA) by using two-dimensional hyperchaotic map (2D-SFHM) derived from Sine map and mathematical function. The performance of the proposed map is numerically investigated, and the results show that 2D-SFHM has larger Lyapunov exponents (up to 8.766), more stable sample entropy, C0 complexity and permutation entropy (all almost 1) than other existing chaotic maps, which indicates that the chaotic sequences generated by 2D-SFHM are highly stochastic. Then a cross-channel color image encryption algorithm combining peripheral-pixel extension technique is proposed, which uses circular-shift permutation and bidirectional-parallel diffusion to obtain strong confusion and diffusion properties. The experimental result with chi-square=229.2843, Pearson correlation coefficient and mutual information are almost 0, information entropy can reach 7.9998, average NPCR=99.6098% and average UACI=33.4632%, which proves the ability of the proposed algorithm to securely resist various illegal attacks.
Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China

This study examines Shenzhen's potential for utilizing photovoltaics (PV) on buildings in terms of residential electricity consumption. Based on its geographic information system (GIS) data, typical local meteorological data, and electricity demand, the solar potential of Shenzhen has been thoroughly studied, critically analyzed, and compared with different urban morphology characteristics. Urban morphology influences the solar radiation received by individual buildings. First, four representative residential areas have been chosen. Using the 3D architectural modeling software Rhinoceros with its Grasshopper and Ladybug plug-ins, the solar potential of the areas was then evaluated. Following this, an economic analysis was carried out. Due to the high solar radiation of the rooftop areas, the results demonstrated that a combination of rooftops and high-performance facades (over 700 kWh/m2/year) produces a promising payback period. Therefore, this paper calls for action to start a large-scale program to promote and then mandate the use of renewable energy resources (RES), specifically solar, for the residential sector in the cities located in the southern region of China.
Large margin distribution multi-class supervised novelty detection

As one of state-of-the-art supervised novelty detection models, support vector machine-supervised novelty detection (SVM-SND) can recognize whether a test instance is a novelty or which class the test instance comes from if it is a normal instance through a single model. The SVM-SND inherits the idea of maximizing minimum margin from one-class support vector machine. However, current research has proved that maximizing minimum margin is not the necessary condition to ensure the generalization in classification. This paper introduces margin distribution into multi-class supervised novelty detection and proposes a large margin distribution-supervised novelty detection (LMD-SND) model in which margin distribution is used to enhance the performance for multi-class supervised novelty detection. The margin distribution is represented by margin mean and margin variance. The LMD-SND maximizes the sum of margin means of all classes and minimizes the sum of margin variance of all classes through extra two terms, simultaneously. Compared with SVM-SND, LMD-SND inherits its merits for multi-class supervised novelty detection but has better generalization. The experimental results show that LMD-SND is superior to SVM-SND and can demonstrate comparative performance with shallow multi-class supervised novelty detection models, KNFST and KLMNN, and deep novelty detection models, DSVDD and DMSVDD.
Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems

Recent years have witnessed the development and adoption of blockchain technology in intelligent transportation systems (ITS) because of its authenticity and traceability. However, increasing ITS devices impose grand challenges in privacy-preserving and efficient data sharing. Recent research has demonstrated that integrating searchable symmetric encryption in blockchain enables privacy-preserving data sharing among ITS devices. However, existing solutions focus only on single-keyword searches over encrypted ITS data on the blockchain and suffer from privacy and efficiency issues when extended to multi-keyword scenarios. This work proposes a bloom filter-based multi-keyword search protocol for ITS data with enhanced efficiency and privacy preservation. We design a bloom filter to select a low-frequency keyword from the multiple keywords input by the ITS data owner. The low-frequency keyword can filter out a large portion of the ITS data from the search result, thus significantly reducing the computational cost. Furthermore, each identifier-keyword pair is attached with a pseudorandom tag that enables the completion of a search operation in only one round. In this manner, privacy is preserved because there are no intermediate rounds and results. In addition to the multi-keyword search protocol, we specify the addition and deletion protocols to enable dynamic updates of data records. We conducted a comprehensive performance evaluation of the protocols. The experimental results indicate that the proposed multi-keyword search protocol saves 14.67% query time and 59.96% financial cost.
Data drift in medical machine learning: implications and potential remedies

Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between the data sampled for training and used in clinical operation, differences between medical practices or context of use between training and clinical use, and time-related changes in patient populations, disease patterns, and data acquisition, to name a few. In this article, we first review the terminology used in ML literature related to data drift, define distinct types of drift, and discuss in detail potential causes within the context of medical applications with an emphasis on medical imaging. We then review the recent literature regarding the effects of data drift on medical ML systems, which overwhelmingly show that data drift can be a major cause for performance deterioration. We then discuss methods for monitoring data drift and mitigating its effects with an emphasis on pre- and post-deployment techniques. Some of the potential methods for drift detection and issues around model retraining when drift is detected are included. Based on our review, we find that data drift is a major concern in medical ML deployment and that more research is needed so that ML models can identify drift early, incorporate effective mitigation strategies and resist performance decay.
Automate Appliances via Gestures Recognition for Elderly Living Assistance

Smart homes have grown in popularity not only as a luxury but also because of the numerous benefits they provide. In this research, a home automation system is developed for the elders because as the number of elders rises, so does the probability that patients will develop geriatric problems, which necessitates society to address the issue. It is especially beneficial for senior citizens and disabled youngsters. Many research and innovation are conducting on in the field of gestures recognition. In this project, home automation is performed through the use of gestures to control appliances and contradicting the computer vision approaches as an elder person is not capable for ensuring the environment for the computer vision techniques as it requires proper lightning conditions and angle to ensure the parameters. Sensor embedded Hand glove that collects hand motions has been discussed in this study. The wearable device detects and records tilting, rotation, and acceleration of the hand movement using accelerometers and gyroscopes. Our proposed human gestures recognition (HGR) system recognizes nine different hand gestures taken from benchmarked dataset. We used a combination of features extraction algorithms and a random forest classifier to compare our system's performance with other well-known classifiers. We have achieved an accuracy of 94% over the benchmark HGR dataset. Experiments have shown that the proposed approach has the capability to recognize gestures for controlling home appliances and can be used in healthcare, residences, offices, and educational environments.
Analytic Theory for the Dynamics of Wide Quantum Neural Networks

Parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on practical problems are heuristic in nature. In particular, the convergence rate for the training of quantum neural networks is not fully understood. Here, we analyze the dynamics of gradient descent for the training error of a class of variational quantum machine learning models. We define wide quantum neural networks as parametrized quantum circuits in the limit of a large number of qubits and variational parameters. Then, we find a simple analytic formula that captures the average behavior of their loss function and discuss the consequences of our findings. For example, for random quantum circuits, we predict and characterize an exponential decay of the residual training error as a function of the parameters of the system. Finally, we validate our analytic results with numerical experiments.
Context in augmented reality marketing: Does the place of use matter?

Abstract Augmented reality (AR) integrates virtual content into a consumer's perception of the real world. While academic interest in AR is growing, most prior research has focused on consumer evaluations of AR content and neglected the physical context in which AR content is consumed. Addressing this research gap, two experimental studies showed that context (e.g., experiencing a virtual sofa at home vs. in a university classroom) impacts consumer judgments and evaluations. The results reveal two primary effects of context. First, contexts in which virtual objects meet users' personal and cultural expectations associated with a specific location (e.g., a sofa in a living room) increase plausibility. However, such functionally appropriate contexts (counterintuitively) decrease local presence (i.e., the perception that the virtual product is “here”). Study 2 extends this model by showing that plausibility (a rational and deliberate assessment of AR content) and local presence both impact utilitarian benefits, whereas local presence has a stronger effect on perceived physical tangibility. The findings extend prior theory on the psychological mechanisms impacting judgment and presence in AR, and they provide managers with important insights regarding the influence of context on downstream variables in their AR and metaverse marketing strategies.
Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach

Traditional methods of diagnosing breast cancer (BC) suffer from human errors, are less accurate, and consume time. A computer-aided detection (CAD) system can overcome the above-stated limitations and help radiologists with accurate decision-making. However, the existing studies using single imaging modalities have shown limited clinical use due to its low diagnostic accuracy and reliability when compared to multimodal system. Thus, we aim to develop a hybrid deep learning bimodal CAD algorithm for the classification of breast lesions using mammogram and ultrasound imaging modalities combined. A combined convolutional neural network (CNN) and long-short term memory (LSTM) model is implemented using images from both mammogram and ultrasound modalities to improve the early diagnosis of BC. A new real-time dataset consisting of 43 mammogram images and 43 ultrasound images collected from 31 patients is used in this work. Further, each group consists of 25 benign and 18 malignant images. The number of images is increased to 1032 (516 for each modality) using different data augmentation techniques. The proposed bimodal CAD algorithm achieves a classification accuracy of 99.35% and the area under the receiver operating characteristic curve (AUC) of 0.99 over the traditional unimodal CAD systems, which attain the classification accuracy of 97.16% and 98.84% using mammogram and ultrasound, respectively. The proposed bimodal CAD algorithm using combined mammogram and ultrasound outperforms the traditional unimodal CAD systems. The bimodal CAD algorithm can avoid unnecessary biopsies and encourage its clinical application.
Instance segmentation ship detection based on improved Yolov7 using complex background SAR images

It is significant for port ship scheduling and traffic management to be able to obtain more precise location and shape information from ship instance segmentation in SAR pictures. Instance segmentation is more challenging than object identification and semantic segmentation in high-resolution RS images. Predicting class labels and pixel-wise instance masks is the goal of this technique, which is used to locate instances in images. Despite this, there are now just a few methods available for instance segmentation in high-resolution RS data, where a remote-sensing image’s complex background makes the task more difficult. This research proposes a unique method for YOLOv7 to improve HR-RS image segmentation one-stage detection. First, we redesigned the structure of the one-stage fast detection network to adapt to the task of ship target segmentation and effectively improve the efficiency of instance segmentation. Secondly, we improve the backbone network structure by adding two feature optimization modules, so that the network can learn more features and have stronger robustness. In addition, we further modify the network feature fusion structure, improve the module acceptance domain to increase the prediction ability of multi-scale targets, and effectively reduce the amount of model calculation. Finally, we carried out extensive validation experiments on the sample segmentation datasets HRSID and SSDD. The experimental comparisons and analyses on the HRSID and SSDD datasets show that our model enhances the predicted instance mask accuracy, enhancing the instance segmentation efficiency of HR-RS images, and encouraging further enhancements in the projected instance mask accuracy. The suggested model is a more precise and efficient segmentation in HR-RS imaging as compared to existing approaches.
Data Security in Healthcare Industrial Internet of Things With Blockchain

The industrial Internet of Things (IIoT) has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integration of E-healthcare and IIoT provides an analytical platform to handle a large amount of data with a low cost of cloud-enabling scalable storage. Throughout the transformation, patients’ personal information is at risk while exchanging records over centralized server-based systems. Thus, the rate of node connectivity, failure of parallel data sharing, and deliverance-related issues increase. In this article, we present solutions in three folds. First, the article proposes a novel and secure architecture for E-healthcare data security using a blockchain-distributed ledger technology named BHIIoT. Second, the transformation in the lifecycle of medical wireless sensor networks (WSNs) for data management and optimization with a distributed layered hierarchy is developed, which enhances the network resources and increases trust in the blockchain-enabled peer-to-peer (P2P) environment. Third, the proposed BHIIoT uses the NuCypher threshold re-encryption mechanism for data encryption and protects shared resources in the form of blocks preserved in a blockchain immutable storage. For instance, chain codes are deployed to automate authentication, logging, index information deliverance, and trace transactions to resist illegal activities in the E-healthcare distributed application. The customized lightweight blockchain multi-proof-of-work (PoW) and multi-proof-of-stake (PoS) are designed with a digital signature for improving the consumption of resources and reducing the load of storage while the transaction process of E-healthcare IIoT is scheduled.
Demonstrating multi-round subsystem quantum error correction using matching and maximum likelihood decoders

Quantum error correction offers a promising path for performing high fidelity quantum computations. Although fully fault-tolerant executions of algorithms remain unrealized, recent improvements in control electronics and quantum hardware enable increasingly advanced demonstrations of the necessary operations for error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. We encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow for the correction of any single fault in the circuitry. Using real-time feedback, we reset syndrome and flag qubits conditionally after each syndrome extraction cycle. We report decoder dependent logical error, with average logical error per syndrome measurement in Z(X)-basis of ~0.040 (~0.088) and ~0.037 (~0.087) for matching and maximum likelihood decoders, respectively, on leakage post-selected data.