Reviewerly/exHarmony huggingface dataset:
A Client Selection Method Based on Loss Function Optimization for Federated Learning

Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the global model with the selected local models. We propose FedChoice, a client selection method based on loss function optimization, to select appropriate local models to improve the convergence of the global model. It firstly sets selected probability for clients with the value of loss function, and the client with high loss will be set higher selected probability, which can make them more likely to participate in training. Then, it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction, respectively, and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviation of the local gradient caused by the Non-IID data. We make experiments to verify the validity of FedChoice on CIFAR-10, CINIC-10, MNIST, EMNITS, and FEMNIST datasets, and the results show that the convergence of FedChoice is significantly improved, compared with FedAvg, FedProx, and FedNova.
SA-Model: Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small. It does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetic sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension (BERT-wwm-ext) and Enhanced representation through knowledge integration (ERNIE) to enrich text vector information; Secondly, it incorporates numerous encoders to remove text features at multiple levels, thereby increasing text feature information, improving text semantics accuracy, and enhancing the model’s learning and generalization capabilities; finally, multi-feature fusion poetry sentiment analysis model is constructed. The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus. Compared with other baseline models, the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.
Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics. Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities, and it is found that compared with the existing methods, the accuracy of our model can reach 93.85%, and the Micro-F1 Score is 94.02%.
Blockchain Technology-Based Smart Cities: A Privacy-Preservation Review

The proper functioning of smart cities depends on the security of communications and data collection by a vast number of sensors. With the generation of ever-increasing amounts of data in direct transactions between devices, blockchain technology, by definition, can be used to secure platforms in that context. The ability of smart cities to take advantage of the vast amount of data generated allows users to obtain a wealth of custom services. However, such services are only possible by using large amounts of multidimensional data, which puts users' privacy at risk. Although within the context of smart cities based on blockchain technology, there are also numerous papers in the literature that address the data-preserving issue, there is no proposal yet applicable in any real use-case scenario. The main contribution of this chapter is the theoretical review carried out on the available literature in the context of blockchain based-smart cities, and how they managed the privacy-preserving issue of the users, providing informative data and a reference point to urban researchers and practitioners alike.
Security in Electronic Health Records System: Blockchain-Based Framework to Protect Data Integrity

Electronic Health Records (EHRs) are intended to improve the quality, efficiency, and safety of patient care. When a patient visits a different provider or an external hospital system, record transmittal to the next point of care is not always guaranteed unless a reciprocity agreement is already in place. A functional interoperable system should include secure network infrastructure, verification of identity, and authentication of all participants as well as consistent proof of authorization of electronic health records. In this research, we consider data security and transparency and introduce a novel blockchain-based database framework for electronic health records of patients and providers that allows data sharing among providers and would bring major benefits such as decreased cost of accessing healthcare. The framework will be able to overcome the interoperability barrier between commercial EHR systems and allow patients to take full control of their health records by granting healthcare providers permission to share data seamlessly based on need. The framework shall be efficient in timely accessing the patient data by providers, improving clinical workflow and quality of care from other organizations without interruptions to their workflow. The proposed framework also addresses the security concern by providing a transparent, decentralized, and secure network of digital EHR records. We perform a case study using Hyperledger fabric for teleradiology application for secure and efficient medical image sharing.
Understanding the dynamic and microscopic traits of typical Ethereum accounts

As the most ecologically active cryptocurrency platform, Ethereum has attracted the attention of many researchers. Leveraging its fully public transaction data, most existing analysis models all account interactions as a network and explores it from a static and global perspective. However, their work ignored the investigation of dynamic and microscopic features of accounts. Therefore, we conduct the first work about these features of different kinds of accounts on Ethereum. We select six account types on Ethereum, including exchanges, phishing, etc. Then we characterize and compare the dynamics of their transactions. Next, we construct a transaction ego network for each account, and investigate the network features from the perspective of microscopic structure. Experimental results show that different kinds of accounts have their own traits in terms of transaction features and properties of ego networks, which greatly contributes to understanding their roles. Additionally, there are obvious differences between normal accounts and illegal accounts in some characteristics such as transaction neighbors and interaction patterns. Moreover, we observe that criminal gangs may be participating in phishing scams. Finally, based on the conclusions of the account analysis, we design a variety of account features and use them for the account classification task. The experimental results prove that the dynamic and microscopic features we proposed are beneficial to distinguish different types of accounts. We believe our research can provide reference value for account classification tasks in Ethereum and other blockchains.
Differential Privacy High-Dimensional Data Publishing Based on Feature Selection and Clustering

As a social information product, the privacy and usability of high-dimensional data are the core issues in the field of privacy protection. Feature selection is a commonly used dimensionality reduction processing technique for high-dimensional data. Some feature selection methods only process some of the features selected by the algorithm and do not take into account the information associated with the selected features, resulting in the usability of the final experimental results not being high. This paper proposes a hybrid method based on feature selection and a cluster analysis to solve the data utility and privacy problems of high-dimensional data in the actual publishing process. The proposed method is divided into three stages: (1) screening features; (2) analyzing the clustering of features; and (3) adaptive noise. This paper uses the Wisconsin Breast Cancer Diagnostic (WDBC) database from UCI’s Machine Learning Library. Using classification accuracy to evaluate the performance of the proposed method, the experiments show that the original data are processed by the algorithm in this paper while protecting the sensitive data information while retaining the contribution of the data to the diagnostic results.
New and Simplified Manual Controls for Projection and Slice Tours, With Application to Exploring Classification Boundaries in High Dimensions

This article describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is useful for exploring the sensitivity of structure to particular variables. The user can also interactively shift the center of a slice, for example, to explore how structure changes in local subspaces. The Mathematica package as well as example notebooks are provided, which contain functions enabling the user to experiment with these new manual controls, with one specifically for exploring regions and boundaries produced by classification models. The advantage of Mathematica is its linear algebra capabilities and interactive cursor location controls. Some limited implementation has also been made available in the R package tourr. Supplementary materials for this article are available online.
A Stan tutorial on Bayesian IRTree models: Conventional models and explanatory extension

IRTree models have been receiving increasing attention. However, to date, there are limited sources that provide a systematic introduction to Bayesian modeling techniques using modern probabilistic programming frameworks for the implementation of IRTree models. To facilitate the research and application of IRTree models, this paper introduces how to perform two families of Bayesian IRTree models (i.e., response tree models and latent tree models) in Stan and how to extend them in an explanatory way. Some suggestions on executing Stan codes and checking convergence are also provided. An empirical study based on the Oxford Achieving Resilience during COVID-19 data was conducted as an example to further illustrate how to apply Bayesian IRTree models to address research questions. Finally, strengths and future directions are discussed.
Color Deconvolution as a Simple and Rapid Tool in Quantitative Biomedical Research

The quality, efficiency, and speed of a quantitative analysis are critical factors in cytopathology. In this scenario, reliable and reproducible tools are needed to improve results in a shorter period,[1] mainly routine immunohistochemistry (IHC) slides and biomedical research.
Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .
A New Enhanced Chaotic Key-Based Medical Image Cryptosystem

In this paper, an effective medical image cryptosystem is discussed. It uses two logistics function mix based on Sine-Tent map for the creation of encryption keys, adding to the Arnold’s Cat Map for pixel permutation. The performance analysis shows that the suggested cryptosystem is extremely secure through satisfying theoretical analysis and simulation outcomes.
FPGA Implementation of Secure Video Network Communication using Chaotic Cryptosystem

This paper presents a new approach for designing real-time video network communication and its securing using a chaos-based cryptosystem. The proposed design is based on Hardware/Software co-design approach that has implemented a TCP-IP protocol for video transmission. The synchronization has been realized by making use of the FPGA PL-PS communication by implementing a customized algorithm for both the hardware and software parts between both transmitting and receiving ends of an OV7670 camera input video. The obtained results have proven the feasibility of the communication securing by means of a chaos-based cryptosystem design and integration that has been validated by security analysis. Moreover, the implemented approach has given good results in terms of hardware resources consumption and speed.
An optimized fuzzy computed torque control for the robot manipulator PUMA 560

This research intends to introduce a novel fuzzy computed torque control (FCTC) for the robot manipulator PUMA 560. This controller is obtained by improving the computed torque controller (CTC) through the addition of a nonlinear element, that is, the fuzzy controller, which compensates for the limitation of the CTC control action. The Archimedes optimization algorithm is utilized to optimize the FCTC parameters. The performances of the proposed controller are compared to the optimized CTC and PID controllers for different trajectories of the end effector. The obtained results demonstrate that the proposed controller can control complex nonlinear systems such as robots and demonstrates superior tracking performances over the optimized CTC and PID controllers.
Image Watermarking Technique Using Convolutional Autoencoder

Due to the rapid growth in communication networks and data sharing, protecting copyrights has become increasingly important. Digital watermarking is seen as a promising solution to resolve this issue. A new blind digital watermarking based on deep convolutional auto-encoders is presented in this paper. The system facilitates the embedding and extraction of a watermark in an image for its protection. The proposed architecture comprises four modules: an “encoder” that compresses the original image and watermark, an “embedder” that combines the two, a “decoder” that upsamples the combined result to reconstruct the watermarked image, and an “extractor” for watermark retrieval. The experimental results indicate that the watermarked images maintain their visual quality after insertion, and the extracted watermark remains robust against various various attacks such as filtering, rotation, and noise.
Adoption and Application of Blockchain Technology in IoT: Survey and Open Issues

The massive expansion of Internet of Things (IoT) in various application domains creates a critical need for security and privacy. Blockchain (BC) is a very promising solution that sets up a decentralized mutual trust relationship between the network nodes, resulting in secure and immutable data exchange. However, adopting and applying such technology in IoT environment is still a challenging issue. In fact, the devices that make up this environment are very constrained in terms of computing, storage, and communication capacity, making their security task a real challenge. This paper focuses on the means of integrating Blockchain technology into IoT networks by analyzing a significant number of recent works in this research area. We highlight some of their strengths and weaknesses according to several comparison criteria. This work ends with some important issues that should be addressed toward a lightweight and efficient IoT-BC integration, followed by recommendations for a potential solution.
Anomaly Detection of Underwater Gliders Verified by Deployment Data

This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.
Intelligent system for detecting faults in the industrial area

In this work, we propose a system capable of performing two tasks, recognition and prediction of human action using the surveillance cameras in the industrial environment to detect employees who violate the safety regulations by not wearing the safety cloths. This system is based on deep learning methods (convolutional neural networks, long-short term memory). For the human action recognition and prediction we proposed a new action representation, based on the human skeleton (body parts key-points), we used these points to create a vector containing the most important features of a descriptor (invariant to rotation, scale and position in the frame), and storing the spatial information of the video, we also used a position map to reduce its size and get very simple representation. Finally, we used LSTM network to preserve the temporal information, by training on these features using the dataset we created. The other part of this system is responsible of detecting employees who violate the safety regulation. So, in order to achieve this, we used the state of the art algorithm (You Only Look Once) for object detection and we adapted it to our dataset that contains four classes (Wearing-helmet, Without-helmet, Wearing-boot and Without-boot).
Towards sensor agnostic artificial intelligence for underwater imagery

Underwater images in global datasets are gathered by different cameras and under different lighting and altitude conditions. Image formation models can potentially compensate for these known differences and improve machine learning (ML) performance. In this paper we will investigate how ML trained on images from one system can classify images taken by another. We will train two ML classification models based on two different underwater camera systems. We are going to evaluate the performance of each model with data from both platforms and will demonstrate the improvement of using image formation models to make an image look-like it has been gathered with a different system and how ML performance is affected.
A comprehensive study on multimedia DeepFakes

Since the entry of so-called DeepFakes in the development of fake multimedia, this late has marked a turning point and emerged as a major issue, although visual and aural media manipulations date back to the beginning of media itself. Thanks to this technology, the detection of altered and generated material has recently received more attention since the human ability to identify DeepFakes has significantly been far less effective than that of deep learning models. organizations need to be ready as there are countless ways to deceive using convincingly altered photos, videos, and audio, such as perpetrating fraud, damaging reputations, extorting money or influencing public opinion during elections, which undoubtedly impacts society. In this regard, there is a critical need for automated solutions that can identify fake multimedia material and prevent the spread of dangerous misinformation. This article aims to give a comprehensive review of DeepFakes and a summary of the technology that underpins it. We provide information on various DeepFake detection algorithms, identify potential dangers of this frightening modern phenomenon, and highlight future research challenges.
Practitioner Perceptions of Ansible Test Smells

The practice of infrastructure as code (IaC) recommends automated management of computing infrastructure with application of quality assurance, such as linting and testing. To that end, researchers recently have investigated quality concerns in IaC test manifests by deriving a catalog of test smells. The relevance of the identified smells need to be quantified by obtaining feedback from practitioners. Such feedback can help the IaC community understand if smells have relevance amongst practitioners, and derive future research directions. We survey 30 practitioners to assess the relevance of three Ansible test smell categories namely, assertion roulette, local only testing, and remote mystery guest. We observe local only testing to be the most agreed upon test smell category, whereas, assertion roulette is the least agreed upon test smell category. Our findings provide a nuanced perspective of test smells for IaC, and lays the groundwork for future research.