Reviewerly/exHarmony huggingface dataset:
Compression of Multiscale Features of FPN with Channel-Wise Reduction for VCM

With the development of deep learning technology and the abundance of sensors, machine vision applications that utilize vast amounts of image/video data are rapidly increasing in the autonomous vehicle, video surveillance and smart city fields. However, achieving a more compact image/video representation and lower latency solutions is challenging for such machine-based applications. Therefore, it is essential to develop a more efficient video coding standard for machine vision applications. Currently, the Moving Picture Experts Group (MPEG) is developing a new standard called video coding for machines (VCM) with two tracks, each mainly dealing with compression of the input image/video (Track 2) and compression of the features extracted from it (Track 1). In this paper, an enhanced multiscale feature compression (E-MSFC) method is proposed to efficiently compress multiscale features generated by a feature pyramid network (FPN), which is the backbone network of machine vision networks specified in the VCM evaluation framework. The proposed E-MSFC reduces the feature channels to be included in a single feature map and compresses the feature map using versatile video coding (VVC), the latest video standard, rather than the single stream feature compression (SSFC) module in the existing MSFC. In addition, the performance of the E-MSFC is further enhanced by adding a bottom-up structure to the multiscale feature fusion (MSFF) module, which performs the channel-wise reduction in the E-MSFC. Experimental results reveal that the proposed E-MSFC significantly outperforms the VCM image anchor with a BD-rate gain of up to 85.94%, which includes an additional gain of 0.96% achieved by the MSFF with the bottom-up structure.
NFT-Vehicle: A Blockchain-Based Tokenization Architecture to Register Transactions over a Vehicle’s Life Cycle

The sale of second-hand vehicles is a popular trade worldwide, and vehicle fraud is currently a common issue, mainly because buyers can lack a complete view of the historical transactions related to their new acquisition. This work presents a distributed architecture for stakeholders to register transactions over a vehicle’s life cycle in a blockchain network. The architecture involves a non-fungible token (NFT) linked to a physical motorized vehicle after a tokenization process, which denote as the NFT-Vehicle. The NFT-Vehicle is a hierarchical smart contract designed using an object-oriented paradigm and a modified version of the ERC721 standard. Every stakeholder engages with the NFT-Vehicle through distinct methods embedded within a smart contract. These methods represent internal protocols meticulously formulated and validated based on a finite-state machine (FSM) model. We implemented our design as a proof of concept using a platform based on Ethereum and a smart contract in the Solidity programming language. We carried out two types of proof: (a) validations, following the FSM model to ensure that the smart contract remained in a consistent state, and (b) proofs, to achieve certainty regarding the amount of ETH that could be spent in the life cycle of a vehicle. The results of the tests showed that the total transaction cost for each car throughout its life cycle did not represent an excessive cost considering the advantages that the system could offer to prevent fraud.
Vibration Response Law of Aircraft Taxiing under Random Roughness Excitation

Understanding the vibration response patterns of aircraft taxiing under runway roughness excitation is crucial for aircraft design and runway performance evaluation. In this paper, we establish pavement roughness models for both asphalt and concrete surfaces, taking into account their unique structural characteristics. We construct a six-degrees-of-freedom aircraft model using multi-rigid-body system dynamics theory and employ the pseudo-excitation method to examine the influence of pavement roughness types on the steady vibration response of aircraft taxiing at constant speeds. Furthermore, we analyze the non-stationary vibration response patterns of aircraft during takeoff and landing taxiing using the method of instantaneous frequency response function with space frequency. Lastly, we explore the effect of stochastic structural parameters on aircraft vibration response using the Monte Carlo method. Our findings reveal that the roughness power spectrum differs between asphalt and concrete pavements, and the established roughness models in this paper demonstrate a strong fit (R2 &gt; 0.95). The type of pavement roughness has a relatively minor impact on the power spectral density distribution of the aircraft vibration response, suggesting that the same roughness model can be used for both asphalt and concrete pavements when high accuracy is not required. The power spectral density distribution of aircraft vibration response varies across different motion attitudes, with the vibration response during landing being significantly larger than that during takeoff. Among the aircraft structural parameters, the randomness of the sprung mass has the most substantial effect on the aircraft vibration response, potentially causing the variation coefficient of dynamic load on the front landing gear to exceed 0.11. Tire stiffness comes next, which can lead to the variation coefficient of dynamic load on the main landing gear reaching 0.07. The results have a guiding role in optimizing aircraft structure design and ensuring pavement performance.
A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area

Newly built offshore wind farms (OWFs) create a collision risk between ships and installations. The paper proposes a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method was tested in offshore waters in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, and ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.
Privacy-Preserving Federated Singular Value Decomposition

Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses. In recent years, the need for privacy-preserving computations has been increasing constantly, which concerns SVD as well. Federated SVD has emerged as a promising approach that enables collaborative SVD computation without sharing raw data. However, existing federated approaches still need improvements regarding privacy guarantees and utility preservation. This paper moves a step further towards these directions: we propose two enhanced federated SVD schemes focusing on utility and privacy, respectively. Using a recommendation system use-case with real-world data, we demonstrate that our schemes outperform the state-of-the-art federated SVD solution. Our utility-enhanced scheme (utilizing secure aggregation) improves the final utility and the convergence speed by more than 2.5 times compared with the existing state-of-the-art approach. In contrast, our privacy-enhancing scheme (utilizing differential privacy) provides more robust privacy protection while improving the same aspect by more than 25%.
Summarizing source code with Heterogeneous Syntax Graph and dual position

Code summarization attempts to summarize the semantics of source code by automatically producing brief natural-language descriptions. Most existing work proposes to learn from the Abstract Syntax Tree (AST) and plain text of source code for summary generation. However, little attention has been paid to the structural heterogeneity and layout features of source code. In this paper, we present a novel framework titled HetSum to address these issues. Specifically, a Heterogeneous Syntax Graph (HSG) is first built by designing six types of augmented edges in AST, which indicates the heterogeneous structure of source code. Meanwhile, a dual position is designed for each token in the source code by considering the layout information. Moreover, we develop a heterogeneous graph neural network in HetSum to encode the HSG while extracting the code layout features with the Transformer encoder. By assimilating the learned code token vectors into the HSG encoder, HetSum can capture the relations between its two encoders for improved code representation. To facilitate the generation of high-quality summaries, we integrate a copying mechanism into the decoding procedure while expanding the Transformer decoding sublayer. Extensive experiments on the Java and Python datasets prove that HetSum is superior to seventeen state-of-the-art baselines. To promote reproducibility studies, we make the implementation of HetSum available at https://github.com/GJCEXP/HETSUM.
Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment

For the problem of multi-mode state estimation in actual train operation, this paper proposes a nonlinear non-gaussian high-precision parallel Kalman filter group (NN-HEKFG) integrated Particle Filter. A multi-model Gaussian decomposition of the probability density function for state equations and measurement equations is performed, and each local state model is represented by a multi-dimensional high-order polynomial to establish the expanded dimensional state model. Then, by updating the mean and variance of the local state expanded dimensional model and in turn solving the particle filtering posterior probability density distribution function, the global estimation results are obtained. In reducing the number of Gaussian terms, a new parameter reduction criterion is established, which can effectively carry out the re-identification of parameters such as weights and means, so as to avoid the problem of parameter explosion. The superiority of NN-HEKFG over particle filters and Gaussian sum filters and its effectiveness for train running state estimation are verified by simulating the multi-model running state of trains.
Hybrid AES model with elliptic curve and ID based key generation for IOT in telemedicine

With the aid of various medical sensors, a remote patient monitoring system will be designed for Internet of Things (IoT)-based telemedicine applications. With the aid of AI algorithms, the healthcare sensor data will eventually be identified and forecasted. In the current healthcare sector, a lot of sensitive data is exchanged. The integration of an increasing number of digital technologies in hospitals, together with laboratory and outpatient examinations of patients and their accompanying files, have all contributed to the growth of data related to patient care in recent years. Electronic health record systems are medical management systems that include all of a patient's personal and medical data. As a consequence, it is crucial that this data be stored and secured in the cloud. This paper describes a data storage and security solution that is well suited for the Internet of Medical Things. Because of the shorter key length, the improved Elliptic Curve Cryptography (ECC) approach using Blake2 hashing takes substantially less time to encrypt. The data is encrypted with the created key and the Advanced Encrypted Standard (AES). The proposed security approach is a hybrid cryptography technique that combines symmetric and asymmetric key encryption, resulting in a more secure system.
Questions Are All You Need to Train a Dense Passage Retriever

Abstract We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples. ART, in contrast, only requires access to unpaired inputs and outputs (e.g., questions and potential answer passages). It uses a new passage-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence passages, and (2) the passages are then used to compute the probability of reconstructing the original question. Training for retrieval based on question reconstruction enables effective unsupervised learning of both passage and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning. Extensive experiments demonstrate that ART obtains state-of-the-art results on multiple QA retrieval benchmarks with only generic initialization from a pre-trained language model, removing the need for labeled data and task-specific losses.1 Our code and model checkpoints are available at: https://github.com/DevSinghSachan/art.
Tilting at 5G Towers: Rethinking Infrastructural Transition in 2020

5G has the potential to expand the horizons of digital inclusion by providing higher speeds, lower latency, and support for more devices on a given network. However, mis‐ and disinformation about 5G has proliferated in recent years and stands to be a persistent barrier to the adoption of this generation of wireless technologies. After rumours linking 5G to Covid‐19 emerged in the wake of the pandemic, isolated actors attempted to disrupt infrastructure with a perceived connection to 5G. Media coverage of these incidents inadvertently spread such claims, engendering lasting uncertainty about 5G. Infrastructure scholars have long held to the maxim that “the normally invisible quality of working infrastructure becomes visible when it breaks” (Star, 1999, p. 482), but efforts to interpret the uptake of mis‐ and disinformation have struggled to define the technical difference 5G makes and describe diffused acts of anti‐5G sentiment that exploited its slippery symbolic associations. What broke to make 5G so visible? This article reassesses interference with infrastructure through the lens of a literary metaphor derived from Miguel de Cervantes’ epic novel Don Quixote. Using the Don’s famed joust with windmills, I examine what efforts to disrupt the development of 5G in 2020 can tell us about infrastructural transition. With reference to Quixote’s tilt, I contend that the disruptions of 2020 illustrate conflicting imperatives of inclusion and exclusion underlying neoliberal schemes of telecommunication development.
WYDISWYG: A Method to Design User Interfaces Combining Design Principles and Quality Factors

The field of human–computer interaction is constantly evolving and facing new challenges. In this study, we propose an experimental method for designing e-commerce user interfaces with varying levels of aesthetics and usability while maintaining a similar level of user experience. The method is based on the Deming Cycle and Nielsen’s framework for design principles. Four e-commerce websites were designed with low/high levels of aesthetics and usability. Based on 160 surveys, the experimental results demonstrated that it is possible to design user interfaces with varying levels of aesthetics and usability while maintaining a similar level of user design perception. Our findings suggest that usability is more crucial than aesthetics in enhancing user experience, which aligns with previous studies. We also emphasize considering aesthetics and usability in UI design and propose the principle “What You Design Is What You Get” (WYDISWYG) as a foundation for future research. Among the evaluated websites, Website1 (the best design) achieved the highest UI quality, with users perceiving it positively at an impressive rate of almost 0.91 percent. In contrast, Website4 (the worst design) had the lowest UI quality, receiving a perception score of only 0.38 percent. The websites designed with a combination of aesthetics and usability, Website2 and Website3, garnered different perception values of 0.79 and 0.51, respectively. While this study has some limitations, such as the subjective selection of features and the focus on e-commerce websites only, it provides a starting point for further investigation into the selection of design principles, consideration of other website domains, and clarification of users’ perception of UI elements to align with Nielsen’s principles.
Quaternary Reversible Circuit Optimization for Scalable Multiplexer and Demultiplexer

Information loss is generally related to power consumption. Therefore, reducing information loss is an interesting challenge in designing digital systems. Quaternary reversible circuits have received significant attention due to their low-power design applications and attractive advantages over binary reversible logic. Multiplexer and demultiplexer circuits are crucial parts of computing circuits in ALU, and their efficient design can significantly affect the processors’ performance. A new scalable realization of quaternary reversible <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 1$ </tex-math></inline-formula> multiplexer and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> demultiplexer, based on quaternary 1-qudit Shift, 3-qudit Controlled Feynman, and 2-qudit Muthukrishnan-Stroud gates, is presented in this paper. Moreover, the corresponding generalized quaternary reversible <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\times 1$ </tex-math></inline-formula> multiplexer and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times n$ </tex-math></inline-formula> demultiplexer circuits are proposed. The comparison, with respect to the current literature, shows that the proposed circuits are more efficient in terms of quantum cost, the number of garbage outputs, and the number of constant inputs.
Intelligent Recommendation System Based on the Infusion Algorithms with Deep Learning, Attention Network and Clustering

Abstract The creation and use of big data have driven the intelligent development of e-commerce. The information generated in e-commerce provides a good means to analyze the behavior of users. How to use this information to give customer recommendations, improve the accuracy of recommendations and protect information security is a topic worth studying. For improving the accuracy of recommendations, analysis of users and tagging of resources are key. The current popular session recommendation algorithms face many problems, such as user interest drift which is difficult to be handled by these algorithms, thus affecting the recommendation accuracy. Based on these problems, this paper proposes a recommendation model based on deep learning, applies it to the clustering analysis of user tagging system, and designs a personalized recommendation algorithm for the tagging system. The model proposed in this paper can effectively analyze not only the interests exhibited by users in the current session, but also their potential long-term interests. By comparing the different performances of different datasets, the experimental results of this paper show that the proposed algorithmic model in this paper helps to dig the interests of different users, thus improving the quality of the recommendation system.
A novel parameter decoupling approach of personalised federated learning for image analysis

Abstract Given the importance of privacy protection in databases and other institutions, federated learning (FL) is used to benefit training machine learning models based on these decentralised and private data so as to address the growing vision tasks. However, for federated learning, statistical heterogeneity continues to be a major problem. Recently, plenty of personalised federated learning methods have been explored to solve the problem of statistical heterogeneity. The usage of trained base layers and the effect of feature extraction in personalised layers, however, are hardly considered in those methods that employ the learning personalised models approach. To address the problem of the statistical heterogeneity in image analysis, PCCFED, a personalised federated learning method utilizing the strategy of parameter decoupling is proposed. It should be emphasised that the authors’ personalised federated learning method decouples the personalised (P) layers into a connecting (C) layer and classifier (C) layer in order to enhance the effectiveness of feature learning for personalised layers. Further, an approach is proposed to fully use the base layers to adapt a personalised model based on the newly admitted institution's dataset through meta‐transfer. The performance of the proposed PCCFED on three datasets is evaluated under the practical non‐independent and identically distributed (non‐IID) setting. Extensive experiments demonstrate that compared with baseline methods, the proposed framework achieves the best performance in federated learning and fine‐tuning. Through FL, the investigation reveals a method to reduce statistical heterogeneity while protecting the institutions' privacy.
Using a deep learning neural network for the identification of malignant cells in effusion cytology material

To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.In this retrospective study, we selected 90 cases of effusion cytology samples over 2 years. There were 52 cases of metastatic adenocarcinoma and 38 benign effusion samples. In each case, an average of five microphotographs from the representative areas were taken at 40× magnification from Papanicolaou-stained samples. A total of 492 images were obtained from these 90 cases. We applied a deep convolutional neural network (DCNN) model to identify malignant cells in the cytology images of effusion cytology smears. The training was performed for 15 epochs. The model consisted of 783 layers with 188 convolution-max pool layers in between.In the test set, the DCNN model correctly identified 54 of 56 images of benign samples and 49 out of 56 images of malignant samples. It showed 88% sensitivity, 96% specificity and 96% positive predictive value in the screening of malignant cases in effusion. The area under the receiver operating curve was 0.92.DCNN is a unique technology that can detect malignant cells from cytological images. The model works rapidly and there is no bias in cell selection or feature extraction. The present DCNN model is promising and can have a significant impact on the diagnosis of malignancy in cytology.