Reviewerly/exHarmony huggingface dataset:
FedDual: Pair-Wise Gossip Helps Federated Learning in Large Decentralized Networks

There is a significant recent interest in collaboratively training a machine learning (ML) model without collecting data to a central server. Federated learning (FL) emerges as an efficient solution mitigating systemic privacy risks and communication costs. However, conventional FL inherited from parameter server designs relies too much on a central server, which may lead to privacy risks, communication bottlenecks, or a single point of failure. In this paper, we propose an asynchronous and hierarchical local gradient aggregation and global model update algorithm, FedDual, under three different security considerations for FL in large decentralized networks. Particularly, FedDual preserves privacy by introducing local differential privacy (LDP) and aggregates local gradients asynchronously and hierarchically via a pair-wise gossip algorithm, which is more competitive than previous gossip-based decentralized FL methods in terms of privacy preservation and communication efficiency, and offers more computational efficiency compared to existing blockchain-assisted decentralized FL methods. Further, we devise a noise cutting trick based on Private Set Intersection (PSI) to mitigate the prediction performance loss of the global model caused by the leveraged LDP. Rigorous analyses show that FedDual helps decentralized FL achieve the same convergence rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}\left({\frac {1}{T}}\right) $ </tex-math></inline-formula> as centralized ML theoretically. Ingenious experiments on MNIST, CIFAR-10, and FEMNIST confirm that the model prediction performance gained from FedDual is close to centralized ML. More importantly, the proposed noise cutting trick helps FedDual to train better global models than LDP-based FL methods in terms of prediction performance and convergence rate.
A Provable Secure Cybersecurity Mechanism Based on Combination of Lightweight Cryptography and Authentication for Internet of Things

Internet of Things devices, platform programs, and network applications are all vulnerable to cyberattacks (digital attacks), which can be prevented at different levels by using cybersecurity protocol. In the Internet of Things (IoT), cyberattacks are specifically intended to retrieve or change/destroy sensitive information that may exceed the IoT’s advantages. Furthermore, the design of a lightweight cybersecurity mechanism experiences a critical challenge that would perfectly fit resource-constrained IoT devices. For instance, identifying the compromised devices and the users’ data and services protection are the general challenges of cybersecurity on an IoT system that should be considered. This paper proposes a secure cybersecurity system based on the integration of cryptography with authentication (ELCA) that utilizes elliptic curve Diffie–Hellman (ECDH) to undertake key distribution while the weak bits problem in the shared secret key is resolved. In this paper, three systems of integration are investigated, while ELCA proposes secure integration between authentication and encryption to facilitate confidentiality and authenticity transfer messages between IoT devices over an insecure communication channel. Furthermore, the security of ELCA is proven mathematically using the random oracle model and IoT adversary model. The findings of the emulation results show the effectiveness of ELCA performance in terms of a reduced CPU execution time by 50%, reduced storage cost by 32–19.6%, and reduced energy consumption by 41% compared to the baseline cryptographic algorithms.
Twitter Sentiment Analysis using Naive Bayes Algorithm

Sentiment analysis mines social media networks like Twitter for views, attitudes, and sentiments. It is presently a popular study subject. The traditional sentiment analysis approach emphasises textual data the most. Twitter is the most well-known microblogging social networking service, where users transmit updates on numerous topics as tweets. This research uses a publicly available labelled dataset on Kaggle. A comprehensive series of data cleaning and preparation techniques are organised to progressively make the tweets more comprehensible to typical language handling algorithms. Because each dataset sample consists of a pair of tweets and a sentiment, the Supervised machine learning concept was used in this work. Additionally, Nave Bayes-based sentiment analysis model is presented. The primary purpose is to discover tweet analysis more efficiently. Twitter sentiment analysis divides tweets into positive and negative attitudes. Using training data, machine learning algorithms correctly categorise the tweets. As a result, this strategy eliminates the requirement for a word database, making machine-learning approaches more effective and faster for sentiment analysis.
Using Highly Compressed Gradients in Federated Learning for Data Reconstruction Attacks

Federated learning (FL) preserves data privacy by exchanging gradients instead of local training data. However, these private data can still be reconstructed from the exchanged gradients. Deep leakage from gradients (DLG) is a classical reconstruction attack that optimizes dummy data to real data by making the corresponding dummy and real gradients as similar as possible. Nevertheless, DLG fails with highly compressed gradients, which are crucial for communication-efficient FL. In this study, we propose an effective data reconstruction attack against highly compressed gradients, called highly compressed gradient leakage attack (HCGLA). In particular, HCGLA is characterized by the following three key techniques: 1) Owing to the unreasonable optimization objective of DLG in compression scenarios, we redesign a plausible objective function, ensuring that compressed dummy gradients are similar to the compressed real gradients. 2) Instead of simply initializing dummy data through random noise, as in DLG, we design a novel dummy data initialization method, Init-Generation, to compensate for information loss caused by gradient compression. 3) To further enhance reconstruction quality, we train an ad hoc denoising model using the methods of “first optimizing, next filtering, and then reoptimizing”. Extensive experiments on various benchmark data sets and mainstream models show that HCGLA is an effective reconstruction attack even against highly compressed gradients of 0.1%, whereas state-of-the-art attacks can only support 70% compression, thereby achieving a 700-fold improvement.
Design and Implementation of New Collision Resistant Fast Hash Function Family for IoT Healthcare Blockchain Ledger

Health care is one of the many fields where blockchain technology is being put to use in new and creative ways. When it comes to the medical industry, blockchain technology is being utilised to store and share patient data across healthcare organisations. It is possible to discover severe errors in the medical field using blockchain technology, which may even be potentially lethal. As a result, medical data interchange may become more efficient, secure, and transparent. Medical facilities may acquire insight and improve their analysis of patient data with the help of this technology. Using blockchain technology, we show how IoT devices in the healthcare industry might be more secure.The development of a novel hash function is based on the Unpredictability principle. Given that the IoT has constrained resources and power, we employed an S-box of AES-256 with an XOR operator. We also created a collision-proof, lightweight, and fast hash function. The outcomes of the suggested hash functions are as follows: (256, 384, and 512).
A deep thermal-guided approach for effective low-light visible image enhancement

Low-light visible image enhancement is important for various visual computing applications under conditions of poor lighting or hazardous weather. However, existing low-light image enhancement methods are mostly based on a single visible channel and cannot achieve satisfactory performance when processing real-captured nighttime images. In this paper, we attempt to utilize the complementary edge/texture features presented in thermal images to provide a stable guidance map to facilitate the enhancement of features extracted on low-light visible images. For this purpose, we propose a novel Central Difference Convolution-based Multi-Receptive-Field (CDC-MRF) module to effectively extract multi-scale edge/texture features on thermal images. Then, we design a thermal-guided convolutional block (TGCB) to enhance the low-light visible features under the guidance of thermal features. To our best knowledge, the proposed thermal-guided low-light image enhancement network (TGLLE-Net) represents the first attempt to perform low-light visible image enhancement by incorporating complementary information presented in both visible and thermal channels. The advantages of the proposed TGLLE-Net are twofold. Firstly, it is capable of suppressing severe noise disturbance presented in low-light visible images under the guidance of low-frequency components in thermal images. Moreover, TGLLE-Net can promote detail/appearance restoration of objects with distinctive thermal features (e.g., pedestrians, vehicles, and buildings). Both objective and subjective evaluation results demonstrate that our proposed TGLLE-Net outperforms state-of-the-art methods in terms of restoration accuracy, visual perception, and computational efficiency.
Attention meets involution in visual tracking

In visual tracking, both convolution and attention are widely employed for feature enhancement and fusion. However, convolution does not adequately model global dependencies of samples due to its operation on local neighbors, while attention gives too much attention to global dependencies and too little to local dependencies. It is intrinsically infeasible to combine both methods to integrate global and local information. However, a recently-proposed model called involution uses kernels differing in spatial extent but sharing across channels, making it possible to take advantage of both convolution and attention. We propose an attention-involution (Att-Inv) model that uses an attention mechanism to generate involution kernels to take both global and local dependencies of samples into account. To improve the performance of our tracker, we develop and implement strategies of backbone network modification, template updates, and regression of bounding box distributions. We evaluate our tracker using benchmarks such as GOT10k, LaSOT, TrackingNet and OxUvA. Experimental results show that it is competitive with state-of-the-art trackers.
Missing values imputation using Fuzzy K-Top Matching Value

Missing data occurs when variables or observations are missing. Researchers exclude or impute influenced variables and data. This study proposes Fuzzy K-Top Matching Value (FKTM) for missing value imputation. It imputes missing numerical and categorical data with intelligent estimates based on similar records, decreasing bias. Expectation-maximization is used, where it employs fuzzy clustering to find a group of similar data and estimate them. We compare FKTM with original datasets on Immunotherapy and Cryotherapy. Multiple classification techniques are used on the imputed datasets. Random Forest achieved the best, with 93.3% for cryotherapy and 85.6% for Immunotherapy. The proposed approach is compared with Multivariate Imputation by Chained Equations (MICE) utilizing a Support Vector Machine. The proposed approach beats MICE with 82.2% accuracy. On the Cryotherapy dataset, the proposed approach surpasses existing strategies with 86.6% accuracy. Levene and Shapiro-Wilk were used to examine the homoscedasticity and normality of data after imputation. The proposed imputation procedure has no detrimental influence on the dataset. Finally, execution time and RMSE of imputed values are determined for three datasets with varied sample sizes and data dimensions. The proposed system exhibits a fast execution time and low RMSE. The proposed FKTM works well in experiments and looks promising.
Quantitative resource assessment of hydrothermal gold deposits based on 3D geological modeling and improved volume method: Application in the Jiaodong gold Province, Eastern China

Quantitative assessment of deep mineral resources is important for the mineral exploration decisions of governments and companies. Complex structural controls make the volume method difficult to apply to hydrothermal gold deposits. Quantitative assessment of Au resources in hydrothermal goldfields remains a challenging task. Here we proposed a quantitative assessment approach for hydrothermal gold deposits via an improved volume method combined with 3D geology modeling. A highly reliable 3D model of ore-controlling structure was first constructed to obtain the spatial association between Au mineralization and structure. On this basis, we made an optimal quantitative estimation of the ore-bearing ratio and similarity index, which are the most critical parameters in the volume method, before evaluating gold resources. Specifically, (1) an ore-bearing ratio prediction model was established by support vector machine based on measured data and 3D models, which was further applied in predicting the ore-bearing ratio of the evaluation area at depth and peripherals; (2) the calculation of the similarity index was improved by novelly using the Mahalanobis distance of the regional geochemical data with regard to the similar geochemical anomalies of gold mineralization in adjacent space. This proposed method was applied to quantitatively assess the gold resources in the Jiaojia and Dayingezhuang goldfields in the Jiaodong gold province, China, where gold deposits show significant structure controls on mineralization. The results of gold grade prediction are statistically significantly correlated to measured values by the Wilcoxon signed rank test, showing the model for gold grade prediction can be extensible in the deep parts. Our approach produced similarity indices for the model and evaluation areas at Jiaojia and Dayingezhuang that accurately reflected the geology complex. The above lines suggest that the proposed approach can be used to estimate the gold resources of Jiaodong. Potential gold resources in the Jiaojia and Dayingezhuang districts were estimated to be 1221.82 t and 254.95 t.
Predicting individual quality ratings of compressed images through deep CNNs-based artificial observers

Unlike traditional objective approaches aimed at MOS prediction, subjective experiments provide individual opinion scores that allow, for instance, to estimate the distribution of users’ opinion scores. Unfortunately, the current literature is lacking objective quality assessment approaches that simulate the process of a subjective test. Therefore, this work focuses on modeling an individual subject through a deep CNN that, once trained, is expected to mimic the subject in terms of quality perception; for this reason, we call it “Artificial Intelligence-based Observer” (AIO). Several AIOs, modeling subjects with different characteristics, can be derived and used to simulate the process of a subjective test, thus yielding a more complete objective quality assessment. However, the training of the AIOs is hindered by two major issues: (i) the lack of training sets containing a large number of individual opinion scores; (ii) the noisy nature of individual opinion scores used as ground truth. To overcome these issues, we motivate a two-step learning approach. During the first learning step, the architecture of the well-known ResNet50 is appropriately modified and its initial weights are updated using a large scale synthetically annotated dataset of JPEG compressed images created for quality assessment purpose. This yields a new deep CNN called JPEGResNet50 that can accurately evaluate the quality of JPEG compressed images. The second learning step, conducted on a subjectively annotated dataset, refines the generic perceptual quality features already learned by the JPEGResNet50 to derive the AIO of each subject. Extensive computational experiments show the potential and effectiveness of our approach.
Multivariable fuzzy rule-based models and their granular generalization: A visual interpretable framework

Fuzzy rule-based models have been widely used due to their interpretability and effectiveness. However, they still encounter challenges when dealing with multivariable and large-scale data. In this study, we first propose a novel approach to establish a selective sampling and mapping data reduction method. The method focuses on reducing data variables while decreasing the number of samples, and an appropriate scaling size can be chosen for different situations. Then, a multivariable data-driven fuzzy rule-based model is developed based on the processed data. Moreover, the data projection approach using the distance metric helps to preserve the structural characteristics of the original data. The results are visually presented to facilitate an interpretable description of the subsequent rule-based modeling. Furthermore, due to the inevitable inaccuracy in the projection process of numeric modeling, we introduce the allocation of information granularity to extend the model to a granular form at a more abstract level. Experimental studies on both synthetic and publicly available datasets demonstrate that the proposed method has superior effectiveness and efficiency compared to the existing state-of-the-art regression algorithms.
Augmented and Mixed Reality for Shipbuilding

Industry 4.0 is paving the way for the automation of many industrial fields that can improve the efficiency of manufacturing processes. Such an improvement can be remarkable on industries that involve a relevant number of complex processes, which need to be optimized individually in order to increase their overall performance. Shipbuilding is one of such industrial fields that can benefit from the application of the principles of Industry 4.0 and from the use of the latest technologies. Among the different Industry 4.0 technologies, augmented reality (AR) and mixed reality (MR) can be especially helpful for enhancing shipbuilding tasks, since they can be applied to many of them, while providing useful and attractive visual interfaces that enable shipyard operators to receive information on the tasks they are working on and that allow them to interact with physical and virtual elements. This chapter first reviews the state of the art on the application of commercial and academic AR/MR solutions to shipbuilding. Moreover, the most relevant shipbuilding tasks to be enhanced with AR/MR and their challenges are analyzed. Furthermore, this chapter also details the latest and most promising AR and MR hardware, software, and communication architectures aimed at being deployed in shipyard workshops and on ships under construction. As a result, this chapter provides a thorough review of the most recent developments on the application of AR and MR to shipbuilding and includes useful guidelines for future developers.
History of Augmented Reality

Augmented reality (AR) is emerged in the last decade as a potentially disruptive technology capable of superimposing virtual objects generated by a computer onto the real world surrounding the user. By using see-through displays, AR systems make the user perceive both the real surrounding environment and virtual elements in a consistent way with regard to user point of view and virtual content size. The recent developments of low-cost AR technologies and mixed reality (MR) devices such as Google Glass, Microsoft Hololens, Vuzix, and many others are capturing interest of users and researchers, suggesting that AR could be the next springboard for technological innovation as also highlighted but its inclusion as enabling technology of Industry 4.0 paradigm. However, the AR is not as young as it seems. The concept of AR was formulated in the 1960s, and the first commercial AR tools appeared in the late 1980s. This chapter presents an overview of AR, the historical evolution since its first appearance, its usage in the most relevant domains including the emerging instance of Mobile/Wearable AR applications, the technical challenges in implementing AR systems, as well as main technological and applicative trends for the near future.
Internal Structures of Placer Gold as an Indicator of Endogenous and Exogenous Processes

The study of the internal structures of placer gold on the territory of the east of the Siberian platform, overlain by a thick cover of Mz-Kz deposits, where traditional methods of searching for gold deposits are not effective, allowed us to determine, for the first time, the stages of ore formation and conditions of its occurrence. The identified indicators of the internal structures of placer gold (structures of primary recrystallization, secondary recrystallization, thick high-grade shells) indicate that placer gold content is formed mainly due to the supply and repeated redeposition of native gold from ancient gold-bearing deposits of the Precambrian stage of ore formation to younger ones. The discovered coarse-, medium-grained, mono-grained, unclear-zonal, granulation and disintegration structures suggest a supply of gold from nearby ore sources of the Mesozoic stage of ore formation. In the weathering crust, a high-grade shell is formed. In the hydrodynamic environment, the internal structures of gold practically do not change and fully correspond to the internal structures of endogenous gold. In aeolian conditions, the internal structures are transformed. In ancient gold-bearing conglomerates, under the impact of lithostatic pressure, as well as in metamorphogenic conditions, when the PT conditions change, the internal structure changes. Thus, for the first time, on a huge factual material, it is proved that the internal structures contain extensive information both about the endogenous origin of gold (the stages of ore formation—Precambrian and Mesozoic) and about its transformation in various exogenous conditions. The identified indicator of the internal structures of placer gold for certain types of sources contribute to a more correct selection of methods for searching for ore and placer gold deposits in closed territories and assessing their prospects. The use of this method makes it possible to develop criteria for forecasting different sources and types of gold deposits based on internal structures.
A nine-degree-of-freedom modular redundant robotic manipulator: Development and experimentation

Performing interactive tasks in complex obstacle environments is a formidable challenge for manipulators. Redundant manipulators have the advantage of being highly flexible and can be used to perform interactive tasks in complex environments. This paper presents a nine-degree-of-freedom (DOF) modular redundant manipulator with nine identical modular joints. To improve the obstacle avoidance capability in narrow environments, the bidirectional rapidly-exploring random trees (bi-RRT) algorithm and the oriented bounding box (OBB) are combined for trajectory planning of redundant robotic manipulators. To enhance the interaction capability in uncalibrated dynamic environments, an improved virtual semi-active damping control (VSADC) algorithm based on particle swarm optimization (PSO) is proposed, and it can effectively estimate impedance parameters and positions of the dynamic environment. Finally, combined with the proposed control schemes, simulation and experimental results demonstrate that the designed redundant manipulator can traverse complex obstacle environments and interact with the unknown dynamic environment.