Reviewerly/exHarmony huggingface dataset:
Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effect of fusion techniques on model performance

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value (HSV), Brovey, principal component analysis (PCA), Gram-Schmidt (GS), wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR-SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements' prediction models.
Complete Blood Cell Detection and Counting Based on Deep Neural Networks

Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.
A robust clustering method with noise identification based on directed K-nearest neighbor graph

Obtaining the optimal cluster number and generating reliable clustering results in nonlinear manifolds are necessary but challenging tasks. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns, while graph-based clustering has shown impressive performance in identifying clusters in such data patterns. In this paper, we propose a robust clustering method with noise cutting based on directed k-nearest neighbor graph (CDKNN) to identify the desired cluster number automatically and produce reliable clustering results simultaneously on nonlinear, non-overlapping but locally tight-connected data patterns. This method draws support from the k-nearest neighbor graph to represent the complex nonlinear datasets and applies parameter adaptive process to make the proposed clustering method better adapt to specific data patterns. The proposed method is robust to the noises of arbitrary shape datasets because it uses a directed K-nearest neighbor to cut out sparse nodes. We use simulation and UCI real-world datasets to prove the validity of the innovatory method by comparing it to k-means, DBSCAN, OPTICS, AP, SC, and CutPC algorithms in terms of clustering ACC, ARI, NMI, and FMI. The experimental results confirm that the proposed method outperforms the alternative nonlinear clustering methods.
Dual Subgraph-Based Graph Neural Network for Friendship Prediction in Location-Based Social Networks

With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship from online social relations and offline trajectory data is of great value to improve the platform service quality and user satisfaction. Existing methods mainly focus on some hand-crafted features or graph embedding models based on the user-location bipartite graph, which cannot precisely capture the latent mobility similarity for the majority of users who have no explicit co-visit behaviors and also fail to balance the tradeoff between social features and mobility features for friendship prediction. In this regard, we propose a dual subgraph-based pairwise graph neural network (DSGNN) for friendship prediction in LBSNs, which extracts a pairwise social subgraph and a trajectory subgraph to model the social proximity and mobility similarity, respectively. Specifically, to overcome the co-visit data sparsity, we design an entropy-based random walk to construct a location graph that captures the high-level correlation between locations. Based on this, we characterize the pairwise mobility similarity from trajectory level instead of location level, which is modeled by a graph neural network (GNN) on a labeled trajectory subgraph composed of the two trajectories of the target user pair. Besides, we also utilize another GNN to extract social proximity based on social subgraph of the target user pair. Finally, we propose a gate layer to adaptively balance the fusion of the social and mobility features for friendship prediction. We conduct extensive experiments on the real-world datasets and demonstrate the superiority of our approach, which outperforms other state-of-the-art methods. In particular, the comparative experiments on the trajectory level mobility similarity further validate the effectiveness of the designed trajectory subgraph-based method, which can extract predictive mobility features.
Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques

Request for quotation (RFQ) is a lengthy document soliciting vendor products and services according to rigid specifications. This research develops an integrated natural language processing (NLP), text mining, and machine learning approach for intelligent RFQ summarization. Over 1,300 power transformer RFQ requests are used to build a word-embedding model for training and testing. Domain keywords are extracted using N-gram TF-IDF. The method automatically extracts essential specifications such as voltage, capacity, and impedance from RFQs using text analytics. The K-means algorithm groups the sentences of each specification. The TextRank algorithm identifies important sentences of all specifications to generate RFQ summaries. The summarization system helps engineers shorten the time to identify all specifications and reduces the risk of missing important requirements during manual RFQ reading. The system helps improve the complex product design for manufacturers and improve the cost estimation and competitiveness of quotations in a highly competitive marketplace.
A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person's activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.
Digital image correlation assisted absolute phase unwrapping

This paper presents an absolute phase unwrapping method for high-speed three-dimensional (3D) shape measurement. This method uses three phase-shifted patterns and one binary random pattern on a single-camera, single-projector structured light system. We calculate the wrapped phase from phase-shifted images and determine the coarse correspondence through the digital image correlation (DIC) between the captured binary random pattern of the object and the pre-captured binary random pattern of a flat surface. We then developed a computational framework to determine fringe order number pixel by pixel using the coarse correspondence information. Since only one additional pattern is used, the proposed method can be used for high-speed 3D shape measurement. Experimental results successfully demonstrated that the proposed method can achieve high-speed and high-quality measurement of complex scenes.
SoK: A Systematic Literature Review of Knowledge-Based Authentication on Augmented Reality Head-Mounted Displays

The adoption of Augmented Reality (AR) technology has increased over the years. AR enhances various activities for consumers and businesses, particularly in industrial contexts. The three-dimensional virtual experience is realized by the usage of Head-Mounted Displays (HMD). These devices provide access to sensitive data and services. Thus, secure and usable authentication schemes are essential to control access to the HMD and the stored data as well as schemes to authenticate to the services one wants to use with the AR device. We conducted a systematic literature review on knowledge-based authentication schemes for AR HMD. 31 different schemes were identified. These schemes were assessed regarding various aspects including the type of AR HMD, the type of secret, how users input their secret, as well as usability and security aspects. We discuss gaps for future work.
Time shifts to reduce the size of reservoir computers

A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accurate results, the reservoir usually contains hundreds to thousands of nodes. This high dimensionality makes it difficult to analyze the reservoir computer using tools from the dynamical systems theory. Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers. We demonstrate here that a reservoir computer may be divided into two parts: a small set of nonlinear nodes (the reservoir) and a separate set of time-shifted reservoir output signals. The time-shifted output signals serve to increase the rank and memory of the reservoir computer, and the set of nonlinear nodes may create an embedding of the input dynamical system. We use this time-shifting technique to obtain excellent performance from an opto-electronic delay-based reservoir computer with only a small number of virtual nodes. Because only a few nonlinear nodes are required, construction of a reservoir computer becomes much easier, and delay-based reservoir computers can operate at much higher speeds.
Joint Graph Attention and Asymmetric Convolutional Neural Network for Deep Image Compression

Recent deep image compression methods have achieved prominent progress by using nonlinear modeling and powerful representation capabilities of neural networks. However, most existing learning-based image compression approaches employ customized convolutional neural network (CNN) to utilize visual features by treating all pixels equally, neglecting the effect of local key features. Meanwhile, the convolutional filters in CNN usually express the local spatial relationship within the receptive field and seldom consider the long-range dependencies from distant locations. This results in the long-range dependencies of latent representations not being fully compressed. To address these issues, an end-to-end image compression method is proposed by integrating graph attention and asymmetric convolutional neural network (ACNN). Specifically, ACNN is used to strengthen the effect of local key features and reduce the cost of model training. Graph attention is introduced into image compression to address the bottleneck problem of CNN in modeling long-range dependencies. Meanwhile, regarding the limitation that existing attention mechanisms for image compression hardly share information, we propose a self-attention approach which allows information flow to achieve reasonable bit allocation. The proposed self-attention approach is in compliance with the perceptual characteristics of human visual system, as information can interact with each other via attention modules. Moreover, the proposed self-attention approach takes into account channel-level relationship and positional information to promote the compression effect of rich-texture regions. Experimental results demonstrate that the proposed method achieves state-of-the-art rate-distortion performances after being optimized by MS-SSIM compared to recent deep compression models on the benchmark datasets of Kodak and Tecnick. The project page with the source code can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn</uri> .
Comments on “A Secure and Lightweight Drones-Access Protocol for Smart City Surveillance”

Recently, Akram et al. (2021) proposed a drones-access protocol for monitoring urban security. Akram et al. claimed that their protocol is secure and can resist known security attacks. However, in this comment article, we demonstrate that their protocol is still vulnerable to drone capture attacks and stolen-verifier attacks. In addition, their protocol does not provide perfect forward secrecy. Finally, we present an improved protocol to manage the security weaknesses we found.
Design of a UAV for Autonomous RFID-Based Dynamic Inventories Using Stigmergy for Mapless Indoor Environments

Unmanned aerial vehicles (UAVs) and radio frequency identification (RFID) technology are becoming very popular in the era of Industry 4.0, especially for retail, logistics, and warehouse management. However, the autonomous navigation for UAVs in indoor map-less environments while performing an inventory mission is, to this day, an open issue for researchers. This article examines the method of leveraging RFID technology with UAVs for the problem of the design of a fully autonomous UAV used for inventory in indoor spaces. This work also proposes a solution for increasing the performance of the autonomous exploration of inventory zones using a UAV in unexplored warehouse spaces. The main idea is to design an indoor UAV equipped with an onboard autonomous navigation system called RFID-based stigmergic and obstacle avoidance navigation system (RFID-SOAN). RFID-SOAN is composed of a computationally low cost obstacle avoidance (OA) algorithm and a stigmergy-based path planning and navigation algorithm. It uses the same RFID tags that retailers add to their products in a warehouse for navigation purposes by using them as digital pheromones or environmental clues. Using RFID-SOAN, the UAV computes its new path and direction of movement based on an RFID density-oriented attraction function, which estimates the optimal path through sensing the density of previously unread RFID tags in various directions relative to the pose of the UAV. We present the results of the tests of the proposed RFID-SOAN system in various scenarios. In these scenarios, we replicate different typical warehouse layouts with different tag densities, and we illustrate the performance of the RFID-SOAN by comparing it with a dead reckoning navigation technique while taking inventory. We prove by the experiments results that the proposed UAV manages to adequately estimate the amount of time it needs to read up-to 99.33% of the RFID tags on its path while exploring and navigating toward new zones of high populations of tags. We also illustrate how the UAV manages to cover only the areas where RFID tags exist, not the whole map, making it very efficient, compared to the traditional map/way-points-based navigation.
CAESAR8: An agile enterprise architecture approach to managing information security risks

In theory, implementing an Enterprise Architecture (EA) should enable organizations to increase the accuracy of information security risk assessments. In reality, however, organizations struggle to fully implement EA frameworks because the requirements for implementing an EA and the benefits of commercial frameworks are unclear, and the overhead of maintaining EA artifacts is unacceptable, especially for smaller organizations. In this paper, we describe a novel approach called CAESAR8 (Continuous Agile Enterprise Security Architecture Review in 8 domains) that supports dynamic and holistic reviews of information security risks in IT projects. CAESAR8’s nonlinear design supports continuous reassessment of information security risks, based on a checklist that assesses the maturity of security considerations in eight domains that often cause information security failures. CAESAR8 assessments can be completed by multiple stakeholders independently, thus ensuring consideration of their tacit knowledge while preventing groupthink. Our evaluation with experienced industry professionals showed that CAESAR8 successfully addresses real-world problems in information security risk management, with significant benefits particularly for smaller organizations.
Multi-Channel Man-in-the-Middle attacks against protected Wi-Fi networks: A state of the art review

Multi-Channel Man-in-the-Middle (MitM) attacks are special MitM attacks capable of manipulating encrypted wireless frames between two legitimate endpoints. Since its inception in 2014, attackers have been targeting Wi-Fi networks to perform different attacks, such as cipher downgrades, denial of service, key reinstallation attacks (KRACK) in 2017, and recently FragAttacks in 2021, which widely impacted millions of Wi-Fi devices, especially IoT devices. To the best of our knowledge, there are no studies in the literature that holistically review the different types of Multi-Channel MitM enabled attacks and analyze their potential impact. To this end, we evaluate the capabilities of Multi-Channel MitM and review every reported attack in the state of the art. We examine practical issues that hamper the total adoption of protection mechanisms, i.e., security patches and Protected Management Frames (PMF), and review available defense mechanisms in confronting the Multi-Channel MitM enabled attacks in the IoT context. Finally, we highlight the potential research problems and identify future research approaches in this field.
Growing deep echo state network with supervised learning for time series prediction

Multilayer echo state networks (ESNs) are powerful on learning hierarchical temporal representation. However, how to determine the depth of multilayer ESNs is still an open issue. In this paper, we propose a novel approach to automatically determine the depth of a multilayer ESN, named growing deep ESN (GD-ESN). First, an incremental hierarchical structure is proposed, where the recurrent layers and the pre-trained feedforward layers are alternately added to the network one by one. Then, a control scheme is designed for the growth of the network based on the newly defined averaged mutual information and the full rank criterion. Finally, the proposed GD-ESN is evaluated on both benchmark datasets and real-world applications. The experimental results show the effectiveness of the proposed method. • An approach is proposed to automatically determine the depth of deep ESNs by hierarchically analyzing the valid information. • A simple supervised learning method is developed to pre-train the feedforward layers before being added to the network. • Experimental results show that the proposed method spends less time to find the optimal depth of deep ESNs.
A geometric-based clustering method using natural neighbors

Neighborhood-based and density-based clustering methods are applied in various data analysis applications. However, most of them have low performance in mixed heterogeneous datasets, and high computational cost. To mitigate these challenges, in this paper, a geometric-based clustering method is proposed which uses the concept of natural neighborhoods to extract the local density of data points. At the first stage of the algorithm, primary clusters are formed by identifying dense points that have a higher number of natural neighbors. Then, the heads of the clusters are extracted in dense points, based on the local natural density criterion. Afterward, a novel natural neighborhood-based method is applied to identify the points having a low number of natural neighbors, which called weak or noise data points. Finally, the desired clusters are obtained by ignoring the weak/noise points. The computational complexity of the proposed method is O(nlogn). The proposed method is independent of any specific parameter setting. The results of experiments on benchmark datasets confirm that it achieves a higher performance compared to the competitors.
A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route

Based on Differential Evolution and Cuckoo Search, a hybrid optimization algorithm, called DDECS, is proposed in this paper for solving the ship pipe route layout optimization problem. The purpose of DDECS algorithm is to quickly and accurately optimize the ship pipe route under various kinds of constraints. Firstly, this paper proposed a vector coding method for ship pipe routes by studying the existing coding methods and ship pipe routes’ attributes. The proposed vector coding method has a concise structure, strong compatibility and is independent of the gridding space. And then, the Discrete Hybrid Differential Evolution and Cuckoo Search (DDECS) algorithm was implemented by using the proposed vector coding. The DDECS algorithm has few parameters and high computational efficiency, and it can effectively find the global optimal solution by maintaining the diversity of the population. Finally, three comparative simulations were presented to validate the effectiveness and engineering practicality of DDECS algorithm. The results showed that, the proposed DDECS algorithm can get an optimization scheme with fewer elbows and more remaining areas, which is significant for the layout optimization of the ship pipe route.