Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial Internet of Things and Emerging Digital Technologies–Modeling Professionals’ Learning Behavior

Industrial internet of things (IIoT) and digital technologies have been evolving fast, leading to a challenge in the availability of skills and commotion in job profiles. While existing job profiles are changing, new job profiles are getting created. Professionals face the challenge of obsolescence and pressure for continuous reskilling and prepare for the future of work. The fast-changing innovations in digital technologies of IIoT like the internet of things, robotics, augmented reality, artificial intelligence, and big data analytics trigger in-depth analysis of professionals’ learning behavior. This study extends the individual’s ambidextrous learning theory and unified theory of acceptance and use of technology (UTAUT) to develop a quantitative behavioral model Learning Emerging Digital Skills (LEDS). LEDS model describes the antecedents of professionals’ learning behavior towards fast-changing emerging digital technologies involved in IIoT. A nation-wide structured survey of 685 professionals across 95 firms in India across industry sectors engaged in IIoT product and solution development in sectors like automotive, aerospace, healthcare, and energy were undertaken. Findings from structural equation modeling are validated via a qualitative study. Social influence and personal innovativeness, anxiety, long-term consequence, and job relevance affect behavioral intention to learn. Professionals’ performance level and technology preference moderate the relationship between antecedents and the intention to learn. For exceptional performers, personal innovativeness is the key driver in the intention to learn. For average performers, social influence and anxiety are additional significant factors towards intention to learn. Technology itself moderates the learning behavior, which indicates professionals’ preference to learn a technology over the other based on technology maturity and use potential. This study can help practitioners design ramp-up strategies to meet the current and future demand of emerging digital skills to meet their IIoT strategy. Policymakers can use antecedents of employees’ ambidextrous learning behavior to formulate policies to achieve ambidextrous organization’s goals.
Solidification and microstructures of binary ice-I/hydrate eutectic aggregates

Abstract The microstructures of two-phase binary aggregates of ice-I + salt-hydrate, prepared by eutectic solidification, have been characterized by cryogenic scanning electron microscopy (CSEM). The specific binary systems studied were H2O-Na2SO4, H2O-MgSO4, H2O-NaCl, and H2O-H2SO4; these were selected based on their potential application to the study of tectonics on the Jovian moon Europa. Homogeneous liquid solutions of eutectic compositions were undercooled modestly (ΔT ~ 1.5 °C); similarly cooled crystalline seeds of the same composition were added to circumvent the thermodynamic barrier to nucleation and to control eutectic growth under (approximately) isothermal conditions. CSEM revealed classic eutectic solidification microstructures with the hydrate phase forming continuous lamellae, discontinuous lamellae, or forming the matrix around rods of ice-I, depending on the volume fractions of the phases and their entropy of dissolving and forming a homogeneous aqueous solution. We quantify aspects of the solidification behavior and microstructures for each system and, with these data, articulate anticipated effects of the microstructure on the mechanical responses of the materials.
The Hardness and Approximation of the Densest k-Subgraph Problem in Parameterized Metric Graphs

A complete weighted graph G = (V, E, w) is called Δβ-metric, for some β ≥ 1/2, if G satisfies the β-triangle inequality, i.e., w(u, v) ≤ β • (w(u, x) + w(x, v)) for all vertices u, v, x ∈ V . Given a Δβ-metric graph G = (V, E, w), the Δβ-WEIGHTED DENSEST k-SUBGRAPH (Δβ-WDkS) problem is to find an induced subgraph G[C] with exactly k vertices such that the total edge weight of G[C] is maximized. For β = 1, this problem, Δ-WDkS, is known NP-hard and admits a $\frac{1}{2}$-approximation algorithms. In this paper, we show that for any β > 1/2, Δβ-WDkS is NP-hard. We also show how to modify any α-approximation algorithm for Δ-WDkS to obtain a δα,β-approximation algorithm for Δβ-WDkS with δα,β > α for every β < 1. Moreover, we prove that Δβ-WDkS can be approximated to within a factor $\frac{1}{{2\beta }}$ for any $\beta > \frac{1}{2}$.
A Stacked Patch Antenna With Broadband Circular Polarization and Flat Gains

In this paper, a stacked circular polarization (CP) patch antenna with broad impedance bandwidth, axial-ratio (AR) bandwidth, and flat gains is investigated. The antenna is composed of a square ring, double layers’ stacked patches, and four vertical patches. The corner truncated loop served as a sequential phase feeding structure for four driven patches. To improve the impedance matching, four square patches are stacked on the bottom layer. Furthermore, four vertical patches are introduced on the ground plane to broaden the AR bandwidth. Simulated and measured studies are conducted on an antenna prototype to validate the proposed design. The proposed design shows that the measured impedance bandwidth is 4.65–7.21 GHz (43.2%), measured 3-dB AR bandwidth at broadside is 4.9–6.4 GHz (26.5%) and the 1-dB gain bandwidth is 4.75–6.6 GHz (32.6%). Compared with other similar CP stacked patch antennas, the antenna owns advantages of wide bandwidth, compact size, and flat gain.
Average Dwell Time Based Smooth Switching Linear Parameter-Varying Proportional-Integral-Derivative Control for an F-16 Aircraft

Switching control methods have great potential applications in flight control system design. However, nonsmooth control commands at switching instants could lead to performance degradations or bring in safety risks. This paper presents a smooth switching proportional-integral-derivative (PID) control method for linear parameter-varying (LPV) systems to solve this issue. By introducing a transition parameter subspace between any two neighboring LPV subsystems, the switched controller gains are designed to be smoothly varied when the parameter trajectory passes through these subspaces, and the Lyapunov function in the transition subspace is designed independently to make the algorithm less conservatism. The controller synthesis condition is formulated as a linear matrix inequality (LMI) optimization problem using Finsler’s lemma. The concept of average dwell-time (ADT) is employed to analyze the performance of the resulting closed-loop system. The effectiveness of the ADT-based smooth switching control strategy is demonstrated by applying it to the nonlinear longitudinal model of the F-16 aircraft, and the simulation results are compared with the traditional ADT switching control method. The results show that the proposed method can achieve a better transition performance with a simpler control structure.
FabAsset: Unique Digital Asset Management System for Hyperledger Fabric

Business is innovating with the advent of blockchain that tokenizes digital assets. To expand the blockchain’s potential, Ethereum, a representative permissionless blockchain platform, supports the fungible token (FT) standard ERC-20 and the nonfungible token (NFT) standard ERC-721. Hyperledger Fabric (Fabric), a representative permissioned blockchain platform, proposed FabToken to support tokens in version 2.0. 0 alpha. But FabToken contains only FTs, not NFTs. Given the market share in the enterprise blockchains, Fabric needs to support NFTs as soon as possible. This paper presents a unique digital asset management system called FabAsset so that Fabric can run decentralized applications that require NFTs. This paper describes the design of FabAsset, consisting of chaincode and SDK (Software Development Kit), and the prototype of a decentralized signature service leveraging FabAsset to validate its usefulness.
CROSS: A Crowdsourcing based Sub-Servers Selection Framework in D2D Enhanced MEC Architecture

In a Device-to-Device (D2D) mobile edge computing system, the mobile devices can share computation and communication resources with each other. A mobile device can offload its computation task to a nearby mobile device. In order to release the computation workload of mobile edge servers and enhance the mobile edge service coverage quality, a set of mobile devices can be selected as sub-servers and provide mobile edge service to nearby devices. Based on the crowdsourcing technique, we have proposed a framework, CROSS, to select sub-servers in a D2D enhanced mobile edge computing system. Two major problems in CROSS framework, the sub-server selection problem and the payoff allocation problem, have been formulated. The first problem is proved to be NP-Hard and solved by an approximation algorithm. The second problem is solved by an auction mechanism. The performance of the CROSS framework is evaluated by experiments. The experimental results show that the CROSS framework is efficient and effective.
Rectum Segmentation in Brachytherapy Dataset Using Recurrent Network

In brachytherapy, the segmentation accuracy of the target tumor and surrounding organs is very important. In recent years, deep learning models have improved the performance of organ segmentation and have been widely used. However, it is still a huge challenge for some organs with variable shapes. The basic idea of the work presented in this paper is to accurately divide the rectal computed tomography image dataset so that patients can obtain more accurate brachytherapy. In this work, we used 3D U-Net and Long ShortTerm Memory (LSTM) to improve the accuracy of rectal segmentation. This model was trained and tested on the rectal computed tomography image dataset, which contains 51 patients undergoing radiation therapy. The dice coefficient is used as the evaluation index in all results of organ segmentation. After experiments are done, it can be seen that the proposed method has good performance in rectal segmentation.
Spin-Injected Birefringent VCSELs for Analog Radio-Over-Fiber Systems

We propose and numerically investigate the use of birefringent vertical-cavity surface-emitting lasers (VCSELs) under the injection and modulation of spin polarized electrons for application to analog radio over fiber (RoF) systems. Injection current and electron spin polarization in a birefringent VCSEL are individually modulated for generating data signals and a millimeter-wave carrier and a polarizer is used to combine two orthogonal polarizations. A preliminary experiment without spin injections confirms that 24-GHz carrier generation originating from polarization-mode splitting of the birefringent VCSEL can be used for the heterodyne detection of square-wave signals with a period of 6.4 ns. Our findings mark the first step in applying spin-injected birefringent VCSELs having the capability of ultra-high-frequency modulation for application to analog RoF systems.
Online Incipient Fault Detection Method Based on Improved ℓ1 Trend Filtering and Support Vector Data Description

Poor model generalization, missing or false alarms, and heavy dependence on expert’s experience are some of the major problems which exist in traditional incipient fault detection (IFD) methods. An IFD rolling bearing application method based on combination of improved $\lambda _{1}$ trend filtering (L1TF) and support vector data description (SVDD) is proposed. First, spectral distance index and multi-scale dispersion entropy based on normal vibration data, which is sensitive to incipient faults, are extracted. The improved $\lambda _{1}$ trend filter (IL1TF) method is employed for processing the feature values and obtaining a trend factor with less fluctuation and better incipient fault indication ability. Then, after determining the kernel function bandwidth of the SVDD by analyzing the characteristics of the training data, a suitable offline SVDD model is trained. Finally, incipient faults are identified by estimating the distance between the trend factor of the real-time data and the center of the hypersphere in the SVDD model. This method employs full performance of SVDD to detect abnormal data files, while reducing the influence of abnormal data files on the model via IL1TF. Furthermore, the method increases the discrimination between the incipient fault data and the normal data. By utilizing Intelligent Maintenance Systems of University of Cincinnati bearing laboratory data and Chinese petrochemical company’s centrifugal pump bearing engineering data, the effectiveness of the constructed model is demonstrated. In addition, the proposed method is compared against existing representative IFD methods. The results indicate that the method proposed in this paper can solve false alarms and detect incipient failure data files more accurately without depending on the external expert’s experience. This is of great significance for providing guidelines to enterprises which employ predictive maintenance techniques.
An Energy-Efficient Multi-Modal IoT System Leveraging NB-IoT and BLE

A multitude of wireless communication technologies are available for building Internet of Things (IoT) solutions, ranging from Low Power Wide Area Network (LPWAN) technologies with large coverage and low data rates to Wireless Personal Area Network (WPAN) technologies with shorter range but higher data rates. To satisfy the requirements of more challenging and dynamic IoT use cases, a combination of different technologies might be the best choice. Leveraging on a communication architecture that supports such multi-modal IoT systems, this paper studies the energy efficiency of a prototype device that is able to switch between Narrowband Internet of Things (NB-IoT), an LPWAN technology, and Bluetooth Low Energy (BLE), a WPAN technology. Starting from the energy consumption of the individual technologies, the overall consumption of the multi-modal device is assessed, including the effects of switching, and a prediction of the battery lifetime is made for different application contexts. Our analysis shows that for energy-critical applications, BLE is preferable over NB-IoT wherever possible. However, in case BLE is unavailable, a switch to NB-IoT can be initiated. Based on our findings, a number of approaches to use the multi-modal system in an energy efficient way has been proposed.
Investigation of Gate-Length Scaling of Ferroelectric FET

In this article, the device scaling of FeFET down to 22 nm and its impacts on memory performance and reliability has been investigated in detail using experimentally calibrated data. Lateral nonuniformity along the channel in the polarization of the ferroelectric layer is observed, which is shown to be due to the scaling effects in FeFET. It has been observed that with gate-length scaling, the remnant polarization shows opposite trends, i.e., <inline-formula> <tex-math notation="LaTeX">${P}_{r}$ </tex-math></inline-formula> reduces for program and increases for erase. Also, with scaling, there is a significant rise in the electric field across the interfacial layer for erase state, which is an important concern for reliability. We find a relatively stable memory window with gate-length scaling due to the counter-balancing of <inline-formula> <tex-math notation="LaTeX">$\pm {P}_{r}$ </tex-math></inline-formula>. Gate-length scaling has resulted in a larger decrease in <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> and higher subthreshold slope (SS) for program state due to a reduction in gate control as compared to erase state. Our results provide important insights into the design optimization of nanoscale FeFET for high-density and low-power applications.
Effective and Efficient Dimensionality Reduction of Hyperspectral Image using CNN and LSTM network

Convolutional neural networks (CNN) which is a feature-based machine learning algorithm is very popular in hyperspectral image (HSI) classification. CNN exploits the spatial relationship between HIS. However, HSI intrinsically have a sequence-based data structure called the spectral features. Combining spectral and spatial information offers a more comprehensive classification approach. 3D-CNN can exploit Spatial-spectral relationship but can be computationally expensive. LSTM, an important branch of the deep learning family, is mainly designed to handle Sequential data. In this paper we propose a model that uses the 1D CNN and 2D-CNN for extracting the spatial features and a LSTM for extracting the spectral features. Experimental results show that our method outperforms the accuracies reported in the existing CNN and LSTM based methods.
Understanding Business Intelligence in The Context of Mental Healthcare Sector of Bangladesh for Improving Health Services

Business Intelligence (BI) can produce solutions that can enable different organizations to take fast and effective decision making by extracting valuable information and discovering underlying data patterns. Whilst health is one of the basic needs underlying various quality of life indicators, many health care organizations still lag behind in terms of the effective management and handling of large amounts of data, as compared with other sectors, such as the financial sector for example. In particular, there are many challenges in relation to the management of data within the context of mental healthcare organizations since data is comparatively complex. The aim of this paper is to focus on decision making using BI to improve services for patients and better understand the role of implementing Business Intelligence by examining different definitions of BI and investigating it through the use of a framework.
Statistical Alignment-Based Metagated Recurrent Unit for Cross-Domain Machinery Degradation Trend Prognostics Using Limited Data

Nowadays, machinery degradation trend prognostics have made significant progress with sufficient and related data. Limited data may weaken the generalizations of artificial intelligence-based prognostics, which could worsen under the domain shift phenomenon. To overcome the dual challenges and further abstract the prior degraded knowledge from historical data, a novel prediction architecture entitled as statistical alignment-based metagated recurrent unit, profoundly integrating deep learning, knowledge transfer, and metalearning, is proposed in this study and simultaneously features domain adaptation ability and desired generalizations under limited data. To be specific, a designed high-order measure is first developed to align training and testing data distribution. Then, the well-known encoder–decoder framework is employed as the base model, and subtask & cross-subtask learning architectures are developed to distill sensitive prognostics agents for easily generalizing to new domains using limited data. Finally, FEMTO-ST bearing data sets and industrial data sets collected from petrochemical pumps are conducted to prove their effectiveness.
Unknown Worker Recruitment in Mobile Crowdsensing Using CMAB and Auction

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Although many MCS systems have been built for various applications, most of them either assume that workers’ qualities are known in advance or cannot ensure workers to report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Additionally, we also demonstrate its significant performances through extensive simulations on real-world data traces.
Charging Models for MANO-based 5G Core Networks

SDN/NFV (Software Defined Network/Network Function Virtualization) are key technology enablers for 5G Core Networks. They will introduce new actors into the telecom ecosystem including Virtual Network Function (VNF) vendor, VNF market place, VNF as a Service (VNFaaS) provider and Network Function Virtualization Infrastructure as a Service (NFVIaaS) provider. These new actors will promote competition hence demand for more flexible pricing with new charging models. This research explores these charging models according to the new charging factors identified in SDN/NFV based on the MANO (MANagement and Orchestration) architecture defined by ETSI NFV Industry Specification Group. The results of our research provide a methodology for service providers and telecom operators to price their SDN/NFV products and services.
A Novel Framework for Early Pitting Fault Diagnosis of Rotating Machinery Based on Dilated CNN Combined With Spatial Dropout

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.
Construction of Health Indicators for Rotating Machinery Using Deep Transfer Learning With Multiscale Feature Representation

In many applications, it is not easy to generate enough whole-life data for training a deep neural network, which may reduce the performance of a health indicator (HI). To solve this problem, a new HI construction method based on deep transfer learning is proposed in this article. First, a new multiscale domain-adversarial neural network is proposed to extract representative features from the data collected under different working conditions. By introducing the maximum mean discrepancy regularizer and the Laplace regularizer, this model can enhance features’ discriminant ability for incipient fault and exploit overall degradation information simultaneously. Then, a new HI is obtained via dimensionality reduction based on the extracted features. Moreover, a new HI assessment metric is proposed to effectively estimate the performance of the obtained HI. This new index considers not only the nonlinear correlation but also the geometric similarity of the degradation processes under different working conditions. Comparative experiments are conducted on the IEEE PHM Challenge 2012 bearing data set and the XJTU-SY bearing data set. The results show that the proposed method outperforms several typical transfer learning methods and nontransferable deep learning methods in terms of monotonicity and correlation.