Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategic Alignment Maturity Level Model Using Drivers of Change in a Business Environment

Achieving results, we often used the word “change”, nevertheless the process behind that word is not easy and sometimes intricate. On the other hand, determining whether an organization has changed or not is another matter that requires standardize assessment. It is clear that the business environment is uncertain and faced with tremendous change due to the influence of globalization, information systems and technology, and the knowledge economy which affects the behavior of our society and will continue to do so. Alignment between business strategy and information system strategy remains a challenge for business organizations, especially in the current digital era. The purpose of this research is to produce a Strategic Alignment Maturity Level Model of business-IS alignment through three drivers of change, namely business, technology, customer, and management. Luftman’s Strategic Alignment Maturity Model is the reference used. Preliminary studies, designing and developing an instrument model, determining the strategic alignment maturity level, determining strategic recommendations, and built an application system are the stages of this research. Expert judgment is involved in linguistic assessment and strategy recommendations. The last step was an experiment to see the suitability of the final level and the strategy recommendations issued. The model for determining the alignment level of IS-Business with business, technology and management and customers as a driver of change and dedicated software is the result of this research, can be used as an alternative technique in the information system strategic planning process to determine needs or to evaluate the implementation of recommendations.
[Invited] NDN Based Participatory Crowdsensing Framework with Area-focused Interest Forwarding

Mobile crowdsensing is gaining wide popularity, and its main aim is to collect significant amounts of data by leveraging mobile terminals such as smartphones that provide a ubiquitous communication environment. On the other hand, named data networking (NDN) has become one of the most popular information-centric networks. In this paper, as a new paradigm, we propose an advanced crowdsensing system using NDN. Unlike previous studies, data, including uncertain information such as people’s notions, opinions, and knowledge, are gathered from the outer locations of NDN, thanks to the delay-tolerant feature, which opens up new possibilities for data collection. The request for the data is forwarded in an area-focused manner. In addition, with this approach, we expect to significantly reduce the redundant data responses.
HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

This article proposes a novel hierarchical residual network with attention mechanism (HResNetAM) for hyperspectral image (HSI) spectral-spatial classification to improve the performance of conventional deep learning networks. The straightforward convolutional neural network-based models have limitations in exploiting the multiscale spatial and spectral features, and this is the key factor in dealing with the high-dimensional nonlinear characteristics present in HSIs. The proposed hierarchical residual network can extract multiscale spatial and spectral features at a granular level, so the receptive fields range of this network will be increased, which can enhance the feature representation ability of the model. Besides, we utilize the attention mechanism to set adaptive weights for spatial and spectral features of different scales, and this can further improve the discriminative ability of extracted features. Furthermore, the double branch structure is also exploited to extract spectral and spatial features with corresponding convolution kernels in parallel, and the extracted spatial and spectral features of multiple scales are fused for hyperspectral image classification. Four benchmark hyperspectral datasets collected by different sensors and at different acquisition time are employed for classification experiments, and comparative results reveal that the proposed method has competitive advantages in terms of classification performance when compared with other state-of-the-art deep learning models.
Automated Fiber Embedding for Soft Mechatronic Components

Many recent advances in smart composites for soft robotics have resulted from integration of long fibers to precisely control structural and functional properties. Compared to the time-consuming, laborious and error-prone process of manual long fiber placement typically used, Automated Fiber Embedding (AFE) has an enormous potential in soft robotics due to its repeatability, precision, and ease of integration with commonly used elastomers. In this work, three different approaches for automated layering and embedding of fibers inside silicone elastomers are presented. These techniques enable fabrication of soft composites comprising linear and free-form long fiber patterns without user intervention or manual post processing. To demonstrate the versatility of the techniques, several soft robotic applications ranging from actuators to inductive charging coils are presented.
Nanocomposite as Visible Sensing Platform for Hg²⁺

Owing to their detection performance without the use of any instruments, visual sensing based on gold nanoparticles has become important and attractive for a wide range of applications in various fields, especially in sensing systems. In this study, a novel visual sensing platform for the detection of mercury (II) ion (Hg2+) was developed by using a graphene quantum dots functionalized gold nanoparticles nanocomposite system. To achieve the goal of selective, sensitive, and rapid sensing of Hg2+, gold nanoparticles (AuNPs) were combined with graphene quantum dots (GQDs). GQDs was assembled on AuNPs due to the strong interaction with the terminal amino groups of GQDs. A new procedure for the selective and sensitive detection of Hg2+ has been unveiled based on the prevention of aggregation of AuNPs functionalized with GQDs in the presence of analytes (i.e. Hg2+).
A BiGRU Autoencoder Remaining Useful Life Prediction Scheme With Attention Mechanism and Skip Connection

Remaining Useful Life (RUL) prediction is one of the most common activities to ensure the reliability of a degradation system. In previous RUL prediction schemes based on RNN autoencoder, the multi-dimensional sensor data for each timestep made an equal contribution to the generation of the embedding vector during the encoding process. Besides, the single embedding vector carries the burden of decoding the entire multi-timestep information. To overcome the above shortcomings, two improvements are proposed: (1) For the embedding vectors to highlight critical timestep information, weights are assigned to each timestep information through an attention mechanism. (2) To reduce the decoding burden on a single embedding vector, a skip connection is introduced at each step of the decoding process to improve BiGRU decoding capabilities. The prognostic performance of the proposed method BiGRU-AS is evaluated on two publicly available datasets: the C-MAPSS dataset (simulation dataset) and the milling dataset (experimental dataset). Compared to the latest prediction methods, the experimental results show that the proposed method is competitive in RUL prediction for mechanical systems.
Denoising Algorithm of Environmental Sound Fused NMF and OMLSA in Non-Stationary Noise Environment

The traditional denoising algorithm can reduce the noise from environment, yet an evident downside of this is the sharp decrease of efficacy under non-stationary noise with low signal to noise ratio (SNR). In this case, this paper presents a denoising algorithm for environmental sound based on non-negative matrix factorization (NMF) and optimally-modified log-spectral amplitude (OMLSA). First of all, prior information is obtained from the noise and environmental sound dataset to separate the noise from noisy sound using supervised NMF. Then, decision-direction approach (DD) is used to calculate a priori SNR estimation according to the power spectral density (PSD) of the separated noise. Finally, under the log minimum mean square error criterion, the OMLSA gain function is obtained to filter out the noise in the environment. In this paper, the performance of the proposed algorithm is tested with white noise, F16 and babble under -5dB, 0dB and 5dB SNR conditions respectively. The experimental results show that this algorithm has strong ability of filtering out the noise superimposed in the environmental sound and improving the SNR gains. Moreover, comparing with other noise suppression algorithms, the proposed algorithm preforms better in preserving weak environmental sound, significantly reducing environmental sound distortion and avoiding the introduction of musical noise.
A Deep Learning Algorithm for Classifying Grasp Motions using Multi-session EEG Recordings

The classification of motor imagery tasks using scalp EEG signals is a complicated procedure in BCI especially when the task comprises multiple gestures of the same hand. In this paper, we present a classification method to distinguish three grasp motion classes (cylindrical, spherical, and lumbrical) of one hand over two-day training sessions in 15 subjects in a public dataset. We have developed Two ensemble methods consisting of (anomaly detection + fully connected neural network) and (anomaly detection + convolutional neural network) to classify grasp motion and have achieved more than 80% classification accuracy in 3 subjects and an average accuracy of 57% among the full cohort. Our results confirm the possibility of utilizing neural networks to decode motor movement intentions from scalp EEG in a complicated task.
MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement

Images captured in low-light conditions often suffer from bad visibility, e.g., low contrast, lost details, and color distortion, and image enhancement methods can be used to improve the image quality. Previous methods have generally obtained a smooth illumination map to enhance the image but have ignored details, leading to inaccurate illumination estimations. To solve this problem, we propose a multi-scale attention retinex network (MARN) for low-light image enhancement, which learns an image-to-illumination mapping to obtain a detailed inverse illumination map inspired by retinex theory. In order to introduce more image priors, we introduce a novel illuminance-attention map to guide the model to characterize varying-lighting areas, which we combine with the low-light image as the model input. MARN consists of a multi-scale attention module and a feature fusion module; the former extracts multi-resolution features with attention-based feature aggregation, while the latter further merges the output features of the previous module with the input. To achieve better visibility, we formulate a novel loss function to synthetically measure the illumination, detail, and colorfulness of the image. Extensive experiments are performed on several benchmark datasets. The results demonstrate that our method outperforms other state-of-the-art methods according to both objective and subjective metrics.
Disk Loaded Monopole Antenna Installed in Metal Cylinder

Recently, a compact, conformal and metal-mountable antennas for radio-frequency identification (RFID) tags have been studied to manage metal cylinder of various shapes wirelessly. In addition, the radiation characteristics of RFID tag antennas placed inside a metal cylinder have also been investigated. However, the antennas attached to consumable parts inside a metal cylinder has not been considered. This paper presents the radiation characteristics of disk loaded monopole antenna placed in the metal cylinder.
A Versatile Optical Fiber Sensor Comprising an Excitation-Independent Carbon Quantum Dots/Cellulose Acetate Composite Film for Adrenaline Detection

A novel optical sensor was constructed for the detection of adrenaline (AD) by immobilizing a carbon quantum dots (CQDs)/cellulose acetate (CA) composite film on the tip of an optical fiber via dip-coating method. The emission wavelength of the CQDs was stabled at about 520 nm when it was excited from 410 to 490 nm, showing a special excitation independent characteristic. The fluorescence intensity of the CQDs quenched by AD with high selectivity leads to development of a novel sensitive method for the AD detection. The fluorescence intensity of the optical sensor has a good linear response to the concentration of adrenaline in the range of <inline-formula> <tex-math notation="LaTeX">$5~\mu \text{M}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$45~\mu \text{M}$ </tex-math></inline-formula> (R<sup>2</sup> = 0.99875). This investigation illustrates that optical fiber sensor platform may have great potential for the detection of AD in biomedical fields.
Data Augmentation for P300-based Brain-Computer Interfaces Using Generative Adversarial Networks

A notable problem in Brain-Computer Interface (BCI) is the burden of collecting an adequate amount of training data to be used in estimating a robust classifier model. This study explores the prospect of using Generative Adversarial Networks (GANs), a novel family of generative models, to perform data augmentation for generating artificial training data that are used in the classification of P300 event-related potentials in electroencephalogram (EEG) signals. In this regard, we consider two popular GANs, namely, Deep Convolutional Generative Adversarial Networks (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), and explore their efficacy in generating artificial EEG data in both subject-specific and subject-independent contexts. The results show that data augmentation using both DCGAN and WGAN-GP could lead to improved classification performance. However, the operating conditions that an improvement is observed differ between the subject-specific and subject-independent classification schemes. In particular, we observe that a transition from a relatively small to large training sample size per subject would generally lead to a better classification performance in training subject-specific classifiers; however, when a limited number of subjects is available, this transition could potentially result in an opposite effect in case of subject-independent classification.
Experimental Link Quality Analysis for LoRa-Based Wireless Underground Sensor Networks

A variety of industrial applications are deployed in underground environments, such as soil condition assessment and pipeline monitoring (PM). Wireless underground sensor networks (WUSNs) are capable of continuously monitoring pipelines and promptly alerting any anomaly of entities. However, underground soils significantly influence the traditional WUSNs connectivity success. Long range (LoRa), being a leading low-power wide-area networks (LPWANs) technology, provides a new solution for underground industrial monitoring with its advantages in long-range capability and ultralow power consumption. Nevertheless, the LoRa-based link quality characteristics have not yet been quantitatively evaluated for WUSNs. In this article, the channel models of both the underground-to-aboveground (UG2AG) and aboveground-to-underground (AG2UG) communications are investigated. We experimentally analyze the impact of the propagation direction, burial depth and LoRa physical layer parameters on the in-situ LoRa propagation performance. The received signal strength indictor (RSSI), signal-to-noise ratio (SNR), and packet deliver ratio (PDR) are characterized for both communication channels in LoRa-based WUSNs. The semiempirical path-loss models are successfully verified by our field results, and we demonstrate that the communication range can be greater than 50 m at the burial depth of 0.4 m by adjusting the LoRa transmission/receiving settings. The combination of RSSI and SNR can be a better indicator of PDR than relying on either of them alone. Finally, the frame error rate (FER) is calculated to estimate the link performance with EM interferences. These results successfully demonstrate the advantages of LoRa for PM applications, which serve the first step toward the efficient protocol development of LoRa-based WUSNs.
Big data Predictive Analytics for Apache Spark using Machine Learning

In today's digital world data is producing at a rapid speed and handling this massive diverse data become more challenging. The environment of big data is capable of handling data efficiently from data warehouses and in real-time. In Big data environment, Apache Spark is cluster-based, open-source computing technology explicitly designed for bulky data handling. Apache spark services are to perform composite Analytics through in-memory processing. This plays an active role in making meaningful exploration through machine learning and processes a large amount of data. Machine learning API is known as Mllib. It is highly prominent and efficient for big data platforms also offers excellent functionalities. In this paper, we have performed an experiment to look at the analytical qualities of Mllib in the apache spark environment. Likewise, we have highlighted the modern tendencies of Machine learning in big data studies and provides an understanding of upcoming work.
Condition Monitoring of Overhead Polymeric Insulators Employing Hyperbolic Window Stockwell Transform of Surface Leakage Current Signals

In this article, an efficient technique has been proposed to estimate the contamination level of overhead polymeric insulators. Deposition of contamination on polymeric insulator surface, is a serious issue as it often results in the flashover and even insulator failure. For estimating the severity of contamination level, surface leakage current (SLC) signals of a 11kV polymeric insulator with contaminated surface has been analyzed in time-frequency domain through hyperbolic window stockwell transform (HST). HST is more flexible than classical stockwell transform. Also, HST can able to handle both the low and high frequencies adequately. Considering the advantage, HST has been used here to estimate contamination degree from SLC signature. HST analysis of SLC signal returned a 2d complex time-frequency HS matrix. The complex time-frequency HS matrix has been separated into magnitude and phase spectrum. Based on the phase and magnitude spectrum, 15 statistical features, namely HST features has been extracted. Thereafter, 5 relevant HST features have been selected through least absolute shrinkage and selection operator (LASSO) feature selection technique. Finally, these relevant HST features are fed to four machine learning classifiers for estimation of contamination degree. It has also been observed that, the proposed framework method offered better classification accuracy compared to other standard time-frequency analysis and existing methods available in literature.
A sensor attack detection method based on fusion interval and historical measurement in CPS

Cyber-physical systems (CPS) is a next-generation intelligent system that realizes close integration of computing, communication and physical elements based on environmental perception. The interaction between information technology and the physical world makes CPS vulnerable to various malicious attacks and damage Its security. The paper designs a sensor attack detection method based on fusion interval and historical measurement. The method first builds different fault models for different sensors, and uses system dynamics equations to integrate historical measurements into the attack detection method. Different aspects of sensor measurement are analyzed. In addition, the use of historical measurement and fusion interval solves the problem of whether there is a failure when the measurements of two sensors intersect. The core idea of this method is to use the paired inconsistent relationship between sensors to detect and identify attacks .
Automatic Planning of 300-GHz-Band Wireless Backhaul Link Deployment in Metropolitan Area

This paper proposes an algorithm for automatic planning of 300-GHz-band wireless backhaul link deployment. The algorithm consists of two steps; Selecting the pair of adjacent cells that can get a line of sight, and Deployment of base stations. We succeeded in the automatic planning of 300-GHz-band backhaul link in Shinjuku, Japan by using this algorithm. We conducted radio wave propagation simulation of the wireless backhaul links, and showed that SNR of 99.5 % of the wireless backhaul links deployed by using this algorithm exceeds the standard demand SNR for 100-Gbit/s data transmission even when the rain rate is 100 mm/hr.
Electrical Power Consumption Profile Modelling of Air Conditioner for Smart Grid Load Management

Air conditioning (AC) system will be an inherent part of smart grid (SG) demand response as it is responsible for a substantial amount of power consumption in total infrastructure. It is invaluable to estimate the nature of operational characteristics of AC compressors in order to implement associated optimization strategy for energy preservation in residential premises. In this paper, the electrical power consumption profile, i.e., On-time and Off-time durations and power consumptions during these time durations is modelled for a split type AC compressor in terms of co-efficient of performance, AC capacity and environment condition. Dead-band temperature and room dimensions are considered as environment condition and the effect of various parameters on On-time and Off-time durations and energy consumption is studied. The factors that can be optimized to reduce the energy consumption are also analyzed.
Disclosing Personal Names in Screen Names Predicts Better Final Achievement Levels in Massive Open Online Courses

The anonymity of the Internet used to be considered as an encouraging factor that helped learners engage in online learning. However, academic studies on anonymity have found that its effect on learning is context-dependent or mixed. In this research, we focused on massive open online course (MOOC) learners’ preference for personal name disclosure in their screen names as a predictor of their final achievement levels (FALs) at the end of a course including 2606 active learners. We conducted two studies, one to examine the associations between these two variables and one to demonstrate how such associations can be utilized in MOOC FAL prediction. We found that MOOC learners who included personal names in their MOOC screen names significantly outperformed other learners in their FALs (p < 0.001). We also found that screen name preference improved FAL prediction accuracy utilizing natural language processing and proper machine learning technologies. The error rate was reduced to 4.03% by a random forest algorithm with an appropriate feature combination: the personal name disclosure indicator (PNDI), quiz scores, number of replies, and exam scores. The results are potentially useful for the development of an early intervention to provide different types of help to students who prefer to disclose personal names and those who do not. The practical effects of these interventions will be examined in the future. In addition, whether the course difficulty level or course type affects the associations between personal name disclosure and FAL will also be examined.