Miracle-dz/UniFAR-FTUs huggingface dataset:
Bounded-Input Bounded-Output Stability Tests for Two-Dimensional Continuous-Time Systems

This paper presents two efficient algorithms to determine whether a bivariate polynomial, possibly with complex coefficients, does not vanish in the cross product of two closed right-half planes (is “2-C stable”). A 2-C stable polynomial in the denominator of a two-dimensional analog filter has been proved (not long ago) to imply bounded-input bounded-output (BIBO) stability. The two algorithms are entirely different but both rely on a recently proposed fraction-free (FF) Routh test for complex polynomials in this transaction. The first algorithm tests the 2-C stability of a bivariate polynomial of degree <inline-formula> <tex-math notation="LaTeX">$(n_{1},n_{2})$ </tex-math></inline-formula> in order <inline-formula> <tex-math notation="LaTeX">$n^{6}$ </tex-math></inline-formula> of elementary operations (when <inline-formula> <tex-math notation="LaTeX">$n_{1}=n_{2}=n$ </tex-math></inline-formula>). It is a “tabular type” two-dimensional stability test that can be regarded as a “Routh table” whose scalar entries were replaced by univariate polynomials. The second 2-C stability test is obtained from the first by its telepolation. It carries out the 2-C stability test by a finite collection of FF Routh tests and requires only order <inline-formula> <tex-math notation="LaTeX">$n^{4}$ </tex-math></inline-formula> elementary operations. Both algorithms possess an integer-preserving property that enhances them with additional merits including numerical error-free decision on 2-C stability.
A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature’s dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).
A Security-UTAUT Framework for Evaluating Key Security Determinants in Smart City Adoption by the Australian City Councils

Governments around the world are promoting the establishment of smart cities by equipping their cities with intelligent devices such as IoT sensors to analyze the data generated by these devices. While smart cities offer ample opportunities for local governments to improve services for their citizens, cyber security has become a major concern for smart cities adoption, due to interconnectedness of intelligent devices and the use of public networks. Literature presents the significance of security models for the adoption of smart cities, but these are limited to identifying one or few security related factors. To investigate the security determinants by focusing on the user intentions to adopt smart cities, the Security-Unified Theory of Acceptance and Use of Technology (UTAUT) framework and research hypotheses are presented. This study adopts a mixed method approach for data collection. The structural equation modelling technique will be used to analyze the data and the structural relationships between the measured variables and latent constructs.
Flexible Capacitive Pressure Sensor Based on Laser–Induced Graphene and Polydimethylsiloxane Foam

Flexible pressure sensors have been extensively employed in a range of fields, such as electronic skin (E–skin), humanoid robots, and personal health care. Laser–induced graphene (LIG) is an ideal active material to produce flexible sensors due to its advantages of one–step fabrication, excellent mechanical performance, and high conductivity. This paper presents a flexible capacitive pressure sensor (FCPS) consisting of LIG and polydimethylsiloxane (PDMS) foam. LIG can be fabricated by using a laser to directly write on polyimide (PI) film. By transferring the LIG to a porous PDMS foam, the FCPS acquired a plate–foam–plate integrated structure and it had high sensitivity (<inline-formula> <tex-math notation="LaTeX">$\sim 0.026\,\textit {kPa}^{-1}$ </tex-math></inline-formula> in <inline-formula> <tex-math notation="LaTeX">$15\sim 40\,\textit {kPa}$ </tex-math></inline-formula>) and a fast response time (<inline-formula> <tex-math notation="LaTeX">$\sim 120\,\textit {ms}$ </tex-math></inline-formula>). In dynamic testing, the FCPS exhibited a stable (<inline-formula> <tex-math notation="LaTeX">$\delta _{r}=\sim 1.785$ </tex-math></inline-formula>%) and low–hysteresis (<inline-formula> <tex-math notation="LaTeX">${h}=\sim 9.762$ </tex-math></inline-formula>%) response to pressure. Furthermore, no significant signal distortions were identified in 5000–cycle press/release testing, which demonstrated the long–term durability of the FCPS. The FCPS was capable of distinguishing between different external mechanical stimuli, including stretching, pressing, bending, and twisting by multiple responses (i.e., two electrode resistances and the capacitance between the electrodes). The FCPS was also employed to detect joint movements, body pressure, and arterial pulse. To study the spatial pressure distribution in depth, an FCPS array was developed by designing a LIG pattern into an electrode array. As a result, there was a mapping between the measurements and the spatial pressure. In our study, FCPS and its array were prepared for multiple stimuli identification and tactile sensing using a simple, efficient, and low–cost technique. The results from this study demonstrated that the FCPS and its array demonstrated potential for being fabricated into wearable medical devices, virtual reality/augmented reality (VR/AR) devices, or E–skin.
Measurement Uncertainty Evaluation of Transmission Line Parameters

Transmission line parameters vary with environmental and operating conditions, thus introducing uncertainties in power flow studies and protection zone settings of relay protection equipment. Variations in line parameters are fundamental for the determination of static and dynamic line power ratings. Usually, nominal values are given without an associated confidence interval, and therefore an important piece of information is missing. With the proliferation of phasor measurement units (PMUs) that allows for synchronized measurements at different geographical positions arouse the possibility for simple and efficient measurements of line parameters. This article analyzes uncertainties associated with the estimation of line parameters, based on the equivalent $\pi $ model, valid for short-and medium-length transmission lines. This problem has been addressed in literature, but only from a theoretical perspective and the obtained results were verified with simulations only. Here, measurements conducted on two 220-kV transmission lines are analyzed. It was found that the correlation between voltage and current measurements must be taken into consideration when analyzing uncertainty propagation law. Only then statistical analysis of estimations matches the predictions obtained by applying the theoretical model. Accuracy limits were analyzed as well, but in this case some results were within the calculated limits, while others did not follow predictions. Improvements in accuracy limits’ determination require the separation of environmental and operating condition variations from deviations due to measurement equipment, thus requiring additional information.
Transform Domain-Based Invertible and Lossless Secret Image Sharing With Authentication

Secret Image Sharing (SIS) as a secure data sharing scheme in multiple cover images, has become an increasing researchers’ concern. In traditional SIS, the cover image can’t be recovered losslessly. The distorted cover images would reduce the practicability of the scheme, especially in medical and military images. The lossless recovery of cover images is required since all details of these images are very critical. In current Invertible SIS (ISIS), the secret image and the cover image may not be reconstructed losslessly. In addition, the authentication capability, visual quality of the stego cover image and embedding rate are limited in spatial domain-based ISIS. As an important carrier, the binary cover image is desired in real applications. Therefore, this paper proposes Transform domain-based Invertible and Lossless Secret Image Sharing schemes with Authentication (T-ILSISA), namely Integer Wavelet Transform-based ILSISA (IWT-ILSISA) and Binarization Transform-based ILSISA (BT-ILSISA) respectively. In (k,n) threshold IWT-ILSISA, the pixels of secret image and the data of cover image are regarded as the coefficients of the (k-1) degree polynomial. The values of generated share are embedded into IWT domain of the cover image. In BT-ILSISA, many different cover images are applied. The generated shares are transformed to the meaningful images since noise-like shares are easy to attract the attacker’s attention, are suspected to censors and are difficult for identification and management. In the two schemes, the original secret image and the cover image can be recovered losslessly. The experimental results and theoretical analysis demonstrate that the performances of IWT-ILSISA are better than other similar schemes in the terms of embedding capacity, authentication capability and visual quality of the stego cover image. The BT-ILSISA has a lower computational complexity of the recovery.
Design and Implementation of A Transformerless High Step-Up DC-DC Converter Based on Conventional Boost Converter and Voltage Multiplier Cells

In this study a transform-less high step-up converter has been proposed. The topology of the proposed converter has been built up based on the conventional boost converter and has been modified by two voltage multiplier cell. The mentioned converter has been designed for the continuous current mode. The voltage ratio of the proposed converter has been extracted for both ideal and non-ideal mode. The non-ideal voltage ratio of the proposed converter has been compared with other high step-up DC-DC converters. Moreover, the practical voltage ratio of the converter has been extracted and compared with the result of the relation of the non-ideal voltage gain. Finally, the simulation results have been extracted and compared with experimental results. The PLECS is the simulation engine which has been used in this study.
Rain to Rain: Learning Real Rain Removal Without Ground Truth

Image deraining is a low-level restoration task that has become quite popular during the past decades. Although recent data-driven deraining models exhibit promising results, most of these models are trained on synthetic rain data sets which do not generalize well when applied to real rain images. While recent real-rain data sets have achieved favorable generalization performance, generating rain-free ground-truths can be tedious and time-consuming. To address this problem, in this work, we present rain to rain training, an unsupervised training method for single image deraining. Our experiments show that it is possible to train single image deraining models by using only rain images. This can be achieved by simply training models to map pairs of rain images. We also introduce the idea of using the least overlapping training pairs, a method of selecting adequate training pairs that enables rain to rain training to achieve equivalent deraining performance compared to supervised training.
Sales Prediction based on Machine Learning

With the increasing influence of the Internet on people’s life, the development of e-commerce platforms is more rapid, with users and earnings of these platforms showing a growing trend. In recent years, the strong support of national policies has also provided a good environment for the development of the e-commerce industry. Under the impact of the epidemic this year, the role of the e-commerce industry in the development of the national economy has become more prominent. In such cases, the number and the competitiveness of e-commerce platforms and e-commerce enterprises are increasing. If a platform wants to maintain its advantage in the competition, it must be able to better meet the needs of users, and do a good job in all aspects of coordination and management. At this point, the accurate forecast of the sales volume of e-commerce platforms is particularly important. At present, there are many studies on e-commerce sales prediction, but we are still exploring the prediction model that can be better applied in different scenarios. In this paper, we try and evaluate two linear models, three machine learning models and two deep learning models, finding that machine learning and deep learning models have no advantage in improving the accuracy of sales forecast, but on a predictive basis, models perform better when they include information on calendar and price.
IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data With Changing States

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.
Learning Instance Motion Segmentation With Geometric Embedding

Most existing deep learning-based motion segmentation methods treat motion segmentation as a binary segmentation problem, which is generally not the real case in dynamic scenes. In addition, the object and camera motion are often mixed, making the motion segmentation problem difficult. This paper proposes a joint learning method which fuses semantic features and motion clues using CNNs with deformable convolution and a motion embedding module, to address multi-object motion segmentation problem. The deformable convolution module serves to fusion color and motion information. And the motion embedding module learns to distinguish objects’ motion status with inspiration from geometric modeling methods. We perform extensive quantitative and qualitative experiments on benchmark datasets. Especially, we label over 9000 images of KITTI visual odometry dataset to help training the deformable module. Our method achieves superior performance in comparison to the current state-of-the-art in terms of speed and accuracy.
An Optimized Radiation Tolerant Baseline Correction Filter for HEP Using AI Methodologies

This paper presents an improved baseline correction filter to be used in the readout front-end ASICs of gaseous detector systems in high energy physics experiments. The digital filter is based on an FIR section and used in the data pipeline running in real-time thought the input signals, allowing adequate data compression by the subsequent blocks in the processing chain. The proposed design was optimized to cope with the power and area trade-offs while hardening the block using redundancy and a novel automated design system based on artificial intelligence. This optimizer is also discussed including its logic and improvements using genetic algorithms with dynamic mutation and cross-over probabilities connected to run-time EDA tools. The designed filter was fabricated in 130 nm process, packaged and tested in the Peletron particle accelerator at the University of Sao Paulo. The device was irradiated and received a total dose of 70 krad during about one week of exposition to alpha particles without any measured failure, validating the design and solving the vulnerability with a small increase in the channel area of just 0.07%.
Elucidating the Effect of the Different Buffer Layers on the Thermal Stability of CIGSe Solar Cells

In this contribution, the impact of thermal stress on Cu(In,Ga)Se<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> (CIGSe) thin film photovoltaic devices is investigated. The tolerance of such devices to high temperatures is of particular interest for processing transparent conductive oxides (TCOs) in order to further close the gap to the theoretical efficiency limit and for their potential use as bottom devices in tandem applications in order to overcome the theoretical efficiency limit of single junction solar cells. When CdS-buffered CIGSe high efficiency solar cells are subjected to thermal stress, elemental interdiffusion of Na and Cd between the absorber and the window layers as well as chemical reactions at the CIGSe/CdS interface result in a degraded power conversion efficiency (PCE). Here, we compare the degradation mechanisms of CdS and GaO<inline-formula><tex-math notation="LaTeX">$_{x}$</tex-math></inline-formula> buffered CIGSe solar cells under thermal stress. A model explaining the observed degradation behaviors is proposed.
Analysis and Application of Quasi-Static and Dynamic Phasor Calculus for Stability Assessment of Integrated Power Electric and Electronic Systems

Power system stability is widely assessed based on tools that rely on the representation of voltages and currents through quasi-static phasor calculus, implying that the network itself and the synchronous machine stators are modeled by algebraic equations. Accordingly, the associated fast transients are neglected, assuming those decay rapidly. However, with the increasing penetration of converter interfaced generation in power systems, the reliance on quasi-static phasor calculus is to be questioned. In this paper, the validity of quasi-static phasor calculus models is verified, and dynamic phasor calculus is considered as the alternative. A methodology to systematically compare quasi-static and dynamic phasor calculus is developed. It includes frequency response, modal, and sensitivity analyses. The methodology is applied to an IEEE test network considering penetrations of converter interfaced generation of up to 100%. The models are implemented in MATLAB. The H-infinity norm is proposed as an indicator to identify differences in the applicability of the models. The results show that the quasi-static phasor calculus is suitable for stability analysis only if low bandwidths of converter controls are given. Dynamic phasor calculus instead is suitable and applicable to generic stability studies of integrated power electric and electronic systems with high penetration of renewables.
Sea Ice Classification via Deep Neural Network Semantic Segmentation

Sea ice monitoring plays a critical role in any icebreaker’s journey, where standard procedures are in place to document and report sea ice types and concentration. In this paper, we propose semantic segmentation for automated detection and classification of sea ice types using camera feeds onboard an ice breaker. For this purpose, we evaluate the SegNet and PSPNet101 neural network architectures, which have proven success in navigation and mapping applications such as self-driving cars, remote sensing, and medical imagery. The networks are used to segment images based on two custom datasets, one with four classes: ice, ocean, vessel, and sky, i.e., sea ice detection dataset, and the second with eight classes: ocean, vessel, sky, lens artifacts, first-year ice, new ice, grey ice, and multiyear ice, i.e., sea ice classification dataset. The Nathaniel B. Palmer imagery, which captured 2-month footage of the icebreaker completing an Antarctic expedition was used in the creation of both datasets. A subset of the dataset was labeled to generate a 240-image training set for sea ice detection achieving an accuracy of 98% classification for the 26-image test set. The sea ice classification dataset consists of 1,090 labeled images achieving accuracies of 98.3% or greater for all ice types for the 104-image test set. These results validate the applicability of deep learning methods for sea ice detection and classification using images captured onboard an ice breaker, which can be further enhanced by incorporating additional ice types and operational data to support marine navigation and mapping applications.