Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumers’ Role in The Survival of E-Commerce in Sub-Saharan Africa: Consequences of E-Service Quality on Engagement Formation

Abstract African countries are witnessing tremendous growth in online shopping. E-commerce firms have realized the need to engage consumers in order to increase their performance. Research indicates that consumer engagement goes beyond reuse or repurchase intention to include psychological and behavioral activities of consumers, targeted at the E-commerce firm. The quality of E-services provided by the firms is paramount in determining the extent to which consumers will engage. However, there are external factors that moderate the effects of e-service quality on consumer engagement. Based on Stimuli-Organism-Response theory, this study developed a model to examined how quality E-services influence consumers to engage and how their attention to social comparisons moderates that relationship. The findings indicate that service reliability has no effects on engagement activities unless moderated by consumers attention to social comparison. However, all the other e-service quality dimensions recorded a significant influence on consumer engagement activities, with website security and aesthetic possessing the highest predictor effects. Finally, consumers with great attention to social comparison were coupled with low engagement behaviors whiles highly engaged consumers have lower attention to social comparison.
Historical Perspective: Terminology for Lower Anogenital Tract Pathology

Abstract The terminology for lower anogenital tract (LAT) pathology has evolved over the past 127 years as the understanding of the etiology of the cellular changes of precancer and cancer evolved. Terminology has had significant influence on the management of LAT lesions, often reflecting misunderstanding of both the etiology and the meaning of the terminology. In this section of Pathology Case Reviews on Lower Anogenital Tract, we explore the evolution in terminology, which has generally developed along 2 separate paths, depending on whether the epithelial lesion is mucosal or cutaneous. As we move forward with greater understanding of the common etiology and morphology of human papillomavirus–induced intraepithelial neoplasia throughout the LAT, the next step in this evolutionary process is a common nomenclature.
Charge Plasma High Voltage PIN Diode Investigation

In this paper, we investigate the new approach on designing a high voltage PIN diode without introducing any doping into the silicon. The concept of charge plasma is basically using different metals with appropriate work-function as anode and cathode contacts, which cause the formation of "p" and "n" plasma regions in silicon respectively. We have used the Silvaco Atlas simulation to compare the forward and reverse I-V characteristics of the proposed device with the conventional PIN diode. Also, we looked at the temperature dependency of their reverse saturation current. We demonstrate that by using the charge plasma concept, the proposed CP-PIN diode and conventional PIN diode have identical characteristics.
Privacy Preserving Scheme for Operating Frequency of Incumbents in Citizens Broadband Radio Service

Citizens Broadband Radio Service (CBRS) is a new service band in 3550–3700 MHz spectrum with the federal military radars of the United States as the primary users (also called as incumbents). The privacy of location, operation time, operating frequency, and antenna parameters of the incumbents is crucial for national security. Different schemes have been proposed for ensuring location and operation time privacy of incumbents. However, the privacy of operating frequency of incumbents has not been addressed. The termination of non-incumbent device from a frequency on which the incumbent becomes active can reveal the operating frequency of the incumbent. In this work, we propose a scheme to preserve the privacy of operating frequency of the incumbents by introducing dummy incumbent channels. We analyze the probability of incorrectly identifying an actual incumbent channel by the adversary as the privacy metric for incumbents. Further, the optimum probability of faking the presence of incumbents on a channel is obtained through a trade-off between the privacy of incumbents and utility of non-incumbents for different system parameters. Finally, the proposed results are verified through simulations.
Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition

This paper represents an implementation of Principal Component Analysis (PCA) on masked and non-masked face recognition. Security is an essential term in our today’s life. In various Biometric technology, face recognition is widely used to secure any system because it is better than any other traditional techniques like PIN, password, fingerprint etc. and most reliable to identify or verify a person efficiently. In recent years, face recognition is a very challenging task because of different occlusion or masks like the existence of sunglasses, scarves, hats and different types of make-up or disguise ingredients. The accuracy rate of face recognition is influenced by these types of masks. Many algorithms have been developed recently for non-masked face recognition which are widely used and give better performance. Still in the field of masked face recognition, few contributions has been done. Therefore, in this work a statistical procedure has been selected which is applied in non-masked face recognition and also apply in the masked face recognition technique. PCA is more effective and successful statistical technique and widely used. For this reason in this work, PCA algorithm has been chosen. Finally, a comparative study also done here for a better understanding.
Assessment of China-made Enzyme Immunoassay Kits for Total Antibodies to Hepatitis A Virus Detection

Objective Two China-made enzyme immunoassays kits that detect total antibody to hepatitis A virus ( Anti-HAV ) are evaluated,and to provide reference for selecting kits to detect antibody of hepatitis A virus.Methods On the gold standard confirmed by assay-Abbott HAVAb2.0,the two kits for testing total Anti-HAV antibody widely sold in China were selected,and each of them was tested 8 times with the total Anti-HAV antibody panel.The CO/S ratios were recorded and transformed by ln ( CO/S+0.1 ),and the generalized estimating equation ( GEE ) model was applied to predict the positive probabilities on their gold standard outcomes.Intraclass correlation coefficient ( ICC ) and coefficient of variability ( CV ) were estimated for evaluating reliability;area under curve ( AUC ),partial area under curve ( pAUC ),and sensitivity at a Fixed Specificity [ Se ( FPR=e )] of the receive operation characteristic curve ( ROC ) indices were calculated for validity.Results The ICC were 0.9971 and 0.9952 for assay A and B ( Beetstrap methed,P 0.05 ) respectively.The CV were 5.7840 % and 6.2931 % for assay A and B ( Bootstrap method,P 0.05 ) respectively.Similarly,AUC and pAUC ( specificity ranging from 0.9 to 1 ) were 0.9557 and 0.0717 for assay A,and 0.9404 and 0.0663 for assay B,respectively.The accuracy indices of assay A were statistically different from assay B ( Bootstrap method,P 0.05 ) . When Se( FPR=0.020 ) to Se( FPR=0.070 ) were calculated at 0.005 intervals,higher sensitivities can be observed in assay A ( Bootstrap method,P 0.05 ) if the specificities were 0.945,0.950,0.955 or 0.960;but at other points of the specificities,there were not ( Bootstrap method,P 0.05 ) . Conclusion Both assays are excellent diagnosis capability.Although there are statistical difference in some indicators,however,they have no realistic difference.In the practice,we will select them based on the specific situation.
Decomposition Wavelet Transform as Identification of Outer Race Bearing Damage Through Stator Flow Analysis in Induction Motor

Mechanical damage is the damage most often encountered in induction motors. The survey proved 41% of the damage to the induction motor occurred in bearing damage. Therefore, it is necessary to make a system that is able to identify the damage to the bearing in the induction motor so that the damage can be handled earlier before a larger disturbance occurs. In this study, we discussed the technique of detecting damage to bearings on an induction motor especially on the outer race bearing using stator current analysis in transient conditions. The approach method used is based on wavelet decomposition transformation as signal processing to get the motor in a healthy or damaged condition. load variations are carried out on the motor to get detailed analysis results. while for the level of bearing damage is to provide holes with variations of 1BB, 2BB, 3BB with each diameter of 2mm. The test results prove that the wavelet transform decomposition is able to detect differences in normal or faulty induction motor conditions through analysis of outer race bearing damage.
Deep Motion-Appearance Convolutions for Robust Visual Tracking

Visual tracking is a challenging task due to unconstrained appearance variations and dynamic surrounding backgrounds, which basically arise from the complex motion of the target object. Therefore, the information and the correlation between the target motion and its resulting appearance should be considered comprehensively to achieve robust tracking performance. In this paper, we propose a deep neural network for visual tracking, namely the Motion-Appearance Dual (MADual) network, which employs a dual-branch architecture, by using deep two-dimensional (2D) and deep three-dimensional (3D) convolutions to integrate the local and global information of the target object’s motion and appearance synchronously. For each frame of a tracking video, 2D convolutional kernels of the deep 2D branch slide over the frame to extract its global spatial-appearance features. Meanwhile, 3D convolutional kernels of the deep 3D branch are used to collaboratively extract the appearance and the associated motion features of the visual target from successive frames. By sliding the 3D convolutional kernels along a video sequence, the model is able to learn the temporal features from previous frames, and therefore, generate the local patch-based motion patterns of the target. Sliding the 2D kernels on a frame and the 3D kernels on a frame cube synchronously enables a better hierarchical motion-appearance integration, and boosts the performance for the visual tracking task. To further improve the tracking precision, an extra ridge-regression model is trained for the tracking process, based not only on the bounding box given in the first frame, but also on its synchro-frame-cube using our proposed Inverse Temporal Training method (ITT). Extensive experiments on popular benchmark datasets, OTB2013, OTB50, OTB2015, UAV123, TC128, VOT2015 and VOT2016, demonstrate that the proposed MADual tracker performs favorably against many state-of-the-art methods.
Assessment of Dissaving Risk against Life Expectancy for Elderly People through Anonymous Data and Random Data

In order to detect the capability deterioration of economic activity for elderly people at age of sixty-five or over, we have used an anonymous data obtained from the National Survey of Family Income and Expenditure (NSFIE) carried out by Ministry of Internal Affairs and Communications (MIAC). We develop a method to detect dissaving risk of elderly people. So far the analysis data were divided into test data and training data. Then three kinds of methods were performed in terms of income and savings. Two-step methods were taken to determine dissaving risk. In using anonymous data, however, there is controversy if anonymity is secured. In order to strengthen the anonymity of the data, in this paper, random data is generated with using the anonymous data and then compared with the case of analyzing anonymous data as it is, with a view to performance evaluation. As a result of analysis, it could be concluded that using the random data would be as effective as using anonymous data for evaluating the performance of the proposed method.
Patch-Wise Blind Image Deblurring Via Michelson Channel Prior

Motion blur exists in many computer vision tasks, including faces, texts, and low-illumination images etc. It has been proved that Dark Channel Prior (DCP) and Bright Channel Prior (BCP) can both help the image deblurring by enhancing the dark or bright channel pixels. However, the pixels between the dark channel pixels and the bright channel pixels are not taken into consideration, which limits the deblurring performance. A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels. Secondly, as the image channels are built based on the series of image patches with different blur kernels, a new method is developed to estimate the blur kernel and can measure the similarity between neighbored kernels. Meanwhile, to perform accurate kernel estimation, the $L_{0}$ regularization is applied into the algorithm framework. In the process of image deblurring, by enhancing the Michelson channels and retaining the other channels of the image, we can capture sharper image detail and eliminate the ringing artifacts of the recovered images. Massive experimental results demonstrate that the proposed method is more robust and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.
Comparison of Classification Algorithms to Improve Smart Fire Alarm System Performance

Fire is a threat that is dangerous for the safety of residents. This research designs a fire early warning system that can ignite alerts for human. Technological advancements in the digital era, specifically in machine learning, can make the smart fire alarm system more developed to maximize the performance of the detection, and minimize false alarms. This paper implements four machine learning classification methods to be compared, Decision Tree, Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To implement the training and testing of each model, an IoT system is created. The IoT system consists of an IoT End Device and an IoT Platform. The IoT End Device consists of several sensors, such as DHT11, BMP180, MQ7, and MQ135. The IoT Platform consists of Thingspeak as data collection database and Matlab that performs all the classifier methods. The result of the comparison shows that Naïve Bayes achieves the best performance in predicting fire.
FAMN: Feature Aggregation Multipath Network for Small Traffic Sign Detection

Traffic sign detection has achieved promising results in recent years. Nevertheless, there are still two problems remain to be overcome. One problem is the detection of small traffic signs, which usually occupy less than 2% of the image area. The other problem is fine-grained classification, with difficulties arising from similar appearances between traffic signs. For example, different speed-limit traffic signs have differences solely from the speed numbers. In this paper, we propose a Feature Aggregation MultiPath Network (FAMN) to tackle the problems simultaneously. First, we propose a Feature Aggregation (FA) structure to aggregate regional features from different feature maps by using element-wise Max, then convolution layers are used to extract rich semantic features. Accordingly, objects of different scales can choose the best features to improve performance of small object detection. Second, we propose a Multipath Network (MN) structure to obtain fine-grained features. The MN structure consists of three paths, extracting instance-level, part-level, and context-level features, respectively. The three types of features are then concatenated to form fine-grained features of the proposals. Experimental results demonstrate the effectiveness of our proposed FAMN. Specifically, FAMN is able to obtain an average F1-measure of 93.1% in TT100K dataset, 2.9% higher than the state-of-the-art.
Two-Dimensional Sound Field Recording With Multiple Circular Microphone Arrays Considering Multiple Scattering

A sound field recording method using multiple circular microphone arrays considering the effect of multiple scattering is proposed. To avoid the numerical instability of an open microphone array, a rigid array, i.e., a microphone array mounted on a circular/spherical baffle, exploiting the scattering effect of a single baffle is frequently used for estimating a sound field. Since it is difficult to estimate a sound field in a large region with a single rigid array, several studies have been carried out using relatively small multiple rigid arrays distributed inside the target region. However, mutual interactions between multiple baffles, such as interreflection, have not been taken into consideration in the estimation process. The effect of multiple scattering is considerable, especially when several baffles are closely located. In this paper, we formulate an estimation method with modeling of this multiple scattering effect using the cylindrical wavefunction expansion of a two-dimensional sound field. Numerical simulation results indicated that the proposed method significantly improves the estimation accuracy compared with the method considering only the single scattering effect.
IoU-Related Arbitrary Shape Text Scoring Detector

As a medium of information transmission, text is widely existed in natural scenes, showing diversity in orientation, scale, font, color and shape. Accurate detection of scene text is a prerequisite for subsequent recognition. Though many previous methods have worked well on horizontal and multi-oriented text detection datasets, detecting arbitrary shape scene text still remains as a challenging problem. To solve the problem, this paper proposes an arbitrary shape scene text detection method. Based on Mask R-CNN, our method replaces the original $\ell _{1}$ -smooth loss with the proposed IoU-related loss and adds a text scoring branch to align the confidence score with the text mask IoU to make the model highly relevant to IoU, achieving the goal of improving detection performance by improving IoU directly in a simple but effective way. The proposed method is evaluated on four public datasets: CTW-1500, Total-Text, ICDAR2015 and ICDAR2017-RCTW. For curved text detection datasets CTW-1500 and Total-Text, we have reached 79.2% and 81.1% H-mean respectively, showing that the proposed method has achieved competitive performance in arbitrary scene text detection.
A Firmware Code Gene Extraction Technology for IoT Terminal

With the development of the IoT technology, an unprecedented number of IoT terminals are connected to various networks. Commercial-off-the-shelf (COTS) technology is widely used in the IoT terminal firmware, which results in high code reuse rates. Such firmware is always heterogeneous and closed-source. It is so difficult to detect and investigate the security risks at the firmware level that their impacts are faster and broader. In recent years, some firmware security detection technologies based on similarity are gradually becoming a research hotspot. However, in these studies, the basic issue regarding whether these foundations comprise an essential basis for comparison and their utility as similarity measures has not been addressed theoretically. Inspired by biological genes, this paper attempts to supplement a foundation for cross-platform firmware binary code homology and similarity analysis by mining firmware code genes that can essentially identify code and exhibit stability, antivariability and heritability. The firmware code gene extract system(FCGES) is designed and implemented in this paper. FCGES first extracts the features of firmware code, then numericizes and normalizes them, and finally sublimates them to firmware code genes by the hypothesis margin. The experimental results show that the firmware code gene extracted by FCGES has essentiality, stability, antivariability and heritability on different platforms.
Predictive Maintenance of Induction Motors Using Ultra-Low Power Wireless Sensors and Compressed Recurrent Neural Networks

In real-world applications – to minimize the impact of failures – machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms.
An Improvement of Migration Efficiency in a Distributed Storage System with Dynamic Tiering

We have proposed a distributed storage system which autonomously migrates a data block into a suitable storage node. In our storage system, heavily accessing data blocks will be migrated autonomously to a higher performance storage node (i.e. an upper storage tier), and rarely accessed data blocks will be migrated to a lower performance storage node (i.e. a lower storage tier). Our investigation shows that it may need several migrations in order to locate a data block onto the most suitable storage node when the destination storage tier has a lot of storage nodes. In this paper, we propose a method to enhance data migration efficiency to improve I/O performance of the whole system. Our simulation results show that the proposed method can greatly reduce I/O execution time than the previous one. The execution time was reduced about one-fifth at the maximum in a system with several hundred scale nodes.
Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications

In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.