Miracle-dz/UniFAR-FTUs huggingface dataset:
Skin Lesion Segmentation Using Deep Learning with Auxiliary Task

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of 79.46, Accuracy (ACC) of 94.32, SEN of 88.76 with only one integrated model, which can be learned in an end-to-end manner.
Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.
Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems

Image structures are segmented automatically using deep learning (DL) for analysis and processing. The three most popular base loss functions are cross entropy (crossE), intersect-over-the-union (IoU), and dice. Which should be used, is it useful to consider simple variations, such as modifying formula coefficients? How do characteristics of different image structures influence scores? Taking three different medical image segmentation problems (segmentation of organs in magnetic resonance images (MRI), liver in computer tomography images (CT) and diabetic retinopathy lesions in eye fundus images (EFI)), we quantify loss functions and variations, as well as segmentation scores of different targets. We first describe the limitations of metrics, since loss is a metric, then we describe and test alternatives. Experimentally, we observed that DeeplabV3 outperforms UNet and fully convolutional network (FCN) in all datasets. Dice scored 1 to 6 percentage points (pp) higher than cross entropy over all datasets, IoU improved 0 to 3 pp. Varying formula coefficients improved scores, but the best choices depend on the dataset: compared to crossE, different false positive vs. false negative weights improved MRI by 12 pp, and assigning zero weight to background improved EFI by 6 pp. Multiclass segmentation scored higher than n-uniclass segmentation in MRI by 8 pp. EFI lesions score low compared to more constant structures (e.g., optic disk or even organs), but loss modifications improve those scores significantly 6 to 9 pp. Our conclusions are that dice is best, it is worth assigning 0 weight to class background and to test different weights on false positives and false negatives.
A Vertically Stacked Nanosheet Gate-All-Around FET for Biosensing Application

In this paper, a vertically stacked nanosheet gate-all-around field-effect transistor (GAA-NSFET) as a label-free biosensor has been proposed and investigated. The influences of different biomolecules on the biosensor’s electrical properties are analyzed, and the proposed biosensor exhibits good sensitivity for both neutral and charged biomolecules. Furthermore, a deep sensitivity analysis is performed to evaluate the sensing ability of biosensors with different channel structures. The sensitivity variation of biosensors is analyzed in terms of subthreshold swing (<inline-formula> <tex-math notation="LaTeX">$SS $ </tex-math></inline-formula>), threshold voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\text {th}}$ </tex-math></inline-formula>), and current switching ratio (<inline-formula> <tex-math notation="LaTeX">$I_{\text {on}} / I_{\text {off}}$ </tex-math></inline-formula>). The results show that GAA-NSFET-based biosensor obtains the best sensitivity compared with other biosensors (including nanowire FET-based biosensor and vertically stacked nanowire FET-based biosensor) due to its larger channel width and multi-channels. Also, the influences of filling position on sensitivity are analyzed in various cases for the proposed biosensor. Its sensitivity depends on the filling amounts of biomolecules instead of the filling position. Finally, a status map is presented, which plots the sensitivity of some important works in biosensing application along with the sensitivity of the proposed biosensor, and GAA-NSFET-based biosensor is more sensitive compared with those works in terms of <inline-formula> <tex-math notation="LaTeX">$I_{\text {on}}/I_{\text {off}}$ </tex-math></inline-formula> sensitivity.
Subject Authentication using Time-Frequency Image Textural Features

The growing internet-based services such as banking and shopping have brought both ease to human’s lives and challenges in user identity authentication. Different methods have been investigated for user authentication such as retina, finger print, and face recognition. This study introduces a photoplethysmogram (PPG) based user identity authentication relying on textural features extracted from time-frequency image. The PPG signal is segmented into segments and each segment is transformed into time-frequency domain using continuous wavelet transform (CWT). Then, the textural features are extracted from the time-frequency images using Haralick’s method. Finally, a classifier is employed for identity authentication purposes. The proposed system achieved an average accuracy of 99.14% and 99.9% with segment lengths of one and tweeny seconds, respectively, using random forest classifier.
Design and Implementation Strategy of Adaptive Processor-Based Systems for Error Resilient and Power-Efficient Operation

The contemporary computing systems are facing two major challenges: excessive power consumption and susceptibility to faults. In order to take advantage of techniques that efficiently address these challenges, the classic ASIC design flow requires some modifications. In this paper, we present a simple and convenient strategy for design and implementation of processor-based systems using highly configurable, cross-layer framework that encompasses techniques such as Adaptive Voltage and Frequency Scaling (AVFS) and Triple Modular Redundancy (TMR). The proposed strategy augments the conventional design flow with two additional steps to integrate the framework’s hardware building blocks into the system. Such system is then able to dynamically switch between low power and error resilient operation modes according to the current requirements. By following the proposed strategy, we were able to implement processor-based system that significantly reduces the power consumption / increases the soft error resilience while preserving the performance at negligible area overhead of less than 1%.
An Archimedean Spiral Antenna for Generation of Tunable Angular Momentum Wave

In this paper, an Archimedean spiral antenna has been developed to flexibly radiate orbital angular momentum (OAM) wave with tunable modes. The operation mechanism for generation of the OAM wave is elaborated in terms of phases of the currents flowing on the spiral arm of the Archimedean structure. Two pairs of feeding probes are introduced to excite the Archimedean spiral antenna. With the signals with different phase differences fed into the spiral antenna, the OAM waves with the modes of <inline-formula> <tex-math notation="LaTeX">$l=\pm 1$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$l=\pm 2$ </tex-math></inline-formula>, and their all mixed modes are radiated, respectively. Simulated and measured results are given to demonstrate that the proposed Archimedean spiral antenna radiates the tunable OAM waves from the sole mode to the mixed four modes in an overlapped band of 5~6 GHz.
Juris2vec: Building Word Embeddings from Philippine Jurisprudence

In this research, we trained nine word embedding models on a large corpus containing Philippine Supreme Court decisions, resolutions, and opinions from 1901 through 2020. We evaluated their performance in terms of accuracy on a customized 4,510-question word analogy test set in seven syntactic and semantic categories. Word2vec models fared better on semantic evaluators while fastText models were more impressive on syntactic evaluators. We also compared our word vector models to another trained on a large legal corpus from other countries.
The Influence of Equity Pledge and Shareholding Ratio of Major Shareholders on Earnings Management

This paper will start from the motivation of enterprises to manipulate positive and negative earnings management, and combine the characteristics of controlling accruals and real earnings management, so as to make up for the lack of research on the direction, mode and degree of earnings management in the existing literature. At the same time, this study will provide a new explanation for the relationship between equity pledge of major shareholders and different ways of earnings management in different directions. In this paper, the pledge of major shareholders' equity is used as an external governance factor, and the shareholding ratio and equity nature of major shareholders are used as internal governance factors for simultaneous analysis, so as to explore the synergistic effect under the joint action and better explore the relationship between corporate governance and earnings management. This paper uses the A-share listed companies in China's Shanghai and Shenzhen stock markets from 2013 to 2018 as a research sample to explore the impact of Equity pledge on upward earnings management, and the moderating effect of the shareholding ratio of major shareholders on the relationship between equity pledge and upward earnings management.
A Multifunctional Non-Isolated Dual Input-Dual Output Converter for Electric Vehicle Applications

High voltage conversion dc/dc converters have perceived in various power electronics applications in recent times. In particular, the multi-port converter structures are the key solution in DC microgrid and electric vehicle applications. This paper focuses on a modified structure of non-isolated four-port (two input and two output ports) power electronic interfaces that can be utilized in electric vehicle (EV) applications. The main feature of this converter is its ability to accommodate energy resources with different voltage and current characteristics. The suggested topology can provide a buck and boost output simultaneously during its course of operation. The proposed four-port converter (FPC) is realized with reduced component count and simplified control strategy which makes the converter more reliable and cost-effective. Besides, this converter exhibits bidirectional power flow functionality making it suitable for charging the battery during regenerative braking of an electric vehicle. The steady-state and dynamic behavior of the converter are analyzed and a control scheme is presented to regulate the power flow between the diversified energy supplies. A small-signal model is extracted to design the proposed converter. The validity of the converter design and its performance behavior is verified using MATLAB simulation and experimental results under various operating states.
A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

Cloud storage service has shown its great power and wide popularity which provides fundamental support for rapid development of cloud computing. However, due to management negligence and malicious attack, there still lie enormous security incidents that lead to quantities of sensitive data leakage at cloud storage layer. From the perspective of protecting cloud data confidentiality, this paper proposed a Cloud Secure Storage Mechanism named CSSM. To avoid data breach at the storage layer, CSSM integrated data dispersion and distributed storage to realize encrypted, chucked and distributed storage. In addition, CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage. The experimental results indicate that proposed mechanism is not only suitable for ensuring the data security at storage layer from leakage, but also can store huge amount of cloud data effectively without imposing too much time overhead. For example, when users upload/download 5G sized file with CSSM, it only takes 646seconds/269seconds, which is acceptable for users.
Performance Analysis on Cache-Enabled FR2 IAB Networks

This paper brings the concept of wireless edge caching (WEC) to the realm of integrated access and backhaul (IAB) networks. Traditionally, both access and backhaul are allocated fixed spectrum. However, when WEC is applied to the IAB nodes to pre-fetch some popular files, the backhaul traffic can be drastically reduced. Accordingly, an analytical framework is developed for a cache-enabled IAB network for the wide-band frequency range 2 (FR2) (> 24.25 GHz) channel model. In particular, the optimal spectrum resource allocation policy between access and backhaul for IAB networks is investigated by utilizing WEC. For the considered WEC enabled IAB network, a mathematical framework is presented under the content delivery phase for the calculation of three key performance metrics, namely the average success probability (ASP), throughput, and latency of file delivery with respect to various network parameters such as different caching placement strategies, node density, cache size, antenna number, blockage density, and resource partitioning factor. Extensive numerical results are provided to both verify the theoretical derivations and to demonstrate the precedency of using WEC as a cost-effective measure for the improvement of the IAB network’s performance. In particular, under a normalized cache size of 0.6, compared to the baseline no caching scenario, the percentages of backhaul spectrum that can be saved are 48% (26%), 66% (48%), and 47% (27%) when caching the most popular files (caching uniformly) with respect to the ASP, latency, and throughput of file delivery, respectively. The saved backhaul spectrum can then be shifted to the access for its performance enhancements. Our findings also show that there exists an optimal spectrum partition for IAB with respect to different network parameter settings. Further, there exists a tradeoff in the selection of the optimal partitioning factor with respect to ASP/ throughput and latency of file delivery for varying IAB node densities.
A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.
Analysis and Design of Novel Structured High Torque Density Magnetic-Geared Permanent Magnet Machine

The main limitation of magnetic gears is their relatively low torque density. Therefore, a novel structured high torque density magnetic-geared permanent magnet (MGPM) machine, which combines the advantages of a conventional MGPM machine and magnetic gear to enhance the torque considerably and achieve low-speed high-torque operation, is proposed in this study. However, there are different structures for increasing torque of a machine. Therefore, in order to demonstrate the advantages of the proposed structure, it is compared with other structures, using the finite element method. Moreover, the proposed MGPM machine can transmit an additional 42.3% of torque density when compared with the conventional MGPM machine of the same size.
Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs

Hyperspectral imagery (HSI) clustering aims to assign pixel-wise data with large amount of spectral bands into different groups, where each group indicates one of land-cover objects existed in HSI. Without available label information in clustering task, the clustering performance heavily depends on the reliability of unsupervised feature learned from HSI. Nevertheless,when HSI data are corrupted with noise,the conventional feature learning methods often failed. To address this problem, in this paper, a dual graph-based robust unsupervised feature extraction framework for HSI is proposed to realize reliable clustering. Firstly, low-rank reconstruction and projected learning are incorporated into the proposed framework to improve the data quality and obtain their robust structures. Then, a novel learning schemes is designed to learn two reliable graphs from the above robust structures respectively. We show that the scheme can reveals the latent similarity relationships while removing the noise influence. Meanwhile, the two reliable graphs are also integrated into a comprehensive graph with consistent constraint. At last, a joint learning framework is proposed, in which the data quality improvement, reliable graphs and consistent graph are learned iteratively to benefit from each other. After that, the normalized cut technique is applied to the learned consistent graph to obtain the final unsupervised feature. Several experiments are conducted on the two public HIS datasets to show advantage of our proposed method against the existing methods.
Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors

The energy efficiency in ICT is becoming a grand technological challenge and is now a first-class design constraint in all computing settings. Energy predictive modelling based on performance monitoring counters (PMCs) is the leading method for application-level energy optimization. However, a sound theoretical framework to understand the fundamental significance of the PMCs to the energy consumption and the causes of the inaccuracy of the models is lacking. In this work, we propose a small but insightful theory of energy predictive models of computing, which formalizes both the assumptions behind the existing PMC-based energy predictive models and properties, heretofore unconsidered, that are basic implications of the universal energy conservation law. The theory’s basic practical implications include selection criteria for model variables, model intercept, and model coefficients. The experiments on two modern Intel multicore servers show that applying the proposed selection criteria improves the prediction accuracy of state-of-the-art linear regression models from 31.2% to 18%. Finally, we demonstrate that employing energy models constructed using the proposed theory for energy optimization can save a significant amount of energy (up to 80% for applications used in experiments) compared to state-of-the-art energy measurement tools.
An Active Electrode for Vital Signal Acquisition With Accurately-Tunable Sub-Hz High-Pass-Corner Frequency and 164-mVₚₚ Linear-Input-Range

An active electrode (AE) for vital signal acquisition with accurately-tunable Sub-Hz high-pass-corner frequency (<inline-formula> <tex-math notation="LaTeX">$f_{HP}$ </tex-math></inline-formula>) has been proposed. Thanks to the proposed PVT-insensitive pseudo-resistor (PR) with embedded calibration circuit, the sub-Hz <inline-formula> <tex-math notation="LaTeX">$f_{HP}$ </tex-math></inline-formula> of the AE can be accurately tuned by an external clock frequency. The AE is implemented in a standard 55nm CMOS process with a core area of 0.11 mm<sup>2</sup>. The post-layout simulation results show that up to 310 <inline-formula> <tex-math notation="LaTeX">$\text{G}\Omega $ </tex-math></inline-formula> of the PR value can be achieved. Compared with the conventional PR, the variation of the calibrated PR value is reduced by 3 decades within a temperature range of 0 to 85°C under all process corners. Besides, the bootstrap technique improves the linear-input-range of the AE up to 164 mV<sub>pp</sub>, demonstrating its potential capability to be applied to recording vital signals merged with large artifacts.