Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on IoT-enabled Appliance Management Service Platform Business Model

Although IoT-enabled appliances are commercially available, business models to provide services to those appliances has not been established yet. This study proposes a platform business model and assesses the feasibility of the business model. We identify three areas that are necessary to establish the business model: rules and regulations, technical interface and incentives of potential participants. We investigate current situations and show the first two conditions have been satisfied or will be satisfied in the near future. We consider consumers and OEMs as potential participants of the platform business and show that they have incentives to use the platform service by using an agent-based simulation. These findings help not only platformers but also OEMs to design their strategies.
HARQ performance over massive MIMO channels using chase combining and CZA pilot decontamination

Pilot contamination is known to seriously limit the performance of massive MIMO systems due to degradation of channel estimation. Channel Zooming Algorithm (CZA) has been proposed as an efficient iterative algorithm to ensure the mitigation of the pilot contamination effect. This algorithm is able to zoom through iterations on the signal of the strongest user by taking advantage of the difference in space signatures and transmitted data sequences. CZA has been proved to be efficient for pilot contamination in the massive MIMO systems in the case of large number of antennas and long data packets. In this paper, we propose a novel version of CZA that utilizes the benefits of retransmitted packets combining. We consider a truncated Hybrid Automatic Repeat reQuest (HARQ), where the number of retransmissions is limited using Chase combining (CC). We evaluate the Packet Error Rate (PER) performance of the proposed approach for different order modulations as BPSK, QPSK, 16-QAM and 64-QAM. Simulation results show that very good performances can be achieved after very few transmission attempts with our HARQ scheme using CC and CZA, despite possible different contamination during each retransmission.
Belief Propagation for Energy Efficiency Maximization in Wireless Heterogeneous Networks

In this article, we study an energy efficient management of two-tier heterogeneous cellular networks (HetNets) which consist of one macro base station (BS) and multiple micro base stations. This article presents a distributed user association algorithm that maximizes the network-wide energy efficiency (EE) in HetNets. A subset of BSs that support only a small number of users can be turned off to save the energy consumption. By turning off BSs in the HetNet and offloading serving users to adjacent active BSs, the network-wide energy consumption is minimized, while the sum throughput is maximized. To solve the problem efficiently, we introduce a new approach based on a message-passing framework and derive a distributed load balancing algorithm. The proposed method provides a very efficient solution with reduced computational complexity compared to existing schemes. Simulation results verify that the proposed algorithm outperforms other conventional load balancing strategies.
Enzymatic modification of wood fibres for activating their ability of self bonding

Objective is the use of phenoloxidases like laccases to activate the surfaces of fibres for making fibreboards without any adhesive. The concept of using lignin-oxidising enzymes for bonding applications is based on the reactivity of phenoxy radicals in the plant cell wall. A problem is that laccase can only oxidise the phenolic constituents of lignin, due to its lower oxidation potential. Therefore the use of appropriate low molecular-mass compounds (so-called mediators), in combination with laccase, makes this enzyme competent for the oxidation of nonphenolic substrates. The oxidised mediator can rely on an oxidation mechanism that is not available to the enzyme.
Resonant LLC Low Voltage Converter

The practical experience of designing a resonant LLC converter with a low voltage input is presented, using the example of converting battery energy in guaranteed power supply systems. A comparative analysis of LC and LLC converters was carried out. The basic characteristics of the converter are determined, allowing coordinating the range of operating frequency and battery voltage. The conditions for the transfer of the resonant tank to the secondary winding are determined. The principles of calculating the parameters of the resonant tank are shown taking into account the type of rectifier used. The analysis of switching processes in transistors of the converter is carried out. An experimental verification of the results obtained.
Transform: Complicating Control Flow by Inserting Confusion Instructions

There is a certain mapping relationship between software control flow and data flow. Side channel attacks can take advantage of this relationship, peep some secret data of software through software control flow. To solve this problem, We insert code snippets that is able to obfuscate software control flow inside the. In this way, the connection between the software control flow and the data flow is disrupted, and it greatly increased the difficulty of side channel analysis. We implemented the prototype system, called as transform in the Ubuntu 18.04. Our tests show that transform does not affect the original semantics of the software. Although transform increase the size of software codes on the degree of confusion, it only introduce the performance overhead which is about 5%.
Dermoscopy Image Classification Based on StyleGAN and DenseNet201

Melanoma is considered one of the most lethal skin cancers. However, skin lesion classification based on deep learning diagnostic techniques is a challenging task owing to the insufficiency of labeled skin lesion images and intraclass-imbalanced datasets. It is thus necessary to utilize data augmentation methods based on generative adversarial networks (GANs) to assist skin lesion classification and help dermatologists reach more accurate diagnostic decisions. Moreover, insufficient samples can cause a low classification accuracy in a model by using deep learning in medical diagnosis and reduce the accuracy of skin lesion classification. To solve the above problems, this paper proposes a new skin lesion image classification framework based on a skin lesion augmentation style-based GAN (SLA-StyleGAN) according to the basic architecture of style-based GANs and DenseNet201. The proposed framework redesigns the structure of style control and noise input in the original generator and reconstructs the discriminator to adjust the generator to efficiently synthesize high-quality skin lesion images. We introduce a new loss function that reduces the intraclass sample distance and expands the sample distance between different classes, which can improve the balanced multiclass accuracy (BMA). The experimental results show that our classification framework performs well on the ISIC2019 dataset, and the BMA reaches 93.64%. The proposed method improves the accuracy of skin lesion image classification, assists dermatologists in determining and diagnosing different types of skin lesions, and analyzes skin lesions at different stages as well as those that are difficult to distinguish.
Privacy-Preserving Event-Triggered Quantized Average Consensus

In this paper, we propose a privacy-preserving event-triggered quantized average consensus algorithm that allows agents to calculate the average of their initial values without revealing to other agents their specific value. We assume that agents (nodes) interact with other agents via directed communication links (edges), forming a directed communication topology (digraph). The proposed distributed algorithm can be followed by any agent wishing to maintain its privacy (i.e., not reveal the initial value it contributes to the average) to other, possibly multiple, curious but not malicious agents. Curious agents try to identify the initial values of other agents, but do not interfere in the computation in any other way. We develop a distributed strategy that allows agents while processing and transmitting quantized information, to preserve the privacy of their initial quantized values and at the same time to obtain, after a finite number of steps, the exact average of the initial values of the nodes. Illustrative examples demonstrate the validity and performance of our proposed algorithm.
Vibration Control based on Reinforcement Learning for a Flexible Building-like Structure System with Active Mass Damper against Disturbance Effects

Vibration and displacement control are of critical importance for both high-rise and ultra high-rise building systems. A single-floor building-like structure equipped with an active mass damper (AMD) is investigated in this paper. Optimal vibration control, while dealing with system uncertainties, is realized by the reinforcement learning (RL) technique. When the unexpected natural disasters (such as strong wind excitation) occur, the proposed controller applying to the active mass damper can compensate the increase of the system vibration caused by external disturbances. In addition, a Lyapunov candidate is used to derive a semi-global uniformly ultimately bounded (SGUUB) property. The experimental platform is mainly composed of one flexible floor and a linear cart system. Both the acceleration and the displacement responses of the floor are provided and compared separately for passive mode, proportional-velocity (PV) control and RL control. The experimental results in the form of graphics and tables have shown the effectiveness of the proposed control algorithm.
Constrained Distributed Nonconvex Optimization over Time-varying Directed Graphs

This paper addresses a class of constrained distributed nonconvex optimization problems involving univariate objective functions, considering time-varying directed networks. We propose a novel algorithm named Extended-CPCA (E-CPCA), exploiting the notion of combining Chebyshev polynomial approximation and average consensus. The proposed algorithm is i) able to yield ε globally optimal solutions for any arbitrarily small given tolerance ε, ii) efficient in terms of both oracle complexities and inter-agent communication costs, and iii) distributed terminable when the specified precision requirement is met. The idea of leveraging polynomial proxy and consensus to deal with the mentioned problems over static undirected graphs is first presented in our previous work. The novelties of this work lie in i) the utilization of push-sum average consensus with distributed stopping mechanism to enable agents to acquire a proxy for the global objective over time-varying digraphs without much wastes of extra communications, and ii) the transformation of the optimization of this global proxy into a semidefinite program to help in obtaining solutions in a fast and reliable manner. Both the analysis and simulations are provided to illustrate the efficacy of the proposed algorithm.
Numerical analysis of a MEMS sensor's deformation behavior considering dynamic moisture conditions

Micro Electro Mechanical Semiconductors (MEMS) functionalities have been continuously extended during the last years, and they are applied in a wide range of industrial sectors, including the automotive, consumer electronics, and Internet of Things (IoT) markets. These MEMS sensor packages are a multi-materials composite system. The composite materials involved in MEMS sensor packages show significant effects like thermal expansion and hygroscopic swelling when exposed to environmental loads like temperature and moisture. Due to these effects, the MEMS sensing performance is affected. To understand the effects on sensing performance and mechanical behavior, an advanced simulation approach, “hygro-thermo-mechanical simulation,” needs to be accounted for. This approach considers not only the dynamic changes in temperature and moisture loads but also generalized solubility, which is a function of both temperature and saturated mass concentration. This numerical model helps in optimizing and understanding the sensing performance of the MEMS sensor packages.
Active-Clamp Resonant Power Factor Correction Converter With Output Ripple Suppression

An active-clamp resonant power factor correction converter with output ripple suppression is proposed and analyzed. It combines a buck power factor correction (PFC) unit and a resonant dc-dc unit by sharing one active switch. With another active-clamp switch, a recycling path is built to recover the leakage inductance energy of transformer, which improves the efficiency and the voltage stress of active switch. The effect of voltage ripple of dc link on output can be suppressed by fast voltage mode control loop. Therefore, low output current or voltage ripple can be achieved. With small voltage ripple across output of buck unit, the turn on time of active switch is almost constant in a half line cycle. Therefore, power factor correction can be achieved with inductor current of buck unit operating in discontinuous conduction mode (DCM). Finally, a 65W high power factor (PF) converter prototype for LED driver application with 91.08% peak efficiency and low output current ripple is built to verify the analytical results.
Economic Viability of Solar PV for Domestic Applications in a Middle-Income Country: A case Study of Sri Lanka

This study focuses on the economics of using solar Photovoltaics for residential in a middle-income country like Sri Lanka. It considers solar irradiance in the Colombo district to estimate the power generation potential by a selected 2.16kWp solar PV system throughout the year. It used solar irradiance data by NASA Surface Meteorology and Solar Energy (SSE), satellite solar insolation values for Sri Lanka and used the Liu and Jordan (LJ) method. Furthermore, it considered the economics of four different scenarios as model houses depending on appliance usage with net accounting. It was seen that without net accounting the Levelized cost of electricity could be as high as US$ 0.69/kWh. However, with mechanisms like net accounting, it could be reduced to US$ 0.12 /kWh with full owners' contribution. Under the net accounting scheme houses that consume above 300 kWh/month will have the lowest payback period of 2 years and 9 months.
Accelerating Simulation for Agent-based Epidemic Models using FPGAs

Agent-based models (ABMs) play a critical role in the containment and mitigation of epidemics. An ABM for epidemics involves a population of agents that represent healthy and infected individuals. Users can simulate the interactions of the agents to analyse spreading patterns, make predictions and conduct what-if tests. A major drawback that limits the application of ABMs is the long simulation time for large populations. This paper proposes new techniques to speed up ABM-based simulation. Specifically, we present an agent-based model for epidemic spreading that enables effective acceleration using reconfigurable hardware. The key idea is to compute the infection probability in a novel way so that the amount of on-chip memory usage is independent of the population size. Also, we propose a parameter pre-computation algorithm, a parallel simulation algorithm and an efficient collaboration method between the host computer and the accelerator. We simulate the proposed model on an Intel Arria 10 FPGA and compare it with a software reference that simulates a conventional ABM on an Intel Core i5-9400F CPU with six cores. The two systems produce similar results, but the FPGA-based system achieves 14 times speedup compared to the software reference.
Stability analysis for non-weakly reversible single linkage class chemical reaction networks with arbitrary time delays

In the present paper, our previous proposed method for analyzing the stability of a class of non-weakly reversible chemical reaction networks ( CRNs ) is generalized to be applied to CRNs with time delays. This method is based on the decomposition of the whole network into weakly reversible subnetworks, and application of the Deficiency Zero Theorem to them. On the basis of this method, it is proven that any positive solution to the delay differential equations that describe the dynamics of non-weakly reversible single linkage class CRNs, each pair of complexes of which has disjoint supports, modeled by mass action kinetics with time delays converges to an equilibrium point.
Facial Expression Recognition and Face Recognition Using a Convolutional Neural Network

The human face can be used in various biometrics procedures to identify an individual through face recognition or for facial expression recognition. However, not many studies have addressed the problem of face recognition along with facial expression recognition. In addition, some studies have directed more attention to finding the most suitable feature to extract and feed to a classifier. This study focused on addressing the problem using a convolutional neural network (CNN)-based method. Unlike other methods that require suitable features to be found, this study utilized raw images as the input to the CNN. A total of 16,640 images showing four facial expressions (normal, smiling, surprised, and angry) were used as input data. These data were obtained from 52 people and captured under outdoor conditions (in midday and the afternoon) using a webcam. The CNN-VGG was utilized because it is deep and fast enough for both face recognition and facial expression recognition purposes. The results showed that the VGG-f model architecture could overcome the underfitting and overfitting problems stemming from simpler CNN architectures. The testing results showed that the VGG-f model could recognize faces and facial expressions well. The average accuracies achieved in recognizing 104 faces during the day and in the afternoon were 86.5% and 90.4%, respectively. Additionally, the average accuracies achieved in recognizing the four different facial expressions of 52 people were 72% and 74% during the day and at noon, respectively. Recognition errors may have been caused by similarities between images.
On the Benefits of Glide Symmetries for Microwave Devices

The presence of glide symmetries in periodic structures can introduce beneficial modifications in their electromagnetic properties. The difference between glide and non-glide periodic structures is due to the distinctive coupling between their constituent sub-unit cells. In this paper, we describe the recent discoveries on the remarkable properties of glide-symmetric periodic structures, which include widened stopbands, reduced dispersion, as well as enhanced anisotropy and magnetic response. These properties are explained through canonical structures simulated with two methods: mode matching and multimode transfer-matrix analysis. We also review the recent use of these distinctive properties for solving technological problems in practical devices such as filters, gap waveguide components, low-leakage flanges, compressed lenses, low-reflected material transitions and leaky-wave antennas with applications in 5G terrestrial communication systems, millimetre-wave satellite systems and automated contactless measurement techniques.
Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.
Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on graph-shifted versions of the input signal. We then propose a centralized graph kernel least mean squares (GKLMS) algorithm to identify nonlinear graph filters’ model parameters. To reduce the dictionary size of the centralized GKLMS, we apply the principles of coherence check and random Fourier features (RFF). The resulting algorithms have performance close to that of the GKLMS algorithm. Additionally, we leverage the graph structure to derive the distributed graph diffusion KLMS (GDKLMS) algorithms. We show that, unlike the coherence check-based approach, the GDKLMS based on RFF avoids the use of a pre-trained dictionary through its data-independent fixed structure. We conduct a detailed performance study of the proposed RFF-based GDKLMS, and the conditions for its convergence both in mean and mean-squared senses are derived. Extensive numerical simulations show that GKLMS and GDKLMS can successfully identify nonlinear graph filters and adapt to model changes. Furthermore, RFF-based strategies show faster convergence for model identification and exhibit better tracking performance in model-changing scenarios.
An Effective Classification Method for Hyperspectral Image With Very High Resolution Based on Encoder–Decoder Architecture

Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder–decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improved residual network backbone with 3-D convolution, which fully considers the spatial–spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.
On the Development of a Tool for Tongue Images Analysis

Artificial intelligence is currently being used for medical image analysis in various fields, and particularly for organ and injury segmentation, disease detection and classification, and to assess response to treatment. In this paper we refer about the development of a software module that is in charge of analyzing tongue images in order to support the identification of specific disease. The module extends the system SIMPATICO 3D.