Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Imputation Methods of Small and Unbalanced Datasets in Classifications using Naïve Bayes and Particle Swarm Optimization

The classification method in data mining requires a good learning process to get optimal accuracy. This can be done if the dataset used is ideal, balanced, and has a lot of records, but in reality, it is difficult to get such a dataset. The imputation method is one way to fill in missing values, in a dataset that is not ideal. A large number of missing values can reduce the number of records in the learning process and affect accuracy. This research aims to analyze the effects of zero and mean imputation methods on classification accuracy in small datasets using the Naïve Bayes classifier (NBC) and NBC which have been optimized with Particle Swarm Optimization (PSO). Tests were carried out on five types of datasets originating from the UCI database, where one of the datasets did not require an imputation method because it did not have a missing value. Based on the results of the PSO testing proven to be able to improve the accuracy of the NBC classification on all datasets. While the imputation method can improve classification accuracy up to 4.33% in Biomarker datasets.
NVIDIA GPGPUs Instructions Energy Consumption

In this work, we accurately measure the energy consumption of the different instructions that can be executed in modern NVIDIA GPGPUs. We use three different software techniques to read the GPU on-chip power sensors, which use NVIDIA’s NVML API and provide an in-depth comparison between these techniques. Additionally, we verified the software measurement techniques against a custom-designed hardware power measurement. The results show that Volta GPUs have the best energy efficiency of all the other generations for the different categories of the instructions. This work should give GPU architects and developers a more concrete understanding of these representative NVIDIA GPUs’ microarchitecture. It should also make energy measurements of any GPU kernel both efficient and accurate.
Unsupervised Learning Clustering and Dynamic Transmission Scheduling for Efficient Dense LoRaWAN Networks

Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network’s transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.
Regression and Multiclass Classification Using Sparse Extreme Learning Machine via Smoothing Group L1/2 Regularizer

Extreme learning machine (ELM) is a simple feedforward neural network, and it has been extensively used in applications for its extremely fast learning speed and good generalization performance. Nevertheless, it is implemented normally under the empirical risk minimization scheme and the model trained by ELM is prone to overfitting. In addition, the ELM provides more nodes than it actually needs, this means that the network structure is not sparse enough. To solve above problems, two efficient algorithms for training ELM: Group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization and smoothing Group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization methods are proposed in this article. But, the basic group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization is nondifferentiable at the origin and which causes oscillation. So, we modify the basic group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization by smoothing it at the origin. Simulation results show that the ELM with smoothing group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization can effectively prune redundant nodes and redundant weights of the surviving nodes, which has better performance than traditional ELM, the ELM with <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularization method, and with group <inline-formula> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> regularization method.
Control Strategy of Grid-connected Wind Power Inverter Based on Linear Active Disturbance Rejection Control

Aiming at the problem of insufficient anti-disturbance performance and control accuracy of the traditional double closed-loop control strategy in the grid-connected inverter of permanent magnet direct drive wind power system, this paper proposes a control strategy that applies linear active disturbance rejection control technology to the voltage outer loop controller. To improve the control performance of the DC bus voltage. By introducing the sum disturbance signal in the expanded state observer (ESO) to observe its change trend, an effective early correction signal is generated, thereby improving the control system dynamic disturbance observation ability. The analysis of frequency response characteristics and simulation results under various working conditions show that, compared with traditional control methods, this method not only has a faster response speed, but also has a better anti-interference ability.
Coverage Optimization Algorithm for Multi-robot System based on Virtual Force Refinement

Multi-robot systems have been increasingly employed to explore and detect unknown environments in recent years, such as geological surveys, disaster detection and so on. To ensure effective area detection, area coverage is one of the key issues for multi-robot systems. However, most of the robot systems have the problems of high redundancy and covering holes. Adding robots to the detected holes will lead to increased redundancy and cost of the robot system. Therefore, this paper studies the problem of area coverage hole optimization for multi-robot systems that have limited perception range, and proposes an improved Lloyd-Voronoi algorithm based on virtual force (LVVF). The minimum number of robots required is determined by the inscribed polygon rule according to the circle perception model, and the preliminary coverage is based on Voronoi diagram and Lloyd algorithm. On the basis, a virtual force algorithm based on the Euclidean distance of regions is applied to refine the proposed Lloyd-Voronoi algorithm. The numerical simulations show that the improved Lloyd-Voronoi algorithm can improve the area coverage of the entire system and reduce the system redundancy.
Enhanced LISP Mapping System for Optimizing Service Path in Edge Computing Environment

In the evolution of mobile networks, edge computing is a key technology, which provides services with proximity to users and enables various services to be operated in a virtualized platform for mobile users. Particularly, services commonly used by multiple mobile users can be simultaneously operated at multiple edge nodes, which allows users to use the service of the closest node to their access point. However, to provide session continuity for a service when the user moves, a method for optimizing the service path along with the mobility management of the user is required. In this paper, we propose an enhanced control system using the locator/identifier separation protocol to provide service path optimization with mobility management. This was achieved by integrating the location information of the service and the session information between the service and the users. The operating cost and handoff latency were analyzed. The analysis results show that the proposed system can provide an optimal service path to mobile users through fewer message exchanges and less delay than existing approaches.
Dynamic Resource Management in Next Generation Networks with Dense User Traffic

With the era of the fifth generation (5G) networks, supporting all mobile service users who have different Quality of Service (QoS) requirements becomes the main challenge. To manage and satisfy the heterogeneous requirements, network slicing concept can be a solution over a common physical infrastructure. Splitting the network into slices which have different properties (e.g., bandwidth requirements, delay tolerance, user density, etc.) allows to schedule and optimize the requests under the constraint of limited resources. The network has to decide to accept or reject the requests, and scale up/down the slices by considering the user density in accepted requests, and then, schedule the accepted requests to serve them in an order. In this paper, it is verified that slicing the network and scaling up/down the slices by using deep reinforcement learning (DRL) algorithms with consideration of user density, improve the speed of satisfaction of users with respect to the classical baseline scheduling algorithms.
Best Relay Selection Schemes for NOMA Based Cognitive Relay Networks in Underlay Spectrum Sharing

Cognitive Radio and the emerging Non-Orthogonal Multiple Access (NOMA) techniques hold the potential to fulfill the increasing demands of radio spectrum by 5G-and-B5G wireless systems. It is a usual practice in underlay cognitive radio networks to take assistance from intermediate relays to reach the remote destination. Putting together, NOMA based Cognitive Relay Networks (NCRNs) have recently gained tremendous research attention to improve the spectrum utilization efficiency. This article studies the application of best relay selection (BRS) in downlink scenario of NCRNs in which a base station (BS) being unable to communicate directly with the far user <inline-formula> <tex-math notation="LaTeX">$U_{2}$ </tex-math></inline-formula> takes assistance from the near user <inline-formula> <tex-math notation="LaTeX">$U_{1}$ </tex-math></inline-formula> and from the best Decode-and-Forward (DF) relay selected from the potential NCRN operating in underlay environment. Three BRS schemes are proposed selecting a relay which (i) maximizes signal-to-noise ratio (SNR) on relay-<inline-formula> <tex-math notation="LaTeX">$U_{2}$ </tex-math></inline-formula> link, (ii) minimizes interference on relay-primary user (PU) link, (iii) maximizes the quotient of relay-<inline-formula> <tex-math notation="LaTeX">$U_{2}$ </tex-math></inline-formula> link’s SNR and relay-PU link’s interference power. Scheme 3 considers the existence of <inline-formula> <tex-math notation="LaTeX">$U_{1}-U_{2}$ </tex-math></inline-formula> link as well whereas the other two schemes do not. To characterize the performance of the proposed schemes assuming Rayleigh channel model, closed-form expressions of probability distribution function (PDF) of received SNR at <inline-formula> <tex-math notation="LaTeX">$U_{2}$ </tex-math></inline-formula>, average number of reliable relays, outage probability and bit error rate are obtained. For insight analysis, analytical results are validated through simulations which reveal that relay selection in NCRNs is different from non-cognitive networks and is feasible in low-to-medium SNR regions. The signal from near user further improved the outage performance.
A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

Wind speed is one of the primary renewable sources for clean power. However, it is intermittent, presents nonlinear patterns, and has nonstationary behavior. Thus, the development of accurate approaches for its forecasting is a challenge in wind power generation engineering. Hybrid systems that combine linear statistical and Artificial Intelligence (AI) forecasters have been highlighted in the literature due to their accuracy. Those systems aim to overcome the limitations of the single linear and AI models. In the literature about wind speed, these hybrid systems combine linear and nonlinear forecasts using a simple sum. However, the most suitable function for combining linear and nonlinear forecasts is unknown and the linear relationship assumption can degenerate or underestimate the performance of the whole system. Thus, properly combining the forecasts of linear and nonlinear models is an open question and its determination is a challenge. This article proposes a hybrid system for monthly wind speed forecasting that uses a nonlinear combination of the linear and nonlinear models. A data-driven intelligent model is used to search for the most suitable combination, aiming to maximize the performance of the system. An evaluation has been carried out using the monthly data from three wind speed stations in northeast Brazil, evaluated with two traditional metrics. The assessment is performed for two scenarios: with and without exogenous variables. The results show that the proposed hybrid system attains an accuracy superior to other hybrid systems and single linear and AI models.
Factual Knowledge Acquisition and Remembering Using Digital Flashcard Tool

Teaching for teachers is a particularly important paradigm of modern society in which information and communication technology is the leading generic technology. During the COVID-19 pandemic, we conducted an online course for primary and secondary school teachers (n=24) from Bosnia and Herzegovina. The course entitled E-learning: Teaching for Teachers took place during May and June 2020, and is designed for continual improvement of teacher’s professional knowledge, including helping teachers switch from face to face to online teaching. This paper presents the results of a one group experiment in which participant quiz scores were analyzed in two different course cycles – cycle one presented the basic elements and configuration of the e- learning systems, while cycle two presented the concepts and models of hybrid learning. The research was implemented with a traditional learning management system Moodle (moodle.org) and a digital flashcard system Memory (memory.com). The goal of the research is to determine whether digital flashcard technology in the process of learning and teaching improves the cognitive process of remembering categorie of factual knowledge.
DevOps for AI – Challenges in Development of AI-enabled Applications

When developing software systems that contain Machine Learning (ML) based components, the development process become significantly more complex. The central part of the ML process is training iterations to find the best possible prediction model. Modern software development processes, such as DevOps, have widely been adopted and typically emphasise frequent development iterations and continuous delivery of software changes. Despite the ability of modern approaches in solving some of the problems faced when building ML-based software systems, there are no established procedures on how to combine them with processes in ML workflow in practice today. This paper points out the challenges in development of complex systems that include ML components, and discuss possible solutions driven by the combination of DevOps and ML workflow processes. Industrial cases are presented to illustrate these challenges and the possible solutions.
A 1.45 GHz All-Digital Spread Spectrum Clock Generator in 65nm CMOS for Synchronization-Free SoC Applications

The increase of clock frequency in digital circuits exacerbates the electromagnetic interference (EMI) between devices. Spread-spectrum techniques reduce the electromagnetic noise lowering harmonic peaks of the clock signal by means of frequency modulation. In System-on-Chips (SoCs) another requirement in many applications is the coexistence of both modulated and un-modulated clock domains. In these cases, suitable synchronization systems are used to allow data to cross the boundary between spread and un-spread clock domains. In this paper we present a spread-spectrum clock generator (SSCG) able to provide both spreaded and un-spreaded clocks. The spreaded clock has a specially designed modulation profile, allowing at the same time a seamless synchronization-free interface between spreaded and un-spreaded clock domains and a large EMI reduction. The paper presents the derivation of the new highly discontinuous modulation profile (that allows to achieve an EMI reduction up to 15.8dB) and implementation details of an all-digital SSCG able to provide the developed modulation waveform. A test chip has been fabricated in UMC 65nm CMOS technology, using a novel dual-output digitally controlled delay line. The circuit can generate both spread and un-spread clocks (double output mode) or the spread clock alone (single output mode). Area occupation is 0.102mm2, whereas power consumption is 48.5mW in double output mode and 34mW in single output mode.
Electromagnetic and Semiconductor Modeling of Scanning Microwave Microscopy Setups

This article presents finite difference time domain (FDTD) and finite element method (FEM) based electromagnetic modeling and simulation of an industrial scanning microwave microscopy (SMM) material measurement setup. These two methods have been employed to cross verify each other for classical electromagnetic simulations of the homogeneous conductive materials under SMM. For the SMM simulations involving semiconductor materials, however, a coupled multiphysics solver is required in addition to the pure electromagnetic analysis. As a solution to this problem, an FEM-based semiconductor Poisson–Drift–Diffusion (PDD) solver and its coupling to transient electromagnetic solver is presented in this article. The considered SMM setup consists of a conductive fine tip suspended at a certain height above the sample. For the validation purposes of electromagnetic solvers, the numerical modeling was based on both the time domain FEM (TD-FEM) and FDTD. Both numerical methods extract the scattering parameters from the computed field of the conductive or dielectric samples. At the second stage of the analysis, the TD-FEM solver is coupled with the time domain PDD semiconductor solver in order to simulate charge transport and explain behavior of the charges in semiconducting domains under electromagnetic illumination similar to SMM setups.
Effect of dietary globin, a natural emulsifier, on the growth performance and digestive efficiency of broiler chickens

Abstract The feed utilisation of young chicks is characterised by a suboptimal fat digestibility, which can be improved by means of dietary emulsifiers. The objective of this study was to evaluate the effect of dietary Globin on the energy efficiency and digestibility of starter feeds and on the production performance of broilers throughout the whole rearing cycle. A total of 224-day-old ROSS 708 chickens (14 birds/pen, 8 replicates/treatment) were fed ad libitum with either a basal diet (C) or a basal diet with the addition of 0.05% Globin during the starter (d1–10), growing (d10–25) and finisher (d25–35) periods. Nutrient digestibility (aD), protein metabolisability (aMCP), energy efficiency (EE) and net energy for production (NEp) were assessed during the starter period. The average daily feed intake (ADFI), average daily gain (ADG), feed conversion ratio (FCR) and protein efficiency ratio (PER) were measured of each growth period. Globin significantly decreased FCR (p = .020) and increased aDfat (p = .021), EE (p = .028) and NEp (p = .011) during the starter period. aMCP (p = .049) and PER (p = .039) were higher in the Globin group than in the Control group. The increased availability of energy from dietary fat, as a result of Globin supplementation, possibly shifted the use of the absorbed amino acids towards an anabolic metabolism, and this could explain the increased aMCP and PER but similar aDCP. The overall performance was similar between groups, although Globin tended to increase PER (p = .064) overall. Highlights Globin dietary supplementation was studied in broiler chickens. Globin improved digestibility and nutrient efficiency utilization in the first period. The overall performance was similar between groups.
Constant current and constant voltage output WPT system based on LCC compensation network

In this paper, the LCC compensation network is applied in the MCR-WPT system to achieve constant voltage and constant current characteristics which is required for battery charging. The configuration and parameters of the LCC network are obtained through the analysis of the working principle. It is found that the performance of the LCC network changes as the transmission distance varies. Then a compensation strategy is proposed to eliminate the influence. The performance of the proposed circuit and compensation strategy are verified by simulation. It is shown that the LCC-type magnetic coupling resonant WPT system can achieve constant voltage output and constant current output, and can play a certain compensation effect when the transmission distance is changed.
Compensation of Phase Noise and IQ Imbalance in Multi-Carrier Systems

Compensation for the impacts of phase noise (PN) and in-phase and quadrature (IQ) imbalance on cyclic-prefix (CP) based multi-carrier modulation systems in the presence of imperfect channel estimation is considered in this paper. A unified two-stage algorithm is proposed. In the first stage, IQ imbalance parameters and channel impulse response are estimated based on the transmission of a preamble which is designed in such a way that the estimation of IQ imbalance does not require any knowledge about the channel and PN. Given the estimates from the first stage, the impacts of IQ imbalance and PN are subsequently compensated in the second stage based on the transmission of pilot symbols. The proposed algorithm is further extended to a MIMO system that employs a diversity technique. Simulation results are presented for a wide range of PN and IQ imbalance scenarios to corroborate the effectiveness of the proposed algorithm.
Early childhood online education in the COVID-19 context. Behavioral Patterns for User Interface Design

The outcomes from the lived experience descriptions show that tested indicators for the evaluation of both MOOCs and preschool education are insufficient or inexact for the assessment of our current condition. Most of the main online platforms, based on universities or as products of local entrepreneurships have ignored the preschool group. The already proposed generic solutions do not meet preschool education’s needs. Considering Behavioral Patterns we propose a model useful for both the evaluation of ongoing experiences and the organization of new content.