Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Improvement of Speaker Recognition by MCE-based Score Combination of Multiple Feature Parameters

In this thesis, an enhanced method for the feature extraction of vocal source signals and score combination using an MCE-Based weight estimation of the score of multiple feature vectors are proposed for the performance improvement of speaker recognition systems. The proposed feature vector is composed of perceptual linear predictive cepstral coefficients, skewness, and kurtosis extracted with lowpass filtered glottal flow signals to eliminate the flat spectrum region, which is a meaningless information section. The proposed feature was used to improve the conventional speaker recognition system utilizing the mel-frequency cepstral coefficients and the perceptual linear predictive cepstral coefficients extracted with the speech signals and Gaussian mixture models. In addition, to increase the reliability of the estimated scores, instead of estimating the weight using the probability distribution of the convectional score, the scores evaluated by the conventional vocal tract, and the proposed feature are fused by the MCE-Based score combination method to find the optimal speaker. The experimental results showed that the proposed feature vectors contained valid information to recognize the speaker. In addition, when speaker recognition is performed by combining the MCE-based multiple feature parameter scores, the recognition system outperformed the conventional one, particularly in low Gaussian mixture cases.
Multi-Layer Radio Network Slicing for Heterogeneous Communication Systems

In this paper, we consider the radio access network (RAN) slicing in heterogeneous communication systems. Typically, the infrastructure provider (InP) allocates the radio resources to the mobile virtual network operators (MVNOs) as RAN slices, and then the MVNOs use the allocated resources to provide services for their users. As the RAN being heterogeneous, the users are generally with different quality of service (QoS) requirements. Besides, the MVNOs also have different intrinsic interests in terms of collecting revenue and satisfying users QoS demand and thus require different amount of resources. In this regard, it is challenging for the InP to determine the resource allocation strategy. To deal with this issue, we propose a three-layer Stackelberg game (TLSG) to jointly solve the frequency and power allocation for the InP. We derive the unique equilibrium for the special case where each MVNO uniform users and propose a bi-level differential evolution algorithm for more general cases. Simulation results reveal that both users and the InP can be better off when the RAN is sliced for different types of users, and that the MVNOs focusing more on revenue collecting result in the InP and the users obtaining less payoff.
KPI design as a simulation project

Design of key performance indicators (KPIs) is a repeated and challenging problem for organizations. KPIs are measured at the operational level, but are used to manage organizations at the strategic level. Strategic goals often change, and professionals with different backgrounds must understand and implement new KPIs that correspond to them. Nowadays, KPIs are designed and directly implemented on running organizations, which is disturbing to operational personnel. To avoid the disruption of business processes, we propose to design and test KPIs through simulation. After satisfactory experiments, a KPI can be implemented on the modelled organization. We have developed a conceptual reference model to organize KPI design simulation projects, showing what should be produced in a project. Our model has been built by the application of requirements engineering methods to the review of the literature of performance indicators. The model has been tested on several case studies. In this paper, we show two studies that demonstrate how unspecified concepts from our conceptual reference model damage reliability and improvement orientation of the designed indicators.
Kinetics of photocatalytic removal of imidacloprid from water by advanced oxidation processes with respect to nanotechnology.

In this study, the kinetics of photocatalytic removal of imidacloprid, a systemic chloronicotinoid insecticide, from water using two advanced oxidation systems (ZnO(normal)/H2O2/artificial sunlight and ZnO(nano)/H2O2/artificial sunlight) were investigated. Moreover, the effects of pH, insecticide concentration, catalyst concentration, catalyst particle size, and water type on the photocatalytic removal of imidacloprid were evaluated. Furthermore, total mineralization of imidacloprid under these advanced oxidation systems was evaluated by monitoring the decreases in dissolved organic carbon (DOC) concentrations and formation rate of inorganic ions (Cl- and NO2-) with irradiation time using total organic carbon (TOC) analysis and ion chromatography to confirm the complete detoxification of imidacloprid in water. The degradation rate of imidacloprid was faster under the ZnO(nano)/H2O2/artificial sunlight system than the ZnO(normal)/artificial sunlight system in both pure and river water. The photocatalytic degradation of imidacloprid under both advanced oxidation systems was affected by pH, catalyst concentration, imidacloprid concentration, and water type. Almost complete mineralization of imidacloprid was only achieved in the ZnO(nano)/H2O2/artificial sunlight oxidation system. The photogeneration rate of hydroxyl radicals was higher under the ZnO(nano)/H2O2/artificial sunlight system than the ZnO(normal)/H2O2/artificial sunlight system. Advanced oxidation processes, particularly those using nanosized zinc oxide, can be regarded as an effective photocatalytic method for imidacloprid removal from water.
FUNCODE: Effective Device-to-System Analysis of Field-Coupled Nanocomputing Circuit Designs

Many beyond-CMOS technologies, based on different switching mechanisms, are arising. Field-coupled technologies are the most promising as they can guarantee an extremely low-power consumption and combine logic and memory into the same device. However, circuit-level explorations, like layout verification and analysis of the circuit performance, considering the constraints of the target technology, cannot be done using existing tools. Here, we propose a methodology to take on this challenge. We present function and connection detection (FUNCODE), an algorithm that can detect element connections, functions, and errors of custom layouts and generate its corresponding very high-speed integrated circuits hardware description language netlist. It is proposed for in-plane and perpendicular nanomagnetic logic as a case study. FUNCODE netlists, which take into account the physical behavior of the technology, were verified using circuits with increasing complexity, from 6 up to 1400 gates with a number of layout elements varying from 200 to 2.3e6.
Reprogramming of neural networks: A new and improved machine learning technique

Neural networks can be repurposed via reprogramming to perform new tasks, which are diﬀerent from the tasks they were originally trained for. We introduce new and improved reprogramming technique that, compared to prior works, achieves better accuracy, scalability, and can be successfully applied to more complex tasks. While prior literature focuses on potential malicious uses of reprogramming, we argue that reprogramming can be viewed as an eﬃcient training method. Our reprogramming method allows for re-using existing pre-trained models and easily reprogramming them to perform new tasks. This technique requires a lot less eﬀort and hyperparameter tuning compared training new models from scratch. Therefore, we believe that our improved and scalable reprogramming method has potential to become a new method for creating machine learning models.
Wideband Circularly Polarized Antenna With In-Lens Polarizer for High-Speed Communications

In this contribution, a broadband $G$ -band leaky-wave (LW) fed lens antenna with an integrated dielectric grid polarizer is presented. The proposed wideband polarizer unit cell geometry enables its fabrication at frequencies higher than 100 GHz, presenting high transmission properties and low axial ratio (AR). A quasi-analytic technique based on multilayer spectral Green’s function combined with a numerical Floquet modes’ solver is used to optimize the lens aperture efficiency and AR. The proposed technique is validated via full-wave (FW) simulations. A design is proposed in low dielectric permittivity material, achieving FW simulated aperture efficiency higher than 75% over 44% relative bandwidth, and an AR lower than 3 dB over 35% relative bandwidth. The antenna is able to achieve multiple directive circularly polarized (CP) beams when fed by a focal plane array, preserving the AR bandwidth. A prototype has been fabricated and measured, exhibiting an excellent agreement with quasi-analytic and FW simulations.
Two-phase flash for tight porous media by minimization of the Helmholtz free energy

Thermodynamic modeling of phase behavior is one of the most fundamental components in the study of enhanced oil recovery by gas injection. Robust algorithms exist for multiphase equilibrium problems with no capillary pressure as commonly used in compositional reservoir simulation. However, various convergence problems have been reported even for simple two-phase split problems in the presence of capillary pressure by using the traditional algorithm based on minimization of the Gibbs free energy. In this research, the phase-split problem with capillary pressure is formulated by using the Helmholtz free energy for a given temperature and total volume. The algorithm is based on the successive substitution (SS) for updating K values, which is coupled with the volume update by using the pressure constraint equation. The robustness of the SS algorithm is improved by using the convexity information of the Helmholtz free energy. Case studies present phase-split problems with capillary pressure by using the developed algorithm and highlight several advantages of using the Helmholtz free energy over the Gibbs free energy. The improved robustness comes mainly from the involvement of a single energy surface regardless of the number of phases. The pressure variability that occurs during the phase-split calculation with capillary pressure is inherent in the Helmholtz free energy in volume space.
A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

: Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel model of the packet ordering is applied into the distributed system, and a distributed covert channel of the packet ordering enhancement model based on data compression (DCCPOEDC) is proposed. The data compression algorithms are used to reduce the amount of data and transmission time. The distributed system and data compression algorithms can weaken the hidden statistical probability of information. Furthermore, they can enhance the unknowability of the data and weaken the time distribution characteristics of the data packets. This paper selected a compression algorithm suitable for DCCPOEDC and analyzed DCCPOEDC from anonymity, transmission efficiency, and transmission performance. According to the analysis results, it can be seen that DCCPOEDC optimizes the covert channel of the packet ordering, which saves the transmission time and improves the concealment compared with the original covert channel.
Segregated Linear Decision Rules for Inverter Watt-VAr Control

The modern photovoltaic inverter has an active power-reactive power (Watt-VAr) control curve that is part of its management modes. The Watt-VAr curve, whose settings can be remotely modified, regulates the inverter reactive power output in function of its active power. This paper proposes the optimization of inverter segregated linear decision rules (LDRs), starting from the legacy Volt-VAr optimization in distribution networks. As opposed to classical LDRs that are interfaced with Volt-VAr optimization, the segregated LDRs are piecewise linear continuous curves that directly satisfy the reactive power capability of inverters, and their multiple segments can capture the correlation in solar power variability. The paper presents linear programming formulations for optimizing the segregated linear decision rules using stochastic optimization, robust optimization, and distributionally robust optimization. The optimization schemes are demonstrated on both meshed and radial networks, and their performance is validated via Monte-Carlo simulation.
In vitro Culture of Heat-treated Anthers Induces Embryogenic Callus in Cassava (Manihot esculenta Crantz)

Microspore embryogenesis is the developmental plasticity of juvenile male gametophytes to switch from pollen to embryo development upon exposure to in vitro stress. It is a common method for obtaining haploid and doubled haploid plants in breeding programs for development of superior varieties via haploid-diploidization which allows fast development of homozygous lines from heterozygous parents. In cassava, obtaining haploidization through traditional methods of successive selfing is difficult because of cassava’s long reproductive cycle, high heterozygosity, and inbreeding depression. As a first step towards microspore embryogenesis, callus induction following heat treatment has not been investigated in cassava. We used two elite Ugandan cassava varieties, hereafter called “genotypes”, NASE3 and NASE14, for the study of callus induction. Heat stress of 40°C for 0, 6, 12, 18 and 24hrs and Murashige and Skoog medium supplemented with 2-9% sucrose, and 2,4-dichlorophenoxy acetic acid were used. Heating anthers of NASE3 at 40°C for 6 hrs resulted in a significantly higher percentage of callus induction on MS medium supplemented with 2% sucrose. Callus emerged from inside of the anthers with production influenced by genotype, sucrose concentration, anther density and duration in culture (P≤0.001). Limited in vitro callus differentiation was observed on auxin-and cytokinin-supplemented media. In both genotypes, embryo genic callus was obtained in liquid medium, while green callus was achieved on solid medium. This is a significant step upstream of double haploid plant production pathway in cassava: rigorous optimization of protocols downstream of callus induction are needed for regeneration of microspore-derived embryos and haploid plants.
Neural dynamics of the impact of font style on lexical decision making in adult dyslexia

Good reading comprehension is indispensable in many situations including contract-based transactions that have become so prevalent in our everyday lives. People with dyslexia often exhibit impairments in this important cognitive process. Although the effects of italics – a commonly used style for highlighting important content in a range of documents – and font in general, have been explored with behavioural measures, their impact on human brain dynamics remains poorly understood. Here, we used electroencephalography (EEG) to investigate the specific effects of italics in a sentence reading lexical decision task in adult dyslexics and an age matched non-dyslexia group. Overall, the performance of dyslexics was worse. Cluster-based event-related potential (ERP) analysis revealed that brain responses within the first 300 ms following the decision stimulus differed in amplitude and spatial distribution between dyslexics and non-dyslexics when processing italicised text. An initial ERP component over occipitotemporal electrode sites started to differ between the groups as early as 167 ms following the onset of short italicised decision words. A subsequent ERP component over centrofrontal electrodes showed differences lasting until about 300 ms post-stimulus onset. Inter-individual amplitude differences in this centrofrontal neural signal were predictive of behavioural performance across participants, further highlighting the role of fast post-sensory linguistic processes in lexical decision making. Crucially, our findings emphasise the importance of choosing font style carefully to optimise word processing and reading comprehension by everyone.
Hera-T: an efficient and accurate approach for quantifying gene abundances from 10X-Chromium data with high rates of non-exonic reads

An important but rarely discussed phenomenon in single cell data generated by the 10X-Chromium protocol is that the fraction of non-exonic reads is very high. This number usually exceeds 30% of the total reads. Without aligning them to a complete genome reference, non-exonic reads can be erroneously aligned to the transcriptome reference with higher error rates. To tackle this problem, Cell Ranger chooses to firstly align reads against the whole genome, and at a later step, uses a genome annotation to select reads that align to the transcriptome. Despite its high running time and large memory consumption, Cell Ranger remains the most widely used tool to quantify 10XGenomics single cell RNA-Seq data for its accuracy. In this work, we introduce Hera-T, a fast and accurate tool for estimating gene abundances in single cell data generated by the 10X-Chromium protocol. By devising a new strategy for aligning reads to both transcriptome and genome references, Hera-T reduces both running time and memory consumption from 10 to 100 folds while giving similar results compared to Cell Ranger’s. Hera-T also addresses some difficult splicing alignment scenarios that Cell Ranger fails to address, and therefore, obtains better accuracy compared to Cell Ranger. Excluding the reads in those scenarios, Hera-T and Cell Ranger results have correlation scores > 0.99. For a single-cell data set with 49 million of reads, Cell Ranger took 3 hours (179 minutes) while Hera-T took 1.75 minutes; for another single-cell data set with 784 millions of reads, Cell Ranger took about 25 hours while Hera-T took 32 minutes. For those data sets, Cell Ranger completely used all 32 GB of memory while Hera-T consumed at most 8 GB. Hera-T package is available for download at: https://bioturing.com/product/hera-t
Diakoptics Modelling Applied to Flying Bird-Shape NGD Microstrip Circuit

This brief develops a diakoptics modelling of a flying bird topology constituted by interconnected piece-wises of admittance or Y-matrices. The diakoptics principle is established with passive system teared into several simplex elements as transmission and coupled lines (TLs and CLs). After introduction of subsystem Y-matrix, the global system Y- and S-matrices are established. To verify the model effectiveness, parametric analyses enabling to understand bandpass negative group delay (NGD) behavior of the flying bird topology. Furthermore, a microstrip circuit is designed, fabricated and tested. The modeled, simulated and measured NGD circuit responses are in excellent agreement. NGD value about −2.2 ns under transmission and reflection coefficients better than −2 dB and −13 dB is measured around 1.15 GHz.
Optimal DG Allocation and Volt–Var Dispatch for a Droop-Based Microgrid

Unequal reactive power sharing amongst distributed generators (DG) is a significant concern while operating a droop based microgrid. The reasons for this unequal reactive power sharing include the difference in feeder impedances, uneven distribution of loads and DGs in terms of size and locations. In this article, a mixed-integer linear programming (MILP) problem applicable for a droop based microgrid has been proposed to achieve proportional reactive power sharing amongst DGs while maintaining very low line losses. Firstly, the optimal sizing and placement of DGs are investigated. This includes a detailed discussion on the linearisation of various nonlinear terms involved in the formulation. Subsequently, a day–ahead dispatch is generated for these DGs for a given load profile. Additionally, network reconfiguration has been incorporated for further improvement in performance. The effect of considering a practical constant impedance-current-power (ZIP) load model, instead of simply considering constant impedance loads, has also been assessed. The proposed formulation has been tested on a 33 bus network which has been modified to represent an islanded microgrid. The results obtained validate the accuracy of the proposed planning and dispatch method and demonstrate its utility in achieving proportional reactive power sharing amongst the DGs while incurring very low line losses.
The Effectiveness of a Cloud Computing-based Learning Program in Enhancing Students' Motivation to Learn Islamic Education in UAE

The study aimed to investigate the impact of a cloud Computing-based learning program on enhancing students' motivation to learn Islamic education in the UAE. The sample consisted of (94) students from tenth grade, who were divided into two equal groups: An experimental group that studied the course using a cloud-based learning program and control group studied the same course by the usual way. To achieve the objectives of the study, the motivation measurement (pre-post) was applied. The results showed significant differences in the motivation measurement as a whole and all its dimensions, due to the teaching method in favor of the experimental group that studied the course through the cloud-based learning program. The results also showed no significant difference between the scores of the experimental group individuals on the post-motivation measurement due to the gender variable. According to these findings, the study recommended the activation of cloud-based learning strategies, especially in the future shifting towards the digital curricula.
A hierarchical Bayesian approach for handling missing classification data

Abstract Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these “partial” observations must be modified to include the missing data mechanism to avoid spurious inference. We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data. We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes. We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.