Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Scrum-XP Hybrid Methodology on Quality in Web Development with Distributed Teamwork

The rise of Distributed Software Development (DSD) brings several challenges in communication, coordination, and collaboration within team environments, potentially impacting the overall quality of software development outcomes. This research investigates the impact of integrating a Scrum-XP hybrid methodology in distributed teams on the quality of web development. A case study is conducted on the development of the Clinic Appointment Booking Application (CABA) by Technway Teamwork, a distributed team. The study follows a rigorous process involving sprint planning, task tracking, progress monitoring, collaboration, and continuous integration. The study evaluates the quality of the software based on ISO/IEC 25010:2011 and ISO/IEC 25023:2016 standards, with high functionality and quality observed particularly in usability, functional suitability, reliability, and security. The study highlights the significance of clear project goals, effective communication channels, rigorous testing, and stakeholder involvement in ensuring quality in distributed web development. The integration of the hybrid Scrum-XP methodology and efficient communication and collaboration solutions enables distributed teams to overcome challenges and deliver high-quality products. Evaluation of sprint parameters reveals improved resource management, product stability, and customer satisfaction over time. This research provides empirical evidence of the benefits of the Scrum-XP hybrid methodology in distributed teamwork and offers insights into addressing challenges faced by such teams.
Towards an Enhanced Model For Contextual Topic Identification

With the expansion of the Internet, there has been a swift accumulation of written content available online, encompassing articles, product evaluations, and historical records. It is imperative to categorize this textual content because it contains a substantial amount of concealed information that can be scrutinized to offer valuable insights. Consequently, there is an urgent need for the development of machine learning algorithms to automate this data analysis process. Topic modeling emerges as a solution to meet this demand as it facilitates the discovery of latent thematic patterns that span multiple documents. It assigns labels to documents based on these topics, enabling the organization, retrieval, and summarization of textual information. Our objective is to enhance contextual topic modeling and compare our outcomes with established conventional algorithms like Latent Dirichlet Allocation (LDA) and sentence-level Bidirectional Encoder Representations from Transformers (BERT) embedding clustering. This enhancement aims to cluster review data and rank them according to their topics. The results reveal that employing our improved model, along with optimal hyperparameters for LDA and various pretrained BERT models, leads to superior performance. Specifically, there is a 4% increase in coherence score and a remarkable 240% improvement in silhouette score.
Tunable Plasmonic Graphene Antenna Array for Communications at THz Frequencies

The existence of plasmonic waves at a graphene/dielectric interface at THz frequencies enables the use of graphene as the radiative element of antennas working at this frequency range, but with a smaller lateral dimension compared to a standard metallic antenna. Due to the low carrier mobility in large-area graphene, result of its manufacturing process, the emitted radiation of graphene antennas is still smaller than its metallic counterpart. In this work, we show that the low emission of a graphene antenna can be compensated by antenna arrays. The proposed 1x4 graphene antenna array presents a far-field radiated gain of 9.3 dBi with a resonance frequency at 269 GHz. The resonating frequency of the graphene antenna array can also be tuned by an electrostatic bias.
Development and Characterization of a COC Pillars-Based Microfluidic Device for Nanoparticle Separation

Nanoparticles are used in a variety of industries, including biomedicine, food science, energy and environmental engineering, and petroleum. Efficient and effective nanoparticle separation techniques are needed to ensure progress in these fields. Conventional separation techniques such as ultracentrifugation, filtration, chromatography, and selective precipitation are not continuous and require external energy sources. On the other hand, microfluidics is a simple, inexpensive, and continuous technique for the effective separation of nanoparticles. In this work, we built a graphene oxide (GO)-coated COC pillar-based microfluidic separation device and studied the effect of GO coating on silica nanoparticle deposition.
Novel Generative Classifier for Acoustic Events

In this paper, we describe a novel generative classifier for audio events based on the projected belief network (PBN). The PBN is a layered generative network formed from a feed-forward network, so it can be simultaneously trained as a generative model for a given class, and as a discriminative classifier against “all other classes”. A PBN can also be shortened to any number of layers, allowing the output features of the shortened network to be modeled using an arbitrary probability density function (PDF) estimator. We exploit these properties of PBN to model the features from the output of an early convolutional layer, where a time dimension is still present, using a hidden Markov model (HMM). The special generative/discriminative training of the PBN produces generative features that are also high in discriminative information, forming a generative classifier combining (a) a discriminative deep network and (b) a generative neural network, and (c) a HMM classifier rooted in classical Bayesian approaches. The approach is demonstrated in the task of acoustic event classification.
Validity of Measurable Expertise on Publication Recognition

In recent years, increasing attention has been given to the use of expert opinions in decision-making and problem-solving processes. Several methodical approaches to assessing a person’s expertise have been analyzed. This study aims to develop renewable approach modeling for the measurement of researchers’ expertise. It is a unique methodology for collecting individual knowledge and expertise by collaborating on measuring the concentration of publication results and research themes, accreditation of publication media, author position, citations, and number of studies in each year. The knowledge of experts can be represented in the form of dynamic "research" by carrying out research schemes using important features in detail and with variable concepts. This research aims to measure the strength of the publication trail of a researcher's scientific work using the Collaboration Algorithm, in terms of consistency in the field of research, quantity of research, and quality of published research as important points for expressing their expertise. Through research, important points will be found that can be used as benchmark parameters to ensure that someone who is called an expert is someone who has been scientifically proven to meet the requirements supported by valid data through measurable and presentable expert parameters.
A Two-Stage Prompt Learning Method for Jointly Predicting Topic and Personality

Accurate personality prediction can help management departments analyze users' behaviors and make informed decisions effectively. Existing text-based personality prediction studies mainly rely on deep neural networks or pre-trained language models to extract semantic information and personality traits. However, the text's topic and label description may provide additional personality clues. This paper proposes a topic and personality prediction method based on a large language model (LLM), which utilizes a two-stage prompt strategy to mine the interaction between the topic and personality information. Additionally, labels' descriptions are incorporated to construct cue-based prompts, and a fine-tuning approach is adopted to optimize the model's performance. Experiments on two datasets show the efficacy of the proposed model.
10 THz Bandwidth with a Fiber-Coupled THz Time-Domain Spectrometer

Fiber-coupled terahertz (THz) time-domain spectrometers (TDS) are versatile tools that are used extensively in academia and industry. Here, we compare two different THz sources with the aim to reach the largest spectral bandwidth: A DSTMS crystal and a novel photoconductive membrane emitter. With both sources, we demonstrate record bandwidths around 10 THz. At the same time, the photoconductive emitter leads to a 30 dB higher peak dynamic range of up to 100 dB.
AC Power Flow-Based Transmission Constrained Generation Expansion Planning with Intermittent RES

Transmission Constrained Generation Expansion Planning (TC-GEP) optimization has been solved majorly using the less reliable DC power flow analysis. In this paper, the TC-MOGEP problem is formulated based on AC-power flow analysis and considering presence of variable/intermittent Renewable Energy Sources (vRES). vRES related constraints in terms of resource availability and variability are considered to ensure reliability and security of supply. The paper studies the dynamics brought about by inclusion of both AC-power flow analysis and vRES in TC-GEP optimization. When considering AC power flow constraints, feasible TC-GEP results were achieved up to the fourth year of optimization (45MW). Beyond this load, no feasible solutions could be obtained even with increased investment in generation sources due to divergence of the AC-based power flow analysis. The divergence was caused by unsatisfied constraints majorly overloading of existing transmission lines. Though penetration of vRES in the optimized expansion plans slightly increased the generation investment cost by 2.5% it significantly reduced the operational cost by approximately 50% resulting to an overall cost reduction of up to 19%. Using the proposed formulation and solution methodology a 6.5% and 4.5% annual average share of vRES were realized in installed capacity and energy mix respectively. This penetration level resulted to a 55% reduction in CO2 emissions.
An Explainable SAR Shadow-Aware Transformer with ASC Embedded for Shaded Object Classification

Shadow in synthetic aperture radar (SAR) results in dark areas or shadows on the SAR image, leading to challenges in object classification tasks. Although Convolutional Neural Networks (CNN) have shown good performance in SAR object classification, it cannot generalize well under shadow and features are corrupted due to pooling. In this paper, we propose an explainable SAR shadow-aware transformer with ASC for shaded object classification. Our method integrates attention-based transformers, CutMix, and a physical explainable module to enhance classification accuracy and interpretability. Our method maintains high accuracy on MSTAR datasets under high shadow rate. LRP visualization method further validates the model's interpretability and reliability during the classification process.
Studying the Safety of Electronic Circuits Using an Improved N-Norm-Based Approach

In this article, we present an improved algorithm that identifies for signal propagation paths in electronic devices in a computer simulation system and compare it with an original algorithm. The novel logic of the algorithm uses all possible signal propagation paths, and as an excitation, we apply ultrashort pulses. To get full data on the operation of the two algorithms, we consider five configurations of microstrip structures that represent various complexity. The operation of each algorithm is visualized by N-norms for each configuration, but because of the space limitations, we compare the N-norms for only one configuration, two-conductor microstrip line. It was found that with the improved algorithm, in 3 out of 5 cases, the value and localization of the maximum do not change. In the remaining two, a global maximum was 15% higher than that found with the original algorithm. In addition, with the improved algorithm, in 4 out of 5 cases, a maximum was different from that found with the original algorithm, with the difference reaching 110%.
Fog Computing in Smart Cities: A Systematic Review

An unprecedented fusion of different technologies has been made possible by the proliferation of smart city projects to improve urban life, resource management, and service delivery. Fog computing is one of these technologies that has shown promise in addressing the problems brought on by the expanding demand for real-time data processing, low-latency connectivity, and effective resource use. The role and effects of fog computing in the context of smart cities are thoroughly reviewed in this work. This paper seeks to provide an in-depth overview of the contributions, trends, and challenges related to the implementation of fog computing in smart urban environments by synthesizing and analyzing a wide range of publications. This paper synthesizes existing information to highlight important research gaps and recommended directions for further study at the interface between fog computing and smart cities. For researchers, practitioners, and policymakers involved in the design, implementation, and optimization of smart city infrastructures employing fog computing technologies, the review's findings provide useful insights.
Virtual and Physical Intelligent Engineering in Collaborative Space

This paper analyzes a comprehensive engineering scenario which consists of virtual twin, field operating, and production views of an engineering achievement (EA) together with influence zones of these views. EA is supposed to be in possession of essential cyber physical biological structure (CPBS) features. This scenario is full contextual together with its reactive, autonomous and situation controlled capabilities. Virtual twin is complex model which is proposed to develop in purposeful collaborative space using modeling, simulation, and other capabilities of suitably configurated engineering modeling platform (EMP). The main contribution in this paper is experimental model (EM), which is developed to serve well-defined research, and which is extended to manage full application process of research results. Although EMP products primarily serve research, development, and operation of industrial and commercial products, in this case EA is experimental configuration which serves realization of well-defined research plan and is represented by EM. Scientific and engineering results of research are represented as content items for contextual, generic, reactive, situation controlled and autonomous EM. EM is developed in dedicated collaborative space on the cloud for the lifecycle of a research with geographically unlimited role based participation. Lifecycle of a research starts when research plan is defined and ends when management of research results application is not necessary anymore. Model and simulation representations of biological structures such as human organ including living tissues are included in the proposed EM. Work for results in this paper and a pilot project for the application of these results proceeds at the Virtual Research Laboratory (VRL) which is equipped by own platform in the 3DEXPERIENCE platform operated by the Dassault Systémes S.A. on the cloud. This paper discusses the above outlined scenario, the main problems to solve by the proposed concept and methodology, the main structure of EM, the related issues of collaborative space, and main features of VRL.
Bandit Algorithms Applied in Online Advertisement to Evaluate Click-Through Rates

Reinforcement learning approaches are increasingly used to model complex decision-based problems. The multi-armed bandit problem is a classical instance suitable for reinforcement learning challenges that involves balancing exploration and exploitation trade-offs. Finding a balance between exploration and exploitation is a fundamental aspect of a variety of reinforcement learning applications. Multi-armed bandit algorithms are useful in multiple industry domains such as computer games, clinical trials, telecommunication, and recommender systems. This paper aims to study the multi-armed bandit problem and contextualize the algorithms to provide a framework for optimizing click-through rates in online advertising, thereby improving the customer fidelity. To that end, parameterized bandit algorithms such as upper confidence bound (UCB), epsilon greedy (є-greedy), and SoftMax algorithms were implemented and tweaked to maximize performance in an advertising platform. The results obtained demonstrate optimal records in choosing the best adverts. The UCB approach achieves the highest cumulative mean rewards for selecting the arms over the iterations. Experiments stipulate that the proposed system outperforms the conventional techniques when є and τ are set to 0.1 as it does not rely on the availability of the data over varying cycles.
Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluate wetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the improved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multidimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluation of wetland soil quality variations are realized by pursuing optimum projection direction and projection function value. Therefore, by adopting this new method, any possible human interference can be avoided and sound results can be achieved in researching qua,lity changes and classification of wetland soils.
Time-Series Misalignment Aware DNN Adversarial Attacks for Connected Autonomous Vehicles

Connected Autonomous Vehicle (CAV) technique allows each autonomous vehicle to analyze time-series driving signals (e.g., speed, steering wheel angle) sent by its nearby vehicle using the Deep Neural Network (DNN). This scenario could attract adversarial attacks on the DNNs. To fool the victim's DNN model to make misclassification in the black-box scenario, an attacker generates a small perturbation series on time-series signals to generate an adversarial input. However, as the attacker does not know the start time stamp of each input to the DNN, the start point of the perturbation may not match that of real target input and the misalignment can make the attack unsuccessful. To address this problem, we propose three time-series Misalignment Aware DNN Adversarial Attacks (MA3) for CAVs. The first two methods estimate the start point of the target input, while the third method creates a perturbation for each maneuver that misclassifies all inputs with all time stamps as the start point. Both of our experimental evaluations based on real driving data and real experiments show that MA3 produces up to 63.49% higher success rate, 56% less perturbation generation time, and 63.16% less perturbation amount compared with previous attack method. Our source code is publicly available.
FILOGEOGRAFIA DE POPULAÇÕES BRASILEIRAS DE MARMOSA ( MARMOSA ) MURINA (DIDELPHIMORPHIA, DIDELPHIDAE)

Marmosa , um genero de marsupial sul-americano, tem 17 especies distribuidas nos subgeneros Marmosa e Micoureus . Visando detectar padroes filogeograficos em M . ( Marmosa ) murina , foram analisados o gene mitocondrial Citocromo b e o exon 28 do gene nuclear Fator de Von Willebrand. Analises de maxima verossimilhanca e Bayesiana corroboraram a monofilia de Marmosa (incluindo Micoureus ), e revelaram a monofilia de M. murina . Esta especie aparece distribuida em tres clados, um ao norte do rio Amazonas, outro entre os rios Tapajos e Araguaia, e o terceiro no sudeste do Brasil. O clado do sudeste do Brasil foi a primeira linhagem a se diferenciar, seguido dos outros dois clados, mais relacionados entre si. As analises mostraram estruturacao, com populacoes do lado leste do rio Araguaia separadas das do lado oeste. A formacao deste rio e posterior ao aparecimento da linhagem do clado Tapajos - Araguaia. A separacao do clado norte do rio Amazonas dos demais sugere o rio Amazonas como barreira. Esse rio ja existia antes da diferenciacao de M. murina , estimada em 1,5 Ma, mas mudancas climaticas durante o Pleistoceno produziram expansoes e contracoes de ambientes aridos e umidos, alterando o volume de agua do rio e possibilitando a colonizacao das duas margens. A partir do Holoceno o rio Amazonas tornou-se barreira efetiva.
Sensor-Aided NILM with Gaussian Mixture Models

Energy disaggregation, also known as Non-Intrusive Load Monitoring (NILM), is the process of analyzing energy consumption in a building and identifying individual appliancelevel energy usage. This approach can provide valuable insights into energy consumption patterns and help reduce overall energy usage, costs, and carbon emissions. This paper proposes a new method for tackling the disaggregation problem by using data from low-cost wireless sensor networks. The proposed approach estimates appliance states using a GMM model and uses these states as features to improve energy disaggregation. The performance of the proposed method was evaluated on a real-world dataset called SmartSense deployed in our lab, and the results showed that it significantly improved the accuracy of conventional NILM disaggregation performance.
Resource Optimisation in Cognitive-Based Internet-of-Things with Edge Computing

Cognitive-based internet-of-things (C-IoT) introduces cognitive capabilities into modern internet-of-things network. One main challenge with C-IoT is the limitation in resource availability and usage, making it difficult to achieve the desired quality of service for the network. With the introduction of edge computing, new and emerging technologies, such as the C-IoT, are empowered to perform data processing at the network edge, making it possible to improve resource utilisation for the network. In this paper, an appropriate resource allocation model for edge computing-based C-IoT is developed and analysed. The model is developed as an optimisation problem and then solved using two different approaches. The first approach employs an integer relaxation-based heuristic to solve the problem. The second approach employs an artificial intelligence-based algorithm to solve the problem. Some important results are presented to demonstrate the improvement in performance that the new model achieves over other models, and to also compare the performances of the two approaches employed in this paper.