Miracle-dz/UniFAR-FTUs huggingface dataset:
It's Important Using Beauty Vloggers on Youtube to Create Brand Preference

Using a beauty vlogger with characteristics that match the product being promoted is one of the company's strategies for utilizing social media to improve brand performance through promotion. This study aims to understand the characteristics of beauty vloggers (physical attractiveness, social and building relationships with the audience) in influencing parasocial interactions and their influence on preferences for local cosmetic brands. The research objects were five beauty vloggers for local cosmetic products with the highest number of subscribers in Indonesia, so 400 subscribers were taken as a purposive sample. Then, the questionnaire was distributed online through social media. The collected data were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The study's results prove that physical attractiveness, social attractiveness, and the relationships built by vloggers with audiences influence parasocial interactions to increase preference for local cosmetic product brands. Companies can take advantage of beauty vloggers to improve brand performance by paying attention to the characteristics of beauty vloggers.
Security Attacks on Social Networking: A Review

Social networking sites allow people to share their information and help to create social contact. Nowadays, it has become an integral part of human life. The first thing any person does just after waking up is checking notifications from various social media platforms they have an account on. Hence, we all can imagine the importance of social networking on human beings. However, sharing information and making contact with the unknown are the key concerns of security in social networks. Now it is very much possible for common men to be tricked into traps by attackers. This paper focuses on different aspects of social networking securities and explains the various categories of online attacks possible on social networking. Moreover, this survey tries to find out the drawbacks and limitations in the literature along with a brief analysis of the security attacks on social networks.
Balancing Dual DC Voltage Control in Quasi Single-Stage Converter for Low-Voltage Energy Storage System Grid-Connected

The aim of a bidirectional quasi single-stage converter (QSSC) for low-voltage energy storage systems that are grid-connected is to reduce the power conversion stage of the dc-dc converter, which results in improved overall efficiency. The QSSC configuration relies on a modified modulation strategy due to the unequal dual dc port (upper and lower dc ports) voltage. The control for ensuring that the dual dc port voltages are equal is presented in this paper. When the lower dc port is varied in response to battery voltage, another is controlled to track in response to the lower dc port, so that the voltage ratio between the upper and lower dc port is always equal to one. Furthermore, power sharing in a dual dc port is equal, which means that a dc-dc converter automatically operates at 50% of the battery power. Thus, the existing modulation can be used and the power stage conversion of the dc-dc converter can be decreased. The proposed control for the QSSC is verified with simulation results.
Compliance to GDPR Data Protection and Privacy in Artificial Intelligence Technology: Legal and Ethical Ramifications in Malaysia

AI is becoming increasingly important in cybersecurity. AI-based products detect risks and secure systems and data. Cybercriminals can use technology to launch more sophisticated attacks. AI-based security is in demand due to cyberattacks. With the adoption of AI technology, GDPR requires most countries to have legal measures to protect their citizens' data and privacy. Data protection and privacy issues arise when using AI technology. AI use must comply with GDPR, including obtaining consent for data processing, ensuring data accuracy, and giving individuals the right to access, correct, or delete their data. Organisations must also be transparent about how their AI makes decisions and not discriminate against individuals or groups. This study examines Malaysia's GDPR compliance on AI usage, data protection, and privacy in light of current concerns. This study analyses primary and secondary sources using doctrinal research. In 2022, Malaysia's banking, healthcare, and telecommunications sectors were hit by data breaches, indicating that AI is increasing data breaches. Thus, the government must examine citizen data protection and privacy concerns and re-examine its governance, including legal and regulatory mechanisms, to see if it conforms to international norms and consider reforms.
Task Allocation Method for Multiple Unmanned Marine Vehicles Cooperative Formation

The cluster operation of unmanned marine vehicle (UMV) has become an important trend in the future with the development of marine industry. Nevertheless, the existing methods have several problems such as delayed task allocation, slow convergence and tendency to fall into local optimization. Therefore, a new task allocation method is proposed to control oil pollution, which closely combines the tour planning process with the task allocation process. Firstly, Hungarian Algorithm (HA) is used to establish Multi-UMV cost matrix. Secondly, Ant Colony Optimization (ACO) is used to solve the tour planning problems. Finally, it is feasible to get the result of the shortest closed-loop itinerant path. The simulation results show that the total track length is reduced by 15.13%, and the average running time is reduced by 46.57%, which can be used to improve the rationality of task allocation.
Computational Analysis of Potential Flavonoids Targeting NSP14 of SARS-COV-2: A Molecular Docking Approach

A medical emergency resulted from the SARSCoV-2 worldwide pandemic that is still going on. The COVID19 illness has been spreading globally since December 2019 by contact with an infected person and respiratory aerosols. We targeted the exonuclease (nsp14) of SARS-CoV-2 as a therapeutic protein in an effort to find flavonoids as antagonists and hinder immune-mediated reactions. In total 50 flavonoids were submitted to in silico molecular docking and drug-likeness testing in order to find potential flavonoids that bind to the target protein effectively. The flavonoids were gathered from research publications and COCONUT. Silymarin and nobiletin, two possible flavonoids, presented robust interactions with nsp14 due to their prolonged close contact and minimal binding energies of 7.45 and 7.27 kcal/mol, respectively. Isorhoifolin, Monoxerutin, Neoeriocitrin, Narcissoside, Poncirin, and Engeletin inhibited the activity of the nsp14 replication fork with binding affinity scores of- 7.9, -7.83, -8.18, -8.37, and -8,19kcal/mol, respectively. Potential SARS-CoV-2 flavonoids predicted by computation are known to have a variety of therapeutic benefits.
A DDD Approach Towards Automatic Migration To Microservices

Nowadays, process management is considered to be the huge key success factor in any organization. It is defined mainly in order to reduce the costs of technical infrastructure, development and maintenance while making the organization more efficient and more agile towards customers. Indeed, and with the globalization of the market, companies operate in a constantly changing environment. However, despite the willingness of these companies to remain proactive, the monolithic nature of their information systems complicates their easy maintenance. With the new paradigm so called micro-services-oriented architecture, we can overcome the shortcomings of monolithic systems and consequently decompose it into small, self-contained, coherent and loosely coupled services. In this paper, we aim to propose a micro-services identification approach based on the functional aspect of the organization. This approach starts with a modeling of the sub-domains or the functions of the Bicing system (describing the global domain) using the technique of domain-driven design (DDD) to subsequently examine how the functional dependency can be modeled using a multiple domain matrix (MDM). Through a clustering algorithm, we will identify the micro-services generated from this multi-domain matrix provided.
Evaluation of User Experience in Software Product Lines Derivation Process

In the past decades, it has beenincreasingly acknowledged that user's requirements go beyond usability, leadingto more general concept “the user experience”. Design and evaluation of userexperience proved a positive result in improving users satisfaction. In thecontext of software product lines, assessment of user experience is notavailable. This paper fills this gap by involving the evaluation of userexperience in the product derivation process. Building from pre-existing userexperience questionnaires, a generalized tool is proposed with a list of 14factors and 66 items. Software product line designer can use this tool toconceive an adequate questionnaire for his user depending on the product andthe context of use. By analyzing experiences in interacting with products,designers can better understand future customers and then reduce productiontime and effort.
Parameter Estimation of Fractional-Order Systems via Evolutionary Algorithms and the Extended Fractional Kalman Filter

As fractional-order models increasingly appear as an option to describe complex systems, they generate a demand for parameter estimation methods in the time and frequency domain. The extended Kalman filter (EKF) is a promising technique in the time domain, but it is sensitive to the initial conditions of the state and error covariance matrices. In the case of integer-order systems, evolutionary algorithms (EAs) can tackle EKF’s sensitiveness issues. The algorithm usually uses EAs to optimise the initial conditions for the EK, leading to a better estimate of the system parameters and states. Here, we extend this methodology to fractional-order models to estimate the model’s fractional order and parameters. Finally, we demonstrate the effectiveness of this methodology on a simple mechanical model.
A Resource Allocation Strategy in Earth Observation Orbital Edge Computing-enabled Satellite Networks to minimize Ground Station Energy Consumption

By endowing satellites in Earth Observation (EO) constellations with Orbital Edge Computing (OEC) capability, i.e, with the ability of processing acquired images directly on-board, it is surely possible to use bandwidth more effectively, since only the actual useful information extracted from the image is transferred on ground. However, OEC can be fruitfully leveraged also to minimize the energy consumption on ground stations due to image processing. In fact, in EO satellites energy is allocated in advance by endowing them with appropriate solar panels and batteries, and this amount of energy is always generated, even when it is not necessary. On the contrary, energy on ground station is closely related to its demand. For this reason, we propose and investigate a strategy to allocate processing and bandwidth resources in OEC-endowed EO satellite network to minimize energy consumption on ground stations due to on-ground processing. By taking into account the energy budget on Sentine1-2, having a 26% of available energy being unused, we show how with our approach it is possible to obtain a substantial reduction in energy consumption on ground stations even by using the 1% of the total energy budget when appropriate computational capacity is available on board, while this consumption can even drop to zero by increasing the energy dedicated to OEC operations to the 15% of the energy budget.
Waveform Selection for FMCW and PMCW 4D-Imaging Automotive Radar Sensors

The emerging 4D-imaging automotive MIMO radar sensors necessitate the selection of appropriate transmit wave-forms, which should be separable on the receive side in addition to having low auto-correlation sidelobes. TDM, FDM, DDM, and inter-chirp CDM approaches have traditionally been proposed for FMCW radar sensors to ensure the orthogonality of the transmit signals. However, as the number of transmit antennas increases, each of the aforementioned approaches suffers from some drawbacks, which are described in this paper. PMCW radars, on the other hand, can be considered to be more costly to implement, have been proposed to provide better performance and allow for the use of waveform optimization techniques. In this context, we use a block gradient descent approach to design a waveform set for MIMO-PMCW that is optimized based on weighted integrated sidelobe level in this paper, and we show that the proposed waveform outperforms conventional MIMO-FMCW approaches by performing comparative simulations.
Practical initialization of homoclinic orbits from a Bogdanov-Takens point

In a recent paper [IJBC, 24(04):1450057, 2014], we improved the theoretical base for the initialization of homoclinic orbits. However, practical application of this method is not very robust without the consideration of some numerical issues. We deal with these issues and provide examples from a robust implementation of the initialization procedure in the software package MatCont [ACM Trans. Math. Software, 29(2):141–164, 2003].
Hilbert–Huang Transform Incorporating Analytical Mode Decomposition and Its Application in Fractured-Vuggy Carbonate Reservoir Prediction

High-precision time–frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this letter, a TFA method based on analytical mode decomposition (AMD) and the Hilbert–Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution (TFD) of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data.
Strengthen Aircraft Real-Time Multiple Object Tracking with Optical Flow and Histogram of Oriented Gradient Provided HSMA Implemented in Low-Cost Energy VPU for UAV

Researchers and algorithm developers have always strived to make tracking more accurate than before, by developing deferents Algorithms that prove their efficiency and effectiveness in international competitions (VOT, MOT challenge, …), but taking care of one side without others may be the reason why limiting the fields of application of that software, which makes them confined only to these competitions or confined to applications domains without others and this is not the end object in itself. For example, the use of complex tracking algorithms in the drone makes the processing unit very slow and unsuitable for real-time tracking, instead that the use of highly efficient processing systems for acceleration consumes a lot of energy, which reduces the time the drone stays in flight. In this paper, we suggest our approach; HashSet Matching Algorithm (HSMA) provided by Optical Flow appearance features and HOG for developing an object-tracking algorithm to address the issue of a complex MOT algorithm for real-time tracking. The MOT challenge data-set experiment results show that the suggested technique can handle object occlusion difficulties successfully while reducing processing time. This algorithm is implemented in the Vision Processing Unit (VPU) to enhance the time flying of UAVs and to ensure low energy consumption. Our approach performs very well in real application scenarios, demonstrating that it is more practical.
On efficacy of Meta-Learning for Domain Generalization in Speech Emotion Recognition

Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech, vital for building human-centered context-aware intelligent systems. Here, domain shift, where models' trained on one domain exhibit performance degradation when exposed to an unseen domain with different statistics, is a major limiting factor in SER applicability, as models have a strong dependence on speakers and languages characteristics used during training. Meta-Learning for Domain Generalization (MLDG) has shown great success in improving models' generalization capacity and alleviate the domain shift problem in the vision domain; yet, its' efficacy on SER remains largely unexplored. In this work, we propose a “domain-shift aware” MLDG approach to learn generalizable models across multiple domains in SER. Based on our extensive evaluation, we identify a number of pitfalls that contribute to poor models' DG ability, and demonstrate that log-mel spectrograms representations lack distinct features required for MLDG in SER. We further explore the use of appropriate features to achieve DG in SER as to provide insides to future research directions for DG in SER.
An Analysis of Pneumonia Prediction Approach Using Deep Learning

A bacterial infection in the lungs' alveoli frequently results in pneumonia, an infectious disease. Pus occurs in infected lung tissue as it gets irritated. To ascertain whether a patient has pneumonia, experts perform physical examinations and diagnose their patients using a chest X-ray, ultrasound, or lung biopsy. A patient's mortality may result from a misdiagnosis, ineffective treatment, or disregard for the disease. The development of deep learning aids specialists in their decision-making when it comes to diagnosing pneumonia patients. The study uses six CNN models to predict and identify a patient with and without the condition using an X-ray image of their chest. It combines versatile and affordable deep learning approaches.
Trust Me: Explainable ML in Self-Organized Network Management

Machine Learning (ML) powered Self-Organizing Network (SON) functions are an integral part of the 5G(+) network management to automatically learn and optimize the network performance. Third Generation Partnership Project (3GPP) Release 17 confirmed it by providing a foundation for studying ML-based solutions to tackle network management problems. However, despite their ability to provide high-quality solutions, most ML algorithms lack interpretability, leading to a lack of trust from Mobile Network Operators (MNOs) and delaying the fast integration of ML solutions into operational networks. To address this issue, Explainable Machine Learning (xML) techniques can be used to make complex ML models more interpretable, manageable, and trustworthy. In this work, we apply xML methods to explain an implicit coordination scenario between two conflicting SON functions (SFs): Coverage and Capacity optimization (CCO) and Inter Cell Interference Coordination (ICIC). We show how xML methods can be used to see the coordination problem from an ML model point of view, get meaningful insights, and confirm that the ML model captured all the relevant relationships correctly. This in turn helps to build trust in the ML model which allows it to be used for automatic network management.
Predictive maintenance project implementation based on data-driven & data mining

Today's global economy is experiencing a sharper slowdown than ever before. With a higher risk of deterioration or disappearance of manufacturing companies than usual. As a result, manufacturers must face this situation by adopting strong strategies, including equipment management and cost optimization in connection. This is why predictive maintenance is an important pillar in achieving these objectives. Except that predictive maintenance requires a budget and time for the proper implementation, such investment can only be accepted by investors if it is sure that the expected results will contribute effectively to the reduction of maintenance costs. To do this, it is necessary to assess the risks that could impact this project's success, mainly the data reliability and the machine learning model performance. The aptitude to predict the need for maintenance of a system in a perfect way at a specific time is one of the main challenges in this scope. This paper proposes a methodology for data management in the case of predictive maintenance project implementation. It starts by introducing the project study phase for cost & benefit evaluation based on data-driven. Then it presents the predictive concept based on data mining & machine learning tools for optimal model building, as well for the project performance follow up a monitoring approach is proposed based on the continuous improvement concept.
5G-Advanced Toward 6G: Past, Present, and Future

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as 5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.
Well-posedness and stabilization of an Euler-Bernoulli equation with a localized nonlinear dissipation involving the $p$-Laplacian

We consider an Euler-Bernoulli equation in a bounded domain with a local dissipation of viscoelastic type involving the $p$-Laplacian. The dissipation is effective in a suitable nonvoid subset of the domain under consideration. This equation corresponds to the plate equation with a localized structural damping when both the parameter $p$ and the space dimension equal two. First we prove existence, uniqueness, and smoothness results. Then, using an appropriate perturbed energy coupled with multiplier technique, we provide a constructive proof for the exponential and polynomial decay estimates of the underlying energy. It seems to us that this is the first time that a dissipation involving the $p$-Laplacian is used in the framework of stabilization of second order evolution equations with locally distributed damping.
Electrothermal modeling of Multilayer Graphene Nanoribbon (MLGNR) Interconnect considering Energy-per-Layer Screening

An explicitly precise energy-per-layer screened electrothermal interconnect model has been developed in this paper that contemplates the impact of Fermi screening (FSc) with temperature for different intercalation doped top contact multilayer graphene nanoribbon (TC-MLGNR). Moreover, a comparative investigation is carried out to examine the impact of temperature upon the resistance of the effective number of layers $(R_{\mathrm{L}_{-}\text{efct}})$ participating in current conduction. It has been observed that the thermal response considering FSc is different for each layer. Moreover, with FSc, the rate of change of resistance with temperature is different for different intercalated TC-MLGNR. The finite difference between effective resistance considering with $(R_{\text{efct}_{-}\text{Efs}})$ and without $(R_{\text{efct}_{-}\mathrm{E}\mathrm{f}})$ FSc shows linear distribution over the chip operating temperature range, which supposedly put forth a significant footprint on the existing self-heating models. Hence, it is pertinent that by incorporating FSc, our proposed model, flow through the electrothermal design of GNR interconnects in upcoming VLSI circuits.