10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Helvira Maharani Tresnadi, Rannie Oges Pebina, Permata Chandra Lagitha, Nurul Sukma Lestari Abstract - This research aims to analyze the relationships between career calling, adaptability, and awareness of STARA technology to provide insights into career development during this critical transition phase. The methodology employed in this research is quantitative, with data collected through online questionnaire surveys. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) and Smart PLS software. The participants are students in Jakarta, with 413 respondents completing the survey. The findings indicate that both career exploration and self-efficacy have a positive influence on career adaptability. Furthermore, career exploration and self-efficacy significantly and positively affect career calling, while career calling positively affects career adaptability. The results also indicate that STARA Awareness reduces the influence of career calling on career adaptability, although the findings remain significant. The mediating variable demonstrates a positive and significant effect on the relationship between career exploration, self-efficacy, and career adaptability. The novelty of this research is that it examines career calling in school children, which is still rarely studied compared to employees, to help students recognize their potential and interests early on. For future research, it is recommended to investigate variables within a broader scope at the national and international levels.
Authors - ThiTuyetNga Phu, HongGiang Nguyen Abstract - Inspecting the compressive strength of buildings' concrete is essential for ensuring the safety of households. This paper examined the study samplers collected using the nondestructive testing (NDT) method combined with Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests to check the beams of some apartments over 30 years old. Firstly, research samples were deployed to analyze the level of data variation using the exploratory data analysis (EDA) method to assess the reliability and correlation of data samples. Next, the study focused on the prediction of concrete compressive strength deploying five functions of activation (AF) (tanhLU, tanh, leakyLU, reLU, and sigmoid) by using two deep learning models as long short-term memory (LSTM) and gated recurrent unit (GRU). Lastly, the experimental results showed that the GRU model combined with two kinds of hybrid AFs gave a fairly accurate prediction level; in contrast, the remaining AF showed acceptable results.
Authors - Prathyush Kiran Holla, Manish M, Purvi Hande, Akshay Anand, Nirmala Devi M Abstract - Integrated Circuits (IC) allows attackers to insert malicious implants called Hardware Trojans (HT). These Trojans leak information or alter circuit functionality. This threat is particularly critical in IoT devices, where compromised hardware can lead to drastic consequences across networks potentially exposing entire systems to data loss. Over the past decade, numerous Hardware Trojan Detection (HTD) methods have been developed which is crucial for securing IoT ecosystems, where detecting hardware-level threats early can prevent cascade failures. Current HTD techniques still face challenges with detection accuracy, class imbalance handling and high false positive/negative rates. We propose a HTD method using XGBoost, enhanced with focal loss to better handle class imbalance. XGBoost is combined with both graph-based and structural features to achieve higher accuracy compared to using each feature type individually. This approach is particularly valuable for IoT applications, where interconnected systems require robust detection methods. The proposed model, evaluated on an extensive dataset comprising of 41 combinational and sequential benchmark circuits, achieves an impressive accuracy of 98.85%, demonstrating superior performance in HT detection across diverse circuit architectures. Such high accuracy is essential for IoT deployments where false positives can trigger unnecessary disruptions across connected systems, and false negatives can leave critical infrastructure vulnerable to attacks.
Authors - Andriy Tevjashev, Oleksii Haluza, Dmytro Kostaryev, Anton Paramonov, Natalia Sizova Abstract - The study focuses on estimating the accuracy of aircraft positioning using an infocommunication network of optical-electronic stations (OES). The problem addressed is the numerical estimation of the shape and boundaries of the region where the aircraft is located, with a given probability, at any fixed time during video surveillance in optical and infrared frequency ranges. The method departs from the traditional assumption of normal distribution for random errors in aircraft location estimates and employs Chebyshev's inequality to construct upper bounds for the uncertainty region. It is shown that the dispersion ellipsoid, often used to estimate the metrological characteristics of OES, is a rough approximation of the actual region where the aircraft is located with a given probability. The following results were obtained: – a method for constructing the actual uncertainty region of an aircraft’s location, based on the statistical properties of random errors in video surveillance from each OES and their relative spatial arrangement to the aircraft at each surveillance moment; – a software implementation of the numerical method for constructing and visualizing upper estimates of the shape and boundaries of the uncertainty region in aircraft positioning, using the OES network for trajectory measurements.
Authors - Qian Jiang, Kin Wai Michael Siu, Jiannong Cao Abstract - Immersive technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), are widely used in exhibitions to engage audiences. This study examines immersive technologies in the context of museum learning with a focus on exhibitions. This study screened and analyzed 104 research papers in this scope closely related to the topic of immersive technologies and museums, which were selected based on search results for four keywords-human behavior, immersive technologies, exhibitions, and embedded experiences-to clarify the impact of immersive technologies on visitor behavior from existing exhibition themes. We conceptualized immersive technologies and categorized the literature according to theme and technology to clarify the relationship between immersive technology applications and exhibition topics. Existing research identifies a positive correlation between immersive technology and positive visitor experiences; however, there is less research on immersive technology and museum learning for special populations, and assessment tools for evaluating the effectiveness of technological application in this context have yet to be tested. The method of co-occurrence is used to analyze what factors need to be considered for the application of immersive technologies in the context of museum learning. Ultimately, a framework for immersive technological application is summarized.
Authors - Unaizah Mahomed, Machdel Matthee Abstract - The use of Professional Social Media Platforms (PSMPs) has become more popular in recent years. As COVID-19 spread globally the world was forced to fast-track digitalisation, remote and hybrid working models as well as the need for online hiring. This systematic literature review aims to give insight into understanding the role of artificial intelligence (AI) algorithms in professional social media platforms as well as gauge a deeper understanding for the need of these AI algorithms. This systematic literature review incorporates findings from previously published peer-reviewed literature to understand how AI-driven systems are used to improve hiring through professional social media platforms. The contents of this review address benefits related to hiring that include but is not limited to, the applications of AI algorithms in PSMPs, candidate screen and sourcing, job matching, and efficiency, as well as some concerns such as algorithmic bias, user privacy, regulations and ethical considerations. Significant effects on stakeholders have also been addressed within this review as well as the gaps within the research.
