HighTech and Innovation Journal
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Digital Skills of Human Resources: Exploratory Research of Innovations in Enterprises
Full text linkBackground: This study is conducted in the context of the remarkable development of digital technology that has a profound impact on many facets of life, including the comprehensive transformation of abilities and the working style of businesses and the workforce. To survive and develop in an emerging digital society and ever-changing digital environment, workers and business leaders need to equip themselves with the necessary digital skills to adapt to job requirements. Objective: This study explores the current status of the digital skills of human resources and its impact on the level of digital transformation readiness of Vietnamese enterprises. At the same time, the inverse relationship between digital skills and the digital divide was explored for the first time in Vietnam. Methodology: A secondary research method was used to summarize and analyze the results of surveys conducted by Vietnamese government agencies and previous studies to investigate the current status of digital skills of human resources and factors affecting the digital readiness of Vietnamese enterprises in the context of ongoing DX. Results: The results show that the digital skills of enterprise HR are weak, the level of DX readiness is low, and there is a reciprocal relationship between digital skills and the digital divide. The principal findings contribute to policy implications aimed at enhancing digital skills for the workforce and enterprises' HR, and bridging the digital divide within the population in the long term. Doi: 10.28991/HIJ-2024-05-03-013 Full Text: PD
SARIMAX–GARCH Model to Forecast Composite Index with Inflation Rate and Exchange Rate Factors
Full text linkInvestors should consider the Indonesia Composite Index (ICI) as a key indicator before making investment decisions, as it reflects the performance of industries and the broader economic growth. In Indonesia, the ICI exhibits fluctuating movements, making accurate forecasting essential for understanding the country's economic conditions, which are closely tied to capital flows, growth, and tax revenues. This study aims to forecast the ICI using the SARIMAX-GARCH model, incorporating macroeconomic factors such as the inflation rate and exchange rate. The findings reveal that both variables significantly impact the ICI, with the model achieving a Mean Absolute Percentage Error (MAPE) of 0.952% for training data and 5.233% for test data. The model's performance is supported by an R² value of 0.9782 and a Mean Squared Error (MSE) of 0.0003. This research not only improves the accuracy of ICI forecasts but also supports Indonesia's 8th Sustainable Development Goal (SDG) for decent work and economic growth. Doi: 10.28991/HIJ-2024-05-03-014 Full Text: PD
A Smart IoT Urban Flood Monitoring System Using a High-Performance Pressure Sensor with LoRaWAN
Full text linkThe Philippines faces frequent flooding and significant loss of life and property. Current flood monitoring systems (FMS) are outdated, causing delays in information distribution and mitigation efforts. Therefore, this study presents the development of a pressure sensor-based system with LoRaWAN capability for continuous, remote flood detection. The FMS PVC housing design was iterated upon by changing lengths, diameters, and mounting systems. Moreover, each of the parts was modeled, simulated, and tested in ANSYS and evaluated with simulated real-world physical and environmental conditions. The FMS is equipped with LoRaWAN transmission and solar charging, which transmits data to The Things Network, where it is then visualized in Packetview. The resulting FMS design and mounting were robust and were able to withstand flooding conditions. The battery and solar panel are also sufficient in continuously powering the FMS. Moreover, the FMS was also able to withstand various tests with minimal sensor errors. The FMS holds the potential to enhance flood monitoring in the Philippines, offering localized, cost-effective, and near-real-time solutions for better disaster preparedness and response strategies. The FMS utilized the accurate theory equation that resulted in a flood height error as low as 1.12% in testing and 1.81% in rain. Furthermore, it is resistant to external disturbances as the system takes 0.5 seconds to stabilize, while continuous disturbances resulted in errors ranging from 0% to 3%. Doi: 10.28991/HIJ-2024-05-04-04 Full Text: PD
The Degree of Consistency Through Adopting SMART Objectives for Succession of Feasibility Studies to Infrastructure Projects
Full text linkThe process of formulating the basic strategies in a construction project must go through a set of stages. The desired objectives in feasibility studies should be highlighted to ensure that the packages of the infrastructure projects are moving smoothly in terms of the desired goal. In order to meet ambition for the long term in infrastructure projects, it is required to adopt the five pillars of SMART for rectifying the paths of feasibility studies. The study is a summarizing of fifteen elements that could have negative impacts on outputs of feasibility studies. The designed questionnaires have already been distributed via two stages to the experts/consultants in project management and others. 63 questionnaires were collected to find the negative impact of the elements, and the second stage included 89 participants for measuring their responses about the extent to which previous studies that were prepared over the past five years matched the concept of SMART-objectives. Based on the theoretical principle through analyzing the responses/opinions of participating experts, it was found that the feasibility studies for infrastructure projects obtained the following percentages: 46.07%, 41.57%, 28.09%, 22.47%, and 22.47%. This reflects that the objectives of the infrastructure project were specific, measured, achievable, relevant, and Time-Bound) in sequence consistent with the mentioned percentages. For the improvement outcomes of feasibility studies for infrastructure projects, that required a clear and invaluable link among all feasibility studies and the concept of applying the SMART-five pillars. Doi: 10.28991/HIJ-2024-05-03-05 Full Text: PD
Motion Picture Analysis: A Mechanical Study of Tennis Players during Forehand and Backhand Strokes
Full text linkObjectives: The purpose of this article is to utilize video images for the examination of lower limb biomechanics in tennis players while executing forehand and backhand strokes, providing a reference for training. Methods: This article provides a brief introduction to forehand and backhand strokes in the sport of tennis. Subsequently, a biomechanical analysis of the lower limbs during forehand and backhand strokes was conducted on ten level 2 tennis players and ten specialized tennis students at XX Sports University. Findings: Level 2 athletes who have undergone a long training exhibited higher linear velocity and joint torque in the lower-limb joints during the preparatory and striking phases of forehand and backhand strokes. Additionally, they exhibited more pronounced surface electromyographic signals in the rectus femoris muscle of the lower limbs. Novelty:The novelty of this article lies in the use of video imagery, a non-contact and non-intrusive method that does not affect the athletes' movements, to study the biomechanics of their lower limbs. Doi: 10.28991/HIJ-2024-05-01-07 Full Text: PD
An Adaptive Differential Evolution with Multiple Crossover Strategies for Optimization Problems
Full text linkThe efficiency of a Differential Evolution (DE) algorithm largely depends on the control parameters of the mutation strategy. However, fixed-value control parameters are not effective for all types of optimization problems. Furthermore, DE search capability is often restricted, leading to limited exploration and poor exploitation when relying on a single strategy. These limitations cause DE algorithms to potentially miss promising regions, converge slowly, and stagnate in local optima. To address these drawbacks, we proposed a new Adaptive Differential Evolution Algorithm with Multiple Crossover Strategy Scheme (ADEMCS). We introduced an adaptive mutation strategy that enabled DE to adapt to specific optimization problems. Additionally, we augmented DE with a powerful local search ability: a hunting coordination operator from the reptile search algorithm for faster convergence. To validate ADEMCS effectiveness, we ran extensive experiments using 32 benchmark functions from CEC2015 and CEC2016. Our new algorithm outperformed nine state-of-the-art DE variants in terms of solution quality. The integration of the adaptive mutation strategy and the hunting coordination operator significantly enhanced DE's global and local search capabilities. Overall, ADEMCS represented a promising approach for optimization, offering adaptability and improved performance over existing variants. Doi: 10.28991/HIJ-2024-05-02-02 Full Text: PD
A Study of Android Security Vulnerabilities and Their Future Prospects
Full text linkNowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities, this study provides valuable insights for improving the overall security of Android system applications. Doi: 10.28991/HIJ-2024-05-03-020 Full Text: PD
Optimizing Container Fill Rates for the Textile and Garment Industry Using a 3D Bin Packing Approach
Full text linkThe scarcity of empty containers presents a significant logistical challenge globally. To address this issue, this study proposes the application of the optimal box arrangement in a container with a 3D bin packing problem to enhance fill rates and accommodate the complex packing criteria of the textile and garment industry. The study's objective is to optimize box stacking into containers by considering various factors such as multiple product types, diverse box sizes, varying container sizes, and prioritizing stacking according to purchase orders (PO). In tackling the NP-hard problem with the added constraint of PO-based stacking, this study advocates employing a genetic algorithm combined with a wall-building algorithm to address practical challenges. The genetic algorithm demonstrates optimal efficacy in solving large-scale optimization problems within specified timeframes, yielding high-quality results. In addition, normalization methods are applied to convert box sizes to pallet sizes, expediting problem-solving and facilitating the selection of appropriate container sizes, namely 20- or 40-feet. The research findings indicate that the proposed method achieved a container fill rate of up to 91.67% and minimized the number of containers used. Doi: 10.28991/HIJ-2024-05-02-017 Full Text: PD
Cryptocurrency Forecasting Using Deep Learning Models: A Comparative Analysis
Full text linkBitcoin has recently grown to prominence as a decentralized digital currency, attracting significant interest for its potential transformation of the financial market. Forecasting Bitcoin's price is crucial for investors, traders, and academics, given the currency's inherent volatility, which makes accurately predicting future prices challenging. This article aims to provide a comprehensive and comparative analysis of Deep Learning Forecasting Models in order to predict Bitcoin prices in the short and medium terms: Transformer with XGBoost, Transformer with ANN, Transformer with LSTM, and Transformer with SVR. This study is the first to explore the effectiveness of transformer-based architectures, particularly focusing on feature extraction, in complex financial market predictions. Therefore, we trained these models using historical Bitcoin data from 2016 to 2023 and evaluated their performance on a test dataset. Our experiments demonstrate that the Transformer with the XGBoost model outperforms the baseline models, achieving a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.018. Our findings suggest that the use of advanced deep learning techniques effectively manages the complexities of the cryptocurrency market, offering significant improvements over traditional methods and guiding investors in the cryptocurrency markets. Doi: 10.28991/HIJ-2024-05-04-013 Full Text: PD
Transformer-Based Sequence Modeling Short Answer Assessment Framework
Full text linkAutomated subjective assessment presents a significant challenge due to the complex nature of human language and reasoning characterized by semantic variability, subjectivity, language ambiguity, and judgment levels. Unlike objective exams, subjective assessments involve diverse answers, posing difficulties in automated scoring. The paper proposes a novel approach that integrates advanced natural language processing (NLP) techniques with principled grading methods to address this challenge. Combining Transformer-based Sequence Language Modeling with sophisticated grading mechanisms aims to develop more accurate and efficient automatic grading systems for subjective assessments in education. The proposed approach consists of three main phases: Content Summarization: Relevant sentences are extracted using self-attention mechanisms, enabling the system to effectively summarize the content of the responses. Key Term Identification and Comparison: Key terms are identified within the responses and treated as overt tags. These tags are then compared to reference keys using cross-attention mechanisms, allowing for a nuanced evaluation of the response content. Grading Process: Responses are graded using a weighted multi-criteria decision method, which assesses various quality aspects and assigns partial scores accordingly. Experimental results on the SQUAD dataset demonstrate the approach's effectiveness, achieving an impressive F-score of 86%. Furthermore, significant improvements in metrics like ROUGE, BLEU, and METEOR scores were observed, validating the efficacy of the proposed approach in automating subjective assessment tasks. Doi: 10.28991/HIJ-2024-05-03-06 Full Text: PD