Agile software development projects focus on running software [BBvB + 01]. Requirements engineering will be performed as far as it is required by the development team. The agile procedure seeks to reduce bureaucracy. However, a risk exists that a focus on the completion of development tasks leads to technical debt. Enhanced requirements engineering could face this risk. In order to reduce the risk of technical debt in agile projects the use of a code generator could be solution a more pragmatically. In this paper, a concept for a code generator will be elaborated.

Email spam, characterized by unsolicited and often harmful messages, remains a pervasive issue across digital communication platforms. This study explores advanced Machine Learning (ML) and Deep Learning (DL) methodologies to enhance email spam detection, aiming to protect users from phishing, malware, and other malicious threats. A comprehensive literature review is conducted, detailing the processes involved in developing spam detection models, including preprocessing, feature extraction, and model implementation. This study focuses on three ML algorithms-Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM)-and three DL models-Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformers (BERT). A comparative analysis of these models is presented, highlighting their accuracy and performance. Notably, DL models, particularly BERT, achieved the highest accuracy (up to 99.14%), while ML models like SVM also demonstrated strong performance (up to 99.00%). The findings indicate that DL models outperform traditional ML algorithms in accuracy, although ML models offer advantages in interpretability and computational efficiency. This study concludes with insights into the strengths and weaknesses of both approaches and suggests future research directions to enhance the robustness and effectiveness of spam detection systems.

This article investigates the possibility of the application of blockchain technology in improving cyber security in view of its properties that include; decentralized, immutable and transparent. Decentralization and algorithmic security allow the avoidance of the formation of critical points and hacker attacks. This paper examines the background, structure, and ideas of blockchain and analyzes the consequences for cybersecurity. It underlines the necessity to conduct more studies to overcome the existing shortcomings and effectively apply blockchain for the protection of digital functions.

Zero Trust Architecture (ZTA), a paradigm shift in cybersecurity, has significantly transformed traditional network security with the pivotal strategy based on the principal of "never trust, always verify." This study explores the core components of ZTA through a systematic literature review, focusing on the Principles of Zero Trust, and other components such as Access Management (IAM), data encryption, endpoint security, and multi-factor authentication, based on synthesized findings from recent studies. This paper proposed a comprehensive flowchart implementation, demonstrating how crucial ZTA is in contemporary and future network infrastructures, including IoT and 6G networks. Concluding the study with a detailed proposal framework for effective ZTA implementation, this paper is tailored to diverse organizational needs that highlights ZTA's potential to significantly strengthen organizational security against evolving cyber threats.

As technology advances, the world becomes more interconnected, and cellular networks play a crucial role. However, these networks are increasingly vulnerable to cyber-attacks, notably denial of service (DoS) attacks. Such attacks can be disastrous, causing significant communication disruptions and even financial losses. This paper focuses on illustrating the most common vulnerabilities in the user authentication system that the attackers exploit, thereby exposing the networks to such attacks. Additionally, the paper will conduct a detailed comparison of network security between the fifth generation and previous generations of cellular networks. This will enable us to understand the advancements in network security and the challenges that still need to be addressed to safeguard these networks from potential cyber threats.

The latest wireless technology, 5G, promises increased network capacity, reduced costs, and improved energy efficiency. To achieve these benefits, businesses and service providers use advanced technologies such as Virtualized Network Functions (VNFs) and Software Defined Networking (SDN) to minimize expenses. This study focuses on the challenge of optimizing virtual network functions and routing data through them to create a seamless service chain. To test this scenario, a network was designed with specific requirements and an optimization problem was formulated to minimize costs while meeting service and cloud provider requirements. Quality of service, network, and topology constraints were considered, including a congestion limit on links. As this is a complex problem, a small-scale network was simulated using MOSEK in MATLAB to obtain optimal results, and a heuristic algorithm was developed and compared to the optimal results.

This work presents a robust approach to derive a quasi-optimal ground segment design in Q/V band for providing connectivity to non-geostationary satellites, considering a "N+P" diversity scheme to cope with rain fading. An optimization problem is formulated with the objective of guaranteeing to each satellite a minimum uplink sum-rate while minimizing the total number of GWs via a ℓ0-norm representation. A two-stage algorithm is proposed to tackle the non-convexity of the above and achieve GW diversity. Numerical results demonstrated that the proposal outperforms algorithms in the literature by reducing the required total number of GWs by 47%-67%.

On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest owing to their superior privacy, reduced latency, and bandwidth saving. Nonetheless, the capabilities of on-device LLMs are intrinsically constrained by the limited capacity of edge devices compared to the much more powerful cloud centers. To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing. MEI sits between on-device AI and cloud-based AI, featuring wireless communications and more powerful computing resources than end devices. This article provides a contemporary survey on harnessing MEI for LLMs. We first cover the preliminaries of LLMs, starting with LLMs and MEI, followed by resource-efficient LLM techniques. We then illustrate several killer applications to demonstrate the need for deploying LLMs at the network edge and present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We aim to inspire researchers in the field to leverage mobile edge computing to facilitate LLM deployment in close proximity to users, thereby unleashing the potential of LLMs across various privacy-and delay-sensitive applications.

In this letter, we propose an extremely high scanning-rate slot array antenna utilizing uniplanar-coupled waveguide cavities, allowing for easy fabrication and a lowprofile planar implementation. In contrast to current techniques employing alternating electric and magnetic coupling topologies, our novel approach relies on a pure magnetic coupling topology, serving as the key for uniplanar implementation. We design and fabricate both metallic waveguide and substrate-integrated waveguide implementations to showcase the effectiveness of the proposed technique. Experimental results demonstrate that the two uniplanar-coupled waveguide antennas can scan angle ranges of 65 • and 70 • , respectively, within a 40 MHz frequency band centered at 4 GHz (equivalent to a 1% bandwidth). Compared to state-of-the-art methods, these proposed frequency-scanning antennas exhibit exceptionally high scanning rates, ease of fabrication, and a low-profile implementation.

This study investigates the impact of exam stress on the sleep patterns of adolescents, who naturally experience disrupted sleep due to physiological changes. The research aims to identify how exam-related stress influences sleep quality by analyzing brain waves. To achieve this, the brain waves of two adolescents were recorded using a research-grade EEG device called Muse 2. The study mainly focused on the relative power of delta waves (in Bels) and slow-wave sleep (SWS), both of which are necessary for waking up refreshed and restored. The findings revealed that the power of delta waves were affected by exam stress, but other brain waves such as alpha, theta, and beta remained unchanged. This suggests that exam stress primarily impacts deep or SWS in adolescents, leading to a decline in overall sleep quality. To conclude, exam stress exacerbates sleep fragmentation in adolescents by disrupting SWS, which is essential for physical and mental restoration.

In the digital age, social networking sites (SNS) have become an important part of daily life, providing a platform for communication, entertainment, and information sharing. However, these platforms also pose significant risks to information security. This article explores the phenomenon of information leakage through SNS, a growing concern that threatens personal privacy and organizational security. Through a comprehensive review of related issues, the article delves into the various forms of information leakage, the potential impacts, and the underlying factors contributing to this issue. The article also discusses the solutions to mitigate the risks associated with information leakage on SNS.

Spam, defined as unwanted, malicious and improper content that led to various challenge in digital realm. This paper provides a comprehensive overview and analysis of the different type of spam such as social media spam, SMS spam, call spam and forum/blog spam. Each of these spams has its own characteristics and challenges. This paper analyzes the methods used by spammers such as obfuscation, social engineering, polymorphism and machine learning integration to make the spam message pass through the detection system. The analysis of the consequences of spam resulting financial losses, security breaches and social consequences. In address these challenges, the study proposes a comprehensive method in handling spam such as advanced filtering system, robust email authentication and enhanced the user awareness education about spam.

On-device DNN training faces constraints in storage capacity and energy supply. Existing works primarily focus on optimizing the training of convolutional and batch normalization (BN) layers to improve the compute-to-communication (CTC) ratio and reduce the energy cost of off-chip memory access. However, the training of activation layers remains challenging due to the additional off-chip memory access required for derivative calculations. This paper proposes MASL-AFU, an architecture designed to accelerate the activation layer in on-device DNN training. MASL-AFU leverages non-uniform piecewise linear (NUPWL) functions to speed up the forward propagation (FP) in the activation layer. During backward propagation (BP), derivatives are retrieved from lookup tables, eliminating the need for input data. By storing lookup table indices instead of the original activation inputs, MASL-AFU significantly reduces and accelerates memory access. Compared to other activation function units, MASL-AFU offers up to a 5.8× increase in computational and off-chip memory access efficiency. Additionally, MASL-AFU incorporates two dimensions of scalability: data precision and the number of LUT entries. These scalable, hardware-friendly methods enhance MASL-AFU's area efficiency by up to 3.24× and energy efficiency by up to 3.85×.

Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate system performance of wireless communications. This paper introduces a new method to model a GBSM by training a generative neural network using images formed by channel parameters. Toward this end, we process the data of channel parameters in the form of images and train the generative neural networks where the convolutional layers are mainly employed. Through a case study, we confirm that the use of channel images facilitates the training of the generative model and ensures that the model learns the correlations among multipath components. We show that the outputs of the generative model faithfully represent the distributions of the original data. Furthermore, to corroborate applicability of the trained model, we run simple system-level simulations and show the results from the trained model and ray-tracing data are well matched. Therefore, we argue that the proposed model trained by channel images will ease the burden of GBSM implementations with general wireless conditions and capture the statistical joint distributions of the original channel data.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime's understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an openaccessed SAR data set and reco\(\chi\delta\)gnition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

In the rapidly evolving field of multimedia services, video streaming has become increasingly prevalent, demanding innovative solutions to enhance user experience and system efficiency. This paper introduces a novel approach that integrates user digital twins-a dynamic digital representation of a user's preferences and behaviors-with traditional video streaming systems. We explore the potential of this integration to dynamically adjust video preferences and optimize transcoding processes according to real-time data. The methodology leverages advanced machine learning algorithms to continuously update the user's digital twin, which in turn informs the transcoding service to adapt video parameters for optimal quality and minimal buffering. Experimental results show that our approach not only improves the personalization of content delivery but also significantly enhances the overall efficiency of video streaming services by reducing bandwidth usage and improving video playback quality. The implications of such advancements suggest a shift towards more adaptive, user-centric multimedia services, potentially transforming how video content is consumed and delivered.

The proliferation of internet usage has led to an increase in deepfake attacks, posing significant threats to privacy and data security. Existing detection systems are continually challenged by increasingly sophisticated deepfake techniques. In this paper, we propose a novel method for detecting deepfake anomalies by focusing on the lip region of human faces in videos. This area is often subtle and difficult for humans to scrutinize. Our approach integrates the Minimum Covariance Determinant (MCD) Estimator with the SHA-256 hashing algorithm and RAID technology to identify even the slightest deepfake activities. By employing the Lip Shaping Technique, we evaluate the effectiveness of our method. Experimental results demonstrate the proposed method's promising performance and its significant impact on frame processing speed due to the incorporation of optimized storage techniques.

The rapid growth of digital communication and video transmission has brought about an urgent need for robust encryption methods to ensure the security and integrity of video data. Traditional encryption techniques, while effective, face increasing challenges from advancing computing capabilities. Quantum cryptography has emerged as a promising solution to secure video transmission. In this article, we propose a novel cutting-edge Hybrid Quantum Video Encryption Framework. Our key idea is to integrate the strengths of quantum encryption and the classical video transmission paradigm to enhance the security of video transmission. By combining the proposed quantum encryption framework which uses a pseudorandom number generated key to perform a row-wise XOR operation along with the secure SSL-encrypted HTTP transmission over the internet, this framework provides a robust defense against threats. The experimental simulation conducted under the new computational integrated paradigm shows the strong encryption capability of the proposed method. The superior performance of the proposed method as validated through statistical analysis, outperforms the state-of-the-art video encryption frameworks in terms of Information Entropy and Correlation Coefficient for encrypted plain images offering a significant advancement in the field of secure video transmission.