Traditional Computer Research Articles

An Incremental Mutual Information-Selection Technique for Early Ransomware Detection

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training such a detection model is choosing a set of relevant and non-redundant features, which is challenging when data are scarce. Therefore, this paper proposes an incremental mutual information-selection technique as a method for selecting the relevant features at the early stages of ransomware attacks. It introduces an adaptive feature-selection technique that processes data in smaller, manageable batches. This approach lessens the computational load and enhances the system’s ability to quickly adapt to new data arrival, making it particularly suitable for ongoing attacks during the initial phases of the attack. The experimental results emphasize the importance of the proposed technique in estimating feature significance in limited data scenarios. Such results underscore the significance of the incremental approach as a proactive measure in addressing the escalating challenges posed by ransomware.

DDoS Attack Detection in SDN-Based Instrumented Ad Hoc Networks

The physical characteristics of today's urban intersection structures and the traffic flows caused by unplanned road intersections lead to many negative effects such as time/cash loss, stress, increased fuel consumption, and more. For this reason, many studies are being conducted on traffic management systems, an application of smart cities, in both academic and commercial circles. In recent years, it has been observed that the VANET (Vehicular Ad Hoc Networks) architecture, which easily enables communication between vehicles or with devices on the side of the field, thus transporting relevant traffic data to the center, is frequently used in these studies. When Software Defined Networking emerged as a new technology, it brought many advantages such as high availability, scalability, and performance, but also introduced new security vulnerabilities targeted by attackers. This research primarily focuses on a resource-based detection approach by combining the powers of Software Defined Networking and s-Flow-RT technology against Distributed Denial of Service Attacks. In the simulation study conducted within the scope of this work, an SDN (Software Defined Networking)-based DDoS (Distributed Denial of Service) attack was carried out, and changes in data before and after the attack were examined. Traffic for the DDoS attack was generated with the Hping3 application. The RYU controller (a component-based software-defined networking framework) was selected as the software-defined network controller to create software-defined networks, and the Mininet emulator was used. A traditional computer network's Ubuntu virtual machine was used to carry out the attack in the scope of the work.

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.

Interaction solutions of (2+1)-dimensional Korteweg–de Vries–Sawada–Kotera–Ramani equation via bilinear method

Using the bilinear neural network method (BNNM) and the symbolic computation system Mathematica, this paper explains how to find an exact solution for the (2+1)-dimensional Korteweg–de Vries–Sawada–Kotera–Ramani (KdVSKR) equation. In terms of activation function and weight coefficient, BNNM is a more appealing option for users than traditional symbolic computation methods. It is possible to develop a wide range of solutions and expand the classes of exact solutions by modifying the activation function. The activation function’s versatility allows it to generate a wide range of solutions with several theoretical and practical uses. The analytical solution is obtained by using a double layer type, while the rogue wave solution and mixed solutions are obtained by using a single layer type. The evolution of these waves is then illustrated using various 3D graphs, 2D graphs, and density plots.

Stem–Leaf segmentation and phenotypic trait extraction of individual plant using a precise and efficient point cloud segmentation network

Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.

Fast and accurate 3-D spine MRI segmentation using FastCleverSeg

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review.

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Compact Instruction Set Extensions for Dilithium

Post-quantum cryptography is considered to provide security against both traditional and quantum computer attacks. Dilithium is a digital signature algorithm that derives its security from the challenge of finding short vectors in lattices. It has been selected as one of the standardizations in the NIST post-quantum cryptography project. Hardware-software co-design is a commonly adopted implementation strategy to address various implementation challenges, including limited resources, high performance, and flexibility requirements. In this study, we investigate using compact instruction set extensions (ISEs) for Dilithium, aiming to improve software efficiency with low hardware overheads. To begin with, we propose tightly coupled accelerators that are deeply integrated into the RISC-V processor. These accelerators target the most computationally demanding components in resource-constrained processors, such as polynomial generation, Number Theoretic Transform (NTT), and modular arithmetic. Next, we design a set of custom instructions that seamlessly integrate with the RISC-V base instruction formats, completing the accelerators in a compact manner. Subsequently, we implement our ISEs in a chip design for the Hummingbird E203 core and conduct performance benchmarks for Dilithium utilizing these ISEs. Additionally, we evaluate the resource consumption of the ISEs on FPGA and ASIC technologies. Compared to the reference software implementation on the RISC-V core, our co-design demonstrates a remarkable speedup factor ranging from 6.95 to 9.96. This significant improvement in performance is achieved by incorporating additional hardware resources, specifically, a 35% increase in LUTs, a 14% increase in FFs, 7 additional DSPs, and no additional RAM. Furthermore, compared to the state-of-the-art approach, our work achieves faster speed performance with a reduced circuit cost. Specifically, the usage of additional LUTs, FFs, and RAMs is reduced by 47.53%, 50.43%, and 100%, respectively. On ASIC technology, our approach demonstrates 12, 412 cell counts. Our co-design provides a better tradeoff implementation on speed performance and circuit overheads.

Simulating Urban Expansion from the Perspective of Spatial Anisotropy and Expansion Neighborhood

Exploring spatial anisotropy features and capturing spatial interactions during urban change simulation is of great significance to enhance the effectiveness of dynamic urban modeling and improve simulation accuracy. Addressing the inadequacies of current cellular automaton-based urban expansion models in exploring spatial anisotropy features, overlooking spatial interaction forces, and the ineffective expansion of cells due to traditional neighborhood computation methods, this study builds upon the machine learning-based urban expansion model. It introduces a spatial anisotropy index into the comprehensive probability module and incorporates a gravity-guided expansion neighborhood operator into the iterative module. Consequently, the RF-CNN-SAI-CA model is developed. Focusing on the 21 districts of the main urban area in Chongqing, the study conducts comparative analysis and ablation experiments using different models to simulate the land use changes between 2010 and 2020. Different model comparison results show that the recommended model in this study has a Kappa value of 0.8561 and an FOM value of 0.4596. Compared with the RF-CA model and the FA-MLP-CA model, the Kappa values are higher by 0.0407 and 0.1577, respectively, while the FOM values are improved by 0.0529 and 0.0654, respectively. Ablation experiment results indicate that removing gravity, SAI, and expansion neighborhood operators leads to a decrease in both Kappa and FOM values. These findings demonstrate that the RF-CNN-SAI-CA model, based on the expanded neighborhood iteration algorithm, effectively integrates spatial anisotropy features, captures spatial interaction forces, and resolves neighborhood cell failure issues, thereby significantly improving simulation effectiveness.

Visualization Program Design for Complex Piping Systems in Marine Engine Simulation Systems

This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development.

Geometry-driven self-supervision for 3D human pose estimation

Although 3D human pose estimation has recently made strides, it is still difficult to precisely recreate a 3D human posture from a single image without the aid of 3D annotation for the following reasons. Firstly, the process of reconstruction inherently suffers from ambiguity, as multiple 3D poses can be projected onto the same 2D pose. Secondly, accurately measuring camera rotation without laborious camera calibration is a difficult task. While some approaches attempt to address these issues using traditional computer vision algorithms, they are not differentiable and cannot be optimized through training. This paper introduces two modules that explicitly leverage geometry to overcome these challenges, without requiring any 3D ground-truth or camera parameters. The first module, known as the relative depth estimation module, effectively mitigates depth ambiguity by narrowing down the possible depths for each joint to only two candidates. The second module, referred to as the differentiable pose alignment module, calculates camera rotation by aligning poses from different views. The use of these geometrically interpretable modules reduces the complexity of training and yields superior performance. By adopting our proposed method, we achieve state-of-the-art results on standard benchmark datasets, surpassing other self-supervised methods and even outperforming several fully-supervised approaches that heavily rely on 3D annotations.

PEMANFAATAN LARGE LANGUAGE MODELS CHATGPT-4 UNTUK KONTROL DRONE: STUDI KASUS DJI TELLO

This study explores the potential of using ChatGPT-4, a Large Language Model (LLM) from OpenAI, for creating flight trajectories for drones. The primary focus is on the DJI Tello drone, which is controlled through Python code to execute various flying commands. Unlike traditional approaches that involve direct programming in Python, this research utilizes ChatGPT-4 to automatically generate Python computer programs capable of instructing the drone. Results indicate ChatGPT-4's intriguing capability to produce the necessary code for flying the drone according to given commands. This suggests that LLMs like ChatGPT-4 can be employed to determine drone flight trajectories using human language, facilitating ease of use compared to traditional computer programming languages.

Thermal Computing with Mechanical Transistors

AbstractMechanical computing has garnered significant interest as a supplement to traditional electronic computing, which often grapples with issues like high power consumption, security vulnerabilities, and susceptibility to extreme environmental conditions such as intense heat and radiation. Yet, most research in mechanical computing has been limited to the ad hoc design of simple logic gates and has not fully achieved the implementation of simple arithmetic computation within an electricity‐free framework. Additionally, progress in environmentally adaptive computing, crucial for decentralized intelligence, has also been slow. New ground is broken with the development of a mechanical transistor that synergizes a Kirigami thermomechanical sensor and a bistable actuator, enabling in‐memory computing for combinational and sequential logic. The design stands out by employing modular construction, symmetry breaking, and nonlinear materials, crafting logic gates, and memory units that respond to environmental stimuli through thermal delay. These transistors integrate design and material intelligence to establish nonvolatile memories, essential for logic‐in‐memory, and align thermal transport with mechanical deformation for environmental responsiveness. The mechanical transistor heralds a new age in mechanical computing, proving to be as versatile and vital as the electronic transistor has been in the era of modern computing.

Quantum computing basics, applications and future perspectives

Quantum Computing observed a significant rise to public and technologies in past three decades, the reason behind for the development of quantum computing is to solve various problems which are so complex that traditional (classical) computers were not able to solve. New technologies, hardware components and software advancements are being discovered all around the world in order to use this powerful tool. But in addition to the development of technologies and the attempt to scale up the quantum computers, new challenges and problems too came in light which makes it tough for further progress in the quest to unlock the true development of quantum computers. Various methods has been identified for Quantum Information Processing (QIP), but the error rates were more than what we would expect often resulting in inappropriate computations which eventually gives inaccurate conclusions.In this work, we discuss about the prominent hardware and software methods to build the quantum computers with low error rates and better accuracy, we will look onto the topics related to qubits and its principles which are incorporated in Quantum Processing Units (QPUs) which govern the working of quantum computers, the topics of quantum algorithms and its methodology are also been discussed to provide a clear understanding of the manipulation of qubits according to the purpose needed. In addition to that we will talk about the applications like quantum teleportation and cryptography which utilizes the quantum computers, and discuss about the future enhancements which can be done using this technology.

Attribute Based Encryption Techniques of Cloud Computing Access Control

Cloud computing has emerged as a transformative development in the realm of Information Technology. The advent of services like "on-demand" and "pay-as-you-go" has led to the migration of both government and industrial IT infrastructures up in the clouds. With data and business logic being stored on remote cloud servers, the security concerns connecting to the cloud environment differ from those of traditional computing. In the domain inside cloud computing, data security is administered by Cloud Service Providers (CSPs), leaving owner of the data with limited control over the security policies governing their own data. Privacy and saftey stand out as two critical factors that discourage users from transitioning to untrusted cloud environments. Despite the substantial savings in costs associated with cloud adoption, ensuring secured data sharing consistently remains a top priority for data owners. As a result, these two aspects demand heightened attention to facilitate securely sharing data within the cloud computing landscape. This paper undertakes a comprehensive review and comparison of various attribute-based encryption schemes within the context of cloud computing.

Investigation on the Significance of Incorporating Quantum Computing Technology into the Prevailing Curriculum

In conventional data processing, the use of binary bits is required. Online education is most beneficial to those who are already comfortable with technology. It's possible that classical mechanics will be replaced by quantum mechanics in near future. The processing speed of quantum computers is million times faster than that of traditional computer. Qubits are quantum bits. In the quantum technology, superposition and entanglement are important concepts. Traditional computers can benefit from the complex computational problems that are solved by quantum computing. Astrology, cryptography, and weather forecasting can all be simplified using quantum technology. Quantum theory should be studied by students majoring in Science, Technology, Engineering and Mathematics related fields. This paper discusses the advantages, applications, and tactics that can be used to enhance graduate and undergraduate courses in this rapidly expanding field of work.

Multi-Subject 3D Human Mesh Construction Using Commodity WiFi

This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.

The use of an interactive whiteboard in solfeggio teaching

This paper explores the application of digital technology, that is, interactive whiteboards, in solfeggio lessons. Digital technology, such as computers and interactive whiteboards, is gradually becoming part of the classroom, both in general education and music schools. In addition to traditional teaching tools (blackboard, chalk/markers, piano, and printed textbooks), computer programs and web applications can be beneficial complements to solfeggio lessons. The music classroom for teaching solfeggio is a great place where interactive whiteboards can be integrated. However, to effectively implement any form of digital technology in solfeggio, the teacher must be competent, IT-literate, know basic musical terms in English, and be receptive to the modern possibilities offered by technology. The aim of this paper is to demonstrate the possibility of using an interactive whiteboard in solfeggio teaching. Employing a descriptive methodology, the study entailed the application of the interactive whiteboard for certain classroom activities while covering topics in solfeggio such as intonation, music dictation, and theoretical musical concepts. The research revealed that the interactive whiteboard can primarily function as a conventional whiteboard without its multimedia capabilities, allowing the written content to be preserved. In addition, interactive whiteboards have numerous advantages when utilized for singing musical examples, writing musical dictations, and practicing theoretical musical content.

Exploring the Literature of Data Analytics Services on Cloud Computing: A Comprehensive Summary

Data generated from the internet and modern applications is extensive and rapidly expanding. So, all modern applications must successfully perform tasks using this massive data. Therefore, one of the significant factors for the success of any application is understanding and extracting meaningful information using digital analytics tools to positively impact the application's performance and deal with challenges that can be encountered. On the other hand, cloud computing is simply an environment comprising a collection of high-performance services from various vendors. These services can frequently access and process massive amounts of data faster than a traditional computer. One of these services is cloud analytics, which applies cloud-based analytic algorithms against data in a private or public cloud to get the desired outcome. This paper differentiates between several analytics methodologies to create highly consistent, logical, and information-rich summaries. This research has discussed and analyzed many studies related to this field. Therefore, the results of this paper can be utilized to determine the advantages of these methods, which will help future researchers in their research for a more organized and thorough analysis when dealing with such applications. In addition, this paper attempts to discover new directions and propose new guidelines in this evolving field.

EXECUTION OF GRAPHIC STILL-LIFE WITH INK AS THE BASIS FOR MASTERING THE LANGUAGE OF GRAPHICS

The article represents detailed stages and results of performing graphic still-lifes with ink in three main techniques: "lines", "spots", "hatches". Examples and types for setting still-life or alternative using photo images taken from open sources with mandatory changes and introduced details are given. Also the principles and examples for performing graphic and tonal sketches are presented, as an important stage in the search for a composition, which are reproduced on formats of graphic still-lifes through their spot solution to be the most objective and expressive. The technique for making a "cardboard" – a preparatory drawing and transferring the image to the formats of three graphic still-lifes is presented. The usage of tonal graphic sketches and "cardboard" is suggested along with traditional manual techniques and computer (digital) graphics technology. The principles and methods for using lines when creating a graphic still-life in the appropriate technique and variants of performing graphic still-lifes in the techniques of "lines", "spots" in comparison with each other are presented. The principles of division and combination of white and black with variants of their combinatorics are considered on the examples of graphic still-lifes in the "spot" technique. The stages for performing graphic still-lifes in the "hatching" technique, from the disclosure of large tonal gradations to the generalization of the work, are considered in detail. The methods for using different hatching techniques on different parts of the image of a graphic still-life are given. By the separate block for each section of the specified three graphic techniques the parallel between graphic still-lifes as educational tasks and graphic works of professional artists of Ukraine and the world is demonstrated.