General-purpose Applications Research Articles

YOLO-Faster: An efficient remote sensing object detection method based on AMFFN.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society's evidence base, particularly in politically divisive domains, is a growing concern.

Dual Polarized Reconfigurable MIMO Antenna for Multi-Band Functioning

This work presents the development, fabrication, and measurements of a new Ultra-wideband sensing multi-band frequency reconfigurable MIMO Active Integrated Antenna (AIA). The proposed antenna has two frequency reconfigured (switching element PIN diodes) planar inverted F-shape antennas (PIFA) using meandered line (ML) structures and a Mirror-shape UWB sensing antenna. The Mirror-shape UWB sensing antenna is integrated with 2x2 MIMO Meandered line PIFA elements for spectrum sensing. PIN diodes (BAR 6402V) are used to provide all digital switching on/off combination modes to attain frequency reconfigurability. Varactor diodes are used for tuning over the wide band. The proposed antenna senses a wide frequency band from 0.5GHz-3GHz and provides frequency reconfigurability over the five bands based on BARP6402V switching modes (0.78GHz-1.01GHz, 1.34GHz-1.62GHz, 1.85GHz-2.18GHz, 2.44GHz-2.67GHz, and 2.76GHz-2.80GHz). All the measured resultant performance parameters are compared with simulation software results. The antenna system is fabricated on an FR4 substrate of size 120mmX65mmX1.6mm. The proposed method used to achieve frequency reconfigurable ultra-wideband MIMO provides a high gain 6.15dBi at a frequency of 1.75GHz. The research also analyzed and investigated all 2x2 MIMO resultant parameters like Total Active Reflection Coefficient (TARC), Isolation coefficient, Envelope correlation Coefficients (ECC), Diversity Gain (DG), Multiplexing efficiency, etc. The proposed antenna is applicable for sensing, communication, & UWB sensing, and cognitive radio (CR) applications since it exhibits stable excellent gain and Omni-directional radiation pattern over the desired bands. Because of its reconfigurable nature, the same antenna also supports use in multiple general-purpose applications like GPS tracing, LTE, UMTS, GSM1800, rescue operation, Wi-Fi/Bluetooth/ISM/WLAN, CDMA, etc applications.

Revealing the exploitability of heap overflow through PoC analysis

The exploitable heap layouts are used to determine the exploitability of heap vulnerabilities in general-purpose applications. Prior studies have focused on using fuzzing-based methods to generate more exploitable heap layouts. However, the exploitable heap layout cannot fully demonstrate the exploitability of a vulnerability, as it is uncertain whether the attacker can control the data covered by the overflow. In this paper, we propose the Heap Overflow Exploitability Evaluator (Hoee), a new approach to automatically reveal the exploitability of heap buffer overflow vulnerabilities by evaluating proof-of-concepts (PoCs) generated by fuzzers. Hoee leverages several techniques to collect dynamic information at runtime and recover heap object layouts in a fine-grained manner. The overflow context is carefully analyzed to determine whether the sensitive pointer is corrupted, tainted, or critically used. We evaluate Hoee on 34 real-world CVE vulnerabilities from 16 general-purpose programs. The results demonstrate that Hoee accurately identifies the key factors for developing exploits in vulnerable contexts and correctly recognizes the behavior of overflow.

A novel prognostics solution for accurate identification of degradation patterns in turbo machines with variable observation window

The degradation of a system is a time bound phenomenon, which leads to the deterioration of turbomachinery, in terms of performance and reliability. If undetected and not acted upon in time, this could also lead to sudden system failure, resulting in unplanned unit downtime and maintenance. Unplanned downtime of a turbomachine leads to severe production loss for the end customer and consequent economic damages. Early detection of a degradation pattern would provide the customer with the opportunity to timely carry out corrective actions, preventing an unscheduled down time. The paper evaluates degradation identification methodology currently known from literature and finds them not accurate enough for general purpose application required by the solution. The paper discusses a novel methodology which can accurately detect degradation patterns of timeseries data. Critical features of this methodology are novel time-based correlation enabled regression model with variable observation window, autonomous training, and automatic adjusting capability to incorporate operating behavior change or physical system replacement. This leads to high accuracy, high generalization, and domain agnostic application capability. Moreover, particular focus is given to achieving high probability of detection and a low probability of false alarm. The paper demonstrates the performance achieved by the methodology when applied to the field of prognostics and diagnostics of IoT connected turbomachines through 50+ real application cases.

Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model.

In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.

Development and analysis of medical instruction-tuning for Japanese large language models

In the ongoing wave of impact driven by large language models (LLMs) like ChatGPT, the adaptation of LLMs to the medical domain has emerged as a crucial research frontier. Since mainstream LLMs tend to be designed for general-purpose applications, constructing a medical LLM through domain adaptation is a huge challenge. While instruction-tuning, particularly based on low-rank adaptation (LoRA), has become a frequently employed strategy to fine-tune LLMs recently, its precise roles in domain adaptation remain unknown. Here, we investigated how LoRA-based instruction-tuning improves the performance of Japanese medical question-answering tasks by employing a multifaceted evaluation of multiple-choice questions, including scoring based on “Exact match” and “Gestalt distance” in addition to the conventional accuracy. Our findings suggest that LoRA-based instruction-tuning can partially incorporate domain-specific knowledge into LLMs, with larger models demonstrating more pronounced effects. Furthermore, our results underscore the potential of adapting English-centric models for Japanese applications in domain adaptation, while also highlighting the persisting limitations of Japanese-centric models. This initiative represents a pioneering effort in enabling medical institutions to fine-tune and operate models without relying on external services.

FiPhA: an open-source platform for fiber photometry analysis.

Fiber photometry (FP) is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals. However, analyzing photometry data can be both laborious and time-consuming. We propose the fiber photometry analysis (FiPhA) app, which is a general-purpose FP analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation. FiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface. This application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events, and quality control features. FiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based FP data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to FP analysis.

Speed and Accuracy Comparison of Person Detection Using Pretrained CenterNet and Yolov3

Implementations of object detection by using common devices for general purpose application is not widely used for speed and prone to errors. Most of research in speed detection using known method such as Centernet, Yolov3, Fast-RNN has variant result since the computer used are different. This experiment try to conduct experiments for person detection with Centernet and Yolov3 method using commonly used computer only using CPU. Based on the experiments, Yolov3 can give a much better precision for person detection by 98.42% of mAP point while Centernet only 97%. In terms of processing speed, Centernet can give much better speed where it can detect a person in average 370ms better than Yolov3 with average of 1050 ms or 1 second.

Initial Verification and Validation of a New CASMO5 JENDL-5 Nuclear Data Library for Typical LWR Applications

A new nuclear data library for the advanced lattice physics code CASMO5 has been prepared based on JENDL-5. In JENDL-5, the range of data, such as the number of nuclides, has been dramatically expanded for general-purpose applications. At the same time, many essential nuclides for conventional light water reactor (LWR) analysis have also been modified based on state-of-the-art evaluations. The new JENDL-5–based CASMO5 library was prepared by replacing as much of the nuclear data of the current CASMO5 ENDF/B-VII.1–based library as possible with JENDL-5. This study performs initial verification and validation of the new library under typical LWR conditions. Verifications were performed based on the Organisation for Economic Co-operation and Development/Nuclear Energy Agency burnup credit criticality safety benchmark phase III-C, and the calculated kinf and fuel compositions of the boiling water reactor fuel assembly were compared with reported benchmark results. Comparison with the MCNP6.2 result was also performed using the same benchmark model. In addition, a tank-type critical assembly critical experiment and Takahama-3 postirradiation experiment were used for validation. The results indicate that the new library performs well and is comparable to the ENDF/B-VII.1–based library in predictions of reactivity and fuel compositions for typical LWR systems.

Selection and evaluation of a MES software for a 3D printing center at point of care

3D printing at point of care is an emerging business and many hospitals are on their way of becoming manufacturers of medical devices. In order to comply with medical regulations, production processes need to be managed and documented. Although various manufacturing execution systems (MES) exist for general purpose applications, the medical domain in combination with novel additive manufacturing processes poses special issues, requirements, and adaptation needs to these systems. In this paper we present a structured framework to select and evaluate an appropriate MES software for a 3D printing center at point of care. The results are based on a real-world case of a 3D printing center in a hospital and provide practical and theoretical insights on the selection and evaluation process in this novel manufacturing domain.

Design and Perceived Value of a Novel Solution for Asynchronous Communication in Radiology

Rationale and ObjectivesCommunication with and within the Radiology Department is typically initiated over phone, face-to-face or general-purpose chat, causing frequent interruptions, additional mental workload, workflow inefficiencies and diagnostic errors. We developed and evaluated a new communication solution that aims to reduce avoidable interruptions caused by technologist-radiologist communication. Materials and MethodsFollowing an iterative design process with future end users, a scalable web-based software solution, RadConnect, was developed enabling a chat-based communication workflow between a technologist and a radiologist. As a first experimental implementation, technologists can send categorized tickets to a radiology section account. Radiologists receive the tickets in a worklist that is prioritized by urgency. Consented radiologists and technologists performed scripted tasks in 2 hr sessions and completed a structured questionnaire on perceived value and comparison to standard communication modes. ResultsOf 17 participants from three academic European institutes, 65% (11/17) believed they would use RadConnect frequently; 53% (9/17) believed that it reduces phone calls >80%; and 88% (15/17) believed it adds value compared to general-purpose enterprise chat applications. DiscussionParticipants recognized the value of RadConnect especially its categorized tickets, prioritized worklist and role-based interaction model. Inter-institute differences in perceived value of RadConnect may have been caused by technologist-radiologist proximity and communication alternatives in the institutions. ConclusionChat-based role-based communication might be a viable mode of communication between technologists and radiologists to reduce avoidable interruptions. Tailoring the chat solution to the needs of and tightly integrated with the radiology workflow is valued by future end users after exposure to the tool in a simulated environment.

Development of lignocellulosic-plastic composite from rice husk and polyethylene

As the demand for wood and wood-based resources keeps outstripping its regeneration, forests are depleting rapidly. To curtail this, it is imperative to shift towards the use of alternative raw materials such as agricultural residues. This study entails the development of lignocellulosic-plastic composites from Rice Husk (RH) and Recycled Low-Density Polyethylene (RLDPE) binder with the potential of augmenting the wood-based composites. Herein, RH was milled, graded into two particle sizes (2 mm and 4 mm), and mixed with RLDPE at two binder ratios (40 wt% and 50 wt%). The obtained composites were evaluated by density, water absorption (WA), thickness swelling (TSW), tensile strength (TS) and bending properties - modulus of elasticity (MOE) and modulus of rupture (MOR). In general, the increase of binder from 40% to 50% resulted in significant improvement of both physical and mechanical properties; while the increment of RH particle size showed slight differences in the properties. Composite with 50% binder content and 4 mm particle size was observed to have the best performance which conforms to the requirements for general-purpose application and door core application.

Non-stationary wave relaxation methods for general linear systems of Volterra equations: convergence and parallel GPU implementation

In the present paper, a parallel-in-time discretization of linear systems of Volterra equations of type u¯(t)=u¯0+∫0tK(t-s)u¯(s)ds+f¯(t),0<t≤T,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} \\bar{u}(t) = \\bar{u}_0 + \\int _0^t \ extbf{K}(t-s) \\bar{u}(s)\\,\ ext{ d }s + \\bar{f}(t),\\qquad 0<t\\le T, \\end{aligned}$$\\end{document}is addressed. Related to the analytical solution, a general enough functional setting is firstly stated. Related to the numerical solution, a parallel numerical scheme based on the Non-Stationary Wave Relaxation (NSWR) method for the time discretization is proposed, and its convergence is studied as well. A CUDA parallel implementation of the method is carried out in order to exploit Graphics Processing Units (GPUs), which are nowadays widely employed for reducing the computational time of several general purpose applications. The performance of these methods is compared to some sequential implementation. It is revealed throughout several experiments of special interest in practical applications the good performance of the parallel approach.

Exploring a mobile application for pest and disease symptomatic diagnosis in food crops in Nigeria: Implications of its use by smallholder farmers in sub-Saharan Africa

Crop pests and diseases are major impediments to food security in sub-Saharan Africa. Timely and accurate diagnosis of invasive crop pests and diseases promote crop protection efforts. The use of digital mobile application technology and image processing tools for precision agriculture and pest identification and monitoring is gaining attention but limited research has tested its accuracy, especially in sub-Saharan Africa. In this study, the accuracy of Plantix - your crop doctor (a general-purpose mobile application for plant pests and diseases diagnosis) was evaluated on common pests and diseases of some staple crops grown in South-Western Nigeria. The results showed 90-100% accuracy of major pest and disease symptoms detected on maize (Zea mays), okra (Abelmoschus esculenthus), cassava (Manihot esculenta), and plantain (Musa paradisiaca), and the application showed 100% accuracy when used to diagnose similar crop types, but that are healthy. However, pest symptoms on Celosia (Celosia argentea), Amaranth (Amaranthus sp.), and Roselle (Hibiscus sabdarifa) were not detected by the mobile application, probably because these crops were not yet included in the database of this application. The exploration of this mobile application can provide technical services for farmers. There is a need to update the database of Plantix with local and indigenous crops in sub-Saharan Africa and develop home-grown mobile applications for disease and pest diagnosis and monitoring.

Spread-Spectrum Frequency Modulation in a DC/DC Converter With Time-Based Control

Spread-spectrum frequency modulation is a technique commonly adopted in DC/DC converters with PWM control to reduce electromagnetic interference induced by switching. It typically requires a dedicated circuit. This paper introduces a novel technique to implement spread-spectrum modulation in a DC/DC converter with time-based control, requiring only an additional transconductor. Modulating the bias current of the voltage-controlled oscillators used in the controller via a common-mode signal, the switching frequency of the converter is modulated without affecting the duty cycle. A theoretical analysis providing the link between modulation factor and design parameters is shown, and the circuit implementation in a time-based buck converter for general-purpose applications in a BCD process with 180nm CMOS is described, together with a comparison between the simulated and the measured spectrum. The measured EMI reduction is 11.2dB. The area occupation of the SSFM circuit is 0.032mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> .

Multimedia Resources Available in Senior High Schools and Mathematics Teachers’ Perceived Ability to use them in Teaching Due to their Gender

Purpose: The study investigated the availability and accessibility of multimedia resources for teaching and learning Senior High School (SHS) mathematics. It also investigated how SHS mathematics teachers perceive their ability to use multimedia resources in teaching mathematics as well as the effect of gender on teachers’ perceived ability to use multimedia resources.&#x0D; Methodology: The research design used was a survey. The population for the study was all Public SHS mathematics teachers in the Central, Eastern, and Volta regions of Ghana. Purposive (non-probability) sampling and Convenience (non-probability) sampling methods were used in selecting twelve (12) schools in the three (3) regions and all the mathematics teachers in these schools formed the sample for the study. A questionnaire was used to collect data from the teachers sampled. This was comprised of seventy-three (73) teachers, sixty-four (64) males, and nine (9) females from these three district types: twenty (20) District Assemblies, thirty-three (33) Municipal Assemblies, and twenty (20) Metropolitan Assemblies.&#x0D; Findings: The study found that most of the teachers surveyed had a high perception of their ability to use computers and multimedia to teach mathematics at the SHS level. However, their perceived ability to use multimedia resources was higher for general-purpose applications; peripherals were higher than that of special mathematics applications software. There was no statistically significant difference between gender and teachers’ perceived ability to use multimedia resources. About half, representing 49.3% of the teachers indicated that they have Computer and Computer Laboratories in their schools but without internet connectivity. The labs were accessible for teaching and learning mathematics.&#x0D; Conclusion: The study conclude that gender, age, teaching experience, and school location have no significant influence on the use of multimedia by mathematics teachers.&#x0D; Recommendations: The study recommend more females to be encouraged to pursue mathematics related disciplines at the higher levels of education. Also, the use of more multimedia tools should be incorporated into the curriculum so that teachers will be encouraged to use them frequently in the classroom. Education institution development should be overseen by stakeholders like parents, philanthropists, or donor organizations.&#x0D;

A novel meshless method based on the virtual construction of node control domains for porous flow problems

In this paper, a novel meshless method that can handle porous flow problems with singular source terms is developed by virtually constructing node control domains. By defining a connectable node cloud, this method uses the integral of the diffusion term and generalized finite-difference operators to derive overdetermined equations of the node control volumes. An empirical method of calculating reliable node control volumes and a triangulation-based method to determine the connectable point cloud are developed. The method focuses only on the volume of the node control domain rather than the specific shape, so the construction of node control domains is virtual and does not increase the computational cost. To our knowledge, this is the first study to construct node control volumes in the meshless framework, termed the node control domain-based meshless method (NCDMM), which can also be regarded as an extended finite-volume method (EFVM). Taking two-phase porous flow problems as an example, discrete NCDMM schemes satisfying local mass conservation are derived by integrating the generalized finite-difference schemes of governing equations on each node control domain. Finally, commonly used low-order finite-volume method (FVM)-based nonlinear solvers for various porous flow models can be directly employed in the proposed NCDMM, significantly facilitating general-purpose application. Theoretically, the proposed NCDMM has the advantages of previous meshless methods for discretizing computational domains with complex geometries, as well as the advantages of traditional low-order FVMs for stably handling a variety of porous flow problems with local mass conservation. Four numerical cases are implemented to test the computational accuracy, efficiency, convergence, and good adaptability to the calculation domain with complex geometry and various boundary conditions.

Comparing User Space and In-Kernel Packet Processing for Edge Data Centers

Telecommunication operators are massively moving their network functions in small data centers at the edge of the network, which are becoming increasingly common. However, the high performance provided by commonly used technologies for data plane processing such as DPDK, based on kernel-bypass primitives, comes at the cost of rigid resource partitioning. This is unsuitable for edge data centers, in which efficiency demands both general-purpose applications and data-plane telco workloads to be executed on the same (shared) physical machines. In this respect, eBPF/XDP looks a more appealing solution, thanks to its capability to process packets in the kernel, achieving a higher level of integration with non-data plane applications albeit with lower performance than DPDK. In this paper we leverage the recent introduction of AF_XDP, an XDP-based technology that allows to efficiently steer packets in user space, to provide a thorough comparison of user space vs in-kernel packet processing in typical scenarios of a data center at the edge of the network. Our results provide useful insights on how to select and combine these technologies in order to improve overall throughput and optimize resource usage.

Inquiry into students' familiarity with computer-assisted translation tools

Acquiring knowledge of computer-assisted translation (CAT) technology is the basic requirement not only for translation students but also for professional translators to have good use of CAT tools. The present study aimed to investigate the degree of MA translation students’ familiarity with CAT tools used to support CAT-related activities included in the translator’s workstation. To do so, a questionnaire consisting of 16 questions was prepared for data collection based on translation activities proposed by Fulford and Granell-Zafar. This includes document production activities, business management activities and translation creation activities. As the results indicated, the MA translation students were mainly familiar with general-purpose applications, such as word processing software and machine translations, and rarely with specific-purpose software, namely web publishing software and accounting packages. They also were in full agreement with the effectiveness of CAT tools in their productivity and efficiency. Keywords: Computer-assisted translation (CAT) tools, machine-aided translation (MAT), technology;