Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved YOLOv8 Detection Algorithm for Detecting Contraband in X-ray Security Inspection Image

The automatic detection of prohibited items in X-ray images plays an important role in ensuring public safety. Aiming at the problem of large changes in the shape and scale of prohibited items and severe overlap between items in X-ray images, an improved detection algorithm based on YOLOv8 for detecting contraband items is proposed. Firstly, introducing the ODConv into the backbone to replace the basic convolution operation. While reducing additional parameters, it enhances the feature extraction ability of the model. Secondly, the DCNv3 operator is introduced into the Bottleneck of the C2f module to construct a new C2fD module, which can adapt to different deformation of objects, further strengthen the model’s ability to extract feature information. The experimental results show the mAP of the proposed algorithm on the subset containing contraband in the public SIXray dataset reaches 94.0%, which is 2.1% higher than the original YOLOv8n. Finally, compared with the current mainstream algorithms, it has better detection accuracy and real-time performance.
Soil Test based Fertilizer Recommendation for Mustard (Brassica juncea L.) in Eastern Plain Zone of Uttar Pradesh

Rapeseed-mustard is the second most important oilseed crop in India. Its production has markedly increased after launching of Technology Mission on Oilseeds. However, its productivity is only 1128 kg ha -1 and in Uttar Pradesh Rapeseed and mustard occupies 1.0 mha area with production of 1.12 mt. in 2013-14 (GOI). Over-exploitation of soils over many decades has resulted in the exhaustion of the agricultural production systems and steadily declining productivity has been noticed in long term experiments in Asia (Bhandari et al., 2002; Latha et al., 2003; Manna et al., 2005). The decision on fertilizer use requires knowledge of the expected crop yield response to nutrient application, which is a function of crop nutrient needs, supply of nutrients from indigenous sources, and the fate of the fertilizer applied (Dobermann et al., 2003). The cost of fertilizers has gone up and, hence, their optional use in required quantity mainly depends on resources available to farmers. Imbalanced use of chemical fertilizers results in lower nutrient use efficiency and restricts utilization of the genetic potential of a crop to International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 2 (2017) pp. 155-161 Journal homepage: http://www.ijcmas. com
An Approach for Load Flow and Load Dispatch Analysis of IEEE-9 Bus System

IEEE-9 bus system complexity and real-world applicability make it a benchmark for power system analysis and optimization. This article describes a method for IEEE-9 bus load flow and dispatch analysis. Load dispatch analysis optimizes generator dispatch to meet load demand, whereas load flow analysis determines steady-state operating characteristics such voltage magnitudes, phase angles, and power flows. Bus voltages are repeatedly calculated using the Newton-Raphson technique in load flow analysis to ensure system stability and voltage limitations. Assessing line flows and losses reveals system performance. The economic dispatch framework for load dispatch analysis minimizes generating costs while respecting generator capacity restrictions and load demand. Each generator's optimum generation levels are found via linear programming. The study illustrates these studies step-by-step, emphasizing data gathering, constraint formulation, and optimization. In order to analyze system dependability and performance, voltage profile analysis and sensitivity evaluations are described. This method helps engineers and planners optimize power system operation and setup and choose generator dispatch methods. System dependability, operational costs, and power production and distribution resource efficiency improve with the results.
GA-STC: An Optimization Approach for the Placement of Data-intensive Composite Services in the Cloud Environment

Cloud computing provides a great number of services to users and is rapidly being adopted by various industries due to its convenient service options and cost-effective models. Composite services have been developed to address complex application scenarios and their associated challenges. However, the fast-growing amount of data in the application domain puts a huge burden on the communication between the sub-services and makes the placement of the composite services in different servers of the cloud environment a huge challenge. In the existing research, there is no optimization for the large amount of data transmitted between services, which leads to the problem of uneven communication load and long response time. This paper addresses the placement of data-intensive composite services in the cloud. Firstly, the Data-intensive composite Services Placement Problem (DSPP) is modeled considering service type constraints and resource constraints, with the objectives of shortest execution time and communication load balancing. Then we proposed a heuristic algorithm, called GA- STC (Genetic Algorithm with Service Type Clustering), that clusters services type based on the a priori knowledge of service communication in workflows. The algorithm takes into account communication requirements between sub-services and, as a result, deploys services with intensive data transmission on the same server to reduce communication delay and improve operational efficiency. Extensive experimentation was performed to compare GA-STC, a novel placement algorithm with three traditional alternatives: random, First Fit Decreasing (FFD), and Genetic Algorithm (GA). The outcomes demonstrate that, concerning data-intensive composite services placement, GA-STC outperforms the other algorithms significantly in terms of optimization efficacy and efficiency. This research provides crucial guidance for carrying out efficient and effective placement of data-intensive composite services.
Few-Shot Object Detection Based On Label Constrained Data Augmentation

The majority of object detection methods typically depend on a significant quantity of annotated data, while few-shot object detection (FSOD) endeavors to identify novel classes of objects using a limited number of training instances. However, the limited number of samples leads to the problem of disparate data distributions between the source and target domains, which makes the generalization ability of the detector usually weak. In this paper, we combine data augmentation with fine-tuning to design a pseudo-labeled constrained model called LCDA, aiming to obtain high-quality pseudo-labels to effectively enrich the training examples. Furthermore, we leverage the pre-trained CLIP model to enhance the quality of pseudo-labels by restricting the category information as well as the designed bounding box consistency criterion. The experimental outcomes demonstrate that our model outperforms the existing models on two public datasets across various shot scenarios. The average enhancement of our method on different shots is 1.2AP, 1.6AP, 2.3AP, 2.7AP, and 4.2AP. We also validate the performance of the model in real applications of the USV dataset which shows an improvement of 1.9AP over the baseline methods. All demonstrate the effectiveness of our model.
Compressional And Shear Wave Velocities In Multicomponent Carbonate Media As Porosity Functions.

In many cases in the process of petrophysical inversion it is necessary to calculate P- and S-wave velocities as functions of porosity and mineral skeleton properties. However, the empirical equations such as the time average equation or RHG equation obtained for a single homogeneous mineralogy can not be applied in the case of mixed mineralogy; especially, for carbonate formations when the solid matrix is frequently multimineralic and can contain dolomite and limestone mixture. In this paper we propose a technique for P- and S-wave velocity determination using the symmetric (or polycrystalline) variant of the effective medium approximation (EMA). Each component of the medium is considered as a three-axial ellipsoid. The aspect ratios for grain and pore ellipsoids are introduced as a function of porosity. This technique consists of the following steps: (1) determination of pore and grain aspect ratios as porosity functions; (2) calculation of the elastic velocities for given mineral component concentrations and porosity. The calculation examples are presented for two component solid matrix (limestone and dolomite). We consider the pore aspect ratios as independent of the mineralogy and they are porosity function only. For the determination of solid grains shape we have assumed that the grain aspect ratios for each component are the same as in the case of one-component solid frame. To find the aspect ratios we solve the nonlinear problem of minimizing the squared difference between the theoretical values of P-wave velocity and electrical conductivity predicted by the EMA and calculated from empirical petrophysical equations. We used empirical RHG equation for each solid component independently to calculate the P-wave velocity and Archie’s law for electrical conductivity. Then for determined component geometry we can find the S-wave velocity for multicomponent rock. In the case of limestone-dolomite mixture the S-wave predictions are close to the Castanga et al. (1993) empirical relations for estimating Vs from Vp in multimineralic rocks.
Low-Cost Permittivity Determination of Liquid Materials Using Transmission-Only Measurements

A low-cost microwave non-resonant measurement setup is proposed for permittivity $(\varepsilon_{r})$ determination of liquid samples with substantial lengths using transmission-only amplitude measurements. The proposed method does not require any dispersion model or applies machine-learning method which requires a training process for each new material (time-consuming and complex). A graphical method is applied to monitor the possibility of extracting $\varepsilon_{r}$. It is shown that it is possible to extract $\varepsilon_{r}$ using samples with different lengths. A low-cost measurement setup, implemented by an SDR unit, with coaxial cables, and waveguide sections, was constructed to validate our method. Post-processing techniques were applied to remove redundant ripples in measurement due to a simple normalization procedure.
Causal Knowledge Integrated with Attention for Interpretable Event Detection

In recent years, significant breakthroughs have been made in end-to-end models for event detection tasks. Nevertheless, these models are limited in that they solely rely on the source sentence as input and provide detection results, making them opaque black-box models with limited control over learning outcomes. To tackle this limitation, we introduce a novel event detection model that incorporates causal knowledge with attention by constructing a causal graph of event arguments to acquire prior causal knowledge before detection. This approach greatly enhances the interpretability of event detection tasks. Furthermore, we perform an extensive analysis to evaluate the interpretability of our model. The experimental results not only showcase superior performance compared to existing event detection models but also demonstrate the high interpretability of our predicted results.
MFYOLO: An Improved YOLOv5 Algorithm for Small Target Detection in UAV-Captured Images

With the advancement of unmanned aerial vehicles, the detection of small targets has become a crucial task. However, it is challenging due to the complexity of the scenes and the small size of the targets. In recent years, various object detection methods based on convolutional neural networks (CNN) have made significant progress in small target detection. Despite these advancements, these methods still suffer from a high false detection rate, as well as substantial time and computational overhead, resulting in unsatisfactory performance. Therefore, we propose a multi-fusion YOLOv5 method (MFYOLO) in this paper, which incorporates attention blocks and a multi-scale feature fusion structure to enhance feature fusion and extraction capabilities. To evaluate our proposed method, we conducted extensive comparative experiments on the challenging VisDrone2019 dataset. The experimental results demonstrate that the mAP@0.5 is significantly improved by 10.0% from the original.
Sketched Irregular Military Symbols Recognition via Generative Adversarial Nets

Military situation perception is the key component to accommodate information warfare and enhance command and decision capabilities. Sketched irregular military symbols can swiftly share battlefield information and comprehensively display the combat situation, which is one of the important tasks of the situation perception system. However, the weakness of sketched irregular military symbols recognition is that the major approach is still based on database correlation technology and computer graphics, which result in the inability to accurately identify some situation, leading to a deficit in information exchange. In response to the aforementioned challenges, this paper introduces a novel methodology leveraging Generative Adversarial Networks (GANs). The approach involves cyclic adversarial generation of sketched symbols, which enhances the robustness of the module for sketched irregular military symbols recognition. We design a specific network architecture called RSM-GAN, which including ResNet-based encoder, Style-based generator and Markovianity-based discriminator. In addition, this paper assembles and constructs a new dataset of sketched irregular military symbols, named MMS-Sketch. The experimental results demonstrate that accurate recognition can be achieved on the dataset using the proposed framework.
An Approach to Control Network Traffic Congestion Using Load Balancing and Feedback Exchange Mechanism

The Internet of Things (IoT) is an emerging industry in which diverse items are linked to the web so that they may share information and interact with one another. Over time, IoT has developed as a concept, and its use has spread to new fields including medicine, transportation, industry, and agriculture. In this article, we'll try to trace how the Internet of Things has developed in many contexts. First, it gives a brief introduction to the Internet of Things, its background, and the foundational technologies that have contributed to its explosive expansion. Next, the study looks at how IoT is being utilized in different industries, beginning with healthcare, where it is being used to remotely monitor patients and enhance healthcare results. After discussing how IoT devices are used to monitor cars and enhance traffic control, the paper shifts its focus to the transportation industry. It also takes a look at how manufacturers are using IoT to streamline their operations and cut down on expenses. The study concludes with a discussion of how farmers may utilize IoT to increase their harvests by keeping tabs on things like weather, soil, and crop conditions.
CrackNet: A Backbone Network for Pavement Crack Analysis

Currently, the methods for road crack recognition based on deep learning typically treat crack recognition as a general image detection or classification task without specific analysis of crack images. In this paper, we propose a novel deep learning crack recognition network called CrackNet, which is based on the Transformer and can serve as the backbone network for crack detection or classification. Compared to advanced Transformer models such as PVTv2, CrackNet has several advantages. Firstly, it allows the model to pay more attention to the features of cracks and their surrounding regions. This is achieved by CrackNet explicitly extracting crack features from the images through wavelet transformation, providing reliable mask information for gated convolutions, and then using gated convolutions to better extract features in the crack region. Additionally, by incorporating convolutions into self-attention blocks, CrackNet can compensate for the insufficient capability of Transformer models in extracting local features. To evaluate the effectiveness of our approach, we conducted crack classification experiments on a large-scale asphalt road crack dataset called CQU-BPMCC, and crack detection experiments on a dataset called RDD2022. The experimental results on both datasets demonstrate the superiority of CrackNet in crack recognition comnared to other general backbone network methods.
Underwater Image Denoising Using Adaptive Retinal Processes and Smoothening Filters

The images that were obtained when the camera was submerged in water are an useful source of data that can be used to explore hydrothermal vents that are located on the ocean floor and to acquire a better knowledge of the marine life that lives there. Low contrast, colour distortion, and an unpleasant overall visual appearance are three of the most fundamental obstacles that an underwater photograph must overcome. These problems were caused by the dispersion and refraction of light, which occurred as beams of light went from a less scarce substance to a more dense one. When light is dispersed, the difference in intensity between the colours in the scene is diminished. Not only does the existence of dispersion in the water add to the impact that water has on underwater photography, but the presence of living organisms in the water also contributes to this effect. The research is needed to overcome the challenges of underwater photography, such as light dispersion and color distortion, in order to facilitate accurate exploration of hydrothermal vents and marine life on the ocean floor. The main problem addressed in the paper is the degradation of underwater images due to issues like low contrast and color distortion caused by light dispersion. The paper proposes a solution by introducing an innovative combination of adaptive retinal mechanism and bilateral filter, effectively enhancing image quality by mitigating these challenges. This paper implementation processes a picture by making use of an adaptive retinal mechanism in combination with a bilateral filter for the aim of smoothing. This is done in order to get the desired effect. The MSE and PSNR metrics have both witnessed significant improvements in their quality in recent years.
A Comparative Study of Temporal and Static Networks in Modeling Pathogen Transmission within School Environments

In the domain of epidemiology and infectious disease control, understanding the dynamics of pathogen transmission is crucial. This study shows the dynamics of pathogen spread in school environments by comparing two distinct network models: temporal networks and static networks. The objective is to assess their respective effectiveness in modeling the transmission of pathogens within school settings. Temporal networks take into account the evolving nature of social interactions over time, offering a more realistic representation of human contact patterns. In contrast, static networks provide a simplified but stable view of social connections. This research investigates how these two modeling approaches impact our ability to analyze, and control the spread of pathogens, such as viruses, in school environments. We Propose T-GSI (Temporal Global Structure Influence) to measure the influence of the temporal networks, which outperformed the state-of-the-art methods. Our findings reveal the strengths and weaknesses of temporal and static networks in capturing the influence of pathogen transmission within school communities.
Factors That Influence The Use of Mutual Fund Applications in Indonesia Using UTAUT2 and WOM as a Mediating Variable

Examining the factors influencing interest in and the using of mutual fund applications is the primary objective of this study. Word-of-mouth (WOM) is incorporated as a factor, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the conceptual framework. Surveys were administered to 178 respondents, comprising users of mutual fund applications, and the gathered data underwent analysis using the Partial Least Square - Structural Equation Model. The results show that elements like price value, habit, fascilitating condition, and effort expectancy have a favorable impact on behavioral intention. Furthermore, it has been shown that behavioral influence affects Word-of-mouth and usage patterns. Word-of-mouth, habit, and enabling circumstances all have an impact on use behavior in addition to the direct influence of behavioral elements. The study highlights Word of Mouth's noteworthy role in influencing user engagement by identifying it as a significant mediator of Use Behavior.
Large‐scale ocean‐atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts

Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large‐scale ocean‐atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large‐scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non‐linear relationships between rainfall and large‐scale ocean‐atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location.