A Method of Evaluating Trust and Reputation for Online Transaction

The widespread use of the Internet and evaluater-based technologies has transformed the way business is conducted. Traditional offline businesses have increasingly become online, and there are new kinds of businesses that solely exist online. Unlike offline business environments, interpersonal trust is generally lacking in online business settings. Trading partners might feel insecure about the exchange of products and services over the net as they have limited information about each other's reliability or about the product quality. Considering that enough trust needs to be created to get the online buyer and seller to take actions, trust is a precious asset in online transactions. In order to address the issue of evaluating trust and reputation in online transaction environments, this paper makes use of a social network that graphically represents interpersonal relationships. This paper proposes computational models that systematically evaluate the quantitative level of trust and reputation based on the social network. A method that combines the evaluated trust and reputation levels is also proposed to increase the reliability of online transactions

Deep Cross-Modal Steganography Using Neural Representations

Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neural Networks (DNNs) have recently been utilized in steganography. However, existing deep steganography techniques are limited in scope, as they focus on specific data types and are not effective for cross-modal steganography. Therefore, We propose a deep cross-modal steganography framework using Implicit Neural Representations (INRs) to hide secret data of various formats in cover images. The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions. Experiments on various secret datasets of diverse types demonstrate that the proposed approach is expandable and capable of accommodating different modalities.Comment: ICIP 202

An Investigation on a Low-cost Machine Vision Measuring System for Precision Improvement

614-618In this paper, we describe the investigation on a machine vision size measuring system to improve its precision on the basis of the inexpensive devices from the viewpoint of industrial applications. The uniformity and stability of the system were analyzed. The results showed the maximum gray value standard deviation of the edge as 2.6 pixels, and the maximum error of edge detection results was approximately 9 pixels (0.279 mm). The traditional noise reduction algorithms were applied to reduce random noise and dark current noise, and a novel uniform-background algorithm was proposed to improve the uniformity of image background. In addition, a calibration method based on the average gray value of the specified areas was developed to correct gray value errors of the left and right edges. A large number of experiments were carried out using the combined methods, the results showed that the measuring speed was approximately 1 piece per second, and the maximum error of lengths measured by the proposed method was within 1 μm, whereas the maximum error of uncalibrated results was about 0.25 mm. The measuring precision and speed of the proposed methods can meet the requirement of industrial applications

Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning

Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation. In this paper, we propose Self-Distillation for Fine-Tuning diffusion models (SDFT), a methodology to address these challenges by leveraging diverse features from diffusion models pretrained on large source datasets. SDFT distills more general features (shape, colors, etc.) and less domain-specific features (texture, fine details, etc) from the source model, allowing successful knowledge transfer without disturbing the training process on target datasets. The proposed method is not constrained by the specific architecture of the model and thus can be generally adopted to existing frameworks. Experimental results demonstrate that SDFT enhances the expressiveness of the diffusion model with limited datasets, resulting in improved generation capabilities across various downstream tasks.Comment: WACV 202

An Investigation on a Low-cost Machine Vision Measuring System for Precision Improvement

In this paper, we describe the investigation on a machine vision size measuring system to improve its precision on the basis of the inexpensive devices from the viewpoint of industrial applications. The uniformity and stability of the system were analyzed. The results showed the maximum gray value standard deviation of the edge as 2.6 pixels, and the maximum error of edge detection results was approximately 9 pixels (0.279 mm). The traditional noise reduction algorithms were applied to reduce random noise and dark current noise, and a novel uniform-background algorithm was proposed to improve the uniformity of image background. In addition, a calibration method based on the average gray value of the specified areas was developed to correct gray value errors of the left and right edges. A large number of experiments were carried out using the combined methods, the results showed that the measuring speed was approximately 1 piece per second, and the maximum error of lengths measured by the proposed method was within 1 μm, whereas the maximum error of uncalibrated results was about 0.25 mm. The measuring precision and speed of the proposed methods can meet the requirement of industrial applications

Water-Saving Traits Can Protect Wheat Grain Number Under Progressive Soil Drying at the Meiotic Stage:A Phenotyping Approach

In wheat, water deficit during meiosis of pollen mother cells greatly reduces seed set and grain number. A promising option to avoid grain losses and maintain wheat productivity under water stress is to exploit conservative water-use strategies during reproduction. In this work, two cultivars known to be adapted to different environments were studied. Water stress, with or without a polymer spray known to reduce stomatal conductance, was applied to both cultivars just prior to meiosis. Two experiments were carried out in a phenotyping platform to (1) assess and validate daily non-destructive estimation of projected leaf area and to (2) evaluate different water-use (WU) strategies across the meiotic period and their effect on physiology and yield components. Gladius displays an elevated breakpoint (BP) in the regression of WU against fraction of transpirable soil water (FTSW) for both daily and night-time WU suggesting higher conservative whole-plant response when compared to Paragon. At the same time, Gladius maintained flag leaf gas-exchange with a significant reduction at ~ 0.2 FTSW only, suggesting an uncoupled mechanism of WU reduction that optimized the water resource available for flag leaf gas-exchange maintenance. Under progressive soil drying, seed set and grain number of tillers stressed at GS41 were significantly reduced in Paragon (p < 0.05) thus leading to lower grain yield and grain number at plant level than Gladius. Polymer-induced reduction of transpiration is potentially useful when applied to the non-conservative stressed Paragon, maintaining higher FTSW, water-use efficiency and RWC during the progressive soil drying treatment. This led to better seed set (p < 0.05) and grain number maintenance (p < 0.05) than in the stressed Paragon control. We conclude that the different conservative traits detected in this work, protect grain development around meiosis and therefore maintain grain number under water-limiting conditions. Additionally, non-conservative genotypes (often with a greater expected yield potential) can be protected at key stages by reducing their water use with a polymer spray. Thus, future efforts can integrate both crop breeding and management strategies to achieve drought-resilience during the early reproductive phase in wheat and potentially other cereals

The estimation of crop emergence in potatoes by UAV RGB imagery

Abstract Background Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. Results In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ($$r^{2}$$ r2 ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. Conclusions The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency