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

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

Defining key metabolic roles in osmotic adjustment and ROS homeostasis in the recretohalophyte Karelinia caspia under salt stress

The recretohalophyte Karelinia caspia is of forage and medical value and can remediate saline soils. We here assess the contribution of primary/secondary metabolism to osmotic adjustment and ROS homeostasis in Karelinia caspia under salt stress using multi‐omic approaches. Computerized phenomic assessments, tests for cellular osmotic changes and lipid peroxidation indicated that salt treatment had no detectable physical effect on K. caspia. Metabolomic analysis indicated that amino acids, saccharides, organic acids, polyamine, phenolic acids, and vitamins accumulated significantly with salt treatment. Transcriptomic assessment identified differentially expressed genes closely linked to the changes in above primary/secondary metabolites under salt stress. In particular, shifts in carbohydrate metabolism (TCA cycle, starch and sucrose metabolism, glycolysis) as well as arginine and proline metabolism were observed to maintain a low osmotic potential. Chlorogenic acid/vitamin E biosynthesis was also enhanced, which would aid in ROS scavenging in the response of K. caspia to salt. Overall, our findings define key changes in primary/secondary metabolism that are coordinated to modulate the osmotic balance and ROS homeostasis to contribute to the salt tolerance of K. caspia

Linking dynamic phenotyping with metabolite analysis to study natural variation in drought responses of Brachypodium distachyon

Drought is an important environmental stress limiting the productivity of major crops worldwide. Understanding drought tolerance and possible mechanisms for improving drought resistance is therefore a prerequisite to develop drought-tolerant crops that produce significant yields with reduced amounts of water. Brachypodium distachyon (Brachypodium) is a key model species for cereals, forage grasses and energy grasses. In this study, initial screening of a Brachypodium germplasm collection consisting of 138 different ecotypes exposed to progressive drought, highlighted the natural variation in morphology, biomass accumulation and responses to drought stress. A core set of ten ecotypes, classified as being either tolerant, susceptible or intermediate, in response to drought stress, were exposed to mild or severe (respectively 15% and 0% soil water content) drought stress and phenomic parameters linked to growth and colour changes were assessed. When exposed to severe drought stress, phenotypic data and metabolite profiling combined with multivariate analysis revealed a remarkable consistency in separating the selected ecotypes into their different pre-defined drought tolerance groups. Increases in several metabolites, including for the phytohormones jasmonic acid and salicylic acid, and TCA-cycle intermediates, were positively correlated with biomass yield and with reduced yellow pixel counts; suggestive of delayed senescence, both key target traits for crop improvement to drought stress. While metabolite analysis also separated ecotypes into the distinct tolerance groupings after exposure to mild drought stress, similar analysis of the phenotypic data failed to do so, confirming the value of metabolomics to investigate early responses to drought stress. The results highlight the potential of combining the analyses of phenotypic and metabolic responses to identify key mechanisms and markers associated with drought tolerance in both the Brachypodium model plant as well as agronomically important crops