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

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

Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms

Commonsense norms are defeasible by context: reading books is usually great, but not when driving a car. While contexts can be explicitly described in language, in embodied scenarios, contexts are often provided visually. This type of visually grounded reasoning about defeasible commonsense norms is generally easy for humans, but (as we show) poses a challenge for machines, as it necessitates both visual understanding and reasoning about commonsense norms. We construct a new multimodal benchmark for studying visual-grounded commonsense norms: NORMLENS. NORMLENS consists of 10K human judgments accompanied by free-form explanations covering 2K multimodal situations, and serves as a probe to address two questions: (1) to what extent can models align with average human judgment? and (2) how well can models explain their predicted judgments? We find that state-of-the-art model judgments and explanations are not well-aligned with human annotation. Additionally, we present a new approach to better align models with humans by distilling social commonsense knowledge from large language models. The data and code are released at https://seungjuhan.me/normlens.Comment: Published as a conference paper at EMNLP 2023 (long

Taurine in drinking water recovers learning and memory in the adult APP/PS1 mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a lethal progressive neurological disorder affecting the memory. Recently, US Food and Drug Administration mitigated the standard for drug approval, allowing symptomatic drugs that only improve cognitive deficits to be allowed to accelerate on to clinical trials. Our study focuses on taurine, an endogenous amino acid found in high concentrations in humans. It has demonstrated neuroprotective properties against many forms of dementia. In this study, we assessed cognitively enhancing property of taurine in transgenic mouse model of AD. We orally administered taurine via drinking water to adult APP/PS1 transgenic mouse model for 6 weeks. Taurine treatment rescued cognitive deficits in APP/PS1 mice up to the age-matching wild-type mice in Y-maze and passive avoidance tests without modifying the behaviours of cognitively normal mice. In the cortex of APP/PS1 mice, taurine slightly decreased insoluble fraction of Aβ. While the exact mechanism of taurine in AD has not yet been ascertained, our results suggest that taurine can aid cognitive impairment and may inhibit Aβ-related damages.MIT International Science and Technology InitiativesKorea Health Industry Development Institute (H14C04660000)Korea Institute of Science and Technology (Open Research 2E24582)Korea Institute of Science and Technology (Flagship 2E25023

Anatomy of ultrafast quantitative magneto-acoustics in freestanding nickel thin films

We revisit the quantitative analysis of the ultrafast magneto-acoustic experiment in a freestanding nickel thin film by Kim and Bigot [1] by applying our recently proposed approach of magnetic and acoustic eigenmodes decomposition by Vernik et al. [2]. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as $\propto\omega^2$ for frequencies up to 80~GHz and the peak amplitudes reach $10^{-3}$. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magneto-elastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and non-uniform modes, as confirmed by comparing the resonantly enhanced magneto-elastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of time scales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.Comment: 9 pages, 7 figure

The effects of dapagliflozin on urinary metabolites in patients with type 2 diabetes

AIMS: Previously, a panel of 13 urinary metabolites linked to mitochondrial metabolism was found to be significantly reduced in patients with diabetic kidney disease and eGFR>60 ml/min/1.73m2 . The beneficial effects of SGLT-2 inhibition on cardio-renal outcomes are hypothesized in part due to improved work efficiency at the mitochondrial level. We therefore assessed the effects of the SGLT-2 inhibitor dapagliflozin, on this pre-specified panel of 13 urinary metabolites linked to mitochondrial metabolism in patients with type 2 diabetes and elevated albuminuria. MATERIALS AND METHODS: Urine and plasma samples were used from a double-blind, randomized, placebo controlled crossover trial in 31 patients with type 2 diabetes, albumin:creatinine ratio >100 mg/g, and on a stable dose of an Angiotensin Converting Enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Dapagliflozin or placebo treatment periods each lasted for 6 weeks, with 6 weeks wash-out in between. Urinary and plasma metabolites were quantified by gas-chromatography mass spectrometry, corrected for creatinine, and then combined into a single-valued urinary metabolite index. Fractional excretion of the metabolites was calculated. RESULTS: All 13 urinary metabolites were detectable. After 6 weeks of dapagliflozin therapy, nine of the 13 metabolites were significantly increased from baseline. The urinary metabolite index increased by 42% (95%CI: 8.5 - 85.6, p=0.01) with placebo compared to 121% (69 - 189, p<0.001) with dapaglifozin. Accordingly, the placebo-adjusted effect was 56% (11 - 118, p=0.012). In plasma, seven of the 13 metabolites were detectable, and none were modified by dapagliflozin. CONCLUSIONS: Dapagliflozin significantly increased a panel of urinary metabolites previously linked to mitochondrial metabolism. These data support the hypothesis that SGLT-2 inhibitors may improve mitochondrial function, and improvements in mitochondrial function may be a mechanism for kidney protection. Future studies of longer treatment duration and clinical outcomes are needed to confirm the clinical impact of these findings. This article is protected by copyright. All rights reserved

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir