Research on the Rules of Electronic Data Evidence Authentication

As a new type of evidence, electronic data has been fully confirmed in the legislative aspects of the three major procedural laws. However, there are still some problems in the judicial level, such as the lack of unity of meaning, the uncertainty of attribution and the lack of certification standards. The lack of certification standards is the most intractable problem. In this paper, the author uses the method of theoretical research and empirical research to analyze the judicial application of electronic data evidence, the existing problems, the causes and the corresponding solutions. The author suggests that the electronic data authentication specification should be set up as soon as possible, and the concrete practical work of the judges should be guided from two aspects of principles and rules, so that the concept of judicial standardization and the concept of free heart proof of judges are fully played in the field of electronic data evidence application

Listening for the Norm: Adaptive Coding in Speech Categorization

Perceptual aftereffects have been referred to as “the psychologist’s microelectrode” because they can expose dimensions of representation through the residual effect of a context stimulus upon perception of a subsequent target. The present study uses such context-dependence to examine the dimensions of representation involved in a classic demonstration of “talker normalization” in speech perception. Whereas most accounts of talker normalization have emphasized talker-, speech-, or articulatory-specific dimensions’ significance, the present work tests an alternative hypothesis: that the long-term average spectrum (LTAS) of speech context is responsible for patterns of context-dependent perception considered to be evidence for talker normalization. In support of this hypothesis, listeners’ vowel categorization was equivalently influenced by speech contexts manipulated to sound as though they were spoken by different talkers and non-speech analogs matched in LTAS to the speech contexts. Since the non-speech contexts did not possess talker, speech, or articulatory information, general perceptual mechanisms are implicated. Results are described in terms of adaptive perceptual coding

Image-Based Relighting

This thesis proposes a method for changing the lighting in some types of images. The method requires only a single input image, either a studio photograph or a synthetic image, consisting of several simple objects placed on a uniformly coloured background. Based on 2D information (contours, shadows, specular areas) extracted from the input image, the method reconstructs a 3D model of the original lighting and and 2.5D models of objects in the image. It then modifies the appearance of shading and shadows to achieve relighting. It can produce visually satisfactory results without a full 3D description of the scene geometry, and requires minimal user assistance. While developing this method, the importance of different cues for understanding 3D geometry, such as contours or shadows, were considered. Constraints like symmetry that help determine surface shapes were also explored. The method has potential application in improving the appearance of existing photographs. It can also be used in image compositing to achieve consistent lighting

EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes

Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn increasing attention in recent years. Most of the existing work in this area focuses on feature design, while little attention has been paid to dataset construction. In this work, we introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes, which is superior to existing datasets in four aspects: scale, annotation richness, diversity, and data balance. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators, making it five times larger than the largest existing dataset. EmoSet includes images from social networks, as well as artistic images, and it is well balanced between different emotion categories. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes: brightness, colorfulness, scene type, object class, facial expression, and human action, which can help understand visual emotions in a precise and interpretable way. The relevance of these emotion attributes is validated by analyzing the correlations between them and visual emotion, as well as by designing an attribute module to help visual emotion recognition. We believe EmoSet will bring some key insights and encourage further research in visual emotion analysis and understanding. Project page: https://vcc.tech/EmoSet.Comment: Accepted to ICCV2023, similar to the final versio

Meta-learning for Multi-variable Non-convex Optimization Problems: Iterating Non-optimums Makes Optimum Possible

In this paper, we aim to address the problem of solving a non-convex optimization problem over an intersection of multiple variable sets. This kind of problems is typically solved by using an alternating minimization (AM) strategy which splits the overall problem into a set of sub-problems corresponding to each variable, and then iteratively performs minimization over each sub-problem using a fixed updating rule. However, due to the intrinsic non-convexity of the overall problem, the optimization can usually be trapped into bad local minimum even when each sub-problem can be globally optimized at each iteration. To tackle this problem, we propose a meta-learning based Global Scope Optimization (GSO) method. It adaptively generates optimizers for sub-problems via meta-learners and constantly updates these meta-learners with respect to the global loss information of the overall problem. Therefore, the sub-problems are optimized with the objective of minimizing the global loss specifically. We evaluate the proposed model on a number of simulations, including solving bi-linear inverse problems: matrix completion, and non-linear problems: Gaussian mixture models. The experimental results show that our proposed approach outperforms AM-based methods in standard settings, and is able to achieve effective optimization in some challenging cases while other methods would typically fail.Comment: 15 pages, 8 figure

Effect of Grapevine Age on the Aroma Compounds in ‘Beihong’ Wine

The main aim of this study was to determine the influence of grapevine age (3, 6 and 12 years) on the aromacompounds in ‘Beihong’ wine. Aroma compounds in wine were analyzed by solid-phase microextractiongas chromatography-mass spectrometry (SPME-GC/MS). Thirty-three (33) volatile compounds wereidentified and quantified. The majority of aroma compounds were esters (20) and the concentrationof these totaled 90.63-92.82% (w/w) of the total aroma compounds; particularly, ethyl octanoate andethyl decanoate. Through the descriptive analysis aroma profile for ‘Beihong’ wine, the highest aromacontribution was from the fruity and floral series. As the age of the grapevine increased, the concentrationsof total volatiles and total odor activity values (OAVs) of the wines significantly increased (p < 0.001). Thissuggests that grapevine age could affect berry composition, enhance the content of wine aroma compoundsand improve wine quality

Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models

In this paper, we identify a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g. ChatGPT). LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages. To systematically evaluate the cultural dominance issue, we build a benchmark that consists of both concrete (e.g. holidays and songs) and abstract (e.g. values and opinions) cultural objects. Empirical results show that the representative GPT models suffer from the culture dominance problem, where GPT-4 is the most affected while text-davinci-003 suffers the least from this problem. Our study emphasizes the need for critical examination of cultural dominance and ethical consideration in their development and deployment. We show two straightforward methods in model development (i.e. pretraining on more diverse data) and deployment (e.g. culture-aware prompting) can significantly mitigate the cultural dominance issue in LLMs

The Art of SOCRATIC QUESTIONING: Zero-shot Multimodal Reasoning with Recursive Thinking and Self-Questioning

Chain-of-Thought prompting (CoT) enables large-scale language models to solve complex reasoning problems by decomposing the problem and tackling it step-by-step. However, Chain-of-Thought is a greedy thinking process that requires the language model to come up with a starting point and generate the next step solely based on previous steps. This thinking process is different from how humans approach a complex problem e.g., we proactively raise sub-problems related to the original problem and recursively answer them. In this work, we propose Socratic Questioning, a divide-and-conquer fashion algorithm that simulates the self-questioning and recursive thinking process. Socratic Questioning is driven by a Self-Questioning module that employs a large-scale language model to propose sub-problems related to the original problem as intermediate steps and Socratic Questioning recursively backtracks and answers the sub-problems until reaches the original problem. We apply our proposed algorithm to the visual question-answering task as a case study and by evaluating it on three public benchmark datasets, we observe a significant performance improvement over all baselines on (almost) all datasets. In addition, the qualitative analysis clearly demonstrates the intermediate thinking steps elicited by Socratic Questioning are similar to the human's recursively thinking process of a complex reasoning problem.Comment: 15 pages, 12 figure, 2 algorithm