Towards a Critical Understanding of Deepfakes: Developing a Teaching Module and More

Recently, computer-generated and computer-altered videos known as deepfakes have raised widespread concerns about the harms they may cause to democratic elections, national security, people’s reputation, and people’s autonomy over their words and actions as represented in videos and other media. How can we build towards a critical understanding of not only deepfakes, but also photos, videos, and the role of many other media objects surrounding us that inform us about the world? In this thesis, wanting to take a historical approach and noting the newness of deepfakes, I first investigate a historical case study regarding a manipulated photo from a 1950 U.S. Senate election campaign. Examining hearings conducted by the Senate into the use of misleading media in the election, I investigate how the incident sparked a debate between different groups of people over the trustworthiness of photographs and their proper role in elections. Next, I move forward in time and discuss the nature of deepfakes, presenting a brief history focusing on the different communities—academic, hobbyist, and commercial—that have played a role in the development of different, but related, technologies that all fall under the umbrella term of deepfakes. Some of this history is incorporated into the third part of this thesis, in which I present a teaching module I developed with the goals of guiding students to think critically about photos and videos and of raising awareness about deepfakes

Ranking with Slot Constraints

We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for different majors, to composing a stratified cohort of eligible participants in a medical trial. We show that the conventional Probability Ranking Principle (PRP) can be highly sub-optimal for slot-constrained ranking problems, and we devise a new ranking algorithm, called MatchRank. The goal of MatchRank is to produce rankings that maximize the number of filled slots if candidates are evaluated by a human decision maker in the order of the ranking. In this way, MatchRank generalizes the PRP, and it subsumes the PRP as a special case when there are no slot constraints. Our theoretical analysis shows that MatchRank has a strong approximation guarantee without any independence assumptions between slots or candidates. Furthermore, we show how MatchRank can be implemented efficiently. Beyond the theoretical guarantees, empirical evaluations show that MatchRank can provide substantial improvements over a range of synthetic and real-world tasks

Assessing the efficacy of large language models in generating accurate teacher responses

(Tack et al., 2023) organized the shared task hosted by the 18th Workshop on Innovative Use of NLP for Building Educational Applications on generation of teacher language in educational dialogues. Following the structure of the shared task, in this study, we attempt to assess the generative abilities of large language models in providing informative and helpful insights to students, thereby simulating the role of a knowledgeable teacher. To this end, we present an extensive evaluation of several benchmarking generative models, including GPT-4 (few-shot, in-context learning), fine-tuned GPT-2, and fine-tuned DialoGPT. Additionally, to optimize for pedagogical quality, we fine-tuned the Flan-T5 model using reinforcement learning. Our experimental findings on the Teacher-Student Chatroom Corpus subset indicate the efficacy of GPT-4 over other fine-tuned models, measured using BERTScore and DialogRPT. We hypothesize that several dataset characteristics, including sampling, representativeness, and dialog completeness, pose significant challenges to fine-tuning, thus contributing to the poor generalizability of the fine-tuned models. Finally, we note the need for these generative models to be evaluated with a metric that relies not only on dialog coherence and matched language modeling distribution but also on the model's ability to showcase pedagogical skills

Risk analysis of GM crop technology in China: modeling and governance

This paper aims at analyzing risks management of genetically modified (GM) crop technology in China, including risk classification, risk generating mechanisms and its governance. Firstly, we seek to create a three-dimensional model capable of assessing the risks of GM crop technologies. Based on this model, the risks of GM crop technologies can be divided into eight types, depending on the high or low risks levels associated with social hazard, technology uncertainty and economic harm. China’s GM technology is currently located in the high risk zone of thismodel, particularly in themarket of GMsoybean. In order to tackle this risk, the article introduces the Actor-Network Theory (ANT) as a useful tool to explore its risk assessment and governance. Lastly, we suggest the Chinese government needs to construct an efficient governancemechanismwhich should be able to balance actors’ interests and reduce or avoid risks induced by GM crop technologies

Oligocene clockwise rotations along the eastern Pamir: Tectonic and paleogeographic implications

International audienceDespite the importance of the Pamir range in controlling Asian paleoenvironments and land-sea paleogeography, its tectonic evolution remains poorly constrained in time and space, hindering its potential for understanding deep to surface processes. We provide here new constraints on vertical-axis tectonic rotations from the southwest Tarim Basin along the eastern flank of the Pamir arcuate range based on paleomagnetic results. Two well-dated Eocene to Oligocene sections, previously analyzed using biostratigraphy andmagnetostratigraphy, yield consistently clockwise rotations of 21.6±4.2° in 41 to 36Ma strata then 17.1±6.5° in 33 to 28Ma strata at the Aertashi section and 14.2 ± 11.5° in 41 to 40Ma strata at the Kezi section. Combined with a regional review of existing paleomagnetic studies, these results indicate that most of the clockwise rotations along the eastern Pamir occurred during Oligocene times and did not extend systematically and regionally into the TarimBasin. In contrast, on the western flank of the Pamir tectonic rotations in Cretaceous to Neogene strata are regionally extensive and systematically counterclockwise throughout the Afghan-Tajik Basin. This timing and pattern of rotations is consistent with paleogeographic reconstructions of the regional sea retreat out of Central Asia and supports a two-stage kinematic model: (1) symmetric rotations of either flanks of the Pamir arcuate range until Oligocene times followed by (2) continued rotations on its western flank associated with radial thrusting and, along the eastern flank, no further rotations due to decoupled transfer slip starting in the Early Miocene

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table