Reviewerly/exHarmony huggingface dataset:
Analysis on the Expression Skills of TV Reporters' Live Reports in the Convergence Media Environment

As one of the mainstream methods of news reporting, on-camera reporters' live reporting is a way of reporting on the scene of the news event by the reporters who go deep into the news scene and act as interviewers, witnesses or participants in front of the camera. <sup>[1]</sup> In the news program, the reporter on the scene report is an effective way to close the relationship between the program and the audience, enhance the audience's sense of participation and make it further have an important way of immersive feeling, in the news program has an indispensable role, therefore, the reporter on the scene report expression skills have great influence on the news program. This article starts with the expression characteristics of the live report of the TV reporter in the media environment, the lack of the expression skills of the live report of the TV reporter in the media environment, and the measures to improve the expression skills of the live report of the reporter in the media environment. It is hoped that it can provide reference for improving the comprehensive ability of the live report of the reporter.
Research on the improvement of core quality of "double-qualified" teachers in vocational colleges under the background of new era

The construction of "double-qualified" teachers is an important part of comprehensively improving the teaching quality and efficiency of vocational colleges, and is the key to training more composite application-oriented outstanding talents. By improving the core quality of "double-qualified" teachers in vocational colleges and sending more applied talents to the society, it can promote the rapid development of regional economy and effectively realize the win-win situation between schools and cities.
Research on the Application of Cooperative Activity Learning in Physical Education Teaching in Universities

The continuous advancement of educational reform in colleges and universities requires the teaching of college subjects to continuously integrate new teaching methods and teaching thinking, and physical education is no exception. The application of cooperative learning mode can effectively improve the quality of teacher-student and student-student interaction, change the shortcomings of traditional skills teaching, physical training and other teaching methods, and promote the overall development of students. Based on this, this paper briefly analyses the problems in the application of cooperative learning in college physical education teaching, and finally puts forward several paths to improve the quality of cooperative learning in college physical education teaching.
Image Steganography-based Copyright and Privacy-Protected Image Trading Systems

This paper proposes steganography-based copyright- and privacy-protected image trading systems using image transformation, i.e., either discrete cosine transform (DCT) or Hadamard transform (HT). In the systems, there are a content provider (CP), a consumer, the first trusted third party (TTP), and the second TTP. To protect the copyright of the image, the consumer ID is embedded into the amplitude components of the commercial image by the first TTP using the digital fingerprinting technique, and to protect the consumer's privacy against the first TTP and a malicious third party (s), the image steganography is applied to the commercial image by using image transformation. A color dummy image is used instead of a gray dummy image for security purposes. After applying the image transformation to both images, the coefficient signs of the commercial image are replaced by the coefficient signs of the dummy image pixel-by-pixel so that the inversely transformed commercial image looks like the dummy image instead of the commercial image. Once the consumer receives the fingerprinted image from the first TTP and the coefficient signs of the commercial image from the second TTP, the consumer reconstructs the fingerprinted commercial image without losing the hidden fingerprint at all because of the compatibility of the proposed image steganography method and the amplitude-based fingerprinting method. The experimental results confirm that the stego-images generated by the proposed systems do not look suspicious with higher qualities compared with those generated by existing systems. Moreover, the fingerprinted image quality and the correct fingerprint extracting rate have been improved by the proposed systems.
Dilemmas and Opportunities Facing China's Educational Development in the Digital Era

With the rapid development of today's era and the increasing level of Internet technology and digitization, how to promote the digitization of education and the construction of a learning society is an issue worth exploring. At the traditional campus stage, the teaching mode is very limited, and the digitalization of education requires the collaboration of all parties in society, schools and families to create a digital learning environment, make full use of multimedia, computers and other forms of online learning to achieve the development of teaching and learning across regions and time, and to effectively give full play to the advantages of digital technology to promote the high-quality development of digital education, and to a certain extent to promote equity in education, and to enhance the efficiency and quality of teaching and learning.
SSUIE 1.0: A Dataset for Chinese Space Science and Utilization Information Extraction

In this paper, we present the Chinese Space Science and Utilization Information Extraction dataset, including named entity recognition, relationship extraction, event extraction tasks, consisting of 6926 sentences, 58771 entities, 30338 triplets, 19 entities, 36 relations and 3039 events categorized into 20 event types, along with 8977 event arguments mapped to 26 argument roles, which, named SSUIE 1.0. We design an efficient coarse-to-fine procedure including candidate generation and crowdsourcing annotation, to achieve high data quality. Additionally, we evaluate performances of the most recent state-of-the-art frameworks and pre-trained language models for the named entity recognition, joint extraction of entities and relations and event extraction tasks on the SSUIE 1.0 dataset. Experiment results show that even these most advanced models still have a large space to improve on our dataset. Our analysis points out several challenges and multiple potential future research directions for the task specialized in the space science and utilization information extraction domain.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/AI4fun/DQ-LoRe}{https://github.com/AI4fun/DQ-LoRe.
Group-theoretic error mitigation enabled by classical shadows and symmetries

Estimating expectation values is a key subroutine in many quantum algorithms. However, near-term implementations face two major challenges: a limited number of samples to learn a large collection of observables, and the accumulation of errors in devices without quantum error correction. To address these challenges simultaneously, we develop a quantum error-mitigation strategy which unifies the group-theoretic structure of classical-shadow tomography with symmetries in quantum systems of interest. We refer to our protocol as "symmetry-adjusted classical shadows," as it mitigates errors by adjusting estimators according to how known symmetries are corrupted under those errors. As a concrete example, we highlight global $\mathrm{U}(1)$ symmetry, which manifests in fermions as particle number and in spins as total magnetization, and illustrate their unification with respective classical-shadow protocols. One of our main results establishes rigorous error and sampling bounds under readout errors obeying minimal assumptions. Furthermore, to probe mitigation capabilities against a more comprehensive class of gate-level errors, we perform numerical experiments with a noise model derived from existing quantum processors. Our analytical and numerical results reveal symmetry-adjusted classical shadows as a flexible and low-cost strategy to mitigate errors from noisy quantum experiments in the ubiquitous presence of symmetry.
DP-SGD for non-decomposable objective functions

Unsupervised pre-training is a common step in developing computer vision models and large language models. In this setting, the absence of labels requires the use of similarity-based loss functions, such as contrastive loss, that favor minimizing the distance between similar inputs and maximizing the distance between distinct inputs. As privacy concerns mount, training these models using differential privacy has become more important. However, due to how inputs are generated for these losses, one of their undesirable properties is that their $L_2$ sensitivity can grow with increasing batch size. This property is particularly disadvantageous for differentially private training methods, such as DP-SGD. To overcome this issue, we develop a new DP-SGD variant for similarity based loss functions -- in particular the commonly used contrastive loss -- that manipulates gradients of the objective function in a novel way to obtain a senstivity of the summed gradient that is $O(1)$ for batch size $n$. We test our DP-SGD variant on some preliminary CIFAR-10 pre-training and CIFAR-100 finetuning tasks and show that, in both tasks, our method's performance comes close to that of a non-private model and generally outperforms DP-SGD applied directly to the contrastive loss.
Design and Optimization of Heterogeneous Coded Distributed Computing with Nonuniform File Popularity

This paper studies MapReduce-based heterogeneous coded distributed computing (CDC) where, besides different computing capabilities at workers, input files to be accessed by computing jobs have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files. Furthermore, we design a nested coded shuffling strategy that can efficiently manage the nonuniformity of file popularity to maximize the coded multicasting opportunity. We then formulate the joint optimization of the proposed file placement and nested shuffling design variables to optimize the proposed CDC scheme. To reduce the high computational complexity in solving the resulting mixed-integer linear programming (MILP) problem, we propose a simple two-file-group-based file placement approach to obtain an approximate solution. Numerical results show that the optimized CDC scheme outperforms other alternatives. Also, the proposed two-file-group-based approach achieves nearly the same performance as the conventional branch-and-cut method in solving the MILP problem but with substantially lower computational complexity that is scalable over the number of files and workers. For computing jobs with aggregate target functions that commonly appear in machine learning applications, we propose a heterogeneous compressed CDC (C-CDC) scheme to further improve the shuffling efficiency. The C-CDC scheme uses a local data aggregation technique to compress the data to be shuffled for the shuffling load reduction. We again optimize the proposed C-CDC scheme and explore the two-file-group-based low-complexity approach for an approximate solution. Numerical results show the proposed C-CDC scheme provides a considerable shuffling load reduction over the CDC scheme, and also, the two-file-group-based file placement approach maintains good performance.
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models

Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients. However, two challenges arise when deploying deep learning models to real-world healthcare applications. First, neural models tend to learn spurious correlations instead of desired features, which could fall short when generalizing to new domains (e.g., patients with different ages). Second, these black-box models lack interpretability. When making diagnostic predictions, it is important to understand why a model makes a decision for trustworthy and safety considerations. In this paper, to address these two limitations, we propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model. We systematically evaluate our method on eight medical image classification datasets to verify its effectiveness. On challenging datasets with strong confounding factors, our method can mitigate spurious correlations thus substantially outperform standard visual encoders and other baselines. Finally, we show how classification with a small number of concepts brings a level of interpretability for understanding model decisions through case studies in real medical data.
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise

While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations in specific domains such as Chinese law, hindering their application in these areas. This is typically due to the absence of training data that encompasses such a specific domain, preventing GPT-4 from acquiring in-domain knowledge. A pressing challenge is that it's not plausible to continue training LLMs of such scale on in-domain data. This paper introduces a simple and effective domain adaptation framework for GPT-4 by reformulating generation as an \textbf{adapt-retrieve-revise} process. The initial step is to \textbf{adapt} an affordable 7B LLM to the target domain by continuing learning on in-domain data. When solving a task, we leverage the adapted LLM to generate a draft answer given a task query. Then, the draft answer will be used to \textbf{retrieve} supporting evidence candidates from an external in-domain knowledge base. Finally, the draft answer and retrieved evidence are concatenated into a whole prompt to let GPT-4 assess the evidence and \textbf{revise} the draft answer to generate the final answer. Our proposal combines the advantages of the efficiency of adapting a smaller 7B model with the evidence-assessing capability of GPT-4 and effectively prevents GPT-4 from generating hallucinatory content. In the zero-shot setting of four Chinese legal tasks, our method improves accuracy by 33.3\% compared to the direct generation by GPT-4. When compared to two stronger retrieval-based baselines, our method outperforms them by 15.4\% and 23.9\%. Our code will be released
Sampling via Gradient Flows in the Space of Probability Measures

Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algorithms derived by considering gradient flows in the space of probability measures open up new avenues for algorithm development. This paper makes three contributions to this sampling approach by scrutinizing the design components of such gradient flows. Any instantiation of a gradient flow for sampling needs an energy functional and a metric to determine the flow, as well as numerical approximations of the flow to derive algorithms. Our first contribution is to show that the Kullback-Leibler divergence, as an energy functional, has the unique property (among all f-divergences) that gradient flows resulting from it do not depend on the normalization constant of the target distribution. Our second contribution is to study the choice of metric from the perspective of invariance. The Fisher-Rao metric is known as the unique choice (up to scaling) that is diffeomorphism invariant. As a computationally tractable alternative, we introduce a relaxed, affine invariance property for the metrics and gradient flows. In particular, we construct various affine invariant Wasserstein and Stein gradient flows. Affine invariant gradient flows are shown to behave more favorably than their non-affine-invariant counterparts when sampling highly anisotropic distributions, in theory and by using particle methods. Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods. We establish connections between various Gaussian approximate gradient flows, discuss their relation to gradient methods arising from parametric variational inference, and study their convergence properties both theoretically and numerically.
Pre-Training and Fine-Tuning Generative Flow Networks

Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects from a given unnormalized reward distribution. They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks. However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks. Inspired by recent successes of unsupervised pre-training in various domains, we introduce a novel approach for reward-free pre-training of GFlowNets. By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to explore the candidate space. Specifically, OC-GFN learns to reach any targeted outcomes, akin to goal-conditioned policies in reinforcement learning. We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks. Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an intractable marginalization over possible outcomes. We propose a novel way to approximate this marginalization by learning an amortized predictor enabling efficient fine-tuning. Extensive experimental results validate the efficacy of our approach, demonstrating the effectiveness of pre-training the OC-GFN, and its ability to swiftly adapt to downstream tasks and discover modes more efficiently. This work may serve as a foundation for further exploration of pre-training strategies in the context of GFlowNets.
Neural Language Model Pruning for Automatic Speech Recognition

We study model pruning methods applied to Transformer-based neural network language models for automatic speech recognition. We explore three aspects of the pruning frame work, namely criterion, method and scheduler, analyzing their contribution in terms of accuracy and inference speed. To the best of our knowledge, such in-depth analyses on large-scale recognition systems has not been reported in the literature. In addition, we propose a variant of low-rank approximation suitable for incrementally compressing models, and delivering multiple models with varied target sizes. Among other results, we show that a) data-driven pruning outperforms magnitude-driven in several scenarios; b) incremental pruning achieves higher accuracy compared to one-shot pruning, especially when targeting smaller sizes; and c) low-rank approximation presents the best trade-off between size reduction and inference speed-up for moderate compression.
The Anatomy of Deception: Technical and Human Perspectives on a Large-scale Phishing Campaign

In an era dominated by digital interactions, phishing campaigns have evolved to exploit not just technological vulnerabilities but also human traits. This study takes an unprecedented deep dive into large-scale phishing campaigns aimed at Meta's users, offering a dual perspective on the technical mechanics and human elements involved. Analysing data from over 25,000 victims worldwide, we highlight the nuances of these campaigns, from the intricate techniques deployed by the attackers to the sentiments and behaviours of those who were targeted. Unlike prior research conducted in controlled environments, this investigation capitalises on the vast, diverse, and genuine data extracted directly from active phishing campaigns, allowing for a more holistic understanding of the drivers, facilitators, and human factors. Through the application of advanced computational techniques, including natural language processing and machine learning, this work unveils critical insights into the psyche of victims and the evolving tactics of modern phishers. Our analysis illustrates very poor password selection choices from the victims but also persistence in the revictimisation of a significant part of the users. Finally, we reveal many correlations regarding demographics, timing, sentiment, emotion, and tone of the victims' responses.
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective

Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to prevent the singular solutions that the regular MLL criterion gives rise to, and it is proven that LOO-MLL prevents these. Relying on this guaranteed robustness, the model is extended by adjustable weights for the kernels. In addition, a modified expectation-maximization algorithm is employed to accelerate the optimization speed reliably. The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models. Results show that the proposed models have promising performance, in addition to their singularity prevention guarantees.