Reviewerly/exHarmony huggingface dataset:
Development Of E-Learning-Based Learning Media At Graha Nusantara University

The rapid development of information technology in the age of globalization has enabled the development of better information services in education. One of the new innovations in the use of educational media and technology-based learning resources and information is e-learning. The term e-learning has a very broad meaning and many experts explain the definition of e-learning from different angles. E-learning, or electronic learning, is learning that uses electronic devices as learning media and the Internet for learning activities. The concept of e-learning has influenced the process of transforming traditional education into digital form, both in terms of content and system. In addition, internet media enables real-time or non-real-time interaction between teachers and students. In the current era of communication technology has experienced rapid development, the internet is one of the technologies that is currently developing very rapidly. The internet seems to have become a basic human need from the top to the bottom, from the elderly to the children, even the internet has also gone global so that this world feels without boundaries. We can get information and communication in an instant, even from parts of the world that are very far from where we are.
A study on lifelong learning competencies according to the lifelong learning motivation profile of Chinese adult learners

Objectives This study aims to investigate and understand human behavior by applying a representative analysis method of person-centered approach research to understand the relationship between the motivation and influ-encing factors of Chinese adult lifelong learners to participate in lifelong learning.&#x0D; Methods The method of this study was a potential profile analysis using Mplus 8.8 for 803 Chinese adult lifelong learners who participated in school-affiliated lifelong education, public institution-type lifelong education, private institution-type lifelong education, vocational training and training-type lifelong education institutions (LPA) was performed.&#x0D; Results First, according to the motivation of Chinese adult lifelong learners to participate, the latent profile was divided into ‘nonsense type (6.97%)’, ‘deliberation type (15.94%)’, ‘hesitation type (62.02%)’, and ‘leading type (15.07%)’. has been classified As a result of analyzing whether there is a difference in lifelong learning com-petency by latent profile, it was found that the ‘Hesitant Type’, who had a medium level of motivation to participate in lifelong learning, had high lifelong learning competencies.&#x0D; Conclusions In constructing social policies for lifelong education, it is continuously necessary to carry out re-search based on a person-centered approach that can be subdivided by lifelong learner type in order to design and implement education policies that consider the various needs, capacities, and interests of individuals. This should provide a flexible curriculum that meets the diverse backgrounds and needs of each person, thereby ensuring that all individuals receive appropriate educational opportunities according to their capabilities and interests.
Tech Showdown – Clipchamp vs Screencastify

Tech Showdown is a regular feature of JoHILA. Novel products, software, and technologies will be compared against each other to determine who is the winner, based on available features, ease of use, and price. If you have an idea for Tech Showdown or would like to see a comparison of two particular technologies, please email Cass.
Development of Digital Literacy Competency Model for Teachers in Early Childhood

Objectives This study aims to develop a model of digital literacy competencies for teachers in early childhood.&#x0D; Methods To this end, literature review, 3 rounds of delphi surveys were conducted. The research team and an expert advisory committee executed mutual validation at the end of each delphi survey to ensure objectivity and validity.&#x0D; Results As a result of this study, a digital literacy competency model for teachers in early childhood was derived, consisting of four main competencies and nine sub-competencies. The main competencies include digital cit-izenship consciousness, digital thinking competency, digital utilization and creation competency, and digital teaching competency. The sub-competencies for digital citizenship consciousness include compliance with digi-tal norms and digital responsibility consciousness, while sub-competencies for digital thinking competency in-clude digital critical thinking and problem-solving abilities. The sub-competencies for digital utilization and crea-tion competency include digital technology utilization and digital creation abilities. Finally, the sub-competencies for digital teaching competency include digital teaching design ability, digital teaching execution ability, and digital teaching reflection ability.&#x0D; Conclusions Based on the results of this study, it is expected that an education and training system can be estab-lished to cultivate digital literacy competencies that teachers in early childhood must possess in the educational field of the digital era.
USING PARADATA AND MANUAL REVIEW TO DETECT AUTOMATED ONLINE RESEARCH SURVEY PARTICIPATION

Abstract With an increase in health research recruitment on social media, there is also an increase in prevalence of software known as bots auto-enrolling in online studies. Despite efforts to prevent automated fraudulent enrollment, bots have become more sophisticated with some able to bypass a CAPTCHA and generate false email addresses. This session describes such fraudulent enrollment in one study and our efforts to detect it. For an online study of family caregivers of people with dementia, we recruited via family caregiver groups on Facebook. Potential participants could proceed to the online study and immediately answer screening questions and complete the study. A CAPTCHA was used to screen non-human respondents, but we detected rapid enrollment compared with other methods, raising suspicions. Paradata generated in the process of survey administration and unusual email addresses flagged suspected bot responses. To screen the responses, we reviewed the time to complete surveys compared with known human responses. We also noted email addresses that consisted of long alphanumeric usernames not common with most human respondents. After screening for suspicious respondents, we attempted to contact the email addresses of borderline cases to verify they were real respondents. Only one of 157 (0.006%) were determined to be a real respondent. Lessons learned from this experience include using paradata as part of data quality monitoring, using survey design methods to elude automated bots, and the value of researchers interacting with participants during enrollment.
VidToMe: Video Token Merging for Zero-Shot Video Editing

Diffusion models have made significant advances in generating high-quality images, but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless, existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work, we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames, our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames, facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing, we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging, ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing, rendering favorable results in temporal consistency over state-of-the-art methods.
SECap: Speech Emotion Captioning with Large Language Model

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.
Locally-Minimal Probabilistic Explanations

Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms.
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via collaborative distributed learning by aggregating multiple client models trained with local noisy or clean samples. On the basis of a federated learning framework, recent advances primarily adopt label noise filtering to separate clean samples from noisy ones on each client, thereby mitigating the negative impact of label noise. However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damaging training stability. In this paper, we present FedDiv to tackle the challenges of F-LNL. Specifically, we propose a global noise filter called Federated Noise Filter for effectively identifying samples with noisy labels on every client, thereby raising stability during local training sessions. Without sacrificing data privacy, this is achieved by modeling the global distribution of label noise across all clients. Then, in an effort to make the global model achieve higher performance, we introduce a Predictive Consistency based Sampler to identify more credible local data for local model training, thus preventing noise memorization and further boosting the training stability. Extensive experiments on CIFAR-10, CIFAR-100, and Clothing1M demonstrate that \texttt{FedDiv} achieves superior performance over state-of-the-art F-LNL methods under different label noise settings for both IID and non-IID data partitions. Source code is publicly available at https://github.com/lijichang/FLNL-FedDiv.
PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated across inputs, while the majority, cold neurons, vary based on specific inputs. PowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine: hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed on the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers. PowerInfer further integrates adaptive predictors and neuron-aware sparse operators, optimizing the efficiency of neuron activation and computational sparsity. Evaluation shows that PowerInfer attains an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU, only 18% lower than that achieved by a top-tier server-grade A100 GPU. This significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy.
Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers

Geometric fracture assembly presents a challenging practical task in archaeology and 3D computer vision. Previous methods have focused solely on assembling fragments based on semantic information, which has limited the quantity of objects that can be effectively assembled. Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information. To improve the effectiveness of assembling geometric fractures without semantic information, we propose a co-creation space comprising several assemblers capable of gradually and unambiguously assembling fractures. Additionally, we introduce a novel loss function, i.e., the geometric-based collision loss, to address collision issues during the fracture assembly process and enhance the results. Our framework exhibits better performance on both PartNet and Breaking Bad datasets compared to existing state-of-the-art frameworks. Extensive experiments and quantitative comparisons demonstrate the effectiveness of our proposed framework, which features linear computational complexity, enhanced abstraction, and improved generalization. Our code is publicly available at https://github.com/Ruiyuan-Zhang/CCS.
Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion

Monocular depth estimation has experienced significant progress on terrestrial images in recent years, largely due to deep learning advancements. However, it remains inadequate for underwater scenes, primarily because of data scarcity. Given the inherent challenges of light attenuation and backscattering in water, acquiring clear underwater images or precise depth information is notably difficult and costly. Consequently, learning-based approaches often rely on synthetic data or turn to unsupervised or self-supervised methods to mitigate this lack of data. Nonetheless, the performance of these methods is often constrained by the domain gap and looser constraints. In this paper, we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth data. This approach facilitates the training of supervised models for underwater depth estimation, effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to prior synthetic datasets that merely apply style transfer to terrestrial images without altering the scene content, our approach uniquely creates vibrant, non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically, we introduce a unique Depth2Underwater ControlNet, trained on specially prepared \{Underwater, Depth, Text\} data triplets, for this generation task. Our newly developed dataset enables terrestrial depth estimation models to achieve considerable improvements, both quantitatively and qualitatively, on unseen underwater images, surpassing their terrestrial pre-trained counterparts. Moreover, the enhanced depth accuracy for underwater scenes also aids underwater image restoration techniques that rely on depth maps, further demonstrating our dataset's utility. The dataset will be available at https://github.com/zkawfanx/Atlantis.
CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code

Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribution addresses a notable research gap marked by the absence of a long-term temporal dimension in existing code change datasets, limiting their suitability in lifelong learning scenarios. In contrast, our dataset aims to comprehensively capture code changes across the entire release history of open-source software repositories. In this work, we introduce an initial version of CodeLL, comprising 71 machine-learning-based projects mined from Software Heritage. This dataset enables the extraction and in-depth analysis of code changes spanning 2,483 releases at both the method and API levels. CodeLL enables researchers studying the behaviour of LMs in lifelong fine-tuning settings for learning code changes. Additionally, the dataset can help studying data distribution shifts within software repositories and the evolution of API usages over time.
Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective

Gradient inversion attacks can leak data privacy when clients share weight updates with the server in federated learning (FL). Existing studies mainly use L2 or cosine distance as the loss function for gradient matching in the attack. Our empirical investigation shows that the vulnerability ranking varies with the loss function used. Gradient norm, which is commonly used as a vulnerability proxy for gradient inversion attack, cannot explain this as it remains constant regardless of the loss function for gradient matching. In this paper, we propose a loss-aware vulnerability proxy (LAVP) for the first time. LAVP refers to either the maximum or minimum eigenvalue of the Hessian with respect to gradient matching loss at ground truth. This suggestion is based on our theoretical findings regarding the local optimization of the gradient inversion in proximity to the ground truth, which corresponds to the worst case attack scenario. We demonstrate the effectiveness of LAVP on various architectures and datasets, showing its consistent superiority over the gradient norm in capturing sample vulnerabilities. The performance of each proxy is measured in terms of Spearman's rank correlation with respect to several similarity scores. This work will contribute to enhancing FL security against any potential loss functions beyond L2 or cosine distance in the future.
Quantum steering ellipsoids and quantum obesity in critical systems

Quantum obesity (QO) is new function used to quantify quantum correlations beyond entanglement, which also works as a witness for entanglement. Thanks to its analyticity for arbitrary state of bipartite systems, it represents an advantage with respect to other quantum correlations, like quantum discord for example. In this work we show that QO is a fundamental quantity to observe signature of quantum phase transitions. We also describe a mechanism based on local filtering operations able to intensify the critical behavior of the QO near to the transition point. To this end, we introduce a theorem stating how QO changes under local quantum operations and classical communications. This work opens perspective for the characterization of new phenomena in quantum critical systems through the analytically computable pairwise QO.
Tensor Train Decomposition for Adversarial Attacks on Computer Vision Models

Deep neural networks (DNNs) are widely used today, but they are vulnerable to adversarial attacks. To develop effective methods of defense, it is important to understand the potential weak spots of DNNs. Often attacks are organized taking into account the architecture of models (white-box approach) and based on gradient methods, but for real-world DNNs this approach in most cases is impossible. At the same time, several gradient-free optimization algorithms are used to attack black-box models. However, classical methods are often ineffective in the multidimensional case. To organize black-box attacks for computer vision models, in this work, we propose the use of an optimizer based on the low-rank tensor train (TT) format, which has gained popularity in various practical multidimensional applications in recent years. Combined with the attribution of the target image, which is built by the auxiliary (white-box) model, the TT-based optimization method makes it possible to organize an effective black-box attack by small perturbation of pixels in the target image. The superiority of the proposed approach over three popular baselines is demonstrated for five modern DNNs on the ImageNet dataset.
Mitigating a discrete sign problem with extreme learning machines

An extreme learning machine is a neural network in which only the weights in the last layer are changed during training; for such networks training can be performed efficiently and deterministically. We use an extreme learning machine to construct a control variate that tames the sign problem in the classical Ising model at imaginary external magnetic field. Using this control variate, we directly compute the partition function at imaginary magnetic field in two and three dimensions, yielding information on the positions of Lee-Yang zeros.
Automated Estimation of Plasma Temperature and Density from Emission Spectroscopy

This paper introduces a novel approach for automated estimation of plasma temperature and density using emission spectroscopy, integrating Bayesian inference with sophisticated physical models. We provide an in-depth examination of Bayesian methods applied to the complexities of plasma diagnostics, supported by a robust framework of physical and measurement models. Our methodology is validated through experimental observations, focusing on individual and sequential shot analyses. The results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of plasma parameter estimation, marking a significant advancement in the field of emission spectroscopy for plasma diagnostics. This study not only offers a new perspective in plasma analysis but also paves the way for further research and applications in nuclear instrumentation and related domains.