Reviewerly/exHarmony huggingface dataset:
SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation

Despite their ability to generate high-resolution and diverse images from text prompts, text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to accelerate these models. However, previous distillation methods fail to retain the generation quality while requiring a significant amount of images for training, either from real data or synthetically generated by the teacher model. In response to this limitation, we present a novel image-free distillation scheme named $\textbf{SwiftBrush}$. Drawing inspiration from text-to-3D synthesis, in which a 3D neural radiance field that aligns with the input prompt can be obtained from a 2D text-to-image diffusion prior via a specialized loss without the use of any 3D data ground-truth, our approach re-purposes that same loss for distilling a pretrained multi-step text-to-image model to a student network that can generate high-fidelity images with just a single inference step. In spite of its simplicity, our model stands as one of the first one-step text-to-image generators that can produce images of comparable quality to Stable Diffusion without reliance on any training image data. Remarkably, SwiftBrush achieves an FID score of $\textbf{16.67}$ and a CLIP score of $\textbf{0.29}$ on the COCO-30K benchmark, achieving competitive results or even substantially surpassing existing state-of-the-art distillation techniques.
TaskMet: Task-Driven Metric Learning for Model Learning

Deep learning models are often deployed in downstream tasks that the training procedure may not be aware of. For example, models solely trained to achieve accurate predictions may struggle to perform well on downstream tasks because seemingly small prediction errors may incur drastic task errors. The standard end-to-end learning approach is to make the task loss differentiable or to introduce a differentiable surrogate that the model can be trained on. In these settings, the task loss needs to be carefully balanced with the prediction loss because they may have conflicting objectives. We propose take the task loss signal one level deeper than the parameters of the model and use it to learn the parameters of the loss function the model is trained on, which can be done by learning a metric in the prediction space. This approach does not alter the optimal prediction model itself, but rather changes the model learning to emphasize the information important for the downstream task. This enables us to achieve the best of both worlds: a prediction model trained in the original prediction space while also being valuable for the desired downstream task. We validate our approach through experiments conducted in two main settings: 1) decision-focused model learning scenarios involving portfolio optimization and budget allocation, and 2) reinforcement learning in noisy environments with distracting states. The source code to reproduce our experiments is available at https://github.com/facebookresearch/taskmet
Mobile application to support the teaching of computer networks

Mobile guide for Computer Networking subjects is an application that serves as an aid to Computer Networking objects. The main goal of the work was to create a set of tests that would help students in education.When designing the application, we also created a user profile that allows students to track their progress, we additionally implemented the theory and configuration sections. The theory and configuration section in the application provide quick access to theoretical and configurational instructions. The results of the testing to which the application has been subjected clearly indicate that the application is usable in the teaching process
Utilizing Word Embedding Representations in Word Sense Analysis of Japanese Spelling Variants

Spelling variants are an important linguistic phenomenon. In this paper, we focused on Japanese spelling variants caused by two character types, Hiragana and Kanji and attempted to analyze the differences of their word meanings. To realize an in-depth analysis capable of catching word meanings, we utilized word embedding representations. Using 293 variant pairs, we found that spelling variants caused by character types can have different meanings and that words with some semantic relations have a similar usage of the variants in a text.
YOLOMH:You Only Look Once for Multi-task Perception Network High-efficiency

Abstract Aiming at the requirements of high accuracy, lightweight and real-time performance of the panoptic driving perception system, this paper proposes an efficient multi-task network(YOLOMH). The network uses a shared encoder and three independent decoding heads to simultaneously complete the three major panoptic driving perception tasks of traffic object detection, road drivable area segmentation and road lane segmentation. Thanks to our innovative design of the YOLOMH network structure: first, we design an appropriate information input structure based on the differences information requirements between different tasks, and secondly, we propose a Hybrid Deep Atrous Spatial Pyramid Pooling(HDASPP) module to efficiently complete the feature fusion work of the neck network, and finally effective approaches such as anchor-free detection head and Depthwise Separable Convolution(DCN) are introduced into the network, making the network more efficient while being lightweight. Experimental results show that our model achieves competitive results in both accuracy and speed on the challenging BDD100K dataset, especially in terms of inference speed, The model’s inference speed on NVIDIA TESLA V100 is as high as 107 Frames Per Second(FPS), far exceeding the 49 FPS of the YOLOP network under the same experimental settings. The final visualization shows that YOLOMH can excellently complete the panoptic driving perception tasks, which is conducive to the safe and reliable autonomous driving of autonomous vehicles.
Comparative Performance of Machine Learning Algorithms for Predicting Future Committer in Blockchain Projects

The success of blockchain projects depends, to a large extent, on the developer's involvement. Source code-commit privilege to the project is typically restricted to a few developers known as committers. Project leaders continuously search for new committers to evolve into a high-quality blockchain. However, promoting developers into committers is risky, mainly when the promoted committers exhibit low involvement behaviour. Hence, the committer assessment process is critical to the successful evolution of blockchain projects. The phenomenon of developer induction as code committers has previously been explored in the literature. However, previous studies employed objective measures such as the number of bugs report and code patches to appraise the developers activities for promotion purposes. Although these approaches are significant, behavioural tendencies influencing developers' activities are ignored. There have, however, been comparatively few investigations on a subjective measure that evaluate developers' perception of their activities for promotion purposes. This study aims to appraise blockchain developers' perceptions for predicting future committers in blockchain projects. In this study, 173 blockchain developers’ perceptions were gathered. The study employed hybrid analyses such as Structural Equation Modelling (SEM) to first analyse the survey data and Machine Learning (ML) algorithms to predict future committers. The performance of various ML algorithms was evaluated compared to classification performance indicators such as the F1 score, accuracy, precision, and AUC. In addition, the study also investigates the most important factors that predict future committers. The results indicate that XGBoost algorithm achieves the best performance with an accuracy of 0.94. Moreover, the most important factors in predicting future committers in a blockchain project are project desertion, developer involvement, decision-right delegation and system integration.
Rhetoric in Friday Sermons

This study is a descriptive study using the Modern Rhetorical Theory by Enos and Brown (1993) as the basis of the study. Used content analysis method to examine the textual and contextual text. The primary focus or objective of the study is to identify the type, structure and style of rhetoric found in Friday sermons. The results showed that all types of rhetoric that kind of narrative, descriptive, exposition, argumentative and persuasive in Friday sermons. Based on the analysis of the structure angle, it appears that rhetoric is used in all forms of the same language in the form of words, phrases, clauses, sentences, or discourse. While the analysis of rhetorical style corner resulted in a finding that suggests that the Friday sermon using a variety of rhetorical style style comparison, linkage, and conflict recurrence may be treated as the influence listener audience. In other words, rhetoric in the Friday sermon is an important element used by the preacher in enlivening the atmosphere and influencing the congregation's thinking.
Correction: Youn et al. Research on Cyber ISR Visualization Method Based on BGP Archive Data through Hacking Case Analysis of North Korean Cyber-Attack Groups. Electronics 2022, 11, 4142

There was an error in the original publication [...]
Empirical Validation of Variable Method Interaction Cohesion Metric (VMICM) for Enhancing Reusability of Object-Oriented (O-O) Software

Any object-oriented (O-O) module's primary goal is to build classes with a high level of coherent interaction between variables and methods. To increase the quality of O-O (Object-Oriented) software, various metrics emphasizing cohesiveness have been established so far. These metrics operate on both the design and the code levels. However, these metrics still fall short of fully measuring the cohesion of object-oriented (O-O) software. Based on several concepts of cohesive interlinkages between variables and procedures, the study proposed an enhanced cohesion metric. The four forms of cohesive linkages (VMRv, VMMv, VMRTv, and VMOv) between variables and procedures were the focus of this study. The axiomatic frame of reference was employed for theoretical validation, and univariate logistic regression was applied in the MATLAB environment for empirical validation. The approach of univariate logistic regression has been adopted because it provides incredibly accurate data and can even be applied to datasets that can be linearly separated. The proposed metric exhibits high cohesion, which is the ultimate perspective of a highly reusable Object- Oriented (O-O) module, as evidenced by the testing phase and even training the real dataset with reusability prediction in terms of high values of precision, recall, R2, and low value of RSME of VMICM metric. The study results demonstrated that the proposed metric can act as a measure for predicting the reusability of the Object-Oriented (O-O) system.
The Use of Online Games for English Vocabulary Learning Media in Teacher Working Group: Some Advantages and Barriers

This study aims to measure the extent of the Kahoot! Effectiveness in improving the professional competence of English teachers in terms of memorizing vocabulary. The study was conducted in the teacher working group (KKG or MGMP) by taking 30 English teachers at random. The system that will be carried out is by control and experiment class then compares the scores both of them. In addition, an in-depth exploration (mix method) was conducted by interviewing the teacher whether the Kahoot! may be used in the next forum. The results of the study showed that control group only could memorize 5,33 in average however experiment class got 8,6 in average so it means that there is 33% distinction between both of them. Then, this result is strengthened by calculation on Wilcoxon Signed Rank that showed that the sig value 0,002 is greater than 0.05 so it also means like aforementioned. Kahoot app! found it very helpful in memorizing vocabulary because of its flexibility that it can be accessed from anywhere (e-class) but there are still many senior teachers, who are technology savvy, cannot access kahoot! especially if it will be implemented in the KKG or MGMP of English teachers.
Empowering knowledge with Virtual and Augmented Reality

Recent global advancements in ICT technologies have motivated our research to assess the impact of Virtual and Augmented Reality on teaching and learning. In particular, we conducted a rigorous study on the effects on learning by subjecting classes of students to an experience of these emerging technologies and measuring their feelings before and after the experiment through questionnaires. We carried out the research involving three high schools and a total of 162 students aged between 15 and 20. During the meetings, we made available two applications that we developed. The first is a Virtual Reality environment that enables the user to explore a room in which the system spawns three-dimensional objects defined by the teacher. The objects may represent mathematical functions, physics simulations, scenes relating to historical re-enactments, etc. The second is an Augmented Reality smartphone application that allows three-dimensional figures to be observed via the smartphone screen by framing a marker printed on paper. Both applications are realised with free software, trying to minimise the technical requirements for their operation and guaranteeing reliability and usability. The work concludes by analysing the results obtained and comparing the effectiveness of the two technologies tested. Results of tests conducted in three Italian schools show that Augmented Reality is seen as a useful tool in education by about 94.43%. Importantly, the use of these technologies requires an inclusive design that involves all students, regardless of their level of familiarity with the technology. The overall analysis reveals that both technologies performed well, and students value them for their intuitiveness and level of immersion. The in presence experience is more effective for using immersive applications fostered by student collaboration.
Use of Mobile Apps for Enhanced Vocabulary Acquisition: A Comparative Study among Saudi EFL Students

The paper presents a methodological approach and a summary of the major findings of the study. The main aim of the research was to investigate the effects of using mobile apps on vocabulary learning outcomes and motivation among Saudi EFL learners. The study adopts a survey method, combining quantitative data from a questionnaire. The survey was done among intermediate and elementary students including the type of sampling. The significance of the study lies in the fact that appropriate vocabulary acquisition is essential for good communication, but acquiring new words and using them can be dull. The research aimed to explore innovative ways of learning vocabulary in a foreign language by using mobile technology and learners’ perceptions and attitudes towards such apps that provide fun-filled learning. The main question addressed by the research was whether using mobile apps for vocabulary learning would lead to better outcomes than other methods. The context of the study was Saudi Arabia, where English is taught as a foreign language. The research tools used were mobile apps for vocabulary learning, which were compared with other methods. The results showed that the groups that used mobile apps for vocabulary learning significantly outperformed the other groups on vocabulary tests, indicating that mobile apps can be an effective tool for enhancing vocabulary acquisition among Saudi EFL learners.
Dissecting the EIP-2930 Optional Access Lists

Ethereum introduced Transaction Access Lists (TALs) in 2020 to optimize gas costs during transaction execution. In this work, we present a comprehensive analysis of TALs in Ethereum, focusing on adoption, quality, and gas savings. Analyzing a full month of mainnet data with 31,954,474 transactions, we found that only 1.46% of transactions included a TAL, even though 42.6% of transactions would have benefited from it. On average, access lists can save around 0.29% of gas costs, equivalent to approximately 3,450 ETH (roughly US$ 5 Mio) per year. However, 19.6% of TALs included by transactions contained imperfections, causing almost 11.8% of transactions to pay more gas with TAL than without. We find that these inaccuracies are caused by the unknown state at the time of the TAL computation as well as imperfect TAL computations provided by all major Ethereum clients. We thus compare the gas savings when calculating the TAL at the beginning of the block vs. calculating it on the correct state, to find that the unknown state is a major source of TAL inaccuracies. Finally, we implement an ideal TAL computation for the Erigon client to highlight the cost of these flawed implementations.
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReST$^{EM}$ scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data.
Concurrent Density Estimation with Wasserstein Autoencoders: Some Statistical Insights

Variational Autoencoders (VAEs) have been a pioneering force in the realm of deep generative models. Amongst its legions of progenies, Wasserstein Autoencoders (WAEs) stand out in particular due to the dual offering of heightened generative quality and a strong theoretical backbone. WAEs consist of an encoding and a decoding network forming a bottleneck with the prime objective of generating new samples resembling the ones it was catered to. In the process, they aim to achieve a target latent representation of the encoded data. Our work is an attempt to offer a theoretical understanding of the machinery behind WAEs. From a statistical viewpoint, we pose the problem as concurrent density estimation tasks based on neural network-induced transformations. This allows us to establish deterministic upper bounds on the realized errors WAEs commit. We also analyze the propagation of these stochastic errors in the presence of adversaries. As a result, both the large sample properties of the reconstructed distribution and the resilience of WAE models are explored.
AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentation

The success of deep networks in medical image segmentation relies heavily on massive labeled training data. However, acquiring dense annotations is a time-consuming process. Weakly-supervised methods normally employ less expensive forms of supervision, among which scribbles started to gain popularity lately thanks to its flexibility. However, due to lack of shape and boundary information, it is extremely challenging to train a deep network on scribbles that generalizes on unlabeled pixels. In this paper, we present a straightforward yet effective scribble supervised learning framework. Inspired by recent advances of transformer based segmentation, we create a pluggable spatial self-attention module which could be attached on top of any internal feature layers of arbitrary fully convolutional network (FCN) backbone. The module infuses global interaction while keeping the efficiency of convolutions. Descended from this module, we construct a similarity metric based on normalized and symmetrized attention. This attentive similarity leads to a novel regularization loss that imposes consistency between segmentation prediction and visual affinity. This attentive similarity loss optimizes the alignment of FCN encoders, attention mapping and model prediction. Ultimately, the proposed FCN+Attention architecture can be trained end-to-end guided by a combination of three learning objectives: partial segmentation loss, a customized masked conditional random fields and the proposed attentive similarity loss. Extensive experiments on public datasets (ACDC and CHAOS) showed that our framework not just out-performs existing state-of-the-art, but also delivers close performance to fully-supervised benchmark. Code will be available upon publication.
0.1% Data Makes Segment Anything Slim

The formidable model size and demanding computational requirements of Segment Anything Model (SAM) have rendered it cumbersome for deployment on resource-constrained devices. Existing approaches for SAM compression typically involve training a new network from scratch, posing a challenging trade-off between compression costs and model performance. To address this issue, this paper introduces SlimSAM, a novel SAM compression method that achieves superior performance with remarkably low training costs. This is achieved by the efficient reuse of pre-trained SAMs through a unified pruning-distillation framework. To enhance knowledge inheritance from the original SAM, we employ an innovative alternate slimming strategy that partitions the compression process into a progressive procedure. Diverging from prior pruning techniques, we meticulously prune and distill decoupled model structures in an alternating fashion. Furthermore, a novel label-free pruning criterion is also proposed to align the pruning objective with the optimization target, thereby boosting the post-distillation after pruning. SlimSAM yields significant performance improvements while demanding over 10 times less training costs than any other existing methods. Even when compared to the original SAM-H, SlimSAM achieves approaching performance while reducing parameter counts to merely 0.9% (5.7M), MACs to 0.8% (21G), and requiring only 0.1% (10k) of the SAM training data. Code is available at url{http://github.com/czg1225/SlimSAM}.
Multi-view Inversion for 3D-aware Generative Adversarial Networks

Current 3D GAN inversion methods for human heads typically use only one single frontal image to reconstruct the whole 3D head model. This leaves out meaningful information when multi-view data or dynamic videos are available. Our method builds on existing state-of-the-art 3D GAN inversion techniques to allow for consistent and simultaneous inversion of multiple views of the same subject. We employ a multi-latent extension to handle inconsistencies present in dynamic face videos to re-synthesize consistent 3D representations from the sequence. As our method uses additional information about the target subject, we observe significant enhancements in both geometric accuracy and image quality, particularly when rendering from wide viewing angles. Moreover, we demonstrate the editability of our inverted 3D renderings, which distinguishes them from NeRF-based scene reconstructions.
Artificial Neural Nets and the Representation of Human Concepts

What do artificial neural networks (ANNs) learn? The machine learning (ML) community shares the narrative that ANNs must develop abstract human concepts to perform complex tasks. Some go even further and believe that these concepts are stored in individual units of the network. Based on current research, I systematically investigate the assumptions underlying this narrative. I conclude that ANNs are indeed capable of performing complex prediction tasks, and that they may learn human and non-human concepts to do so. However, evidence indicates that ANNs do not represent these concepts in individual units.