Reviewerly/exHarmony huggingface dataset:
KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching

Dual-lens super-resolution (SR) is a practical scenario for reference (Ref) based SR by utilizing the telephoto image (Ref) to assist the super-resolution of the low-resolution wide-angle image (LR input). Different from general RefSR, the Ref in dual-lens SR only covers the overlapped field of view (FoV) area. However, current dual-lens SR methods rarely utilize these specific characteristics and directly perform dense matching between the LR input and Ref. Due to the resolution gap between LR and Ref, the matching may miss the best-matched candidate and destroy the consistent structures in the overlapped FoV area. Different from them, we propose to first align the Ref with the center region (namely the overlapped FoV area) of the LR input by combining global warping and local warping to make the aligned Ref be sharp and consistent. Then, we formulate the aligned Ref and LR center as value-key pairs, and the corner region of the LR is formulated as queries. In this way, we propose a kernel-free matching strategy by matching between the LR-corner (query) and LR-center (key) regions, and the corresponding aligned Ref (value) can be warped to the corner region of the target. Our kernel-free matching strategy avoids the resolution gap between LR and Ref, which makes our network have better generalization ability. In addition, we construct a DuSR-Real dataset with (LR, Ref, HR) triples, where the LR and HR are well aligned. Experiments on three datasets demonstrate that our method outperforms the second-best method by a large margin. Our code and dataset are available at https://github.com/ZifanCui/KeDuSR.
Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation

This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmentation methods. Unlike conventional IoU measure or its variants, which only consider the proximity of anchor and ground-truth boxes; GmaIoU additionally takes into account the segmentation mask. This enables GmaIoU to provide more accurate supervision during training. We demonstrate the effectiveness of GmaIoU by replacing IoU with our GmaIoU in ATSS, a state-of-the-art (SOTA) assigner. Then, we train YOLACT, a real-time instance segmentation method, using our GmaIoU-based ATSS assigner. The resulting YOLACT based on the GmaIoU assigner outperforms (i) ATSS with IoU by $\sim 1.0-1.5$ mask AP, (ii) YOLACT with a fixed IoU threshold assigner by $\sim 1.5-2$ mask AP over different image sizes and (iii) decreases the inference time by $25 \%$ owing to using less anchors. Taking advantage of this efficiency, we further devise GmaYOLACT, a faster and $+7$ mask AP points more accurate detector than YOLACT. Our best model achieves $38.7$ mask AP at $26$ fps on COCO test-dev establishing a new state-of-the-art for real-time instance segmentation.
CluBear: A Subsampling Package for Interactive Statistical Analysis with Massive Data on A Single Machine

This article introduces CluBear, a Python-based open-source package for interactive massive data analysis. The key feature of CluBear is that it enables users to conduct convenient and interactive statistical analysis of massive data with only a traditional single-computer system. Thus, CluBear provides a cost-effective solution when mining large-scale datasets. In addition, the CluBear package integrates many commonly used statistical and graphical tools, which are useful for most commonly encountered data analysis tasks.
Tighter List-Size Bounds for List-Decoding and Recovery of Folded Reed-Solomon and Multiplicity Codes

Folded Reed-Solomon (FRS) and univariate multiplicity codes are prominent polynomial codes over finite fields, renowned for achieving list decoding capacity. These codes have found a wide range of applications beyond the traditional scope of coding theory. In this paper, we introduce improved bounds on the list size for list decoding of these codes, achieved through a more streamlined proof method. Additionally, we refine an existing randomized algorithm to output the codewords on the list, enhancing its success probability and reducing its running time. Lastly, we establish list-size bounds for a fixed decoding parameter. Notably, our results demonstrate that FRS codes asymptotically attain the generalized Singleton bound for a list of size $2$ over a relatively small alphabet, marking the first explicit instance of a code with this property.
Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math

High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce \textsc{MathPile}, a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens. Throughout its creation, we adhered to the principle of ``\emph{less is more}'', firmly believing in the supremacy of data quality over quantity, even in the pre-training phase. Our meticulous data collection and processing efforts included a complex suite of preprocessing, prefiltering, language identification, cleaning, filtering, and deduplication, ensuring the high quality of our corpus. Furthermore, we performed data contamination detection on downstream benchmark test sets to eliminate duplicates. We hope our \textsc{MathPile} can help to enhance the mathematical reasoning abilities of language models. We plan to open-source different versions of \mathpile with the scripts used for processing, to facilitate future developments in this field.
Large Language Model for Causal Decision Making

Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics. However, their capability to inference based on user-specified structured data and knowledge in corpus-rare concepts like causal decision-making is still limited. In this work, we explore the possibility of fine-tuning an open-sourced LLM into LLM4Causal, which can identify the causal task, execute a corresponding function, and interpret its numerical results based on users' queries and the provided dataset. Meanwhile, we propose a data generation process for more controllable GPT prompting and present two instruction-tuning datasets: (1) Causal-Retrieval-Bench for causal problem identification and input parameter extraction for causal function calling and (2) Causal-Interpret-Bench for in-context causal interpretation. With three case studies, we showed that LLM4Causal can deliver end-to-end solutions for causal problems and provide easy-to-understand answers. Numerical studies also reveal that it has a remarkable ability to identify the correct causal task given a query.
4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

Aided by text-to-image and text-to-video diffusion models, existing 4D content creation pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene. However, as these pipelines generate 4D content from text or image inputs, they incur significant time and effort in prompt engineering through trial and error. This work introduces 4DGen, a novel, holistic framework for grounded 4D content creation that decomposes the 4D generation task into multiple stages. We identify static 3D assets and monocular video sequences as key components in constructing the 4D content. Our pipeline facilitates conditional 4D generation, enabling users to specify geometry (3D assets) and motion (monocular videos), thus offering superior control over content creation. Furthermore, we construct our 4D representation using dynamic 3D Gaussians, which permits efficient, high-resolution supervision through rendering during training, thereby facilitating high-quality 4D generation. Additionally, we employ spatial-temporal pseudo labels on anchor frames, along with seamless consistency priors implemented through 3D-aware score distillation sampling and smoothness regularizations. Compared to existing baselines, our approach yields competitive results in faithfully reconstructing input signals and realistically inferring renderings from novel viewpoints and timesteps. Most importantly, our method supports grounded generation, offering users enhanced control, a feature difficult to achieve with previous methods. Project page: https://vita-group.github.io/4DGen/
Unsupervised Universal Image Segmentation

Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g., STEGO) or class-agnostic instance segmentation (e.g., CutLER), but not both (i.e., panoptic segmentation). We propose an Unsupervised Universal Segmentation model (U2Seg) adept at performing various image segmentation tasks -- instance, semantic and panoptic -- using a novel unified framework. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models followed by clustering; each cluster represents different semantic and/or instance membership of pixels. We then self-train the model on these pseudo semantic labels, yielding substantial performance gains over specialized methods tailored to each task: a +2.6 AP$^{\text{box}}$ boost vs. CutLER in unsupervised instance segmentation on COCO and a +7.0 PixelAcc increase (vs. STEGO) in unsupervised semantic segmentation on COCOStuff. Moreover, our method sets up a new baseline for unsupervised panoptic segmentation, which has not been previously explored. U2Seg is also a strong pretrained model for few-shot segmentation, surpassing CutLER by +5.0 AP$^{\text{mask}}$ when trained on a low-data regime, e.g., only 1% COCO labels. We hope our simple yet effective method can inspire more research on unsupervised universal image segmentation.
Monocular 3D Hand Mesh Recovery via Dual Noise Estimation

Current parametric models have made notable progress in 3D hand pose and shape estimation. However, due to the fixed hand topology and complex hand poses, current models are hard to generate meshes that are aligned with the image well. To tackle this issue, we introduce a dual noise estimation method in this paper. Given a single-view image as input, we first adopt a baseline parametric regressor to obtain the coarse hand meshes. We assume the mesh vertices and their image-plane projections are noisy, and can be associated in a unified probabilistic model. We then learn the distributions of noise to refine mesh vertices and their projections. The refined vertices are further utilized to refine camera parameters in a closed-form manner. Consequently, our method obtains well-aligned and high-quality 3D hand meshes. Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10$\%$ but also achieves state-of-the-art performance. Project page: \url{https://github.com/hanhuili/DNE4Hand}.
Blockchain Participation Games

We study game-theoretic models for capturing participation in blockchain systems. Existing blockchains can be naturally viewed as games, where a set of potentially interested users is faced with the dilemma of whether to engage with the protocol or not. Engagement here implies that the user will be asked to complete certain tasks, whenever she is selected to contribute, according to some stochastic process. Apart from the basic dilemma of engaging or not, even more strategic considerations arise in systems where users may be able to declare participation and then retract (while still being able to receive rewards). We propose two models for studying such games, with the first one focusing on the basic dilemma of engaging or not, whereas the latter focuses on the retraction effects. In both models we provide characterization results or necessary conditions on the structure of Nash equilibria. Our findings reveal that appropriate reward mechanisms can be used to stimulate participation and avoid negative effects of free riding, results that are in line with real world blockchain system deployments.
DISTANCE LERNING AS AN INNOVATIVE EDUCATIONAL TECHNOLOGY

The article presents the features of distance learning as one of the educational technologies. The main purpose of the article is to consider distance learning as an innovative technology that is actively used in the modern educational process. The authors utilized various methodologies, including the examination and analysis of pedagogical literature, the systematic organization, and generalization of experiences, the study of pedagogical practices, and experimental research involving observation and the analysis of activity outcomes. Both quantitative and qualitative analyses, along with mathematical statistics, were applied. The educational process is viewed as a system that encompasses the reproduction of a scientific worldview within its content. Its primary goal is the organization and transmission of knowledge, harmonizing content, forms, means, and methods of learning in a cohesive manner. The authors scrutinized the papers of scholars exploring aspects such as contemporary innovative technologies, the digital revolution, international experiences in economic digitization, and the distinctive features of distance learning during quarantine. Educational technology is assumed as a systematic method of creating, applying and defining the entire educational process of teaching and learning knowledge. Innovative mechanisms of education development are presented. Distance learning is analyzed, it showed the methods used and the features of this technology of education. Distance education is relevant in modern society, especially for those students who are deprived of the opportunity to attend offline classes because of various circumstances. It is concluded that distance learning is an independent, effective form of learning.
Max as a Digital Platform for Noise Music Performance

This article explores Max as a digital platform for performing noise music through a practice-led research method. The practice-led research method was used to explore the possibilities of building Max patches, while content analysis method was used to analyse the outcome of the patches. Several Max patches were created to explore the potential of Max as an alternative approach for performing noise music. Findings show that Max can replicate the audio processing methods used in conventional performance. Due to Max capabilities, some of these methods could be automated and arranged prior to the performance. In addition, Max patches featured changing sound, random pitches, mixture of pre-recorded audio source and live instrument, and drone sound combined with automatic constant real-time audio self-processing and automatic audio panning, a feature that seldom appears in the local noise music scene. In conclusion, this research argues that Max has much potential for creating a variety of digital sounds that are harsh and dissonant to the ears, therefore contributing to the musical diversity in noise music performance. These sounds are the results of the features of audio self-processing, random pitches, automatic audio panning object and self-changing pitched drone audio signals relying on random MIDI values that appeared in Max.
Extracting Sentiments from Youtube Comments Using Deep Learning and Transformers

Abstract: YouTube is the most used social media platform, and it has been the most popular website where users can post the video. The public generally does comment, like or dislike, video-sharing on a YouTube video. Comment plays a vital role in expressing opinions and mindset, and it is used as an expression of public opinion. The massive amount of comment is generated mainly on famous channels where challenges arise to analyse public opinion or behaviour regarding that particular video. This project proposes sentiment analysis on YouTube video by Natural Language Processing (NLP) technique along with Deep Learning techniques Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) models. Sentiment analysis is when comprehension, citation, and processing of text-based data is done, and it directly converts it into sentiment information. This analysis help users to get the report of their YouTube video. The output of this analysis gives the classification of sentiment analysis, as Positive, negative, or neutral.
Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet

The efficiency of the Honeywords approach has been proven to be a significant tool for boosting password security. The suggested system utilizes the Meerkat Clan Algorithm (MCA) in conjunction with WordNet to produce honeywords, thereby enhancing the level of password security. The technique of generating honeywords involves data sources from WordNet, which contributes to the improvement of authenticity and diversity in the honeywords. The method encompasses a series of consecutive stages, which include the tokenization of passwords, the formation of alphabet tokens using the Meerkat Clan Algorithm (MCA), the handling of digit tokens, the creation of unique character tokens, and the consolidation of honeywords. The optimization of the performance of the Meerkat Clan Algorithm (MCA) involves the careful selection of parameters. The experimental findings have exhibited noteworthy levels of precision and optimum efficacy, particularly in tasks such as proposing words with similar meanings, forecasting numerical values, and producing distinctive symbols. The attainment of this achievement is facilitated by a confluence of factors, encompassing the caliber of data, the judicious use of algorithms or models, and the ongoing process of iterative improvement to consistently enhance outcomes. In order to achieve the appropriate levels of accuracy and functionality, it is crucial to engage in the process of conducting experiments, thoroughly testing the system, and making necessary improvements. The empirical findings provide confirmation of the effectiveness of the MCA in producing a varied and protected collection of honeywords. This is especially evident in the case of alphabet tokens, which are distinguished by their autonomous creation and strong security characteristics. The analysis of correction rates, specifically in relation to the password "Lion1999*," demonstrates the aforementioned results. This study reveals an average accuracy of honeyword production up to 0.729847632111541. In the same manner, the accuracy of the password "house2000" is determined to be 0.761325846711256. Additionally, when considering a sample of 100 passwords, the mean accuracy of honeyword creation is calculated to be 0.7073897168887518. The findings collectively highlight the effectiveness of the MCA in generating honeywords that possess improved security characteristics.
Predicting personalized saliency map for people with autism spectrum disorder

People with autism spectrum disorder usually exhibit heterogeneous gaze patterns. Universal saliency prediction, which generates salient regions based on high fixations across all observers, is limited to analyzing the visual attention of autism spectrum disorders. To solve the problem, we propose a learning-based method named PSMANet to predict the personalized saliency map based on personal information. Collecting personal information and collecting large-scale datasets are challenging tasks for people with autism spectrum disorders, since they often suffer from deficits in social communication and interaction. The proposed approach introduces the image-similarity-measure based embedding to extract personal information and transfers the saliency distribution knowledge from universal saliency prediction to personalized saliency prediction. For evaluating our network, two popular metrics, Normalized Scanpath Salience (NSS) and Area Under Curve (AUC), are used. The experimental results show that it achieves good performance on the databases of people with autism spectrum disorder.
Inclusion Probability in Simple Random Sampling by Hypergeometric Distribution

We review important probability issues in sampling from simple random sampling without replacement. The inclusion probability can be calculated by enumerating samples which is formidable for most cases of large samples or large population. A good number of possible situations have been considered. We prove that hypergeometric mass function provides an elegant solution to the problem.
Research on MEMS sensor-based police physical fitness assistance training system

In order to address the problems of lack of methods and low efficiency of coaching in traditional police physical fitness-assisted training systems, this paper proposes a Micro-electromechanical system (MEMS) sensor-based sports assistance training system. Using the MEMS sensor's working principle, the system's hardware design is completed through the design of the logical framework of the physical fitness-assisted training system and the design of the physical fitness-assisted training sensor. At the same time, the trainer's arm motion posture angle is calculated by extracting the body motion trajectory features and combining them with the Kalman filter method, and then the trainer's posture estimation algorithm is designed, and finally, the software design of the system is completed, to realize the design of the motion-assisted training system. The experimental test results show that the MEMS sensor-based police physical fitness-assisted training system can increase the number of assisted training methods and improve coaching efficiency, thus greatly improving the performance of the police physical fitness-assisted training system. The system designed in this paper is of great significance to the promotion of motion capture technology and its application in the field of police physical fitness-assisted training.
The hybrid method for finding the roots of a polynomial over finite fields based on affine expansion

This paper proposes a hybrid method to find the roots of a polynomial in a finite field based on combining the polynomial decomposition into the sum of multiples of the affine polynomial with the analytical method to find the roots of low-order polynomials. The proposed method allows a significant reduction in complexity and is applicable in the design of low-latency BCH and Reed-Solomon decoders.
Advanced Geostatistical Techniques for Building 3D Geological Modeling: A Case Study from Cretaceous Reservoir in Bai Hassan Oil Field

A 3D Geological model was generated using an advanced geostatistical method for the Cretaceous reservoir in the Bai Hassan oil field. In this study, a 3D geological model was built based on data from four wells for the petrophysical property distribution of permeability, porosity, water saturation, and NTG by using Petrel 2021 software. The geological model was divided into a structural model and a property model. The geological structures of the cretaceous reservoir in the Bai Hassan oil field represent elongated anticline folds with two faults, which had been clarified in the 3D Structural model. Thirteen formations represent the Cretaceous reservoir which includes (Shiranish, Mashurah, U.kometan, Kometan Shale, L. Kometan, Gulneni, Dokan, Mauddud, Jawan/Mud, Batiwah, Shuaiba, Garagu, L.Sarmord). According to the property model, the model for each petrophysical property was constructed based on core data and CPI. By using the geostatistical method, the property model was constructed. The Mauddud Formation is considered one of the most promising hydrocarbon reservoirs in the Bai Hassan oil field based on the results of the property model, where the ratio of water saturation is around 30%, the porosity value is reaching up to 31%, and the net to gross ratio is averaging at 70%.