Reviewerly/exHarmony huggingface dataset:
GPT4Point: A Unified Framework for Point-Language Understanding and Generation

Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting progress in 3D language understanding and generation. To solve this problem, we introduce GPT4Point, an innovative groundbreaking point-language multimodal model designed specifically for unified 3D object understanding and generation within the MLLM framework. GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point is equipped with advanced capabilities for controllable 3D generation, it can get high-quality results through a low-quality point-text feature maintaining the geometric shapes and colors. To support the expansive needs of 3D object-text pairs, we develop Pyramid-XL, a point-language dataset annotation engine. It constructs a large-scale database over 1M objects of varied text granularity levels from the Objaverse-XL dataset, essential for training GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D point-language understanding capabilities. In extensive evaluations, GPT4Point has demonstrated superior performance in understanding and generation.
A Comprehensive Study of Vision Transformers in Image Classification Tasks

Image Classification is a fundamental task in the field of computer vision that frequently serves as a benchmark for gauging advancements in Computer Vision. Over the past few years, significant progress has been made in image classification due to the emergence of deep learning. However, challenges still exist, such as modeling fine-grained visual information, high computation costs, the parallelism of the model, and inconsistent evaluation protocols across datasets. In this paper, we conduct a comprehensive survey of existing papers on Vision Transformers for image classification. We first introduce the popular image classification datasets that influenced the design of models. Then, we present Vision Transformers models in chronological order, starting with early attempts at adapting attention mechanism to vision tasks followed by the adoption of vision transformers, as they have demonstrated success in capturing intricate patterns and long-range dependencies within images. Finally, we discuss open problems and shed light on opportunities for image classification to facilitate new research ideas.
Optimization of dedicated domain text classification based on data augmentation using BERT generation pre-trained model

Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data augmentation method based on the BERT model is proposed in this paper for augmenting the small amount of available annotated data. The optimization of this data augmentation method is demonstrated by experiments. In a text classification experiment, this data augmentation method can improve the training effect of the source data by 2.9%.
Research on improved algorithm of FP-growth based on data strong association

FP-Growth algorithm is a classic algorithm in the field of data mining. Compared with Apriori algorithm, all frequent item sets can be obtained by traversing the data set only twice, which improves the efficiency of data mining. However, in practical use, if there are many Frequent one item sets in the data set, the data set is scattered, and a lot of time will be wasted. Moreover, it will cause the problem that there are too many child nodes when FP-Tree is formed, and it is easy to overflow. In view of the above shortcomings, in this paper, a new FP-Growth algorithm based on Data Strong Association (SDA-FP-Growth algorithm) is proposed., which uses frequent two items set for operation integration and merging of the same leaf nodes, which is conducive to the subsequent frequent item set search. Experiments show that the SDA-FPGrowth algorithm reduces the running time and memory space.
A modified method based on the K-nearest neighbor approach for solving PDE global solutions using the Feynman-Kac formula

Inspired by the classification idea of the K-nearest neighbors (KNN) algorithm, this paper proposes a new method for numerically solving partial differential equations (PDE) using the Feynman-Kac formula. The method involves establishing a connection between PDE and stochastic differential equations through the Feynman-Kac formula. Random points are selected within the domain of the equation, and the KNN algorithm is used to partition these points into different regions. Then, Monte Carlo simulation is performed on the random points within each region to obtain a series of simulated values. These simulated values are substituted into the corresponding Feynman-Kac formula for each random point to obtain the solution. Finally, within each region, the average of the solutions for all random points belonging to that region is calculated, resulting in the corresponding approximate solution. By selecting an appropriate partitioning approach, a higher-precision global solution to the PDE can be constructed. Through a series of numerical simulations, the results show that the PDE global solution constructed by the new method achieves higher accuracy compared to traditional interpolation methods.
A Jauharah Hijab Web Site Design

In this modern era, Muslimah fashion in Indonesia has become one of the fastest-growing sectors, making many consumers look for Muslimah fashion to follow this fashion trend. Muslim women in Indonesia have various tastes in wearing Muslim clothing. Muslim clothing known as the hijab now has many views with an interesting variety of creativity, so this has caused many Muslim women in various circles in Indonesia to wear the hijab for their daily clothes. This hijab business is a business that has a very small loss potential because the hijab is a product that is not easily damaged and does not spoil. Correct storage and skills in creating new variations and combining clothes and hijab with trendy styles will make the hijab sell well. The target market is not only adult women but various groups, ages, and social statuses because the modern hijab is now more flexible and can be combined with everyday clothes.
Patterns of Conjunctive Relation (CR) in Phases and Stages of Narratives in Year Three Junior High School English Books and Their Pedagogical Implication

Background: &#x0D; This study is intended to find out the patterns of CR used in developing phases and stages of narrative and their implication in the genre-based model of language teaching of narratives. The study uses 15 different narratives from two nationally recognized Junior High School English books published by Intan Pariwara and Tiga Serangkai as data sources.&#x0D; Methodology: &#x0D; The narratives are analysed based on their functional engagements of phases and rhetorical functions of stages. Then, the patterns of CR in each phase and stage of the narratives are identified in terms of their types, meanings and roles to develop the phases and stages of narratives.&#x0D; Findings: &#x0D; The findings indicate that the logic of space of external addition and internal similar comparison are used in describing Character, Setting, and Activity in Orientation. Meanwhile, the combination of logic of space and time of external different comparison, external successive time and cause consequence are mainly used to develop phases of Introducing Problem, Developing Problems to Climax in Complication, Evaluation, and phases of Effort 1 to N, Problem Solved in Resolution. Coda, finally, applies internal similar comparison and external successive time to make comments and extract the value of the story.&#x0D; Conclusion: &#x0D; The intricate patterns of CR used in different phases and stages of narrative should be taught delicately in guided and scaffolded instructions through four stages of genre-based model of teaching, namely, BKOF, Modelling, JCT and ICT.&#x0D; Originality: &#x0D; This area of research is under widespread scrutiny and investigation especially conjunctive relationship in each step and phase, as well as the instructional implications of narrative texts.
Unravelling DNS Performance: A Historical Examination of F-Root in Southeast Asia

The DNS root server system uses Anycast technology to provide resolution through widely distributed root nodes. In recent years, the F-root node has seen astonishing growth and now boasts the largest number of nodes among the 13 root servers. Based on Ripe Atlas measurement data, we examined the availability and query latency of the F-root within the Southeast Asian region historically. The collected data illustrates how latency varies with changes in the number of root nodes, how the geographic distribution of responding root nodes changes in different periods, and examines the most recent differences between countries in terms of latency distribution. This study sheds light on the evolving landscape of DNS infrastructure in Southeast Asia.
AN ANALYSIS OF DERIVATIONAL AFFIXES FOUND IN THE JAKARTA POST NEWSPAPER

This study focused to analyse derivational affixes found in the Jakarta Post Newspaper. To describe the data source this study used the qualitative method. The observation method is used in this study to collect the data. There were several steps to collecting the data which were, first finding the data on the Jakarta Post website, second reading and underlining all the words containing derivational affixes, and then identifying and classifying based on their categories. This study used the theory proposed by Lieber (2009) as the main theory and supported theory by Katamba (1993) and Plag (2002). This study used formal and informal methods to present the data. After analyzing the data source there are two types of derivational affixes, namely derivational prefixes and derivational suffixes. There were 36 data of derivational affixes found in three newspaper such as 6 data are prefixes -un, -non, -over -ir 16,6% and 30 data are suffixes -y, -ity, -er, -ion, -ly, -al, -ful, -ible -or with percentage 83,4%. Based on the result it can be concluded the derivational suffix is more dominant than the derivational prefixes found in the Jakarta Post newspaper&#x0D; Keywords: derivation; Jakarta post Newspaper; prefix; suffix
Learning to Prevent Input Leakages in the Mobile Cloud Inference

Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-intensive, it has become a trend to process raw data on devices and send the deep neural network (DNN) features to the cloud, where the features are further processed to return final results. However, there is always an unexpected leakage with the release of features, by which an adversary could infer much information on the original data. We propose a privacy-preserving framework on top of the mobile cloud infrastructure from the perspective of DNN structures. Our framework aims to learn a policy to modify the base DNNs to prevent information leakage while maintaining high inference accuracy. The policy can also be readily transferred to large-size DNNs and large-scale datasets to speed up learning. Extensive evaluations on a variety of DNNs have shown that our framework successfully finds privacy-preserving DNN structures to defend privacy attacks.
FingerPattern: Securing Pattern Lock Via Fingerprint-Dependent Friction Sound

Pattern lock is widely used for user authentication in mobile devices due to its simplicity and ease of remembering. However, it is vulnerable to various attacks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , shoulder surfing attacks. In this paper, we propose FingerPattern, a novel enhanced pattern lock authentication system by using friction sound as a second authentication factor. When the user inputs the pattern by swiping his/her fingertip on the screen, the friction sound is generated based on the user's fingerprint and is unique. Thus, FingerPattern can identify and rule out the illegality by utilizing fingerprint-dependent friction sound even if the adversary has inferred the pattern. By this method, FingerPattern secures pattern lock without the need for a change in user unlocking habits. Extensive experiments demonstrate that FingerPattern can identify legitimate users with 97.5% TAR in one attempt and defend against various attacks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , only 16.8% FAR in five attempts even if the adversary can clearly spy on the user's unlocking process). FingerPattern can be incorporated into the existing pattern lock of mobile devices, which is cost-free. Furthermore, a user experience study shows that FingerPattern is well-received by users.
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN

Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature \textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to \textbf{8.3}$\times$ memory saving and \textbf{42.9}$\times$ computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show $\sim$\textbf{70} milliseconds latency on Qualcomm Snapdragon 865 chipset.
Exploring the Hybrid Teaching Model Integrating "Scenario Construction" and Modern Information Technology

The concept of integrating "scenario construction" with modern information technology in educational reform is inspired by the current trend of e-commerce live streaming for product sales. This approach embodies the "student-centered" teaching philosophy and is conducive to fostering students' abilities for independent inquiry, innovation, and teamwork. It serves as an effective exploration for higher education institutions to promote the "Three-Three-One" education model. This article, from the perspective of co-creating value, utilizes a hybrid teaching platform to create a diverse, content-rich, and flexible communication "teaching broadcast room" for students. This enables students to autonomously select learning materials of interest, facilitating in-depth learning from multiple angles and channels, increasing their knowledge reservoir, and improving knowledge conversion rates.
ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images

Abstract Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation .The densely connected network is employed in the encoding stage to enhance the network's feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on two publicly available breast ultrasound datasets, the experimental results demonstrate that our method achieves favorable performance compared to other deep learning approaches.
Identifying Spurious Correlations using Counterfactual Alignment

Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual alignment method to detect and explore spurious correlations of black box classifiers. Counterfactual images generated with respect to one classifier can be input into other classifiers to see if they also induce changes in the outputs of these classifiers. The relationship between these responses can be quantified and used to identify specific instances where a spurious correlation exists as well as compute aggregate statistics over a dataset. Our work demonstrates the ability to detect spurious correlations in face attribute classifiers. This is validated by observing intuitive trends in a face attribute classifier as well as fabricating spurious correlations and detecting their presence, both visually and quantitatively. Further, utilizing the CF alignment method, we demonstrate that we can rectify spurious correlations identified in classifiers.
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness

Adversarial examples are one of the most severe threats to deep learning models. Numerous works have been proposed to study and defend adversarial examples. However, these works lack analysis of adversarial information or perturbation, which cannot reveal the mystery of adversarial examples and lose proper interpretation. In this paper, we aim to fill this gap by studying adversarial information as unstructured noise, which does not have a clear pattern. Specifically, we provide some empirical studies with singular value decomposition, by decomposing images into several matrices, to analyze adversarial information for different attacks. Based on the analysis, we propose a new module to regularize adversarial information and combine information bottleneck theory, which is proposed to theoretically restrict intermediate representations. Therefore, our method is interpretable. Moreover, the fashion of our design is a novel principle that is general and unified. Equipped with our new module, we evaluate two popular model structures on two mainstream datasets with various adversarial attacks. The results indicate that the improvement in robust accuracy is significant. On the other hand, we prove that our method is efficient with only a few additional parameters and able to be explained under regional faithfulness analysis.
Recursive Visual Programming

Visual Programming (VP) has emerged as a powerful framework for Visual Question Answering (VQA). By generating and executing bespoke code for each question, these methods demonstrate impressive compositional and reasoning capabilities, especially in few-shot and zero-shot scenarios. However, existing VP methods generate all code in a single function, resulting in code that is suboptimal in terms of both accuracy and interpretability. Inspired by human coding practices, we propose Recursive Visual Programming (RVP), which simplifies generated routines, provides more efficient problem solving, and can manage more complex data structures. RVP is inspired by human coding practices and approaches VQA tasks with an iterative recursive code generation approach, allowing decomposition of complicated problems into smaller parts. Notably, RVP is capable of dynamic type assignment, i.e., as the system recursively generates a new piece of code, it autonomously determines the appropriate return type and crafts the requisite code to generate that output. We show RVP's efficacy through extensive experiments on benchmarks including VSR, COVR, GQA, and NextQA, underscoring the value of adopting human-like recursive and modular programming techniques for solving VQA tasks through coding.
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks

The MineRL BASALT competition has served to catalyze advances in learning from human feedback through four hard-to-specify tasks in Minecraft, such as create and photograph a waterfall. Given the completion of two years of BASALT competitions, we offer to the community a formalized benchmark through the BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource for algorithm development and performance assessment. BEDD consists of a collection of 26 million image-action pairs from nearly 14,000 videos of human players completing the BASALT tasks in Minecraft. It also includes over 3,000 dense pairwise human evaluations of human and algorithmic agents. These comparisons serve as a fixed, preliminary leaderboard for evaluating newly-developed algorithms. To enable this comparison, we present a streamlined codebase for benchmarking new algorithms against the leaderboard. In addition to presenting these datasets, we conduct a detailed analysis of the data from both datasets to guide algorithm development and evaluation. The released code and data are available at https://github.com/minerllabs/basalt-benchmark .
Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning

Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios.