OpenRaiser/Intern-Atlas huggingface dataset:
Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System
Venue: arXiv cs.IR 2023
Year: 2023

In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS). However, in a recommender system, the correlations among the involved tasks are complex. Therefore, the existing MTL models designed for RS suffer from negative transfer to different degrees, which will injure optimization in MTL. We find that the root cause of negative transfer is feature redundancy that features learned for different tasks interfere with each other. To alleviate the issue of negative transfer, we propose a novel multi-task learning method termed Feature Decomposition Network (FDN). The key idea of the proposed FDN is reducing the phenomenon of feature redundancy by explicitly decomposing features into task-specific features and task-shared features with carefully designed constraints. We demonstrate the effectiveness of the proposed method on two datasets, a synthetic dataset and a public datasets (i.e., Ali-CCP). Experimental results show that our proposed FDN can outperform the state-of-the-art (SOTA) methods by a noticeable margin.
Exploiting Sparsity in Pruned Neural Networks to Optimize Large Model Training
Venue: arXiv cs.LG 2023
Year: 2023

Parallel training of neural networks at scale is challenging due to significant overheads arising from communication. Recently, deep learning researchers have developed a variety of pruning algorithms that are capable of pruning (i.e. setting to zero) 80-90% of the parameters in a neural network to yield sparse subnetworks that equal the accuracy of the unpruned parent network. In this work, we propose a novel approach that exploits these sparse subnetworks to optimize the memory utilization and communication in two popular algorithms for parallel deep learning namely -- data and inter-layer parallelism. We integrate our approach into AxoNN, a highly scalable framework for parallel deep learning that relies on data and inter-layer parallelism, and demonstrate the reduction in communication time and memory utilization. On 512 NVIDIA V100 GPUs, our optimizations reduce the memory consumption of a 2.7 billion parameter model by 74%, and the total communication time by 40%, thus providing an overall speedup of 34% over AxoNN, 32% over DeepSpeed-3D and 46% over Sputnik, a sparse matrix computation baseline.
CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration
Venue: arXiv cs.CV 2023
Year: 2023

Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This whole algorithm includes a checkerboard corner detection network and some post-processing techniques. The network model is a fully convolutional network with improvements of loss function and learning rate, which can deal with the images of arbitrary size and produce correspondingly-sized output with a corner score on each pixel by efficient inference and learning. Besides, in order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering. Evaluations on two different datasets show its superior robustness, accuracy and wide applicability in quantitative comparisons with the state-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.
Tasks Decomposition Approaches in Crowdsourcing Software Development
Venue: arXiv cs.SE 2023
Year: 2023

A main characteristic of crowdsourcing software development (CSD) is the complexity of tasks and skills required by workers to achieve successful software crowdsourcing. The tasks proposed to the crowd in CSD are checked to ensure they are manageable and achievable. In general, individual tasks come from general goal-oriented projects. There are practices for breaking down software projects into manageable tasks, known as task decomposition. This study identified task decomposition techniques in software engineering, particularly in the context of CSD. Then, we defined the experienced developers who lead the requester in decomposing the project, preparing tasks, and reviewing submissions. This study explored and addressed decomposition approaches in CSD. Next, we selected projects in TopCoder to identify the task decomposition process in the CSD context. Finally, we concluded with future research directions for investigating decomposition approaches and their effects in the CSD context to ensure successful crowdsourced software projects.
Text recognition on images using pre-trained CNN
Venue: arXiv cs.CV 2023
Year: 2023

A text on an image often stores important information and directly carries high level semantics, makes it as important source of information and become a very active research topic. Many studies have shown that the use of CNN-based neural networks is quite effective and accurate for image classification which is the basis of text recognition. It can also be more enhanced by using transfer learning from pre-trained model trained on ImageNet dataset as an initial weight. In this research, the recognition is trained by using Chars74K dataset and the best model results then tested on some samples of IIIT-5K-Dataset. The research results showed that the best accuracy is the model that trained using VGG-16 architecture applied with image transformation of rotation 15°, image scale of 0.9, and the application of gaussian blur effect. The research model has an accuracy of 97.94% for validation data, 98.16% for test data, and 95.62% for the test data from IIIT-5K-Dataset. Based on these results, it can be concluded that pre-trained CNN can produce good accuracy for text recognition, and the model architecture that used in this study can be used as reference material in the development of text detection systems in the future
New advances in permutation decoding of first-order Reed-Muller codes
Venue: arXiv cs.IT 2023
Year: 2023

In this paper we describe a variation of the classical permutation decoding algorithm that can be applied to any affine-invariant code with respect to certain type of information sets. In particular, we can apply it to the family of first-order Reed-Muller codes with respect to the information sets introduced in [2]. Using this algortihm we improve considerably the number of errors we can correct in comparison with the known results in this topic.
Conceptual Views on Tree Ensemble Classifiers
Venue: arXiv cs.LG 2023
Year: 2023

Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
A Novel Improved Mask RCNN for Multiple Targets Detection in the Indoor Complex Scenes
Venue: arXiv cs.CV 2023
Year: 2023

With the expansive aging of global population, service robot with living assistance applied in indoor scenes will serve as a crucial role in the field of elderly care and health in the future. Service robots need to detect multiple targets when completing auxiliary tasks. However, indoor scenes are usually complex and there are many types of interference factors, leading to great challenges in the multiple targets detection. To overcome this technical difficulty, a novel improved Mask RCNN method for multiple targets detection in the indoor complex scenes is proposed in this paper. The improved model utilizes Mask RCNN as the network framework. On this basis, Convolutional Block Attention Module (CBAM) with channel mechanism and space mechanism is integrated, and the influence of different background, distance, angle and interference factors are comprehensively considered. Meanwhile, in order to evaluate the detection and identification effects of the established model, a comprehensive evaluation system based on loss function and Mean Average Precision (mAP) is established. For verification, experiments on the detection and identification effects under different distances, backgrounds, angles and interference factors were conducted. The results show that designed model improves the accuracy to a higher level and has a better anti-interference ability than other methods when the detection speed was nearly the same.
Action Dynamics Task Graphs for Learning Plannable Representations of Procedural Tasks
Venue: arXiv cs.CV 2023
Year: 2023

Given video demonstrations and paired narrations of an at-home procedural task such as changing a tire, we present an approach to extract the underlying task structure -- relevant actions and their temporal dependencies -- via action-centric task graphs. Learnt structured representations from our method, Action Dynamics Task Graphs (ADTG), can then be used for understanding such tasks in unseen videos of humans performing them. Furthermore, ADTG can enable providing user-centric guidance to humans in these tasks, either for performing them better or for learning new tasks. Specifically, we show how ADTG can be used for: (1) tracking an ongoing task, (2) recommending next actions, and (3) planning a sequence of actions to accomplish a procedural task. We compare against state-of-the-art Neural Task Graph method and demonstrate substantial gains on 18 procedural tasks from the CrossTask dataset, including 30.1% improvement in task tracking accuracy and 20.3% accuracy gain in next action prediction.
C-rusted: The Advantages of Rust, in C, without the Disadvantages
Venue: arXiv cs.PL 2023
Year: 2023

C-rusted is an innovative technology whereby C programs can be (partly) annotated so as to express: ownership, exclusivity and shareability of language, system and user-defined resources; dynamic properties of objects and the way they evolve during program execution; nominal typing and subtyping. The (partially) annotated C programs can be translated with unmodified versions of any compilation toolchain capable of processing ISO C code. The annotated C program parts can be validated by static analysis: if the static analyzer flags no error, then the annotations are provably coherent among themselves and with respect to annotated C code, in which case said annotated parts are provably exempt from a large class of logic, security, and run-time errors.
Online Algorithms with Randomly Infused Advice
Venue: arXiv cs.DS 2023
Year: 2023

We introduce a novel method for the rigorous quantitative evaluation of online algorithms that relaxes the "radical worst-case" perspective of classic competitive analysis. In contrast to prior work, our method, referred to as randomly infused advice (RIA), does not make any probabilistic assumptions about the input sequence and does not rely on the development of designated online algorithms. Rather, it can be applied to existing online randomized algorithms, introducing a means to evaluate their performance in scenarios that lie outside the radical worst-case regime. More concretely, an online algorithm ALG with RIA benefits from pieces of advice generated by an omniscient but not entirely reliable oracle. The crux of the new method is that the advice is provided to ALG by writing it into the buffer B from which ALG normally reads its random bits, hence allowing us to augment it through a very simple and non-intrusive interface. The (un)reliability of the oracle is captured via a parameter 0 {\le} α {\le} 1 that determines the probability (per round) that the advice is successfully infused by the oracle; if the advice is not infused, which occurs with probability 1 - α, then the buffer B contains fresh random bits (as in the classic online setting). The applicability of the new RIA method is demonstrated by applying it to three extensively studied online problems: paging, uniform metrical task systems, and online set cover. For these problems, we establish new upper bounds on the competitive ratio of classic online algorithms that improve as the infusion parameter α increases. These are complemented with (often tight) lower bounds on the competitive ratio of online algorithms with RIA for the three problems.
Refined $F_5$ Algorithms for Ideals of Minors of Square Matrices
Venue: arXiv cs.SC 2023
Year: 2023

We consider the problem of computing a grevlex Gröbner basis for the set $F_r(M)$ of minors of size $r$ of an $n\times n$ matrix $M$ of generic linear forms over a field of characteristic zero or large enough. Such sets are not regular sequences; in fact, the ideal $\langle F_r(M) \rangle$ cannot be generated by a regular sequence. As such, when using the general-purpose algorithm $F_5$ to find the sought Gröbner basis, some computing time is wasted on reductions to zero. We use known results about the first syzygy module of $F_r(M)$ to refine the $F_5$ algorithm in order to detect more reductions to zero. In practice, our approach avoids a significant number of reductions to zero. In particular, in the case $r=n-2$, we prove that our new algorithm avoids all reductions to zero, and we provide a corresponding complexity analysis which improves upon the previously known estimates.
The Construction of Reality in an AI: A Review
Venue: arXiv cs.AI 2023
Year: 2023

AI constructivism as inspired by Jean Piaget, described and surveyed by Frank Guerin, and representatively implemented by Gary Drescher seeks to create algorithms and knowledge structures that enable agents to acquire, maintain, and apply a deep understanding of the environment through sensorimotor interactions. This paper aims to increase awareness of constructivist AI implementations to encourage greater progress toward enabling lifelong learning by machines. It builds on Guerin's 2008 "Learning Like a Baby: A Survey of AI approaches." After briefly recapitulating that survey, it summarizes subsequent progress by the Guerin referents, numerous works not covered by Guerin (or found in other surveys), and relevant efforts in related areas. The focus is on knowledge representations and learning algorithms that have been used in practice viewed through lenses of Piaget's schemas, adaptation processes, and staged development. The paper concludes with a preview of a simple framework for constructive AI being developed by the author that parses concepts from sensory input and stores them in a semantic memory network linked to episodic data. Extensive references are provided.
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
Venue: arXiv cs.CV 2023
Year: 2023

We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database; 2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D\&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at http://github.com/zhuhao-nju/rafare .
On the Use of Static Analysis to Engage Students with Software Quality Improvement: An Experience with PMD
Venue: arXiv cs.SE 2023
Year: 2023

Static analysis tools are frequently used to scan the source code and detect deviations from the project coding guidelines. Given their importance, linters are often introduced to classrooms to educate students on how to detect and potentially avoid these code anti-patterns. However, little is known about their effectiveness in raising students awareness, given that these linters tend to generate a large number of false positives. To increase the awareness of potential coding issues that violate coding standards, in this paper, we aim to reflect on our experience with teaching the use of static analysis for the purpose of evaluating its effectiveness in helping students with respect to improving software quality. This paper discusses the results of an experiment in the classroom over a period of 3 academic semesters, involving 65 submissions that carried out code review activity of 690 rules using PMD. The results of the quantitative and qualitative analysis shows that the presence of a set of PMD quality issues influence the acceptance or rejection of the issues, design, and best practices-related categories that take a longer time to be resolved, and students acknowledge the potential of using static analysis tools during code review. Through this experiment, code review can turn into a vital part of the educational computing plan. We envision our findings enabling educators to support students with code review strategies to raise students awareness about static analysis tools and scaffolding their coding skills.
Overview of the TREC 2022 Fair Ranking Track
Venue: arXiv cs.IR 2023
Year: 2023

The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents topical content or authors. The 2022 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
Improvement of attention in subjects diagnosed with hyperkinetic syndrome using BIOVIT Simulator
Venue: arXiv cs.HC 2023
Year: 2023

This study aimed to stimulate the brain's executive function through a series of tasks and rules based on dynamic perceptual stimuli using the Biotechnology Virtual Immersion Simulator (BIOVIT) and thus evaluate its usefulness to maintain and increase attention levels in subjects diagnosed with hyperkinetic syndrome. With a quantitative methodology framed in a longitudinal trend design, the cause of the exposure-outcome relationships was studied using the BIOVIT simulator. Exploratory analysis of oscillatory brain activity was measured using a graphical recording of brain electrical activity and attention levels. Data consisted of 77,566 observations from n = 18 separately studied participants. The findings established that the BIOVIT simulator maintained and increased the attention levels of the participants by 77.8%. Furthermore, the hypothesis was tested that virtual reality immersion technologies significantly affect attention levels in participants aged 8 to 12. The evidence shows that the BIOVIT simulator is an alternative to developing learning methodologies in vulnerable populations. The low implementation costs and the diversity of academic applications may allow schools in developing countries to solve this problem that afflicts thousands of children with attention deficit and hyperactivity disorder.
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Venue: arXiv cs.CL 2023
Year: 2023

Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information. Given this new added constraint, it is plausible to expect that the overall quality of the output will be affected, for example, in terms of fluency. Can scaling language models help? Here we examine the relationship between fluency and attribution in LLMs prompted with retrieved evidence in knowledge-heavy dialog settings. Our experiments were implemented with a set of auto-metrics that are aligned with human preferences. They were used to evaluate a large set of generations, produced under varying parameters of LLMs and supplied context. We show that larger models tend to do much better in both fluency and attribution, and that (naively) using top-k retrieval versus top-1 retrieval improves attribution but hurts fluency. We next propose a recipe that could allow smaller models to both close the gap with larger models and preserve the benefits of top-k retrieval while avoiding its drawbacks.