Reviewerly/exHarmony huggingface dataset:
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets

We consider an offline reinforcement learning (RL) setting where the agent needs to learn from a dataset collected by rolling out multiple behavior policies. There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies. Previous methods fail to handle this by only controlling the global trade-off. 2) For a given state, the action distribution generated by different behavior policies may have multiple modes. The BC regularizers in many previous methods are mean-seeking, resulting in policies that select out-of-distribution (OOD) actions in the middle of the modes. In this paper, we address both challenges by using adaptively weighted reverse Kullback-Leibler (KL) divergence as the BC regularizer based on the TD3 algorithm. Our method not only trades off the RL and BC signals with per-state weights (i.e., strong BC regularization on the states with narrow action coverage, and vice versa) but also avoids selecting OOD actions thanks to the mode-seeking property of reverse KL. Empirically, our algorithm can outperform existing offline RL algorithms in the MuJoCo locomotion tasks with the standard D4RL datasets as well as the mixed datasets that combine the standard datasets.
Augmenting Knowledge Transfer across Graphs

Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance? In many high-impact domains (e.g., brain networks and molecular graphs), collecting and annotating data is prohibitively expensive and time-consuming, which makes domain adaptation an attractive option to alleviate the label scarcity issue. In light of this, the state-of-the-art methods focus on deriving domain-invariant graph representation that minimizes the domain discrepancy. However, it has recently been shown that a small domain discrepancy loss may not always guarantee a good generalization performance, especially in the presence of disparate graph structures and label distribution shifts. In this paper, we present TRANSNET, a generic learning framework for augmenting knowledge transfer across graphs. In particular, we introduce a novel notion named trinity signal that can naturally formulate various graph signals at different granularity (e.g., node attributes, edges, and subgraphs). With that, we further propose a domain unification module together with a trinity-signal mixup scheme to jointly minimize the domain discrepancy and augment the knowledge transfer across graphs. Finally, comprehensive empirical results show that TRANSNET outperforms all existing approaches on seven benchmark datasets by a significant margin.
HyperKGQA: Question Answering over Knowledge Graphs using Hyperbolic Representation Learning

Knowledge Graph Question Answering (KGQA) models enable users to acquire entity-based answers from a Knowledge Graph by asking natural language questions (NLQs) without the need to learn a specialized graph query language or knowing the underlying schema of the knowledge graph. This work investigates hyperbolic graph representation learning methods to effectively and efficiently represent knowledge base items and natural questions. Our system, HyperKGQA, proposes a technique that embeds the knowledge graph in a hyperbolic manifold, then learns an adaptive transformation of pre-trained sentence representations into the space of entities and relations. Finally, a post-processing step refines the ranking of the candidate answers by computing the relevance score of the set of relations and the question. An extensive set of experiments conducted on two datasets shows that our method outperforms the current state-of-the-art models when reasoning over sparse graphs to answer multi-hop questions.
An Integrated Vulnerability Assessment Tool for Web Applications

Security criteria fixation and analysis for all the web applications developed and deployed on the internet is very much required due to the excess usage of many users accessing the application. The application has to respond in a fraction of nanoseconds for users to remain attached to the application. In this scenario, the need to ensure the basic security requirements of Confidentiality, Integrity, and Availability is an important task. To analyze the security requirements penetration testing is being done. The process of manual penetration testing is slowly shifting towards automated testing systems. Security experts are designing automation tools to detect vulnerabilities. This paper deals with binding the known multiple vulnerabilities to find the overall vulnerability of a web application. The impact of the vulnerability is analyzed by combining multiple penetration testing tools which automatically classify the exploit model to gather information using the signature patterns (Regular Expressions). Finally, the exploit information can be viewed in the form of a report so that security patches of the vulnerabilities can be performed.
Estimating the Pruned Search Space Size of Subgroup Discovery

Subgroup discovery (SD) is a well-established supervised pattern mining approach. A key practical challenge —in particular considering interactive mining strategies— is that it is difficult to estimate the runtime of an exhaustive search algorithm before actually running the algorithm even for experienced practitioners. This is due to the exponential explosion of the candidate search space, sophisticated pruning strategies, and implementation specifics that can all affect the runtime by orders of magnitude depending on the dataset and the exact mining task parameters. A subgroup discovery run could take mere minutes or literal years. We would not know until afterwards. In this paper, we study the estimation of the complexity and runtime of subgroup discovery algorithms by estimating the pruned search space size, i.e., the number of actually evaluated candidate subgroups. We propose a sampling-based algorithm called SDFASTEST. SDFASTEST can effectively estimate the pruned search space size of a search algorithm. In our extensive evaluation on 1026 different tasks with 2 search algorithms, SDFASTEST was able to reduce the average mean absolute log error of the search space size estimation by ca. 94% compared to the best baseline, a depth-based upper bound.
Deep Learning for Dynamic Hand Gesture Recognition: Applications, Challenges and Future Scope

Hand Gesture Recognition is a very promising area of research over the last decade. It has found useful applications in touchless human-machine interaction in modern real-world problems. Dynamic Hand Gesture Recognition is an important requirement in most problems requiring recognition of moving gestures rather than a static pose. This review article provides a comprehensive survey of the recent deep learning-based systems. The articles have been selected by a systematic approach. We provide a detailed literature review and pictorial comparison of accuracies. The existing systems have been summarized concerning various aspects by using a tabular representation. From a study of the existing models, we find out the existing challenges, research gaps, and future scope in this direction.
Set2Box: Similarity Preserving Representation Learning for Sets

Sets have been used for modeling various types of objects, and measuring similarity between them has been a key building block of a wide range of applications. However, as sets have grown in numbers and sizes, the computational cost and storage required for set similarity computation have become substantial. In this work, we propose SET2Box, which represents sets as boxes to precisely capture overlaps of sets and thus accurately estimate various similarity measures. Additionally, based on the proposed box quantization scheme, we design SET2Box <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , which yields more concise but more accurate box representations of sets. Through extensive experiments on 8 real-world datasets, we show that, compared to baseline approaches, SET2Box <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> is (a) Accurate: achieving up to $40.8\times$ smaller estimation error while requiring 60% fewer bits to encode sets, (b) Concise: yielding up to $96.8\times$ more concise representations with similar estimation error, and (c) Versatile: enabling the estimation of four set-similarity measures from a single representation of each set. For reproducibility, the source code and datasets used in the paper are available at https://github.com/geon0325/Set2Box.
A Maritime Target Identification Algorithm Based on Improved OCRNet

This paper proposes an improved semantic segmentation method based on object contextual representations network (OCRNet) to improve the recognition ability of various objects in the complex marine environment. The proposed maritime target identification algorithm mainly improves the performance of OCRNet from two aspects. First, we introduce the channel attention module in the similar self-attention mechanism module behind the backbone network of OCRNet, allow the computer selectively value or ignore certain characteristics. Second, Focal Loss and Lovász-Softmax Loss are both adopted to improve the accuracy of algorithm recognition during training. By comparing with existing advanced semantic segmentation methods, the experimental results demonstrate that our improved method has significant advantages for semantic segmentation in marine scenes, with a recognition accuracy up to 78.65% in mIoU.
VERIPS: Verified Pseudo-label Selection for Deep Active Learning

Active learning has the power to significantly reduce the amount of labeled data needed to build strong classifiers. Existing active pseudo-labeling methods show high potential in integrating pseudo-labels within the active learning loop but heavily depend on the prediction accuracy of the model. In this work, we propose VERIPS, an algorithm that significantly outperforms existing pseudo-labeling techniques for active learning. At its core, VERIPS uses a pseudo-label verification mechanism that consists of a second network only trained on data approved by the oracle and helps to discard questionable pseudo-labels. In particular, the verifier model eliminates all pseudo-labels for which it disagrees with the actual task model. VERIPS overcomes the problems of poorly performing initial models, e.g., due to imbalanced or too small initial pools, where previous methods select too many incorrect pseudo-labels and recovering takes long or is not possible. Moreover, VERIPS is particularly insensitive to parameter choices that existing approaches suffer from. Our code is available at https://github.com/lmu-dbs/VERIPS.
Mixing Global and Local Features for Long-Tailed Expression Recognition

Large-scale facial expression datasets are primarily composed of real-world facial expressions. Expression occlusion and large-angle faces are two important problems affecting the accuracy of expression recognition. Moreover, because facial expression data in natural scenes commonly follow a long-tailed distribution, trained models tend to recognize the majority classes while recognizing the minority classes with low accuracies. To improve the robustness and accuracy of expression recognition networks in an uncontrolled environment, this paper proposes an efficient network structure based on an attention mechanism that fuses global and local features (AM-FGL). We use a channel spatial model and local feature convolutional neural networks to perceive the global and local features of the human face, respectively. Because the distribution of real-world scene field expression datasets commonly follows a long-tail distribution, where neutral and happy expressions account for the tail expressions, a trained model exhibits low recognition accuracy for tail expressions such as fear and disgust. CutMix is a novel data enhancement method proposed in other fields; thus, based on the CutMix concept, a simple and effective data-balancing method is proposed (BC-EDB). The key idea is to paste key pixels (around eyes, mouths, and noses), which reduces the influence of overfitting. Our proposed method is more focused on the recognition of tail expression, occluded expression, and large-angle faces, and we achieved the most advanced results in occlusion-RAF-DB, 30∘ pose-RAF-DB, and 45∘ pose-RAF-DB with accuracies of 86.96%, 89.74%, and 88.53%.
Object Marking Detection Method Based On Improved DetectoRS

Many CNN based detectors show strong performance in detection tasks. However, for a small amount of sample data and small target detection, there are still problems such as networks are difficult to train and poor robustness. In the context of small sample brand dataset, this paper proposes a small sample brand detection model based on convolutional neural network, which integrates recursive feature pyramid and global context, aiming at the problems of sparse data samples, uneven distribution of brand size, and complex scene transformation. Finally, the Stochastic Weights Averaging is used to further improve the performance of the model and improve the generalization ability of the model. The model can finally achieve 56.2% mAP without too much computational overhead. Improve the low robustness of multi-scale detection, false detection of small targets, missed detection and other problems.
On manipulation in merging epistemic states

In this work we study the problem of manipulation in the framework of merging complex epistemic states. We adopt the techniques concerning representation and impossibility in merging complex epistemic states proposed by Mata Díaz and Pino Pérez in 2017. We introduce here the notion of belief lifting, aiming to capture the preferences of an agent over formulas. This allows us to define a general qualitative notion of manipulability. We prove that, given some rational properties, a merging operator is either manipulable, with respect to any well-behaved belief lifting, or it admits a powerful agent (a dictator or a nominator) who, in some way, imposes his will. We also study the behaviour of some concrete epistemic state merging operators, showing that most of them are manipulable. By means of this study, we prove that strategy-proofness cannot be characterised in terms of dictators or nominators in this qualitative framework of manipulability.
A Novel Differential Evolution Sparrow Search Algorithm

In this paper, a method of improvement with sparrow search algorithm is proposed, namely differential sparrow search algorithm (DSSA). It is inspired by some advanced differential evolution algorithms and sparrow search algorithm. In DSSA, we proposed an operation of differential evolution to improve the ability to search locally. Several results show that the comprehensive performance of DSSA is improved compared with SSA and is better than some classical algorithms.
Retrospection and trend analysis of comprehensive quality evaluation research based on Cite Space Visualization Atlas

In this paper, literature selected from CNKI database is used as research samples, and visual knowledge graph software CiteSpace is used for statistical analysis, and the comprehensive quality evaluation in China is backtracked, and the trend of comprehensive quality evaluation in China is fully discussed.The results show that there are few links between the authors and the institutions, and there is no fixed cooperative team.Its research vein can be divided into four stages: exploratory stage, initial stage, slow development stage, rapid development stage and stable stage.
Research on the Course Assessment Method by Introducing Mathematical Model of Value-added Evaluation

Computer basic course is a public one offered by higher vocational colleges. The assessment methods of the course are mainly based on final examinations, which is hard to reflect the fairness and effectiveness of course assessment. By constructing a process assessment system, this paper analyzes the data of students’ module scores, establishes a mathematical model by using algorithms such as sorting, normalization and weighted average, quantifies value-added, and apply advanced technologies such as process evaluation and value-added evaluation to the evaluation methods of the course to make the assessment and evaluation of computer basic courses more effective and fair.
Literature analysis on the research status of mass entrepreneurship and innovation training projects : Document metrology based on CNKI database

Taking the literature of CNKI as the data source and using the literature metrological visual analysis function of CNKI, this paper makes a quantitative analysis on the relevant literature in the research field of "College Students’ innovation and entrepreneurship training program" from 2011 to 2021 from the aspects of literature quantity and annual distribution, subject analysis, discipline distribution, source journals, and author distribution, Get the research status and development trend of innovation and entrepreneurship training projects. The results show that in recent ten years, Chinese scholars have paid more and more attention to the research of "College Students’ innovation and entrepreneurship training program," and the keywords with high frequency are "innovation and entrepreneurship," "innovation and entrepreneurship education," "practical teaching" and so on; Education and teaching forum, with 4705 papers published as relevant research papers, has the most significant number of papers, followed by education modernization and laboratory research and exploration, with 393 and 333 papers respectively. Huang Suoyi, Jian Guoming, Zhang Liucheng, and the other three authors wrote a lot, but the distribution of authors was scattered. Higher education, medical education, marginal medical disciplines, computer software, and computer applications ranked among the top three in the number of documents issued.
Research on the fusion influence of participant by fusing the text sentiment feature

In order to study the influence of university closure and prevent negative public opinion events, this paper crawls blog posts on the topic of "school closure" based on Weibo and analyzes their influence by fusing the sentiment of the text. The EMBUI algorithm was constructed to calculate the fusion influence. In EMBUI, participant influence is calculated based on the MBUInfluence algorithm and sentiment feature is incorporated, which are obtained by machine learning methods. The experimental results show that the fusion influence can effectively grasp the public opinion, and Logistic Regression works best in sentiment analysis. The influence of the participants is related to their own characteristics and the influence of their fans, and there is a strong correlation between their influence and the sentiment. Influence has different sentiment features, which need to be captured separately.
Optimize Matching for Project Team Members in Practice Course Based on Extenics

For inspiring the motivation for each student during the process of practical course, this paper proposes an optimal method for project team member matching based on Extenics. The method is proposed based on the assumption that a project can be considered as a relation element, each stage of the project can be considered as a matter-element, and each stage contains many components, which is assumed as matter-elements. The project, stages, and components constitute a hierarchical structure. According to the classroom learning performance of each student, Extenics arrange him or her into different phase. Course Software Engineering and UML Modeling is taken as the example to carry out a comparative experiment, which indicates that this method can effectively inspire the advantage of each student, and can balance the practical achievements of each project group. Finally, advices based on the method is given. The result shows that the proposed method can make student with different levels to match with different mission in a project team, and enable them to reach their full potential, and it promotes team spirit among students.
A Study on the Classification of Learners’ Perceptions on Labor Rights: Focusing on J University Students

Objectives In order to prepare a curriculum improvement plan to help students understand the socioeconomic background and current issues surrounding labor with interest in labor rights issues, we analyzed how actual students think about issues related to labor rights.&#x0D; Methods This study explored the perspectives on labor rights issues subjectively perceived by J University students through the Q methodology, categorizing various perceptions into 4 types, and looked into the characteristics of each type in detail.&#x0D; Results The 4 types extracted through the QUANL program were reviewed, and a group of students with similar perceptions and attitudes was analyzed in depth. As a result of the analysis, a big difference in the subjective perception of undergraduate students looking at labor rights issues was found.&#x0D; Conclusions Through this type of classification and analysis process, improving the curriculum is suggested and the learning vital for today's undergraduate school students requires motivation. In the future, this data is expected to become meaningful basic data with which to design a curriculum centered on voluntary and active learners in labor rights education.
From CALL to MALL: Research on the Construction of Foreign Language Teaching Based on Internet and 5G

In the Big Data era, traditional foreign language teaching has transferred from static to IT-supported one, which represents the integration of foreign language teaching and technology. The purpose of this paper is to explore and analyze the trend of FLT from CALL to MALL based on Internet and 5G. First, the literature review of CALL and MALL is given, and then a new smart teaching platform, Unipus smart teaching cloud platform is introduced to illustrate the implementation of MALL into FLT. Finally, teachers’ and students’ feedback will be summarized based on an interview. Hopefully, this research can provide some suggestions and implications for future research concerning smart teaching, blended learning, etc.
Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay

The Neonatal intensive care unit (NICU) is a specialized section for newborn babies. The neonates are in vulnerable conditions in the ICU, so the predictive models will help to indicate the seriousness of the patients and assist the doctors in taking immediate actions. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is used in this research. The medicines medicated in critical newborn children were detected, and how the drugs and the doses of drugs can affect the Length of Stay (LOS) in NICU is analyzed. The predictive result of ICU Length of Stay (LOS) for the patients admitted to NICU for seven days is analyzed. Different Machine Learning algorithms were implemented for developing the classification model, and Logistic Regression Algorithm performed well and showed an F1 score of about 85%, which was better than the F1 score of the deep learning model long Short-Term Memory (LSTM). The automated Machine Learning (AutoML) tool, AutoNLP was also implemented for classifying LOS. But traditional methods demonstrated better performance in comparison to AutoML.