Reviewerly/exHarmony huggingface dataset:
Dynamic Gesture Recognition Based on Two-Scale 3-D-ConvNeXt

As a straightforward method for human–machine interaction, gesture recognition is vital in many practical applications. However, effectively extracting spatiotemporal information from video is still a fundamental problem and designing an accurate and efficient network is a feasible solution. The ConvNeXt, renowned for its superior still image processing capabilities, is chosen as the basis of this work. Then, the network is extended to a 3-D pattern for dynamic data and a two-scale convolution kernel is introduced to focus on the hand region. Therefore, a novel two-scale 3-D-ConvNeXt network (TS3C-Net) is established. Furthermore, the Mixup, Cutmix data augmentation, and label smoothing regularization are also applied to enhance the performance further. The experiments show that the accuracy of the proposed TS3C-Net achieves 95.36%, 97.1%, and 87.55% on EgoGesture, Jester, and NVGesture datasets, respectively.
GROWING TREE METHOD FOR OPTIMISATION OF MULTIFACTORIAL EXPERIMENTS

Context. The task of planning multifactorial experiments is important in science and industrial production. In the context of competition, rising costs, and increasing efficiency, it is necessary to optimize plans for multifactorial experiments in terms of cost and time. To solve this problem, there are a number of approaches and methods, the choice of which for a competitive technical task is an important and difficult task. In this regard, there is a need to develop new methods for optimizing the cost (time) of multifactorial experiment plans, compare them with existing methods, and give recommendations for practical application in the study of real objects.&#x0D; Objective. The purpose of the study is to develop and test the method of growing trees, to evaluate its effectiveness in comparison with other methods. The following tasks has been solved to achieve this goal: the proposed method of growing trees has been implemented in the form of software; the method has been used to optimize plans for multifactorial experiments in the study of real objects; its effectiveness has been evaluated in comparison with other methods; recommendations for its use were given.&#x0D; Method. The proposed method of growing trees is based on the application of graph theory. The advantage of the method is the reduction of time for solving optimization problems related to the construction of optimal plans for multifactorial experiments in terms of cost (time) expenses. Another characteristic feature is the high accuracy of solving optimization problems.&#x0D; Results. The results of experiments and comparisons with other optimization methods confirm the efficiency and effectiveness of the proposed method and allow us to recommend it for the study of objects with the number of significant factors k ≤ 7. It is promising to further expand the range of scientific and industrial objects for their study using this method.&#x0D; Conclusions. A growing tree method has been developed for the optimization of multifactorial experimental plans in terms of cost and time expenditures, along with software that implements it using the Angular framework and the TypeScript programming language.&#x0D; The effectiveness of the growing tree method is shown in comparison with the following methods: complete and limited enumeration, monkey search, modified Gray code application, and bacterial optimization. The growing tree method is faster than complete enumeration and can be applied to optimize multifactorial experimental plans in terms of cost (time) expenses for objects with a number of factors k ≤ 7. In solving optimization problems, the method of growing trees gives better results compared to monkey search, limited enumeration and bacterial optimization.
Layered Exploitation: Exploring the Relationship Between Revenge Porn And Poly-Victimization

Amidst dynamic advancements in the digital landscape, we are witnessing the emergence of new forms of violence and exploitation, with "revenge porn" being one of the most alarming representations. This study undergoes an in-depth investigation into how revenge porn can lead to a situation of poly-victimization, a situation where victims are exposed to multiple types of victimization at once. Revenge porn, the act of publishing explicit material without the consent of the depicted party, often triggers a cascade of additional victimization, ranging from online harassment to discrimination and social stigmatization. The concept of poly-victimization refers to a collection of traumatic experiences experienced by the victim, which in this context includes emotional trauma, reputational damage, and potential legal complications. This research adopts the normative legal method, which integrates statutory, conceptual, comparative, and futuristic approaches. The nature of the research is descriptive-prescriptive, with the collected data analyzed using the content analysis method. The results of this study aim to define and identify the layered impact of revenge porn and poly-victimization, emphasizing how victims often undergo a continuum of traumatic experiences. By exploring the multifaceted dimensions of these crimes, both from a psychological and legal perspective, this research seeks to establish a strong foundation of knowledge that can encourage the creation of more efficient victim protection strategies and stricter legal responses against perpetrators.
Progressive Domain Adaptive Object Detection Based on <scp>Self‐Attention</scp> in Foggy Weather

The quality of captured images is compromised by foggy weather, resulting in poor performance of detection networks. To mitigate this issue, researchers have attempted to employ domain adaptation techniques. However, a significant difference between the domains can create difficulties in the adaptation process, resulting in unstable training procedures and less‐than‐optimal outcomes. A self‐attention‐based progressive domain adaptive object detection framework is proposed to address this issue. We solved this problem by building an intermediate domain through physical synthesis. Moreover, the domain features of different levels are aligned in the feature space to achieve a better domain adaptation effect. In addition, we add a plug‐and‐play attention module to enhance the feature discriminability in the shallow feature extraction stage. Our method performs better than the existing state‐of‐the‐art techniques in synthetic and real‐world datasets, according to the experimental results. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Fuzzy Control Algorithm of Intelligent Cantilever Based on Neural Network

Modern industry is developing towards scale and integration, and its manufacturing processes are becoming increasingly complex. Due to its high degree of nonlinearity, time-varying, uncertainty, and strong coupling between variables, many systems are difficult to accurately describe using traditional theoretical methods. Therefore, it is necessary to conduct in-depth research on new intelligent control methods. A neural network-based intelligent cantilever beam fuzzy controller is proposed. On this basis, a fuzzy control(FC) method based on neural networks is proposed. Each input of a neuron is the axon output of the relevant neuron, and the weight of the input is the connection strength between the neuron and the synapse of the relevant neuron, and the value of the weight can be positive or negative, positive indicates excitatory synapse, negative indicates inhibitory synapse. The fuzzy control system is mapped to a kind of neural network(NN) structure, so that the self-organization problem of fuzzy rules is transformed into the self-learning problem of neural network, and it is changed from a hierarchical discrete controller to a continuous controller, which improves the control accuracy and adaptive ability. After this study, the algorithm in this paper has remarkable effect and is suitable for wide application.
Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems

We consider the problem of learning control policies in discrete-time stochastic systems which guarantee that the system stabilizes within some specified stabilization region with probability 1. Our approach is based on the novel notion of stabilizing ranking supermartingales (sRSMs) that we introduce in this work. Our sRSMs overcome the limitation of methods proposed in previous works whose applicability is restricted to systems in which the stabilizing region cannot be left once entered under any control policy. We present a learning procedure that learns a control policy together with an sRSM that formally certifies probability 1 stability, both learned as neural networks. We show that this procedure can also be adapted to formally verifying that, under a given Lipschitz continuous control policy, the stochastic system stabilizes within some stabilizing region with probability 1. Our experimental evaluation shows that our learning procedure can successfully learn provably stabilizing policies in practice.
FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW.
Cross-heterogeneity Graph Few-shot Learning

In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges. The existing methods have achieved good performance by transferring generalized knowledge extracted from rich-labeled classes in source HG(s) to few-labeled classes in a target HG. However, these methods only consider the single-heterogeneity scenario where the source and target HGs share a fixed set of node/edge types, ignoring the more general scenario of cross-heterogeneity, where each HG can have a different and non-fixed set of node/edge types. To this end, we focus on the unexplored cross-heterogeneity scenario and propose a novel model for Cross-heterogeneity Graph Few-shot Learning, namely CGFL. In CGFL, we first extract meta-patterns to capture heterogeneous information and propose a multi-view heterogeneous graph neural network (MHGN) to learn meta-patterns across HGs. Then, we propose a score module to measure the informativeness of labeled samples and determine the transferability of each source HG. Finally, by integrating MHGN and the score module into a meta-learning mechanism, CGFL can effectively transfer generalized knowledge to predict new classes with few-labeled data. Extensive experiments on four real-world datasets have demonstrated the superior performance of CGFL over the state-of-the-art methods.
A Flash Attention Transformer for Multi-Behaviour Recommendation

\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.
Improving Adversarial Transferability via Frequency-based Stationary Point Search

Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS
Label-Dependent Hypergraph Neural Network for Enhanced Multi-label Text Classification

Multi-label text classification (MLTC) is a challenging task in natural language processing. Improving the performance of MLTC through building label dependencies remains a focus of current research. Previous researches used label tree structure or label graph structure to build label dependencies. However, these label dependency structure building methods suffer from complexity and lack of interpretability of label relationships. To solve these problems, we propose a new model LHGN: Label-Dependent Hypergraph Neural Network for Enhanced Multi-label Text Classification, which introduces hypergraph structure to build label-dependent relationships, enhance the correlation between labels, reduce graph complexity and improve the interpretability of label relationships. In addition, we build hypergraph structures for each text instance to capture its structural information, and use the BERT model to capture the semantic information of texts. By integrating text information and combining the hypergraph label structure dependencies for multi-label text classification. Experimental results on three benchmark datasets demonstrate that the LHGN model outperforms state-of-the-art baseline models.
Sustainability at UAM-Azcapotzalco for academic programs with virtual classroom methodologies

Abstract. The post-pandemic educational processes due to Covid-19 have generated an interest in structuring new learning trends. The Autonomous Metropolitan University (UAM) Azcapotzalco identified the use of methodologies with digital tools known as virtual classrooms during the pandemic to continue academic activities. These digital tools are still being used in different academic programs, including undergraduate and postgraduate programs to promote the professionalism of the university community. This paper provides a general breakdown of the virtual classroom format as a methodology in the teaching-learning processes to improve the educational environment at a higher level and ensure educational sustainability through the innovation of new technologies and digital tools that can be applied at a national and international level. The expected results from this methodology are part of an improvement process aimed at generating academic programs in virtual environments to enrich the educational models at higher and postgraduate levels and creating collaborative groups within each teaching department that can be extrapolated to a unit or campus level.Keyword:Sustainable Academic, Educational Methodology, Educational Process, Virtual Classroom.
HYBRID OF ADABOOST ALGORITHM AND NAÏVE BAYES CLASSIFIER ON SELECTION OF CONTRACEPTION METHODS

Stunting is a growth failure in children. Stunting can be avoided by adjusting birth spacing or implementing a Family Planning program by using appropriate contraception. Therefore, it is necessary to develop appropriate and rapid contraceptive selection techniques to assist family planning programs. This study develops a model for determining contraceptive methods using a Naïve Bayes Classifier. In addition, an Adaboost algorithm was used to handle the independent between attributes on Naïve Bayes. The performance evaluation of model was measured by combining k-fold cross validation and confusion matrix. Based on the results testing was obtained an optimal parameter of learning rate was 0.1 and the number of iterations was 50. The evaluation using optimal parameters produce the best accuracy of 87.5%, precision of 87.6%, recall of 87.5%, and f1-measure of 87.5%. This result was better than applying the Naïve Bayes without implementing Adaboost, which had 70% accuracy. The used of Adaboost was proven to increase the accuracy of Naive Bayes by 17.5%.
PENGEMBANGAN E-MODUL BERBASIS ICARE BERBANTUAN FLIPPINGBOOK PADA MATERI SISTEM PERSAMAAN LINEAR DUA VARIABEL

The aims of this study were to (1) determine the implementation of ICARE-based e-module development supported by FlippingBook in SPLDV material, (2) ensure the feasibility and practicality of the product developed. The research was conducted at SMPN 4 Abung Timur. The type of research used is development and the model used is 4D development (Define, Design, Develop, Disseminate). The data obtained in the research process are qualitative data and quantitative data. The instruments to collect data used were interview forms, validation questionnaires, and student response questionnaires. Based on expert validation, the e-module is categorized as very feasible with a large percentage of 85%. Based on the results of the limited trial, the e-module was stated to be very practical with a large percentage of 87.33%. So it can be concluded that the ICARE-based e-module assisted by FlippingBook on SPLDV material is feasible and practical to use for the learning process.
Extraction of target structures in learners' corpora

Foreign language (FL) or second language (L2) corpora are sets of productions by non-native speakers, learners of a given language, which contemplate the errors and well-formed structures produced. These serve different research objectives, such as studies on language acquisition (LE and L2), phenomena of linguistic interference or analysis and diagnosis of LE/L2 proficiency levels. In the context of this research, the definition of the learner's proficiency level is often relevant, and this is done, typically, through the analysis of the presence or absence of errors in the learners' productions, based on mappings of typical or expected errors and well-formed structures for a given level of proficiency. However, contrary to the learner's error – which is explicitly marked in the corpus and whose typology and methodology of analysis constitutes a subtopic of investigation on its own –, the well-formed structures, and in particular the target structures (well-formed structures expected in the learners' productions of a given level of proficiency), are not easily identifiable in the corpora. The work presented here aims to fill this gap in COPLE2 – Corpus of Portuguese Foreign/Second Language through the use of expressions in CQL – Corpus Query Language. Based on pre-identified target structures and on the information made available in COPLE2, such as morphosyntactic tagging and different levels of information and annotation (learner production, teacher correction, normalized form, lemma, etc.), we propose query expressions in CQL that easily allow any user to immediately extract examples of target structures by proficiency level. The construction of the query expressions implies the definition and testing of the best strategies for each case and requires the systematization of linguistic rules and patterns of occurrence of the phenomena in question, but also the definition of ways to circumvent the limitations inherent to the corpus annotation, on the one hand, and the query language, on the other.
Marcadores discursivos com funções modais

The modal functions performed by some discourse markers have been the subject of analysis for several languages. This paper analyzes the different values conveyed by the Portuguese discourse marker claro, and takes a contrastive perspective with French, based on monolingual and parallel, written and spoken, corpora, in order to identify discourse markers that are functional equivalents of claro in different contexts. Contexts of claro and its French equivalents in corpora show a meaning of agreement and shared knowledge, as well as the elimination of alterity (bien sûr), factuality (de fait, c'est un fait), opposition and even irony (bien entendu) and the insertion in argumentative structures preceding contrastive or concessive segments (certes). Even in contexts in which claro does not responds to or comments on the discourse of a direct interlocutor, it establishes a relationship between the speaker's discourse and another discourse, either the discourse of an epistemic community that includes the speaker or the implicit discourse of a real or virtual speaker.