Reviewerly/exHarmony huggingface dataset:
PoseSDF: Simultaneous 3D Human Shape Reconstruction and Gait Pose Estimation Using Signed Distance Functions

Vision-based 3D human pose estimation and shape reconstruction play important roles in robot-assisted healthcare monitoring and personal assistance. However, 3D data captured from a single viewpoint always encounter occlusions and exhibit substantial heterogeneity across different views, resulting in significant challenges for both tasks. Extensive approaches have been proposed to perform each task separately, but few of them present a unified solution. In this paper, we propose a novel network based on signed distance functions, namely PoseSDF, to simultaneously reconstruct 3D lower limb shape and estimate gait pose by two dedicated branches. To promote multi-task learning, several strategies are developed to ensure that these two branches leverage the same latent shape code while exchanging information between them. More importantly, an auxiliary RotNet is incorporated into the inference phase, overcoming the inherent limitations of implicit neural functions under cross-view scenarios. Experimental results demonstrate that our proposed PoseSDF can achieve both high-quality shape reconstruction and precise pose estimation, generalizing well on the data from novel views, gait patterns, as well as real-world.
Expression recognition method combining convolutional features and Transformer

Expression recognition has been an important research direction in the field of psychology, which can be used in traffic, medical, security, and criminal investigation by expressing human feelings through the muscles in the corners of the mouth, eyes, and face. Most of the existing research work uses convolutional neural networks (CNN) to recognize face images and thus classify expressions, which does achieve good results, but CNN do not have enough ability to extract global features. The Transformer has advantages for global feature extraction, but the Transformer is more computationally intensive and requires a large amount of training data. So, in this paper, we use the hierarchical Transformer, namely Swin Transformer, for the expression recognition task, and its computational power will be greatly reduced. At the same time, it is fused with a CNN model to propose a network architecture that combines the Transformer and CNN, and to the best of our knowledge, we are the first to combine the Swin Transformer with CNN and use it in an expression recognition task. We then evaluate the proposed method on some publicly available expression datasets and can obtain competitive results.
Communication-Efficient Federated Learning for Digital Twin Systems of Industrial Internet of Things

With the rapid development and deployment of Industrial Internet of Things technology, it promotes interconnection and edge applications in smart manufacturing. However, challenges remain, such as yet-to-improve communication efficiency and trade-offs between computing power and energy consumption, which limits the application and further development of IIoT technology. This paper proposes the digital twin systems into the IIoT to build model between physical objects and digital virtual systems to optimize the structure of IIoT. And we further introduce federal learning to train the digital twins model and to improve the communication efficiency of IIoT. In this paper, we first establish the digital twins model of IIoT based on industrial scenario. Moreover, to optimize the communication overhead allocation problem, this paper proposes an improved communication-efficient distribution algorithm, which speeds up the training performance of federated model and ensures the performance of industrial system model by changing the update training mode of client and server and allowing some industrial equipment to participate in federated training. This paper simulates the real-word intelligent camera detection to validate the proposed method. Comparing our proposed method with the existing traditional methods, the results show the advantages of the proposed method can improve the communication performance of the training model.
Improving Discriminative Learning for Zero-Shot Relation Extraction

Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training.While most previous studies have focused on fully supervised relation extraction and achieved considerably high performance, less effort has been made towards ZSRE.This study proposes a new model incorporating discriminative embedding learning for both sentences and semantic relations.In addition, a self-adaptive comparator network is used to judge whether the relationship between a sentence and a relation is consistent.Experimental results on two benchmark datasets showed that the proposed method significantly outperforms the state-of-the-art methods.
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e.g., word and sentence information.Hence, we propose a task-free enhancement module termed as Heterogeneous Linguistics Graph (HLG) to enhance Chinese pre-trained language models by integrating linguistics knowledge.Specifically, we construct a hierarchical heterogeneous graph to model the characteristics linguistics structure of Chinese language, and conduct a graphbased method to summarize and concretize information on different granularities of Chinese linguistics hierarchies.Experimental results demonstrate our model has the ability to improve the performance of vanilla BERT, BERTwwm and ERNIE 1.0 on 6 natural language processing tasks with 10 benchmark datasets.Further, the detailed experimental analyses have proven that this kind of modelization achieves more improvements compared with previous strong baseline MWA.Meanwhile, our model introduces far fewer parameters (about half of MWA) and the training/inference speed is about 7x faster than MWA.
Anomaly Detection in Self-Organizing Networks: Conventional Versus Contemporary Machine Learning

This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks.While deep learning has gained significant traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong statistical alternatives, especially when using proper learning representations.For instance, support vector machines have previously demonstrated state-ofthe-art potential in many binary classification applications and can be further exploited with different representations, such as one-class learning and data augmentation.We demonstrate for the first time, on a previously published and publicly available dataset, that conventional machine learning can outperform the previous state-of-the-art using deep learning by 15% on average across four different application scenarios.Our results further indicate that with nearly two orders of magnitude improvement in computational speed and an order of magnitude reduction in trainable parameters, conventional machine learning provides a robust alternative for 5G self-organizing networks especially when the execution and detection times are critical.
Formal sources of finance boost innovation: Do immigrants benefit as much as natives?

Objective:The article investigates the relative benefit of formality/informality of finance sources for innovation and compares this benefit amongst immigrant and native entrepreneurs.The authors investigate whether formal finance (here, bank loan) benefit innovation more than informal sources (personal savings and friend/family loan).Then, they explore whether an entrepreneur's status strengthens or weakens the benefit finance sources for innovation. Research Design & Methods:This study applies a quantitative approach to conduct the research.The data of 15,850 entrepreneurs surveyed by Global Entrepreneurship Monitor in 2015 were analysed using the hierarchical linear modelling (HLM) technique.Findings: Results indicated that formal finance benefits innovation more than informal sources, and this advantage is the same for both immigrant and native entrepreneurs.Implications & Recommendations: Despite the prevalence of some stereotypes regarding the simplistic and repetitive nature of immigrants ' businesses, the study recommends that financial institutions and policymakers plan to enhance entrepreneurs ' access to formal financial resources irrespective of their migrant status.Policymakers also can plan to increase the immigrant entrepreneurs ' access to the formal sources of finance by tailored educations to boost innovation. Contribution & Value Added:The results highlight that immigrant entrepreneurs benefit from formal finance towards innovation similarly to their native counterparts.Such a clarification informs the studies on the liability of foreignness and innovation finance that immigration status cannot be a barrier to innovation as portrayed and conceptualized by some studies.
Open-Ended Evolution for <i>Minecraft</i> Building Generation

This paper proposes a procedural content generator which evolves Minecraft buildings according to an open-ended and intrinsic definition of novelty.To realize this goal we evaluate individuals' novelty in the latent space using a 3D autoencoder, and alternate between phases of exploration and transformation.During exploration the system evolves multiple populations of CPPNs through CPPN-NEAT and constrained novelty search in the latent space (defined by the current autoencoder).We apply a set of repair and constraint functions to ensure candidates adhere to basic structural rules and constraints during evolution.During transformation, we reshape the boundaries of the latent space to identify new interesting areas of the solution space by retraining the autoencoder with novel content.In this study we evaluate five different approaches for training the autoencoder during transformation and its impact on populations' quality and diversity during evolution.Our results show that by retraining the autoencoder we can achieve better open-ended complexity compared to a static model, which is further improved when retraining using larger datasets of individuals with diverse complexities.
Anomaly detection in rotating machinery using autoencoders based on bidirectional LSTM and GRU neural networks

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur.The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring.Several algorithms have been used to classify anomalies successfully.However, the time series anomaly detection algorithm was not studied well.We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research.An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration sensors.The features taken from new data are then reconstructed using the trained model.If the model accurately reconstructs the features, the machine is in good working order.If the reconstruction exceeds a certain error threshold, the machine is functioning strangely and needs to be serviced.
Survey on Highly Imbalanced Multi-class Data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic.Data distribution in machine learning has been an essential aspect in providing unbiased solutions.From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems.Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required.This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains.Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset.This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data.The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data.Finally, for highly imbalanced multi-class data, this paper presents a novel framework.We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining

Large-scale pretrained language models have achieved SOTA results on NLP tasks.However, they have been shown vulnerable to adversarial attacks especially for logographic languages like Chinese.In this work, we propose ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc.It is pretrained with the contrastive learning objective which maximizes the label consistency under different synthesized adversarial examples.The model takes as input multimodal information including the semantic, phonetic and visual features.We show all these features are important to the model robustness since the attack can be performed in all the three forms.Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under three blackbox adversarial algorithms without sacrificing the performance on clean testset.It also performs the best in the toxic content detection task under human-made attacks.
Image Captioning using CNN and LSTM

In the model we proposed, we examine the deep neural networks-based image caption generation technique. We give image as input to the model, the technique give output in three different forms i.e., sentence in three different languages describing the image, mp3 audio file and an image file is also generated. In this model, we use the techniques of both computer vision and natural language processing. We are aiming to develop a model using the techniques of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to build a model to generate a Caption. Target image is compared with the training images, we have a large dataset containing the training images, this is done by convolutional neural network. This model generates a decent description utilizing the trained data. To extract features from images we need encoder, we use CNN as encoder. To decode the description of image generated we use LSTM. To evaluate the accuracy of generated caption we use BLEU metric algorithm. It grades the quality of content generated. Performance is calculated by the standard calculation matrices.
Web Scraping Techniques and Applications: A Literature Review

Big data analytics gives organizations a way to analyze huge data sets and gather new information. It helps answer basic questions about business operations and business performance. It also helps discover unknown patterns in vast datasets or combinations thereof. In the current data-driven world, it becomes increasingly essential that big data techniques are applied and analyzed for organizational growth. More specifically, with the large availability of data on the Web, whether from social media, websites, online portals, or platforms, to name but a few, it is important for organizations to know how to mine that data in order to extract useful knowledge. Web scraping represents a fundamental approach in this regard. Therefore, this paper aims to provide an updated literature review about the most advanced Web Scraping techniques to better equip scholars and managers with helpful knowledge on how to mine most effectively online data. The paper starts with presenting the basic design of a web scraper and the applications of web scraping in diverse sectors and areas. Next, the different Web scraping methods and Web scraping technologies are presented. Finally, a procedure to develop Web scraping with various tools is proposed before a conclusion wraps up the paper.
Learning Degradation Uncertainty for Unsupervised Real-world Image Super-resolution

Acquiring degraded images with paired high-resolution (HR) images is often challenging, impeding the advance of image super-resolution in real-world applications. By generating realistic low-resolution (LR) images with degradation similar to that in real-world scenarios, simulated paired LR-HR data can be constructed for supervised training. However, most of the existing work ignores the degradation uncertainty of the generated realistic LR images, since only one LR image has been generated given an HR image. To address this weakness, we propose learning the degradation uncertainty of generated LR images and sampling multiple LR images from the learned LR image (mean) and degradation uncertainty (variance) and construct LR-HR pairs to train the super-resolution (SR) networks. Specifically, uncertainty can be learned by minimizing the proposed loss based on Kullback-Leibler (KL) divergence. Furthermore, the uncertainty in the feature domain is exploited by a novel perceptual loss; and we propose to calculate the adversarial loss from the gradient information in the SR stage for stable training performance and better visual quality. Experimental results on popular real-world datasets show that our proposed method has performed better than other unsupervised approaches.
Finland Education System

If viewed from a geographical perspective, Finland is a Scandinavian country in Northern Europe. A quarter of its territory falls within the Arctic Arctic Ocean Circle. Therefore, the population of this small country must be able to maximize the use of all its psychological and physical potential to maintain and prosper their lives. The country has an area of ​​338,145 km with a population of around 5,518,371 inhabitants and adheres to the socialist (Luthrean-communist) philosophy. Before 1990 Finland relied on the country's income in the agricultural sector, but now Finland is famous as one of the world's technology centers. Finland is able to produce students who are superior compared to students in America in the fields of Science and Mathematics. Like other countries in Europe Finland also adheres to pragmaticism. Religious education is the responsibility of each parent. The Finnish government does not charge tuition fees to its citizens. The core curriculum is handled by tripartite training committees (government, industry and education and training providers). The change of leadership of the state does not change the education policy, so that what was previously programmed by the holders of authority on education policy can continue. The result is that in just 14 years Finland has become the number one education country in the world. In one class there are three teachers (two teachers who focus on delivering the material, one teacher accompanying students who are still lagging behind in the lesson). All teachers must have a master's degree. Undergraduate students are no longer allowed to become teachers even in primary school. Prospective teachers come from the top 10 students on campus who will still be screened more closely. In the evaluation of the Finnish education system there are no Minimum Mastery Criteria (KKM) as in Indonesia, and also no national examination (UAN), but they adhere to policies. "Automatic promotion", move up the class automatically. The teacher is always ready to help students who are left behind so that all go up to class. Their motto is "Test Less Learn More" (reduce the test multiply learning). The measure of educational progress according to them is the character of the population not national income, technological advances and military strength. The Finnish government provides a budget of 5,200 Euros per year per student.
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.
Lexicase selection at scale

Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.