Reviewerly/exHarmony huggingface dataset:
Research on Pedestrian Detection Based on Multimodal Infor-mation Fusion

Aiming at the matter that pedestrian detection in the autonomous driving system is vulnerable to the influence of the external environment and the detector supported single sensor modal detector has poor performance beneath the condition of enormous amendment of unrestricted light-weight, this paper proposes a fusion of light and thermal infrared dual mode pedestrian detection methodology. Firstly, 1 × 1 convolution and expanded convolution square measure are introduced within the residual network, and also the ROI Align methodology is employed to exchange the ROI Pooling method-ology to map the candidate box to the feature layer to optimize the Faster R-CNN. Secondly, the loss performance of the generalized intersection over union (GIoU) is employed because of the loss performance of the prediction box positioning regression; finally, supported by the improved Faster R-CNN, four forms of multimodal neural network structures square measure designed to fuse visible and thermal infrared pictures. According to experimental findings, the proposed technique outperforms current mainstream detection algorithms on the KAIST dataset. As compared to the conventional ACF + T + THOG pedestrian detector, the AP is 8.38 percentage points greater. Compared to the visible light pedestrian detector, the miss rate is 5.34 percentage points lower.
Anomaly Detection Algorithm with Blockchain to Detect Potential Security Attacks in the IIoT Model of Industry 5.0

The research presents a model for detecting potential security attacks in the Industry 5.0’s Internet of Things (IIoT) model using an Anomaly Detection Algorithm, with Blockchain technology to further enhance security. One-class Support Vector Machines (SVM) is used as the Anomaly Detection Algorithm, to identify any unusual behavior in the IIoT system. The proposed model ensures the integrity of data by implementing the decentralized features of Blockchain technology. This paper aims to address the current security challenges faced by Industry 5.0 and enhance the reliability of the IIoT model. Since Industry 5.0 is not here yet, hypothetical data is used to train the model which is generated after seeding using Numpy. The Blockchain technology enhanced the overall security of the Industrial Internet of Things (IIoT) model whereas, to secure it even further by detecting anomalous activities, the machine learning algorithm is proposed. Anomaly detection algorithm with Gaussian distribution is proposed through One-class SVM. The threshold for an activity to be classified as unusual or anomalous is discussed in the paper along with the difference between classification algorithm and anomaly detection algorithm. The research implemented One-class SVM algorithm to train the model by randomly seeding data using Numpy with an average accuracy of 92.8% after 5 different runs with different datasets. The algorithm also focused on other applications of the model like detection of faulty driver, device, or equipment.
Few-shot Sentiment Analysis Based on Adaptive Prompt Learning and Contrastive Learning

Traditional deep learning-based strategies for sentiment analysis rely heavily on large-scale labeled datasets for model training, but these methods become less effective when dealing with small-scale datasets. Fine-tuning large pre-trained models on small datasets is currently the most commonly adopted approach to tackle this issue. Recently, prompt-based learning has gained significant attention as a promising research area. Although prompt-based learning has the potential to address data scarcity problems by utilizing prompts to reformulate downstream tasks, the current prompt-based methods for few-shot sentiment analysis are still considered inefficient. To tackle this challenge, an adaptive prompt-based learning method is proposed, which includes two aspects. Firstly, an adaptive prompting construction strategy is proposed, which can capture the semantic information of texts by utilizing a dot-product attention structure, improving the quality of the prompt templates. Secondly, contrastive learning is applied to the implicit word vectors obtained twice during the training stage to alleviate over-fitting in few-shot learning processes. This improves the model’s generalization ability by achieving data enhancement while keeping the semantic information of input sentences unchanged. Experimental results on the ERPSTMT datasets of FewCLUE demonstrate that the proposed method have great ability to construct suitable adaptive prompts and outperforms the state-of-the-art baselines.
ResyDuo: Combining Data Models and CF-Based Recommender Systems to Develop Arduino Projects

While specifying an IoT-based system, software developers have to face a set of challenges, spanning from selecting the hardware components to writing the actual source code. Even though dedicated development environments are in place, a non-expert user might struggle with the over-choice problem in selecting the proper component. By combining MDE and recommender systems, this paper proposes an initial prototype, called ResyDuo, to assist Arduino developers by providing two different artifacts, i.e., hardware components and software libraries. In particular, we make use of a widely adopted collaborative filtering algorithm by collecting relevant information by means of a dedicated data model. ResyDuo can retrieve hardware components by using tags or existing Arduino projects stored on the ProjectHub repository. Then, the system can eventually retrieve corresponding software libraries based on the identified hardware devices. ResyDuo is equipped with a web-based interface that allows users to easily select and configure the under-developing Arduino project. To assess ResyDuo's performances, we run the ten-fold cross-validation by adopting the grid search strategy to optimize the hyperparameters of the CF-based algorithm. The conducted evaluation shows encouraging results even though there is still room for improvement in terms of the examined metrics.
A hybrid neural network-based intelligent body posture estimation system in sports scenes

&lt;abstract&gt;&lt;p&gt;Body posture estimation has been a hot branch in the field of computer vision. This work focuses on one of its typical applications: recognition of various body postures in sports scenes. Existing technical methods were mostly established on the basis of convolution neural network (CNN) structures, due to their strong visual information sensing ability. However, sports scenes are highly dynamic, and many valuable contextual features can be extracted from multimedia frame sequences. To handle the current challenge, this paper proposes a hybrid neural network-based intelligent body posture estimation system for sports scenes. Specifically, a CNN unit and a long short-term memory (LSTM) unit are employed as the backbone network in order to extract key-point information and temporal information from video frames, respectively. Then, a semi-supervised learning-based computing framework is developed to output estimation results. It can make training procedures using limited labeled samples. Finally, through extensive experiments, it is proved that the proposed body posture estimation method in this paper can achieve proper estimation effect in real-world frame samples of sports scenes.&lt;/p&gt;&lt;/abstract&gt;
Information Theoretical Importance Sampling Clustering and Its Relationship with Fuzzy C-Means

A current assumption of most clustering methods is that the training data and future data are taken from the same distribution. However, this assumption may not hold in most real-world scenarios. In this paper, we propose an information theoretical importance sampling based approach for clustering problems (ITISC) which minimizes the worstcase of expected distortions under the constraint of distribution deviation. The distribution deviation constraint can be converted to the constraint over a set of weight distributions centered on the uniform distribution derived from importance sampling. The objective of the proposed approach is to minimize the loss under maximum degradation hence the resulting problem is a constrained minimax optimization problem which can be reformulated as an unconstrained problem using the Lagrange method. The optimization problem can be solved by either an alternating optimization algorithm or a general optimization routine by commercially available software. Experiment results on synthetic datasets and a real-world load forecasting problem validate the effectiveness of the proposed model. Furthermore, we demonstrate that fuzzy c-means is a special case of ITISC with the logarithmic distortion, and this observation provides an interesting physical interpretation for fuzzy exponent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> .
Web API Recommendation via Combining Adaptive Multi-channel Graph Representation and xDeepFM Quality Prediction

With the increasing number of Web services, how to provide developers with Web APIs that meet their Mashup requirements accurately and efficiently has become a challenge. Even though the existing methods show improvements in service recommendation, the efficiency and accuracy of them still need to be improved due to their limited representation in fusing network topology and node feature of Web service, and the neglected higher-order feature interactions of Web service. To address this problem, this paper proposes a Web APIs recommendation method via combining adaptive multichannel graph representation and xDeepFM (eXtreme Deep Factorization Machine) quality prediction. In this method, firstly, specific embedding and shared embedding in Web API node isomorphic network are extracted from the nodes’ feature space, topology space, and the combination of the two spaces. Then, attention mechanism is used to adaptively learn the importance weight of each embedding. Next, these embeddings are adaptively integrated to generate the multi-channel graph representation of Web APIs for service classification. Finally, aiming at the Web APIs in the service cluster, it utilizes xDeepFM to model and mine the complex feature interactions and predict and rank the scores of Web APIs for Mashup creation. The experimental results on the real datasets of ProgrammableWeb show that compared with DeepFM, WDL, FNN, NFM, and MLR, the method proposed in this paper has an average improvement in AUC of 2.3%, 7.9%, 8.0%, 9.6%, and 13.3%.
La dimensión educativa de la Sección 22 (SNTE) durante la pandemia del covid-19

This article seeks to account for the educational proposals deployed by Section 22 of the National Union of Education Workers (SNTE) of Mexico during the covid-19 pandemic (2020-2021). Section 22 has a leading place within the Oaxacan educational system, which means that its decisions have an impact on the educational future in the State. Section 22 has been characterized by practicing a belligerent trade unionism in which its alternative education proposal is a fundamental dimension. In this article we will detail their educational proposals for an exceptional moment such as a pandemic. In our research we use the case study technique and qualitative data construction instruments such as the semi-structured interview and work with secondary data.
AR-Speaking Buddy: Designing Content and User Interface of An Augmented Reality (AR)-Based Application for Malaysian Children Speaking Practice

The application of augmented reality (AR) as a learning tool is crucial for incorporating virtual objects into real-world environments to support learners in their educational journey. Despite the growing interest in AR applications for learning, research in this area is still in its early stages, particularly concerning the development of AR applications tailored for children, taking into account their needs and design preferences. This study aims to gain insights into children's perspectives on interface and content design, specifically for augmented reality application development. By involving children in the design process, the study seeks to identify their interface design preferences for AR characters and explore the topics that interest them the most. To achieve these objectives, the paper outlines the development process of an AR mobile application named "AR-Speaking Buddy," which was designed by considering children's content needs and interface design preferences. The application focuses on providing English language learning support to children for practicing their English communication skills. The research involved 25 children aged between 9 to 12 years old as users, and their feedback on the AR-Speaking Buddy application is presented, highlighting content and interface design elements. The findings from the children's feedback shed light on essential aspects of AR application design, which contribute to the development of future interface and content design strategies for augmented reality applications in education. By understanding children's preferences and incorporating them into AR application development, educators can create more effective and engaging learning experiences for young learners.
Exploring the Proof of Work Based Block Chain Algorithm for Pharmaceutical Industry Supply Chain Innovation

Block chain technology offers numerous promising opportunities for improving supply chain management. This article provides a brief outline of how Block chain technology is being utilized in the supply chain industry. The article is proposed to explain how the Block chain might help suppliers, manufacturers, regulators, quality assurance labs, and logistics operators by cooperating on a shared ledger, as well as to establish provenance for speciality products at the package level and to reduce income leakage to pharmaceuticals. A Block chain-based pharmacy supply chain management track and trace system could tackle this growing illegal economy by tracking medicine through suppliers, manufacturers, wholesale distributors, pharmacies, hospitals, regulators, and logistics. In this paper it is proposed to display proof of work (PoW) based Block chain algorithm to stop counterfeit medications entering a pharmaceutical supply chain and to enhance transparency, traceability, and security highlighting the merit of PoW algorithm.
Live Objects All The Way Down: Removing the Barriers between Applications and Virtual Machines

Object-oriented languages often use virtual machines (VMs) that provide mechanisms such as just-in-time (JIT) compilation and garbage collection (GC). These VM components are typically implemented in a separate layer, isolating them from the application. While this approach brings the software engineering benefits of clear separation and decoupling, it introduces barriers for both understanding VM behavior and evolving the VM implementation. For example, the GC and JIT compiler are typically fixed at VM build time, limiting arbitrary adaptation at run time. Furthermore, because of this separation, the implementation of the VM cannot typically be inspected and debugged in the same way as application code, enshrining a distinction in easy-to-work-with application and hard-to-work-with VM code. These characteristics pose a barrier for application developers to understand the engine on top of which their own code runs, and fosters a knowledge gap that prevents application developers to change the VM. We propose Live Metacircular Runtimes (LMRs) to overcome this problem. LMRs are language runtime systems that seamlessly integrate the VM into the application in live programming environments. Unlike classic metacircular approaches, we propose to completely remove the separation between application and VM. By systematically applying object-oriented design to VM components, we can build live runtime systems that are small and flexible enough, where VM engineers can benefit of live programming features such as short feedback loops, and application developers with fewer VM expertise can benefit of the stronger causal connections between their programs and the VM implementation. To evaluate our proposal, we implemented Bee/LMR, a live VM for a Smalltalk-derivative environment in 22057 lines of code. We analyze case studies on tuning the garbage collector, avoiding recompilations by the just-in-time compiler, and adding support to optimize code with vector instructions to demonstrate the trade-offs of extending exploratory programming to VM development in the context of an industrial application used in production. Based on the case studies, we illustrate how our approach facilitates the daily development work of a small team of application developers. Our approach enables VM developers to gain access to live programming tools traditionally reserved for application developers, while application developers can interact with the VM and modify it using the high-level tools they use every day. Both application and VM developers can seamlessly inspect, debug, understand, and modify the different parts of the VM with shorter feedback loops and higher-level tools.
Performance Evaluation of Artificial Neural Network Methods Based on Block Machine Learning Classification

Traditional (pixel-by-pixel) classification techniques are time-consuming, whereas semantic segmentation in machine learning requires assigning class labels to each pixel in an image. This study proposes a block-by-block (5x5 chunks) segmentation method for semantic segmentation, which involves image dissection, feature extraction, and model training based on specific color and textural properties. Thirty cat photos from the Oxford-IIIT Pet dataset were used for evaluation. Five different Artificial Neural Network (ANN) models, including LM, BGFGS, RP, SCG, and GDX, were trained and assessed for both pixel-based and block-based methods. The accuracy of the block-based classification ranges from 82.94% to 85.83%, surpassing the pixel-based approach, which ranges from 70.82% to 76.47%. The processing time for the models also improved with the block-based method. For the pixel-based approach, RP model takes the longest processing time i.e., 242.39 seconds, while GDX model takes the shortest processing time i.e., 49.89 seconds. For the block-based approach, LM model takes the longest processing time i.e., 13.86 seconds, while GDX still has the shortest processing time i.e., 5.98 seconds. Therefore, block-based methods can be seen as more efficient and accurate for classification models. The LM model achieved the highest accuracy on test images, ranging from 94.72% to 89.81%, while the GDX model had the lowest accuracy, ranging from 92.96% to 81.15%. The remaining models, RP, SCG, and BFG, have intermediate levels of accuracy.
Boosting the Transferability of Adversarial Attacks in Deep Neural Networks

This article presents a means of boosting the transferability of adversarial attacks in deep neural networks. The research includes background, methodologies, and outcomes, encompassing single-attack approaches and ensemble attack strategies such as I-FGSM and MI-FGSM. We delve into the notion of retraining using adversarial examples. Our contributions reveal the limitations of single-attack methods regarding transferability and demonstrate the superiority of ensemble attack methods. We highlight how algorithm selection impacts attack effectiveness and how model variations enhance transferability. Through these investigations, we offer valuable insights for bolstering deep neural networks’ adversarial robustness while acknowledging existing constraints.
The Museum: From Digital To Metaverse

The use of digital technology has greatly facilitated the interaction between viewers and museum collections. Although the idea of digital museums has received considerable focus in the last decade, the lack of interactivity remains a challenge for digital museums. In this paper, we discuss the combination of the metaverse and traditional museums. The study finds that the integration of the metaverse and museums allows museums to create deeper engagement and achieve higher levels of interactivity.
A Crowd Behavior Analysis Method for Large-Scale Performances

This study combines visual and athletic information to analyze crowd performance, using performance density entropy and performance consistency as visual descriptors and group collectivity as an athletic descriptor. We used these descriptors to develop a crowd performance behavior classification algorithm that can distinguish between different behaviors in large-scale performances. The study found that the descriptors were weakly correlated, indicating that they capture different dimensions of performance. The crowd behavior classification experiments showed that the descriptors were valid for qualitative analysis and consistent with human perception. The proposed algorithm successfully differentiated and described performance behavior in the dataset of a large-scale crowd performance and was demonstrated to be effective.
Group Perception Based Self-adaptive Fusion Tracking

Multi-object tracking (MOT) is an important and representative task in the field of computer vision, while tracking-by-detection is the most mainstream paradigm for MOT, so that target detection quality, feature representation ability, and association algorithm greatly affect tracking performance. On the one hand, multiple pedestrians moving together in the same group maintain similar motion pattern, so that they can indicate each other’s moving state. We extract groups from detections and maintain the group relationship of trajectories in tracking. We propose a state transition mechanism to smooth detection bias, recover missing detection and confront false detection. We also build a two-level group-detection association algorithm, which improves the accuracy of association. On the other hand, different areas of the tracking scene have diverse and varying impact on the detections’ appearance feature, which weakens the appearance feature’s representation ability. We propose a self-adaptive feature fusion strategy based on the tracking scene and the group structure, which can help us to get fusion feature with stronger representative ability to use in the trajectory-detection association to improve tracking performance. To summary, in this paper, we propose a novel Group Perception based Self-adaptive Fusion Tracking (GST) framework, including Group concept and Group Exploration Net, Group Perception based State Transition Mechanism, and Self-adaptive Feature Fusion Strategy. Experiments on the MOT17 dataset demonstrate the effectiveness of our method. The method achieves competitive results compared to the state-of-the-art methods.
Simple and Secure Digital Color Image Steganography

A simple and secure method of digital color image steganography method will be introduced, the method will hide a digital color image in another image, the covering image will be any type and with any size (the covering image can be smaller than the image to be hidden, thus the capacity of the covering image will be unlimited. The proposed method will apply image resizing, the resizing factors will form the private key to protect the hidden image from being hacked, this key will provide a good key space to secure the image, the extracted image will be very sensitive to the selected values of the private key components. A simple way of image bits hiding and extracting operation will be used, the image bit matrix will converted to one column matrix and embedded in the LSBs of the covering bytes using one assignment operation, the LSBs of the stego bytes will extracted using one operation to create a one column matrix, which will be converted to 8 columns matrix to form the image binary matrix. The method will be implemented and tested using various covering images and various color images to be hidden. The obtained results will be analyzed to prove the quality, speed and sensitivity of the proposed method.