Reviewerly/exHarmony huggingface dataset:
Small target detection algorithm based on YOLOv4

In recent years, deep learning has been rapidly developed in the field of target detection. How to accurately and efficiently locate small target objects is one of the main difficulties in target detection research. This paper proposes a small target detection method based on YOLOv4 algorithm. In the process of data preprocessing, the small target enhancement method is used instead of the Mosaic method to increase the number of small targets in the image and improve the small target detection ability of the network model. Using the K-means to get the best size of the anchor box to improve the accuracy and speed of small target detection. Experiments on the DOTA data set show that it meets the performance requirements and can effectively and accurately locate the target object in the image with the accuracy of 93.2%.
A short survey on deep learning for skeleton-based action recognition

Motion recognition is an essential aspect of computer vision used in a wide range of fields and has received much attention as one of the most popular research topics. Traditional motion recognition studies are mainly based on RGB images and videos, but the lighting and viewpoint of RGB data can easily affect the model performance. Skeleton sequences are the most common type of coordinate data and avoid these problems. Therefore, more and more research has been conducted to combine skeleton sequences with deep learning to solve action recognition problems, and awe-inspiring results have been obtained. In particular, the recent rapid emergence of GCN methods, which fit well with the characteristics of skeletal data, offers a promising future for action recognition based on skeletal sequences. In this paper, we first introduce the acquisition of skeletal data and some common datasets, summarise some of the research in the field of skeletal sequence-based action recognition, and briefly discuss the future directions of this kind of research.
Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs

Web logs have been widely used to represent the web page visits of online users. However, we found that web logs in Chrome’s browsing history only record 57% of users’ visited websites, i.e., nearly half of a user’s website visits are not recorded. Additionally, 5.1% of the visits recorded in the web log occur because of unconscious user actions, i.e., these page visits are not initiated from users. We created a Google Chrome plugin and recruited users to install the plugin to collect and analyze the conscious URL visits, unconscious URL visits, and “missing” URL visits (i.e., the visits unrecorded in the traditional web log). We reported the statistics of these behaviors. We showed that sorting popular website categories based on traditional web logs differs from the rankings obtained when including missing visits or excluding unintentional visits. We predicted users’ future behaviors based on three types of training data – all the visits in modern web logs, the intentional visits in web logs, and the intentional visits plus missing visits in web logs. The experimental results indicate that missing visits in web logs may contain additional information, and unintentional visits in web logs may contain more noise than information for user modeling. Consequently, we need to be careful of the observations and conclusions derived from web log analyses because the web log data could be an incomplete and noisy dataset of a user’s visited web pages.
Discerning appropriate reviews based on hierarchical deep neural network for answering product-related queries

In recent years, E-Commerce is globally increasing among online purchaser, in which customer post product related queries for finding the best product in online shopping. Manually answering the product related queries in real-time, cause online traffic and practically not possible. So, automatic answering system is helpful for answering product related queries. But, the product queries are always in product-explicit, so discovering related product queries and recovering its responds is distinctly be impractical. Accordingly, we propose Hierarchical Deep Neural Network (HiDeNN) model using MOQA framework to discern the appropriate reviews based on Mixtures of Opinions Question Answering (MOQA). The Hierarchical Deep Neural Network provides discerning the most relevant review for queries and it also provides the relevant answer for specific product category queries. The proposed method is executed on Python and it provides 9.594% and 7.574% higher accuracy value for Discerning Appropriate Reviews compared with the existing method like Relevant Reviews for Answering Product-related Queries (MOQA-BERTQA+FLTR+PT) and IQA: Interactive Query Construction on Semantic Question Answering Systems (IQC-SQA). The experimental result indicates that the proposed MOQA- HiDeNN method can efficiently and accurately get the optimal global solutions for recognizing the appropriate discerning of most relevant review for queries and it also provides the relevant answer for specific product category queries.
Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition

This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available. Generative Adversarial Networks (GANs) provide an approach to model the distribution of a dataset and can be used to augment data to reduce the amount of labelled data required to train accurate classifiers. By introducing another fully connected neural network as classifier into a conditional GAN framework, we utilise the adversarial learning approaches between discriminator and generator and between discriminator and classifier to perform semi–supervised learning on labelled and unlabelled samples. We evaluate our approach on the recognition of 8 modes of transportation and locomotion using a publicly available dataset. This dataset is well established and has led to 3 public machine learning challenges, which allows us to contrast our approach to the state of the art. Our GAN operates on 150 features extracted from 5s windows captured by a smartphone acceleration sensor carried at the hips. The most promising features are selected based on maximum relevance – minimum redundancy feature selection. We use Bayesian Search for hyperparameter optimisation. The resulting GAN classifier achieves 49% F1 score on a user independent evaluation, drastically outperforming our baseline at 35% F1 score.
Application of Improved Dijkstra Path Planning Algorithm for Industrial Stacking Robots

In the path planning research of industrial robots, it is necessary to find the shortest path and reduce the search time. In this paper, A* algorithm, Dijkstra algorithm and Dynamic Programming algorithm are used to find the shortest path in the industrial workshop scenario. An improved Dijkstra algorithm is used to add additional paths considering turning angle, scene complexity and device wear.
A remote attestation mechanism using group signature for the perception layer in centralized networking

Abstract As the important transmission approach of the perception data, the security and privacy of the perception layer nodes have been paid more and more attention. To evaluate the credibility of the perception nodes and protect autologous identity privacy, a remote attestation mechanism using group signature (GS-RAM) for the perception layer of centralized networking is proposed in this paper. First, a RAM, based on the computational Diffie–Hellman (CDH) problem and GS, is established for the data source of the perception nodes. Second, the specific construction of the proposed GS-RAM is given to identify the trusted state of data sources without exposing the privacy of the perception nodes. Then, a strict security certification was carried out to verify the correctness, unforgeability, anonymity, traceability, unrelatedness, non-framing, anti-joint aggression and forward security of the proposed GS-RAM. Finally, simulation experiments are carried out to verify that the proposed scheme has better security and dynamic adaptability.
Virtual Activities to Strengthen Basic Math Skills in Children

This article presents the results obtained from an investigation carried out in Ecuador during the COVID 19 pandemic to determine how the use of virtual activities strengthens basic mathematical skills in children aged four to five years old. A quantitative study was carried out with sixty-one children who were assessed using the Early Mathematical Assessment Test (EMAT), in its Spanish version, to indicate the level of mathematical skills of the participants before and after the course. We used Moodle for the delivery of the virtual activities and synchronous sessions over a 4-week period. Results obtained were positive after participation in the Basic Mathematical Skills (BMS) course. There was an increase in the scores of the posttest compared to the pre-test, in the concepts of comparison, classification, seriation and correspondence in all age groups. Regarding activities in the platform, seriation and classification were the math skill that stood out. In contrast, comparison was the least developed math skill. Additionally, the development of math skills in virtual environments seemed to be influenced by student age. Finally, it can be added that virtual platforms help to generate better performance in the classroom and can be used as a complement to educational programs for a better learning.
Transfer Learning Using Variational Quantum Circuit

Quantum computing and particularly quantum machine learning has witnessed a surge in the direction of fruitful research being done in recent years. Variational quantum algorithms have gained popularity due to their resilience to error, which is a common occurrence in the near term intermediate scale quantum (NISQ) hardware. In such an algorithm, an optimal function is mapped by a quantum circuit which is designed using weighted parameterized gates to be trained based on a cost function that is optimized using gradient descent. A Variational Quantum Classifier (VQC) is such an algorithm and is used for supervised learning to perform data classification. But since the quantum hardware available today is not powerful enough for a VQC to come of any practical use in the current setting, we implement a transfer learning pipeline in which a pre-trained image processing model is fine tuned using a dressed VQC; for a specific, binary classification task. Our aim was to study the working of a VQC and test it’s behaviour with respect to multiple hyperparameters such as circuit depth, number of qubits, etc. We trained our transfer learning-VQC model for 4 different datasets and for different hyperparameters and were able to get close to state of the art performance in most of our tests, solidifying the prospect of VQC as an efficient quantum-classical hybrid supervised learning algorithm. It should be noted that the study of VQC was chosen over that of other algorithms available for classification because recent advancements in the understanding of VQCs that show promise in favour of the algorithm.
Adversarial Adaptive Interpolation in Autoencoders for Dually Regularizing Representation Learning

Linear interpolation in the latent space may induce mismatch between the constructed data and the distribution a model was trained on. In this article, we propose an Adversarial Adaptive Interpolation-based AutoEncoder (AdvAI-AE). To constrain the interpolation path on the underlying manifold, an additional interpolation correction module is trained to offset the deviation between the linearly interpolated data points and the statistics of real ones in latent space. Furthermore, we apply prior matching to control the characteristics of the representation. Toward this end, the maximum mean discrepancy-based and adversarial regularizers are incorporated into the model. The synthesized data from random variables are in turn leveraged to regularize the interpolation process. The proposed improvement strategies lead to significant performance gains in downstream classification, clustering, and synthesis tasks on multiple benchmark datasets.
Utilization of HVS paper waste with collage art and paper quilling techniques as a 2-dimensional medium for hair styling in beauty education program

Abstract The accumulation of paper waste encourages the utilization of HVS paper waste with Collage and Paper Quilling Techniques as a 2-dimensional medium for hair styling learning. The purpose of this study was to understand the process of utilizing HVS paper waste with collage and paper quilling techniques as a 2-dimensional medium for hair styling learning; to explore the design and shape produced in the utilization of HVS paper waste with collage and paper quilling techniques as a 2-dimensional medium for hair styling learning; to learn the validity of 2-Dimensional media for Hair Styling Learning by utilizing HVS Paper Waste using Collage and Paper Quilling Techniques. Development research with qualitative descriptive data analysis was carried out, the process of utilizing HVS paper waste with collage and paper quilling techniques using loose scrolls and multi strips scrolls was explored. Design and shape of hair styling for 5 face shapes. Validity by 5 experts with an average of 89.33% and, the results of final product assessment by 60 students obtained an average of 92.70% based on indicators of aspects of regulation, balance, color, conformity, attractiveness. Utilization of HVS paper waste with Collage and Paper Quilling Techniques as a 2-dimensional medium for hair styling learning is declared very feasible.
Analyzing the Effects of Hyperparameters on Convolutional Neural Network &amp; Finding the Optimal Solution with a Limited Dataset

Convolutional neural networks are composed of many hidden layers and each layer has its properties and may require some parameters. Such training models train on the weights derived from the training dataset. These weights determine how the input will have an impact on the output. The efficiency of the training model can be increased by fine-tuning the parameters of each layer. Manually tuned parameters that are known as hyperparameters require a heuristic approach for defining their values. In this paper, the hyperparameters, namely learning rate, filter size, number of filters, number of layers, and validation frequency, have been studied. This paper also examines the effect of the size of the dataset and the number of layers. It is observed that learning rate and number of layers have more impact on the accuracy as compared to validation frequency.
Digital Tools to Promote Healthy Eating for Working-Age Individuals: A Scoping Review

In this scoping review, we aimed to understand current developments of digital tools for promoting healthy eating behaviors in a work context among working-age individuals and identify research gaps for future design opportunities. The papers published over the last decade (2010-2021) were searched in three databases: the Association for Computing Machinery (ACM) digital library, the interdisciplinary library Scopus, and the PubMed database. Initially, 2098 papers were identified, of which 16 papers were included in the final analysis. These 16 papers were published in 15 various conference proceedings or journals between 2010 and 2021, and mainly focused on tracking eating moment and promoting healthy food intake. Our findings showed that four types of digital tools for healthy eating promotion were commonly used, including mobile applications, wearables, service, and multicomponent (i.e., a combination between mobile apps and wearables). Moreover, we found that current digital tools made small using a range of existing working infrastructures. Future design research could focus on personalized, interactive, and playful digital tools in human-computer interaction field with behavior change techniques and user-centered approaches to promote healthy eating behaviors in daily work routines.
Natural Image Investigation Using Masked Image Encoding for Text Editing

Recognising and updating text is a typical photographic technique for changing text while maintaining the image's integrity. It offers several advantages, including the ability to rectify errors and restore text after an image has been damaged. Because the background is easily distorted, this is a difficult task. Masked autoencoders are self-supervised computer vision learners that are scalable. It works on the basis of two simple ideas: random parts of the original picture are masked, and missing pixels are restored. This study demonstrates how to alter the look of visual text down to the letter and digit level while maintaining the background's authenticity. Based on a masked autoencoder, LDN is built to update text on the natural images while maintaining the background texture. When it comes to assessing the 126 photographs of streets, posters, retail malls, and school settings, LDN performed brilliantly. It may effectively keep the background texture when modifying capital, lowercase, and numbers.
Exploring User Experience and Design Opportunities of Desktop Social Virtual Reality for Group Learning Activities

Conducting group learning activities (GLAs), e.g., group discussion and presentation, can improve course outcomes. But when GLAs are carried out online, they may face challenges such as a reduced sense of social presence. Social virtual reality (VR) that enables a group of people to interact in virtual space could better support social presence than the video conferencing systems commonly used for online courses. However, few works explore the use of social VR for online GLAs. In this paper, we conduct a co-design study with five small groups of students in an online course to investigate user experience and design opportunities of desktop-based social VR for GLAs. Participants experience a desktop social VR platform Mozilla Hubs in their group projects and use techniques like storyboarding and prototyping to propose new social VR designs to support GLAs. We contribute design ideas about social VR applications that support various kinds of online GLAs.
Safety Helmet Wearing Detection and Recognition Based on YOLOv4

In specific working conditions, construction personnel need to wear safety helmets. Safety hazards occur when workers or other people enter the site without helmets. In order to monitor whether operators meet safety standards, we improved YOLOv4 algorithm to detect and identify whether people entering special places are wearing safety hats. We improve the loss function of the convolutional neural network and change its attention mechanism to make the network more sensitive to smaller objects. Moreover, we added some parameters to solve the imbalance between classes. Experimental results show that our improvement improves the accuracy of the network model in detecting and identifying the wearing condition of safety helmet.
Semantic Segmentation in Immunohistochemistry Breast Cancer Image using Deep Learning

Cancer is affecting many people's lives. Uncontrollable growth of cells causes cancer. Thus, there is a need to find accurate results for the treatment of a patient by using the proper computation method. Immunohistochemistry (IHC) image is the study of stained cancerous tissue at a microscopic level. For IHC images, cancerous tissue taken during a biopsy is inspected to identify the protein expression region. Pathologists inspect the stained specimen and manually find the Region of Interest (ROI) and quantify the protein (brown color), which is subjective and time-consuming. Thus, there is a need to develop an assistive system to segment the protein expression region. In this paper, we segment the protein expression in the membranous region - HER2 expression of Breast Cancer using various segmentation models like FCN, SegNet and U-Net. U-Net performed well with an accuracy of 94%.
SparkAC: Fine-Grained Access Control in Spark for Secure Data Sharing and Analytics

With the development of computing and communication technologies, an extremely large amount of data has been collected, stored, utilized, and shared, while new security and privacy challenges arise. Existing access control mechanisms provided by big data platforms have limitations in granularity and expressiveness. In this article, we present SparkAC, a novel access control mechanism for secure data sharing and analysis in Spark. In particular, we first propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">purpose-aware access control</i> (PAAC) model, which introduces new concepts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data processing purpose</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data operation purpose</i> and an automatic purpose analysis algorithm that identifies purposes from data analytics operations and queries. Moreover, we develop a unified access control mechanism that implements PAAC model in two modules. GuardSpark++ supports structured data access control in Spark Catalyst and GuardDAG supports unstructured data access control in Spark core. Finally, we evaluate GuardSpark++ and GuardDAG with multiple data sources, applications, and data analytics engines. Experimental results show that SparkAC provides effective access control functionalities with very small (GuardSpark++) or medium (GuardDAG) performance overhead.
EDCOSUM: Text extractive summarization framework based on edge information with coreference resolution

Automatic Text Summarization(ATS) is distinctly beneficial due to a vast amount of textual data and time-consuming manual summarization. In order to enhance ATS for single document in huge datasets, a new extractive graph framework - text extractive SUMmarization framework based on EDge information with COreference resolution EDCOSUM is proposed in this paper that relies on coreference resolution, adding edge information in word-level graph and a sentence-ranking strategy. EDCOSUM combines the graph-based and statistical-based extractive summarization methods. It is a general method for any document (not limited to a specific domain). Moreover, two ranking strategies(sentence and LSA ranking strategy) are proposed for sentence selection. A set of extensive experiments on CNN/Daily Mail and NEWSROOM are conducted for investigating the proposed method. The widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation(ROUGE) is utilized to evaluate EDCOSUM. Compared to the state-of-the-art ATS methods, EDCOSUM achieves a competitive result by improvements of over the highest scores in the literature for metrics ROUGE-1, ROUGE-2 and ROUGE-L respectively.
Reducing Communication Costs of Federated Contrastive Learning by Particle Swarm Optimization

Federated learning and contrastive learning are two important machine learning paradigms, and federated contrastive learning is their combination, allowing pretraining models using unlabeled data in the context of data silos while protecting the data privacy and security. However, existing federated contrastive learning algorithms require transmission of model weights and data representations among the sever and clients, which is costly and greatly increases data security risks. Therefore, how to reduce the amount of data communication, especially the representations, while maintaining the performance of the model is an important problem that has not yet received any attention. In this study, we propose Federated Contrastive Learning Algorithm using Particle Swarm Optimization to reduce data communication by transmitting the client model scores instead. In the experiments, our proposed method can reduce 40% weight uploads and 78.97% representation uploads compared with the baseline algorithm, while with only 1.93% decrease in accuracy.