Reviewerly/exHarmony huggingface dataset:
Footwear segmentation and recommendation supported by deep learning: an exploratory proposal

The management of an online footwear retail store - also known as marketplace - usually involves activities that directly or indirectly end up to interface with customers, wherein communication efficiency and effectiveness is crucially relevant. Critical factors concerning entities developing remote business in these areas or similar include: (i) production of appealing catalogues and (ii) digital tools to shorten the distance between customers and marketplaces. The former requires using specific third parties - often technically complex - to arrange and prepare photographic entries acquired in studio-like environments. This can delay the diffusion of products supply that may result in financial losses. The latter prevents the retailer of reaching critical mass at a higher potential. Considering such issues, this paper proposes a couple of modules for footwear marketplaces, powered by deep learning: one to segment shoes as a fully automatic background removal tool for easing and quickening catalogue creation activities in a back-office perspective; and another to provide visual search services that allow a customer to submit photographs of footwear of interest to obtain recommendations of similar products directly retrieved from online retail databases, establishing another digital bridge with potential buyers. Preliminary implementation and pilot tests point out Mask-RCNN as a promising approach for shoes segmentation. The same applies to ResNet101 and Xception, but for shoes recommendation, based on multi-label classification.
Digital Transformation of Teaching and Perception at TU Graz from the Students’ Perspective: Developments from the Last 17 Years

The use of digital technologies in teaching to make it more varied, better, more diverse, or even more accessible is being pursued systematically at many universities. This article shows the developments in the digital transformation of teaching at Graz University of Technology (TU Graz) over the last 17 years. In the process, the various activities of Graz University of Technology and of the central department of teaching and learning technologies about the digital transformation of teaching and its focus and change during this period are described in the form of a workshop report. The consequences and developments of the Covid-19 pandemic on digital transformation efforts are also addressed. This is contrasted with results of two students’ surveys from 2014 (N = 1,502 complete questionnaires) and 2021 (N = 1,386 complete questionnaires). Within this contribution, the authors use the survey’s data to assess how students’ attitudes towards technology-enhanced teaching were changing at TU Graz. Mean indices were created to be able to compare the two surveys. This shows that despite the less good experience with teaching at TU Graz during the pandemic the attitude towards digital teaching is relatively satisfying. Nevertheless, the authors point out that the students clearly indicate that digital (distance) learning has a negative impact on communication between students and teachers as well as between students themselves, and that measures would be desirable here.
Comparative Analysis between Flutter and React Native

Today’s well know technology is mobile applications and mobile application development depends on demand skills after the invention of smartphones. The current mobile market shares are divided into two powerful technologies giant Android and iPhone. The Native Android mobile applications development Java-Kotlin are used, and on the other hand, for iPhone Apps are used Swift and Objective-C native IOS development. Those applications of Android and iPhone increased the development process and cost the development cost. Recently, cross-platform proved that developers quickly develop Android and iPhone applications with the same code known as a cross-platform mobile application, reducing company and customer effort and cost. In this research paper, the performance is compared between React Native Vs Flutter and efficiency, effectiveness, compatibility, community growth, documentation, Architecture, developer productivity, and Testing Automation Support CI/CD Support.
Can AI infringe moral rights of authors and should we do anything about it? An Australian perspective

While artificial intelligence technologies (AI), such as machine learning (ML), hold significant potential for the economy and social wellbeing, it is unclear to what extent copyright laws stimulate or impede the development of these promising technologies. The unauthorised use of copyright-protected works in the ML process and its possible implications on economic rights of authors have been previously explored, however, the implications of such use on the moral rights of authors – the rights of attribution and integrity – have not been examined. This paper, by focusing on Australia as a case study, explores whether the use of works as training data in the ML process could amount to the infringement of moral rights of authors and, if so, whether law reform in the area is needed.
Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.
Application of the Certainty Factor Method for Diagnosing Osteoarthritis Using the Python Programming Language

Osteoarthritis is a disease that causes the joints in the bones to become weaker and less able to function properly. Osteoarthritis can cause pain, stiffness, and even deformity in the joints. The solution to the problem of diagnosing Osteoarthritis is to use a tool, namely a system (expert system) that uses computer technology to make decisions more easily, effectively and efficiently. This study uses the Certainty Factor method in diagnosing osteoarthritis and uses 8 symptoms, namely, pain in the joints, joints feel stiff, clicks or cracks appear when the joints are bent or moved, joints lose their flexibility properties, joints feel softer when pressure is applied, spurs appear bones around the joints, namely hard and sharp bony protrusions, swelling around the joints, and muscles around the joints. The conclusions from the calculations and program implementation, the application of the certainty factor in diagnosing osteoarthritis with existing test data, results in 95.38% that the patient has severe symptoms of osteoarthritis, so he must be taken to an orthopedic specialist.
Quantum all-subkeys-recovery attacks on 6-round Feistel-2* structure based on multi-equations quantum claw finding

Exploiting quantum mechanisms, quantum attacks have the potential ability to break the cipher structure. Recently, Ito et al. proposed a quantum attack on Feistel-2* structure (Ito et al.’s attack) based on the Q2 model. However, it is not realistic since the quantum oracle needs to be accessed by the adversary, and the data complexity is high. To solve this problem, a quantum all-subkeys-recovery (ASR) attack based on multi-equations quantum claw-finding is proposed, which takes a more realistic model, the Q1 model, as the scenario, and only requires 3 plain-ciphertext pairs to quickly crack the 6-round Feistel-2* structure. First, we proposed a multi-equations quantum claw-finding algorithm to solve the claw problem of finding multiple equations. In addition, Grover’s algorithm is used to speedup the rest subkeys recovery. Compared with Ito et al.’s attack, the data complexity of our attack is reduced from $$O(2^n)$$ to O(1), while the time complexity and memory complexity are also significantly reduced.
Comparison of Target Prediction in VR and MR using Inverse Reinforcement Learning

Numerous research were undertaken to predict pointing targets in Graphical User Interfaces (GUI). This paper extends target prediction for Extended reality (XR) platforms through Sampling-based Maximum Entropy Inverse Reinforcement Learning (SMEIRL). The SMEIRL algorithm learns the underlying reward distribution for the pointing task. Results show that SMEIRL achieves better accuracy in both VR and MR (for example accuracy in VR and accuracy in MR at of pointing task) compared to Artificial Neural Network (ANN) and Quadratic Extrapolation (QE) during early stage of pointing task. For later stage, QE performs better (for example accuracy in VR and accuracy in MR at of the pointing task) than SMEIRL and ANN. All the three algorithms, SMEIRL, ANN and QE reported higher target prediction accuracy in MR than in VR.
Efficient Human-in-the-loop System for Guiding DNNs Attention

Attention guidance is used to address dataset bias in deep learning, where the model relies on incorrect features to make decisions. Focusing on image classification tasks, we propose an efficient human-in-the-loop system to interactively direct the attention of classifiers to regions specified by users, thereby reducing the effect of co-occurrence bias and improving the transferability and interpretability of a deep neural network (DNN). Previous approaches for attention guidance require the preparation of pixel-level annotations and are not designed as interactive systems. We herein present a new interactive method that allows users to annotate images via simple clicks. Additionally, we identify a novel active learning strategy that can significantly reduce the number of annotations. We conduct both numerical evaluations and a user study to evaluate the proposed system using multiple datasets. Compared with the existing non-active-learning approach, which typically relies on considerable amounts of polygon-based segmentation masks to fine-tune or train the DNNs, our system can obtain fine-tuned networks on biased datasets in a more time- and cost-efficient manner and offers a more user-friendly experience. Our experimental results show that the proposed system is efficient, reasonable, and reliable. Our code is publicly available at https://github.com/ultratykis/Guiding-DNNs-Attention.
A Container Pre-copy Migration Method Based on Dirty Page Prediction and Compression

Container live migration can move the running container between different physical machines without interrupting the operations of container, which brings great flexibility to cloud computing environments. The pre-copy algorithm is a commonly used migration algorithm in live migration. Aiming at the problem of repeated transmission of dirty pages in the pre-copy algorithm, this paper proposes a container pre-copy migration method called MBDPC, which based on dirty page prediction and page compression. The MBDPC method exploits the locality principle of program operations, uses the Random Forest model for dirty page prediction, and skips the transmission of repeated dirty pages. The MBDPC also uses the page incremental compression method in the transmission process, which further reduces the amount of data transmitted and the migration time. Experimental results show that the MBDPC method can significantly reduce the time and data transmission overhead of live migration compared with traditional pre-copy method.
YOLOMask: Real-time Instance Segmentation With Integrating YOLOv5 and OrienMask

In this paper, we propose a real-time framework for instance segmentation, which we call YOLOMask and which builds on the real-time project OrienMask. In YOLOMask, we integrate the YOLOv5 object detection framework with the OrienMask instance segmentation framework to form a new real-time instance segmentation framework and we integrate CBAM into YOLOMask, which can help the network to find regions of interest in images with large area coverage. Using this method, our YOLOMask can achieve 47.8/44.3 msak/box AP on Pascal 2012 SBD dataset evaluated at 84.3 fps with a V100 GPU. Compared to OrienMask, YOLOMask improves box AP by about 5.8% and mask AP by 4.5%, which is encouraging and competitive. Given its simplicity and efficiency, we hope that our YOLOMask can serve as a simple but strong baseline for a variety of instance-wise prediction tasks.
Text grouping: a comprehensive guide

Text keywords have huge variance and to bridge the gap between the country business segment which provides negligible information and the keywords that have a huge longtail it is imperative for us to categorize the queries that provide a middle ground and also serve a few other purposes. The paper will present those in-depth. Query categorization falls into the segment of 'Multi-Class Classification' in the domain of natural language processing (NLP). However, business requirements require the implementation of any technique that could provide as accurate results as possible. So, to solve this problem the paper discusses an amalgamation of approaches like TF-IDF (term frequency-inverse document frequency), neural networks, cosine similarity, transformers-all of which fix specific issues.
An adaptable sentence segmentation based on Indonesian rules

&lt;p&gt;Sentence segmentation that breaks textual data strings into individual sentences is an important phase in natural language processing (NLP). Each word in the string that is added a punctuation mark such as a period, question mark, or exclamation point, becomes the location for splitting the string. Humans can easily see the punctuation and split the string into sentences, but not machines. Basically, the three punctuation marks also perform other functions so that the sentence segmentation process must really be able to detect whether a word marked with punctuation is a sentence boundary or not. This research proposes a sentence segmentation system called segmentasi kalimat bahasa Indonesia (SKBI) or Indonesian language Sentence Segmentation by applying a set of rules and can be used in Indonesian texts and can be adapted for English. There are 34 rules built with a combination of 27 fairly complete features that contribute to this research. The experimental results for the Indonesian text show that the SKBI is able to achieve an F1-Score of 96.89% and 97.07% for English. Both need to be improved but now better than previous research.&lt;/p&gt;
Program for international student assessment-based analysis for Javanese test

This study aimed to analyze the test items of Javanese for junior high school in Special Region of Yogyakarta (DIY) using Program for International Student Assessment (PISA) literacy parameters. It was qualitative descriptive research employing content analysis methods. The instruments used were test items of PISA and Javanese reading literacy. The data collected was primary data in the forms of words, phrases, sentences, paragraphs, and discourses. The purposive sampling method was used to choose the research subjects. Data were analyzed using content analysis methods. The data collection was conducted through detailed documentation on test items of PISA reading literacy published by the Organization for Economic Co-operation and Development (OECD) and Curriculum 2013 Javanese reading test items for junior high school obtained from final examinations of four regions and one city in DIY. The semantic validity was used in this study. The results of the data analysis showed that out of 25 characteristics of PISA parameters of test items, there were only 13 (52%) characteristics a lied in test items of Javanese for junior high school in DIY, while the rest (48%) was not found in the Javanese test items. Based on these findings, it is expected that teachers can develop test items for Javanese reading literacy with higher thinking ability levels.
Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine

This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs).The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data.The hybrid approach of the Hunger Games Search (HGS) and the Arithmetic Optimization Algorithm (AOA) was adopted.This paper tested the effectiveness of the OKEML-HGS-AOA on Chinese Obstetric EMR (COEMR) datasets.Compared with other models, the proposed model outperformed the state-of-the-art experimental results on the COEMR, Arxiv Academic Paper Dataset (AAPD), and the Reuters Corpus Volume 1 (RCV1) datasets, with an accuracy of 88%, 90%, and 91%, respectively.
Research on Vehicle Re-identification for Vehicle Road Collaboration

Abstract Vehicles and roads cooperate to perceive traffic targets, which can reduce the perception blind spots of vehicles and improve driving safety. In this paper, we proposes a vehicle re-identification method oriented to vehicle-road coordination. This method first designs a lightweight vehicle re-identification network based on ShufflenetV2 to solve the computational efficiency problem of vehicle-road coordination scenarios, which can efficiently complete vehicle feature extraction; then, due to the real-time requirements of scenario communication, an adaptive feature conversion mechanism is designed in combination with the LSH algorithm, which can make the re-identification module to dynamically perform binary bit feature conversion and adjust the dimension according to the communication channel state; finally, a loss function for the conversion of vehicle re-identification features is designed, which can greatly reduce the accuracy loss rate of converting floating-point features to bit features. Experiments show that our method can efficiently complete the information extraction and comparison of vehicle re-identification features in the vehicle-road coordination scenario, and can improve the perception efficiency of vehicle-road coordination while taking into account performance and bandwidth.
A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed

Processing speed is defined as the ability to quickly process information, which is generally considered as one of the affected cognitive functions of multiple sclerosis and schizophrenia. Paper-pencil type tests are traditionally used in the assessment of processing speed. However, these tests generally need to be conducted under the guidance of clinicians in a specific environment, which limits their application in cognitive assessment or training in daily life. Therefore, this paper proposed an intelligent evaluation method of processing speed to assist clinicians in diagnosis.We created an immersive virtual street embedded with Stroop task (VR-Street). The behavior and performance information was obtained by performing the dual-task of street-crossing and Stroop, and a 50-participant dataset was established with the label of standard scale. Utilizing Pearson correlation coefficient to find the relationship between the dual-task features and the cognitive test results, and an intelligent evaluation model was developed using machine learning.Statistical analysis showed that all Stroop task features were correlated with cognitive test results, and some behavior features also showed correlation. The estimated results showed that the proposed method can estimate the processing speed score with an adequate accuracy (mean absolute error of 0.800, relative accuracy of 0.916 and correlation coefficient of 0.804). The combination of Stroop features and behavior features showed better performance than single task features.The results of this work indicates that the dual-task design in this study better mobilizes participants' attention and cognitive resources, and more fully reflects participants' cognitive processing speed. The proposed method provides a new opportunity for accurate quantitative evaluation of cognitive function through virtual reality.
Regularized Denoising Masked Visual Pretraining for Robust Embodied PointGoal Navigation

Embodied PointGoal navigation is a fundamental task for embodied agents. Recent works have shown that the performance of the embodied navigation agent degrades significantly in the presence of visual corruption, including Spatter, Speckle Noise, and Defocus Blur, showing the weak robustness of the agent. To improve the robustness of embodied navigation agents to various visual corruptions, we propose a navigation framework called Regularized Denoising Masked AutoEncoders Navigation (RDMAE-Nav). In a nutshell, RDMAE-Nav mainly consists of two modules: a visual module and a policy module. In the visual module, a self-supervised pretraining method, dubbed Regularized Denoising Masked AutoEncoders (RDMAE), is designed to enable the Vision Transformers (ViT)-based visual encoder to learn robust representations. The bidirectional Kullback-Leibler divergence is introduced in RDMAE as the regularization term for a denoising masked modeling task. Specifically, RDMAE mitigates the gap between clean and noisy image representations by minimizing the bidirectional Kullback-Leibler divergence. Then, the visual encoder is pretrained by RDMAE. In contrast to existing works, RDMAE-Nav applies denoising masked visual pretraining for PointGoal navigation to improve robustness to various visual corruptions. Finally, the pretrained visual encoder with frozen weights is applied to extract robust visual representations for policy learning in the RDMAE-Nav. Extensive experiments show that RDMAE-Nav performs competitively compared with state of the arts (SOTAs) on various visual corruptions. In detail, RDMAE-Nav performs the absolute improvement: 28.2% in SR and 23.68% in SPL under Spatter; 2.28% in SR and 6.41% in SPL under Speckle Noise; and 9.46% in SR and 9.55% in SPL under Defocus Blur.
Privacy-Preserving Asynchronous Federated Learning Framework in Distributed IoT

To solve the data island issue in the distributed Internet of Things (IoT) without privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, existing PPFL solutions still suffer from a single point of failure and incur untrusted aggregation results caused by a malicious central server, and even cause a loss of model accuracy in an asynchronous setting. To solve these issues, we propose a privacy-preserving asynchronous federated learning scheme by using blockchain. Specifically, we use blockchain to address single points of failure and untrustworthy aggregation results, implement reliable model aggregation utilizing a practical byzantine fault-tolerant protocol in an asynchronous setting, and leverage differential privacy to improve system robustness. Formal security analysis and convergence analysis demonstrate that the proposed scheme is secure and robust, and extensive experiments demonstrate that our scheme can effectively ensure the accuracy of the system when compared with state-of-the-art schemes.