Reviewerly/exHarmony huggingface dataset:
Research on garment image classification and detection algorithm based on improved deep learning

With the development of e-commerce economy, online shopping has become an indispensable part of people's life. Therefore, the study of clothing image classification is of great significance to realize the effective operation of online clothing change and clothing recommendation system. This paper uses two classical deep learning algorithms: LeNet and ResNet to construct the clothing image classification model and realize the classification and detection of. At the same time, on the basis of Yolov5, combined with ResNet to improve its backbone network and further improve the accuracy of clothing image classification and detection. Experiments show that on the FashionMnist dataset, the accuracy and precision of LeNet and ResNet are about 93%, the accuracy and precision of improved Yolov5 are more than 98%, which is much higher than the former.
Initial Clustering Center Optimization Algorithm Based on Weighted Dissimilarity

The traditional k-means algorithm has randomness in the selection of the initial clustering center, which is easy to fall into local optimum. In order to select the appropriate initial clustering center to obtain the optimal clustering effect, an optimized initial clustering center algorithm based on density and dimension weighting is proposed. Euclidean distance after dimension weighting was used to construct dissimilarity matrix, and mean dissimilarity and overall dissimilarity were calculated. Introducing Gaussian kernel function to determine initial cluster centers. Through the test on UCI dataset, compared with the classical k-means algorithm,k-means++ algorithm and related improved algorithms,the clustering indexes are optimized and improved, with high feasibility. High time consumption occurs in high dimensional data computation. The proposed algorithm has less iterations and better clustering performance on high-dimensional datasets.
Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction

Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCNN) to simultaneously predict the action semantics and human motion of future human movements. Specifically, we first extract a general spatiotemporal representation of partial observations via two regression blocks. Then, we propose a regression head and a classification head for predicting future human motion and action semantics of human motion, respectively. For the regression head, another two stacked regression blocks and two convolutional layers are applied to predict future poses from the general representation learning. For the classification head, we propose a classification block and stack two regression blocks to predict action semantics from the general representation. In this way, the regression and classification heads are incorporated into a unified framework. During the backward propagation of the network, the human motion prediction and the semantic prediction may be enhanced by each other. NTU RGB+D is a widely used large-scale dataset for action recognition, which was collected by 40 different subjects from three views. Based on the official protocols, we use the skeletal modality and process action sequences with fixed lengths for the evaluation of our action prediction task. Experiments on NTU RGB+D show our model’s state-of-the-art performance. Furthermore, the experimental results also show that semantic information is of great help in predicting future human motion.
DeepVis: A Visual Interactive System for Exploring Performance of Deep Learning Models

Nowadays, deep learning (DL) models have been an emerging technology because of their performances and wide acceptance in various fields. However, in most cases, the performance analysis of DL models is not viable to understand how they predict because they are inherently considered black-boxes, and different models have different performance rates. Ad-ditionally, due to a lack of highly technical expertise and domain knowledge, people struggle to choose a proper model for their work. Therefore, to understand and improve the performance of the D L model, careful selection of model, layer, epoch, optimizer, hyperparameter tuning, and model visualization is essential. In this paper, we design an interactive visualization system named DeepVis with a wide range of performance evaluation methods that assist the non-expert in adopting an appropriate model. Finally, we demonstrate use cases and expert opinion using a publicly available dataset to validate the usability and effectiveness of Deep Vis.
Fault detection in GPU-enabled Cloud Systems – An Overview

Fault detection and handling are crucial tasks in cloud systems. As these infrastructures are growing and evolving, manual monitoring and interaction have become less feasible. To deal with this issue, monitoring systems are developed to track the behavior of the various components (e.g. nodes) in cloud systems, as well as the served applications in the virtual environment. Nowadays, most cloud environments provide graphics accelerators for their users leading to different problems. However, the application of GPUs in deep learning could also help the detection of incorrect behavior. In this paper, a short overview of cloud monitoring and fault detection methods is given focusing on GPU-enabled nodes.
Generalized Undersampling of Center Points Based on Granular Ball

In this paper, we propose a new generic sampling classification method based on granular Ball sampling because of good robust noise immunity and possessing properties that can be classified as an undersampling method, called Granular Ball Center-based Common Under-sampling. The method uses adaptively generated Ball to cover the data space, and the sampled data are supported by the centroid properties of the Ball. Unlike the boundary sampling of granular ball sampling, this method mainly uses the center points to smooth out the noise, which not only can clearly represent the attributes, but also can reduce some computational effort, and in most of the time, it can obtain almost the same accuracy as the original model, or even can perform better. Compared with undersampling, it can be better adapted to the task of unbalanced classification as well as non-unbalanced classification. The above can show that the method is able to be generalized without the restriction of specific classifiers and also has a strong adaptability to the dataset. The method can also be effectively used as an undersampling method for unbalanced classification because it is supported by the centroid property, and the method has a speedup effect for most classifiers. These advantages form a strong advantage of the method in improving classifier performance.
English-Speaking Teaching System with Natural Language Processing Technology Based on Artificial Intelligence Algorithms

English speaking is a very important skill of English ability for students. At the same time, English speaking learning needs a demanding environment. Because English teachers have heavy teaching tasks and limited time. This research developed an English-speaking teaching system with natural language processing technology based on artificial intelligence algorithms, which can solve this problem very well. The system engages the most advanced natural language processing technology based on artificial intelligence algorithms to correct and feedback students' oral problems in a timely, fast, and targeted manner, which is beneficial to improve students' speaking learning efficiency and interest. This system proposes an improved Bidirectional Encoder Representations from Transformers model, which is simply called BERT and based on Transformer's bidirectional encoding representation. The accuracy of this algorithm for spoken English recognition is significantly higher than other classical algorithms. This system has been tested and evaluated in some universities, and the learning efficiency and satisfaction of students is significantly higher than other oral English learning systems.
Simulation framework for fringe projection profilometry using ray tracing and light transport coefficient measurement

Fringe projection profilometry is widely used in optical metrology, and fringe analysis is important to improve measurement accuracy. However, the fringe images captured by cameras are influenced by many factors, an analytical study of which, to characterize the imaging process, is difficult to perform. We propose a method to accurately simulate the real imaging system in the virtual environment using ray tracing algorithm. The light transport coefficients of the cameras are measured to simulate defocus instead of using Gaussian function. Experimental results show that the proposed method can simulate a physical system in the virtual environment more accurately than the Gaussian function at large defocus condition.
Implementation and Analysis of AI-Based Gesticulation Control for Impaired People

This paper presents an intelligent human PC intuitive framework. In this proposed work, artificial intelligence is utilized for home mechanization, which perceives human motions with the assistance of a camera and performs tasks appropriately. The idea of perceiving the motions depends on three layers: detection, tracking, and recognition. We use a camera-PC interface that can catch the developments, and later use PC vision innovation and AI calculations to comprehend the fundamental example and match the information with a preprepared dataset. When it comes to safes, an extra layer of security is provided by using face recognition, and the safe is opened if the individual is recognized from the dataset.
Knowledge Annotation within Research Data Management System for Oxygen-Free Production Technologies

Abstract The comprehensive implementation of digital technologies in product manufacturing leads to changes in engineering processes and requires new approaches to data management. An important role belongs to the processes of organizing the collection, storage and reuse of research data obtained and used in the process of product, system or technology development, taking into account the FAIR data principles. This article describes a Research Data Management System for the organization of documentation and measurement requests in the research and development of new oxygen-free production technologies.
A Geographical and Social Society Attributes Based Privacy Preserving Recommendation Method for POIs

Point-of-interests (POIs) recommendation technology using user’s check-in data has attracted great attentions in recent years. However, user’s check-in data often contains sensitive information such as time and location data. Due to privacy considerations, many users are unwilling to share their check-in data with untrusted service providers, which has a great negative impact on recommendation quality. Trying to solve this problem, geographical and social society attributes based privacy preserving recommendation method for POIs, named GSSA-PPRM, is proposed in the paper. In the proposed method, a local differentially private matrix factorization algorithm is firstly designed to learn user’s preference with social attribute in client/server style. Then, according to the learned preference and considering geographical distance of POIs, a self-adaptive kernel density estimation algorithm is devised to study user’s check-in behavior. And an algorithm that tallies POI visit count and computes POI popularity by securely collecting user’s check-in data through random response (RR) mechanism is presented. Finally, a rating rule is given to predict the ratings of users for POIs by integrating kernel density estimation and POI popularity. The experimental results on two real datasets validate that the proposed method achieves better POI recommendation quality in condition of keeping user’s privacy.
Recognition System for Behavior &amp; Activities of Daily Living Among Patients with Dementia Using Smart Algorithms and Assistive Technology

Dementia is a syndrome consisting of a group of symptoms that cause progressive brain dysfunction and impairment of cognition or intellects. Dementia causes problems with memory, orientation and thinking processes to the extent that it decreases functionality in Activities of Daily Living (ADL) and causes behavior change. Several studies have focused on the unusual behavior detection and pattern detection of daily activities using in-home sensors. Activities are detected either through specific rules or by applying learning techniques. This study focuses on utilizing adaptive algorithms where the relationship between environmental and cognitive state will be predicted. To automatically assess cognitive health based on activity and behavior, this research will focus on introducing a method to automatically predict the behavior of patients at homes in a smart way and predicting the patient's cognitive state.
From Worklife Competencies to Educational Virtual Reality Implementations

In this paper, we discuss how to implement educational virtual reality (VR) applications based on worklife competencies. VR is a technology increasingly used in education, and VR applications can be used, for example, in workplace learning and vocational education as well as in learning evaluation. We present a development workflow based on the good practices found and applied in multiple development cases. First, we describe the cases and use examples to illustrate competencies and their implementation in educational VR applications, with our observations of relevant good practices. Then, we construct a generalized workflow for developing similar competence-based educational VR applications. Finally, we evaluate the applicability of the workflow and compare it with the typical existing workflows used in software development.
Appearance Adaptive Visual Tracking Algorithm Based on the KCF and SURF Features

The correlation filter-based trackers have demonstrated excellent performance for visual tracking according to the fast response in the frequency domain and the discriminant approach applied to it. However, it is difficult to maintain stable and robust tracking in such scenes like appearance deformation and occlusion. To address this issue, we employ an appearance adaptive algorithm based on the combination of SURF features and traditional KCF method in feature extraction, target localization strategy, and model updating strategy, to alleviate the algorithm accuracy reduction. Experiments show that our proposed method is competitive with other present algorithms and the average of distance precision becomes 6.7% higher than the KCF.
Facial Expression Recognition for OoD Generation Using Risk Extrapolation

Humans express emotions through facial expressions. It&#x0027;s easy for humans to recognize these emotions, but it&#x0027;s a very challenging task for computers. Every face image will be different because of the differences in brightness, contrast, resolution, angle, etc. when taking an image. which is why Out-of-distribution (OoD)Generalizing for facial recognition expression is very difficult. In pursuit of strong OoD generalization, We introduce the principle of Risk Extrapolation (REx) into the field of face recognition. The robustness of affine combination of training environments is enhanced by making each training risk equal. In this paper, we identified seven basic facial expressions and improve their adaptability based on risk extrapolation from multiple datasets.
A Trustworthy Decentralized Onkart Ecommerce Platform based on Blockchain Technology

Onkart is a blockchain decentralized e-commerce platform for buying and selling e-commerce products. The seller can sell the product and the buyer can purchase the product directly without the intervention of a third party. When a seller adds a product to the platform, they stake 1% of the price of the product in their stake on the platform, and similarly place an equal number of tokens together when a buyer places an order for the product. Price tokens for items in the platform's stake. If the buyer confirms receipt of the product without dispute, the seller will receive their stock tokens and the price of the product. Similarly, buyers receive their stake on the platform. This incentive mechanism will make the admins do their work without any manipulation. This mechanism avoids the seller/ buyer from doing malpractices since their amount is in stake in the platform. Hence, this provides decentralized application which is friendly to use. This system follows Consensus based Blockchain network which is more secure, and its functionality is protected by smart contracts. In terms of latency and throughput demonstrating the Onkart decentralization effectiveness in the products of e-commerce, the results are discussed for the betterment of safety of products and to ensure that there is sustainability in terms of society and finances.
Improving GAN for Image Super-Resolution by Using Attention Mechanism and Dense Module

With the gradual maturity of the convolutional neural networks (CNN) in image recognition, deep-learning based singe image superresolution (SISR) has shown great promise but also poses a challenge to current researches. Specifically, most SR methods have problems such as failing to reconstruct finer texture details, difficulty in determining the interdependence between each channel of the feature map, to name a few. To solve these problems, we propose a multi-level dense skip network (MDSN) and adapt it into SRGAN. This is a powerful model designed for SISR tasks, which combines confrontation training and extracts abundant local features via the contiguous memory mechanism. We achieve a trade-off between model performance and complexity by introducing the ECA module, which ensures the appropriate cross-channel interaction. The testing results on the public dataset demonstrate richer texture details and better visual effects of superresolution images restored by our model.
Clothing Parsing Based on Context Prior and Flow Alignment Pyramid

Clothing parsing plays an important role in the fashion industry, and it is also a significant branch of semantic segmentation. The advanced related work explores various methods to efficiently aggregate contextual information, but they do not clearly distinguish the difference of different contextual relationships. In this paper, we propose a novel network based on Context Prior and Flow Alignment Pyramid for clothing parsing. Specifically, we effectively establish an original context map to exploit intra-class and inter-class contextual information. We devise a new loss function to supervise the learning of the original context map mentioned above by using a standard context map. Considering the sizes of different classes have a large degree of variation in clothing images, we fuse semantic information from multiple levels of the network to avoid small-sized items from disappearing during network propagation. To circumvent the offset that occurs in fusion process, the Flow Alignment Module (FAM) is designed to make the output contains more spatial detail information. Experiments show that our method achieves promising performance on the CFPD dataset (51.71&#x0025; of mIoU and 93.23&#x0025; of PA) compared with other state-of-the-art methods.
An Approach on Developing a Dynamic Wind-Solar Map for Tracking Electricity Production Potential and Energy Harvest

Increasing energy demand brings in technical, environmental and economic problems as the production processes evolve. In this parallel, many countries are trying to satisfy the increasing energy demand using renewable energy sources. The scope of this study is to develope a mathematical model and data monitoring-evaluation software for wind and solar renewable energy sources, which can dynamically evaluate meteorological data measurements and make more precise energy harvest estimation with the data obtained. It is aimed to create a web based “Dynamic Wind-Sun Map of Turkey”. Through the developed software; instant data can be analyzed and instantaneous electrical energy values produced from wind and solar energy sources can be calculated. Thus, it can help create a system that allows regional management in energy production and is compatible. Within the scope of the study, a mathematical model expressing the problem was created by using some mathematical optimization methods. The created model was converted into a web-based software. PHP software development platform and MySQL database language were used during the software creation. The software developed within the scope of the study has analogues in the world literature. However, no study has been found in this context in Turkey. Especially since it can use real-time data and includes wind-photovoltaic calculations together, this software distinguishes it from its peers.
Research on Expert System of Japanese Auxiliary Teaching Based on BP Neural Network

College Japanese teaching is the cradle of Japanese professional development. With the rising frequency of interactions with Japan in our country in the fields of politics, economy, trade, and other fields, Japanese as a professional discipline is exhibiting popularization, universalization, and folkization features. However, the ever-emerging trend of Japanese as an application tool for worldwide communication has rendered professional Japanese instruction in colleges insufficient. To meet the needs and growth of society, as well as to address the problems of teacher shortages and a lack of attention to students’ fundamental knowledge in the reform of Japanese language teaching in colleges, the study uses an expert system as the theoretical foundation and combines BP neural network technology to design an auxiliary teaching system with a friendly interface, strong versatility, and extensibility for Japanese language teachers and students. Teachers can use this technique to organize the test by classifying and summarizing the test questions based on the knowledge points and complexity of the questions. Students can utilize this system to learn on their own, and by identifying weak links in their knowledge points, they can practice more effectively and create a multiplier impact with half the work. Finally, the whole system is designed and implemented in accordance with the software development process. It has been demonstrated that the system can provide realistic results and has good application value after a huge quantity of data testing and operation.
Rotate-Yolov5 for Aerial Images

Abstract In Recent years, great progress has made in object detection. However, since the orientation of object in aerial image is random, the regular horizontal object detection method is not suitable for aerial images. In this paper, we present a Rotate-Yolov5 network based on Yolov5. We use an Adaptive Rotating Anchor Generation Module (ARAGM) to generate anchors with object orientation information. Then the orientation information is used for Rotate-Deformable Convolution Module (R-DCM) to extract features. Finally, we use a decouple detection head as Oriented Object Detection Module (OODM) to yield classification and regression results. Moreover, Rotate-Smooth L1 is used to optimize the loss function. We evaluate the proposed Rotate-Yolov5 on DOTA datasets and the mAP reached 75.4, which demonstrate the superiority of its effectiveness.