Reviewerly/exHarmony huggingface dataset:
Design of Place Recognition Algorithm Based on VLAD Code and Convolutional Neural Network

Visual place recognition is an important and challenging research topic. With the increasing complexity of vision recognition tasks, the traditional image recognition algorithm can not deal with the problem of large-scale illumination change and the interference caused by object occlusion in image. In recent years, deep learning has made many achievements. The main solution proposed based on the deep learning method is to design an end-to-end recognition network, and use pre-labeled place datasets to train the network and finally obtain the classification results of the network. In this paper, a network of place recognition algorithm is proposed which combines deep convolutional features. The sensing field is expanded by adding convolution module, and the extracted convolution features are sent to the local aggregation vector module to obtain the place description vector. The training result of Tokyo Time Machine dataset and Pittsburgh dataset shows that the improved algorithm proposed in this paper has a better recognition recall rate. The generalization performance of the general place retrieval dataset, such as Oxford architecture and Paris architecture, is better than that of the contrast algorithm.
Question Classification Using Universal Sentence Encoder and Deep Contextualized Transformer

One of the most vital steps in automatic Question Answer systems is Question classification. The Question classification is also known as Answer type classification, identification, or prediction. The precise and accurate identification of answer types can lead to the elimination of irrelevant candidate answers from the pool of answers available for the question. High accuracy of Question Classification phase means highly accurate answer for the given question. This paper proposes an approach, named Question Sentence Embedding(QSE), for question classification by utilizing semantic features. Extracting a large number of features does not solve the problem every time. Our proposed approach simplifies the feature extraction stage by not extracting features such as named entities which are present in fewer questions because of their short length and features such as hypernyms and hyponyms of a word which requires WordNet extension and hence makes the system more external sources dependent. We encourage the use of Universal Sentence Embedding with Transformer Encoder for obtaining sentence level embedding vector of fixed size and then calculate semantic similarity among these vectors to classify questions in their predefined categories. As it is the time of the Global pandemic COVID-19 and people are more curious to ask questions about COVID. So, our experimental dataset is a publicly available COVID-Q dataset. The acquired result highlights an accuracy of 69% on COVID questions. The approach outperforms the baseline method manifesting the efficacy of the QSE method.
A Novel Cost-sensitive Capsule Network for Audit Fraud Detection

In the face of increasing audit tasks, it is imperative to realize the transformation from manual audit based on domain expertise to intelligent audit based on algorithm, in order to improve the efficiency and quality of audit. However, the serious imbalance of data distribution and strong dependence on domain experts pose a huge challenge to the application of the algorithm in the field of audit. In response to the above challenges, this paper proposes a cost-sensitive capsule network (CSCN) to identify abnormal accounting vouchers. It can not only successfully extract features from multiple types of input data and improve the accuracy through capsule network, but also deal with extremely unbalanced data by introducing cost-sensitive loss function. The comparative experiment results show that the proposed CSCN algorithm accurately identifies all abnormal accounting vouchers, the G-mean index is increased by 5.3% and the average cost index is reduced by 64.6%, which fully verifies its effectiveness.
A Language-Independent Approach to Classification of Textual File Fragments: Case Study of Persian, English, and Chinese Languages

With the advent of communications systems in recent decades, the transmission of electronic files on computer networks has dramatically increased. In this situation, identifying the type of files is important in many applications such as digital forensics and file carving. The state-of-the-art methods for identifying the file type of a file fragment are based on the content of the fragments. To the best of the authors' knowledge, there is no study addressing the effect of context language in identifying the file type of textual file fragments. In this paper, we have considered a machine learning approach for the classification among five types of common text file formats: PDF, DOC, DOCX, RTF, and TXT. Also, we have examined the effect of context language on the classification of the file fragments. Two scenarios are considered. In the first one, the language for both training and testing phases are the same, that the best results are achieved; the accuracies of the test for Persian, English, and Chinese languages are 85.6%, 76.4%, 86.1%, respectively. In the second scenario, the languages of training and testing sets are not the same, in which the training is done using one language and the evaluation is performed on the two other languages. In this case, the average accuracy values for Persian, English, and Chinese languages are 60.0%, 58.5%, and 71.4%, respectively. The evaluations of the second scenario show that the language-independent machine learning approach is robust in the identification of DOC, DOCX, and RTF formats.
The Relationship of Design Principles and The Types of Virtual Reality from Novice Designers' Perspective

Virtual Reality(VR) is a new frontier of the digital world by providing users with fully immersive. Many industrials take its capability to create valuable experiences for different purposes: edutainment, tourist, commercial, medical, and academic domains. VR can provide user experiences by various components, especially an initial visual design, immersion, and interactivities. This paper aims to verify the fundamental principles of design for VR from novice designer perspectives. The result showed a statistically significant difference in fundamental principles variables used to create virtual reality. Moreover, VR design principles effect different types of VR and specific domains. For example, the navigation principle suits the design of AR in learning and MR on tourists while affordance proper VR design for tourists. Feedback principle influences MR design in a commercial, and color influences VR design for commercials. The finding can be further used to design a VR system for supporting the local hill-tribe product, organic coffee.
Transferable Graph Neural Networks on Large-Scale Stochastic Graphs

Graph neural networks (GNNs) stack layers alternating graph convolutions and pointwise nonlinearities. Since graph convolutions are parametrized locally, their weights are independent of the underlying graph. This decouples the number of parameters and the graph size; however, it does not alleviate the computational cost of training GNNs on large graphs. In this paper, we address this by unveiling the ability of graph neural networks to transfer from small to large graphs. Relying on graphons as both graph limits and stochastic graph models, we define graphon neural networks (WNNs) and demonstrate that they can be used to sample GNNs. We derive a probabilistic bound for the difference between the outputs of the WNN and the GNN and, using a simple triangle inequality argument, compose it to derive the GNN transferability bound. When the convolutional filters are bandlimited in the spectral domain, this bound is seen to vanish with the size of the graph.
A Survey of Deep Learning Target Detection Algorithms under Candidate Regions

Target detection is a research hotspot in the field of computer vision. The target detection algorithm based on candidate regions has high accuracy, but the structure is complex, and the detection speed is slow. The regression-based target detection algorithm has simple structure and fast detection speed, and it is in real-time target detection The field has high application value, but the detection accuracy is relatively low. Based on this research background, this paper first analyses the mechanism of shadow generation, understands the characteristics of shadows and human visual characteristics, and summarizes and classifies the current existing features. Based on the similar shadow detection algorithm, a shadow detection algorithm based on the RGB colour model is proposed. The algorithm in this paper is verified through experiments. The article proves that the algorithm can detect and remove the shadows of moving targets well.
A Software for Rapid Annotation of Scene Objects Based on Saliency Object Ranking

With the development of artificial intelligence (AI), a mainstream research line in building a non-invasive intelligent system for assisting people with visual disabilities is to capture images with a camera, and then using AI to identify the content of images and convert visual information into auditory information. The key point is to establish high-quality personalized datasets based on real open scenes for AI model training. However, most of existing annotation software is designed for face recognition, automatic driving and other tasks, whose annotation information type is monotonous. Also, when converting an image with multiple objects into audio information, the order of objects needs to be determined. Few studies have focused on this issue currently. Therefore, we develop an image annotation software for rapid construction of small sample databases of AI models that can be used in intelligent blindness assistance systems. The software can additionally mark information such as size and affiliation. Moreover, it has a database module to facilitate the management of multiple annotation tasks. Then, a saliency object ranking network based on multi-task cascade is designed. The object ranking results can provide a reference for the sequence of audio information. The Hit Rate, Relative Ranking accuracy and Saliency Object Ranking score of the ranking network are better than that existing saliency object network on the validation set. Finally, the network is deployed into the software to realize the AI-assisted annotation, and to quickly establish the personalized dataset.
Research on Query System of Knowledge Service Search Engine Based on Internet Big Data

Providing users with effective personalized knowledge services is one of the development directions of information services in institutions such as libraries. The article constructs a personalized knowledge service system based on ontology by introducing ontology theory. The system uses the ontology to uniformly describe and reconstruct the multi-domain and cross-database document library. On this basis, a user personality characteristic model and a personalized knowledge retrieval model are further proposed, so that the system can realize personalized retrieval on the semantic level among multiple document databases. The system improves the efficiency and quality of knowledge services.
Preserving Privacy for Edge Weights in Communication Networks

The anonymity mechanism in the communication network plays an extremely important role in the protection of user privacy. However, the existing anonymous system not only has low performance, but also consumes high computing and network resources. In order to address this problem, this paper proposes a preserving privacy solution for the edge weights of communication network by a linear programming model. The linear programming model preserves the shortest communication path, and its solution is the anonymity result of the edge weights. To plan the shortest path between multiple sources, we use the Floyd algorithm for modeling. And we conduct experiments to verify the anonymity of Floyd's algorithm with different edge weights.
Adaptive Data Masking-based Named Entity Recognition

To address the deficiencies of traditional data masking techniques, this paper focuses on the identification of unstructured sensitive data, and constructs an adaptive data masking-named entity recognition model (Adm-NER). Based on the Bi-LSTM-CRF model, Adm-NER applies adversarial transfer learning to the field of data desensitization and introduces self-attention mechanism, which can effectively identify sensitive data in the lack of sample fields. The results of five comparative experiments show that Adm-NER has significantly improved the accuracy of identifying sensitive data. In addition, the transfer learning experiment proves that Adm-NER can adaptively learn common features by using large-scale labeled samples to achieve accurate positioning and recognition of sensitive data in the lack of sample field, which is conducive to subsequent data desensitization. Adm-NER provides a new idea for the intelligent design of big data masking systems.
An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems

This paper suggests using Mahalanobis distance to regenerate a new whale position to increase the performance of the whale optimization algorithm. Learning from previous evolutionary searches allows the probability parameters to be self-adapted. The suggested approach was compared to the classical whale optimization algorithm (WOA), particle swarm optimization (PSO), and differential evolution algorithm (DE) on 11 well-known benchmark functions. The results of the experiments showed that the proposed algorithm was effective in solving optimization problems.
A Modified SiamRPN for Visual Tracking

Siamese network based trackers have achieved state-of-the-art performance on multiple benchmarks. SiamRPN can predict the size of target thanks to RPN module. This paper proposes a modified SiamRPN based on IoU, under the framework of SiamRPN, Siamese feature extraction, and proposal generation for target, followed by the loss minimization. Aiming to the loss of both the classification branch and regression branch, we introduce IoU between GT box and the anchor into the regression loss function to form the joint optimization of IoU&smooth L1 norm, which is useful to refine the tracking target box prediction. Then, we define IoU between GT box and the predicted box to weight positive samples. Weighted positive samples establish the connection between the classification branch and regression branch, which is helpful to eliminate the inconsistency in the optimal prediction of two branches. Experimental evaluations on the datasets OTB2013, OTB2015, demonstrate that compared with the state-of-the-art tracker such as SiamFC, SiamRPN and other algorithms, our proposed tracker achieves higher tracking accuracy and stronger robustness in most challenges of the tracking situation.
Social Gardening: Integrated Social Network with Smart Systems

In recent years, the demand of planting trees as hobby has been increasing. This is because some plants with unique characteristics such as spotted leaf plants, have high bid of price. However, caring for these plants is still difficult and time-consuming. Therefore, we have developed a software system that assists in caring for plants as a hobby. The system automates watering, fertilizing and measuring different factors that affect the growth of plants. To increase the user engagement, we have developed our system as a social network that lets users view and interact with their friends' trees through different features such as posting, emotional responding, commenting and private chats, as well as trading. These features aims to increase user interaction and help to expand the community. Our proposed system serves as an example that provide a guideline of how to develop an automated system in the hobbyist communities.
A Study on the Creation of 3D Models of Cultural Properties by SfM Using an Elaborate Diorama of a Non-Existing Excavation Site

In order to protect local cultural properties, it is important for citizens to know the vivid views of the excavation sites. However, it is difficult to recreate the situations of the old excavation sites and make them available as digital archives for citizens to view at any time. When the Tateiwa Site in Iizuka City was excavated in 1963, there was no technology for 3D digital recording of the situations at the excavation site. Also, it was not possible to accurately record the 3D situation with paper drawings. Instead, the excavators of the time took the effort to create an elaborate diorama. This diorama has preserved the 3D situation of the excavation site to the present. Nowadays, SfM (Structure from Motion) can be used to generate a 3D model of an object using photographic images from many viewpoints. Therefore, we attempted to create a 3D model of the excavation site using SfM in order to generate a good quality 3D model at a low cost. In SfM, the processing time and the quality of the created 3D model vary depending on the number of photos used, so we investigated the relationship between the number of photos and the quality of the model. In addition to creating a 3D model using images of a diorama taken with a DSLR(Digital Single Lens Reflex) camera, as is commonly done in SfM, we also experimented with creating a 3D model using images from a smartphone camera. We evaluated the differences in the 3D models generated by the expensive DSLR camera and the smartphone camera that many people carry around. In this evaluation, we confirmed that we could create 3D models with the same quality as those of DSLR cameras even with images from smartphone cameras. Finally, we created a VR (Virtual Reality) system using the 3D model created in this study, and evaluated the quality of the 3D model for VR use.
Intelligent transportation big data retrieval algorithm based on blockchain and aggregation signature

In view of the low retrieval efficiency of data ciphertext and the insecurity of centralized database in the current intelligent transportation big data system, this paper introduces the blockchain technology into the intelligent transportation big data system, and each information collection department stores it in the blockchain after normalization according to the data standard. According to the unmodifiability and traceability of the blockchain, the trustworthiness and transparency of the data in the system can be guaranteed, The system is better than the current mainstream scheme in data sharing. In this paper, an efficient ciphertext retrieval algorithm scheme for data in intelligent transportation big data system based on blockchain technology is proposed. Based on the theories of bilinear yangshe and difficulty hypothesis theory in cryptography, the problem of low efficiency of multi keyword and multi ciphertext retrieval in large database on traditional platform is solved, This paper uses the aggregation signature algorithm to improve the ciphertext retrieval efficiency in the big data system. For multiple keyword retrieval conditions, an aggregation key is generated by the aggregation signature key algorithm, and the aggregation trap is generated with the aggregation key, retrieval keyword and retrieval file set as inputs for ciphertext retrieval, The final result of the retrieval is based on the smart contract technology of the blockchain to verify the effectiveness of the data and ensure the integrity of the data. In Chapter 4, the security and performance of the efficient aggregation signature retrieval algorithm are analyzed. The results show that the key generated by the aggregation signature is safe and reliable, the aggregation trap used in ciphertext retrieval is irreversibly regenerated, and the algorithm is verified to be trusted in terms of security. The simulation results show that the algorithm is better than other literature algorithms in terms of time overhead, effectively reduces the system overhead and improves the retrieval efficiency of the algorithm.
Recursive Hierarchical Regression Clustering

Regression Clustering (RC) is a combination of unsupervised and supervised learning techniques that aims to address the problems arising with performing regression that results in a varied set of parameters over a single dataset. RC approaches attempt to form optimal subsets of a dataset then apply a regression algorithm to each subset, with the goal of achieving a more accurate prediction. Selection of the optimal number of clusters that improve the goodness-of-fit of the overall regression outcome without prior knowledge about the data is a challenging task. In this paper, we investigate the adaptation of hierarchical clustering to the RC problem. The proposed recursive hierarchical regression clustering (RHRC) algorithm utilizes a top-down hierarchical clustering approach to obtain optimized clusters. Moreover, our approach addresses premature stopping of the recursive algorithm by incorporating a patience hyper-parameter which ensures the stability of the overall algorithm. To evaluate our approach, we compared the proposed method with previously proposed regression clustering algorithms. The paper also provides an evaluation using different flat clustering techniques with RHRC to test the performance of different regression algorithms when integrating them with RHRC. Our method achieves a 53.79&#x0025; increase in accuracy as an average of improvement across ten datasets with respect to linear regression. Finally, compared to Random Forest, our algorithm obtained equal or higher accuracy (up to 23&#x0025;) on seven out of ten datasets with a fraction of the runtime.
Simulation of MobiFall Dataset for Fall Detection Using MATLAB Classifier Algorithms

Fall accidents are considered one of the significant global public health concerns, and the largest proportion of fatal accidents are experienced by elderly people. Currently, there is a demand for creating an effective machine learning-based fall detection system that is significantly portable at a low cost. Public datasets are available for simulating an effective classifier for development. Hence, the current study is aimed at simulating the MobiFall fall detection dataset to acquire an effective machine learning classifier. The methodology included a study of the various fall detection systems as well as general features for machine learning classifications. The most suitable potential combination of machine learning algorithms that will provide the best accuracy, precision, sensitivity, specificity and lowest training time was developed via simulation models using MATLAB. Input data was selected from the MobiFall dataset for simulations. Up to 23 algorithms, including Decision Trees, Discriminant Analysis, Naïve Bayes Classifiers, Support Vector Machines, KNN and available Ensemble Classifiers, were simulated. Four sets of experiments were done using accelerometers with varying features and cross-validation. Currently, a combination of Quadratic SVM, Cubic SVM and Fine KNN was chosen to be used as the most appropriate classifier to train a fall detection system.