Reviewerly/exHarmony huggingface dataset:
Optical color image encryption scheme with a novel DNA encoding algorithm based on a chaotic circuit

An optical setup with a unique color image cryptography technique employing a chaotic system is suggested in this study. For this purpose, deoxyribonucleic acid (DNA) encoding is used as an encryption method. Rössler attractor is employed as a chaotic system and it consists of three differential equations. In order to implement the chaotic system by analog electronic components, a circuit design including operational transconductance amplifier (OTA) is also suggested. Circuit simulations have been performed utilizing LT spice program. In the proposed study, the number of DNA encoding rules has excessively been increased compared to previous studies on DNA encoding technique. Thus, the security of the encryption scheme is enhanced. Numerous security tests such as correlation coefficient, histogram, Shannon entropy, secret key and initial condition sensitivities, plaintext and ciphertext attacks and differential attack have been carried out to evaluate functioning of the introduced cryptography algorithm. The numerical test results prove that the suggested encryption algorithm can resist statistical and differential attacks. Moreover, the cryptology scheme indicates the behavior of sensitivity to initial condition and secret key. The presented algorithm has great performance from the point of encoding rules number and being robust against differential attack. Moreover, enhanced double random phase encoding (DRPE) technique is used in optical stage of the study. By modifying the conventional DRPE method, correlation value between R, G, B components for same pixel have extremely reduced. All numerical analyses regarding chaotic system and encryption scheme have been performed in MATLAB program.
Learning to Know Where to See: A Visibility-Aware Approach for Occluded Person Re-identification

Person re-identification (ReID) has gained an impressive progress in recent years. However, the occlusion is still a common and challenging problem for recent ReID methods. Several mainstream methods utilize extra cues (e.g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem. Although achieving inspiring progress, these methods severely rely on the fine-grained extra cues, and are sensitive to the estimation error in the extra cues. In this paper, we show that existing methods may degrade if the extra information is sparse or noisy. Thus we propose a simple yet effective method that is robust to sparse and noisy pose information. This is achieved by discretizing pose information to the visibility label of body parts, so as to suppress the influence of occluded regions. We show in our experiments that leveraging pose information in this way is more effective and robust. Besides, our method can be embedded into most person ReID models easily. Extensive experiments validate the effectiveness of our model on common occluded person ReID datasets.
An improved symbiotic organisms search algorithm for higher dimensional optimization problems

The symbiotic organisms search (SOS) algorithm was introduced by considering the relationships among the creatures in a natural ecosystem. Despite the superior efficiency of SOS, it has been observed that fixing benefit factors of mutualism phase at 1 or 2; the algorithm obstructs itself from an extensive and diverse search of the search region. Moreover, alteration of random dimensions in the parasitism phase increases the computational burden of the algorithm. Considering these limitations, a modified SOS algorithm, namely nwSOS, has been proposed in this study to solve higher dimensional optimization problems. In the suggested nwSOS, the benefit factors are calculated by a non-linear approach. The mutual vector is modified, and weights of both benefit factors are utilized to effectively explore and exploit the search region. Moreover, the parasitism phase is tailored to lessen the computational overhead. The modified method is then used to evaluate twenty basic benchmark functions using 100 and 500 dimensions. Results are compared with six state-of-the-art algorithms and with SOS and its five modified variants. Four designing issues from both unconstrained and constrained classifications are solved utilizing nwSOS. Complexity analysis, statistical analysis, and convergence analysis are executed to measure the algorithm’s effectiveness from different aspects. Moreover, the proposed algorithm has been used for segmenting COVID-19 chest X-ray images with the help of multi-level thresholding approach using different thresholds. All the results confirmed the enhancement of the proposed algorithm.
Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

Abstract Owing to technological advancements in artificial intelligence, voice assistants (VAs) offer speech as a new interaction modality. Compared to text-based interaction, speech is natural and intuitive, which is why companies use VAs in customer service. However, we do not yet know for which kinds of tasks speech is beneficial. Drawing on task-technology fit theory, we present a research model to examine the applicability of VAs to different tasks. To test this model, we conducted a laboratory experiment with 116 participants who had to complete an information search task with a VA or a chatbot. The results show that speech exhibits higher perceived efficiency, lower cognitive effort, higher enjoyment, and higher service satisfaction than text-based interaction. We also find that these effects depend on the task’s goal-directedness. These findings extend task-technology fit theory to customers’ choice of interaction modalities and inform practitioners about the use of VAs for information search tasks.
Self-attention neural architecture search for semantic image segmentation

Self-attention can capture long-distance dependencies and is widely used in semantic segmentation. Existing methods mainly use two kinds of self-attentions, i.e., spatial attention and channel attention, which can capture the relations in HW dimension (image plane, height and width) and C dimension (channels), respectively. Very little research investigates self-attention along other dimensions, which can potentially improve the segmentation performance. In this work, we investigate the self-attentions along all the possible dimensions {H,W,C,HW,HC,CW,HWC}. Then we explore the aggregation of all the possible self-attentions. We apply the neural architecture search (NAS) technique to achieve optimal aggregation. Specifically, we carefully design (1) the search space and (2) the optimization method. For (1), we introduce a building block, a basic self-attention search unit (BSU), which can model self-attentions along all the dimensions. And the search space contains within-BSU and cross-BSU operations. In addition, we propose an attention-map splitting method, which can reduce the computations by 1/3. For (2), we apply an efficient differentiable optimization method to search the optimal aggregation. We conduct extensive experiments on Cityscapes and ADE20K datasets. The results show the effectiveness of the proposed method, and we achieve very competitive performance against state-of-the-art methods.
Comparative study of regressor and classifier with decision tree using modern tools

Machine Learning is one of the importantareas for modeling the data and itcan be saidthat this is the core part of the field of Data Science. Supervised Machine Learning (SML)has many algorithms to train the machine. The decision tree as the classifier is used to trainthe model based onthe categorical label and the Decision Tree as Regressor is used to trainthe model based ona non-categorical label. There are two kinds of algorithms like Classification and Regression. In this paper, we focus on the Decision Tree as a Regressor and Classifier and compare the metrics. This paper describes the decision tree with the analysis as well as a comparison with the most efficient algorithm based on the different datasets using python programming. Results show the accuracy and comparison of the decision tree as a Regressor and Classifier. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Confusion Matrix are performance parametersused to analysis of decision treesand also using different python libraries to analyze and visualize the result.In this paper, we used a shopping mall dataset for classification as a case study from UCI (Machine Learning Repository) to predict that users purchase an item or not. This dataset contains 400records. Decision Tree as a Regressor is used as the dataset from the Kaggle repository for analysis and visualization of results and show comparison. In this paper, the Accuracy score is the most important measure to compare decision treesbased on regression and classification but Mean Squared Error (MSE) is also an important factor to decide and split the node into two or more nodes.
Identifying fraud in medical insurance based on blockchain and deep learning

With the rapid growth of medical costs, the control of medical expenses has been becoming an important task of Health Insurance Department. Traditional medical insurance settlement is paid on a per-service basis, which leads to lots of unreasonable expenses. To cope with this problem, the single-disease payment mechanism has been widely used in recent years. However, the single-disease payment also has a risk of fraud. In this work, we propose a framework to identify fraud of medical insurance based on consortium blockchain and deep learning, which can recognize suspicious medical records automatically to ensure valid implementation on single-disease payment and lighten the work of medical insurance auditors. An explainable model BERT-LE is designed to evaluate the reasonability of ICD disease code for Medicare reimbursement by predicting the probability of a disease according to the chief complaint of a patient. We also put forward a storage and management process of medical records based on consortium blockchain to ensure the security, immutability, traceability, and auditability of the data. The experiments on two real datasets from two 3A hospitals demonstrate that the proposed solution can identify fraud effectively and greatly improve the efficiency in medical insurance reviews.
Trade or Trick?

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.
Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing

In this paper, deep learning models trained with real seismic data are proposed and proven to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The proposed neural network architectures cover the three classical deep learning paradigms: fully connected artificial neural networks (FC-ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Results demonstrate that training these networks with seismic waveforms measured by traditional broadband seismometers can extract and learn relevant features of earthquakes, enabling the reliable detection of seismic waves in DAS measurements. The intrinsic differences between DAS and seismograph waveforms, and eventual errors in the labelling of the DAS data, slightly reduce the performance of the models when tested with the distributed acoustic measurements. Despites of that, trained models can still reach up to 96.94% accuracy in the case of CNN and 93.86% in the case of CNN+RNN. The method and results here reported could represent an important contribution to the development of an early warning earthquake system based on DAS technology.
From video streaming to virtual reality worlds: an academic, reflective, and creative study on live theatre and performance in the metaverse

With the advent of the Covid-19 pandemic, theatre and performing artists needed to find creative and safe solutions for continuing to do their work. Online video conference tools and virtual reality (VR) platforms seemed like good opportunities to explore new ways of connecting their art with their audiences. In this emergent ecosystem, there has been a specific interest in experimenting with VR social platforms given that its inherent technologies enable profoundly interactive experiences. Theatre companies and other creative projects like the Royal Shakespeare Company, Double Eye Productions or La Cuarta Pared VR have been producing cutting-edge live theatre experiences in VR that question the very essence of what live performance is all about. Triggered by the need to understand what is conceptually and artistically involved in these emergent experiences, the authors outlined an in-conversation piece combining both academic insight and reflective and creative writing. Drawing from netnographical and arts-based fieldwork in VR platforms, as well as key studies in theatre, performance, immersive media, gaming and other related fields, the goal of this paper is to present a gateway for a conceptual framework consisting of a series of foundational features and practical guidelines that define live theatre and performance in VR.
Review of Graph Neural Network in Text Classification

Text classification is one of the fundamental problems in Natural Language Processing (NLP). Several research studies have used deep learning approaches such as Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification. Over the past decade, graph-based approaches have been used to solve various NLP tasks including text classification. This paper reviews the most recent state-of-the-art graph-based text classification, datasets, and performance evaluations versus baseline models.
An attention-based U-Net for detecting deforestation within satellite sensor imagery

In this paper, we implement and analyse an Attention U-Net deep network for semantic segmentation using Sentinel-2 satellite sensor imagery, for the purpose of detecting deforestation within two forest biomes in South America, the Amazon Rainforest and the Atlantic Forest. The performance of Attention U-Net is compared with U-Net, Residual U-Net, ResNet50-SegNet and FCN32-VGG16 across three different datasets (three-band Amazon, four-band Amazon and Atlantic Forest). Results indicate that Attention U-Net provides the best deforestation masks when tested on each dataset, achieving average pixel-wise F1-scores of 0.9550, 0.9769 and 0.9461 for each dataset, respectively. Mask reproductions from each classifier were also analysed, showing that compared to the ground reference Attention U-Net could detect non-forest polygons more accurately than U-Net and overall it provides the most accurate segmentation of forest/deforest compared with benchmark approaches despite its reduced complexity and training time, thus being the first application of an Attention U-Net to an important deforestation segmentation task. This paper concludes with a brief discussion on the ability of the attention mechanism to offset the reduced complexity of Attention U-Net, as well as ideas for further research into optimising the architecture and applying attention mechanisms into other architectures for deforestation detection. Our code is available at https://github.com/davej23/attention-mechanism-unet.
Attentive WaveBlock: Complementarity-Enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-Identification and Beyond

Unsupervised domain adaptation (UDA) for person re-identification is challenging because of the huge gap between the source and target domain. A typical self-training method is to use pseudo-labels generated by clustering algorithms to iteratively optimize the model on the target domain. However, a drawback to this is that noisy pseudo-labels generally cause trouble in learning. To address this problem, a mutual learning method by dual networks has been developed to produce reliable soft labels. However, as the two neural networks gradually converge, their complementarity is weakened and they likely become biased towards the same kind of noise. This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels. Specifically, we first introduce a parameter-free module, the WaveBlock, which creates a difference between features learned by two networks by waving blocks of feature maps differently. Then, an attention mechanism is leveraged to enlarge the difference created and discover more complementary features. Furthermore, two kinds of combination strategies, i.e. pre-attention and post-attention, are explored. Experiments demonstrate that the proposed method achieves state-of-the-art performance with significant improvements on multiple UDA person re-identification tasks. We also prove the generality of the proposed method by applying it to vehicle re-identification and image classification tasks. Our codes and models are available at: AWB.
Dual-view 3D object recognition and detection via Lidar point cloud and camera image

When it comes to the accuracy of autonomous motion, it is necessary to consider object detection and recognition, especially for the robot application of the complex environment. This paper investigates novel dual-view 3D object detection networks combined with the Lidar point cloud and RGB image in engineering scenarios. The developed system is applied for autonomous vehicles that the detected objects are cars, cyclists, and pedestrians. Firstly, a feature extraction network based on the residual module is presented, and the specific features are from the RGB image. The point cloud is transformed into Bird’s Eye View (BEV), and the BEV feature extraction network is built based on sparse convolution. Besides, the feature maps are input into the region proposal network (RPN) to obtain the optimal proposal so that the object classification and the bounding box regression are obtained. Finally, to evaluate the flexibility of the developed framework, extensive data sets are generated through the CARLA simulator and verified on the KITTI data set and unmanned motion platform (BIT-NAZA robot), indicating that the proposed networks can achieve satisfactory performance in the real-world scenario.
A Breast Cancer Diagnosis Method Based on VIM Feature Selection and Hierarchical Clustering Random Forest Algorithm

Breast cancer is a neoplastic disease which seriously threatens women's health.It is regard as the most common cause of cancer death in women.Accurate detection and effective treatment are of vital significance to lower the death rate of breast cancer.In recent years, machine learning technique has been considered to be an effective method for accurate diagnosis of various diseases, among which Random Forest (RF) has been widely applied.However, decision trees with poor classification performance and high similarity may be generated during the training process, which affects the overall classification performance of the model.In this paper, a Hierarchical Clustering Random Forest (HCRF) model is developed.By measuring the similarity among all the decision trees, the hierarchical clustering technique is used to carry out clustering analysis on decision trees.The representative trees are selected from divided clusters to construct the hierarchical clustering random forest with low similarity and high accuracy.In addition, we use Variable Importance Measure (VIM) method to optimize the selected feature number for the breast cancer prediction.Wisconsin Diagnosis Breast Cancer (WDBC) database and Wisconsin Breast Cancer (WBC) database from the UCI (University of California Irvine) Machine Learning repository are employed in this study.The performance of the proposed method is evaluated by utilizing accuracy, precision, sensitivity, specificity and AUC (Area Under ROC Curve).Experimental results indicate that the classification based on HCRF algorithm with VIM as a feature selection method reaches the best accuracy of 97.05% and 97.76% compared to Decision Tree, Adaboost and Random Forest on both the WDBC and WBC datasets.The method proposed in this study is an effective tool for diagnosing breast cancer.
JointE: Jointly utilizing 1D and 2D convolution for knowledge graph embedding

Knowledge graph embedding is a popular method to predict missing links for knowledge graphs by projecting entities and relations into continuous low-dimension embeddings. Some recent embedding models employ translation-based operations to learn the representations of entities and relations with shallow and linear structures, and others leverage neural networks, especially convolution neural networks, to embed the entities and relations with deep and non-linear structures. However, shallow and linear models limit the extraction capacity of the latent knowledge while deep and non-linear models lead to the overabundance of parameters and the loss of surface and explicit knowledge. In this paper, we propose JointE, which utilizes 1D and 2D convolution operations jointly to alleviate these issues effectively. More specifically, we utilize 2D convolution operations to facilitate the interactions between entities and relations, thereby capturing the latent knowledge sufficiently. To reduce the number of parameters significantly, we innovatively construct 2D convolution filters from internal embeddings rather than using external filters which costs plenty of redundant parameters. Furthermore, we appropriately employ 1D convolution filters over input embeddings to extract the surface and explicit knowledge and preserve it by element-wise addition. Experimental evaluation on five benchmark datasets demonstrates that our model outperforms all other state-of-the-art convolution-based models and simultaneously enhances the parameter efficiency.