Reviewerly/exHarmony huggingface dataset:
On the Security of the Dandelion Protocol

In this paper, we review the peer-to-peer blockchain transaction protocol, Dandelion, and develop an oracle-based model for its network and security. We formalize a series of security and functional criteria, such as unforgeability, non-repudiation, and immutability, into security experiments. In our model, we consider a quantum-capable adversary who seeks to undermine any of the security criteria while using oracles to simulate and interact with the Dandelion network. We then prove the security of Dandelion in our model with a series of (tight) security reductions as our main result. In addition, we prove that Dandelion is resistant to double-spending attacks.
Loan Default Prediction Using Machine Learning Techniques

Loans are a very fundamental source of any bank’s revenue, so they work tirelessly to make sure that they only give loans to customers who will not default on the monthly payments. They pay a lot of attention to this issue and use various ways to detect and predict the default behaviors of their customers. However, a lot of the time, because of human error, they may fail to see some key information. This paper proposes a better approach using machine learning approaches like KNN, decision tree, SVM and logistic regression to predict defaulters. The accuracy of these methods will also be tested using metrics like log loss, Jaccard similarity coefficient and F1 Score. These metrics are compared to determine the accuracy of prediction. This can help banks conserve their manpower and fiscal resources by reducing the number of steps they have to take in order to check if somebody is eligible for a loan.
The Sense of Presence between Volumetric-Video and Avatar-Based Augmented Reality and Physical-Zoom Teaching Activities

Abstract Various types of Augmented Reality (AR) have been successfully applied to many highly operable and spatial teaching courses in medicine, chemistry, and others. AR has already shown advantages over traditional face-to-face and online teaching, such as Zoom or Teams meetings. However, it is unclear when comparing teaching activity whether new AR technologies can offer similar advantages disciplines that are non-highly operational or non-spatial in nature, such as Introduction to Psychology. In order to present teaching activities in three dimensions and all directions and realize the presence of a teacher's face movement, we developed two new methods: Volumetric video Augmented Reality (VAR) and Avatar-based Augmented Reality (AAR). We compared the effects of teaching psychology using four methods: VAR, AAR, face-to-face, and Zoom. The participants’ data were collected via questionnaires with which we conducted variance analysis and Pearson correlation analysis. Our experiments showed that there were no significant differences in the effects of teaching under the four different teaching methods, but the AAR and VAR groups had significantly higher curriculum novelty and satisfaction, 3D sensation and presence, and teacher's attractiveness than face-to-face and Zoom groups. The research demonstrated that both VAR and AAR can improve the sense of presence and satisfaction in teaching psychology-related courses.
Deep Colorization: A Channel Attention-based CNN for Video Colorization

We investigate the video colorization problem which aims to convert a grayscale video to a colorful version. Video colorization is a difficult problem due to the temporal and spatial correlation between video frames. It can be widely used in many fields, such as restoring black-and-white film back to color and cross color prediction for video coding. We propose an end-to-end framework to solve the video colorization task. Our work is based on temporal convolutional neural networks with attention mechanisms. The proposed method can control the contribution of each channel when extracting features. Our network can colorize multiple frames at the same time and can handle long video sequences colorization by giving only one frame as a reference. Quantitative analysis shows that our method outperforms existing approaches on the tested video sequences.
Mask-Guided Deformation Adaptive Network for Human Parsing

Due to the challenges of densely compacted body parts, nonrigid clothing items, and severe overlap in crowd scenes, human parsing needs to focus more on multilevel feature representations compared to general scene parsing tasks. Based on this observation, we propose to introduce the auxiliary task of human mask and edge detection to facilitate human parsing. Different from human parsing, which exploits the discriminative features of each category, human mask and edge detection emphasizes the boundaries of semantic parsing regions and the difference between foreground humans and background clutter, which benefits the parsing predictions of crowd scenes and small human parts. Specifically, we extract human mask and edge labels from the human parsing annotations and train a shared encoder with three independent decoders for the three mutually beneficial tasks. Furthermore, the decoder feature maps of the human mask prediction branch are further exploited as attention maps, indicating human regions to facilitate the decoding process of human parsing and human edge detection. In addition to these auxiliary tasks, we further alleviate the problem of deformed clothing items under various human poses by tracking the deformation patterns with the deformable convolution. Extensive experiments show that the proposed method can achieve superior performance against state-of-the-art methods on both single and multiple human parsing datasets. Codes and trained models are available https://github.com/ViktorLiang/MGDAN .
Machine Learning Techniques for Prediction and Analysis of Benign and Malignant in Breast Cancer

In today&#x0027;s technological world, the medical area has become one of the most popular research topics, and cancer is one of them. One of the major diseases caused for women&#x0027;s life today is breast cancer. Breast cancer claims the lives of most women all over the world, not just in India. Breast cancer originates due to aberrant cell division, leading to benign or malignant cancer formation. Patients with this disease can only be saved if and when found in the early stages. If it is discovered in the later stages, the chances of survival are less. Early diagnosis will help physicians who will significantly enhance the survival rate of cancer patients. Then only patients can able to avail best possible care by providing better treatment. During the early stage, symptoms must be thoroughly investigated. It is also necessary to develop an automatic prediction system that can classify tumors as benign or malignant. This paper will focus on some Machine Learning techniques for prediction and analyze whether a disease is a malignant or benign stage in breast cancer.
Prior image-based medical image reconstruction using a style-based generative adversarial network

Computed medical imaging systems require a computational reconstruction procedure for image formation. In order to recover a useful estimate of the object to-be-imaged when the recorded measurements are incomplete, prior knowledge about the nature of object must be utilized. In order to improve the conditioning of an ill-posed imaging inverse problem, deep learning approaches are being actively investigated for better representing object priors and constraints. This work proposes to use a style-based generative adversarial network (StyleGAN) to constrain an image reconstruction problem in the case where additional information in the form of a prior image of the sought-after object is available. An optimization problem is formulated in the intermediate latent-space of a StyleGAN, that is disentangled with respect to meaningful image attributes or “styles”, such as the contrast used in magnetic resonance imaging (MRI). Discrepancy between the sought-after and prior images is measured in the disentangled latent-space, and is used to regularize the inverse problem in the form of constraints on specific styles of the disentangled latent-space. A stylized numerical study inspired by MR imaging is designed, where the sought-after and the prior image are structurally similar, but belong to different contrast mechanisms. The presented numerical studies demonstrate the superiority of the proposed approach as compared to classical approaches in the form of traditional metrics.
Anomaly detection for high-dimensional space using deep hypersphere fused with probability approach

Abstract Data distribution presents sparsity in a high-dimensional space, thus difficulty affording sufficient information to distinguish anomalies from normal instances. Moreover, a high-dimensional space may exist many subspaces, obviously, anomalies can exist in any subspaces. This also creates trouble for anomaly mining. Consequently, it is a challenge for anomaly mining in a high-dimensional space. To address this, here proposed a deep hypersphere method fused with probabilistic approach for anomaly mining. In the proposed method, the deep neural network is used as a feature extractor to capture those layered low-dimensional features from the data lying in a high-dimensional space. To promote the ability of the deep neural network to capture these features, the probability approach of sample binary-classification is fused into the loss function, thereby forming the probability deep neural network Then, the hypersphere is used as an anomalous detector. In the low-dimensional features extracted by the deep neural network, the anomalous detector separates anomaly features from normal features. Finally, experimental results on synthetic and real-world data sets show that the proposed method not only outperforms the state-of-the-art methods in the precision of mined anomalies, but also this hybrid method consisting of deep neural networks and traditional detection methods has outstanding capabilities of mining high-dimensional anomalies. We find that deep neural networks fusing the probabilistic method of sample multi-classification can capture these desired low-dimensional features; moreover, these captured low-dimensional features present more obvious layered characteristics. We also demonstrate that as long as these captured features represent a fewer anomaly instances, it can sufficiently identify anomalies from normal instances.
Inverse limits which are P-spaces

We investigate inverse systems of height ω1 of discrete topological spaces. Inverse limits of such systems are P-spaces, which allows us to explore dimensional types of such P-spaces.
The relationship between knowledge-based economies and economic growth: an empirical analysis on the Asia-Pacific region 2011–2018

Purpose This paper aims to examine the relationship between knowledge-economy and economic growth in 16 Asia-Pacific (AP) countries during the period 2011–2018. The study also aims to investigate a diversity of knowledge-economy pillars, including tertiary education, domestic innovation, foreign innovation, economic incentives and institutional regime and information and communications technologies (ICTs) and their relation to economic growth. Design/methodology/approach The study applies a comparative empirical analysis using pooled ordinary least squares (OLS), one-step difference generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVc) estimators to test this relationship. Findings Pooled OLS estimators deemed suboptimal to the panel data under study, while GMM results reveal a significant relationship between tertiary education, domestic and foreign innovation, government expenditure and investments with economic growth. Of these results, domestic innovation, investments and government consumption are positively correlated with economic growth, whereas tertiary education and foreign innovation show a negative relation. Meanwhile, institutions and ICT have insignificant relationships with economic growth. LSDVc results coincide with GMM results with respect to tertiary education, whereas institutions is the only additional significant and negatively correlated variable with economic growth. Research limitations/implications The main limitation of this research lies in the unavailability of proxy data for knowledge economy pillars in monetary terms, and hence, the paper relies on indices. Originality/value The novelty of the study lies in its aim to investigate economic growth in the AP region that is enhanced by domestic innovation, foreign innovation or both – an area which is empirically understudied in the knowledge-economy context. Further, the paper’s novelty lies in its application of a comparative empirical analysis between the most popular dynamic panel estimators – dynamic GMM and bias-corrected LSDVc for AP countries.
Conditions for steady-state entanglement of quantum systems in a stationary environment under Markovian dissipation

We study the entanglement dynamics of an open quantum system composed of two identical two-level subsystems in a common stationary environment undergoing Markovian dissipation, with the help of a set of physical parameters defined with the collective transition coefficients of the system. We then systematically investigate the steady-state entanglement of such a system and obtain the necessary and sufficient condition for steady-state entanglement when it is initial-state dependent. We also show in particular conditions for steady-state entanglement in some circumstances and propose in general a conjecture for the necessary and sufficient condition when it is independent of the initial state.
Data Storage on Cloud using Split-Merge and Hybrid Cryptographic Techniques

Data protection is the most important consideration when storing data in the cloud. We use hybrid cryptography to provide data protection in the cloud, while most current approaches use a single cryptography algorithm. As a result, we need a method that is both efficient and safe. In this paper, we employ hybrid cryptography, which makes use of existing cryptographic algorithms. We use cloud platform to promote the tremendous benefits of distributed computing in a variety of ways, from reducing enterprise sizes to lowering development costs. Organizations are most concerned about the security of their data. A single algorithm is used for data encryption and decryption in current systems, which may not be sufficient to achieve high security. A problem of stable key transmission and storage arises when using a public key cryptography algorithm. We use hybrid cryptography and file splitting for data protection. To data less accessible to attackers, components of the files are sorted on separate data.
Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension

Advances in artificial intelligence-based autonomous applications have led to the advent of domestic robots for smart elderly care; the preliminary critical step for such robots involves increasing the comprehension of robotic visualizing of human activity recognition. In this paper, discrete hidden Markov models (D-HMMs) are used to investigate human activity recognition. Eleven daily home activities are recorded using a video camera with an RGB-D sensor to collect a dataset composed of 25 skeleton joints in a frame, wherein only 10 skeleton joints are utilized to efficiently perform human activity recognition. Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. The novelty indicated D-HMM theory is not only promising in terms of speech signal processing but also is applicable to visual signal processing and applications.
Detection using Yolov5n and Yolov5s with small balls

Detection on small objects is a tough task compared with large objects. It is even harder on smaller-size models because the accuracy might decline significantly. The main purpose of this paper is to explore and evaluate the performance of small object detection based on yolov5 series networks, including yolov5n, which is the latest released version and has a smaller size than yolov5s. Meanwhile, this paper will narrow down the general small object to small balls detection, including baseball, football, and tennis, as an example of general small objects in real life. The idea of this paper is basically to see if there is an easier way to detect missing little stuff in daily life, such as lost keys or chargers, to provide help to people who might have difficulty to see or find them. The method applied is to train the dataset containing the three balls sport involved and run them on yolov5n and yolov5s to evaluate their performance. The dataset collection is from Kaggle and labeled by this paper’s author, to simulate other small objects detection.
Application of Digital Technology-Based TPACK in English Translation

Internet teaching encourages the societal improvement of teaching and learning systems. In this paper, we examine big data and interactive teaching modes and the design process of interactive teaching modes. We propose an interactive teaching model that uses big data in learning analytics and content analysis to optimize the whole teaching practice application process. We also show how to use current technology, build a great teaching management platform, and raise the teaching management level. In this paper, we also examine and develop an English translation teaching model based on TPACK and propose the use of TPACK in technical English translation based on big data platforms, with the purpose of “cultivating students to improve translation abilities”. The model is effective and promotes the improvement of translation teaching levels.
Development on Fovea-Lens Camera System for Human Recognition in Robot System

This paper describes the development on the fovea-lens camera system for a robotic system which recognize and track human object. The fovea lens has the characteristics imitating human eye: wide range of view and high resolution at the center of image. The wide range of view enables to find human earlier and the high resolution enables to recognize human well. We realized the realtime system to recognize human by using AdaBoost machine learning. An experiment shows that our fovea-lens camera is suitable for the robotic camera. The human recognition rate with the fovea lens is more than 91 % even though the rate with the wide-range lens is less than 80 % with 5 m distance between the human and the camera. The detection range in horizontal plane of the fovea lens is almost same as the wide-range lens.
Privacy-Preserving Minority Oversampling Protocols with Fully Homomorphic Encryption

In recent years, blockchain and machine-learning techniques have received increasing attention both in theoretical and practical aspects. However, the applications of these techniques have many challenges, one of which is the privacy-preserving issue. In this paper, we focus on, specifically, the privacy-preserving issue of imbalanced datasets, a commonly found problem in real-world applications. Built based on the fully homomorphic encryption technique, this paper presents two new secure protocols, Privacy-Preserving Synthetic Minority Oversampling Protocol (PPSMOS) and Borderline Privacy-Preserving Synthetic Minority Oversampling Protocol (Borderline-PPSMOS). Our analysis reveals that PPSMOS is generally more efficient in performance than Borderline-PPSMOS. However, Borderline-PPSMOS achieves a better TP rate and F-Value than PPSMOS.
Self-attention technology in image segmentation

Although the traditional Convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And we think about whether the self-attention module in this field can replace convolution operation in the future. The answer to this review is yes, so it is recommended that the focus of future research be on the self-attention module
Feature Analysis and Extraction for Specific Emitter Identification Based on the Signal Generation Mechanisms of Radar Transmitters

In this study, a feature analysis and extraction method was proposed for specific emitter identification based on the signal generation mechanisms of radar transmitters. The generation of radar signals by radar transmitters was analyzed theoretically and experimentally. In the analysis, the main source of unintentional modulation in radar signals was identified, and the frequency stabilization of the solid-state frequency source, the nonlinear characteristics of the radio frequency amplifier chain, and the envelope of the pulse front edge were extracted as features for specific emitter identification. Subsequently, these characteristics were verified through simulation. The results revealed that the features extracted by this method exhibit "fingerprint characteristics" and can be used to identify specific radar emitters.
A compact fuzzy min max network with novel trimming strategy for pattern classification

Hyperbox classifier has large contribution to the field of pattern classification, because of its efficiency and transparency. Hyperbox classifier is efficiently implemented by using fuzzy min–max (FMM) neural network. FMM was modified many times to improve the classification accuracy. Moreover, there still exists a space for increasing the accuracy of hyperbox based classifiers. In this paper, four modifications are proposed to FMM network for increasing the classification accuracy rate. First, centroid and K-highest (CCK) based criteria to select the expandable hyperbox. Second, a new set of overlap test cases to consider all types of overlapping regions. Third, a new set of contraction rules to settle the overlapped regions. Fourth, novel hyperbox trimming strategy to reduce the system complexity. The proposed method is compared with FMM, enhanced FMM (EFMM) and Kn_FMM using five datasets. Experimental results clearly reflect the improved efficiency of proposed method. Proposed FMM (PFMM) network is also used to classify the histopathological images for knowing the best magnifying factor.