Reviewerly/exHarmony huggingface dataset:
Vehicle Detection Method Based on GhostNet-SSD

The traditional vehicle target detection algorithm needs to select appropriate features for different image scenes, resulting in poor generalization ability. In order to solve this problem, this paper proposes an image vehicle detection method based on SSD. This method combines GhostNet and SSD to extract feature maps from GhostNet for classified and location prediction. To some extent, the detection accuracy and speed of the vehicle are improved. Experimental results show that this method has a high recognition rate and is better than the traditional algorithm.
Neural Architecture Search Based on Evolutionary Algorithms with Fitness Approximation

Designing advanced neural architectures to tackle specific tasks involves weeks or even months of intensive investigation by experts with rich domain knowledge. In recent years, neural architecture search (NAS) has attracted the interest of many researchers due to its ability to automatically design efficient neural architectures. Among different search strategies, evolutionary algorithms have achieved significant successes as derivative-free optimization algorithms. However, the tremendous computational resource consumption of the evolutionary neural architecture search dramatically restricts its application. In this paper, we explore how fitness approximation-based evolutionary algorithms can be applied to neural architecture search and propose NAS-EA-FA to accelerate the search process. We further exploit data augmentation and diversity of neural architectures to enhance the algorithm, and present NAS-EA-FA V2. Experiments show that NAS-EA-FA V2 is at least five times faster than other state-of-the-art neural architecture search algorithms like regularized evolution and iterative neural predictor on NASBench-101, and it is also the most effective and stable algorithm on NASBench-201. All the code used in this paper is available at https://github.com/fzjcdt/NAS-EA-FA.
Integrating IoT with Blockchain: A Systematic Review

With the advent of IoT, Internet dominance has been extended far and wide, resulting in management of billions of smart devices online. However, all the management frameworks in IoT developed so far are based on centralized models, which have their own set of issues like single point of failure and security constraints. To encounter major issues like this, Blockchain provides an effective alternative to encounter the issues of security and privacy. Blockchain is being a distributed and decentralized ledger framework, when used with IoT helps to encounter a lot of security related constraints. In this paper, a detailed study related to Blockchain has been conducted and how well it works in collaboration with IoT to overcome all the major security and privacy issues in an IoT ecosystem. Many other applications of Blockchain in an IoT environment were also studied, with the intention of exploring all the potential benefits it can provide while working with IoT.
Structured Variational Bayesian Inference for Gaussian State-Space Models With Regime Switching

Linear and Gaussian models with regime switching are popular in signal processing. In this letter, we revisit Bayesian inference in such models under the variational Bayesian framework. We propose a structured but implicit variational distribution which can be seen as the posterior distribution of an alternative statistical model in which the first and second order moments of the filtering distribution can be computed exactly. A critical property of this alternative model is that the Kullback-Leibler Divergence between its associated posterior distribution and that of linear and Gaussian models with regime switching can also be computed exactly, at a cost linear in the number of observations; hence, we propose a parameter estimation method of our variational model but also of the original one from a sequence of observations. Once the parameters are estimated, our inference method is efficient as compared to the Rao-Blackwell Particle Filter.
Planning in Dynamic and Partially Unknown Environments

Motion planning in dynamic and partially unknown environments is a difficult problem requiring both perception and control components. We propose a solution to the control component while cleanly abstracting perception. We show that this clean abstraction can be used to synthesize verifiably safe reference trajectories using a combination of reachability analysis and Mixed Integer Linear Programming. Experiments with a prototype implementation of this algorithm show promise as it has subsecond synthesis performance for nonlinear vehicle models in scenarios with hundred plus obstacles on standard hardware.
When Hearing the Voice, Who Will Come to Your Mind

Speech is a carrier containing rich biological information, such as speaker identity information including age, gender, race. In this paper, we explore the use of a self-supervised method to obtain speaker identity information from high-dimensional speech representations to generate face image. At the same time, considering that the biological information contained in the same piece of speech has different expression forms (such as images), we designed a cross-modal knowledge distillation method to transform the feature information from the visual domain to the speech domain. The feature vectors obtained through self-supervised learning and knowledge distillation are fed into a GAN-based generative model to obtain facial images containing speaker information. Subjective experiments show that our model can reach a well performance in the task of speaker identification. Experiments show that our proposed method can effectively establish the connection between different modalities and generate a face with rich biological information.
Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding

At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users’ privacy.
Deep sea nodule mineral image segmentation algorithm based on Mask R-CNN

In the exploration of deep-sea nodule mineral resources, computer vision-based methods have been widely used to assess nodule mineral resources, such as abundance, particle size, coverage and other information. Among them, the separation of nodule minerals from the background is an important part of deep-sea mineral resource assessment. Threshold segmentation, clustering segmentation, multispectral segmentation and other traditional segmentation methods need to rebuild the model from scratch once the distribution of feature space changes. However, the data-driven deep learning method provides a general framework to solve this problem, which has strong generalization and robustness. In order to improve the segmentation performance, a novel deep sea nodule mineral images segmentation algorithm based on Mask R-CNN was proposed. The comparative analysis is also processed on some other different deep learning methods, such as U-Net, and the Generative Adversarial Network. The experimental results show that, the method based on Mask R-CNN is better than the method based on U-Net, improced U-Net and Conditional Generative Adversarial Networks on the dataset.
Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020

Since 2007, knowledge graphs, an important research tool, have been applied to education and many other disciplines. This paper firstly overviews the application of knowledge graphs in education and then samples the knowledge graph applications in CSSCI- (Chinese Social Sciences Citation Index-) indexed journals in the past two years. These samples were classified and analyzed in terms of research institute, data source, visualization software, and analysis perspective. Next, the situation of knowledge graph applications in education was summarized and evaluated in detail. Furthermore, the authors discussed and assessed the normalization of knowledge graph applications in education. The results show that in the past 15 years, knowledge graphs have been widely used in education. The academia has reached a consensus on the paradigm of the research tool: examining the hotspots, topics, and trends in the related fields from the angles of keyword cooccurrence network (KCN), time zone map, clustering network, and literature/author cocitation, with the aid of CiteSpace and other visualization software and text analysis. However, there is not yet a thorough understanding of the limitations of the visualization software. The relevant research should be improved in terms of scientific level, normalization level, and quality.
A Backdoor Attack against 3D Point Cloud Classifiers

Vulnerability of 3D point cloud (PC) classifiers has become a grave concern due to the popularity of 3D sensors in safety-critical applications. Existing adversarial attacks against 3D PC classifiers are all test-time evasion (TTE) attacks that aim to induce test-time misclassifications using knowledge of the classifier. But since the victim classifier is usually not accessible to the attacker, the threat is largely diminished in practice, as PC TTEs typically have poor transferability. Here, we propose the first backdoor attack (BA) against PC classifiers. Originally proposed for images, BAs poison the victim classifier's training set so that the classifier learns to decide to the attacker's target class whenever the attacker's backdoor pattern is present in a given input sample. Significantly, BAs do not require knowledge of the victim classifier. Different from image BAs, we propose to insert a cluster of points into a PC as a robust backdoor pattern customized for 3D PCs. Such clusters are also consistent with a physical attack (i.e., with a captured object in a scene). We optimize the cluster's location using an independently trained surrogate classifier and choose the cluster's local geometry to evade possible PC preprocessing and PC anomaly detectors (ADs). Experimentally, our BA achieves a uniformly high success rate (> 87%) and shows evasiveness against state-of-the-art PC ADs.
Sentiment Analysis of Movie Reviews Using Machine Learning Techniques

Sentiment analysis is the interpretation and classification of emotions and opinions from the text. The scale of emotions and opinions can vary from positive to negative and maybe neutral. Customer sentiment analysis helps businesses to point out the public’s thoughts and feelings about their products, brands, or services in online conversations and feedback. Natural language processing and text classification are crucial for sentiment analysis. That means we can predict or classify customers’ opinions given their comments. In this paper, we do sentiment analysis in the two different movie review datasets using various machine learning techniques including decision tree, naïve Bayes, support vector machine, blending, voting, and recurrent neural networks (RNN). We propose a few frameworks of sentiment classification using these techniques on the given datasets. Several experiments are conducted to evaluate them and compared with an outstanding natural language processing tool (Stanford CoreNLP) at present. The experimental results have shown our proposals can achieve higher performance, especially, the voting and RNN-based classification models can result in better predictions.
Minimal Unique Palindromic Substrings After Single-Character Substitution

A palindrome is a string that reads the same forward and backward. A palindromic substring w of a string T is called a minimal unique palindromic substring (MUPS) of T if w occurs only once in T and any proper palindromic substring of w occurs at least twice in T. MUPSs are utilized for answering the shortest unique palindromic substring problem, which is motivated by molecular biology [Inoue et al., 2018]. Given a string T of length n, all MUPSs of T can be computed in O(n) time. In this paper, we study the problem of updating the set of MUPSs when a character in the input string T is substituted by another character. We first analyze the number d of changes of MUPSs when a character is substituted, and show that d is in \(O(\log n)\). Further, we present an algorithm that uses O(n) time and space for preprocessing, and updates the set of MUPSs in \(O(\log \sigma + (\log \log n)^2 + d)\) time where \(\sigma \) is the alphabet size. We also propose a variant of the algorithm, which runs in optimal \(O(1+d)\) time when the alphabet size is constant.
FastNeRF: High-Fidelity Neural Rendering at 200FPS

Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints. Rendering these images is very computationally demanding and recent improvements are still a long way from enabling interactive rates, even on high-end hardware. Motivated by scenarios on mobile and mixed reality devices, we propose FastNeRF, the first NeRF-based system capable of rendering high fidelity photorealistic images at 200Hz on a high-end consumer GPU. The core of our method is a graphics-inspired factorization that allows for (i) compactly caching a deep radiance map at each position in space, (ii) efficiently querying that map using ray directions to estimate the pixel values in the rendered image. Extensive experiments show that the proposed method is 3000 times faster than the original NeRF algorithm and at least an order of magnitude faster than existing work on accelerating NeRF, while maintaining visual quality and extensibility.
A Review of Opinion Mining Techniques.

Opinion mining also known as Sentiment analysis is one of the most recent challenges in Natural Language Processing (NLP). Individuals expressing their opinions on various platforms such as Facebook, Twitter, and Yelp are also a difficult task because innovation has increased exponentially. With the popularity of social media, a massive amount of data namely comments, reviews and opinions have been generated. According to researchers, analysis of sentiments is based on a sentence level, document level, aspect level and user level. The analysis of this data consumes more time and is difficult for processing. As a result, there is a need to create an intelligent system that can classify or determine positive, negative, and neutral opinions. The main goal of this paper is to provide a brief summary of opinion mining using techniques like machine learning, deep learning, transfer learning, and the Hadoop framework.
Hard parameter sharing for compressing dense-connection-based image restoration network

The dense connection is a powerful technique to build wider and deeper convolution neural networks (CNNs) for handling several computer vision tasks. Despite the excellent performance, it consumes numerous parameters and produces a large weight model file. We studied the distribution of convolution layers and proposed a hard parameter sharing approach known as convolution pool (CP) for compressing dense-connection-based image restoration CNN models. CP is used to reallocate the parameters to specific convolution layers to ensure that some can be shared in different layers. We design a set of dense-connection-based baselines for three typical image restoration tasks, including image denoising, super-resolution, and JPEG deblocking, to validate the performance of the proposed method. Moreover, we comprehensively analyze the potential problems by introducing CP, including group convolution, dilated convolution, and modeling efficiency. Experimental results demonstrate that the proposed method can efficiently achieve an impressive compression rate with negligible performance reduction.
Semi-Supervised Anomaly Detection Algorithm Using Probabilistic Labeling (SAD-PL)

To detect abnormal data via semi-supervised learning, unlabeled data are generally assumed to be normal data. This assumption, however, causes inevitable performance degradation when a small fraction of abnormal data is included in the unlabeled dataset. To overcome the degradation and to maintain stable detection performance, we propose a semi-supervised anomaly detection algorithm using probabilistic labeling (SAD-PL) for unlabeled data. The proposed SAD-PL is composed of two steps: (1) estimating local outlier factor (LOF) scores of latent vectors from both labeled and unlabeled data and (2) estimating labeling probability on the unlabeled data by using the prior missing probability of the labeled data via the Neyman-Pearson (NP) criterion. The SAD-PL runs iteratively by using the proposed complementary learning functions until the rate of label changes is lower than the predefined threshold. Experimental results reveal that the SAD-PL shows superior detection probability over the existing algorithms and stable performance regardless of the normal to abnormal data ratio in unlabeled data and the ratio of change variation of unlabeled data statistics to labeled data statistics.
A blockchain-based information management system for academic institutions: a case study of international students’ workflow

Purpose This study aims to explore the impact of applying a blockchain-based information system in the international students’ administrative workflow in the context of a public research university in the USA. Design/methodology/approach Specifically, a prototype of the blockchain-based information system was developed and tested. The system has the potential advantages of being more efficient, more transparent, faster and safer. Findings The results show that digital registration could increase student registration efficiency with a blockchain-based application. Research limitations/implications There are also some limitations of this study. As an early exploration of the use of blockchain technology in higher education, this study could be further extended in several aspects: different institutions (e.g. private vs public school) may have different operational strategies towards international student affairs. The different administrative processes should be discussed in the context of different institutions, and thus the proposed system should be tested and evaluated based on the requirements of different institutions. Use experience and feedback from different users such as university administrators and students should be considered and investigated with respect to the necessary improvement of the proposed system. The extensibility and scalability of the system should be assessed to accommodate any potential modifications of the administrative process. Originality/value The findings show that a blockchain-based information management system has the potential to improve data transmission efficiency and protect information privacy.
Type or Individual? Evidence of Large-Scale Conceptual Disarray in Wikidata

The distinction between types and individuals is key to most conceptual modeling techniques. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of modeling mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an individual and a type, and specialization (or subtyping), which is a relation between two types.
CrackDect: Detecting crack densities in images of fiber-reinforced polymers

<h2>Abstract</h2> CrackDect is a tool to detect cracks in a given direction from a series of images. It is specialized to detect multiple matrix cracks in composite laminates to yield the crack density but can also be used as a general line detection. The package is written in Python, and includes classes and functions to efficiently handle large image stacks, pre-process images and perform the crack detection. Due to its modular structure it is easily expandable to other crack detection or feature recognition algorithms. Pre-processing of whole image stacks can be customized to account for different image capturing techniques. Since image processing tends to be computational and memory expensive, special focus is put on efficiency.
Image Quality Assessment in the Modern Age

This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA). From an actionable perspective, we will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. We will then present in detail the design principles of objective quality assessment models, supplemented by an in-depth analysis of their advantages and disadvantages. Both hand-engineered and (deep) learning-based methods will be covered. Moreover, the limitations with the conventional model comparison methodology for objective quality models will be pointed out, and novel comparison methodologies such as those based on the theory of "analysis by synthesis" will be introduced. We will last discuss the real-world multimedia applications of IQA, and give a list of open challenging problems, in the hope of encouraging more and more talented researchers and engineers devoting to this exciting and rewarding research field.
Viewing from Frequency Domain

Video-based person re-identification (Re-ID) aims to match the target pedestrians under non-overlapping camera system by video tracklets. The key issue of video Re-ID focuses on exploring effective spatio-temporal features. Generally, the spatio-temporal information of a video sequence can be divided into two aspects: the discriminative information in each frame and the shared information over the whole sequence. To make full use of the rich information in video sequences, this paper proposes a Discrete Cosine Transform based Information Enhancement Network (DCT-IEN) to achieve more comprehensive spatio-temporal representation from frequency domain. Inspired by the principle that average pooling is one of the special frequency components in DCT (the lowest frequency component), DCT-IEN first adopts discrete cosine transform to convert the extracted feature maps into frequency domain, thereby retaining more information that embedded in different frequency components. With the help of DCT frequency spectrum, two branches are adopted to learn the final video representation: Frequency Selection Module (FSM) and Lowest Frequency Enhancement Module (LFEM). FSM explores the most discriminative features in each frame by aggregating different frequency components with attention mechanism. LFEM enhances the shared feature over the whole video sequence by frame feature regularization. By fusing these two kinds of features together, DCT-IEN finally achieves comprehensive video representation. We conduct extensive experiments on two widely used datasets. The experimental results verify our idea and demonstrate the effectiveness of DCT-IEN for video-based Re-ID.