Reviewerly/exHarmony huggingface dataset:
Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network

Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection.However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm.In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN.We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation.Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset, with object detection CNN algorithms such as YOLOv3 (You Only Look Once), Faster R-CNN (Region-based CNN), and SSD (Single Shot Multibox Detector).Each CNN model shows performance loss for varying sharpness and brightness, ranging between -80% to 80%.It was further shown that compared to random augmentation, the augmented dataset with weak photometric changes delivered high performance, but the photometric augmentation range differs for each model.Concurrently, we discuss some research questions that benefit the direction of the study.The results prove the importance of adaptive augmentation for individual CNN model, subjecting towards the robustness of object detection.
A two-stage modular blind denoising algorithm based on real scene

Abstract The use of deep learning methods has developed rapidly in image denoising. Now deep learning methods have a good improvement in denoising effects compared to traditional methods, but there are also some problems. On the one hand, these denoisers are more used to remove the noise of a specific distribution, while the image noise distribution in real life is not fixed, so it is more difficult and more practical to denoise the real image. On the other hand, more and more complex network structures and deeper and deeper network models seem to have become a necessary condition for better denoising effects, but its improvement is not linear, and deeper networks are likely to be removed. The improvement in noise effect is minimal. Because of this, we designed a relatively simple network structure to remove real image noise, which includes the noise level estimation stage using the channel attention mechanism, and the non-blind noise reduction stage using the micro-branch structure we designed. Experiments show that our method has good visual perception quality compared with other methods on commonly used image denoising data sets.
Blurry Facial-Image Deconvolution via Model-Guided Deep Neural Network Inspired From Edge Regularization

The blind facial-image deconvolution problem is ill-posed so that the estimation of latent image and blur kernel is hard to obtain without additional priors or heuristics. Although data-driven methods with deep neural networks have achieved excellent performance in this issue, most existing methods are designed like black boxes without transparency and interpretability. Thus, a model-guided explainable approach is proposed to address the above issues on blurry-facial images’ recovery, named the model-guided deep neural network (MG-DNN). To break the barrier between the network design and interpretability, we optimize blind image deconvolution based on salient edge regularization (BID-SER) and use it to guide the proposed MG-DNN. First, basic units for processing image and kernel features are designed with convolutional operators. Then, unfolding the optimization of BID-SER, the multiblocks concatenation is designed by several kernel networks and image networks with basic units. Finally, comprehensive experiments evaluated by several metrics demonstrate that the proposed MG-DNN performs favorably with existing state-of-the-art methods while processing on three different face data sets.
A Comparative Analysis on Blockchain Technology Considering Security Breeches

Nowadays, blockchain technology become a positive pandemic by holding the Internet by storm. Thanks to its free accessibility and safety in nature where the technology of blockchain had emerged as a technology of revolution for the next coming waves of a buzz industrial profile. One is Stuff's network which is sponsored by the company Cloud Infrastructure and Internet things (IoT). To analyze the effect of cyber-attacks in the traditional cyber-framework and study the blockchain cryptocurrency architecture to assess the short comings of blockchain technology. This paper focuses on the various articles of blockchain technology and identified the issues related to security, confidentiality, and accessibility. In this paper, a comparison is done on various articles based on the methodology and merits.
Design of Middle School Chemistry Experiment Simulation System Based on Apriori Algorithm

Aiming at the safety problems of toxic, flammable and explosive chemicals used in middle school chemical experiments, such as human poisoning, skin corrosion, fire or explosion caused by improper experimental operation, a virtual simulation method of chemical experiments based on unity is proposed.Due to the need to analyze and compare the data in chemical experiments, summarize the experimental characteristics and data relevance.Therefore, based on the Apriori algorithm, this method deeply excavates the data obtained in the chemical experiment, uses Maya to model the experimental environment, uses unity to design the interactive functions in the experimental process, and uses visual effect graph technology, shader graph technology and other methods to realize the special effect simulation of various chemical experiments.With this method, the virtual simulation prototype system of middle school chemistry experiment is developed.The example shows that this method can vividly and realistically reproduce the simulation effect of chemical experiment, find the correlation between the experimental data, classification, properties and the overall characteristics of the data, describe and predict the development trend of the data, so as to make better use of these data to provide support for chemical experiment, and not only solve the problems of poisoning, corrosion of skin and It also solves the problems that it is difficult to scientifically compare data and find data relevance in traditional chemical experiments.
CAPTCHA Robustness

Human verification is important to avoid spamming over the internet. Internet has grown tremendously in the last decade. Introduction to preference personalization make the users experience and attract more people. This has deemed it necessary to verify that some of the actions are carried out by a human rather than a computer program to generation of invalid traffic and prevent fraudulence. Methods like Captcha and ReCaptcha have extensively used to overcome this challenge but with the advancements in machine leaning and artificial intelligence, these techniques have started to become obsolete. So, to address this issue we are proposing a phase based human verification process using a combination of neural network and machine learning.
No-Reference Image Quality Assessment Based on Image Naturalness and Semantics

Automatically providing feedback about the quality of natural images could be of great interest for image-driven applications. Toward this goal, this paper proposes a novel no-reference image quality metric capable of effectively evaluating the image quality without requring the information of the original image. The proposed method delivers a comprehensive analysis of the image quality through exploring its statistical natural properties and high-level semantics. Specifically, we adopt the NSS regularities based method to characterize the image naturalness, and take the semantic information of the image through the deep neural networks. The quality prediction model is then derived by SVR to analyze the relationship between these extracted features and the image quality. Experiments conducted on LIVEC and CID2013 databases manifest the effectiveness of the proposed metric as compared to existing representative image quality assessment techniques.
Notes on the Architecture, League Training and PFSP in AlphaStar

AlphaStar, a multi-agent intelligence program, is a new member of Alpha’s gamer software. Rated at Grandmaster level in the empirical evaluation shows that AlphaStar has been a great success in engineering, which is another milestone in AI history. After a brief introduction to AlphaStar, we discussed its computational architecture, league training, PFSP, and proposed an interesting topic direction on the basis of our suggestions, which was how to combine the capacities of diverse ANNs that successful completed learning. Additionally, we proposed the viewpoint that is big data— space, intelligence— man’ world, parallel computing—time.
Unsupervised Monocular Depth Estimation with Attention Based Inception Pipe and Overlap Regularized Loss

Unsupervised learning methods that treat depth estimation as image reconstruction has achieved promising results recently. Most existing methods generate disparity maps from multi-layers residual network by training image reconstruction loss for left right view image pairs. Even the smooth regularized loss that allows for the depth discontinuities at object contours has been introduced, depth maps generated by these methods still suffer from border blur or dilation at image gradients. To overcome this problem, we improve the unsupervised depth estimation network by attaching the attention based inception pipe and overlap regularized loss. While using residual layer to generate the disparity maps, we also enforce the global inception pipe to auto-adjust depth changes according to the spatial attention values. In addition, we use the overlap regularized weights in final loss computation to reduce the reconstruction errors at non-overlapping area for the aligned image pairs. Our methods can present more depth details at object contours and boundaries and produce superior performance for depth estimation on KITTI dataset compared to the typical methods.
Comparison between traditional model and causal intervention model: A case study baesd on directed acyclic graph

With the development of machine learning, the advantages of causal learning over statistics have been pointed out. This paper investigates whether causal logic can significantly improve machine learning performance by comparing the performance of the traditional model and the model after causal intervention. However, there is no unified process and lack of real data application in this kind of research now. To this end, the paper proposed a process of exploring hidden variables, constructing directed acyclic graphs, and eliminating confounding through backdoor criteria, to explore the cause-and-effect relationship. At the same time, this paper applies the strategy to establish the causal intervention strategies under the multiple linear regression model to forecast the grade of human living environment. Finally, the results of grade classification according to the specified criteria were compared with the results of traditional multiple linear regression model without causal intervention strategy, and it was found that the model with causal intervention strategy had better performance, which may suggest that causal learning is likely to eliminate or reduce the negative effects of pseudo-correlation in traditional models.
Tractable Parsing for CCGs of Bounded Degree

Abstract Unlike other mildly context-sensitive formalisms, Combinatory Categorial Grammar (CCG) cannot be parsed in polynomial time when the size of the grammar is taken into account. Refining this result, we show that the parsing complexity of CCG is exponential only in the maximum degree of composition. When that degree is fixed, parsing can be carried out in polynomial time. Our finding is interesting from a linguistic perspective because a bounded degree of composition has been suggested as a universal constraint on natural language grammar. Moreover, ours is the first complexity result for a version of CCG that includes substitution rules, which are used in practical grammars but have been ignored in theoretical work.
The Future Around Journalism

Journalism has a context. Before one can consider the future of journalism, one has to know something about the context – the setting of our future Earth. And while some aspects of the future are unknown, a number of dramatic changes are known with near certainty. The climate is entering into a period with no historical precedent. There is no near-term way to reverse the climate disruption being caused by the ever-rising amount of carbon in our atmosphere. Climate change will not be a story, but a setting: new and ever-changing rules of daily and seasonal life that will form the backdrop of everything that happens. One of the most evident consequences will be hundreds of millions of climate refugees—even in rich nations—with both international and regional borders hardened accordingly. In parallel to these climate-related changes, the quality of algorithmically generated text and videos will continue to advance. For any rapidly quantifiable task, deep-learning algorithms can quickly learn to outperform any human.
Comparative Analysis of AttnGAN, DF-GAN and SSA-GAN

In computer vision, generating visuals based on the associated text is important, demanding, and interesting task. GANs (Generative Adversarial Networks) are the most powerful generative models for computer vision and natural language processing. GANs ensure that the synthesized image matches the input text semantically. The purpose of this paper is to compare three algorithms for producing images from text. These include Attentional Generative Adversarial Networks (AttnGAN), Deep-Fusion Generative Adversarial Networks (DF-GAN) and Semantic-Spatial Aware Generative Adversarial Networks (SSA-GAN). AttnGAN extends StackGAN++ by including attention in a multi-stage refining pipeline. DF-GAN, and SSA-GAN on the other hand are based on one stage architecture. DF-GAN incorporates Deep Fusion Block (DFBlock) that effectively fuses textual and visual information whereas SSA-GAN uses novel Semantic-Spatial Aware Convolutional Network (SSACN) block for efficient and deep text and image features fusion.
Product Information Browsing Support System Using Analytic Hierarchy Process

Large-scale e-commerce sites can collect and analyze a large number of user preferences and behaviors, and thus can recommend highly trusted products to users. However, it is very difficult for individuals or non-corporate groups to obtain large-scale user data. Therefore, we consider whether knowledge of the decision-making domain can be used to obtain user preferences and combine it with content-based filtering to design an information retrieval system. This study describes the process of building a product information browsing support system with high satisfaction based on product similarity and multiple other perspectives about products on the Internet. We present the architecture of the proposed system and explain the working principle of its constituent modules. Finally, we demonstrate the effectiveness of the proposed system through an evaluation experiment and a questionnaire.
Security and Privacy Aspects of AI, IoT, Big Data and Blockchain in Healthcare Industry

The rapid development of machine-learning techniques, especially artificial intelligence, has the potential to revolutionize medical practice, from resource allocation to complex disease diagnosis. On the other hand, ML brings with it a slew of threats and obstacles, including serious concerns about patient privacy. AI, IoT, Big Data, and Blockchain pose challenges to the notice and consent approach to data protection and privacy law and regulation, which is much more important when we talk about the healthcare sector. In this chapter, we talk about the various approaches, like K-anonymity, T-Closeness, HybrEx, and many more approaches, that are important to maintain individual privacy and also deeply investigate the pros and cons of the different techniques used in AI, IoT, Big Data, and Blockchain. We also suggest that recent methods of privacy preservation in big data will make it extremely difficult or impossible to achieve anonymization, avoidance of identity loss, or differential privacy; Mondrian approach techniques are used to publish big data and data stream and machine learning.
Sparse nonlinear dimension reduction algorithm

After we go into the big data age, the scale of data sets becomes more and more massive. The dimension of the data is also higher. For a task, there are a huge number of features in the ultra-high-dimensional data set that are irrelevant to the learning task. Manifold learning is proposed to reduce the dimensions of high-dimensional data sets and to use a manifold and other geometric tools to extract features from the data sets to obtain valid features and eliminate invalid features. However, the traditional manifold learning algorithms still have some unresolved problems by themselves. At present, the classic manifold learning algorithm can not deal with some large scale data sets. The main reason is that the cost of memory is very high during the algorithm construction. Therefore, this article makes some improvement for the basic manifold algorithm and proposes a sparse algorithm to reduce the memory cost and the time complexity. In experiments, we are using three sets of combinational data sets for comparative experiments. The experimental results show that the improved algorithm has obvious advantages in terms of time consumption.
Electronic Medical Record classification method based on LSTM of text word features dimensionality reduction

LSTM can effectively achieve text classification, but it cannot directly and specifically achieve efficient classification of Electronic Medical Record (EMR). Therefore, we propose an EMR classification method based on LSTM of text word features dimensionality reduction. Firstly, the EMR text word features are analyzed; secondly, the EMR topic words are extracted based on the text word features and LDA, the topic revealing probability distribution matrix of document words is generated, and then the dimensionality reduction of EMR is realized based on this matrix; finally, the LSTM-based targeted classification of EMR is achieved. The experiments show that the text word features weighting can effectively guide the topic extraction of LDA; the LSTM based on text word features dimensionality reduction has higher classification accuracy and precision rate and better computing performance compared with traditional LSTM.
Aircraft Intelligent Guidance Technology for Evasion and Penetration

In this paper, in order to improve the performance of aircraft evasion and penetration, an intelligent guidance algorithm is proposed by combining twin delayed deep deterministic policy gradient (TD3) and interfered fluid dynamical system (IFDS). First, the guidance model and simulation environment are established. On this basis, the basic guidance algorithm IFDS and the deep reinforcement learning method TD3 are introduced respectively, and the intelligent guidance algorithm is designed. Then, this paper compares and analyzes the simulated flight path of the aircraft obtained by the traditional guidance algorithm and the intelligent guidance algorithm. Finally, simulations show that the intelligent guidance algorithm can guide the aircraft to avoid obstacles and missiles, and demonstrate the advantages of the intelligent guidance algorithm.
Data in Archival Systems

An archive is a repository that holds documents of permanent historical information (a vast plurality of Itoms) that are dated and stored on different kinds of media in order that this information can be accessed and analyzed at some future date. The documents can be kept in analogue or digital form. In a computer archive the data are stored in digital form on some protected digital storage media.
CLZT: A Contrastive Learning Based Framework for Zero-Shot Text Classification

Zero-shot text classification aims to predict classes which never been seen in training stage. The lack of annotated data and huge semantic gap between seen and unseen classes make this task extremely hard. Most of existing methods employ binary classifier-based framework, and regard it as a relatedness (yes/no) prediction problem between instances and every candidate class. However, these methods only consider the similarities between one instance and one class at a time, and ignore semantic relations between candidate classes. To alleviate this problem, we propose a novel Contrastive Learning based Zero-shot Text classification framework (CLZT). With the contrastive optimized objects, we can capture the semantic relations between classes that need to be predicted and build more discriminative embeddings. Main experiment shows that our method achieves the best overall f1 score compared with baselines in three different datasets.
Types of novelty in the semiotics of creativity

The purpose of the article is to identify the main types of novelty from the standpoint of the semiotic theory of creativity. The methodological framework of the research is general semiotics, Friedrich Dessauers theory of potential space and the transcendentalist scheme of cognition and activity. Creativity is considered as the transformation of the rules of semiosis at the level of sensory perception, reason or mind in receptive (cognitive) or projective (actually creative) activity. The source and condition for the possibility of creativity is the heterogeneous structure of reality, ontological pluralism, fixed in the concept of three worlds by K.R. Popper and the four kingdoms by Fr. Dessauer. The subject, object and process of activity are singled out as the main environments for the emergence of novelty, in semiotic terminology presented as a shift in pragmatic, semantic and syntactic rules of sensory perception, reason and mind. The identified types of novelty are illustrated with examples from literary science fiction.
Weight-Guided Loss for Long-Tailed Object Detection and Instance Segmentation

The long-tailed characteristic leads to a significant performance drop for various models on long-tailed distribution datasets. Existing works mainly focus on mitigating the data shortage in tail classes at dataset level by data re-sampling, loss re-weighting or knowledge transfer from head to tail. While in this paper, we focus on another perspective which is also related to the performance drop: the gap between total dataset class number and training batch size. To address this issue, we propose a Weight-Guided (WG) loss which utilizes the classifier weights as auxiliary tail samples. It can be easily deployed to different models. By simply adding WG loss to Mask R-CNN with ResNet-50 backbone, we improve the performance by (i) 0.5 box AP and 0.4 mask AP on COCO dataset, (ii) 0.4 box and mask AP (1.8 mask AP for rare classes) on LVIS v1.0 dataset. Codes will be released.
Parallel DBSCAN-Martingale Estimation of the Number of Concepts for Automatic Satellite Image Clustering

The necessity of organising big streams of Earth Observation (EO) data induces the efficient clustering of image patches, deriving from satellite imagery, into groups. Since the different concepts of the satellite image patches are not known a priori, DBSCAN-Martingale can be applied to estimate the number of the desired clusters. In this paper we provide a parallel version of the DBSCAN-Martingale algorithm and a framework for clustering EO data in an unsupervised way. The approach is evaluated on a benchmark dataset of Sentinel-2 images with ground-truth annotation and is also implemented on High Performance Computing (HPC) infrastructure to demonstrate its scalability. Finally, a cost-benefit analysis is conducted to find the optimal selection of reserved nodes for running the proposed algorithm, in relation to execution time and cost.