Reviewerly/exHarmony huggingface dataset:
A chaos-based true random number generator based on OTA sharing and non-flipped folded Bernoulli mapping for high-precision ADC calibration

An operational amplifier sharing (op-amp sharing), non-flipped folded Bernoulli mapping chaos-based true random number generator (TRNG) is proposed in this paper. Based on the concept of op-amp sharing for non-flipped folded Bernoulli mapping, the number of stages is reduced from two stages to one, which improves power efficiency of the system. Among them, a novel transconductance enhancement recycling amplifier is adopted for power reduction. The entire system has been implemented in a standard 180 nm process with power supply of 1.2V, and the output speed of random number is 500k Bits/s with power consumption of only 4.68 μW. The TRNG system is verified over a wide range of process and temperature variations, including TT corner 25 °C, FF corner 0 °C, and SS corner 85 °C with post-layout simulation. All the generated random bits pass National Institute of Standards and Technology (NIST) SP800-22 randomness tests successfully.
Improved Boosted Regression Forests Through Non-Greedy Tree Optimization

Regression forests (ensembles of trees) are considered as the leading off-the-shelf method for regression. One of the main approaches of constructing such forests is based on boosting. However, majority of the current boosting implementations employ an axis-aligned tree as a base learner, where each decision node tests for a single feature. Moreover, such trees are usually trained by greedy top-down algorithms such as CART which is shown to be suboptimal. We instead use oblique trees, where each decision node tests for a linear combination of features and train them with the recently proposed non-greedy tree learning method-Tree Alternating Optimization (TAO). We embed the TAO algorithm into the boosting framework and show its effectiveness in the regression setting. We show that it produces much better forests than other types of tree ensembling methods in terms of error, model size and inference time. The result has an immense practical impact on various applications such as in signal processing, data mining, computer vision, etc.
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach Using <i>ℓ</i> <sub> <i>p</i> </sub>-Regression for Outlier Resistance

Low-rank tensor completion (LRTC) that aims to restore the latent clean data from an incomplete and/or degraded observation, shows promising results in ubiquitous tensorial data completion applications. Most tensor completion approaches are vulnerable to outliers since their derivations are based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> -space to be robust against Gaussian noise. In this work, to tackle this issue, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> -regression <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(0 &lt; p &lt; 2)$ </tex-math></inline-formula> is employed to achieve outlier resistance, where a factored form of tensor train (TT)-format representation is regularized by the low-TT-rank prior to exploit the inter-fibers correlation. On the basis of that, an effective iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> -regression TT completion method (referred to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> -TTC) is proposed, with the advantage of not requiring the hard-to-determine user-defined weights in TT rank model. Extensive experiment results are presented to demonstrate the outlier resistance of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> -TTC, and showing the effective and superior performance in both bistatic MIMO radar localization and color image inpainting and denoising, compared with state-of-the-art tensor completion approaches.
Mapping the barriers of AI implementations in the public distribution system: The Indian experience

With increasing digitization, private organizations have started adopting modern technology to improve the effectiveness and transparency of their system. In countries like India, where most public services are under government control, technology adoption is nascent due to various obstacles. The study considered artificial intelligence (AI) the most popular technology and identified 18 critical adoption barriers in India's public distribution systems (PDS). The study is further extended to find the contextual relationship among barriers using interpretative structural modeling (ISM) and prioritize them using the analytical network process (ANP) method. The study identifies lack of trust in technology, lack of AI literacy, and political issues as significant barriers to AI adoption in PDS. The hybrid methodology used in this study proposed five different strategies for effective and smooth implementation of AI in PDS, which would help the policymaker plan the same.
Multilabel Feature Selection: A Local Causal Structure Learning Approach

Multilabel feature selection plays an essential role in high-dimensional multilabel learning tasks. Existing multilabel feature selection approaches mainly either explore the feature-label and feature-feature correlations or the label-label and feature-feature correlations. A few of them are able to deal with all three types of correlations simultaneously. To address this problem, in this article, we formulate multilabel feature selection as a local causal structure learning problem and propose a novel algorithm, M2LC. By learning the local causal structure of each class label, M2LC considers three types of feature relationships simultaneously and is scalable to high-dimensional datasets as well. To tackle false discoveries caused by the label-label correlations, M2LC consists of two novel error-correction subroutines to correct those false discoveries. Through local causal structure learning, M2LC learns the causal mechanism behind data, and thus, it can select causally informative features and visualize common features shared by class labels and specific features owned by an individual class label using the learned causal structures. Extensive experiments have been conducted to evaluate M2LC in comparison with the state-of-the-art multilabel feature selection algorithms.
Bibliometric Analysis in the Field of Quantum Technology

The second quantum technological revolution started around 1980 with the control of single quantum particles and their interaction on an individual basis. These experimental achievements enabled physicists, engineers, and computer scientists to utilize long-known quantum features—especially superposition and entanglement of single quantum states—for a whole range of practical applications. We use a publication set of 54,598 papers from Web of Science, published between 1980 and 2018, to investigate the time development of four main subfields of quantum technology in terms of numbers and shares of publications, as well as the occurrence of topics and their relation to the 25 top contributing countries. Three successive time periods are distinguished in the analyses by their short doubling times in relation to the whole Web of Science. The periods can be characterized by the publication of pioneering works, the exploration of research topics, and the maturing of quantum technology, respectively. Compared to the USA, China’s contribution to the worldwide publication output is overproportionate, but not in the segment of highly cited papers.
Developing a System for Automatic Detection of Books

Managing a large number of books is a challenging task for humans. Our proposed system would assist librarians in efficiently sorting books and keeping an up-to-date inventory of huge books. Many methods for detecting only the book spine in a picture have been created. However, not every strategy is perfect. So, this paper presents an automated book detection system that can quickly identify books by reading their titles. Firstly, images are captured from random position. Secondly books in the image are segmented by using Canny Edge Detection and line segmentation. After book spine segmentation stage, an OCR engine is applied to the segmented images to extract book titles and ready for recognition of next stage. Then maximum string matching score is calculated with extracted text for all data of a column of database where all book’s Title are enlisted. For which index of database system will get the maximum score, book category information will be extracted of that index.
Illuminating diverse neural cellular automata for level generation

We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels. While NCAs have so far only been trained via supervised learning, we present a quality diversity (QD) approach to generating a collection of NCA level generators. By framing the problem as a QD problem, our approach can train diverse level generators, whose output levels vary based on aesthetic or functional criteria. To efficiently generate NCAs, we train generators via Covariance Matrix Adaptation MAP-Elites (CMA-ME), a quality diversity algorithm which specializes in continuous search spaces. We apply our new method to generate level generators for several 2D tile-based games: a maze game, Sokoban, and Zelda. Our results show that CMA-ME can generate small NCAs that are diverse yet capable, often satisfying complex solvability criteria for deterministic agents. We compare against a Compositional Pattern-Producing Network (CPPN) baseline trained to produce diverse collections of generators and show that the NCA representation yields a better exploration of level-space.
Fossil brachiopod identification using a new deep convolutional neural network

• We propose TCNN mode to automatically identify brachiopod fossils at species level. • TCNN can achieve a high identification accuracy using a small training data set of brachiopods. The identification of brachiopods requires specialist knowledge held by a limited number of researchers and is very time-consuming. The new technique of deep learning by artificial intelligence offers promising tools to break these shackles to develop computer automatic identification. However, we found that the traditional convolution neural network is not sufficient to automatically identify brachiopod species. Thus, we propose a new tailored Transpose Convolutional Neural Network (TCNN) in order to automatically identify brachiopod fossils with high efficiency. In this network, we add an “upsampling” Transpose Convolutional Layer and synthesize the data of this layer with the data of a Convolutional Layer to fully mix the small and large scales features extracted by the neural network. Compared with the traditional Convolution Neural Network (CNN), the Transpose Convolutional Neural Network (TCNN) can achieve a high identification accuracy using a smaller training data set of images of brachiopods. Results from this study show that the TCNN can achieve 98%, 98% and 97% identification accuracy respectively, with training data sets of 400 images of 3 species, 484 images of 4 species and 630 images of 5 species. In contrast, the traditional CNN can achieve only a low identification accuracy (67%) with 400 images of 3 species and requires 3000 images per 3 species to achieve a 95% identification accuracy. For most of brachiopod species, it is almost an impossible task to collected thousands of samples and as more brachiopod species are fitted into automatic identification, it is significant to have a reliable network which can achieve high accuracy on a small data set. In summary, the TCNN is a more efficient neural network that could be better applied to automatically identify brachiopod fossils.
Deep reinforcement learning based multi-AUVs cooperative decision-making for attack–defense confrontation missions

This paper mainly focuses on using deep reinforcement learning (RL) to deal with the cooperative decision-making problem of multiple Autonomous Underwater Vehicles (multi-AUVs) under limited perception and limited communication in attack–defense confrontation missions. Firstly, a novel Coding-Convolutional Network (CCN) is proposed, which can encode the raw sensor information of AUVs and extract representative features from limited perception. Secondly, utilizing the approximator of the state value function and policy function composed of CCN and fully connected network, we put forward a centralized decision-making architecture for multi-AUVs system based on actor–critic framework and give the corresponding algorithm combined with asynchronous training. Moreover, with respect to multi-AUVs attack–defense confrontation tasks, we develop a simulation platform to train and evaluate the proposed algorithms under different parameters. It can be directly observed from the visualization of the simulation that the algorithm enables multi-AUVs to complete the attack–defense confrontation missions excellently and generate some interesting collaborative behaviors. The average winning probability data of the simulation results also indicate that the designed method is feasible for multi-AUVs to achieve cooperative decision-making in attack–defense confrontation missions.
SDTP: Semantic-Aware Decoupled Transformer Pyramid for Dense Image Prediction

Although transformer has achieved great progress on computer vision tasks, the scale variation in dense image prediction is still the key challenge. Few effective multi-scale techniques are applied in transformer and there are two main limitations in the current methods. On the one hand, self-attention module in vanilla transformer fails to sufficiently exploit the diversity of semantic information because of its rigid mechanism. On the other hand, it is difficult to build attention and interaction among different levels due to the heavy computational burden. To alleviate this problem, we first revisit multi-scale problem in dense prediction, verifying the significance of diverse semantic representation and multi-scale interaction, and exploring the adaptation of transformer to pyramidal structure. Inspired by these findings, we propose a novel Semantic-aware Decoupled Transformer Pyramid (SDTP) for dense image prediction, consisting of Intra-level Semantic Promotion (ISP), Cross-level Decoupled Interaction (CDI) and Attention Refinement Function (ARF). ISP explores the semantic diversity in different receptive space through more flexible self-attention strategy. CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation. Besides, ARF is further added to refine the attention in transformer. Experimental results demonstrate the validity and generality of the proposed method, which outperforms the state-of-the-art by a significant margin in dense image prediction tasks. Furthermore, the proposed components are all plug-and-play, which can be embedded in other methods.
Investigating Entropy for Extractive Document Summarization

Automatic text summarization aims to cut down readers time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document summary generated by an algorithm, and selecting sentences that capture the essence of a document is the primary goal of extractive document summarization. In this paper, we employ Shannon entropy to capture informativeness of sentences. We employ Non-negative Matrix Factorization (NMF) to reveal probability distributions for computing entropy of terms, topics, and sentences in latent space. We present an information theoretic interpretation of the computed entropy, which is the bedrock of the proposed E-Summ algorithm, an unsupervised method for extractive document summarization. The algorithm systematically applies information theoretic principle for selecting informative sentences from important topics in the document. The proposed algorithm is generic and fast, and hence amenable to use for summarization of documents in real time. Furthermore, it is domain-, collection-independent and agnostic to the language of the document. Benefiting from strictly positive NMF factor matrices, E-Summ algorithm is transparent and explainable too. We use standard ROUGE toolkit for performance evaluation of the proposed method on four well known public data-sets. We also perform quantitative assessment of E-Summ summary quality by computing its semantic similarity w.r.t the original document. Our investigation reveals that though using NMF and information theoretic approach for document summarization promises efficient, explainable, and language independent text summarization, it needs to be bolstered to match the performance of deep neural methods.
Spam message detection using Danger theory and Krill herd optimization

Due to proliferation of online posts and rise in the active social media users, fraudulent activities related with spam messages have taken a spike drift. Spam is an activity by which hackers use electronic messaging system to unsolicited messages in mass content to unknown users. It can be also taken as one of the major attraction of attackers in the form of short message service (SMS) messages. Spam messages can be categorized in different categories such as business opportunity spam, trending topic spam, banking services spam etc. These problems can be tackled by confirming to the actions taken by users towards these messages. There is an urgent explicit need of practical medium in order to assist the users against these spam messages. This paper proposes a novel SMS spam filtering model based on Danger theory of Artificial Immune System (AIS). Several feature extraction and selection techniques have been applied for optimizing the algorithm and claiming an admissible accuracy. This paper uses a biologically inspired algorithm named Krill herd Optimization (KHO) for the task of feature selection and various optimization functions like Quing function, Sumsquare function, Levy function etc. are applied for enhancing its performance. The Dendritic Cell Algorithm (DCA) is also incorporated with KHA as an added advantage towards achieving efficiency. Comparative results between Dendritic Cell Algorithm (DCA) with KHA and other spam filtering models have been shown in comparison with several state-of-the-art machine learning classifiers. The algorithms have been experimented by using varied optimization functions illustrated using visualization tools and results have been validated in the paper. The obtained results demonstrate an admissible accuracy of 96% that is calculated using different information retrieval metrics using recall, F-measure and precision.
Overview of the ImageCLEF 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

This paper presents an overview of the ImageCLEF 2021 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2021. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2021, the 19th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a nature coral task about segmenting and labeling collections of coral reef images, (iii) an Internet task addressing the problems of identifying hand-drawn and digital user interface components, and (iv) a new social media aware task on estimating potential real-life effects of online image sharing. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 38 groups submitting more than 250 runs.
Inheritance Attention Matrix-Based Universal Adversarial Perturbations on Vision Transformers

Universal Adversarial Perturbations (UAPs), which is type of image-agnostic adversarial attack, has been deeply investigated for Convolutional Neural Networks due to its high efficiency. On the other hand, as an architecture based on self-attention mechanism, Vision Transformers (ViTs) have boomed and been widely applied to solve various computer vision problems since most recent years. In this letter, we delve into the robustness of ViTs against universal adversarial attack and propose an inheritance attention matrix-based UAPs (IAM-UAP). Specifically, we introduce the inheritance attention weight matrix (IAM), which represents the integration of global information of the input patch sequence. Further, we propose a perturbation optimization objective based on IAM to confuse the global information integration of ViTs. The empirical results confirm the attacking capability of IAM-UAP on ViTs with a moderate attacking rate. In addition, we also disclose that the patch size of ViTs is an latent factor influencing the robustness against the universal attack.
Degradation Prediction of PEMFCs Using Stacked Echo State Network Based on Genetic Algorithm Optimization

Durability is considered as one of the main technical obstacles to the large-scale commercialization of proton exchange membrane fuel cells (PEMFCs), which can be e↵ectively improved through prognostics prediction techniques.This paper proposes a stacked echo state network (ESN) based on the genetic algorithm (GA) to predict the future degradation trend of PEMFCs.By alternately using the projection layer and the encoding layer, the proposed method can make full use of the temporal kernel property of the ESN to encode the multi-scale and multi-level dynamics of the stack voltage, thereby obtaining more robust generalization performance and higher accuracy than existing methods.Specifically, a stack voltage time series of PEMFCs is projected into the high-dimensional echo state space of the reservoir.Then, an auto-encoder projects the echo state representation into the low-dimensional feature space.After that, the genetic algorithm is utilized to optimize the hyperparameters of the developed model.Based on two open-source datasets of PEMFCs with di↵erent accelerated test conditions, this paper systematically tested the proposed degradation prediction methods based on di↵erent model structures.Test results demonstrate that the proposed method is superior to traditional prediction methods in terms of accuracy and generalization performance.