Reviewerly/exHarmony huggingface dataset:
Target group distribution pattern analysis with bagged convolutional neural networks for UAV distribution pattern identification

Purpose The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features. Design/methodology/approach This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns. Findings Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy. Originality/value Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.
Research on Educational Informatization Platform Based on Cloud Computing

In the information age, educational informatization is the inevitable choice to promote the progress and development of contemporary education and cultivate innovative talents. Today, with the continuous development of information technology, such as Internet of things, cloud computing, big data, mobile communication, ubiquitous network and so on, all kinds of intelligent terminal devices (mobile phones, computers, tablets and so on) have become more and more popular, and people are beginning to step into the intelligent internet life. The change of life style not only provides a broad base of population and resources for the development of information education platform, but also puts forward new challenges for the information education platform to be able to serve education and to adapt to the healthy development of life in the future. Combined with the current situation of educational informatization, we apply cloud computing to the information process of basic education in China. By learning the concept and characteristics, the three-tier architecture, the advantages and key technologies of cloud computing, the focus of this research is to construct a resource-sharing and cloud computing-based information education platform structure. The results of this research have a certain guiding role for the development of education information. It plays an active role for optimizing the structure of education, rationally configuring educational resources, reducing the gap between urban and rural education, improving the level of education and teaching management and promoting the information education.
Adaptive black widow optimisation algorithm for data clustering

Generally, local search approaches are better than population-based approaches in exploiting the search space and worse for exploration. Recently, the black widow optimisation (BWO) algorithm was p...
Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

Abstract Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

Ensembles of neural networks achieve superior performance compared to stand-alone networks not only in terms of predictive performance, but also uncertainty calibration and robustness to dataset shift. Diversity among networks is believed to be key for building strong ensembles, but typical approaches, such as \emph{deep ensembles}, only ensemble different weight vectors of a fixed architecture. Instead, we propose two methods for constructing ensembles to exploit diversity among networks with \emph{varying} architectures. We find that the resulting ensembles are indeed more diverse and also exhibit better uncertainty calibration, predictive performance and robustness to dataset shift in comparison with deep ensembles on a variety of classification tasks.
FantasyCoref: Coreference Resolution on Fantasy Literature Through Omniscient Writer’s Point of View

This paper presents a new corpus and annotation guideline for a novel coreference resolution task on fictional texts, and analyzes its unique characteristics.FantasyCoref contains 211 stories of Grimms' Fairy Tales and 3 other fantasy literature annotated in the omniscient writer's point of view (OWV) to handle distinctive aspects in this genre.This task is more challenging than general coreference resolution in two ways.First, documents in our corpus are 2.5 times longer than the ones in OntoNotes, raising a new layer of difficulty in resolving long-distant referents.Second, annotation of literary styles and concepts raise several issues which are not sufficiently addressed in the existing annotation guidelines.Hence, considerations on such issues and the concept of OWV are necessary to achieve high interannotator agreement (IAA) in coreference resolution of fictional texts.We carefully conduct annotation tasks in four stages to ensure the quality of our annotation.As a result, a high IAA score of 87% is achieved using the standard coreference evaluation metric.Finally, state-of-the-art coreference resolution approaches are evaluated on our corpus.After training with our annotated dataset, there was a 2.59% and 3.06% improvement over the model trained on the OntoNotes dataset.Also, we observe that the portion of errors specific to fictional texts declines after the training.
A Coinductive Version of Milner’s Proof System for Regular Expressions Modulo Bisimilarity

By adapting Salomaa’s complete proof system for equality of regular expressions under the language semantics, Milner (1984) formulated a sound proof system for bisimilarity of regular expressions under the process interpretation he introduced. He asked whether this system is complete. Proof-theoretic arguments attempting to show completeness of this equational system are complicated by the presence of a non-algebraic rule for solving fixed-point equations by using star iteration. We characterize the derivational power that the fixed-point rule adds to the purely equational part Mil- of Milner’s system Mil: it corresponds to the power of coinductive proofs over Mil- that have the form of finite process graphs with the loop existence and elimination property LEE. We define a variant system cMil by replacing the fixed-point rule in Mil with a rule that permits LEE-shaped circular derivations in Mil- from previously derived equations as a premise. With this rule alone we also define the variant system CLC for combining LEE-shaped coinductive proofs over Mil-. We show that both cMil and CLC have proof interpretations in Mil, and vice versa. As this correspondence links, in both directions, derivability in Mil with derivation trees of process graphs, it widens the space for graph-based approaches to finding a completeness proof of Milner’s system.
Artificial Intelligence in Education (AIEd): from Applied Problems of Informatization to Studying a Topic in a Computer Science Course

The article provides an overview of two main possibilities of using artificial intelligence in education: as new educational tools and as the development of the theoretical and practical foundations of artificial intelligence in the school computer science course. A comparison of approaches to the use in education and the study of artificial intelligence issues at the level of secondary general education in different countries is given. The development of the topic at all levels of general education is considered.
Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

Although label distribution learning has made significant progress in the field of face age estimation, unsupervised learning has not been widely adopted and is still an important and challenging task. In this work, we propose an unsupervised contrastive label distribution learning method (UCLD) for facial age estimation. This method is helpful to extract semantic and meaningful information of raw faces with preserving high-order correlation between adjacent ages. Similar to the processing method of wireless sensor network, we designed the ConAge network with the contrast learning method. As a result, our model maximizes the similarity of positive samples by data enhancement and simultaneously pushes the clusters of negative samples apart. Compared to state-of-the-art methods, we achieve compelling results on the widely used benchmark, i.e., MORPH.
Improvement of nuclear facilities DNP3 protocol data transmission security using super encryption BRC4 in SCADA systems

Data transmissions using the DNP3 protocol over the internet in SCADA systems are vulnerable to interruption, interception, fabrication, and modification through man-in-the-middle (MITM) attacks. This research aims to improve the security of DNP3 data transmissions and protect them from MITM attacks.This research describes a proposed new method of improving DNP3 security by introducing BRC4 encryption. This combines Beaufort encryption, in which plain text is encrypted by applying a poly-alphabetic substitution code based on the Beaufort table by subtracting keys in plain text, and RC4 encryption, a stream cipher with a variable-length key algorithm. This research contributes to improving the security of data transmission and accelerating key generation.Tests are carried out by key space analysis, correlation coefficient analysis, information entropy analysis, visual analysis, and time complexity analysis.The results show that to secure encryption processes from brute force attacks, a key of at least 16 characters is necessary. IL data correlation values were IL1 = -0.010, IL2 = 0.006, and IL3 = 0.001, respectively, indicating that the proposed method (BRC4) is better than the Beaufort or RC4 methods in isolation. Meanwhile, the information entropy values from IL data are IL1 = 7.84, IL2 = 7.98, and IL3 = 7.99, respectively, likewise indicating that the proposed method is better than the Beaufort or RC4 methods in isolation. Both results also show that the proposed method is secure from MITM attacks. Visual analysis, using a histogram, shows that ciphertext is more significantly distributed than plaintext, and thus secure from MITM attacks. The time complexity analysis results show that the proposed method algorithm is categorized as linear complexity.
Introduction to quantum error correction and fault tolerance

These lecture notes from the 2019 Les Houches Summer School on 'Quantum Information Machines' are intended to provide an introduction to classical and quantum error correction with bits and qubits, and with continuous variable systems (harmonic oscillators). The focus on the latter will be on practical examples that can be realized today or in the near future with a modular architecture based on superconducting electrical circuits and microwave photons. The goal and vision is 'hardware-efficient' quantum error correction that does not require exponentially large hardware overhead in order to achieve practical and useful levels of fault tolerance and circuit depth.
Privacy-Preserving Attribute-Based Access Control in Education Information Systems

Nowadays, the more growth of data science, the more information is collected and analyzed for a variety of educational purposes. Information systems in government organizations, corporations, and companies often tend to expand and integrate multi-platform application systems with shared databases during operation to bring convenience for customers. Therefore, the access control and privacy protection are considered as one of the top concerns. In this paper, we propose an Attribute-based Access Control (ABAC) solution combined with protecting the privacy of data attributes when querying. It is an approach to attribute-based access management that allows attributes of subjects and objects to take part in authorization decisions that deliver more efficiency, flexibility, granularity, scalability, and security. To enhance privacy protection for access control, we suggest mechanisms to preserve the privacy of attributes such as data masking, anonymization, and encryption in some different contexts. We also provide an implementation that demonstrates the feasibility of the ABAC model for storing enforcement policies on the MongoDB database.
A Lexicon for Profane and Obscene Text Identification in Bengali

Bengali is a low-resource language that lacks tools and resources for profane and obscene textual content detection.Until now, no lexicon exists for detecting obscenity in Bengali social media text.This study introduces a Bengali obscene lexicon consisting of over 200 Bengali terms that can be considered filthy, slang, profane or obscene.A semiautomatic methodology is presented for developing the obscene lexicon that leverages an obscene corpus, word embedding, and part-ofspeech (POS) taggers.The developed lexicon achieves coverage of around 0.85 for obscene and profane content detection in an evaluation dataset.The experimental results imply that the developed lexicon is effective at identifying obscenity in Bengali social media content.
Blurring kernel extraction and super-resolution image reconstruction based on style generative adersarial networks

The point spread function (PSF) is the main index used to evaluate the imaging resolution and further improve the quality of an optical image. Its measurement is significant for system development and pattern recognition. However, the precision of current measurement methods is low owing to a complicated modelling process, the pairing of various camera parameters, and disturbances by external factors. In this paper, we propose a method to extract blurring kernels and reconstruct super-resolution images based on style generative adversarial networks (StyleGANs). First, an improved StyleGAN model is introduced and an ideal blurry image generation model based on StyleGAN is trained to obtain a series of ideal Gaussian light-source images with a regular Airy disk; as the intensity distribution in the Airy disk is closer to its theoretical distribution. Second, the blurring kernels are extracted at different depth positions from the generated Gaussian light-source images to replace the PSF. This allows the evaluation of the blurry property of the optical system and effectively avoids the enrolment of noise in parameter identification or curve fitting in PSF representation. Finally, both the blurring kernels are used to deblur the blurry images of the Gaussian light source with a single wavelength and the blurry images of microbeads under visual light illumination at different depths based on the learnable convolutional half-quadratic splitting and convolutional preconditioned Richardson (LCHQS-CPCR) model. Compared to other image deblurring methods, our proposed method achieves high-resolution image reconstruction with blurring kernels from the generated optical images of the Gaussian light source.
Adversarial Examples Make Strong Poisons

The adversarial machine learning literature is largely partitioned into evasion attacks on testing data and poisoning attacks on training data. In this work, we show that adversarial examples, originally intended for attacking pre-trained models, are even more effective for data poisoning than recent methods designed specifically for poisoning. Our findings indicate that adversarial examples, when assigned the original label of their natural base image, cannot be used to train a classifier for natural images. Furthermore, when adversarial examples are assigned their adversarial class label, they are useful for training. This suggests that adversarial examples contain useful semantic content, just with the ``wrong'' labels (according to a network, but not a human). Our method, adversarial poisoning, is substantially more effective than existing poisoning methods for secure dataset release, and we release a poisoned version of ImageNet, ImageNet-P, to encourage research into the strength of this form of data obfuscation.
A multi-target tracking algorithm suitable for road scenes

Traditionally, multiple target tracking and target detection are carried out using separate models, which leads to non-sharing of features and inability to track in real time. Now with the rapid development of target detection and re-identification (re-ID) technology, this paper proposes a conceptually simple and effective joint detection and tracking model. Its detection module and tracking module are isomorphic networks, which are based on anchor- The CenterNet network of the free mechanism is used as the backbone network, and all the convolutional layers in the up sampling are replaced with deformable convolutions, so as to adapt to the geometric deformation of different tracking targets. Second, the tracking module calculates the re-ID feature for each pixel, outputs the calculated similarity between the detection frame and the trajectory frame, uses the Hungarian algorithm and the correlation metric of the measurable bounding box overlap, and uses the Kalman algorithm to perform data association frame by frame. The trajectory filling strategy is used to balance the number of missed and false positives. Finally, experiments are carried out on the open target tracking data set, which proves the superiority and effectiveness of the proposed model, and realizes the tracking of pedestrians, vehicles, bicycles and tricycles in four target categories.
CNN-RNN Based Handwritten Text Recognition

At present most of the scripts are handwritten due to the ease of using a pen tip in place of a keyboard, hence errors are common due to illegibility of the human handwriting. To avoid this problem handwriting recognition is essential. Offline handwritten Text recognition (OHTR) has become one of the major areas of research in recent times because of the need to eliminate errors due to misinterpretation of handwritten text and the need for automation to improve efficiency. The application of this system can be seen in fields like handwritten application interpretations, postal address recognition, signature verification, and various others. In this project, offline handwritten Text recognition is performed using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural network (RNN) and Connectionist Temporal Classification (CTC). The neural network is trained and tested using the IAM database containing handwritten English text. The implementation of this work is done using image segmentation-based handwritten text recognition where OpenCV is used for performing image processing and TensorFlow is used for training and text recognition. This whole system is developed using python and the output is displayed in a word file.
QoE-Aware Edge Computing Through Service Function Chaining

Mobile multimedia Internet services, such as mobile virtual reality (VR), request for advanced quality of experience (QoE) provision. In the future Internet, such a QoE provision will be leveraged through edge computing, which can be facilitated through advanced programmable frameworks, such as service function chaining (SFC). To this direction, we provide in this work, the design, development, and experimentation of a QoE-aware edge computing framework using SFC. In particular, the QoE-aware edge computing is provided by a network function virtualization-enabled SFC architecture. The SFC architecture provides adaptive media services through programmable service functions (SFs). Our solution provides multimedia dynamic adaptive streaming HTTP services with QoE under certain network conditions. The proposed QoE-aware edge computing is guaranteed with both data and computation offloading techniques, including a required QoE-aware edge resource management. Specific use cases are designed in order to test our solution that shows better QoE in comparison with the baseline use case, where neither data nor computational offloading is considered.
The Application of Computer Speech Recognition Technology in Oral English Teaching

With the rapid development of computer speech recognition technology, how to better apply it to oral English teaching has become a concern of majority English Teachers. This paper probes into the operation method of speech recognition technology and its application in oral English teaching, and emphatically introduces several main forms of the application of speech recognition technology in the field of oral English teaching. It including: oral assessment, learning records, intelligent platform, multimedia information retrieval, etc., which expounds the practical significance of the application of speech recognition technology in the field of oral English teaching. The oral English teaching mode based on speech recognition can improve students' oral English ability more effectively, which helps students to build up confidence, improve their interest in learning and achieve good learning results.
Fast algorithms for solving the Hamilton Cycle problem with high probability

We study the Hamilton cycle problem with input a random graph G ~ G(n,p) in two different settings. In the first one, G is given to us in the form of randomly ordered adjacency lists while in the second one, we are given the adjacency matrix of G. In each of the two settings we derive a deterministic algorithm that w.h.p. either finds a Hamilton cycle or returns a certificate that such a cycle does not exist for p = p(n) ≥ 0. The running times of our algorithms are O(n) and respectively, each being best possible in its own setting.* The full version of the paper can be accessed at https://arxiv.org/abs/2111.14759