Reviewerly/exHarmony huggingface dataset:
Design of quantum neuron model for quantum neural networks

Quantum EngineeringVolume 3, Issue 3 e75 RESEARCH ARTICLE Design of quantum neuron model for quantum neural networks Yao Zhang, Yao Zhang School of Computing and Communications, Lancaster University, Lancaster, UKSearch for more papers by this authorQiang Ni, Corresponding Author Qiang Ni q.ni@lancaster.ac.uk School of Computing and Communications, Lancaster University, Lancaster, UK Correspondence Qiang Ni, School of Computing and Communications, Lancaster University, Lancaster, UK. Email: q.ni@lancaster.ac.ukSearch for more papers by this author Yao Zhang, Yao Zhang School of Computing and Communications, Lancaster University, Lancaster, UKSearch for more papers by this authorQiang Ni, Corresponding Author Qiang Ni q.ni@lancaster.ac.uk School of Computing and Communications, Lancaster University, Lancaster, UK Correspondence Qiang Ni, School of Computing and Communications, Lancaster University, Lancaster, UK. Email: q.ni@lancaster.ac.ukSearch for more papers by this author First published: 24 August 2021 https://doi.org/10.1002/que2.75 Funding information: Huawei Technologies, HIRP HO2018105132 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Quantum neural network is a network which is to be composed of several quantum neurons, which is required to have a certain topological structure. In this article, we propose a new method to encode classical input data into quantum states to represent a quantum neuron, which has a low-complexity form and can be easily utilized to construct a quantum neural network. The numerical simulation of two classification tasks with the proposed quantum neuron model is conducted to show its validity. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Volume3, Issue3September 2021e75 RelatedInformation
Methods for Decentralized Identities: Evaluation and Insights

Recent technological shifts have pressured businesses to reshape the way they operate and transact. At the hart of this restructuring, identity management established itself as an essential building block in both B2C and B2B business models. Trustworthy identities may refer to customers, businesses, suppliers or assets, and enable trusted communications between different actors. Unfortunately, traditional identity management systems rely on centralized architectures and trust in third party services. With the inception of blockchain technology, new methods for managing identity emerged, which promise better decentralization and self-sovereignty. This paper provides an evaluation of a selection of distributed identity methods, and analyzes their properties based on the categorization specified in the W3C recommendation rubric.
Internet Traffic Classification Using an Ensemble of Deep Convolutional Neural Networks

Network traffic classification (NTC) has attracted considerable attention in recent years. The importance of traffic classification stems from the fact that data traffic in modern networks is extremely complex and ever-evolving in different aspects, e.g. volume, velocity and variety. The inherent security requirements of Internet-based applications also highlights further the role of traffic classification. Gaining clear insights into the network traffic for performance evaluation and network planning purposes, network behavior analysis, and network management is not a trivial task. Fortunately, NTC is a promising technique to gain valuable insights into the behavior of the network, and consequently improve the network operations. In this paper, we provide a method based on deep ensemble learning to classify the network traffic in communication systems and networks. More specifically, the proposed method combines a set of Convolutional Neural Network (CNN) models into an ensemble of classifiers. The outputs of the models are then combined to generate the final prediction. The results of performance evaluation show that the proposed method provides an average accuracy rate of 98% for the classification of traffic (e.g., FTP-DATA, MAIL, etc.) in the Cambridge Internet traffic dataset.
Information literacy: a desideratum of the 21st century

Purpose The research surveyed the competency of information literacy of senior high school students in Fiji. This is to evaluate the strong predictors of information literacy. Design/methodology/approach The study adopted a survey research design whereby a five-point Likert scale self-reporting questionnaire was administered to Year 12 and Year 13 secondary school students. The data were analysed using the Statistical Package for the Social Sciences (SPSS) software-descriptive statistics of calculating the mean and standard deviation, a correlation and linear regression analysis to deduce the strong predictors of information literacy. Findings The study showed that 81% of the students surveyed were average to above average information literate. The strong predictors of information literacy were the ability of an individual to collaborate and share safely online, the ability to share files securely and the ability to access the credibility of any resource assessed on the digital platform. Research limitations/implications The current study evaluates information literacy of a cohort – stating how information literate the participants are, comprehending the strong predicators of information literacy so that there is an appropriate and effective implementation of interventions for the desired improvements. Practical implications The results can be used to improve information literacy of students at all levels of education in the Fiji Islands. Social implications If the youths are information literate they will be able to effectively contribute towards the development of their economy. Since the work environment today is technology oriented and involves a lot of information, being information literate means knowing how to use the information and differentiate between good and bad information. Hence, contributing effectively towards whatever task is performed. Originality/value This research if the first ever research done on evaluating the information literacy of individuals in Fiji.
VNLP: Visible natural language processing

In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.
Coupled Patch Similarity Network FOR One-Shot Fine-Grained Image Recognition

One-shot fine-grained image recognition (OSFG) aims to distinguish different fine-grained categories with only one training sample per category. Previous works mainly focus on learning a global feature representation through only a using single similarity metric branch, which is unsuitable for OSFG to effectively capture subtle and local differences under limited supervision. In this work, we propose a Coupled Patch Similarity Network (CPSN) for OSFG. Firstly, we propose a Feature Enhancement Module (FEM) to extract more discriminative features of the fine-grained samples. Then, we develop two coupled and symmetrical branches to capture discriminative parts of the samples and reduce the deviation of the distance metric. For each branch, we design a Patch Similarity Module (PSM) to calculate the patch similarity map for the sample pair. Especially, a Patch Weight Generator (PWG) is proposed to generate the patch weight map, which indicates the degree of importance for each position in the patch similarity map, so that the model can focus on diverse and informative parts. We analyze the effect of the different components in the proposed network, and extensive experimental results demonstrate the effectiveness and superiority of the proposed method on two fine-grained benchmark datasets.
Information Fingerprint for Secure Industrial Big Data Analytics

Data-driven artificial intelligence (AI) models have been widely used in industrial systems helping big data analytic due to its convenience and flexibility. However, adversarial attacks have the ability to mislead AI models to make incorrect predictions just by adding specific perturbation to actual samples. With the high integration of industrial systems and information technology, the reliability and safety of AI models in industrial systems have been seriously threatened. In the article, fault diagnosis models and soft sensing models rely on AI technology are verified to be vulnerable facing adversarial attack. To this end, the concept of information fingerprint for industrial data is introduced to distinguish actual samples from adversarial samples with small perturbation. With fault diagnosis models and soft sensing models as the background, information fingerprint exaction networks based on deep learning is developed to extract the information fingerprint for further analysis. It utilizes supervised contrastive pretraining and unsupervised training to realize parameter learning for the structure of siamese neural network and autoencoder. Finally, the effectiveness and feasibility of the proposed information fingerprint for adversarial sample detection are verified in two industrial benchmark cases.
Perceptions of police training needs in cyber-crime

A common observation in the literature on cyber-crime policing is the need for more training. However, there is little detail of who within the police organisation requires training and what type of training may be needed. Based on survey and interview data from three specialist cyber-crime units in Australia, this article identifies that ‘lack of training’ is likely to have distinct meanings for different groups within the police: (a) front-line officers, (b) higher management, (c) generalist investigators, and (d) specialist investigators and civilians in cyber-crime units. Each of these groups is likely to face unique training needs that undermines the overall effectiveness of police organisations to respond to cyber-crime. The article explores the perceived training requirements across each of these groups and some potential ways in which they can be addressed in an effort to stimulate further research in this area focusing on the differentiated internal needs of police organisations.
Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level

Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.
Hovering Swarm Particle Swarm Optimization

PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.
Mineralisation potential assessment using analytical hierarchy process (AHP) modeling technique: A case study of Ilesha schist belt, southwestern Nigeria

A mineralisation potential assessment in part of Ilesha Schist belt of southwestern Nigeria was carried out using multi criteria decision analysis (MCDA) in the context of analytical hierarchy process (AHP). Three parameters sourced from aeromagnetic, electrical resistivity and geological data were identified to favour gold mineralisation in the study area. These parameters include lineament density (Ld), lithology (Lt), and electrically derived coefficient of anisotropy (An). The application of data enhancement techniques such as total horizontal derivative, 3-D Euler deconvolution and source edge detection were employed in the production of lineament map of the study area. Two hundred and sixty-five (265) vertical electrical sounding (VES) points were investigated using Schlumberger configuration. Coefficient of anisotropy derived from the Dar-Zarouk parameters was obtained from the synthesized VES data. The lithologic information was obtained from the existing geological map of the area. The orientations of lineaments displayed on the aeromagnetic results suggest predominantly NE-SW trending structures, which are characteristic of the Pan African orogenic events in the area. However, few of the E-W trending lineaments are imprints of secondary structural alteration. Furthermore, the Euler deconvolution solutions revealed that the area is characterised by geological contacts with estimated depth of 94–600 m. The coefficient of anisotropy results obtained from the geo-electric parameters vary from 1.00–2.87. AHP weighting and ranking technique was used to develop a reliable mineralisation potential model (MPM). In line with the recommended lower limit of 10 %, the consistency ratio of the developed model is 9.61 %. The produced MPM was classified into low, moderate and high mineralisation potential zones. Preliminary validation of the MPM using the existing pits in the area showed 79 % success rate. The validation of the model using the existing geochemical information from the previous studies indicated 85 % agreement with the conceptual model; thus, confirming the reliability of the produced MPM. The developed model is therefore believed to serve as a future reference in the resource management of the study area.
Challenges in chart image classification

Charts are commonly used forms of visualizing scientific observations from research findings or commercial trends. They provide an abstraction of the underlying information in a more understandable way. Over time, different forms of charts are developed. With the increase in the number of scientific documents present on the internet with different types of charts, automatic chart classification is becoming an important task for various applications. There have been several studies on chart classification with methods ranging from traditional machine learning approaches like SVM, KNN, and HMM to recent deep learning models like VGG, ResNet, and Xception. However, inconsistencies in experimental results are evident. This paper evaluates nine of the recently proposed deep learning-based models on three datasets (one curated and annotated by authors, and two publicly available), and systematically studies their performances over various setups to understand the reason for observing inconsistent results.
Criticality in reservoir computer of coupled phase oscillators

Accumulating evidence shows that the cerebral cortex is operating near a critical state featured by power-law size distribution of neural avalanche activities, yet evidence of this critical state in artificial neural networks mimicking the cerebral cortex is still lacking. Here we design an artificial neural network of coupled phase oscillators and, by the technique of reservoir computing in machine learning, train it for predicting chaos. It is found that when the machine is properly trained, oscillators in the reservoir are synchronized into clusters whose sizes follow a power-law distribution. This feature, however, is absent when the machine is poorly trained. Additionally, it is found that despite the synchronization degree of the original network, once properly trained, the reservoir network is always developed to the same critical state, exemplifying the "attractor" nature of this state in machine learning. The generality of the results is verified in different reservoir models and by different target systems, and it is found that the scaling exponent of the distribution is independent of the reservoir details and the bifurcation parameters of the target system, but is modified when the dynamics of the target system is changed to a different type. The findings shed light on the nature of machine learning, and are helpful to the design of high-performance machines in physical systems.
Understanding the Reason for Misclassification by Generating Counterfactual Images

Explainable AI (XAI) methods contribute to understanding the behavior of deep neural networks (DNNs), and have attracted interest recently. For example, in image classification tasks, attribution maps have been used to indicate the pixels of an input image that are important to the output decision. Oftentimes, however, it is difficult to understand the reason for misclassification only from a single attribution map. In this paper, in order to enhance the information related to the reason for misclassification, we propose to generate several counterfactual images using generative adversarial networks (GANs). We empirically show that these counterfactual images and their attribution maps improve the interpretability of misclassified images. Furthermore, we additionally propose to generate transitional images by gradually changing the configurations of a GAN in order to understand clearly which part of the misclassified image cause the misclassification.
Cryptanalysis of an Online/Offline Certificateless Signature Scheme for Internet of Health Things

Recently, Khan et al. [An online-offline certificateless signature scheme for internet of health things,” Journal of Healthcare Engineering, vol. 2020] presented a new certificateless offline/online signature scheme for Int... | Find, read and cite all the research you need on Tech Science Press
A novel particle filtering for nonlinear systems with multi-step randomly delayed measurements

For nonlinear discrete-time systems where measurements can be randomly delayed by multiple sampling periods, measurements are dependent conditioned on the state trajectory, and the dependence becomes more complicated with the increase of step of random delay. A particle filtering for this system is developed, which is novel in that the likelihood is computed allowing multi step of delay and dependence of measurements. Multi step of delay is dealt with through utilizing the formula of total probability skillfully, and dependence is dealt with through estimating the filtering probability distribution of random delay. The novel particle filtering is applied to two examples to validate its effectiveness and superiority.
Self-Organized Residual Blocks For Image Super-Resolution

It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approximation theorem states that a multi-layer neural network can approximate any non-linear function with the desired precision, it does not reveal the best network architecture to do so. Recently, operational neural networks (ONNs) that choose the best non-linearity from a set of alternatives, and their “self-organized” variants (Self-ONN) that approximate any non-linearity via Taylor series have been proposed to address the well-known limitations and drawbacks of conventional ConvNets such as network homogeneity using only the McCulloch-Pitts neuron model. In this paper, we propose the concept of self-organized operational residual (SOR) blocks, and present hybrid network architectures combining regular residual and SOR blocks to strike a balance between the benefits of stronger non-linearity and the overall number of parameters. The experimental results demonstrate that the proposed architectures yield performance improvements in both PSNR and perceptual metrics.