Reviewerly/exHarmony huggingface dataset:
Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine

Reference evapotranspiration (ET0) is usually calculated as a pre-requisite to obtain the actual evapotranspiration (ET). Estimation of ET0 using extreme learning machine (ELM) is a new norm due to its excellent computation efficiency and its lower data dependency. However, the lack of stochastic tuning often results in the convergence to the local optima instead of the global optimum. This study attempts to address this issue by hybridizing the ELM with three swarm-based optimization algorithms, namely the particle swarm optimization (PSO), the moth-flame optimization (MFO) and the whale optimization algorithm (WOA) with different fitness functions. This study was conducted with three stations in Sabah and Sarawak (states in East Malaysia) as this region is to a very large extent covered with oil palm plantation. The water resources management is thus rather crucial for the area. The results showed that the WOA-ELM achieved a higher average rank score relative to the PSO-ELM and MFO-ELM, especially when the simple Taylor skill score was used as the fitness function. The application of different fitness functions did not show any significant effect. The reported standard deviations of the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) for the three variations of the hybridized ELM were close to 0; thus reflecting their similarity in accuracy. However, in terms of computational efficiency, the simple Taylor skill score exhibited better stability where it could effectively reduce the number of required hidden neurons when the input meteorological parameters were decreased. The WOA was suggested as the best optimization algorithm for this study. For the three stations, respectively it achieved an average MAE, RMSE and R2 of 0.0007 to 0.1443, 0.0011 to 0.1927 and 1.0000 to 0.9486, from cases C1 to C6 with minimum time cost.
Deformable Kernel Networks for Joint Image Filtering

Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing noise. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result. In this paper, we instead learn explicitly sparse and spatially-variant kernels. We propose a CNN architecture and its efficient implementation, called the deformable kernel network (DKN), that outputs sets of neighbors and the corresponding weights adaptively for each pixel. The filtering result is then computed as a weighted average. We also propose a fast version of DKN that runs about seventeen times faster for an image of size $$640 \times 480$$ . We demonstrate the effectiveness and flexibility of our models on the tasks of depth map upsampling, saliency map upsampling, cross-modality image restoration, texture removal, and semantic segmentation. In particular, we show that the weighted averaging process with sparsely sampled $$3 \times 3$$ kernels outperforms the state of the art by a significant margin in all cases.
Blockchain Technology Adoption in Supply Chain Finance

Supply Chain Finance (SCF) faces the complex problem of implementing inventory, purchase order and accounts receivable financing automation in terms of transaction data trust and validation. This paper aims to explore how blockchain technology adoption solves the SCF problem using a multi-case method based on the Technological Acceptance Model (TAM). With purposive sampling, 30 cases were selected on the criteria of perceived usefulness and perceived ease of use in solving SCF problems. The results show that trust, validity and distributed ledger transaction data as perceived usefulness are the main drivers of blockchain adoption because it provides solutions to SCF automation problems such as Know Your Customer (KYC), accounting, and transaction settlement. Smart contracts offer easy and fast transactions such as in L/C export processing as perceived ease to use. Of the 30 blockchain projects, 21 offer the usefulness of automated accounts receivable financing, 15 offer easy-to-use purchase order financing and 8 offer easy-to-use inventory financing processes. This study provides the current state of blockchain technology adoption by exploring 30 real application cases in SCF globally. Blockchain advantages provide automation solutions in global supply SCF practices with smart contracts, transparency and security of distributed ledger data feature.
OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce

With the paradigm shift in business strategy in terms of online marketing and e-commerce and to comprehend the World Wide Web transforming into an intelligent semantic web, there arises a perpetual need for semantically driven e-commerce systems which gives preference to the users. In this paper, OntoCommerce which is an e-commerce system that incorporates semantic algorithms for product recommendation has been proposed. The proposed strategy uses the enriched normalised pointwise mutual information measure for semantic similarity computation. OntoCommerce assimilates ontologies and recommends products based on the user query, recorded user navigation as well as the user profile analysis thereby encompassing personalisation. In order to make the recommendations more relevant, OntoCommerce uses parametric fuzzification to increase the number of relevant recommendable products. OntoCommerce yields an average accuracy of 88.68 % with a low false discovery rate of 0.13 which makes it a best-in-class semantically driven product recommendation system for online e-commerce.
A Modified Hierarchical Attribute-Based Encryption Access Control Method for Mobile Cloud Computing

Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on-demand. It is an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.
E-learning in higher inclusive education: needs, opportunities and limitations

Purpose The purpose of this paper is to study the limitations, opportunities and conditions for the development of e-learning in the inclusive education system in the universities. Design/methodology/approach The paper reviews the literature dedicated to e-learning, its application and adaptation in higher inclusive education. Systemic and social approaches were applied to the perception of higher inclusive education for the purposes of this study. The principles of evolutionary economics and institutional theory were used for determining the possibility of using e-learning in higher inclusive education. The findings are confirmed by an empirical study of the integration of e-learning into the higher inclusive education system by the example of Russia. Findings It has been shown that the development of e-learning in the national higher education system and its perception by the higher inclusive education system depends on the level of development of social and information interrelation in the society. In addition, e-learning can only be used in higher inclusive education when it is interrelated with traditional learning. The efficiency of e-learning in higher inclusive education depends on the systemic institutional environment which was formed both at the level of the state and at the level of a particular university. In this case, the institutional environment should be focused not only on the development of e-learning and inclusive education, but also on their collaboration. Practical implications The results of the study, which identified special aspects, opportunities and limitations of e-learning in higher inclusive education, can be applied to improve its effectiveness both at the level of individual universities and at the level of formation of national strategies for the development of higher education. Originality/value Given the growing relevance of higher inclusive education in the contemporary world and the limited number of studies of adaptation and the use of e-learning in it, the results obtained can contribute to the implementation of strategic planning of this direction at the national and local levels.
Enhancement of a Lightweight Attribute-Based Encryption Scheme for the Internet of Things

In this paper, we present the enhancement of a lightweight key-policy attribute-based encryption (KP-ABE) scheme designed for the Internet of Things (IoT). The KP-ABE scheme was claimed to achieve ciphertext indistinguishability under chosen-plaintext attack in the selective-set model but we show that the KP-ABE scheme is insecure even in the weaker security notion, namely, one-way encryption under the same attack and model. In particular, we show that an attacker can decrypt a ciphertext which does not satisfy the policy imposed on his decryption key. Subsequently, we propose an efficient fix to the KP-ABE scheme as well as extending it to be a hierarchical KP-ABE (H-KP-ABE) scheme that can support role delegation in IoT applications. An example of applying our H-KP-ABE on an IoT-connected healthcare system is given to highlight the benefit of the delegation feature. Lastly, using the NIST curves secp192k1 and secp256k1, we benchmark the fixed (hierarchical) KP-ABE scheme on an Android phone and the result shows that the scheme is still the fastest in the literature.
Stacked capsule autoencoders

Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts to reason about objects. Since these relationships do not depend on the viewpoint, our model is robust to viewpoint changes. SCAE consists of two stages. In the first stage, the model predicts presences and poses of part templates directly from the image and tries to reconstruct the image by appropriately arranging the templates. In the second stage, the SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses. Inference in this model is amortized and performed by off-the-shelf neural encoders, unlike in previous capsule networks. We find that object capsule presences are highly informative of the object class, which leads to state-of-the-art results for unsupervised classification on SVHN (55%) and MNIST (98.7%).
Improved Convolutional Neural Network Based on Fast Exponentially Linear Unit Activation Function

The activation functions play increasingly important roles in deep convolutional neural networks. The traditional activation functions have some problems such as gradient disappearance, neuron death and output offset, and so on. To solve these problems, we propose a new activation function in this paper, Fast Exponentially Linear Unit (FELU), aiming to speed up exponential linear calculations and reduce the time of network running. FELU has the advantages of Rectified Linear Unit (RELU) and Exponential Linear Unit (ELU), leading to have better classification accuracy and faster calculation speed. We test five traditional activation functions such as ReLU, ELU, SLU, MPELU, TReLU, and our new activation function on the cifar10, cifar100 and GTSRB data sets. Experiments show that the proposed activation function FELU not only improves the speed of the exponential calculation, reducing the time of convolutional neural network running, but also effectively enhances the noise robustness of network to improve the accuracy of classification.
Detection of SQL injection based on artificial neural network

The SQL injection, a common web attack, has been a challenging network security issue which causes annually millions of dollars of financial loss worldwide as well as a large amount of users’ privacy data leakage. This work presents a high accuracy SQL injection detection method based on neural network. We first acquire authentic user URL access log data from the Internet Service Provider(ISP), ensuring that our approach is real, effective and practical. We then conduct statistical research on normal data and SQL injection data. Based on the statistical results, we design eight types of features and train an MLP model. The accuracy of the model maintains over 99%. Meanwhile, we compare and evaluate the training effect of other machine learning algorithms(LSTM, for example), the results reveal that the accuracy of our method is superior to the relevant machine learning algorithms.
ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications

The growing role artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of vision-aided wireless communications, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.
An integrated model of word processing and eye-movement control during Chinese reading.

In the Chinese writing system, there are no interword spaces to mark word boundaries. To understand how Chinese readers conquer this challenge, we constructed an integrated model of word processing and eye-movement control during Chinese reading (CRM). The model contains a word-processing module and an eye-movement control module. The word-processing module perceives new information within the perceptual span around a fixation. The model uses the interactive activation framework (McClelland & Rumelhart, 1981) to simulate word processing, but some new assumptions were made to address the word segmentation problem in Chinese reading. All the words supported by characters in the perceptual span are activated and they compete for a winner. When one word wins the competition, it is identified and it is simultaneously segmented from text. The eye-movement control module makes the decision regarding when and where to move the eyes using the activation information of word units and character units provided by the word-processing module. The model estimates how many characters can be processed during a fixation, and then makes a saccade to somewhere beyond this point. The model successfully simulated important findings on the relation between word processing and eye-movement control, how Chinese readers choose saccade targets, how Chinese readers segment words with ambiguous boundaries, and how Chinese readers process information with parafoveal vision during Chinese sentence reading. The current model thus provides insights on how Chinese readers address some important challenges, such as word segmentation and saccade-target selection. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

With the development of the Internet of things (IoT) and wearable devices, the sensor-based human activity recognition (HAR) has attracted more and more attentions from researchers due to its outstanding characteristics of convenience and privacy. Meanwhile, deep learning algorithms can extract high-dimensional features automatically, which makes it possible to achieve the end-to-end learning. Especially the convolutional neural network (CNN) has been widely used in the field of computer vision, while the influence of environmental background, camera shielding, and other factors are the biggest challenges to it. However, the sensor-based HAR can circumvent these problems well. Two improved HAR methods based on Gramian angular field (GAF) and deep CNN are proposed in this paper. Firstly, the GAF algorithm is used to transform the one-dimensional sensor data into the two-dimensional images. Then, through the multi-dilated kernel residual (Mdk-Res) module, a new improved deep CNN network Mdk-ResNet is proposed, which extracts the features among sampling points with different intervals. Furthermore, the Fusion-Mdk-ResNet is adopted to process and fuse data collected by different sensors automatically. The comparative experiments are conducted on three public activity datasets, which are WISDM, UCI HAR and OPPORTUNITY. The optimal results are obtained by using the indexes such as accuracy, precision, recall and F-measure, which verifies the effectiveness of the proposed methods.
Face Morphing Attack Generation and Detection: A Comprehensive Survey

Face recognition has been successfully deployed in real-time applications, including secure applications such as border control. The vulnerability of face recognition systems (FRSs) to various kinds of attacks (both direct and indirect attacks) and face morphing attacks has received great interest from the biometric community. The goal of a morphing attack is to subvert an FRS at an automatic border control (ABC) gate by presenting an electronic machine-readable travel document (eMRTD) or e-passport that is obtained based on a morphed face image. Since the application process for an e-passport in the majority of countries requires a passport photograph to be presented by the applicant, a malicious actor and an accomplice can generate a morphed face image to obtain the e-passport. An e-passport with a morphed face image can be used by both the malicious actor and the accomplice to cross a border, as the morphed face image can be verified against both of them. This can result in a significant threat, as a malicious actor can cross the border without revealing the trace of his/her criminal background, while the details of the accomplice are recorded in the log of the access control system. This survey aims to present a systematic overview of the progress made in the area of face morphing in terms of both morph generation and morph detection. In this article, we describe and illustrate various aspects of face morphing attacks, including different techniques for generating morphed face images and state-of-the-art morph attack detection (MAD) algorithms based on a stringent taxonomy as well as the availability of public databases, which allow us to benchmark new MAD algorithms in a reproducible manner. The outcomes of competitions and benchmarking, vulnerability assessments, and performance evaluation metrics are also provided in a comprehensive manner. Furthermore, we discuss the open challenges and potential future areas that need to be addressed in the evolving field of biometrics.
Criteria for assessing practical work in higher education

The article substantiates the advantages of an improved approach to the criteria for assessing students' knowledge in practical classes on technology disciplines in the system of higher education
‘The game has changed.’ AI triumphs at protein folding

In milestone, software predictions finally match structures calculated from experimental data.
Efficient generation of entangled multiphoton graph states from a single atom

Entanglement is a powerful concept with an enormous potential for scientific and technological advances. A central focus in modern research is to extend the generation and control of entangled states from few to many qubits, and protect them against decoherence. Optical photons play a prominent role as these qubit carriers are naturally robust and easy to manipulate. However, the most successful technique to date for creating photonic entanglement is inherently probabilistic and therefore subject to severe scalability limitations. Here we avoid these by implementing a deterministic protocol with a single memory atom in a cavity. We interleave controlled single-photon emissions with tailored atomic qubit rotations to efficiently grow Greenberger-Horne-Zeilinger states of up to 14 photons and linear cluster states of up to 12 photons with a fidelity lower bounded by 76(6)% and 56(4)%, respectively. Thanks to a source-to-detection efficiency of 43.18(7)% per photon we measure these large states about once every minute, orders of magnitude faster than in any previous experiment. In the future, this rate could be increased even further, the scheme could be extended to two atoms in a cavity, or several sources could be quantum mechanically coupled, to generate higher-dimensional cluster states. Our work therefore removes a long-standing obstacle towards scalable measurement-based quantum computation and communication.
The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education

Abstract Chatbot usage is evolving rapidly in various fields, including higher education. The present study’s purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student’s question. A pretest–posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students’ experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot’s integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.