Reviewerly/exHarmony huggingface dataset:
Towards Blockchain and Semantic Web

Blockchain has become a pervasive technology in a wide number of sectors like industry, research, and academy. In the last decade a large number of tailored-domain problems have been solved thanks to the blockchain. Due to this reason, researchers expressed their interest in combining the blockchain with other well-known technologies, like Semantic Web. Unfortunately, as far as we known, in the literature no one has presented the different scenarios in which Semantic Web and blockchain can be combined, and the further benefits for both. In this paper, we aim a providing an in-depth view of the beneficial symbiotic relation that these technologies may reach together and report the different scenarios that we have identified in the literature to combine Semantic Web and blockchain.
Improved Proofs Of Retrievability And Replication For Data Availability In Cloud Storage

Abstract For a high level of data availability and reliability, a common strategy for cloud service providers is to rely on replication, i.e. storing several replicas onto different servers. To provide cloud users with a strong guarantee that all replicas required by them are actually stored, many multi-replica integrity auditing schemes were proposed. However, most existing solutions are not resource economical since users need to create and upload replicas of their files by themselves. A multi-replica solution called Mirror is presented to overcome the problems, but we find that it is vulnerable to storage saving attack, by which a dishonest provider can considerably save storage costs compared to the costs of storing all the replicas honestly—while still can pass any challenge successfully. In addition, we also find that Mirror is easily subject to substitution attack and forgery attack, which pose new security risks for cloud users. To address the problems, we propose some simple yet effective countermeasures and an improved proofs of retrievability and replication scheme, which can resist the aforesaid attacks and maintain the advantages of Mirror, such as economical bandwidth and efficient verification. Experimental results show that our scheme exhibits comparable performance with Mirror while achieving high security.
Temporal Context-Aware Representation Learning for Question Routing

Question routing (QR) aims at recommending newly posted questions to the potential answerers who are most likely to answer the questions. The existing approaches that learn users' expertise from their past question-answering activities usually suffer from challenges in two aspects: 1) multi-faceted expertise and 2) temporal dynamics in the answering behavior. This paper proposes a novel temporal context-aware model in multiple granularities of temporal dynamics that concurrently address the above challenges. Specifically, the temporal context-aware attention characterizes the answerer's multi-faceted expertise in terms of the questions' semantic and temporal information simultaneously. Moreover, the design of the multi-shift and multi-resolution module enables our model to handle temporal impact on different time granularities. Extensive experiments on six datasets from different domains demonstrate that the proposed model significantly outperforms competitive baseline models.
Dynamic Recognition of Phishing URLs Using Deep Learning Techniques

Phishing is a critical issue that faces the digital security. The straightforwardness of the web and Internet uncovered open doors for offenders to transfer malevolent substance at the same time with the upgrade of online business trades, for example, phishing – the demonstration of taking individual data which ascends in number. Internet clients’ costs have been increased to billions of dollars for each year due to phishing. Phishers use parodied email, Uniform Resource Locator (URL) locations of phony sites, and phishing programming to take individual data and monetary record subtleties, for example, usernames and passwords. The boycott system is definitely not a sufficient method to remain safe from the cybercriminals. Hence, phishing site pointers must be considered for this reason, with the presence and utilization of machine learning calculations. The current techniques make utilization of all separated attributes in the phishing URL location, prompting high false positive rate. In this manner, the proposed work manages strategies for distinguishing phishing web destinations by investigating different attributes of genuine and phishing URLs utilizing profound learning procedures, for example, deep Boltzmann machine (DBM), stacked auto-encoder (SAE), and deep neural network (DNN). DBM and SAE are utilized for pre-preparing the model with a superior portrayal of data for attribute determination, among which SAE has accomplished lower misclassification mistake with nine and includes a diminished list of attributes and DNN is utilized for twofold grouping in distinguishing obscure URL as either a phishing URL or a genuine URL. The proposed framework accomplishes higher location rate of 94% with low false positive rate than other machine learning strategies.
Electronic Voting Recording System Based on Blockchain Technology

Blockchain has been said to be one of the technologies for the future, and researchers have argued that this technology is going to disrupt many industries in the coming years, and democratic elections are one of the key areas blockchain is going to transform. Several organizations have begun experimenting on blockchain-enabled e-voting platforms, such as Democratic Earth, Horizon State and Follow My Vote. This study seeks to conceptualize a blockchain architecture for the storage of election results that provide trust, transparency, and immutability using distributed ledger technology (DLT). One of the main issues with elections in Ghana and other sub-Saharan African countries is the inaccurate recording of votes from polling stations, constituencies and at the national office. There are instances where votes recorded at the polling station changes at the constituencies either intentionally or accidentally. The study discussed the basic properties of the blockchain, such as distributed ledger, consensus mechanisms and cryptographic hash function, and how it can be used to address the current challenges in vote recording during elections. The study evaluates current blockchain-enabled e-voting systems and designs a blockchain-based vote recording system that provides immutability, trust, and transparency. The proposed design addresses the issues of vote tempering because transactions added to the block are secure with a cryptographic hash function which makes tempering of the votes stored in the blockchain nearly impossible and make it immutable.
Phishing Attack Detection using a Search Engine and Heuristics-based Technique

The language used in the textual content of the webpage is the barrier in most of the existing anti-phishing methods. Most of the existing anti-phishing methods can identify the fake webpages written in the English language only. Therefore, we present a search engine-based method in this article, which identifies phishing webpages accurately regardless of the textual language used within the webpage. The proposed search engine-based method uses a lightweight, consistent and language independent search query to detect the legality of the suspicious URL. We have also integrated five heuristics with the search engine-based mechanism to improve the detection accuracy, as some newly created legitimate sites may not appear in the search engine. The proposed method can also correctly classify the newly created legitimate sites that are not classified by available search engine-based methods. Evaluation results show that our method outperforms the available search-based techniques and achieves 98.15% TPR of and only 0.05% FPR.
Self‐adaptive brainstorming for jobshop scheduling in multicloud environment

Summary Cloud computing is a popular platform for processing the tasks by utilizing Virtual Machines as executing elements. The problems such as utilization and makespan persist in task scheduling in cloud which has to be solved and hence this article presents a human‐inspired approach for solving the job shop scheduling issue in the cloud environment. Since the job shop scheduling is challenging under multicloud environment, this article improves the well‐known method which is termed as self‐adaptive Brain Storm Optimization scheme. As a result, the recommendation of solutions is improved and so the desired updating is done. With this context, the scheduling process is performed. Here, the allocation of jobs for resources of heterogeneous cloud is encoded as brain storming process. Furthermore, the resultant scheduling scheme is evaluated for different performance constraints such as resource utilization rate, job completion, and makes span and the outcomes are verified. Next, to the implementation, the proposed model is compared with BSO, Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and the analysis proves its better performance.
Image Overlay Surgery Based on Augmented Reality: A Systematic Review

Augmented Reality (AR) applied to surgical guidance is gaining relevance in clinical practice. AR-based image overlay surgery (i.e. the accurate overlay of patient-specific virtual images onto the body surface) helps surgeons to transfer image data produced during the planning of the surgery (e.g. the correct resection margins of tissue flaps) to the operating room, thus increasing accuracy and reducing surgery times. We systematically reviewed 76 studies published between 2004 and August 2018 to explore which existing tracking and registration methods and technologies allow healthcare professionals and researchers to develop and implement these systems in-house. Most studies used non-invasive markers to automatically track a patient’s position, as well as customised algorithms, tracking libraries or software development kits (SDKs) to compute the registration between patient-specific 3D models and the patient’s body surface. Few studies combined the use of holographic headsets, SDKs and user-friendly game engines, and described portable and wearable systems that combine tracking, registration, hands-free navigation and direct visibility of the surgical site. Most accuracy tests included a low number of subjects and/or measurements and did not normally explore how these systems affect surgery times and success rates. We highlight the need for more procedure-specific experiments with a sufficient number of subjects and measurements and including data about surgical outcomes and patients’ recovery. Validation of systems combining the use of holographic headsets, SDKs and game engines is especially interesting as this approach facilitates an easy development of mobile AR applications and thus the implementation of AR-based image overlay surgery in clinical practice.
Ten maxims for out of class learning to outclass the academic challenges of COVID-19

This article was migrated. The article was marked as recommended. Responding to the COVID-19 pandemic, universities world over are aggressively moving their learning, teaching and assessment to online platforms. Educators suddenly feel unsettled, unprepared and unsupported. This inevitable segue from a traditional to online environment will need faculty to grapple quickly and efficiently with the technological imperatives. The purpose of this article is to present ten maxims to navigate the academic challenges posed by out of class learning. The article includes a user-friendly checklist to facilitate the blooming of online teachers.
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has been spent on improving RNN architectures and on understanding the underlying neural mechanisms for performance gain. In this paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical results show that the network can autonomously learn to abstract sub-goals and can self-develop an action hierarchy using internal dynamics in a challenging continuous control task. Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch. We also found that improved performance can be achieved when neural activities are subject to stochastic rather than deterministic dynamics.
Three-dimensional imaging technique based on Gray-coded structured illumination

Three-dimensional (3D) imaging technique based on structured illumination has been deeply studied and widely applied in recent years. Among numerous 3D imaging methods, the imaging technique based on Gray-coded structured illumination has been applied in industrial inspection, digitalization of antique, biomedicine detection and so on, owing to its robustness and anti-noise ability. To realize 3D imaging, the projected Gray-coded pattern can either be used to modulate the height information or be used to assist acquisition and computation of the height information of the tested object. In this paper, a review was given to introduce principles and progresses of the imaging techniques based on Gray-coded structured illumination and the research achievements of the author's research group about this technique were also shown. In addition, the advantages, disadvantages and application scope of binary Gray-coded patterns in 3D shape measurement were discussed and the developing trends of this technique was pointed out as well.
Modified Convolutional Neural Network of Tamil Character Recognition

The handwritten Tamil characterVinotheni,C. recognition in offline mode isLakshmana Pandian,S. challenging tasks as there are virtually different people who have different styles of writing the same characters. Deep convolution neural networks are playing a virtual role nowadays in recognizing handwritten character by automatically learning discriminative features from high dimensionality of input data. This work presents a modified convolution neural network $$\left( \text {M-CNN} \right) $$ architecture to achieve a faster convergence rate and also to get the highest recognition accuracy. The M-CNN on different aspects along with layers design, activation function, loss function and optimization is discussed. Systematic experiments on isolated handwritten Tamil character dataset collected from various schools by ourselves. For these collected datasets, the proposed system recognized the characters with 97.07%.
Co-design for a Competency Self-assessment Chatbot and Survey in Science Education

This paper describes a co-design process for a formative assessment tool in the area of core competencies such as creativity, communication, collaboration, and critical thinking. This process has been carried out in the context of non-formal science education institutions in 19 countries in Europe and the Middle East. The results of the co-design have been influential in the design of a formative assessment chatbot and survey that supports learner competency and self-regulation. We also discuss some preliminary results from the use of the tool and introduce additional considerations with regard to inclusion, technical aspects, and ethics, towards a set of best practices in this area.
Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network

Bitcoin has attracted extensive attention from investors, researchers, regulators, and the media. A well-known and unusual feature is that Bitcoin’s price often fluctuates significantly, which has however received less attention. In this paper, we investigate the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation. In this paper, three kinds of features are presented for the price fluctuation prediction, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder. We evaluate these features using an Attentive LSTM network and an Embedding Network (ALEN). In particular, an attentive LSTM network can capture the time dependency representation of Bitcoin price and an embedding network can capture the hidden representations from related cryptocurrencies. Experimental results demonstrate that ALEN achieves superior state-of-the-art performance among all baselines. Furthermore, we investigate the impact of parameters on the Bitcoin price fluctuation prediction problem, which can be further used in a real trading environment by investors.
Quantifying the Efficiency of State Preparation via Quantum Variational Eigensolvers

Recently, there has been much interest in the efficient preparation of complex quantum states using low-depth quantum circuits, such as Quantum Approximate Optimization Algorithm (QAOA). While it has been numerically shown that such algorithms prepare certain correlated states of quantum spins with surprising accuracy, a systematic way of quantifying the efficiency of QAOA in general classes of models has been lacking. Here, we propose that the success of QAOA in preparing ordered states is related to the interaction distance of the target state, which measures how close that state is to the manifold of all Gaussian states in an arbitrary basis of single-particle modes. We numerically verify this for several examples of non-integrable quantum models, including Ising models with two- and three-spin interactions and the cluster model in an external field. Our results suggest that the structure of the entanglement spectrum, as witnessed by the interaction distance, correlates with the success of QAOA state preparation, and that this correlation also contains information about different phases present in the model. We conclude that QAOA typically finds a solution that perturbs around the closest free-fermion state.
Transformation of educational roles of library-supported mobile learning: a literature review from 2009 to 2018

Purpose This study aims to explore the transformation of the roles of libraries, application trends and potential research issues of library-supported mobile learning. Design/methodology/approach The publications in the Scopus database from 2009 to 2018 are reviewed and analyzed from various aspects, such as the roles of libraries in mobile learning, types of libraries, research foci and sensing or location-based technologies. Findings The role of libraries as learning material providers is examined the most in library-supported mobile learning studies, followed by the role as inquiry context providers and as knowledge-sharing platforms. In terms of the role as learning material providers, academic libraries were investigated the most and radio frequency identification (RFID) was mainly adopted. In terms of the role as inquiry context providers, special libraries were explored the most; adopted sensing technologies were more diverse (e.g. QR code, augmented reality, RFID and Global Positioning System). Only special libraries played a role as knowledge-sharing platforms, adopting augmented reality. Most research on library-supported mobile learning mainly focused on investigating the affective domain during mobile learning. Practical implications Five potential applications of educational roles in library-supported mobile learning are suggested based on the findings of the present study. Originality/value The current study provides insights relevant to the educational roles of library-supported mobile learning. The findings and suggestions can serve as references for researchers and school teachers conducting library-supported mobile learning.
Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule

This paper proposes a method to accelerate the training process of general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potential candidates of the expansion step of existing hyperboxes to cover a new input pattern in the online learning algorithms or candidates of the hyperbox aggregation process in the agglomerative learning algorithms. Our proposed approach is based on the mathematical formulas to form a new solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in turn decreasing the training time of learning algorithms. The efficiency of the proposed method is assessed over a number of widely used data sets. The experimental results indicated the significant decrease in training time of proposed approach for both online and agglomerative learning algorithms. Notably, the training time of the online learning algorithms is reduced from 1.2 to 12 times when using the proposed method, while the agglomerative learning algorithms are accelerated from 7 to 37 times on average.
Strength in Numbers: Improving Generalization with Ensembles in Machine Learning-based Profiled Side-channel Analysis

The adoption of deep neural networks for profiled side-channel attacks provides powerful options for leakage detection and key retrieval of secure products. When training a neural network for side-channel analysis, it is expected that the trained model can implement an approximation function that can detect leaking side-channel samples and, at the same time, be insensible to noisy (or non-leaking) samples. This outlines a generalization situation where the model can identify the main representations learned from the training set in a separate test set. This paper discusses how output class probabilities represent a strong metric when conducting the side-channel analysis. Further, we observe that these output probabilities are sensitive to small changes, like selecting specific test traces or weight initialization for a neural network. Next, we discuss the hyperparameter tuning, where one commonly uses only a single out of dozens of trained models, where each of those models will result in different output probabilities. We show how ensembles of machine learning models based on averaged class probabilities can improve generalization. Our results emphasize that ensembles increase a profiled side-channel attack’s performance and reduce the variance of results stemming from different hyperparameters, regardless of the selected dataset or leakage model.
MDA: Multimodal Data Augmentation Framework for Boosting Performance on Sentiment/Emotion Classification Tasks

Multimodal data analysis has drawn increasing attention with the explosive growth of multimedia data. Although traditional unimodal data analysis tasks have accumulated abundant labeled datasets, there are few labeled multimodal datasets due to the difficulty and complexity of multimodal data annotation, nor is it easy to directly transfer unimodal knowledge to multimodal data. Unfortunately, there is little related data augmentation work in multimodal domain, especially for image–text data. In this article, to address the scarcity problem of labeled multimodal data, we propose a Multimodal Data Augmentation framework for boosting the performance on multimodal image–text classification task. Our framework learns a cross-modality matching network to select image–text pairs from existing unimodal datasets as the multimodal synthetic dataset, and uses this dataset to enhance the performance of classifiers. We take the multimodal sentiment analysis and multimodal emotion analysis as the experimental tasks and the experimental results show the effectiveness of our framework for boosting the performance on multimodal classification task.