Reviewerly/exHarmony huggingface dataset:
Faithful Edge Federated Learning: Scalability and Privacy

Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. First, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Faithful Federated Learning (FFL) mechanism</i> which approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Further, the time complexity in the number of agents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\log (K))$ </tex-math></inline-formula> . Second, by partitioning agents into several clusters, we present a scalable VCG mechanism approximation. We further design a scalable and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Differentially Private FFL (DP-FFL) mechanism</i> , the first differentially private faithful mechanism, that maintains the economic properties. Our DP-FFL mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.
Immersive on-the-job training module development and modeling users’ behavior using parametric multi-group analysis: A modified educational technology acceptance model

An on-the-job-training approach is required for sharing firsthand knowledge or experience with students in various contexts such as medicine, arts, architectural design, archaeology, construction, mining, and civil engineering. Large classes of university students and numerous professionals can learn from best case practices as on-the-job training. However, the job is not often available, the best case practices are not accessible to everyone, and the construction field is hazardous, so it may not be safe for a large group of students to attend operating construction sites. The large size of classes and, currently, the COVID-19 pandemic is also hindering the application of authentic and case-based training. This paper presents the process of developing and implementing innovative virtual tour (VT) modules to support on-the-job training needs where the teaching approach is the case-based storytelling scenario. The paper shows how the VTs were utilized for students’ learning, and their behavior was examined to see if it could support the development of a novel virtual teaching acceptance model (VTAM) as a theoretical framework for measuring educational technology adoption. VTAM comprises an amalgamation of technology attributes and learning factors, including perceived usefulness, engagement, situated learning, immersion, social presence, perceived utility, information-rich sources, and perceived satisfaction. VTAM was validated by conducting a longitudinal three-year survey with the participation of 339 VT users and an interview of 31 users to triangulate the quantitative analysis outcomes. The survey data was analyzed by using structural equation modeling (SEM) and parametric multi-group analysis (PMGA), and the interview transcriptions were analyzed using coding techniques. The outcome shows that VTAM, accompanied by situated learning, immersion, and social presence, has the strongest impact on engagement which positively affects students' satisfaction. VTAM helps to understand the critical factors affecting the usefulness of immersive technologies in education. The outcome is crucial to virtual system developers and online education designers for understanding students’ behavior in implementing virtual technologies successfully.
Application of bio-inspired optimization algorithms in food processing

Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.
Differential evolution with modified initialization scheme using chaotic oppositional based learning strategy

Differential evolution (DE) is a popular optimization algorithm with easy implementation and fast convergence rate. For evolutionary algorithms such as DE, the initialization process of solution members is crucial because the distribution of initial population can govern the overall quality of final solution obtained in terms of accuracy and convergence speed. This study leverages the strengths of both chaotic maps and oppositional-based learning strategy to design a new DE variant with modified initialization scheme, namely chaotic oppositional DE (CODE) in order to generate the initial population with good quality of mean fitness and diversity of the solutions. The effectiveness of CODE variants incorporated with seven different chaotic maps are investigated using CEC 2014 benchmark functions and the chaotic circle oppositional DE (CCODE) is revealed as the best performing CODE variants. The optimization performance of CCODE is further compared with other existing optimization algorithms in terms of solution accuracy and convergence speed. Extensive simulation studies prove that the proposed algorithm is able to outperform its peers by achieving better trade-off between two contradicting requirements of fast convergence speed and population diversity preservation.
A Multichannel CNN-GRU Model for Human Activity Recognition

Human activity recognition (HAR) is one of the important research areas in pervasive computing. Among HAR, sensor-based activity recognition refers to acquiring a high-level knowledge about human activities from readings of many low-level sensor. In recent years, although traditional methods of deep learning (DL) have been widely used for sensor-based HAR with some good performance, they still face such challenges as feature extraction and characterization, continuous action segmentation in dealing with time series problems. In this study, a multichannel fusion model is proposed by the idea of dividing. In this proposed architecture, a multichannel convolutional neural network (CNN) is used to enhance the ability to extract features at different scales, and then the fused features are fed into gated recurrent unit (GRU) for feature labeling and enhanced feature representation, through the learning of temporal relationships. Finally, the multichannel CNN-GRU model is designed using global average pooling (GAP) to connect the feature maps with the final classification. The model performance was conducted on three benchmark datasets of WISDM, UCI-HAR, and PAMAP2 with the accuracy of 96.41%, 96.67%, and 96.25% respectively. The results show that the proposed model demonstrates better activity detection capability than some of the reported results.
Deep Learning and Smart Contract-Assisted Secure Data Sharing for IoT-Based Intelligent Agriculture

The recent development of Internet of Things (IoT) and Unmanned Aerial Vehicles has revolutionized traditional agriculture with intelligence and automation. In a typical Intelligent Agriculture (IA) ecosystem, massive and real-time data are generated, analyzed, and sent to the Cloud Server (CS) for the purpose of addressing complex agricultural issues, such as yield prediction, water feed calculation, and so on. This helps farmer and associated stakeholders to take correct decision that improves the yield and quality of agricultural product. However, the distributed nature of IA entities and the usage of insecure wireless communication open various challenges related to data sharing, monitoring, storage, and further makes the entire IA ecosystem vulnerable to various potential attacks. In this article, we exploit deep learning and smart contract to propose a new IoT-enabled IA framework for enabling secure data sharing among its various entities. Specifically, first we develop new authentication and key management scheme to ensure secure data transmission in IoT-enabled IA. The encrypted transactions are then used by the CS to analyze and further detect intrusions by a novel deep learning architecture. In CS, the smart contract (SC)-based consensus mechanism is executed on legitimate transactions that verifies and adds the formed blocks into blockchain by a peer-to-peer CSs network. In comparison to existing competing security solutions, a rigorous comparative research demonstrates that the proposed approach provides greater security and more utility characteristics.
Color image encryption algorithm based on Double layer Josephus scramble and laser chaotic system

In this paper, a color image encryption algorithm is presented, the new algorithm uses double-layer Joseph’s scramble, XOR diffusion, and is built on a laser chaos system. Firstly, a laser chaotic system is chosen in the scheme and it has the advantages of extended bandwidth and rapid propagation speed. Secondly, the structure of the image encryption scheme follows the Fridrich structure. In the scrambling part, a modified Joseph’s ring is adapted to process the chaotic sequence and the picture pixel sequence to achieve the scrambling of the plaintext image pixel positions. In the diffusion part, the XOR diffusion technique is employed to reduce the statistical characteristics of the plaintext picture, which can resist external attacks better. Finally, the presented encryption algorithm is demonstrated utilizing simulation results and security analysis. The simulation performance demonstrates that the new proposed method is practical. The security test performance indicates that the algorithm can validly encrypt the image with good security performance. In summary, the new scheme designed in this paper can offer better technical backing for the wide applications of image encryption.
Efficient Implementation of Pattern Database Heuristics for Classical Planning

Despite their general success in the heuristic search community, pattern database (PDB) heuristics have, until very recently, not been used by the most successful classical planning systems. We describe a new efficient implementation of pattern database heuristics within the Fast Downward planner. A planning system using this implementation is competitive with the state of the art in optimal planning, significantly improving over results from the previous best PDB heuristic implementation in planning.
Addressing the Cold-Start Problem Using Data Mining Techniques and Improving Recommender Systems by Cuckoo Algorithm: A Case Study of Facebook

One of the most common problems in recommender systems is a “cold-start” problem, which is related to users who do not indicate any behavior on social media. This paper proposes a solution for tackling this problem by using data mining techniques and improving the recommender systems by using the Cuckoo algorithm.
A Real-time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.
Two-mode squeezed states of two separated nitrogen-vacancy-center ensembles coupled via dissipative photons of superconducting resonators

We propose an efficient method for generating two-mode squeezed states of the collective excitation of two nitrogen-vacancy-center ensembles (NVEs), which are magnetically coupled to two interacting superconducting transmission line resonators via photon hopping, respectively. By applying the external driving fields to induce Raman transitions between the ground-state sublevels of the nitrogen-vacancy center, an effective interaction between the spin ensembles and the resonators can be engineered. We show that the dissipative quantum dynamical process of the two resonators can be employed to steer the two NVEs into a two-mode squeezed state. By comparison with previous proposals, our scheme does not need either an extra superconducting qubit, or external squeezing resource. The present work may have potential applications for implementing NVE-based hybrid quantum computation.
An analysis of digital competence as expressed in design patterns for technology use in teaching

Abstract Teachers cannot presume that their learners have the competence to use the technology brought to the classroom. Therefore, the learners’ abilities to use technology may be a concern for teachers. This paper reports on digital competence through an analysis of designs for learning in design patterns, written by upper secondary teachers. Learning activities found in the design patterns were analysed with the aim to understand how teachers perceive the learners’ digital competence when using technology. A framework that compromises digital competence was utilised for inferring the digital competencies. The qualitative analysis of these learning activities reveals that competences of information and data literacy, and of communication and collaboration predominate. By analysing the characteristics of learning activities and hence the teachers’ ideas of technology use in teaching, it is concluded that design patterns can be used to identify the competences teachers believe are relevant for the learners to acquire. The result therefore involves aspects of how teachers perceive learners’ digital competence when using technology in teaching.
Enhanced sparse representation classifier for text classification

Classification of text based on its substance is an essential part of analysis to organize enormously large text data and to mine the salient information contained in it. It is gaining greater attention with the surge in the volume of on-line data available. Classical algorithms like k-NN (k-nearest neighbor), SVM (Support Vector Machine) and their variations have been observed to yield only reasonable results in addressing the problem, leaving enough room for further improvement. A class of algorithms commonly referred to as Sparse Methods has been emerged recently from compressive sensing and found numerous effective applications in many areas of data analysis and image processing. Sparse Methods as a tool for text analysis is an alley that is largely unexplored rigorously. This paper presents exploration of sparse representation-based methods for text classification. Based on the success of sparse representation based methods in different areas of data analysis, we intuitively hypothesized that it should work well on text classification problems as well. This paper empirically reinforces the hypothesis by testing the method on Reuters and WebKB data sets. The empirical results on Reuters and WebKB benchmark data show that it can outperform classical classification algorithms like SVM and k-NN. It has been observed that obtaining the basis of representation and sparse codes are computationally costly operations affecting the performance of the system. We also propose a class-wise dictionary refinement algorithm and dynamic dictionary selection algorithm to make sparse coding faster. The addition of dictionary refinement to the classification system not only reduces the time taken for sparse coding but also gives improved classification accuracy. The outcomes of the study are empirical verification of sparse representation classifier as a text classification tool and a computationally efficient solution for the bottleneck operation of sparse coding.
A trust and privacy framework for smart manufacturing environments

Operators in industrial manufacturing environments are under pressure to cope with the ever increasing flexibility and complexity of work. Transitioning towards data-driven smart manufacturing environments in the context of Industry 4.0 with wearable technologies and sensors brings the opportunity to (1) use the recorded data to deliver timely assistance to the operator and (2) employ it in retrospective for work process optimisation. Despite these clear opportunities challenges are raised concerning the perceived threats of the usage of the data that outweigh the perceived benefits. Thus, it is paramount to strongly consider privacy concerns when designing a system for a smart manufacturing environments from the onset rather than an afterthought. This paper presents a trust and privacy framework to address these challenges by facilitating the understanding of the role of trust and privacy in complex smart manufacturing systems. The framework is instantiated in the context of a smart manufacturing system developed in the EU H2020 HUMAN project and evaluated in three studies towards its perceived usefulness and its impact on the privacy awareness in a concrete smart manufacturing application scenario. The evaluation results show that the framework helps to structure and understand the data-flow in a smart manufacturing scenario from a privacy perspective.
Deep 3D morphable model refinement via progressive growing of conditional Generative Adversarial Networks

3D face reconstruction from a single 2D image is a fundamental Computer Vision problem of extraordinary difficulty. Statistical modeling techniques, such as the 3D Morphable Model (3DMM), have been widely exploited because of their capability of reconstructing a plausible model grounding on the prior knowledge of the facial shape. However, most of these techniques derive an approximated and smooth reconstruction of the face, without accounting for fine-grained details. In this work, we propose an approach based on a Conditional Generative Adversarial Network (CGAN) for refining the coarse reconstruction provided by a 3DMM. The latter is represented as a three channels image, where the pixel intensities represent the depth, curvature and elevation values of the 3D vertices. The architecture is an encoder–decoder, which is trained progressively, starting from the lower-resolution layers; this technique allows a more stable training, which leads to the generation of high quality outputs even when high-resolution images are fed during the training. Experimental results show that our method is able to produce reconstructions with fine-grained realistic details and lower reconstruction errors with respect to the 3DMM. A cross-dataset evaluation also shows that the network retains good generalization capabilities. Finally, comparison with state-of-the-art solutions evidence competitive performance, with comparable or lower error in most of the cases, and a clear improvement in the quality of the generated models.
Key elements of an information security culture in organisations

Purpose The purpose of this paper is to report on a study that investigated the information security culture in organisations in South Africa, with the aim of identifying key aspects of the culture. The unique aspects for building an information security culture were examined and presented in the form of an initial framework. These efforts are necessary to address the critical human aspect of information security in organisations where risky cyber behaviour is still experienced. Design/methodology/approach Literature was investigated with the focus on the main keywords security culture and information security. The information security culture aspects of different studies were compared and analysed to identify key elements of information security culture after which an initial framework was constructed. An online survey was then conducted in which respondents were asked to assess the importance of the elements and to record possible missing elements/aspects regarding their organisation’s information security culture to construct an enhanced framework. Findings A list of 21 unique security culture elements was identified from the literature. These elements/aspects were divided into three groups based on the frequency each was mentioned or discussed in studies. The number of times an element was found was interpreted as an indication of how important that element/aspect is. A further four aspects were added to the enhanced framework based on the results that emerged from the survey. Originality/value The value of this research is that an initial framework of information security culture aspects was constructed that can be used to ensure that an organisation incorporates all key aspects in its own information security culture. This framework was further enhanced from the results of the survey. The framework can also assist further studies related to the information security culture in organisations for improved security awareness and safer cyber behaviour of employees.
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings

Word embeddings typically represent different meanings of a word in a single conflated vector.Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts.We present a large dataset based on manual Wikipedia annotations and word senses, where word senses from different words are related by semantic classes.This is the basis for novel diagnostic tests for an embedding's content: we probe word embeddings for semantic classes and analyze the embedding space by classifying embeddings into semantic classes.Our main findings are: (i) Information about a sense is generally represented well in a single-vector embedding -if the sense is frequent.(ii) A classifier can accurately predict whether a word is single-sense or multi-sense, based only on its embedding.(iii) Although rare senses are not well represented in single-vector embeddings, this does not have negative impact on an NLP application whose performance depends on frequent senses.