Reviewerly/exHarmony huggingface dataset:
Using the security triad to assess blockchain technology in public sector applications

This conceptual paper explores the impact of blockchain technology on public sector processes through the lens of information security. It includes an overview of the evolution of e-government, a synopsis of existing applications of blockchain technology, and innovative blockchain developments. We utilize the Confidentiality-Integrity-Accessibility (CIA) triad to guide our discussion of the security, governance, and regulatory implications of this technology. Leveraging the CIA triad model, we provide context for public managers who may consider blockchain technologies, and we highlight certain advantages arising from the “non-reputability” of distributed ledgers. In particular, we highlight the advantages of blockchain technologies with regards to non-reputability to help public managers understand how to best leverage blockchain technology to transform operations.
Tourists’ perceptions regarding the use of anthropomorphic robots in tourism and hospitality

Purpose Amidst the COVID-19 outbreak, service organizations rushed to deploy robots to serve people in quarantine, again igniting the ongoing dispute regarding robots in tourism. This study aims to investigate tourists’ perceptions regarding the use of robots and, more specifically, anthropomorphic robots in the tourism domain. Design/methodology/approach A qualitative inquiry was used to delve deep into the issue of tourists’ perceptions regarding the usage of anthropomorphic robots in tourism, with a total number of 78 interviews with tourists being retained in the study. Findings The findings reveal that tourists favor the use of anthropomorphic robots over any other type of robot. The use of anthropomorphic robots in tourism may result in an overall enhanced experiential value. Even so, informants also expressed frustration, sadness and disappointment vis-à-vis the use of robots in a human-driven industry. Research limitations/implications A conceptual continuum of tourists’ perceptions and concerns over the use of robots is presented that can guide future studies. Tourism stakeholders may look at the possibility of incorporating carefully designed anthropomorphic robots in key service positions, but should not give the impression that robots are replacing the human face of the organization. Practical implications Tourism stakeholders may look at the possibility of incorporating carefully designed anthropomorphic robots in key service positions, but should not give the impression that robots are replacing the human face of the organization. Originality/value Tourism organizations that make use of robots run the risk of being perceived as nonanthropocentric. This leads to the conclusion that anthropomorphism could be used but should not replace the sector’s anthropocentrism. The study conveys tourists’ concerns over technological (robot) determinism.
Attention Guided Anomaly Localization in Images

Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous training images to compute a class-specific threshold to localize anomalies. Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss where we encourage CAVGA to focus on all normal regions in the image. Furthermore, in the weakly-supervised setting we propose a complementary guided attention loss, where we encourage the attention map to focus on all normal regions while minimizing the attention map corresponding to anomalous regions in the image. CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting and when using only 2% anomalous images in the weakly-supervised setting. CAVGA also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10, Fashion-MNIST, MVTAD, mSTC and LAG datasets.
Smart Contract-Based Blockchain-Envisioned Authentication Scheme for Smart Farming

A blockchain-based smart farming technology provides the agricultural data to the farmers and other users associated with smart farming on a single integrated platform. Moreover, persistence and auditability of stored data in blocks into the blockchain provide the confidence of using the correct data when needed later and adds transparency, anonymity, and traceability at the same time. To fulfill such a goal, in this article, we design a new smart contract-based blockchain-envisioned authenticated key agreement mechanism in a smart farming environment. The device-to-device (D2D) authentication phase and device-to-gateway (D2G) authentication phase support mutual authentication and key agreement between two Internet-of-Things (IoT)-enabled devices and between an IoT device and the gateway node (GWN) in the network, respectively. The blocks are created by the edge servers on the authenticated data of IoT devices received from the GWNs and then sent to the cloud server (CS). The smart contract-based consensus mechanism allows verification and addition of the formed blocks by a peer-to-peer (P2P) CSs network. The security of the proposed scheme is done through formal and informal security analysis, and also using the formal security verification tool. A detailed comparative study reveals that the proposed scheme offers superior security and more functionality features as compared to existing competing authentication protocols. Finally, the blockchain-based simulation has been conducted to measure computational time for a varied number of mined blocks and also a varied number of transactions per block.
Computer-aided diagnosis system for breast ultrasound images using deep learning

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for the classification of malignant and benign masses in the breast using ultrasonography based on a convolutional neural network (CNN), a state-of-the-art deep learning technique. We explored the regions for the correct classification by generating a heat map that presented the important regions used by the CNN for human malignancy/benign classification. Clinical data was obtained from a large-scale clinical trial previously conducted by the Japan Association of Breast and Thyroid Sonology. Images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations were collected, with each breast mass captured from various angles using an ultrasound (US) imaging probe. We constructed an ensemble network by combining two CNN models (VGG19 and ResNet152) fine-tuned on balanced training data with augmentation and used the mass-level classification method to enable the CNN to classify a given mass using all views. For an independent test set consisting of 154 masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9% (95% confidence interval 84.5–97.3), a specificity of 87.0% (79.5–94.5), and area under the curve (AUC) of 0.951 (0.916–0.987) compared to that of the two CNN models. In addition, our study indicated that the breast masses themselves were not detected by the CNN as important regions for correct mass classification. Collectively, this CNN-based CAD system is expected to assist doctors by improving the diagnosis of breast cancer in clinical practice.
An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimisation of mathematical test functions with a unique neighbourhood search mechanism. This neighbourhood search mechanism could not be directly applied to combinatorial discrete optimisation problems. In order to tackle combinatorial discrete optimisation problems, the employed and onlooker bees need to be equipped with problem-specific perturbative heuristics. However, a large variety of problem-specific heuristics are available, and it is not an easy task to select an appropriate heuristic for a specific problem. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The Lin-Kernighan (LK) local search strategy is integrated to improve the performance of the proposed model. To demonstrate the effectiveness of the proposed model, 64 Traveling Salesman Problem (TSP) instances available in TSPLIB are evaluated. On average, the proposed model solves the 64 instances to 0.055% from the known optimum within approximately 2.7 min. A performance comparison with other state-of-the-art algorithms further indicates the effectiveness of the proposed model.
Improved salp swarm algorithm for feature selection

Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. This paper tries to improve the structure of basic SSA to enhance solution accuracy, reliability and convergence speed. A new control parameter, inertia weight, is added to adjust the present best solution. The new method known as improved salp swarm algorithm (ISSA) is tested in feature selection task. The ISSA algorithm is consolidated with the K-nearest neighbor classier for feature selection in which twenty-three UCI datasets are utilized to assess the performance of ISSA algorithm. The ISSA is compared with the basic SSA and four other swarm methods. The results demonstrated that the proposed method produced superior results than the other optimizers in terms of classification accuracy and feature reduction.
Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem

In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimization (KSO) algorithm inspired by the kernel method has been proposed. KSO can simplify the optimization process by transforming the optimization process of nonlinear function into the linear optimization process. Despite its advantage, the original KSO requires a large amount of computation, and has no powerful exploitation search, resulting in its inability to obtain more accurate results. In the present study, a local search of the hill-climbing algorithm is adopted, and the calculation of the kernel parameter is simplified to improve the original KSO. In an experiment using 50 benchmark functions, the new algorithm outperformed KSO and some well-known algorithms in accuracy and running time. Moreover, when applied in the real-world economic emission dispatch problem, the improved algorithm achieved a better performance than other algorithms compared. An online repository will support this research at https://aliasgharheidari.com.
Self-Alignment Pretraining for Biomedical Entity Representations

Despite the widespread success of selfsupervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal.To address this challenge, we propose SAPBERT, a pretraining scheme that selfaligns the representation space of biomedical entities.We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts.In contrast with previous pipelinebased hybrid systems, SAPBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets.In the scientific domain, we achieve SOTA even without taskspecific supervision.With substantial improvement over various domain-specific pretrained MLMs such as BIOBERT, SCIBERT and PUB-MEDBERT, our pretraining scheme proves to be both effective and robust.
Educational Uses of Augmented Reality (AR): Experiences in Educational Science

Augmented Reality (AR) is an emerging technology that is gaining greater influence on teaching every day. AR, together with mobile technology, is defined as one of the most efficient pairs for supporting significant and ubiquitous learning. Purpose of the study: the Instructional Material Motivational Survey (IMMS), by Keller, was used to determine the degree of motivation possessed by the Pedagogy students on the utilization of the notes enriched with AR in the classroom, available for their didactic use through mobile devices. Methods: through an app designed for the courses Education Technology (ET) and Information and Communication Technologies (ICT) Applied to Education, the motivation gained when participating in this experience, and how it influences the improvement of academic performance, was evaluated. Results and conclusions: the most notable main result was finding a strong relationship between the motivation of the students when using the enriched notes and the increase of performance in the academic subject where it was used. Likewise, it was proved that the use of Augmented Reality benefited the learning process itself.
Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed.Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path.The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing.In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method.This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots.Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness.Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented.The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer.Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.
Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.
SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications

Chimp Optimization Algorithm (ChoA) is a recently developed meta-heuristic approach which is inspired by the individual intelligence and sexual motivation of chimps. It is designed for trapping the local optima to alleviate the slow convergence speed. In this paper, a hybrid algorithm is developed which is based on the sine–cosine functions and attacking strategy of Spotted Hyena Optimizer (SHO). This hybrid algorithm is termed as Sine–cosine and Spotted Hyena-based Chimp Optimization Algorithm (SSC). This algorithm is used to find the best optimal solutions of real-life complex problems. The sine–cosine and attacking strategy of SHO algorithm is responsible for better exploration and exploitation. These strategies are applied to update the equations of chimps during the searching process to overcome the drawbacks of the ChoA algorithm such as slow convergence and local minima. Experimental results based on IEEE CEC’17 and six real-life engineering problems such as welded beam design, tension/compression spring design, pressure vessel design, multiple disk clutch brake design, gear train design, and car side crashworthiness, demonstrate the robustness, effectiveness, efficiency, and convergence analysis of the proposed SSC algorithm in comparison with other competitor approaches. Note that the source codes are available at http://www.dhimangaurav.com.
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

Weakly supervised semantic segmentation produces a pixel-level localization from class labels; but a classifier trained on such labels is likely to restrict its focus to a small discriminative region of the target object. AdvCAM is an attribution map of an image that is manipulated to increase the classification score produced by a classifier. This manipulation is realized in an anti-adversarial manner, which perturbs the original images along pixel gradients in the opposite direction from those used in an adversarial attack. It forces regions initially considered not to be discriminative to become involved in subsequent classifications, and produces attribution maps that successively identify more regions of the target object. In addition, we introduce a new regularization procedure that inhibits both the incorrect attribution of regions unrelated to the target object and excessive concentration of attributions on a small region of that object. Our method is a post-hoc analysis of a trained classifier, which does not need to be altered or retrained. On PASCAL VOC 2012 test images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised semantic segmentation respectively, which represent a new state-of-the-art.
Optimal Robot–Environment Interaction Under Broad Fuzzy Neural Adaptive Control

This article proposes a novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment. Compared with the conventional fuzzy neural network (NN), a prominent feature can be achieved by taking the advantage of the broad learning system (BLS) to explicitly tackle the problem of how to choose a sufficient number of NN units to approximate the unknown dynamic model. Aiming at providing a soft compliant contact scheme without the requirement of the environment model, an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment. Meanwhile, the problems related to the state constraints are addressed by incorporating a barrier Lyapunov function (BLF) into the design of a trajectory tracking controller. The proposed method can achieve desired tracking and interaction performance while guaranteeing the stability of the closed-loop system. In addition, simulation and experimental studies are performed to verify the effectiveness of BFNN under optimal impedance control with a two degree-of-freedom (DOF) manipulator and a Baxter robot, respectively.
Capsule Network Algorithm for Performance Optimization of Text Classification

In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.
Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly relies on the features that they use, and thus, their success strictly depends on the ability of these features by successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning-based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning-based technique constructs a deep belief network of the restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example for successfully using the restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts.
More-than-human participation

In this forum we highlight innovative thought, design, and research in the area of interaction design and sustainability, illustrating the diversity of approaches across HCI communities. --- Roy Bendor, Editor