Reviewerly/exHarmony huggingface dataset:
PHISHGEM: a mobile game-based learning for phishing awareness

Phishing attacks have become the most effective means of gaining unauthorized access to confidential information in the cyberspace. Unfortunately, many Internet users cannot identify phishing strategies and fall victim to these attacks. This work presents a mobile game-based learning called PHISHGEM for enhancing the awareness of phishing attacks. PHISHGEM educates users on five (5) major types of phishing techniques, namely, URL Manipulation, Email Spoofing, Website Cloning, Smishing and Social Media Attacks. The users of the game will identify phishing attacks in a wide range of real-life scenarios using a mobile application. The functionalities of the game were run on JAVA programming language, XML (Extensible Markup Language) was used to create the layout and Android studio IDE Version 4.1 was used to run the program. A user study with 100 participants was conducted to determine the effectiveness of PHISHGEM. Three (3) prominent factors such as the playability, usability and users’ learning outcome were used for the evaluation of the game. The study analysis revealed that PHISHGEM attained a 98% Awareness Level, a Perceived Ease of Use of 94% and a New Knowledge Acquired of 94%. Also, the Perceived Effectiveness of the game and Users’ Experience were given positive responses of 90% and 89%, respectively.
Chatbots as Part of Digital Government Service Provision – A User Perspective

Chatbots are taken up as part of digital government service provision. While the success of chatbots for this purpose depends on these being accepted by their intended users, there is a lack of knowledge concerning user perceptions of such chatbots and the implications of these for intention to use. In response to this, an exploratory qualitative interview study was conducted with 15 users of a chatbot for municipality service provision. The interviews showed the importance of performance expectations, effort expectations, and trust. In particular, while a municipality chatbot supporting service triaging may be perceived as beneficial for their availability and to provide support navigation of municipality services and information, this benefit is compared by users to the benefit of other digital government channels. On the basis of the findings, we present key implications to theory and practice, and suggest avenues for future research.
A Framework and Content Analysis of Social Cues in the Introductions of Customer Service Chatbots

Organizations are increasingly implementing chatbots to address customers' inquiries, but customers still have unsatisfactory encounters with them. In order to successfully deploy customer service chatbots, it is important for organizations and designers to understand how to introduce them to customers. Arguably, how a chatbot introduces itself as well as its services might influence customers' perceptions about the chatbot. Therefore, a framework was developed to annotate the social cues in chatbot introductions. In order to validate our framework, we conducted a content analysis of introductions of customer service chatbots (n = 88). The results showed that the framework turned out to be a reliable identification instrument. Moreover, the most prevalent social cue in chatbot introductions was a humanlike avatar, whereas communication cues, indicating the chatbot's functionalities, hardly occurred. The paper ends with implications for the design of chatbot introductions and possibilities for future research.
A structural developmental neural network with information saturation for continual unsupervised learning

Abstract In this paper, we propose a structural developmental neural network to address the plasticity‐stability dilemma, computational inefficiency, and lack of prior knowledge in continual unsupervised learning. This model uses competitive learning rules and dynamic neurons with information saturation to achieve parameter adjustment and adaptive structure development. Dynamic neurons adjust the information saturation after winning the competition and use this parameter to modulate the neuron parameter adjustment and the division timing. By dividing to generate new neurons, the network not only keeps sensitive to novel features but also can subdivide classes learnt repeatedly. The dynamic neurons with information saturation and division mechanism can simulate the long short‐term memory of the human brain, which enables the network to continually learn new samples while maintaining the previous learning results. The parent‐child relationship between neurons arising from neuronal division enables the network to simulate the human cognitive process that gradually refines the perception of objects. By setting the clustering layer parameter, users can choose the desired degree of class subdivision. Experimental results on artificial and real‐world datasets demonstrate that the proposed model is feasible for unsupervised learning tasks in instance increment and class increment scenarios and outperforms prior structural developmental neural networks.
SuperConText: Supervised Contrastive Learning Framework for Textual Representations

In the last decade, Deep neural networks (DNNs) have been proven to outperform conventional machine learning models in supervised learning tasks. Most of these models are typically optimized by minimizing the well-known Cross-Entropy objective function. The latter, however, has a number of drawbacks, including poor margins and instability. Taking inspiration from the recent self-supervised Contrastive representation learning approaches, we introduce Supervised Contrastive learning framework for Textual representations (SuperConText) to address those issues.We pretrain a neural network by minimizing a novel fully-supervised contrastive loss. The goal is to increase both inter-class separability and intra-class compactness of the embeddings in the latent space. Examples belonging to the same class are regarded as positive pairs, while examples belonging to different classes are considered negatives. Further, we propose a simple yet effective method for selecting hard negatives during the training phase. In an extensive series of experiments, we study the impact of a number parameters on the quality of the learned representations (e.g. the batch size). Simulation results show that the proposed solution outperforms several competing approaches on various large-scale text classification benchmarks without requiring specialized architectures, data augmentations, memory banks, or additional unsupervised data. For instance, we achieve top-1 accuracy of 61.94% on the Amazon-F dataset, which is 3.54% above the best result obtained when using the cross-entropy with the same model architecture.
Impact of Avatar Representation in a Virtual Reality-Based Multi-user Tunnel Fire Simulator for Training Purposes

Virtual Reality (VR) technology is playing an increasingly important role in the field of training. The emergency domain, in particular, can benefit from various advantages of VR with respect to traditional training approaches. One of the most promising features of VR-based training is the possibility to share the virtual experience with other users. In multi-user training scenarios, the trainees have to be provided with a proper representation of both the other peers and themselves, with the aim of fostering mutual awareness, communication and cooperation. Various techniques for representing avatars in VR have been proposed in the scientific literature and employed in commercial applications. However, the impact of these techniques when deployed to multi-user scenarios for emergency training has not been extensively explored yet. In this work, two techniques for avatar representation in VR, i.e., no avatar (VR Kit only) and Full-Body reconstruction (blending of inverse kinematics and animations), are compared in the context of emergency training. Experiments were carried out in a training scenario simulating a road tunnel fire. The participants were requested to collaborate with a partner (controlled by an experimenter) to cope with the emergency, and aspects concerning perceived embodiment, immersion, and social presence were investigated.
Devanagari Handwritten Character Recognition using Transfer Learning with Deep CNN and SVM

This paper proposes how Transfer Learning can be utilized in recognition of handwritten Devanagari characters. A Deep Convolution Neural Network (Deep CNN) is used for features extraction and classification, whereas Deep CNN with Support Vector Machine (SVM) is used as a concept of Transfer Learning, where the features extracted by the Deep CNN are fed to SVM. Experiments conducted on real-world dataset show that the proposed approach not only achieved 99.413% accuracy on test set but also reduced the number of parameters used for classification.
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.
Toward Unsupervised Outlier Model Selection

Today there exists no shortage of outlier detection algorithms in the literature, yet the complementary and critical problem of unsupervised outlier model selection (UOMS) is vastly understudied. In this work, we propose ELECT, a new approach to select an effective candidate model, i.e. an outlier detection algorithm and its hyperparameter(s), to employ on a new dataset without any labels. At its core, ELECT is based on meta-learning; transferring prior knowledge (e.g. model performance) on historical datasets that are similar to the new one to facilitate UOMS. Uniquely, it employs a dataset similarity measure that is performance-based, which is more direct and goal-driven than other measures used in the past. ELECT adaptively searches for similar historical datasets, as such, it can serve an output on-demand, being able to accommodate varying time budgets. Extensive experiments show that ELECT significantly outperforms a wide range of basic UOMS baselines, including no model selection (always using the same popular model such as iForest) as well as more recent selection strategies based on meta-features.
Unifying Graph Contrastive Learning with Flexible Contextual Scopes

Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling information for graph representation learning. The core of GCL is to maximise the mutual information between the representation of a node and its contextual representation (i.e., the corresponding instance with similar semantic information) summarised from the contextual scope (e.g., the whole graph or 1-hop neighbourhood). This scheme distils valuable self-supervision signals for GCL training. However, existing GCL methods still suffer from limitations, such as the incapacity or inconvenience in choosing a suitable contextual scope for different datasets and building biased contrastiveness. To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short). Our algorithm builds flexible contextual representations with tunable contextual scopes by controlling the power of an adjacency matrix. Additionally, our method ensures contrastiveness is built within connected components to reduce the bias of contextual representations. Based on representations from both local and contextual scopes, UGCL optimises a very simple contrastive loss function for graph representation learning. Essentially, the architecture of UGCL can be considered as a general framework to unify existing GCL methods. We have conducted intensive experiments and achieved new state-of-the-art performance in six out of eight benchmark datasets compared with self-supervised graph representation learning baselines. Our code has been open sourced <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/zyzisastudyreallyhardguy/UGCL
On Mean-Optimal Robust Linear Discriminant Analysis

Linear discriminant analysis (LDA) is widely used for dimensionality reduction under supervised learning settings. Traditional LDA objective aims to minimize the ratio of squared Euclidean distances that may not perform optimally on noisy data sets. Multiple robust LDA objectives have been proposed to address this problem, but their implementations have two major limitations. One is that their mean calculations use the squared $\ell_{2}$-norm distance to center the data, which is not valid when the objective does not use the Euclidean distance. The second problem is that there is no generalized optimization algorithm to solve different robust LDA objectives. In addition, most existing algorithms can only guarantee the solution to be locally optimal, rather than globally optimal. In this paper, we review multiple robust loss functions and propose a new and generalized robust objective for LDA. Besides, to better remove the mean value within data, our objective uses an optimal way to center the data through learning. As one important algorithmic contribution, we derive an efficient iterative algorithm to optimize the resulting non-smooth and non-convex objective function. We theoretically prove that our solution algorithm guarantees that both the objective and the solution sequences converge to globally optimal solutions at a sub-linear convergence rate. The experimental results demonstrate the effectiveness of our new method, achieving significant improvements compared to the other competing methods.
Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation

Despite achieving exceptional performance, deep neural networks (DNNs) suffer from the harassment caused by adversarial examples, which are produced by corrupting clean examples with tiny perturbations. Many powerful defense methods have been presented such as training data augmentation and input reconstruction which, however, usually rely on the prior knowledge of the targeted models or attacks. A clean example and its adversarial version are very similar but have different high-level representations in a victim model. If we can obtain a space in which the representations of similar examples are also similar, then adversarial examples can be picked out by comparing the representations of input examples in this space and the high-level space of the victim model. Inspired by this, we propose a novel approach for detecting adversarial images, which can protect any pre-trained DNN classifiers and resist an endless stream of new attacks. Specifically, we first adopt a dual autoencoder to project images to a latent space. The dual autoencoder uses the self-supervised learning to ensure that small modifications to samples do not significantly alter their latent representations. Next, the mutual information neural estimation is utilized to enhance the discrimination of the latent representations. We then leverage the prior distribution matching to regularize the latent representations. To easily compare the representations of examples in the two spaces, and not rely on the prior knowledge of the targeted model, a simple fully connected neural network is used to embed the learned representations into an eigenspace, which is consistent with the output eigenspace of the targeted model. Through the distribution similarity of an input example in the two eigenspaces, we can judge whether the input example is adversarial or not. Extensive experiments on MNIST, CIFAR-10, and ImageNet show that the proposed method has superior defense performance and transferability than state-of-the-arts.
Explainable Deep Learning for Alzheimer Disease Classification and Localisation

Alzheimer’s disease is an irreversible neurological brain disorder that causes nuero-degenerative cognitive function like memory loss and thinking abilities. The accurate diagnosis of Alzheimer’s disease at an early stage is very crucial for patient care and conducting future treatment. Deep learning can help to reach the diagnosis: for this reason we propose a method aimed to distinguish and properly classify four Alzheimer disease’s stages. Two different deep learning models are exploited: Alex_Net and a model designed by authors, obtaining an average accuracy equal to 0.97 with the deep learning network developed by authors applying a colormap to brain magnetic resonance images. Our method provides also the localization areas used by the model to perform the classification (by adopting the heatmap overlapping provided by Gradient-weighted Class Activation Mapping algorithm) in order to ensures the explainability of the method.
Application of elastic wave tomography method for damage evaluation in a large-scale reinforced concrete structure

Quality control of construction works and characterization of defects in newly built reinforced concrete structures is an important issue. Usually, these kinds of defects can be evaluated by means of visual inspection, however, there are cases, in which damage cannot be reliably evaluated visually. In this situation, Non-Destructive Testing methods can be used. In this study we report the results of application of Elastic Wave Tomography method for evaluation of a defect in a newly built reinforced concrete bridge pier. A large void located inside the bridge pier was detected using Elastic Wave Tomography method. Due to the presence of the large defect the structure was demolished. Elements of the demolished bridge pier were visually observed, which allowed to confirm the results of Elastic Wave Tomography method.
National-Scale Geochemical Baseline and Anomalies of Chromium in Papua New Guinea

Papua New Guinea (PNG) is located at the convergence edge of the Pacific Plate and the Indo-Australian Plate, consisting of three units. There are three chromium mineralization types in PNG. Based on national-scale geochemical mapping in PNG during 2015–2018, 1399 samples of stream sediments were collected from Highland Region, Papua Peninsula, and New Guinea Islands. This paper preliminarily studied chromium’s geochemical background, spatial distribution characteristics, and geochemical anomalies. The chromium concentration ranged from 3 ppm to 74,600 ppm, with a median value of 145 ppm, which was higher than the upper crustal abundance of chromium and the chromium geochemical baseline of Europe, Australia, North America, and China. In terms of stream sediment samples in different tectonic units, as mafic–ultramafic magmatic rocks are widely developed, the median chromium values of the New Guinea Orogen, including the Papuan Fold Belt, the New Guinea Thrust Belt, the Finisterre Terrane, the Aure Fold Belt, the Eastern Fold Belt, and the Eastern Papuan Composite Terrane, were higher than the value of the Melanesian Arc. The ophiolitic complexes, such as the April ophiolite, the Marum ophiolite, and the Papua ultramafic belt, significantly correlated with the higher chromium concentration. Eleven chromium high anomalies with mineralization potential were delineated, including three laterite and podiform prospecting areas and eight placer prospecting areas. Based on the chromium–nickel integrated anomaly map, comprehensive exploration and exploitation of nickel and chromium can be carried out in 1 and 11 high anomaly areas related to lateritic mineralization.
Handling Imbalanced and Overlapped Medical Datasets: A Comparative Study

An imbalanced dataset with class overlapping is a challenging issue in medical research. Imbalanced data points lead to overfitting for the majority class while overlapped classes cause misclassification for both classes. Hence, this combination makes it challenging for classic machine learning algorithms to define a decision boundary between minority and majority classes. In our study, different algorithms with different techniques have been compared for example oversampling, undersampling, combined over and under sampling, and the ensemble methods to deal with class imbalance along with class overlapping. Two well-known highly imbalanced and overlapped medical datasets are used to compare the performance of different approaches and performance is evaluated by sensitivity and specificity. On the sleep apnea dataset, oversampling combined with ensemble classifier AdaBoost with the specificity and sensitivity of 0.72 and 0.46 which proved better than other techniques and classifiers. On the diabetes dataset, SMOTE-TOMEK oversampling combined with the Random Forrest classifier with the specificity and sensitivity of 0.91 and 0.77 proved to be better than all the combinations that have been tried for the classification with minimal number of features.
Initial insights of laundry detergent and additive impact on e-textile surface resistivity

Smart clothing represents less than 1% of the multibillion dollar wearables market. This lagging representation is due to many factors; one predominant challenge which includes performance reliability post-laundering/care. Due to smart clothing intimately contacting the human body, it naturally becomes soiled and requires laundering. However, insight into the impact of consumer detergents and other additives on e-textile conductive functionality post laundering has yet to be explored. The purpose of this research was to study laundering conditions, specifically the influence of select laundry detergents and additives, on surface resistivity of select e-textiles to provide initial insights into the impact of consumer-available laundering treatments to contribute to knowledge around laundering of e-textiles. Understanding initial impacts of laundering conditions, particularly detergents and other common laundry additives can inform future studies, laundering text methods of e-textiles, and care label development.