Reviewerly/exHarmony huggingface dataset:
A Virtual Versus an Augmented Reality Cooking Task Based-Tools: A Behavioral and Physiological Study on the Assessment of Executive Functions

Virtual reality (VR) and augmented reality (AR) are two novel graphics immersive techniques (GIT) that, in the last decade, have been attracting the attention of many researchers, especially in psychological research. VR can provide 3D real-life synthetic environments in which controllers allow human interaction. AR overlays synthetic elements to the real world and the human gaze to target allow hand gesture to act with synthetic elements. Both techniques are providing more ecologically environments than traditional methods, and most of the previous researches, on one side, have more focused on the use of VR for treatment and assessment showing positive effectiveness results. On the other, AR has been proving for the treatment of specific disorders but there are no studies that investigated the feasibility and effectiveness of augmented reality in the neuropsychological assessment. Starting from these premises, the present study aimed to compare the performance and sense of presence using both techniques during an ecological task, such as cooking. The study included 50 cognitively healthy subjects. The cooking task consisted of 4 levels that increased in difficulty. As the level increased, additional activities appeared. The order of presentation of each exposure condition (AR and VR) was counterbalanced for each participant. The virtual reality-cooking task has been performed through “HTC/VIVE” and augmented reality through “Microsoft HoloLens”. Furthermore, the study recorded and compared the psychophysiological changes (heart rate and skin conductance response) during the cooking task in both conditions. To measure the sense of presence occurring during the two exposure conditions, subjects completed the SUSQ and the ITC-SOPI immediately after each condition. The behavioral results showed that times are always lower in VR than in AR, increasing constantly in accordance with the difficulty of the tasks. Regarding physiological responses, the findings showed that AR condition produced more individual excitement and activation than VR. Finally, VR was able to produce higher levels of sense of presence than AR condition. The overall results support that VR currently represents the GIT with greater usability and feasibility compared to AR, probably due to the differences in the human-computer interaction between the two techniques.
Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail to capturelong distance relationship in graphs; (2) they require users to label data from several classes to facilitate the learning of discriminative models; whereas in reality, users may only provide labels of a small number of nodes in a single class. To overcome these limitations, this paper presents a novel long-short distance aggregation networks (\textttLSDAN ) for positive unlabeled (PU) graph learning. Our theme is to generate multiple graphs at different distances based on the adjacency matrix, and further develop a long-short distance attention model for these graphs. The short-distance attention mechanism is used to capture the importance of neighbor nodes to a target node. The long-distance attention mechanism is used to capture the propagation of information within a localized area of each node and help model weights of different graphs for node representation learning. A non-negative risk estimator is further employed, to aggregate long- short-distance networks, for PU learning using back-propagated loss modeling. Experiments on real-world datasets validate the effectiveness of our approach.
Thermal Facial Landmark Detection by Deep Multi-Task Learning

We present a neural network to jointly consider facial landmark detection and emotion recognition for thermal face images. The first part of this network is based on the U-Net structure, targeting at extracting good features for advanced analysis. Using U - Net as the basic structure enables modeling context information based on a limited number of training data. The second part of this network contains two branches that are designed for landmark detection and emotion recognition, respectively. We propose a two-stage training mechanism to learn this network, and demonstrate the effectiveness of the proposed approach. This work is believed to be one of the few studies on thermal face image analysis.
Better together: Comparing vulnerability prediction models

Vulnerability Prediction Models (VPMs) are an approach for prioritizing security inspection and testing to find and fix vulnerabilities. VPMs have been created based on a variety of metrics and approaches, yet widespread adoption of VPM usage in practice has not occurred. Knowing which VPMs have strong prediction and which VPMs have low data requirements and resources usage would be useful for practitioners to match VPMs to their project's needs. The low density of vulnerabilities compared to defects is also an obstacle for practical VPMs. The goal of the paper is to help security practitioners and researchers choose appropriate features for vulnerability prediction through a comparison of Vulnerability Prediction Models. We performed replications of VPMs on Mozilla Firefox with 28,750 source code files featuring 271 vulnerabilities using software metrics, text mining, and crash data. We then combined features from each VPM and reran our classifiers. We improved the F-score of the best VPM (.20 to 0.28) by combining features from three types of VPMs and using Naive Bayes as the classifier. The strongest features in the combined model were the number of times a file was involved in a crash, the number of outgoing calls from a file, and the string "nullptr". Our results indicate that further work is needed to develop new features for input into classifiers. In addition, new analytic approaches for VPMs are needed for VPMs to be useful in practical situations, due to the low density of vulnerabilities in software (less than 1% for our dataset).
AI + Co-Design

In this report, we review our early steps in leveraging the promise of Artificial Intelligence to potentially create more inclusive co-design experiences for marginalized populations. We discuss our research agenda and the first phases where we utilized Wizard of Oz prototypes to engage participants in an online co-design environment. We found that the types of interactions between the facilitator and participant determined the amount of engagement with the co-design process.
Differentially Private Nonparametric Hypothesis Testing

Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we study differentially private tests of independence between a categorical and a continuous variable. We take as our starting point traditional nonparametric tests, which require no distributional assumption (e.g., normality) about the data distribution. We present private analogues of the Kruskal-Wallis, Mann-Whitney, and Wilcoxon signed-rank tests, as well as the parametric one-sample t-test. These tests use novel test statistics developed specifically for the private setting. We compare our tests to prior work, both on parametric and nonparametric tests. We find that in all cases our new nonparametric tests achieve large improvements in statistical power, even when the assumptions of parametric tests are met.
CTBRNN: A Novel Deep-Learning Based Signal Sequence Detector for Communications Systems

In this letter, a deep-learning based method is proposed for signal sequence detection. A novel neural network (NN) architecture, in communications systems called Cooperative and Time-varying Bidirectional Recurrent Neural Network (CTBRNN), is developed, which learns from the training data and estimates the transmitted signal sequence without knowing the underlying channel model. Furthermore, we develop a chemical communication experimental platform to collect real data, which is used to train the NN and evaluate the performance of the developed detector. Experimental results demonstrate that, the proposed detection method outperforms the existing NN-based and NN-free candidate solutions in terms of the detection accuracy.
DBkWik: extracting and integrating knowledge from thousands of Wikis

Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the potential use of DBkWik as a benchmark for knowledge graph matching.
Phase harmonic correlations and convolutional neural networks

Abstract A major issue in harmonic analysis is to capture the phase dependence of frequency representations, which carries important signal properties. It seems that convolutional neural networks have found a way. Over time-series and images, convolutional networks often learn a first layer of filters that are well localized in the frequency domain, with different phases. We show that a rectifier then acts as a filter on the phase of the resulting coefficients. It computes signal descriptors that are local in space, frequency and phase. The nonlinear phase filter becomes a multiplicative operator over phase harmonics computed with a Fourier transform along the phase. We prove that it defines a bi-Lipschitz and invertible representation. The correlations of phase harmonics coefficients characterize coherent structures from their phase dependence across frequencies. For wavelet filters, we show numerically that signals having sparse wavelet coefficients can be recovered from few phase harmonic correlations, which provide a compressive representation.
Matching training to individual learning styles improves information security awareness

Purpose This paper aims to introduce the concept of a framework of cyber-security controls that are adaptable to different types of organisations and different types of employees. One of these adaptive controls, namely, the mode of training provided, is then empirically tested for its effectiveness. Design/methodology/approach In total, 1,048 working Australian adults completed the human aspects of the information security questionnaire (HAIS-Q) to determine their individual information security awareness (ISA). This included questions relating to the various modes of cyber-security training they had received and how often it was provided. Also, a set of questions called the cyber-security learning-styles inventory was used to identify their preferred learning styles for training. Findings The extent to which the training that an individual received matched their learning preferences was positively associated with their information security awareness (ISA) level. However, the frequency of such training did not directly predict ISA levels. Research limitations/implications Further research should examine the influence of matching cyber-security learning styles to training packages more directly by conducting a controlled trial where the training packages provided differ only in the mode of learning. Further research should also investigate how individual tailoring of aspects of an adaptive control framework (ACF), other than training, may improve ISA. Practical implications If cyber-security training is adapted to the preferred learning styles of individuals, their level of ISA will improve, and therefore, their non-malicious behaviour, whilst using a digital device to do their work, will be safer. Originality/value A review of the literature confirmed that ACFs for cyber-security does exist, but only in terms of hardware and software controls. There is no evidence of any literature on frameworks that include controls that are adaptable to human factors within the context of information security. In addition, this is the first study to show that ISA is improved when cyber-security training is provided in line with an individual’s preferred learning style. Similar improvement was not evident when the training frequency was increased suggesting real-world improvements in ISA may be possible without increasing training budgets but by simply matching individuals to their desired mode of training.
Towards zero-shot learning generalization via a cosine distance loss

With the knowledge learned from some labelled training images, zero-shot learning (ZSL) aims to recognize new visual concepts by leveraging some intermediate information for both seen and unseen classes. Despite the existence of various methods, few work tends to comprehensively evaluate the generalization ability towards some practical data, which is important for their popularization and application. In this paper, we illustrate that it is inadequate and unconvincing to evaluate the ZSL methods using the current fixed data split. 19 existing methods are investigated on two practical datasets using 5-fold cross-validation to evaluate their generalization performances. More specifically, to alleviate the hubness problem in the high-dimensional visual space, we propose a cosine distance-based objective function to learn the transformation from semantic to visual features. Extensive experiments on up to 9 practical subsets demonstrate that the proposed method significantly outperforms the other baseline approaches.
Hierarchical Learning Water Cycle Algorithm

In order to improve the global searching ability of Water Cycle Algorithm (WCA), the hierarchical learning concept is introduced and the Hierarchical Learning WCA (HLWCA) is proposed in this paper. The underlying idea of HLWCA is to divide the solutions into collections and give these collections with hierarchy differences. One of the collections has a higher hierarchy than others and utilizes an exploration-inclined updating mechanism. The solutions in this high hierarchy collection are the exemplars of other collections. The other collections are sorted according to the exemplars’ function value and the solutions in these collections actively choose whether to follow their own exemplar or not. Through different updating mechanisms of collections, the global searching ability is improved while the fast convergence and strong local search ability of WCA are retained. The proposed HLWCA is firstly experimented on IEEE CEC 2017 benchmark suite to testify its performance on complex numerical optimization tasks. Then, it is tested on four practical design benchmark problems to verify its ability of solving real-world problems. The experimental results illustrate the efficiency of the proposed algorithm.
You Are Who You Appear to Be

The public key infrastructure (PKI) provides the fundamental property of authentication: the means by which users can know with whom they are communicating online. The PKI ensures end-to-end authenticity insofar as it verifies a chain of certificates, but the true final step in end-to-end authentication comes when the user verifies that the website is what they expect. To this end, users are expected to evaluate domain names, but various "domain impersonation" attacks threaten their ability to do so. Indeed, if a user could be easily tricked into believing that amazon.com-offers.com is actually amazon.com, then, coupled with security indicators like a lock icon, users could believe that they have a secure connection to Amazon.
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos

We propose a novel approach that disentangles the identity and pose of objects for image generation. Our model takes as input an ID image and a pose image, and generates an output image with the identity of the ID image and the pose of the pose image. Unlike most previous unsupervised work which rely on cyclic constraints, which can often be brittle, we instead propose to learn this in a self-supervised way. Specifically, we leverage unlabeled videos to automatically construct pseudo ground-truth targets to directly supervise our model. To enforce disentanglement, we propose a novel disentanglement loss, and to improve realism, we propose a pixel-verification loss in which the generated image's pixels must trace back to the ID input. We conduct extensive experiments on both synthetic and real images to demonstrate improved realism, diversity, and ID/pose disentanglement compared to existing methods.
Walkable Mixed Reality Map as interaction interface for Virtual Heritage

Studies in the Virtual Heritage (VH) domain have led to underlining the significance of a contextual relationship between users, immersive reality technologies and interactive and engaging cultural context as essential aspects towards enabling cultural learning in VH applications. Interaction methods, therefore, play a great role in terms of enabling interactive and engaging experience under various VH settings. This paper proposes a novel approach to use maps as interaction interfaces in a Mixed Reality (MxR) environment that could be applied to specific VH settings with a predefined cultural and historical context. The primary focus of the proposed interaction method named 'Walkable MxR Map' is to use interactive, immersive and walkable maps to allow users interact with cultural content, 3D models, and different multimedia content at museums and heritage sites. Hence, the applicability of the proposed systems will not be limited to museums' indoor settings; its applicability extends outdoors at the natural location of cultural heritage assets. To this end, immersive reality technologies, interaction methods, development platforms and mapping and cloud storage services have been combined to realise the interaction method. The Walkable MxR Map allows users to interact with virtual objects via maps that are virtually projected on the floor and viewable through MxR devices, specifically the Microsoft HoloLens. The projected maps are room-scale and walkable with a potential global scalability. Besides movement-based interaction, users can interact with virtual objects, multimedia content and 3D models using HoloLens's standard gesture, gaze and voice interaction methods.
Linear canonical Stockwell transform

The linear canonical transform (LCT) has long been recognized as a competent tool for optics and signal processing. However, the LCT cannot reveal the local LCT-frequency contents due to its global kernel and as such makes the LCT incompetent in situations demanding a joint information of time and linear canonical domain-frequency. In this paper, we propose the linear canonical Stockwell transform (LCST) in order to rectify the limitations of the LCT and the Stockwell transform (ST). Firstly, we examine the resolution of the novel transform in the time and LCT domains and then derive some of its basic properties, such as Moyal's formula, inversion formula and provide a characterization of the range. Furthermore, we derive a direct relationship between the well known Wigner distribution and the proposed LCST. In sequel, a discrete version of the linear canonical Stockwell transform is also presented. Finally, we extend the scope of the proposed work by studying the LCST in the realm of almost periodic functions.
Label Relation Inference for Multi-Label Aerial Image Classification

Multi-label aerial image classification is a challenging visual task and obtaining increasing attention recently. Most of the existing methods resort to training independent classifier for each label, while underlying label correlations are not fully exploited while making predictions. To this end, we propose an innovative inference network, which takes advantage of pairwise label relations to infer multiple object labels of a high-resolution aerial image. Specifically, we first employ a feature extraction module to extract high-level feature representations of an aerial image, and then, feed them into a relational inference module to predict the presence of each object label. We evaluate our network on the UCM multilabel dataset and experiment with various popular convolutional neural networks (CNNs) as the backbone of the feature extraction module. Experimental results demonstrate that the proposed network behaves superiorly in comparison with other existing methods.
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement

We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data. While many recent attempts at factor disentanglement have focused on sophisticated learning objectives within the VAE framework, their choice of a standard normal as the latent factor prior is both suboptimal and detrimental to performance. Our key observation is that the disentangled latent variables responsible for major sources of variability, the relevant factors, can be more appropriately modeled using long-tail distributions. The typical Gaussian priors are, on the other hand, better suited for modeling of nuisance factors. Motivated by this, we extend the VAE to a hierarchical Bayesian model by introducing hyper-priors on the variances of Gaussian latent priors, mimicking an infinite mixture, while maintaining tractable learning and inference of the traditional VAEs. This analysis signifies the importance of partitioning and treating in a different manner the latent dimensions corresponding to relevant factors and nuisances. Our proposed models, dubbed Bayes-Factor-VAEs, are shown to outperform existing methods both quantitatively and qualitatively in terms of latent disentanglement across several challenging benchmark tasks.
Leveraging Textual Paralanguage and Consumer–Brand Relationships for More Relatable Online Brand Communication: A Social Presence Approach

The recent proliferation of social Web applications has facilitated an array of new customer engagement behaviors. Creating social presence in brand communication is key to creating positive outcomes for brands. Present research integrates social presence theory and the consumer–brand relationship (CBR) perspective in examining how the use of textual paralanguage (TPL; e.g., emojis and emoticons) and CBRs can be leveraged to enhance message relatability in the context of responding to product reviews. A 2 (CBR strength) × 3 (TPL) online experiment is conducted. Findings show that TPL use increases the social presence of brand messages, leading to more positive attitude toward the brand, greater purchase intention, and the strengthening of brand relationships. Further, while CBR strength did not influence social presence of responses, actively engaging with consumers increases purchase intention and significantly strengthens weak brand relationships.