Reviewerly/exHarmony huggingface dataset:
The power of interpretation

Cybersecurity is a hot topic and researchers have published extensively on studies conducted using a variety of different research methods. This paper aims to determine which qualitative research methods were most used and for studying which topics. A systematic literature review on Web of Science, Scopus and ACM DL has been conducted to achieve an overview of quantitative methods used in cybersecurity. The review covered the most recent research in different areas of cybersecurity (i.e., personal, organizational and state cybersecurity) in the period of 2017 to 2019. After careful inspection of papers, we identified 160 papers reporting on the use of qualitative methods. The most common qualitative methods are interviews, followed by case studies and observation. Other studied qualitative methods (i.e., focus groups, grounded theory, action research and Delphi method) seem to be much less frequent. Although qualitative methods are used when studying all key cybersecurity areas, they often lack the necessary rigor and detail observed in other research areas where qualitative methods are well-established.
Distributional Deep Reinforcement Learning with a Mixture of Gaussians

In this paper, we propose a novel distributional reinforcement learning (RL) method which models the distribution of the sum of rewards using a mixture density network. Recently, it has been shown that modeling the randomness of the return distribution leads to better performance in Atari games and control tasks. Despite the success of the prior work, it has limitations which come from the use of a discrete distribution. First, it needs a projection step and softmax parametrization for the distribution, since it minimizes the KL divergence loss. Secondly, its performance depends on discretization hyperparameters such as the number of atoms and bounds of the support which require domain knowledge. We mitigate these problems with the proposed parameterization, a mixture of Gaussians. Furthermore, we propose a new distance metric called the Jensen-Tsallis distance, which allows the computation of the distance between two mixtures of Gaussians in a closed form. We have conducted various experiments to validate the proposed method, including Atari games and autonomous vehicle driving.
Forecasting global solar irradiance for various resolutions using time series models - case study: Algeria

(2019). Forecasting global solar irradiance for various resolutions using time series models - case study: Algeria. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. Ahead of Print.
Assessing Alzheimer’s Disease from Speech Using the i-vector Approach

One of the world's chronic neuro-degenerative diseases, Alzheimer's Disease (AD), leads its sufferers, among other symptoms, to suffer from speech difficulties. In particular, the inability to recall vocabulary which makes patients' speech different. Furthermore, Mild Cognitive Impairment (MCI) is usually considered as a prodromal neuro-degenerative state of AD. The key to abate the progress of both disorders is their early diagnosis. However, actual ways of diagnosis are costly and quite time-consuming. In this study, we propose the extraction of features from speech through the use of the i-vector approach, by which we seek to model the speech pattern of the three mental conditions from the subjects. To the best of our knowledge, no previous studies have utilized i-vector features to assess Alzheimer's before. These i-vectors are extracted from Mel-Frequency Cepstral Coefficients (MFCCs), then they are given to a SVM classifier in order to identify the speech in one of the following manners: AD - Alzheimer Disease, MCI - Mild Cognitive Impairment, HC - Healthy Control. We tested these i-vector features by performing a 5-fold cross-validation and we achieved an F1-score of 79.2%.
Architectural Design in Virtual Reality and Mixed Reality Environments: A Comparative Analysis

Virtual reality (VR) provides a completely digital world of interaction which enables the users to modify, edit, and transform digital elements in a responsive way. Mixed reality (MR), which is the result of blending the digital world and the physical world together, brings new advancements and challenges to human, computer and environment interactions. This paper focuses on adapting the already-existing methods and tools in architecture to both VR and MR environments under sustainable architectural design domain. For this purpose, we benefit from the semantically enriched data platforms of Building information modelling (BIM) tools, the performance calculation functions of building energy simulation tools while transcending these data into VR and MR environments. In this way, we were able to merge these diverse data for the virtual design activity. Nine participants have already tested the initial prototype of MR-based only interaction environment in our previous study [1]. According to the feedbacks, the user interface and interaction mechanisms were updated and the environment was made accessible also in VR. These updates made four types of interactions possible in MR and VR: 1) MR environment using HoloLens with gestures, 2) MR environment using HoloLens with a clicker, 3) VR environment using HTC Vive with two controllers, and 4) HoloLens emulator with a mouse. All these interaction cases were tested by 21 architecture students in an in-house workshop. In this workshop, we collected data on presence, usability, and technology acceptance of these cases. Our results show that interaction in a VR environment is the most natural interaction type and the participants were eager to use both MR and VR environments instead of an emulator. To our best of knowledge, this is the first comparative study of a BIM-based architectural design medium in both VR and MR environments.
Towards Reversible Storage Network Covert Channels

The use of network covert channels to improve privacy or support security threats has been widely discussed in the literature. As today, the totality of works mainly focuses on how to not disrupt the overt traffic flow and the performance of the covert channels in terms of undetectability and capacity. To not void the stealthiness of the channel, an important feature is the ability of restoring the carrier embedding the secret information into its original form. However, the development of such techniques mainly targets the domain of digital media steganography. Therefore, this paper applies the concept of reversible data hiding to storage network covert channels. To prove the effectiveness of our idea, a prototypical implementation of a channel exploiting IPv4 flows is presented along with its performance evaluation.
Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning

Differential privacy is a standard mathematical framework to quantify the degree to which individual privacy in a statistical dataset is preserved. We derive an optimal $$(\epsilon ,\delta )$$ –differentially private noise adding mechanism for real-valued data matrices meant for the training of models by machine learning algorithms. The aim is to protect a machine learning algorithm from an adversary who seeks to gain an information about the data from algorithm’s output by perturbing the value in a sample of the training data. The fundamental issue of trade-off between privacy and utility is addressed by presenting a novel approach consisting of three steps: (1) the sufficient conditions on the probability density function of noise for $$(\epsilon ,\delta )$$ –differential privacy of a machine learning algorithm are derived; (2) the noise distribution that, for a given level of entropy, minimizes the expected noise magnitude is derived; (3) using entropy level as the design parameter, the optimal entropy level and the corresponding probability density function of the noise are derived.
A novel Error-Correcting Output Codes algorithm based on genetic programming

Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this paper proposes a genetic programming (GP) based ECOC algorithm (GP-ECOC). In the design of individual of our GP, each terminal node represents a class, and nonterminal nodes combine the classes in their child nodes. In this way, an individual is a class combination tree, and represents an ECOC codematrix. A legality checking process is embedded in our algorithm to check each codematrix, so as to ensure each codematrix satisfying ECOC constraints. Those violating the constraints will be corrected by a proposed Guided Mutation operator. Before fitness evaluation, a local optimization algorithm is proposed to append new columns for tough classes, so as to improve the generalization ability of each individual and accelerate the evolutionary speed. In this way, our GP can evolve optimal codematrices through the evolutionary process. Experiments show that compared with other ensemble algorithms, our algorithm can achieve stable and high performances with relatively small ensemble scales on various UCI data sets. To the best of our knowledge, it is the first time that GP has been applied to implement the ECOC encoding algorithm. Our Python code is available at https://github.com/samuellees/gpecoc.
Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.
Improving cross-dimensional weighting pooling with multi-scale feature fusion for image retrieval

In this paper, we aim to achieve effective image representation for image retrieval in an unsupervised manner. To this end, we propose a fully cross-dimensional weighting pooling (FCroW) method to improve the weight strategy of the cross-dimensional weighting pooling (CroW). More specifically, FCroW weights both the non-zero parts and zero-parts of convolutional layers, aiming to obtain robust image representations. In particular, we aggregate multi-scale features extracted by convolutional neural networks using the proposed FCroW, taking into account multiple aspects of visual features captured by the networks. Different weights can be assigned to the features extracted by different layers of the networks. To reduce the effort for parameter tuning, we propose an initial strategy to prune the searching space of the weights, which is achieved by designing constraint rules based on the prior knowledge on relations between the layers of the networks. Based on this, we propose weighted multi-layer feature fusion for similar image representations. Extensive experiments conducted on four public real-world datasets demonstrate the effectiveness of the proposed FCroW method and the pruning strategy for image retrieval.
DeepAge: Deep Learning of face-based age estimation

The estimation of a person’s age based on a face image is a common biometric task conducted effortlessly by human observers. We present a dual Convolutional Neural Network (CNN) and Support Vector Regression (SVR) approach for face-based age estimation. A CNN is trained for representation learning, followed by Metric Learning, after which SVR is applied to the learned features. This allows to overcome the lack of large datasets with age annotations, by initially training the CNN for face recognition. The proposed scheme was applied to the MORPH-II and FG-Net datasets and compares favorably with contemporary state-of-the-art approaches. In particular, we show that domain adaptation which is essential for analyzing small-scale datasets, such as the FG-Net, can be achieved by retraining the SVR layer, rather than the CNN.
Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain

A novel combined probabilistic forecasting method based on an improved Markov chain for photovoltaics (PVs) to enhance the accuracy of probabilistic PV power forecasting is presented. First, a Markov chain (MC) forecasting structure combining precise factors is proposed that considers more influence factors beyond the statistical information of historical data as compared with conventional MCs. Rough set theory is then used to refine the major factors to quantify the influence of those factors. Furthermore, a k-nearest neighbours algorithm is used to select similar samples for building an accurate forecasting model. Based on similar samples, the changing PV trends are more obvious than when using whole historical samples, thus further improving forecasting accuracy. Finally, the effectiveness and superiority of the proposed method are verified by comparing results from simulations with the results from competing methods for two cases using datasets from DESERT KNOWLEDGE AUSTRALIA Solar Centre and GEFCom2014. The simulation results show that the proposed method can provide probabilistic forecasting results with better performance, also, the proposed method can be adapted to various forecasting scenarios.
A Decentralized Marketplace for M2M Economy for Smart Cities

Data Marketplace will be the engine of economic progress in smart cities. To foster the new Business Models of the Data and the Machine-to-Machine Economy, there are two needs: managing the plethora of sources of data and link them by a trusted, decentralized and scalable infrastructure. The two disruptive technologies able to foster this innovation are the Internet of Things (IoT) on the data-sources side and the Blockchain on the data marketplace side. In this paper we present a distributed data marketplace allowing different actors to purchase and monitor data streams coming from the smart city thanks to the use of IOTA technology, a Blockchain specifically developed for IoT networks. The marketplace is public and can be the starting point of other IOTA-based solutions.
A crosswalk pedestrian recognition system by using deep learning and zebra‐crossing recognition techniques

Summary Pedestrian detection is essential for improving pedestrian safety in an intelligent traffic system. The efficiency of the system is affected by real‐time processing and the error rate of detection. These concerns have not been completely addressed in previous studies. Therefore, this study proposes a real‐time pedestrian recognition system that ensures high accuracy by using a deep learning classifier and zebra‐crossing recognition techniques. The proposed system was designed to improve pedestrian safety and reduce accidents at intersections. Environmental feature vectors were first used to detect zebra crossings and to determine crossing areas. An adaptive mapping technique was then used to map the pedestrian waiting area based on the crossing area. A dual camera mechanism was used to maintain detection accuracy and improve system fault tolerance. Finally, the you‐only‐look‐once model was used to recognize pedestrians at intersections. A system prototype was implemented to verify the feasibility of the proposed system. The results revealed that the proposed scheme outperforms the conventional histogram of oriented gradients and Haarcascade schemes.
The Supernumerary Hand Illusion in Augmented Reality

The classic rubber hand illusion (RHI) experiment studies the sense of embodiment over a fake limb. Distinguished subcomponents of embodiment are ownership (sense of self-attribution of a body), agency (sense of having motor control), and self-location (the spatial experience of being inside a body), and are typically evoked in either reality or virtual reality. In augmented reality (AR), however, visually present real limbs can be augmented with (multiple) fake virtual limbs, which results in a variation of the RHI, the augmented reality supernumerary hand illusion (ARSHI). Such conditions occur, for example, in first-person AR games and in AR-interfaces for tele-robotics. In this article, we examined to what extent humans can experience the sense of embodiment over a supernumerary virtual arm in addition to one or two real arms. We also examine how embodiment is affected by the perceptual visual-tactile synchronicity of the virtual and real limbs, and by the synchronicity of active movement of the virtual and real hand. Embodiment was measured subjectively by questionnaire and objectively by skin conductance responses (SCRs). Questionnaire responses show that ownership, agency, and self-location can be evoked over the virtual arm in the presence of a real arm, and that they are significantly stronger for synchronous conditions than for asynchronous conditions. The perceptual and motorical synchronous condition with three visible hands led to an experience of owning the virtual hand. These responses further show that agency was also strongly experienced over the supernumerary virtual arm, and responses regarding self-location suggest a shift in sensed location when one real arm was in view and an additional location when both real arms where in view. SCRs show no significant effect of condition, but do show a significant habituation effect as a function of the number of conditions performed by participants. When analyzing the relations at the individual participant level between the questionnaire data and skin conductance, we found two clusters of participants: (1) participants with low questionnaire responses and low-medium SCRs and (2) participants with high questionnaire responses and low-high SCRs. Finally, we discuss how virtual hand appearance/realism and willingness to accept virtual limbs could play an important role in the ARSHI, and provide insights on intricacies involved with measuring and evaluating RHIs.
Symmetric Primitives with Structured Secrets

Securely managing encrypted data on an untrusted party is a challenging problem that has motivated the study of a wide variety of cryptographic primitives. A special class of such primitives allows an untrusted party to transform a ciphertext encrypted under one key to a ciphertext under another key, using some auxiliary information that does not leak the underlying data. Prominent examples of such primitives in the symmetric setting are key-homomorphic (weak) PRFs, updatable encryption, and proxy re-encryption. Although these primitives differ significantly in terms of their constructions and security requirements, they share two important properties: (a) they have secrets with structure or extra functionality, and (b) all known constructions of these primitives satisfying reasonably strong definitions of security are based on concrete public-key assumptions, e.g., DDH and LWE. This raises the question of whether these objects inherently belong to the world of public-key primitives, or they can potentially be built from simple symmetric-key objects such as pseudorandom functions. In this work, we show that the latter possibility is unlikely. More specifically, we show that: In addition, we show that unbounded (or exact) key-homomorphic weak PRFs over abelian groups are impossible in the quantum world. In other words, over abelian groups, bounded key-homomorphism is the best that we can hope for in terms of post-quantum security. Our attack also works over other structured primitives with abelian groups and exact homomorphisms, including homomorphic one-way functions and input-homomorphic weak PRFs.
Construct-A-Vis: Exploring the Free-Form Visualization Processes of Children

Building data analysis skills is part of modern elementary school curricula.Recent research has explored how to facilitate children's understanding of visual data representations through completion exercises which highlight links between concrete and abstract mappings.This approach scaffolds visualization activities by presenting a target visualization to children.But how can we engage children in more free-form visual data mapping exercises that are driven by their own mapping ideas?How can we scaffold a creative exploration of visualization techniques and mapping possibilities?We present Construct-A-Vis, a tablet-based tool designed to explore the feasibility of free-form and constructive visualization activities with elementary school children.Construct-A-Vis provides adjustable levels of scaffolding visual mapping processes.It can be used by children individually or as part of collaborative activities.Findings from a study with elementary school children using Construct-A-Vis individually and in pairs highlight the potential of this free-form constructive approach, as visible in children's diverse visualization outcomes and their critical engagement with the data and mapping processes.Based on our study findings we contribute insights into the design of free-form visualization tools for children, including the role of tool-based scaffolding mechanisms and shared interactions to guide visualization activities with children.