Reviewerly/exHarmony huggingface dataset:
An e-Health care services framework for the detection and classification of breast cancer in breast cytology images as an IoMT application

One of the primary causes of mortality among women aged 20–59 worldwide is breast cancer. Early detection and getting proper treatment can reduce the rate of morbidity of breast cancer. In this paper, we proposed a framework which combines machine learning and computational intelligence-based approaches in e-Health care service as an application of the Internet of Medical Things (IoMT) technology, for the early detection and classification of malignant cells in breast cancer. In the proposed approach, the detection of malignant cells is achieved by extracting various shapes and textured based features, whereas the classification is performed using three well-known classification algorithms. The most innovative part of the proposed approach is the use of Evolutionary Algorithms (EA) for the selection of optimal features, which reduces the computational complexity and accelerates the classification process in cloud-based e-Health care service. Similarly, an ensemble based classifier is used to select the best classifier by adopting the majority voting technique. The performance of the proposed approach is validated through experiments on real data sets which provide an accuracy of 98.0% in the detection and classification of malignant cells in breast cytology images.
P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays, and can be used in various smart city applications, such as traffic management, ehealth, and surveillance.In IoMT applications, multimedia data is generated from devices with sensing capabilities, e.g., Multimedia Sensor Nodes (MSNs).These devices come with limited computational and storage resources, and cannot hold captured multimedia data for a long time if the connection between a base station and cloud server is down.In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server.However, such a data collection raise privacy issues, such as revealing identities and location information of MSNs to mobile sinks.Therefore, there is a need to preserve the privacy of MSNs during mobile data collection.In this paper, we propose an efficient privacy-preserving-based data collection and analysis framework for IoMT applications.The proposed framework distributes an underlying Wireless Multimedia Sensor Networks (WMSNs) into multiple clusters.Each cluster is represented by a cluster head.The cluster heads are responsible to protect the privacy of member nodes through data and location aggregation.Later, aggregated multimedia data is analyzed at cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation.Experimental results show that our proposed framework outperforms other stateof-the-art techniques, and can be used to collect multimedia data in various IoMT applications.
Analysis and Optimization of the Memory Hierarchy for Graph Processing Workloads

Graph processing is an important analysis technique for a wide range of big data applications. The ability to explicitly represent relationships between entities gives graph analytics a significant performance advantage over traditional relational databases. However, at the microarchitecture level, performance is bounded by the inefficiencies in the memory subsystem for single-machine in-memory graph analytics. This paper consists of two contributions in which we analyze and optimize the memory hierarchy for graph processing workloads. First, we perform an in-depth data-type-aware characterization of graph processing workloads on a simulated multi-core architecture. We analyze 1) the memory-level parallelism in an out-of-order core and 2) the request reuse distance in the cache hierarchy. We find that the load-load dependency chains involving different application data types form the primary bottleneck in achieving a high memory-level parallelism. We also observe that different graph data types exhibit heterogeneous reuse distances. As a result, the private L2 cache has negligible contribution to performance, whereas the shared L3 cache shows higher performance sensitivity.
On the Capacity and Straggler-Robustness of Distributed Secure Matrix Multiplication

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typical of concern. In this paper, we first study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product AB of two finite field private matrices A and B from an information-theoretic perspective. In this problem, the user exploits the computational resources of N servers to compute the matrix product but simultaneously tries to conceal the private matrices from the servers. Our goal is twofold: (i) to maximize the downlink communication rate, and (ii) to minimize the effective number of server observations needed to determine AB, while preserving security, where we allow for up to ℓ ≤ N servers to collude. To this end, we propose two schemes - an aligned secret sharing scheme (A3S) and a secure cross subspace alignment (SCSA) scheme. For A3S, we optimize the partitioning of matrices A and B in order to either optimize objective (i) or (ii) as a function of the system parameters (e.g., N and ℓ). A proposed inductive approach gives us analytical, close-to-optimal solutions for both (i) and (ii). The SCSA, on the other hand, is shown to be (rate) capacity-optimal for the general J-sided distributed secure matrix multiplication problem Π <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</sup> M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> . We show this by developing a recursive information-theoretic upper bound (converse) on the downlink rate for the J-sided secure matrix multiplication problem. With respect to (i), both A3S and SCSA, significantly outperform the state-of-the-art in terms of (a) communication rate, (b) maximum tolerable number of colluding servers, and (c) computational complexity. Overall SCSA (A3S) is the preferred choice when the focus is on the downlink (uplink).
DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality

We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV). For training data, we collect videos of various reflective spheres placed within the camera's FOV, leaving most of the background unoccluded, leveraging that materials with diverse reflectance functions reveal different lighting cues in a single exposure. We train a deep neural network to regress from the LDR background image to HDR lighting by matching the LDR ground truth sphere images to those rendered with the predicted illumination using image-based relighting, which is differentiable. Our inference runs at interactive frame rates on a mobile device, enabling realistic rendering of virtual objects into real scenes for mobile mixed reality. Training on automatically exposed and white-balanced videos, we improve the realism of rendered objects compared to the state-of-the art methods for both indoor and outdoor scenes.
Voice as a Design Material

While there is a renewed interest in voice user interfaces (VUI) in HCI, little attention has been paid to the design of VUI voice output beyond intelligibility and naturalness. We draw on the field of sociophonetics - the study of the social factors that influence the production and perception of speech - to highlight how current VUIs are based on a limited and homogenised set of voice outputs. We argue that current systems do not adequately consider the diversity of peoples' speech, how that diversity represents sociocultural identities, and how voices have the potential to shape user perceptions and experiences. Ultimately, as other technological developments have influenced the ideologies of language, the voice outputs of VUIs will influence the ideologies of speech. Based on our argument, we pose three design strategies for VUI voice output design - individualisation, context awareness, and diversification - to motivate new ways of conceptualising and designing these technologies.
PersoNet: Friend Recommendation System Based on Big-Five Personality Traits and Hybrid Filtering

Friend recommendation system (FRS) is an essential part of any social network system. With the popularity of social network sites, many FRSs have been proposed in the past few years. However, most of them are homophily based systems, homophily is the propensity to associate and bond with similar others. In other words, these systems will recommend people that you share common features with them as friends. Homophily based FRS is accurate when the common feature is a physical or social feature, such as age, race, location, job, or lifestyle. However, it is not the case with personality types. Having a given personality type does not necessarily mean that you are compatible with people that have the same personality type. Therefore, in this paper, we present and evaluate an FRS based on the big-five personality traits model and hybrid filtering, in which the friend recommended process is based on personality traits and users' harmony rating. To validate the proposed system's accuracy, a personality-based social network site that uses the proposed FRS named PersoNet is implemented. Users' rating results show that PersoNet performs better than collaborative filtering (CF)-based FRS in terms of precision and recall.
Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast uncertainties can help users of solar energy to make better solar radiation based decisions related to the stability of the electrical grid. To achieve this, we apply statistical post-processing techniques that determine relationships between observations of global radiation (made within the KNMI network of automatic weather stations in the Netherlands) and forecasts of various meteorological variables from the numerical weather prediction (NWP) model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Those relationships are used to produce probabilistic forecasts of global radiation. We compare 7 different statistical post-processing methods, consisting of two parametric and five non-parametric methods. We find that all methods are able to generate probabilistic forecasts that improve the raw global radiation forecast from HA according to the root mean squared error (on the median) and the potential economic value. Additionally, we show how important the predictors are in the different regression methods. We also compare the regression methods using various probabilistic scoring metrics, namely the continuous ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.
Discrete pigeon-inspired optimization algorithm with Metropolis acceptance criterion for large-scale traveling salesman problem

Pigeon-inspired optimization (PIO) algorithm, which is a newly proposed swarm intelligence algorithm, has been mainly applied to continuous optimization problems. In this paper, a discrete PIO (DPIO) algorithm, which uses the Metropolis acceptance criterion of simulated annealing algorithm, is proposed for Traveling Salesman Problems (TSPs). A new map and compass operator with comprehensive learning ability is designed to enhance DPIO's exploration ability. A new landmark operator, which has cooperative learning ability and can learn from the heuristic information of TSP instance, is designed to improve DPIO's exploitation ability. Aim to enhance its ability to escape from premature convergence, the Metropolis acceptance criterion is used to decide whether to accept newly produced solutions. Systematic experiments were performed to analyze the behaviours of the map and compass operator and the landmark operator. The performance of DPIO algorithm was tested on 33 large-scale TSP instances from TSPLIB with city number from 1000 to 85900. Simulation results show that the proposed algorithm is effective and is competitive with most other state-of-the-art meta-heuristic algorithms.
Unsupervised Wireless Spectrum Anomaly Detection With Interpretable Features

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based anomaly detector for wireless spectrum anomaly detection using power spectral density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alarm rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features, such as signal bandwidth, class, and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120× and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its anomaly detection capabilities.
Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.
The Right to the Sustainable Smart City

Environmental concerns have driven an interest in sustainable smart cities, through the monitoring and optimisation of networked infrastructures. At the same time, there are concerns about who these interventions and services are for, and who benefits. HCI researchers and designers interested in civic life have started to call for the democratisation of urban space through resistance and political action to challenge state and corporate claims. This paper contributes to an emerging body of work that seeks to involve citizens in the design of sustainable smart cities, particularly in the context of marginalised and culturally diverse urban communities. We present a study involving co-designing Internet of Things with urban agricultural communities and discuss three ways in which design can participate in the right to the sustainable smart city through designing for the commons, care, and biocultural diversity.
Deep Neural Network-Based Severity Prediction of Bug Reports

Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90%.
What.Hack

Phishing attacks are a major problem, as evidenced by the DNC hackings during the 2016 US presidential election, in which staff were tricked into sharing passwords by fake Google security emails, granting access to confidential information. Vulnerabilities such as these are due in part to insufficient and tiresome user training in cybersecurity. Ideally, we would have more engaging training methods that teach cybersecurity in an active and entertaining way. To address this need, we introduce the game What.Hack, which not only teaches phishing concepts but also simulates actual phishing attacks in a role-playing game to encourage the player to practice defending themselves. Our user study shows that our game design is more engaging and effective in improving performance than a standard form of training and a competing training game design (which does not simulate phishing attempts through role-playing).
From photo to 3D to mixed reality: A complete workflow for cultural heritage visualisation and experience

The domain of cultural heritage is on the verge of adopting immersive technologies; not only to enhance user experience and interpretation but also to satisfy the more enthusiastic and tech-savvy visitors and audiences. However, contemporary academic discourse seldom provides any clearly defined and versatile workflows for digitising 3D assets from photographs and deploying them to a scalable 3D mixed reality (MxR) environment; especially considering non-experts with limited budgets. In this paper, a collection of open access and proprietary software and services are identified and combined via a practical workflow which can be used for 3D reconstruction to MxR visualisation of cultural heritage assets. Practical implementations of the methodology has been substantiated through workshops and participants’ feedback. This paper aims to be helpful to non-expert but enthusiastic users (and the GLAM sector) to produce image-based 3D models, share them online, and allow audiences to experience 3D content in a MxR environment.
UnstructuredFusion: Realtime 4D Geometry and Texture Reconstruction Using Commercial RGBD Cameras

A high-quality 4D geometry and texture reconstruction for human activities usually requires multiview perceptions via highly structured multi-camera setup, where both the specifically designed cameras and the tedious pre-calibration restrict the popularity of professional multi-camera systems for daily applications. In this paper, we propose UnstructuredFusion, a practicable realtime markerless human performance capture method using unstructured commercial RGBD cameras. Along with the flexible hardware setup using simply three unstructured RGBD cameras without any careful pre-calibration, the challenge 4D reconstruction through multiple asynchronous videos is solved by proposing three novel technique contributions, i.e., online multi-camera calibration, skeleton warping based non-rigid tracking, and temporal blending based atlas texturing. The overall insights behind lie in the solid global constraints of human body and human motion which are modeled by the skeleton and the skeleton warping, respectively. Extensive experiments such as allocating three cameras flexibly in a handheld way demonstrate that the proposed UnstructuredFusion achieves high-quality 4D geometry and texture reconstruction without tiresome pre-calibration, liberating the cumbersome hardware and software restrictions in conventional structured multi-camera system, while eliminating the inherent occlusion issues of the single camera setup.
Unsupervised Saliency Model with Color Markov Chain for Oil Tank Detection

Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by view angle and illumination. Therefore, we propose an unsupervised saliency model with Color Markov Chain (US-CMC) to deal with oil tank detection. To avoid the influence of shadows, we make use of the CIE Lab space to construct a Color Markov Chain and generate a bottom-up latent saliency map. Moreover, we build a circular feature map based on a radial symmetric circle, which makes true targets to be strengthened for a subjective detection task. Besides, we combine the latent saliency map with the circular feature map, which can effectively suppress other salient regions except for oil tanks. Extensive experimental results demonstrate that it outperforms 15 saliency models for remote sensing images (RSIs). Compared with conventional oil tank detection methods, US-CMC has achieved better results and is also more robust for view angle, shadow, and shape similarity problems.