Reviewerly/exHarmony huggingface dataset:
Blind single image super-resolution with a mixture of deep networks

Existing deep neural network based image super-resolution (SR) methods are mostly designed for non-blind cases, where the blur kernel used to generate the low-resolution (LR) images is assumed to be known and fixed. However, this assumption does not hold in many real scenarios. Motivated by the observation that SR of LR images generated by different blur kernels are essentially different but also correlated, we propose a mixture model of deep networks, which is capable of clustering SR tasks of different blur kernels into a set of groups. Each group is composed of correlated SR tasks with similar blur kernels and can be effectively handled by a combination of specific networks in the mixture model. To achieve automatic SR tasks clustering and network selection, we model the blur kernel with a latent variable, which is inferred from the input image by an encoder network. Since the ground-truth of the latent variable is unknown in the training stage, we initialize the encoder network by pre-training it on the blur kernel classification task to avoid trivial solutions. To jointly train the mixture model and the encoder network, we further derive a lower bound of the likelihood function, which circumvents the intractability in direct maximum likelihood estimation. Extensive evaluations are performed on benchmark data sets and validate the effectiveness of the proposed method.
Researchers' attitudes and perceptions towards data sharing and data reuse in the field of food science and technology

This work analyses the perception and practice of sharing, reusing, and facilitating access to research data in the field of food science and technology. The study involved the coordination of a focus group discussion and an online survey, to understand and evince the behaviour of researchers regarding data management in that field. Both the discussion group and the survey were performed with researchers from several institutes of the Spanish National Research Council. The lack of a data sharing culture, the fear of being scooped, and confusion between the concepts of the working plan and the data management plan were some of the issues that emerged in the focus group. Respondents' previous experience with sharing their research data has been mainly in the form of appendices to peer‐reviewed publications. From the survey (101 responses), the most important motivations for publishing research data were found to be facilitating the reproducibility of the research, increasing the likelihood of citations of the article, and compliance with funding body mandates. Legal constraints, intellectual property, data ownership, data rights, potential commercial exploitation, and misuse of data were the main barriers to publishing data as open data. Citation in publications, certification, compliance with standards, and the reputation of the data providers were the most relevant factors affecting the use of other researchers' data. Being recent or recently updated, well documented, with quality metadata and ease of access were the most valued attributes of open research data.
Evaluation of Solar Radiation Transposition Models for Passive Energy Management and Building Integrated Photovoltaics

Incident solar radiation modelling has become of vital importance not only in architectural design considerations, but also in the estimation of the energy production of photovoltaic systems. This is particularly true in the case of buildings with integrated photovoltaics (PV) systems having a wide range of orientations and inclinations defined by the skin of the building. Since solar radiation data at the plane of interest is hardly ever available, this study presents the analysis of two of the most representative transposition models used to obtain the in-plane irradiance using as input data the global and diffuse irradiation on the horizontal plane, which can be obtained by satellite-based models or ground measurements. Both transposition models are validated with experimental measurements taken in Murcia (southeast of Spain) and datasets provided by the photovoltaic geographical information system (PVGIS) and the National Renewable Energy Laboratory (NREL) for vertical surfaces facing the four cardinal points. For the validation, the mean bias deviation, root mean square error and forecasted skill were used as indicators. Results show that the error rate decreases slightly for clear days. Better results are also obtained by dismissing data with low solar elevation angles so as to avoid shadowing effects from the surroundings in the early and late hours of the day, which affects mainly the performance of the transposition models for west and east surfaces. The results highlight the potential of equator-facing façades in winter time when the received irradiation can be twice as much as the one collected by the horizontal plane. It is also noteworthy that the operating conditions of all façades are mainly low irradiance and medium temperature at these locations.
Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels

A good model of business credit evaluation is an important tool for risk management. Although the dynamic imbalanced data flow is more consistent with the form of collected financial data in the actual situation, existing studies seldom research financial data as this form. This paper proposes a new ensemble model for dynamic imbalanced business credit evaluation based on the improved Learn++ and fuzzy c-means (FCM). To handle dynamic imbalanced financial data, Learn++ is improved by using a sliding time window (STW) and weight sampling (WS). This method is termed Learn++.STW-WS. STW can divide data with the same concept into the same dataset to solve the problem of concept drift which characteristic in dynamic data. Additionally, WS can redistribute the weights for samples of different classes to resolve the issue of imbalance. To satisfy the demand of Learn++.STW-WS on the prediction accuracy of a base classifier, FCM is improved by multiple kernels (MK), and is designated as MK-FCM. Several kernel functions are integrated to construct MK by the mean method, and MK is adopted to improve the calculation method of distances among points for FCM. Therefore, this new ensemble model can solve the problems of dynamic data and imbalanced classes at the same time. In the empirical research, financial data from Chinese listed companies are selected to evaluate business credit risk, and the associated models are adopted to make comparative analysis. The experiment results can fully demonstrate the good performance of the new ensemble model in terms of handling dynamic imbalanced financial data.
Ensemble Learning Approach on Indonesian Fake News Classification

The news is information about a recently changed situation or a recent event. Serving as popular media information the internet has the power spread the news not only real news but fake news as well. We propose an ensemble learning approach on Indonesian fake news in order to separate fake news from the real one and to tackle imbalanced data problem which we face on the given dataset. Our experiment result shows that random forest classifier as the ensemble classifier which obtained 0.98 f1-score is superior to multinomial naive bayes and support vector machine as non-ensemble classifiers which achieve 0.43 and 0.74 f1-score respectively across 660 evaluation documents. We also compare our result against other research that using the same data and our approach achieved better results.
Tangible Interfaces and Interactions in Sci-Fi Movies

Science-Fiction (Sci-Fi) movies have long been a frontier in showcasing futuristic computer interfaces and their associated interactions. Unconstrained by technological limitations, they are free to depict the most imaginative systems, including augmenting objects attributes that are not yet possible in reality. We present a case study on Sci-Fi movies where tangible objects are part of these systems, and examine how they illustrate Tangible User Interfaces (TUIs) concepts. We provide three examples of tangible systems and one that deviates considerably (holographic system), and analyze them using a well-established interaction model (MCRpd). We found that TUIs in movies exhibit various levels of the model's characteristics and demonstrate an inclusive and diverse context through combining interaction modalities and catering to audience needs. We argue that these aspects provide valuable lessons and implications in designing future TUIs and hope to broaden the design space by initiating discussions on the fascinating worlds in Sci-Fi movies.
Balancing the Tradeoff Between Clustering Value and Interpretability

Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This paper addresses the problem of generating interpretable clusters, given features of interest that signify interpretability to an end-user, by optimizing interpretability in addition to common clustering objectives. We propose a β-interpretable clustering algorithm that ensures that at least β fraction of nodes in each cluster share the same feature value. The tunable parameter β is user-specified. We also present a more efficient algorithm for scenarios with β\!=\!1$ and analyze the theoretical guarantees of the two algorithms. Finally, we empirically demonstrate the benefits of our approaches in generating interpretable clusters using four real-world datasets. The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.
User's guide to the interpretation of sandstones using whole-rock chemical data, exemplified by sandstones from Triassic to Miocene passive and active margin settings from the Southern Neotethys in Cyprus

Sandstone geochemistry provides useful insights into source-rock weathering, erosion, sedimentary transport, deposition, diagenesis and provenance. We identify and discuss useful geochemical methods and plots for the description and interpretation of whole-rock chemical data for a wide range of sandstone compositions, which we present as a user's guide. As a test, we apply this to late Triassic-late Miocene sandstones from two contrasting depositional-tectonic settings in Cyprus that are already extensively documented and interpreted. The chemical approach adds useful information/interpretation and deserves to be used more widely. Late Triassic-early Cretaceous sandstones in southwest and southern Cyprus (Mamonia Complex and Moni Melange) were ultimately derived from felsic-dominated source rocks (e.g., granodiorite) that became more weathered through time, as conditions became more humid; sorting and recycling effects increased over the same time interval. The sandstones are interpreted as being mainly recycled from Paleozoic siliciclastic shelf deposits in southernmost Turkey (Taurides) during the rift/passive margin development of the Southern Neotethys. For the late Cretaceous-late Miocene sandstones in northern Cyprus (Kyrenia Range), the chemical signals are more complex, related to a more variable provenance. Paleocurrent and other geological evidence support initial supply during the late Eocene-Oligocene from a mostly felsic continental source to the north in southern Turkey, with accumulation in a trench-type setting in north Cyprus, related to northward subduction. Paleocurrent data for during and after the early-middle Miocene indicate sediment westward supply, including mafic/ultramafic material from ophiolite-related rocks, related to suturing of the Southern Neotethys in southeast Turkey. Overall, sandstone composition in Cyprus was strongly influenced by the regional Cadomian basement that was amalgamated during the late Precambrian-Cambrian Pan-African orogeny.
SAR Image Colorization Using Multidomain Cycle-Consistency Generative Adversarial Network

Synthetic aperture radar (SAR) images are widely used for aerial and spatial image applications. However, Most of SAR images are usually grayscale images with no color information. Hence, the study of SAR image colorization is meaningful. At present, deep learning has become the mainstream method of SAR coloring, and with the most advanced pix2pix method, it achieves satisfactory results. However, such an approach is limited to the corresponding paired data, which may be difficult to get. We then notice that the cycle-consistency loss can remove this constraint to some extent. In this letter, we present a novel method to colorize the SAR image using a multidomain cycle-consistency generative adversarial network (MC-GAN). The proposed method improves the performance of coloring SAR images from two aspects: first, we propose a mask vector for images of every particular terrain combined with cycle-consistency loss, which does not need the paired SAR-optical images to train the model. Second, we define the multidomain classification loss, which can together get the correct output image with the color we hope it to be. We examined the proposed method on the newly SEN1-2 data set compared with the pix2pix and CycleGAN methods, which demonstrates the effectiveness of our proposed method.
A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

In refrigerators production, vacuum creation is fundamental to guarantee the correct manufacturing of the product. Before inserting the refrigerant in the refrigerator cabinet, the vacuum is tested through a Pirani gauge that assesses the pressure within the cabinet. Such readings are used to evaluate the vacuum creation process and to verify if leakings are present. In this work, we employ a Deep Learning-based Anomaly Detection approach to associate an Anomaly Score to each pressure profile; this score can be exploited to optimize actions performed by human operators like more detailed inspections or unit exclusion from the downstream production stages. We propose a native time series-based approach based on Deep Learning and compare it with classic ones based on hand-craft features. The proposed approach is designed to be deployed in a Decision Support System for assisting human operators in the following testing operations, helping them in reducing evaluation bias and attention losses that are inevitable in production line environment. Moreover, costs associated with false positives (normally operating units detected as anomalous) and false negatives (undetected anomalies) are considered here to optimize decision making in a cost-reduction perspective. We also describe promising results obtained on real industrial data spanning on a 5-month period and consisting of thousands of tested household units.
Mobile Streaming of Live 360-Degree Videos

Live streaming of immersive multimedia content, e.g., 360-degree videos, is getting popular due to the recent availability of commercial devices that support interacting with such content such as smartphones/tablets and head-mounted displays. Streaming live content to mobile users using individual connections (i.e., unicast) consumes substantial network resources and does not scale to large number of users. Multicast, on the other hand, offers a scalable solution but it introduces multiple challenges, including handling user interactivity, ensuring smooth quality, conserving the energy of mobile receivers, and achieving fairness among users. We propose a new solution for the problem of live multicast streaming of 360-degree videos to mobile users, which addresses the aforementioned challenges. The proposed solution, referred to as VRCast, is designed for cellular networks that support multicast, such as LTE. We show through trace-driven simulations that VRCast outperforms the closest algorithms in the literature by wide margins across several performance metrics. For example, compared to the state-of-the-art, VRCast improves the viewport quality by up to 2.5 dB. We have implemented VRCast in an LTE testbed to show its practicality. Our experimental results show that VRCast ensures smooth video quality and saves energy for mobile devices.
FPGA-based Accelerators of Fully Pipelined Modular Multipliers for Homomorphic Encryption

Homomorphic encryption (HE) is an important cryptographic primitive which allows privacy preserving computations. Current HE schemes are all based on modular arithmetic. Modular multiplication (ModMult) is one of the most frequently used modular operations, but in practice it is often prohibitively slow due to a reduction operation with high computational complexity. To address this speed problem, we demonstrate a set of novel FPGA-based accelerators for fully pipelined ModMults in this paper. For a high-throughput integer multiplier (IntMult) in the ModMult designs, digital signal processing (DSP) slices on FPGAs are efficiently exploited with optimized IntMult designs. For the full RNS-HEAAN scheme, which is our target HE scheme, our proposed Barrett ModMult design is optimized using specific moduli and extended to the Shoup ModMult algorithm. Our proposed Barrett and Shoup ModMult designs implemented on a Xilinx Virtex UltraScale FPGA show a 2 × shorter delay, 14× higher throughput at the same frequency, and 3× higher throughput/DSP than the previous non-fully pipelined Barrett ModMult design on average. In particular, our Barrett ModMult design with the specific moduli shows the highest throughput/DSP value although precomputation required in the Shoup ModMult design is not used. Compared with a reference software implementation, our ModMult designs show 679× faster average processing speeds when we deploy multiple ModMult cores that fully use DSP slices on our target FPGA.
Reflective-AR Display: An Interaction Methodology for Virtual-to-Real Alignment in Medical Robotics

Robot-assisted minimally invasive surgery has shown to improve patient outcomes, as well as reduce complications and recovery time for several clinical applications. While increasingly configurable robotic arms can maximize reach and avoid collisions in cluttered environments, positioning them appropriately during surgery is complicated because safety regulations prevent automatic driving. We propose a head-mounted display (HMD) based augmented reality (AR) system designed to guide optimal surgical arm set up. The staff equipped with HMD aligns the robot with its planned virtual counterpart. In this user-centric setting, the main challenge is the perspective ambiguities hindering such collaborative robotic solution. To overcome this challenge, we introduce a novel registration concept for intuitive alignment of AR content to its physical counterpart by providing a multi-view AR experience via reflective-AR displays that simultaneously show the augmentations from multiple viewpoints. Using this system, users can visualize different perspectives while actively adjusting the pose to determine the registration transformation that most closely superimposes the virtual onto the real. The experimental results demonstrate improvement in the interactive alignment a virtual and real robot when using a reflective-AR display. We also present measurements from configuring a robotic manipulator in a simulated trocar placement surgery using the AR guidance methodology.
Improving Imbalanced Dataset Classification Using Oversampling and Gradient Boosting

Imbalanced data classification is challenging task for various datasets in the real world. One of technique to enlarge the sample in minority class is oversampling to fix size as majority class. This research aims to test SMOTE, Borderline-SMOTE, and ADASYN to handle dataset imbalance and to observe its impact toward classification accuracy. Gradient Boosting applied as a classifier and seven datasets are used in this research. Accuracy, recall, precision, F1-Score, AUC were also implemented to measure classifier performance. Experiments showed that oversampling technic increase accuracy from 2% to 11% for the dataset Mammography, Liver Disorders, Diabetes (Pima Indian), Indian Liver, Habberman, and Immunotherapy. Borderline-SMOTE increases higher accuracy compared to other oversampling method. Surprisingly, Breast Cancer Wisconsin has steady accuracy with or without oversampling. Even though, oversampling good for data imbalanced, the sensibility of oversampling algorithm and the nature of dataset must considered.
FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities

The number of zero-day attacks has been exponentially increasing due to a variety of heterogeneous Internet of Things (IoT) protocols. Many centralized-based techniques have been introduced to identify malicious activities in IoT environments. However, these techniques have suffered including a high latency to satisfy IoT requirements. This paper proposes a fog-based attack detection (FBAD) framework using an ensemble of online sequential extreme learning machine (EOS-ELM) for efficiently detecting malicious activities. We indicate a high-level view of the efficient proposed framework for deploying the distributed attack detection technique in fog computing due to high accuracy and low latency, as it is closer to the IoT devices at the network edge. Furthermore, we compare the performance of our framework with other existing approaches including ELM and OS-ELM. The results of the experiment demonstrate that distributed architecture outperforms centralized architecture in terms of the detection time and classification accuracy.
Short Keynote Paper: Mainstreaming Personalized Healthcare-Transforming Healthcare through new era of Artificial Intelligence

Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.
Learning to recommend third-party library migration opportunities at the API level

The manual migration between different third-party libraries represents a challenge for software developers. Developers typically need to explore both libraries Application Programming Interfaces, along with reading their documentation, in order to locate the suitable mappings between replacing and replaced methods. In this paper, we introduce RAPIM, a machine learning model that recommends mappings between methods from two different libraries. Our model learns from previous migrations, manually performed in mined software systems, and extracts a set of features related to the similarity between method signatures and method textual documentations. We evaluate our model using 8 popular migrations, collected from 57,447 open-source Java projects. Results show that RAPIM is able to recommend relevant library API mappings with an average accuracy score of 87%. Finally, we provide the community with an API recommendation web service that could be used to support the migration process.