Reviewerly/exHarmony huggingface dataset:
Fast Decryption Algorithm for Paillier Homomorphic Cryptosystem

With the shift in storage paradigm, there is an increasing need for privacy of dataset and also for an encryption scheme that permits computation on encrypted data. Paillier cryptosystem is a good example of such a homomorphic encryption scheme. To improve the efficiency of the Paillier homomorphic encryption scheme in terms of its decryption speed and overall computational cost, we propose an improved decryption process. Specifically, the inclusion of a variable k to reduce the modular multiplicative arithmetic. The variable k is combined with the L function and CRT recombination method, to arrive at a fast and improved decryption process, showing the mathematical correctness of the decryption algorithm. Experimental results validate that our scheme is significantly efficient in its decryption speed.
Inversion of provenance data and sediment load into spatially varying erosion rates

Abstract Sediment fingerprinting methods aim to determine the relative contribution of different source areas in detrital sediments based on natural properties – fingerprints – of the source areas. Here, we use U/Th–Pb age signatures as fingerprints, assuming that the age signal is not altered during erosion–transportation–deposition events, and given that recent technological advances enable precise dating of large amounts of grains. We introduce a formal inversion method that allows to disentangle the amalgamation of source contributions in detrital zircon data and enables to convert this information into an erosion rate map starting from the spatial distribution of zircon age signatures. Relying on the least‐squares method and using prior and covariance information to deal with non‐uniqueness, we show, using synthetic and natural examples, that we are able to retrieve erosion rate patterns of a catchment when the age distribution and zircon fertility for each source area are well known. Moreover, we show that not only zircon age fingerprints but also other tracers such as mineral content can be used. Furthermore, we found that adding data from samples taken at the outlet of tributaries improves the estimation of erosion rate patterns. We conclude that the least squares inverse model applied to detrital data has great potential for investigating erosion rates. © 2020 John Wiley &amp; Sons, Ltd.
Recurrent Convolution Basket Map for Diversity Next-Basket Recommendation

Next-basket recommendation plays an important role in both online and offline market. Existing methods often suffer from three challenges: information loss in basket encoding, sequential pattern mining of the shopping history, and the diversity of recommendations. In this paper, we contribute a novel solution called Rec-BMap ("Recurrent Convolution Basket Map"), to address those three challenges. Specifically, we first propose basket map, which encodes not only the items in a basket without losing information, but also static and dynamic properties of the items in the basket. A convolutional neural network followed by the basket map is used to generate basket embedding. Then, a Time-LSTM with time-gate is proposed to learn the sequence pattern from consumer's historical transactions with different time intervals. Finally, a deconvolutional neural network is employed to generate diverse next-basket recommendation. Experiments on two real-world datasets demonstrate that the proposed model outperforms existing baselines.
Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers

Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning without data transfer that is mathematically equivalent to single site training with centralized data. We evaluate two scenarios: a simulated multi-site dataset for handwritten digit classification with MNIST and a real multi-site dataset with head CT hemorrhage segmentation. We compare federated gradient averaging to single site training, federated weight averaging (FWA), and cyclic weight transfer. In the MNIST task, we show that training with FGA results in a weight set equivalent to centralized single site training. In the hemorrhage segmentation task, we show that FGA achieves on average superior results to both FWA and cyclic weight transfer due to its ability to leverage momentum-based optimization.
Design and Implementation of Advanced Encryption Standard Algorithm on 7th Series Field Programmable Gate Array

Transmission of data digitally is always susceptible to attacks. Analysis of cipher strength is a necessary part of the protection assessment of either corporate or academic institutions. So, a strong encryption method is required to secure data. Therefore, in 2001 United States National Institute of Standards and Technology i.e. (NIST) amended (AES) Advanced Encryption Standard, a specification for the encryption which was better than encoding Standard (DES). The AES Algorithm is purely based upon the design principle of substitution-permutation network and it is efficient in both software as well as hardware for encryption. The algorithm described is a symmetric-key algorithm, i.e. for encryption and decryption, a single identical secret key is used. It's absolved to use by public or private, commercial, etc. programs. So far, extensive research is being currently administered to spot techniques to further secure the AES algorithm. This research proposes the architecture as well as the execution of the AES i.e. Advanced Encryption Standard simulation, on two 7th series capital to investigate the comparison of cost and performance.
Human Action Recognition Using Bone Pair Descriptor and Distance Descriptor

The paper presents a method for the recognition of human actions based on skeletal data. A novel Bone Pair Descriptor is proposed, which encodes the angular relations between pairs of bones. Its features are combined with Distance Descriptor, previously used for hand posture recognition, which describes relationships between distances of skeletal joints. Five different time series classification methods are tested. The selection of features, input joints, and bones is performed. The experiments are conducted using person-independent validation tests and a challenging, publicly available dataset of human actions. The proposed method is compared with other approaches found in the literature achieving relatively good results.
Gaussian Process Regression Ensemble Model for Network Traffic Prediction

Network traffic prediction is substantial for network optimization and resource management. However, designing an efficient predictive model considering different traffic characteristics, including periodicity, nonlinearity, and nonstationarity, is challenging. Recently, ensemble learning is attracting much attention from researchers in the machine learning community. Although ensemble learning has proven exceptional performance in modelling the intricate problems, it may not be able to handle varying patterns and chaotic behaviour, which are typical properties of traffic data (and many other time-series problems). For this reason, ensemble methods show a limited prediction accuracy in network traffic modelling. We address this issue by proposing an ensemble of learners for time-series prediction that considers the accuracy of individual learners as well as diversity among their outcomes. Each learner contributes to the optimization process by finding the optimal accuracy-diversity balance in a segment of feature space. This divide-and-conquer approach avoids complicated objective functions with many local optimums while fitting the ensemble model on large datasets. Experimental results on the real traffic traces show our proposed method outperforms other state-of-the-art predictors with 12% on average in prediction accuracy for different datasets.
Blockchain-Based Federated Learning in Medicine

Worldwide epidemic events have confirmed the need for medical data processing tools while bringing issues of data privacy, transparency and usage consent to the front. Federated Learning and the blockchain are two technologies that tackle these challenges and have been shown to be beneficial in medical contexts where data are often distributed and coming from different sources. In this paper we propose to integrate these two technologies for the first time in a medical setting. In particular, we propose a implementation of a coordinating server for a federated learning algorithm to share information for improved predictions while ensuring data transparency and usage consent. We illustrate the approach with a prediction decision support tool applied to a diabetes data-set. The particular challenges of the medical contexts are detailed and a prototype implementation is presented to validate the solution.
Cascaded Attention Guidance Network for Single Rainy Image Restoration

Restoring a rainy image with raindrops or rainstreaks of varying scales, directions, and densities is an extremely challenging task. Recent approaches attempt to leverage the rain distribution (e.g., location) as prior to generate satisfactory results. However, concatenation of a single distribution map with the rainy image or with intermediate feature maps is too simplistic to fully exploit the advantages of such priors. To further explore this valuable information, an advanced cascaded attention guidance network, dubbed as CAG-Net, is formulated and designed as a three-stage model. In the first stage, a multitask learning network is constructed for producing the attention map and coarse de-raining results simultaneously. Subsequently, the coarse results and the rain distribution map are concatenated and fed to the second stage for results refinement. In this stage, the attention map generation network from the first stage is used to formulate a novel semantic consistency loss for better detail recovery. In the third stage, a novel pyramidal "whereand- how" learning mechanism is formulated. At each pyramid level, a two-branch network is designed to take the features from previous stages as inputs to generate better attention-guidance features and de-raining features, which are then combined via a gating scheme to produce the final de-raining results. Moreover, the uncertainty maps are also generated in this stage for more accurate pixel-wise loss calculation. Extensive experiments are carried out for removing raindrops or rainstreaks from both synthetic and real rainy images, and CAG-Net is demonstrated to produce significantly better results than state-of-the-art models. Code will be publicly available after paper acceptance.
Notice of Removal: Blockchain-based Database to Ensure Data Integrity in Cloud Computing Environments

Data is nowadays an invaluable resource; indeed, it guides all business decisions in most of the computer-aided human activities. Threats to data integrity are thus of paramount relevance, as tampering with data may maliciously affect crucial business decisions. This issue is especially true in cloud computing environments, where data owners cannot control fundamental data aspects, like the physical storage of data and the control of its accesses. Blockchain has recently emerged as a fascinating technology which, among others, provides compelling properties about data integrity. Using the Blockchain to face data integrity threats seems to be a natural choice, but its current limitations of low throughput, high latency, and weak stability hinder the practical feasibility of any Blockchain-based solutions. In this paper, by focusing on a case study from the European SUNFISH project, which concerns the design of a secure by-design cloud federation platform for the public sector, we precisely delineate the actual data integrity needs of cloud computing environments and the research questions to be tackled to adopt Blockchain-based databases. First, we detail the open research questions and the difficulties inherent in addressing them. Then, we outline a preliminary design of an effective Blockchain-based database for cloud computing environments.
Scan-Based Self Anomaly Detection: Client-Side Mitigation of Channel-Based Man-in-the-Middle Attacks Against Wi-Fi

In recent years, Wi-Fi has been used as a means of near-field high-speed communication across personal computers, smartphones and IoT devices such as hospital healthcare devices. Meanwhile, there have been many attempts to exploit equipment leveraging Wi-Fi. Among those exploits and attacks, an attack called channel-based man-in-the-middle (MITM) attack is a serious threat, since it can be used to exploit WPA2, which is a standard encryption and authentication scheme currently and widely in use. In this paper, we propose a scan-based self anomaly detection (SSAD), which is a client-side solution to detect and mitigate channel-based man-in-the-middle attacks using access point (AP) scans. SSAD enables wireless devices to verify the authenticity of wireless access points without the support of the access points, but running anomaly detection by themselves. This characteristic of SSAD, independent from access points, is especially favorable to mobile clients such as smartphones and IoT devices since they usually connect to multiple wireless access points. We implemented SSAD into an open source Wi-Fi client software and evaluated the effectiveness. With our experiments in some operational fields, we achieved 99% detection rate if an attacker was in the same room of a legitimate AP, and over 91% detection rate if an attacker and a legitimate AP were in different rooms.
Y-Autoencoders: Disentangling latent representations via sequential encoding

In the last few years there have been important advancements in disentangling latent representations using generative models, with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). However, standard Autoencoders (AEs) and closely related structures have remained popular because they are easy to train and adapt to different tasks. An interesting question is if we can achieve state-of-the-art latent disentanglement with AEs while retaining their good properties. We propose an answer to this question by introducing a new model called Y-Autoencoder (Y-AE). The structure and training procedure of a Y-AE enclose a representation into an implicit and an explicit part. The implicit part is similar to the output of an AE and the explicit part is strongly correlated with labels in the training set. The two parts are separated in the latent space by splitting the output of the encoder into two paths (forming a Y shape) before decoding and re-encoding. We then impose a number of losses, such as reconstruction loss, and a loss on dependence between the implicit and explicit parts. Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses. We provide significant experimental results on various domains, such as separation of style and content, image-to-image translation, and inverse graphics.
Phase unwrapping error correction based on phase edge detection and classification

A method is proposed to detect and correct the phase unwrapping error (PUE). Phase edge is defined to help detect points potential with PUEs. Then, phase derivatives of points crossing phase edges are used to inspect whether the phase edges are from surface discontinuity or PUE. This makes it possible to dismiss phases at discontinuous regions during the PUE correction. Hence, the proposed method evidently improves the robustness of PUE correction with little additional time consumption. Experiments confirm that it is capable of correcting PUE with outstanding robustness and efficiency. To the best of our knowledge, it is the first time that phase edge is defined with aid of unwrapped phase, and used for PUE correction.
Multi-View Robust Feature Learning for Data Clustering

Multi-view feature learning can provide basic information for consistent grouping, and is very common in practical applications, such as judicial document clustering. However, it is a challenge to combine multiple heterogeneous features to learn a comprehensive description of data samples. To solve this problem, many methods explore the correlation between various features across views by assuming that all views share the same semantic information. Inspired by this, in this paper we propose a new multi-view robust feature learning (MRFL) method. In addition to projecting features from different views to a shared semantic subspace, our approach also learns the irrelevant information of data space to capture the feature dependencies between views in potential common subspaces. Therefore, the MRFL can obtain flexible feature associations hidden in multi-view data. A new objective function is designed to derive, and solve the effective optimization process of MRFL. Experiments on real-world multi-view datasets show that the proposed MRFL method is superior to the state-of-the-art multi-view learning methods.
Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach

Abstract For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.
BQC: A free web service to quality control solar irradiance measurements across Europe

Classical quality control (QC) methods of solar irradiance apply easy-to-implement physical or statistical limits that are incapable of detecting low-magnitude measuring errors due to the large width of the intervals. We previously presented the bias-based quality control (BQC), a novel method that flags samples in which the bias of several independent gridded datasets is larger for consecutive days than the historical value. The BQC was previously validated at 313 European and 732 Spanish stations finding multiple low-magnitude errors (e.g., shadows, soiling) not detected by classical QC methods. However, the need for gridded datasets, and ground measurements to characterize the bias, was hindering the BQC implementation. To solve this issue, we present a free web service, www.bqcmethod.com, that implements the BQC algorithm incorporating both the gridded datasets and the reference stations required to use the BQC across Europe from 1983 to 2018. Users only have to upload a CSV file with the global horizontal irradiance measurements to be analyzed. Compared to previous BQC versions, gridded products have been upgraded to SARAH-2, CLARA-A2, ERA5, and the spatial coverage has been extended to all of Europe. The web service provides a flexible environment that allows users to tune the BQC parameters and upload ancillary rain data that help in finding the causes of the errors. Besides, the outputs cover not only the visual and numerical QC flags but also daily and hourly estimations from the gridded datasets, facilitating the access to raster data.
An overview of blockchain-based systems and smart contracts for digital coupons

Among the accessory applications of the blockchain, the idea of using it as an immutable register for tracking and certifying documents is recently gaining interest in research and industry. The problems of traceability, non-counterfeiting and unique usage of digital coupons fall within this area; many couponing platforms are hence exploring the possibility of addressing the above limitations with blockchain technologies. In view of the foregoing, in this work we analyse and compare several blockchain-based couponing systems. To do so, we first propose a general schema of digital coupon and define the desirable properties of a couponing system. Then, we select a sample of these systems and we examine them, describing their design choices and summarizing their relevant properties. Finally, we inspect their code and study how the notion of couponing system is interpreted in their smart contracts. We also highlight their distinctive features and relevant implementation solutions. We conclude by discussing what emerged from our analysis and proposing some possible future investigations.
Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de

The task "recommend a video to watch next?" has been in the focus of recommender systems' research for a long time. However, adequately exploiting the clues hidden in the sequences of actions of user sessions in order to reveal users' short-term intentions moved only recently into the focus of research. Based on a real-world application scenario, in this paper, we propose a Markov Chain-based transition probability matrix to efficiently reveal the short-term preferences of individuals. We experimentally evaluated our proposed method by comparing it against state-of-the-art algorithms in an offline as well as a live evaluation setting. In both cases our method not only demonstrated its superiority over its competitors, but exposed a clearly stronger engagement of users on the platform. In the online setting, our method improved the click-through rate by up to 93.61%. This paper therefore contributes real-world evidence for improving the recommendation effectiveness, by considering sequence-awareness, since capturing the short-term preferences of users is crucial in the light of items with a short life span such as tv programs (news, tv shows, etc.).
82‐2: <i>Invited Paper:</i> Latest Progress of Laser Phosphor Projection Display

In recent years, the laser phosphor technology is becoming a mainstream one in the industry of projection display. In this paper, we first review the development of different light source technologies used in projectors. Thereafter, we review the evolution of our laser phosphor technology and then present its latest progress ‐ RGB laser phosphor.
ASMCNN: An efficient brain extraction using active shape model and convolutional neural networks

Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnetic Resonance Imaging (MRI) scans, namely ASMCNN, by combining the Active Shape Model (ASM) and Convolutional Neural Network (CNN) for taking full of their advantages to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in the sagittal plane. First, we divide images into different groups such that, in each group, shapes and structures of brain boundaries have similar appearances. Second, a modified version of ASM is used to detect brain boundaries by utilizing prior knowledge of each group. Finally, CNN and post-processing methods, including Conditional Random Field (CRF), Gaussian processes, and several special rules are applied to refine the segmentation contours. Experimental results show that our proposed method outperforms current state-of-the-art algorithms by a significant margin in all experiments.