Reviewerly/exHarmony huggingface dataset:
ESVSSE: Enabling Efficient, Secure, Verifiable Searchable Symmetric Encryption

Symmetric Searchable Encryption(SSE) is deemed to tackle the privacy issue as well as the operability and confidentiality in data outsourcing. However, most SSE schemes assume that the cloud is honest but curious. This assumption is not always applicable. In this paper, we propose an efficient SSE scheme based on B+-Tree and Counting Bloom Filter (CBF) which supports secure verification, dynamic updating, and multi-user queries. Comparing with the previous state of the arts, we design the new data structure CBF to support dynamic updating and boost verification. we evaluate our scheme through comprehensive experiments. The results are consistent with our analysis and show that our scheme is secure, and more efficient compared with the previous schemes with the same functionalities.The average performance can be improved by about 20% for both the cloud servers and users when the missing rate of the searching keywords is 20%. And the higher the missing rate is, the more the performance can be improved.
One- versus multi-component regular variation and extremes of Markov trees

Abstract A Markov tree is a random vector indexed by the nodes of a tree whose distribution is determined by the distributions of pairs of neighbouring variables and a list of conditional independence relations. Upon an assumption on the tails of the Markov kernels associated to these pairs, the conditional distribution of the self-normalized random vector when the variable at the root of the tree tends to infinity converges weakly to a random vector of coupled random walks called a tail tree. If, in addition, the conditioning variable has a regularly varying tail, the Markov tree satisfies a form of one-component regular variation. Changing the location of the root, that is, changing the conditioning variable, yields a different tail tree. When the tails of the marginal distributions of the conditioning variables are balanced, these tail trees are connected by a formula that generalizes the time change formula for regularly varying stationary time series. The formula is most easily understood when the various one-component regular variation statements are tied up into a single multi-component statement. The theory of multi-component regular variation is worked out for general random vectors, not necessarily Markov trees, with an eye towards other models, graphical or otherwise.
Manifold Projection for Adversarial Defense on Face Recognition

Although deep convolutional neural network based face recognition system has achieved remarkable success, it is susceptible to adversarial images: carefully constructed imperceptible perturbations can easily mislead deep neural networks. A recent study has shown that in addition to regular off-manifold adversarial images, there are also adversarial images on the manifold. In this paper, we propose Adversarial Variational AutoEncoder (A-VAE), a novel framework to tackle both types of attacks. We hypothesize that both off-manifold and on-manifold attacks move the image away from the high probability region of image manifold. We utilize variational autoencoder (VAE) to estimate the lower bound of the log-likelihood of image and explore to project the input images back into the high probability regions of image manifold again. At inference time, our model synthesizes multiple similar realizations of a given image by random sampling, then the nearest neighbor of the given image is selected as the final input of the face recognition model. As a preprocessing operation, our method is attack-agnostic and can adapt to a wide range of resolutions. The experimental results on LFW demonstrate that our method achieves state-of-the-art defense success rate against conventional off-manifold attacks such as FGSM, PGD, and C&W under both grey-box and white-box settings, and even on-manifold attack.
On the Semantics of Abstract Argumentation Frameworks: A Logic Programming Approach

Abstract Recently there has been an increasing interest in frameworks extending Dung’s abstract Argumentation Framework (AF). Popular extensions include bipolar AFs and AFs with recursive attacks and necessary supports. Although the relationships between AF semantics and Partial Stable Models (PSMs) of logic programs has been deeply investigated, this is not the case for more general frameworks extending AF. In this paper we explore the relationships between AF-based frameworks and PSMs. We show that every AF-based framework Δ can be translated into a logic program P Δ so that the extensions prescribed by different semantics of Δ coincide with subsets of the PSMs of P Δ . We provide a logic programming approach that characterizes, in an elegant and uniform way, the semantics of several AF-based frameworks. This result allows also to define the semantics for new AF-based frameworks, such as AFs with recursive attacks and recursive deductive supports.
A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks

In recent years, Event-based social network (EBSN) applications, such as Meetup and DoubanEvent, have received popularity and rapid growth. They provide convenient online platforms for users to create, publish, and organize social events, which will be held in physical places. Additionally, they not only support typical online social networking facilities (e.g., sharing comments and photos), but also promote face-to-face offline social interactions. To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research. CARS in EBSNs provide the suitable recommendation to target users by incorporating the contextual factors into the recommendation process. This paper provides an overview on the development of CARS in EBSNs. We begin by illustrating the concept of the term context and the paradigms of conventional context-aware recommendation process. Subsequently, we introduce the formal definition of an EBSN, the characteristics of EBSNs, the challenges that are faced by CARS in EBSNs, and the implementation process of CARS in EBSNs. We also investigate which contextual factors are considered and how they are represented in the recommendation process. Next, we focus on the state-of-the-art computational techniques regarding CARS in EBSNs. We also overview the datasets and evaluation metrics for evaluation in this research area, and discuss the applications of context-aware recommendation in EBSNs. Finally, we point out research opportunities for the research community.
A blockchain-based decentralized energy management in a P2P trading system

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity.
Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks

In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics and data-efficiency-both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.
Motion‐based frame interpolation for film and television effects

Frame interpolation is the process of synthesising a new frame in‐between existing frames in an image sequence. It has emerged as a key algorithmic module in motion picture effects. In the context of this special issue, this study provides a review of the technology used to create in‐between frames and presents a Bayesian framework that generalises frame interpolation algorithms using the concept of motion interpolation. Unlike existing literature in this area, the authors also compare performance using the top industrial toolkits used in the post production industry. They find that all successful techniques employ motion‐based interpolation, and the commercial version of the Bayesian approach performs best. Another goal of this study is to compare the performance gains with recent convolutional neural network (CNN) algorithms against the traditional explicit model‐based approaches. They find that CNNs do not clearly outperform the explicit motion‐based techniques, and require significant compute resources, but provide complementary improvements in certain types of sequences.
Synthesis of Satellite-Like Urban Images From Historical Maps Using Conditional GAN

One method for encouraging the public interest in the use of historical maps as a source of reliable knowledge is to represent them in a more familiar aspect, such as the style of the current-day popular application Google Maps’ satellite view. We present a method for synthesizing satellite-images from historical maps, translating their visuals using conditional generative adversarial networks (conditional GANs). We discuss a typical representation of these dated documents to allow such translations. We observe how the semantics involved in the process influence the outcomes. Finally, we discuss the effective result of bringing the past to a familiar look for the viewer.
Application of Pre-training Models in Named Entity Recognition

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task to extract entities from unstructured data. The previous methods for NER were based on machine learning or deep learning. Recently, pre-training models have significantly improved performance on multiple NLP tasks. In this paper, firstly, we introduce the architecture and pre-training tasks of four common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task. The experiment results showed that RoBERTa achieved state-of-the-art results on the MSRA-2006 dataset.
Power Range: Forward Private Multi-Client Symmetric Searchable Encryption with Range Queries Support

Symmetric Searchable encryption (SSE) is an encryption technique that allows users to search directly over their outsourced encrypted data while preserving the privacy of both the files and the queries. In this paper, we present Power Range - a dynamic SSE scheme (DSSE) that supports range queries in the multi-client model. We prove that our construction captures the very crucial notion of forward privacy in the sense that additions and deletions of files do not reveal any information about the content of past queries. Finally, to deal with the problem of synchronization in the multi-client model, we exploit the functionality offered by Trusted Execution Environments and Intel’s SGX.
Glomerulus Classification via an Improved GoogLeNet

Glomerulosclerosis is a pathomorphological feature of glomerular lesions. Early detection, accurate judgement and effective prevention of the glomeruli is crucial not only for people with kidney disease, but also for the general population. We proposed a method in combination of traditional image analysis with modern machine learning diagnosis system model based on GoogLeNet for recognizing and distinguishing different categories of glomerulus in order to efficiently capture the important structures as well as to minimize manual effort and supervision. We proposed a novel deep learning model based on GoogLeNet with added batch-normalization layers to extract useful features and subsequently entered the features into SoftMax for classification. We also incorporated Bayesian Optimization algorithm and k-fold cross validation in this system for achieving a more reliable result. Our method has eventually achieved an overall accuracy of 95.04±4.99%, and F1 score of 94.44±3.11% for no glomerulus category, 96.73±5.23% for normal glomerulus category and 93.66±7.82% for globally sclerosed glomerulus category, which means this method can accurately determine the degree of glomerulosclerosis with little supervision. The experimental result also shows that this method has better performance when compared with other state-of art methods.
Moving Vehicle Candidate Recognition and Classification Using Inception-ResNet-v2

Vehicle detection and classification are important tasks in the automatic traffic monitoring system. The proposed work focuses on vehicle detection and classification. Vehicle detection is carried out using the combination of dense optical flow method and integrated binary projection profile. Inception-ResNet-v2 is used as a feature extraction technique and extracted features are fed to two different classifiers such as Support Vector Machine and Random Forest to classify the vehicle type. The recognition performance of Inception-ResNet-v2 with these classifiers is significantly high and the proposed approach obtained an output accuracy as 99.89% and 98.615% in Support Vector Machine and Random forest respectively.
Human Action Recognition Using Smartphone Sensors

As smartphones are becoming ubiquitous, many studies using smartphones are being investigated in recent years. Further, these smartphones are being laden with several diverse and sophisticated sensors like GPS sensor, vision sensor (camera), acceleration sensor, audio sensor (microphone), light sensor, and direction sensor (compass). Activity Recognition is one of the potent research topics, which can be used to provide effective and adaptive services to users. Our paper is intended to evaluate a system using smartphone-based sensors used for acceleration, referred to as an accelerometer. To understand six different human activities using supervised machine learning classification; to execute the model a compiled accelerometer data from different sixteen users are collected as per their usual day to day routine consisting of sitting, standing, laying down, walking, climbing up and down the staircase. The sample data thus generated then have been aggregated and combined into examples upon which supervised machine learning algorithms have been applied to generate predictive models. To address the limitations of laboratory settings, we have used the Physics Toolbox Sensor Suite with the Google Android platform to collect these time-series data generated by the smartphone accelerometer. This kind of activity prediction model can be used to provide insightful information about millions of human beings merely by making them contain a smartphone with them.
Fast multi-feature image segmentation

Multi-feature segmentation has demonstrated its superiority against one-dimensional feature approaches based on only grayscale information. Mean shift (MS) is an algorithm that has been used commonly for multi-feature segmentation. In spite of its interesting results, MS maintains a computational cost that is prohibitive for segmentation scenarios where the feature map consists of multi-dimensional features. In this paper, a new competitive segmentation algorithm for grayscale images is introduced. The proposed approach considers a two-dimensional feature map that includes the grayscale value and the local variance for each pixel in the image. To reduce the computational cost, the Mean shift (MS) algorithm is modified to operate with a very limited number of points from all available data. Under such conditions, two sets of elements are differentiated: involved data (the reduced dataset considered in the MS operation) and not involved data (the rest of the available data). Different from the classical MS, which employs Gaussian functions, in our approach, the process of estimating the feature map is carried out using a more accurate approach such as the Epanechnikov kernel function. Once the MS results are obtained, they are generalized to include the not involved data. Therefore, each unused element is assigned to the same cluster of the closest used data. Finally, clusters with the fewest elements are fused with other neighboring clusters. The proposed segmentation method has been compared with other state-of-art algorithms considering the full number of images from the Berkeley dataset. Experimental results confirm that the proposed scheme produces segmented images with a 50% better quality of visual perception approximately two times (≈ 1.8 − 2) faster than its competitors.
Evaluation on interactive visualization data with scatterplots

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data. A recent technique, called Linkable Scatterplots, provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction, linking and brushing. This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time, Multiple-Scatterplots who number of plots can be specified and shown, and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix. Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization, particularly in comparison with the Simultaneous-Scatterplots.​ While the time taken to complete tasks was longer in the Multiple-Scatterplots technique, compared with the simpler Sequential-Scatterplots, Multiple-Scatterplots is inherently more accurate. Moreover, the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study. Overall, results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
Nourish Your Tree! Developing a Persuasive Exergame for Promoting Physical Activity Among Adults

In this paper, we present the design and development of a persuasive game for promoting physical activity and discouraging sedentariness. The game is based on the tree metaphor which links a player's physical activity level (operationalized using step count) to the health of a tree. Thus, as the player becomes physically active (such as walking or running), the tree's health improves. The tree withers if player's physical activity level reduces. The game employs various persuasive strategies based on the Persuasive Systems Design (PSD) framework to boost players' motivation and provide an enjoyable experience. We believe that our game will yield improved physical activity levels among adults by encouraging them to regularly engage in light-intensity activity (e.g., slow-walking), moderate-intensity activity (e.g., brisk-walking), and vigorous-intensity activity (e.g., running). By being physically active, players protect themselves from chronic health conditions and improve their emotional and mental well-being.
Spatial and Transform Domain CNN for SAR Image Despeckling

The speckle interference seriously degrades the quality of synthetic aperture radar (SAR) image. The existing despeckling algorithms still struggle to remove noise and preserve details simultaneously. In order to enhance the noise suppression and detail restoration performance, this article specially presents a spatial and transform domain convolutional neural network (STD-CNN) model, which yields an integrated feature representation and learning framework for despeckling. In addition, an innovative feature refinement strategy is proposed to further reduce the detail loss by isolating detail features from noise features. Extensive experiments on synthetic and real SAR images demonstrate that the proposed method outperforms the existing SAR despeckling methods on both quantitative and qualitative assessments. With partial modification, the STD-CNN model can still be extended to other image restoration tasks.
Secure Federated Learning in 5G Mobile Networks

Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower communication overhead than previous work, without affecting ML performance.
Dagbase: A Decentralized Database Platform Using DAG-Based Consensus

As the infrastructure to provide support for distributed database management systems, the distributed database platform is very important to unify the management of data distributed in intricate environments. However, a traditional distributed database platform with centralized entities faces diverse and serious threats when the central entity is compromised. Consensus mechanisms in distributed ledger technology (DLT) can enhance the capability of defending threats by decentralizing the platform, but the efficiency and cost of consensus mechanisms in classic blockchain techniques are notable issues. In this paper, we propose a novel decentralized database platform, named Dagbase, with the support of an efficient and cost-effective consensus mechanism that uses the directed acyclic graph (DAG) as the structure. Dagbase decentralizes the management and distributes data to prevent threats in untrustworthy environments, which gains benefits from recent DLT. The performance of near-native data reading and high-efficiency data writing is ensured by a layered architecture and DAG-based consensus. Furthermore, we ensure flexibility by decoupling the consensus mechanism from the architecture. Dagbase is also easy-to-use and can be integrated with mainstream database products seamlessly on account of great interoperability. The implementation demonstrates our work and the security and performance analysis are enforced for evaluation.