Reviewerly/exHarmony huggingface dataset:
Cloud reliability and efficiency improvement via failure risk based proactive actions

Due to the huge magnitude and complexity of cloud computing systems (CCS), failures are inevitable, which lead to reliability and efficiency losses. Failure mitigation, fault tolerance, and recovery actions can be performed to improve CCS reliability and efficiency. Using data collected during CCS operation, failure prediction and risk identification techniques could anticipate such failure occurrences. In this paper, we develop a framework to combine risk identification with follow-up proactive actions for CCS reliability and efficiency improvement. We start by analyzing cloud failures and the related operational data. Then a tree based predictive model is trained to diagnose high risk cloud tasks. By proactively terminating these high risk tasks, both the number of CCS failures and the resource consumption could be significantly reduced. The impact of these proactive actions can be simulated to quantify the improvement to both system reliability and efficiency. The new approach has been applied on the Google cluster dataset, covering approximately 400GB of operational data over 29 consecutive days, to demonstrate its viability and effectiveness.
Continuous-Discrete Multiple Target Filtering: PMBM, PHD and CPHD Filter Implementations

This article develops models and algorithms for continuous-discrete multiple target filtering, in which the multi-target system is modelled in continuous time and measurements are available at discrete time steps. In order to do so, this paper first proposes a statistical model for multi-target appearance, dynamics and disappearance in continuous time, based on continuous time birth/death processes and stochastic differential equations. The multitarget state is observed at known time instants based on the standard measurement model, and the objective is to compute the distribution of the multi-target state at these time steps. For the Wiener velocity model, we derive a closed-form formula to obtain the best Gaussian Poisson point process fit to the birth density based on Kullback-Leibler minimisation. The resulting discretised model gives rise to the continuous-discrete Gaussian Poisson multi-Bernoulli mixture (PMBM) filter, the continuous-discrete Gaussian mixture probability hypothesis density (PHD) filter and the continuous-discrete Gaussian mixture cardinality PHD (CPHD) filter. The proposed filters are specially useful for multi-target estimation when the time interval between measurements is non-uniform.
PASPORT: A Secure and Private Location Proof Generation and Verification Framework

Recently, there has been a rapid growth in location-based systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and nontransferability of LPs. Furthermore, the PASPORT scheme is resilient to prover-prover collusions and significantly reduces the success probability of Prover-Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions.
Source Camera Identification With Dual-Tree Complex Wavelet Transform

Sensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI).However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border.To fill this gap, we propose a dual tree complex wavelet transform (DTCWT) based method to extract the SPN from a given image, which achieves better performance in the area around strong edges.Furthermore, symmetric boundary extension instead of the periodized boundary extension is used for enhancing the quality of SPN along with the image border.Extensive experimental results on both synthetic noisy images and real-world photographs clearly demonstrate the superior SCI performance of the proposed method over state-of-the-arts.Moreover, the proposed method also shows potential in the application of image tampering localization.
Construction of Cubic Timmer Triangular Patches and its Application in Scattered Data Interpolation

This paper discusses scattered data interpolation by using cubic Timmer triangular patches. In order to achieve C1 continuity everywhere, we impose a rational corrected scheme that results from convex combination between three local schemes. The final interpolant has the form quintic numerator and quadratic denominator. We test the scheme by considering the established dataset as well as visualizing the rainfall data and digital elevation in Malaysia. We compare the performance between the proposed scheme and some well-known schemes. Numerical and graphical results are presented by using Mathematica and MATLAB. From all numerical results, the proposed scheme is better in terms of smaller root mean square error (RMSE) and higher coefficient of determination (R2). The higher R2 value indicates that the proposed scheme can reconstruct the surface with excellent fit that is in line with the standard set by Renka and Brown’s validation.
Aplos: Smart Contracts Made Smart

Smart contract is a programming interface to interact with the underlying blockchain storage models. It is a database abstraction layer for blockchain. Existing smart contract platforms follow the imperative style programming model since states are shared. As a result, there is no concurrency control mechanism when executing transactions, resulting in considerable latency and hindering scalability. To address performance and scalability issues of existing smart contract platforms, we design a new smart contract platform called “Aplos” based on the Scala functional programming language and Akka actors. In Aplos, all blockchain-related smart contract functions are implemented with Akka actors. The Aplos platform is built over Mystiko—a highly scalable blockchain storage for big data. Mystiko supports concurrent transactions, high transaction throughput, data analytics and machine learning. With Aplos smart contracts over Mystiko, we have developed a blockchain for highly scalable storage that aligns with big data requirements.
Performance Comparison of the type-2 hypervisor VirtualBox and VMWare Workstation

This paper presents a performance comparison of two hypervisors, VirtualBox and VMWare Workstation 15 Player. The performances of the hypervisors are evaluated through the performance of the applied file system. For the testing procedure, we have used Filebench benchmark tool. We have used CentOS 7 Linux distribution as the virtual machine operating system. The hypervisor performances are compared taking into consideration tests performed for the system with one, two and three virtual machines launched.
Blockchain for Identity Management: The Implications to Personal Data Protection

Identity symbolizes a personal level of an individual and becomes an embodiment of who we are, and Identity Management in Information Communication Technologies field represents the basis of authenticating a specific individual. Therefore, it is important to protect and control identity as personal information so that unfortunate misconduct would be avoided. Currently, how individuals are represented in the digital ecosystem appears to be permissive with the management of centralized control on identity. Recently, blockchain technology has been making its mark as a revolutionized approach to exceed expectations with its decentralized approach. Popularized with the cryptocurrency, blockchain technology could also create a disruptive effect in the area of identity management. In the domain of identity management, privacy protection is crucial to the security of personal data. However, there is a lack of systematic study on how the identity management aspect of blockchain technology supports personal data protection based on the requirement of regulations. In this paper, we aim to close this gap through a systematic review to examine how the identity management aspect of blockchain technology supports or contradicts personal data protection based on the requirements of the General Data Protection Regulation (GDPR) requirements.
cBAD: ICDAR2019 Competition on Baseline Detection

Baseline detection is a simplified text-line extraction that typically serves as pre-processing for Automated Text Recognition. The cBAD competition benchmarks state-of-the-art baseline detection algorithms. It is the successor of cBAD 2017 with a larger dataset that contains more diverse document pages. The images together with the manually annotated groundtruth are made publicly available which allows other teams to benchmark and compare their methods. We could also evaluate the winning method of cBAD 2017 on the newly introduced dataset which now serves as baseline. This competition shows that the performance of automated baseline detection increased substantially since 2017.
Document Binarization via Multi-resolutional Attention Model with DRD Loss

Document binarization which separates text from background is a critical pre-processing step for many high level document analysis tasks. Conventional document binarization approaches tend to use hand-craft features and empirical rules to simulate the degradation process of document image and accomplish the binarization task. In this paper, we propose a deep learning framework where the probability of text areas is inferred through a multi-resolutional attention model, which is consequently fed into a convolutional conditional random field (ConvCRF) to obtain the final binarized document image. In the proposed approach, the features of degraded document image are learned by neural networks and the relations between text areas and backgrounds are inferred by ConvCRF, which avoids the dependence of domain knowledge from researchers and has more generalization capabilities. The experimental results on public datasets show that the proposed method has superior binarization performance than the existing state-of-the-art approaches.
Application of Smart Contracts based on Ethereum Blockchain for the Purpose of Insurance Services

The insurance business is looking for solutions to minimize its operational costs and time to process claims for losses. Blockchain technology gives advantages such as transaction security, process identification, process automation and payment speed. This paper presents an experimental implementation of smart contracts based on Ethereum blockchain for insurance services. A decentralized crypto-token, based on ERC20 standard for smart contract, is implemented. A web-based interface is created for sales of these crypto-tokens. The results from the experimental tests are presented.
Grounding Language Attributes to Objects using Bayesian Eigenobjects

We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase. Our system is designed to learn from only a small amount of human-labeled language data and generalize to viewpoints not represented in the language-annotated depth image training set. By decoupling 3D shape representation from language representation, this method is able to ground language to novel objects using a small amount of language-annotated depth-data and a larger corpus of unlabeled 3D object meshes, even when these objects are partially observed from unusual viewpoints. Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions. Our method also enables viewpoint transfer; trained on human-annotated data on a small set of depth images captured from frontal viewpoints, our system successfully predicted object attributes from rear views despite having no such depth images in its training set. Finally, we demonstrate our approach on a Baxter robot, enabling it to pick specific objects based on human-provided natural language descriptions.
Separate Your Domains: NIST PQC KEMs, Oracle Cloning and Read-Only Indifferentiability

It is convenient and common for schemes in the random oracle model to assume access to multiple random oracles (ROs), leaving to implementations the task—we call it oracle cloning—of constructing them from a single RO. The first part of the paper is a case study of oracle cloning in KEM submissions to the NIST Post-Quantum Cryptography standardization process. We give key-recovery attacks on some submissions arising from mistakes in oracle cloning, and find other submissions using oracle cloning methods whose validity is unclear. Motivated by this, the second part of the paper gives a theoretical treatment of oracle cloning. We give a definition of what is an “oracle cloning method” and what it means for such a method to “work,” in a framework we call read-only indifferentiability, a simple variant of classical indifferentiability that yields security not only for usage in single-stage games but also in multi-stage ones. We formalize domain separation, and specify and study many oracle cloning methods, including common domain-separating ones, giving some general results to justify (prove read-only indifferentiability of) certain classes of methods. We are not only able to validate the oracle cloning methods used in many of the unbroken NIST PQC KEMs, but also able to specify and validate oracle cloning methods that may be useful beyond that.
Blind de-convolution of images degraded by atmospheric turbulence

Atmospheric turbulence can change the path and direction of light during the imaging of a target in space due to the random motion of the turbulent medium, resulting in severe image distortion. To correct geometric distortion, and reduce spatially and temporally varying blur, this paper proposes a convolutional network for blind deblurring atmospheric turbulence (BDATNet) that includes a feature extraction noise suppression block (FENSB), an asymmetric U-net, and an image reconstruction subnetwork (IRSubnetwork). A deblurring noise suppression block (DNSB) is used instead of the traditional convolution layer for the U-net. The core principle of this model is to suppress noise before deblurring. During convolutional encoding, the FENSB and DNSB can suppress noise and capture rich feature maps. To fuse information obtained from low-level and high-level features, the FENSB and IRSubnetwork are skip-connected to ensure the integrity of the former during image reconstruction. Moreover, the method of gradually increasing the difficulty of data to train the network is used to cause it to gradually converge from simple to complex, so that it can deal with images severely degraded by turbulence. The experimental results of real data and simulation data show that the BDATNet can restore details of the image and sharpen its edges, and can suppress noise.
A 2020 perspective on “Information asymmetry in initial coin offerings (ICOs): Investigating the effects of multiple channel signals”

An initial coin offering (ICO) is an innovative way for entrepreneurs of blockchain companies to raise capital by selling tokens to a crowd of investors. Regardless of its popularity, ICO is still considered as a controversial activity that has emerged in the domain of financial technology (fintech). However, even though ICOs have attracted much attention in the cryptocurrency market, research in this area is still sparse. In this article, we comment on recent work discussing information asymmetry in ICOs. We explore the significance of the research in the fintech domain. We also point out interesting research questions in this area that are worthy of investigation and some directions for future research.
BACC: Blockchain-Based Access Control For Cloud Data

Controlling the access over the stored data in the cloud is one of the fundamental security requirements, especially with the wide usage of cloud storage servers for nearly most of the enterprise applications. Traditional cloud-based access control solutions are based on a centralized approach (i.e. a cloud server becomes the central authority to control accesses to the data), which makes it difficult to prevent malicious cloud servers from disclosing user's data; and therefore compromising the privacy of the stored data. Additionally, the centralization of authority can cause a single point of failure. Furthermore, to provide confidentiality, which is one of the essential security requirements, user's data is encrypted before it is stored on the cloud. Most of the cloud servers store the decryption keys, after they encrypt the data, in their premises. This compromises data privacy. In this paper we propose a new model that addresses the aforementioned issues. To address the centralization problem, we distributed the access control tasks to smart contracts over a decentralized network, i.e. blockchain. To address the latter, we used Shamir secret sharing scheme to manage the encryption keys. Then we introduced a new type of node, called master node, to our blockchain platform, to store the decryption key parts.
A Comparative Study of Attention-Based Encoder-Decoder Approaches to Natural Scene Text Recognition

Attention-based encoder-decoder approaches have shown promising results in scene text recognition. In the literature, models with different encoders, decoders and attention mechanisms have been proposed and compared on isolated word recognition tasks, where the models are trained on either synthetic word images or a small set of real-world images. In this paper, we investigate different components of the attention based framework and compare its performance with a CNN-DBLSTM-CTC based approach on large-scale real-world scene text sentence recognition tasks. We train character models by using more than 1.6M real-world text lines and compare their performance on test sets collected from a variety of real-world scenarios. Our results show that (1) attention on a two-dimensional feature map can yield better performance than one-dimensional one and an RNN based decoder performs better than CNN based one; (2) attention-based approaches can achieve higher recognition accuracy than CNN-DBLSTM-CTC based approaches on isolated word recognition tasks, but perform worse on sentence recognition tasks; (3) it is more effective and efficient for CNN-DBLSTM-CTC based approaches to leverage an explicit language model to boost recognition accuracy.
Virtual Reality for Anxiety Disorders: Rethinking a Field in Expansion

The principal aim to this chapter is to present the latest ideas in virtual reality (VR), some of which have already been applied to the field of anxiety disorders, and others are still pending to be materialized. More than 20 years ago, VR emerged as an exposure tool in order to provide patients and therapists with more appealing ways of delivering a technique that was undoubtedly effective but also rejected and thus underused. Throughout these years, many improvements were achieved. The first section of the chapter describes those improvements, both considering the research progresses and the applications in the real world. In a second part, our main interest is to expand the discussion of the new applications of VR beyond its already known role as an exposure tool. In particular, VR is enabling the materialization of numerous ideas that were previously confined to a merely philosophical discussion in the field of cognitive sciences. That is, VR has the enormous potential of providing feasible ways to explore nonclassical ways of cognition, such as embodied and situated information processing. Despite the fact that many of these developments are not fully developed, and not specifically designed for anxiety disorders, we want to introduce these new ideas in a context in which VR is experiencing an enormous transformation.
Open Set Domain Adaptation for Hyperspectral Image Classification Using Generative Adversarial Network

Hyperspectral image (HSI) classificationNirmal, S. attracted lotsSowmya, V. of attentionSoman, K. P. due to its complexity in dealing with large dimensions. In recent years, the techniques for dealing with the HSI have been evolved, ensuring the increase in efficiency to some extent in classification and other perspectives. Domain adaptation is a well-established technique for using any trained classification model, when the feature space from target domain is a subset of feature space from source domain. The objective of this paper is to create an efficient and effective model for HSI classification by implementing open set (OS) domain adaptation and generative adversarial network (GAN). This has advantages in quite few ways, such as creating a single training model that deals with various HSI data set with common classes, classifying the features in any data to specific trained classes and unknown (to be labelled) making it easy to annotate. The proposed open set domain adaptation for HSI classification is evaluated using Salinas and Pavia. The proposed method resulted in the classification accuracy for unknown classes as 99.07% for Salinas and 81.65% for Pavia.
Performing Co-membership Attacks Against Deep Generative Models

In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, membership attack aims to check whether a given instance x was used in the training data or not. A co-membership attack checks whether the given bundle of n instances were in the training, with the prior knowledge that the bundle was either entirely used in the training or none at all. Successful membership attacks can compromise the privacy of training data when the generative model is published. Our main idea is to cast membership inference of target data x as the optimization of another neural network (called the attacker network) to search for the latent encoding to reproduce x. The final reconstruction error is used directly to conclude whether x was in the training data or not. We conduct extensive experiments on a variety of datasets and generative models showing that: our attacker network outperforms prior membership attacks; co-membership attacks can be substantially more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs.