Reviewerly/exHarmony huggingface dataset:
Is Stellar As Secure As You Think?

Stellar is one of the top ten cryptocurrencies in terms of market capitalization. It adopts a variant of Byzantine fault tolerance (BFT), named federated Byzantine agreement (FBA), which generalizes the traditional BFT algorithm to make it more suitable for open-membership blockchains. To this end, FBA introduces a quorum slice concept, which consists of a set of nodes. In FBA, a node can complete one consensus round when it receives specific messages from nodes in a quorum slice appointed by the node. In this study, we analyze FBA, whose security is highly dependent on the structure of quorum slices, and demonstrate that it is not superior to the traditional BFT algorithm in terms of safety and liveness. Then, to analyze the security of the Stellar consensus protocol (SCP), which is a construction for FBA, we investigate the current quorum slices in Stellar. We analyze the structure of quorum slices and measure the influence of each node quantitatively using two metrics, PageRank (PR) and the newly proposed NodeRank (NR). The results show that the Stellar system is significantly centralized. Thereafter, to determine how the centralized structure can have a negative impact on the Stellar system, we study the cascading failure caused by deleting only a few nodes (i.e., validators) in Stellar. We show that all of the nodes in Stellar cannot run SCP if only two nodes fail. To make matters worse, these two nodes are run and controlled by a single organization, the Stellar foundation.
Empirically Analyzing Ethereum's Gas Mechanism

Ethereum's Gas mechanism attempts to set transaction fees in accordance with the computational cost of transaction execution: a cost borne by default by every node on the network to ensure correct smart contract execution. Gas encourages users to author transactions that are efficient to execute and in so doing encourages node diversity, allowing modestly resourced nodes to join and contribute to the security of the network. However, the effectiveness of this scheme relies on Gas costs being correctly aligned with observed computational costs in reality. In this work, we performed the first large scale empirical study to understand to what degree this alignment exists in practice, by collecting and analyzing Tera-bytes worth of nanosecond-precision transaction execution traces. Besides confirming potential denial-of-service vectors, our results also shed light on the role of I/O in transaction costs which remains poorly captured by the current Gas cost model. Finally, our results suggest that under the current Gas cost model, nodes with modest computational resources are disadvantaged compared to their better resourced peers, which we identify as an ongoing threat to node diversity and network decentralization.
Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias--when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, with a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of the drift for each cohort, which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.
A Subjective Study on User Perception Aspects in Virtual Reality

Three hundred and sixty degree video is becoming more and more popular on the Internet. By using a Head-Mounted Display, 360-degree video can render a Virtual Reality (VR) environment. However, it is still a big challenge to understand Quality of Experience (QoE) of 360-degree video since user experience during watching 360-degree video is a very complex phenomenon. In this paper, we aim to investigate four QoE aspects of 360-degree video, namely, perceptual quality, presence, cybersickness, and acceptability. In addition, four key QoE-affecting factors of encoding parameters, content motion, rendering device, and rendering mode are considered in our study. To the best of our knowledge, this is the first work that covers a large number of factors and QoE aspects of 360-degree video. In this study, a subjective experiment is conducted using 60 video versions generated from three original 360-degree videos. Based on statistical analysis of the obtained results, various findings on the impacts of the factors on the QoE aspects are provided. In particular, regarding the impacts of encoding parameters, it is found that the difference of QoE is negligible between video versions encoded at 4 K and 2.5 K resolutions. Also, it is suggested that 360-degree video should not be encoded at HD resolution or lower when watching in VR mode using Head Mounted Display. In addition, the bitrate for good QoE varies widely across different video contents. With respect to the content motion factor, its impact is statistically significant on the perceptual quality, presence, and cybersickness. In a comparison of two rendering device sets used in this study, there is no statistically significant difference found for the acceptability and cybersickness. However, the differences of the perceptual quality and presence are indicated to be statistically significant. Regarding the rendering mode, a comparison between VR and non-VR modes is also conducted. Although the non-VR mode always achieves higher perceptual quality scores and higher acceptability rates, more than a half of the viewers prefer the VR mode to the non-VR mode when watching versions encoded at the resolutions of fHD or higher. By contrast, the non-VR mode is preferred at the HD resolution.
Discrimination-Aware Projected Matrix Factorization

Non-negative Matrix Factorization (NMF) has been one of the most popular clustering techniques in machine leaning, and involves various real-world applications. Most existing works perform matrix factorization on high-dimensional data directly. However, the intrinsic data structure is always hidden within the low-dimensional subspace. And, the redundant features within the input space may affect the final result adversely. In this paper, a new unsupervised matrix factorization method, Discrimination-aware Projected Matrix Factorization (DPMF), is proposed for data clustering. The main contributions are threefold: (1) The linear discriminant analysis is jointly incorporated into the unsupervised matrix factorization framework, so the clustering can be accomplished in the discriminant subspace. (2) The manifold regularization is introduced to perceive the geometric information, and the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm is utilized to improve the robustness. (3) An efficient optimization algorithm is designed to solve the proposed problem with proved convergence. Experimental results on one toy dataset and eight real-world benchmarks show the effectiveness of the proposed method.
Learning Code Context Information to Predict Comment Locations

Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. In our developer survey, commenting has become an important, yet often-neglected activity when programming. Moreover, there is a lack of formal and automatic way in current practice to remind developers where to comment in the source code. To provide informative guidance on commenting during development, we propose a novel method CommentSuggester to recommend developers regarding appropriate commenting locations in the source code. Because commenting is closely related to the context information of source code, we identify this important factor to determine comment positions and extract it as structural context features, syntactic context features, and semantic context features. Subsequently, machine learning techniques are applied to identify possible commenting locations in the source code. We evaluated CommentSuggester using large datasets from dozens of open-source software systems in GitHub. The encouraging experimental results and user study demonstrated the feasibility and effectiveness of our commenting suggestion method.
Scaling reconfigurable emulation of quantum algorithms at high precision and high throughput

The general approach for emulating quantum algorithms on classical platforms has been through representing them as gate-based quantum circuits. However, direct implementation of quantum circuits significantly increases the hardware resource utilization and system latency of classical emulators. In this paper, we investigate multiple implementation models alternative to conventional emulation approaches, as feasible solutions to the scalability problem in classical emulation of quantum circuits. In the first model, the quantum circuit functionality is reduced to equivalent, arithmetic (multiply-and-accumulate) operations and combined with two computation methods, based on lookup and dynamic generation. In the second model, a kernel operation is extracted from the quantum circuit based on its functionality, and the kernel is iterated through all input quantum states. The proposed emulation models provide space and time optimizations, significantly reducing resource utilizations and latencies and improving scalability. We use these models to develop a highly scalable hardware emulator that is based on reconfigurable technology for efficient simulations of full quantum algorithms and circuits. Our hardware implementations support single precision floating point arithmetic for improved accuracy, and contain fully pipelined designs for high throughput. We investigate quantum algorithms such as quantum Fourier transform and quantum wavelet transform and explore different hardware architectures and optimizations. Experimental results and analysis are provided for the architectures in terms of resource consumption and emulation time. The experiments are carried out on a high-performance reconfigurable computing system and the obtained results show that the proposed emulator is feasible for running and testing a variety of quantum algorithms.
Dynamic Traffic Feature Camouflaging via Generative Adversarial Networks

Traffic analysis attacks, including website fingerprinting and protocol fingerprinting, are widely adopted by Internet censorship to block a specific type of traffic. To mitigate these attacks, some advanced approaches such as traffic morphing and protocol tunneling techniques have been proposed. However, the existing traffic morphing/protocol tunneling techniques suffer from showing a strong traffic pattern or can be uncovered with a low false positive. Further, they mainly rely on learning the pattern for specific traffic, which makes it highly possible to be identified due to a lack of dynamics. In this paper, we propose a dynamic traffic camouflaging technique, coined FlowGAN, to dynamically morph traffic feature as another “normal” network flow to bypass Internet censorship. The core idea of FlowGAN is to automatically learn the features of the “normal” network flow, and dynamically morph the on-going traffic flows based on the learned features by the adoption of the recently proposed Generative Adversarial Networks (GAN) model. To measure the indistinguishability of the target traffic and the morphed traffic, we introduce a novel concept of ϵ-indistinguishability. We evaluate the proposed method on a dataset involving 10,000 realworld flows, and experimental results show that the effectiveness and the efficiency of FlowGAN regarding ϵ-indistinguishability, AUC, and latency. To the best of our knowledge, our work is the first one to adopt GAN for automatic traffic generation and censor circumvention.
Verifying commuting quantum computations via fidelity estimation of weighted graph states

Abstract The instantaneous quantum polynomial time (IQP) model is one of promising models to demonstrate a quantum computational advantage over classical computers. If the IQP model can be efficiently simulated by a classical computer, an unlikely consequence in computer science can be obtained (under some unproven conjectures). In order to experimentally demonstrate the advantage using medium or large-scale IQP circuits, it is inevitable to efficiently verify whether the constructed IQP circuits faithfully work. There exist two types of IQP models, each of which is the sampling on hypergraph states or weighted graph states. For the first-type IQP model, polynomial-time verification protocols have already been proposed. In this paper, we propose verification protocols for the second-type IQP model. To this end, we propose polynomial-time fidelity estimation protocols of weighted graph states for each of the following four situations where a verifier can (i) choose any measurement basis and perform adaptive measurements, (ii) only choose restricted measurement bases and perform adaptive measurements, (iii) choose any measurement basis and only perform non-adaptive measurements, and (iv) only choose restricted measurement bases and only perform non-adaptive measurements. In all of our verification protocols, the verifier’s quantum operations are only single-qubit measurements. Since we assume no independent and identically distributed property on quantum states, our protocols work in any situation.
Learning Bayesian network structures using weakest mutual-information-first strategy

In Bayesian network structure learning, the quality of the directed graph learned by the constraint-based approaches can be greatly affected by the order of choosing variable pairs and the order of selecting condition sets for testing conditional independence. Inspired by the strong connection between the degree of mutual information shared by two variables and their conditional independence, we introduce the M-ordering concept, where a matrix is precomputed from the observational data with variables ordered increasingly by their respective degree of mutual information with the target variable under concern. Given the M-ordering matrix, we propose a strategy called Weakest Mutual-Information-First Strategy (WMIF), which is integrated into the PC-algorithm in two aspects: an MI-based edge removal strategy, and an MI-based condition set generation strategy. The MI-based edge removal strategy is to always select the variable pair with the weakest mutual information to test their conditional independence; the condition set generation strategy is to construct a conditioning set where variables bearing a weaker degree of mutual information with the target variable are always considered first. We prove that the weakest MI-based edge removal strategy is sound, and our PC-MI algorithm, a PC variant empowered by the WMIF strategy, is order-independent. Moreover, in PC algorithms, the number of conditional independence tests increases exponentially with the number of random variables; we show that the WMIF strategy can effectively reduce the complexity (bounded by o(|V|(2|adj(X)|−|V|22))). We have conducted experiments with both low-dimensional and high-dimensional data sets, and the results indicate that PC-MI outperforms the state-of-the-art approaches. More importantly, the order-agnostic property of PC-MI can be extremely useful when it is hard to prescribe a meaningful variable ordering as needed in some other PC algorithms.
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Determining the Number of Hidden Layers in Neural Network by Using Principal Component Analysis

One of the challenges faced in the success of Deep Neural Network (DNN) implementation is setting the values for various hyper-parameters, one of which is network topology that is closely related to the number of hidden layers and neurons. Determining the number of hidden layers and neurons is very important and influential in DNN learning performance. However, up to now, there has been no guidance on it. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process. In this study, we propose the method used for determining the number of hidden layers was through the number of components formed on the principal component analysis (PCA). By using Forest Type Mapping Data Set, based on PCA analysis, it was found out that the number of hidden layers that provide the best accuracy was three. This is in accordance with the number of components formed in the principal component analysis which gave a cumulative variance of around 70%.
Region Proposal Network Based Small-Footprint Keyword Spotting

We apply an anchor-based region proposal network (RPN) for end-to-end keyword spotting (KWS). RPNs have been widely used for object detection in image and video processing; here, it is used to jointly model keyword classification and localization. The method proposes several anchors as rough locations of the keyword in an utterance and jointly learns classification and transformation to the ground truth region for each positive anchor. Additionally, we extend the keyword/non-keyword binary classification to detect multiple keywords. We verify our proposed method on a hotword detection data set with two hotwords. At a false alarm rate of one per hour, our method achieved more than 15% relative reduction in false rejection of the two keywords over multiple recent baselines. In addition, our method predicts the location of the keyword with over 90% overlap, which can be important for many applications.
Towards Explainable Face Aging with Generative Adversarial Networks

Generative Adversarial Networks (GAN) are being increasingly used to perform face aging due to their capabilities of automatically generating highly-realistic synthetic images by using an adversarial model often based on Convolutional Neural Networks (CNN). However, GANs currently represent black box models since it is not known how the CNNs store and process the information learned from data. In this paper, we propose the first method that deals with explaining GANs, by introducing a novel qualitative and quantitative analysis of the inner structure of the model. Similarly to analyzing the common genes in two DNA sequences, we analyze the common filters in two CNNs. We show that the GANs for face aging partially share their parameters with GANs trained for heterogeneous applications and that the aging transformation can be learned using general purpose image databases and a fine-tuning step. Results on public databases confirm the validity of our approach, also enabling future studies on similar models.
Local energy oriented pattern for image indexing and retrieval

A novel image indexing algorithm for Content Based Image Retrieval (CBIR) using Local Energy Oriented Patterns (LEOP) is proposed in this paper. LEOP encodes pixel level energy orientations to find minute spatial features of an image whereas existing methods use neighborhood relationship. LEOP maps four pixel progression orientations to find top two maximum energy changes for each reference pixel in the image i.e. for each reference 3×3 grid, two more 3×3 grids out of four pixel progression are extracted. Finally, LEOP encodes the relationship among pixels of three 3×3 local grids extracted. LEOP is applied on four different image databases named MESSIDOR, VIA/I-ELCAP, COREL and ImageNet Database using traditional CBIR framework. To test the robustness of proposed feature descriptor the experiment is extended to a learning based CBIR approach on COREL database. The LEOP outperformed state-of-the-art methods in both traditional as well as learning environments and hence it is a strong descriptor.
An Incentive Mechanism Using Shapley Value for Blockchain-Based Medical Data Sharing

With the development of big data and machine learning techniques, medical data sharing for the use of disease diagnosis has received considerable attention. Blockchain, as an emerging technology, has been widely used to resolve the efficiency and security issues in medical data sharing. However, the existing studies on blockchain-based medical data sharing have rarely concerned about the reasonable incentive mechanism. In this paper, we propose a cooperation model where medical data is shared via blockchain. We derive the topological relationships among the participants consisting of data owners, miners and third parties, and gradually develop the computational process of Shapley value revenue distribution. Specifically, we explore the revenue distribution under different consensuses of blockchain. Finally, we demonstrate the incentive effect and rationality of the proposed solution by analyzing the revenue distribution.
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning

Sensational headlines are headlines that capture people's attention and generate reader interest.Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention.In this paper, we propose a model that generates sensational headlines without labeled data.We first train a sensationalism scorer by classifying online headlines with many comments ("clickbait") against a baseline of headlines generated from a summarization model.The score from the sensationalism scorer is used as the reward for a reinforcement learner.However, maximizing the noisy sensationalism reward will generate unnatural phrases instead of sensational headlines.To effectively leverage this noisy reward, we propose a novel loss function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance reinforcement learning (RL) with maximum likelihood estimation (MLE).Human evaluation shows that 60.8% of samples generated by our model are sensational, which is significantly better than the Pointer-Gen baseline (See et al., 2017) and other RL models.