Reviewerly/exHarmony huggingface dataset:
Consensus Crash Testing: Exploring Ripple’s Decentralization Degree in Adversarial Environments

The inception of Bitcoin as a peer-to-peer payment system, and its underlying blockchain data-structure and protocol, has led to an increased interest in deploying scalable and reliable distributed-ledger systems that build on robust consensus protocols. A critical requirement of such systems is to provide enough fault tolerance in the presence of adversarial attacks or network faults. This is essential to guarantee liveness when the network does not behave as expected and ensure that the underlying nodes agree on a unique order of transactions over a shared state. In comparison with traditional distributed systems, the deployment of a distributed-ledger system should take into account the hidden game theoretical aspects of such protocols, where actors are competing with each other in an environment which is likely to experience various well-motivated malicious and adversarial attacks. Firstly, this paper discusses the fundamental principles of existing consensus protocols in the context of both permissioned and permissionless distributed-ledger systems. The main contribution of this work deals with observations from experimenting with Ripple’s consensus protocol as it is embodied in the XRP Ledger. The main experimental finding suggests that, when a low percentage of malicious nodes is present, the centralization degree of the network can be significantly relaxed ensuring low convergence times. Those findings are of particular importance when engineering a consensus algorithm that would like to balance security with decentralization.
Recovery of Damaged Information and the Out-of-Time-Ordered Correlators

The evolution with a complex Hamiltonian generally leads to information scrambling. A time-reversed dynamics unwinds this scrambling and thus leads to the original information recovery. We show that if the scrambled information is, in addition, partially damaged by a local measurement, then such a damage can still be treated by application of the time-reversed protocol. This information recovery is described by the long-time saturation value of a certain out-of-time-ordered correlator of local variables. We also propose a simple test that distinguishes between quantum and reversible classical chaotic information scrambling.
Distributed Error Correction Coding Scheme for Low Storage Blockchain Systems

This article presents a novel way to reduce blockchain nodes’ memory requirements using error correcting codes. In particular, LDPC codes are taken as examples to explicitly demonstrate the scheme. The proposed coding scheme encodes data across multiple blocks, respectively, block headers, in the blockchain. This leads to a significant reduction in required memory at each node. We then apply the proposed coding technique to blockchains organized in two different ways. Our first scheme has the same protocol for mining, broadcasting, and verification of blocks, as Bitcoin-type blockchains. Our scheme is different in that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full nodes</i> do not have to store all blocks. Instead they will need to store only one block of a group of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> blocks. In the second scheme, we consider a new block verification protocol and an account-based model under the assumption that transmission between any two nodes can be established, as well as the broadcast transmission. Our block verification protocol uses the Byzantine fault tolerance algorithm and requires sending a newly mined block to only a small number of verification nodes, instead of broadcasting it to the entire network, which leads to a reduction of the network load.
Fuzzy-Based Parameter Optimization of Adaptive Unscented Kalman Filter: Methodology and Experimental Validation

This study introduces a fuzzy based optimal state estimation approach. The new method is based on two principles: Adaptive Unscented Kalman filter, and Fuzzy Adaptive Grasshopper Optimization Algorithm. The approach is designed for the optimization of an adaptive Unscented Kalman Filter. To find the optimal parameters for the filter, a fuzzy based evolutionary algorithm, named Fuzzy Adaptive Grasshopper Optimization Algorithm, is developed where its efficiency is verified by application to different benchmark functions. The proposed optimal adaptive unscented Kalman filter is applied to two nonlinear systems: a robotic manipulator, and a servo-hydraulic system. Different simulation tests are conducted to verify the performance of the filter. The results of simulations are presented and compared with a previous version of the unscented Kalman filter. For a realistic test, the proposed filter is applied on the practical servo-hydraulic system. Practical results are discussed, and presented results approve the capability of the presented method for practical applications.
Multi-view semantic learning network for point cloud based 3D object detection

Point cloud based 3D objection plays a crucial role in real-world applications, such as autonomous driving. In this paper, we propose the Multi-view Semantic Learning Network (MVSLN) for 3D object detection, an approach considering the feature discrimination for LIDAR point cloud. Since the discrete and disordered nature of point cloud, most existing methods ignore the low-level information and focus more on the spatial details of point cloud. To capture the discriminative feature of objects, our MVSLN takes advantages of both spatial and low-level details to further exploit semantic information. Specifically, the Multiple Views Generator (MVG) module in our approach observes the scene from four views by projecting the 3D point cloud to planes with specific angles, which preserves much more low-level features, e.g., texture and edge. To correct the deviation brought by different projection angles, the Spatial Recalibration Fusion (SRF) operation in our approach adjusts the locations of features of these four views, enabling the interaction between different projections. Then the recalibrated features of SRF are sent to the developed 3D Region Proposal Network (RPN) to detect objects. The experimental results on challenging KITTI benchmark verify that our approach achieves a promising performance and outperforms state-of-the-art methods. Furthermore, the discriminative feature extractor brought by exploiting the conspicuous semantic information, leads to encouraging results in the hard-level difficulty of both BEV and 3D object detection tasks, without any help of camera image.
Simple and Easy: Transfer Learning-Based Attacks to Text CAPTCHA

CAPTCHA, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, is a common mechanism used to protect commercial accounts from malicious computer bots, and the most widely used scheme is text-based CAPTCHA. In recent years, newly emerged deep learning techniques have achieved high accuracy and speed in attacking text-based CAPTCHAs. However, most of the existing attacks have various disadvantages, the attack process made high complexity or manually collecting and labeling a large number of samples to train a deep learning recognition model is time-consuming and expensive. In this paper, we propose a transfer learning-based approach that greatly reduces the attack complexity and the cost of labeling samples, specifically, by pre-training the model with randomly generated samples and fine-tuning the pre-trained model with a small number of real-world samples. To evaluate our attack, we tested 25 online CAPTCHAs achieving success rates ranging from 36.3% to 96.9%. To further explore the effect of the training sample characteristics on the attack accuracy, we elaborately imitate some samples and apply a generative adversarial network to refine the samples, sequentially we use these two kinds of generated samples to pre-train the models, respectively. The experimental results demonstrate that the similarity between randomly generated samples and elaborately imitated samples has a negligible impact on the attack accuracy. Instead, transfer learning is the key factor; it reduces the cost of data preparation while preserving the model's attack accuracy.
A case study of agile software development for safety-Critical systems projects

This study explores the introduction of agile software development within an avionics company engaged in safety-critical system engineering. There is increasing pressure throughout the software industry for development efforts to adopt agile software development in order to respond more rapidly to changing requirements and make more frequent deliveries of systems to customers for review and integration. This pressure is also being experienced in safety-critical industries, where release cycles on typically large and complex systems may run to several years on projects spanning decades. However, safety-critical system developments are normally highly regulated, which may constrain the adoption of agile software development or require adaptation of selected methods or practices. To investigate this potential conflict, we conducted a series of interviews with practitioners in the company, exploring their experiences of adopting agile software development and the challenges encountered. The study also explores the opportunities for altering the existing software process in the company to better fit agile software development to the constraints of software development for safety-critical systems. We conclude by identifying immediate future research directions to better align the tempo of software development for safety-critical systems and agile software development.
MotionRec: A Unified Deep Framework for Moving Object Recognition

In this paper we present a novel deep learning framework to perform online moving object recognition (MOR) in streaming videos. The existing methods for moving object detection (MOD) only computes class-agnostic pixel-wise binary segmentation of video frames. On the other hand, the object detection techniques do not differentiate between static and moving objects. To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i.e. MOR in a single-stage deep learning framework. We achieve this by labelling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. In the proposed MotionRec, both temporal and spatial features are learned using past history and current frames respectively. First, the background is estimated with a temporal depth reductionist (TDR) block. Then the estimated background, current frame and temporal median of recent observations are assimilated to encode spatiotemporal motion saliency. Moreover, feature pyramids are generated from these motion saliency maps to perform regression and classification at multiple levels of feature abstractions. MotionRec works online at inference as it requires only few past frames for MOR. Moreover, it doesn't require predefined target initialization from user. We also annotated axis-aligned bounding boxes (42,614 objects (14,814 cars and 27,800 person) in 24,923 video frames of CDnet 2014 dataset) due to lack of available benchmark datasets for MOR. The performance is observed qualitatively and quantitatively in terms of mAP over a defined unseen test set. Experiments show that the proposed MotionRec significantly improves over strong baselines with RetinaNet architectures for MOR.
Learning refined attribute-aligned network with attribute selection for person re-identification

Effective person re-identification (Re-ID) is often required in real applications. While most exiting approaches either assume the detected pedestrian bounding box well-aligned or utilize limited human structural information (pose, attention, segmentation) to calibrate the misalignment. However, the value of utilizing attributes for pedestrian alignment is still under explored. Furthermore, the hierarchy of attributes in previous works has been largely ignored, appearance feature and attribute feature are often fused in a rigid way. This directly limits the discriminatory and robustness of feature representation. In this paper, we propose a Refined Attribute-aligned Network (RAN), which consists of a coarse-alignment and a fine-alignment module. First, the pre-trained part and attribute predictor are used to generate body parts and candidate attributes. Then the body parts are used for coarse alignment and the attributes are selected by an agent. The agent is optimized with policy gradient algorithm, which can maximize the accumulative reward to increase the probability of proper attribute selection. Finally, for the fine-alignment, the attribute maps and body part features are aggregated by a bilinear-pooling layer to support accurate Re-ID. Extensive experimental results based on multiple datasets including CUHK03, DukeMTMC and Market-1501 demonstrate the superiority of our method over state-of-the-art methods.
Indistinguishability Obfuscation Without Maps: Attacks and Fixes for Noisy Linear FE

Candidates of Indistinguishability Obfuscation ($$\mathsf {iO}$$) can be categorized as "direct" or "bootstrapping based". Direct constructions rely on high degree multilinear maps [28, 29] and provide heuristic guarantees, while bootstrapping based constructions [2, 7, 33, 36, 38, 39] rely, in the best case, on bilinear maps as well as new variants of the Learning With Errors ($$\mathsf {LWE}$$) assumption and pseudorandom generators. Recent times have seen exciting progress in the construction of indistinguishability obfuscation ($$\mathsf {iO}$$) from bilinear maps (along with other assumptions) [2, 7, 33, 38]. As a notable exception, a recent work by Agrawal [2] provided a construction for $$\mathsf {iO}$$ without using any maps. This work identified a new primitive, called Noisy Linear Functional Encryption ($$\mathsf {NLinFE}$$) that provably suffices for $$\mathsf {iO}$$ and gave a direct construction of $$\mathsf {NLinFE}$$ from new assumptions on lattices. While a preliminary cryptanalysis for the new assumptions was provided in the original work, the author admitted the necessity of performing significantly more cryptanalysis before faith could be placed in the security of the scheme. Moreover, the author did not suggest concrete parameters for the construction. In this work, we fill this gap by undertaking the task of thorough cryptanalytic study of $$\mathsf {NLinFE}$$. We design two attacks that let the adversary completely break the security of the scheme. Our attacks are completely new and unrelated to attacks that were hitherto used to break other candidates of $$\mathsf {iO}$$. To achieve this, we develop new cryptanalytic techniques which (we hope) will inform future designs of the primitive of $$\mathsf {NLinFE}$$. From the knowledge gained by our cryptanalytic study, we suggest modifications to the scheme. We provide a new scheme which overcomes the vulnerabilities identified before. We also provide a thorough analysis of all the security aspects of this scheme and argue why plausible attacks do not work. We additionally provide concrete parameters with which the scheme may be instantiated. We believe the security of $$\mathsf {NLinFE}$$ stands on significantly firmer footing as a result of this work.
Toward Secure Storage in Cloud-based eHealth Systems: A Blockchain-Assisted Approach

Cloud-based eHealth systems enable patients and medical institutions to manage their EHRs in a flexible and efficient way. Since EHRs are the most sensitive data for people and have significant value to adversaries, outsourced EHRs suffer from both internal and external threats. In this article, we first identify the threats toward the security of outsourced EHRs and present system and security criteria that a secure cloud-based eHealth system should achieve. Then we provide a blockchain-assisted eHealth framework for securing outsourced EHRs that meets the identified criteria. We also show a case study where the blockchain technology is applied to ensure the security of outsourced EHRs. Finally, we discuss several promising issues and research directions on blockchain-assisted secure EHRs in cloud-based eHealth systems.
A Novel Perspective to Zero-Shot Learning: Towards an Alignment of Manifold Structures via Semantic Feature Expansion

Zero-shot learning aims at recognizing unseen classes (no training example) with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space shared by both seen and unseen classes, i.e., attribute or word vector, as the bridge. One common practice in zero-shot learning is to train a projection between the visual and semantic feature spaces with labeled seen classes examples. When inferring, this learned projection is applied to unseen classes and recognizes the class labels by some metrics. However, the visual and semantic feature spaces are mutually independent and have quite different manifold structures. Under such a paradigm, most existing methods easily suffer from the domain shift problem and weaken the performance of zero-shot recognition. To address this issue, we propose a novel model called AMS-SFE. It considers the alignment of manifold structures by semantic feature expansion. Specifically, we build upon an autoencoder-based model to expand the semantic features from the visual inputs. Additionally, the expansion is jointly guided by an embedded manifold extracted from the visual feature space of the data. Our model is the first attempt to align both feature spaces by expanding semantic features and derives two benefits: first, we expand some auxiliary features that enhance the semantic feature space; second and more importantly, we implicitly align the manifold structures between the visual and semantic feature spaces; thus, the projection can be better trained and mitigate the domain shift problem. Extensive experiments show significant performance improvement, which verifies the effectiveness of our model.
Engineering trustable choreography-based systems using blockchain

The adoption of model-driven engineering methodologies contributes to reduce the complexity of developing distributed systems. A key point to master such complexity is the use of modelling languages, such as the BPMN standard. This permits to specify choreography diagrams describing, from a global point of view, the interactions that should occur among distributed components in order to reach given goals. Even though BPMN choreographies are promising to increase business possibilities, their concrete adoption has been challenging and faced complex hurdles. On the one hand, there is a lack of concrete support to the different phases of the choreography life-cycle, especially in relation to the choreography execution. Another obstacle consists in the lack of distributed infrastructures allowing the participants involved in the cooperation to trust each other, and in particular to get enough guarantees that all of them will behave as prescribed by the choreography model.
Syntactic complexity in assessing young adolescent EFL learners’ writings: Syntactic elaboration and diversity

This study investigates the relationship between syntactic complexity and writing quality, measured by both syntactic elaboration and diversity, based on 410 narrative essays of adolescent beginner and intermediate Chinese EFL learners. Syntactic elaboration measures were analyzed by the L2 Syntactic Complexity Analyzer. Syntactic diversity was measured by the corrected type-token ratio of dependency relations, a new metric proposed by this study. Three regression analyses were conducted, each with writing scores as an outcome variable, but the syntactic elaboration model employed traditional syntactic elaboration measures as explanatory variables; the syntactic diversity model employed the syntactic diversity measure; and the combined model used all of the measures. The results show that 1) syntactic elaboration measures account for 36.6% of the variance in writing scores, with mean length of sentence, complex nominals per clause, and clauses per T-unit as the best predictors; 2) the syntactic diversity measure alone could predict 32.1% of the variance in writing scores; 3) the combined model, including both syntactic elaboration and syntactic diversity measures, could explain 45.3% of the variance (the largest amount of variance in the study) in writing scores.
Investigating a Mixed-Initiative Workflow for Digital Mind-Mapping

Abstract In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion—the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate its construction process as a sequential interaction workflow wherein a human user and an intelligent agent take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (IA). This paper is an extension of our prior work (Chen et al., 2019, “Mini-Map: Mixed-Initiative Mind-Mapping Via Contextual Query Expansion,” AIAA Scitech 2020 Forum, p. 2347) wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of “commonsense” knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools.
Journal research data sharing policies: a study of highly-cited journals in neuroscience, physics, and operations research

Abstract The practices for if and how scholarly journals instruct research data for published research to be shared is an area where a lot of changes have been happening as science policy moves towards facilitating open science, and subject-specific repositories and practices are established. This study provides an analysis of the research data sharing policies of highly-cited journals in the fields of neuroscience, physics, and operations research as of May 2019. For these 120 journals, 40 journals per subject category, a unified policy coding framework was developed to capture the most central elements of each policy, i.e. what, when, and where research data is instructed to be shared. The results affirm that considerable differences between research fields remain when it comes to policy existence, strength, and specificity. The findings revealed that one of the most important factors influencing the dimensions of what, where and when of research data policies was whether the journal’s scope included specific data types related to life sciences which have established methods of sharing through community-endorsed public repositories. The findings surface the future research potential of approaching policy analysis on the publisher-level as well as on the journal-level. The collected data and coding framework is provided as open data to facilitate future research and journal policy monitoring.