Reviewerly/exHarmony huggingface dataset:
One-More Unforgeability of Blind ECDSA

In this paper, we give the first formal security analysis on the one-more unforgeability of blind ECDSA. We start with giving a general attack on blind ECDSA, which is similar to the ROS attack on the blind Schnorr signature. We formulate the ECDSA-ROS problem to capture this attack.Next, we give a generic construction of blind ECDSA based on an additive homomorphic encryption and a corresponding zero-knowledge proof. Our concrete instantiation is about 40 times more bandwidth efficient than the blind ECDSA in AsiaCCS 2019.After that, we give the first formal proof of one-more unforgeability for blind ECDSA, under a new model called algebraic bijective random oracle. The security of our generic blind ECDSA relies on the hardness of a discrete logarithm-based interactive assumption and an assumption of the underlying elliptic curve.Finally, we analyze the hardness of the ECDSA-ROS problem in the algebraic bijective random oracle model.
A Bayesian semiparametric accelerate failure time mixture cure model

The accelerated failure time mixture cure (AFTMC) model is widely used for survival data when a portion of patients can be cured. In this paper, a Bayesian semiparametric method is proposed to obtain the estimation of parameters and density distribution for both the cure probability and the survival distribution of the uncured patients in the AFTMC model. Specifically, the baseline error distribution of the uncured patients is nonparametrically modeled by a mixture of Dirichlet process. Based on the stick-breaking formulation of the Dirichlet process, the techniques of retrospective and slice sampling, an efficient and easy-to-implement Gibbs sampler is developed for the posterior calculation. The proposed approach can be easily implemented in commonly used statistical softwares, and its performance is comparable to fully parametric method via comprehensive simulation studies. Besides, the proposed approach is adopted to the analysis of a colorectal cancer clinical trial data.
SmartReviews: Towards Human- and Machine-Actionable Reviews

Review articles summarize state-of-the-art work and provide a means to organize the growing number of scholarly publications. However, the current review method and publication mechanisms hinder the impact review articles can potentially have. Among other limitations, reviews only provide a snapshot of the current literature and are generally not readable by machines. In this work, we identify the weaknesses of the current review method. Afterwards, we present the SmartReview approach addressing those weaknesses. The approach pushes towards semantic community-maintained review articles. At the core of our approach, knowledge graphs are employed to make articles more machine-actionable and maintainable.
Learning a Hierarchical Intent Model for Next-Item Recommendation

A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.
Which Parts Determine the Impression of the Font?

Various fonts give different impressions, such as legible, rough, and comic-text. This paper aims to analyze the correlation between the local shapes, or parts, and the impression of fonts. By focusing on local shapes instead of the whole letter shape, we can realize more general analysis independent from letter shapes. The analysis is performed by newly combining SIFT and DeepSets, to extract an arbitrary number of essential parts from a particular font and aggregate them to infer the font impressions by nonlinear regression. Our qualitative and quantitative analyses prove that (1) fonts with similar parts have similar impressions, (2) many impressions, such as legible and rough, largely depend on specific parts, and (3) several impressions are very irrelevant to parts.
A Methodology For Large-Scale Identification of Related Accounts in Underground Forums

Underground forums allow users to interact with communities focused on illicit activities. They serve as an entry point for actors interested in deviant and criminal topics. Due to the pseudo-anonymity provided, they have become improvised marketplaces for trading illegal products and services, including those used to conduct cyberattacks. Thus, these forums are an important data source for threat intelligence analysts and law enforcement. The use of multiple accounts is forbidden in most forums since these are mostly used for malicious purposes. Still, this is a common practice. Being able to identify an actor or gang behind multiple accounts allows for proper attribution in online investigations, and also to design intervention mechanisms for illegal activities. Existing solutions for multi-account detection either require ground truth data to conduct supervised classification or use manual approaches. In this work, we propose a methodology for the large-scale identification of related accounts in underground forums. These accounts are similar according to the distinctive content posted, and thus are likely to belong to the same actor or group. The methodology applies to various domains and leverages distinctive artefacts and personal information left online by the users. We provide experimental results on a large dataset comprising more than 1.1M user accounts from 15 different forums. We show how this methodology, combined with existing approaches commonly used in social media forensics, can assist with and improve online investigations.
The thickening of futures

This paper engages with biodiversity loss. In particular, it focuses on observations and scientific facts: the decline of pollinators and what that entails for the co-living of humans and more-than-humans. This kind of work often reaches the publics as thin stories of limited futures. The article explores how to situate the issue of out-of-sync plant–pollinator relationships into thick, ongoing presents rather than as a distant future that is out of one's own hands. This is done through a collaborative design project that experiments with various formats for staging more material, embodied and experiential ways to sensitise and invite humans to experience the issue of pollination. We therefore explore and give an account of how we have situated the issues in a thick, ongoing present as an anticipatory practice. We thus suggest a practice that becomes both sticky and sweaty; in addition, the practice moves some pollination facts into not only matters of concern but also matters of care. In doing so, we forward the role that design researchers can play in environmental and collaborative anticipation by engaging with emerging approaches to both biodiversity loss and collaborative future-making that are simultaneously conflicting and harsh as well as hopeful.
Language Translation as a Socio-Technical System:Case-Studies of Mixed-Initiative Interactions

Seamless access to information in a rapidly globalizing world demands for availability of information across, ideally all but at the least a large number of, languages. Machine translation has been proposed as a technological solution to this complex problem. However, despite seven decades of research, and recently seen rapid progress in the field - thanks to deep learning and availability of large data-sets, perfect machine translation across a large number of the world's languages still remains elusive. In fact, it is a distant and perhaps even an impossible goal. Erroneous translations, on the other hand, can be detrimental in critical situations such as talking to a law enforcement officer; or, they could potentially perpetuate social biases or stereotypes, for instance, by producing mis-gendered translations. In this work, we argue that language translation is inherently a socio-technical system, which has to be viewed, studied, and optimized for, as such. The need and context of translation, the socio-demographic factors behind the human translators as well as the consumers of the translated content affect the complexity of the translation system, as much as the accuracy of the technology and its interface. Through a series of case studies on mixed-initiative interaction based approach to translation, we bring out the various socio-technical factors and their complex interactions that one has to bear in mind while designing for the ideal human-machine translation systems. Through these observations, we make multiple recommendations which, at the core, suggest that "solving" translation in the real sense would require more coordinated efforts between the technical (NLP) and social communities (HCI + CSCW + DEV).
SADRL: Merging human experience with machine intelligence via supervised assisted deep reinforcement learning

Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies in decision-making problems by directly interacting with environments. Meanwhile, supervised learning methods also show great capability of learning from data. However, how to combine DRL with supervised learning and leverage additional knowledge and data to assist the DRL agent remains difficult. This study proposes a novel Supervised Assisted Deep Reinforcement Learning (SADRL) framework integrating deep Q-learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC). Specifically, the proposed DQfDD-BC method leverages historical demonstrations to pre-train a behavioral cloning model and consistently update it by learning the dynamically updated demonstrations. A supervised expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach accelerates the learning processes, and meanwhile, adapts to different performance levels of demonstrations. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms using a BC model contribute to improving the learning convergence performance compared with the baseline models. We believe that SADRL provides an elegant framework and the proposed method can promote the integration of human experience and machine intelligence.
Secure Random Sampling in Differential Privacy

Differential privacy is among the most prominent techniques for preserving privacy of sensitive data, oweing to its robust mathematical guarantees and general applicability to a vast array of computations on data, including statistical analysis and machine learning. Previous work demonstrated that concrete implementations of differential privacy mechanisms are vulnerable to statistical attacks. This vulnerability is caused by the approximation of real values to floating point numbers. This paper presents a practical solution to the finite-precision floating point vulnerability, where the inverse transform sampling of the Laplace distribution can itself be inverted, thus enabling an attack where the original value can be retrieved with non-negligible advantage.
Auto-learning Convolution-Based Graph Convolutional Network for Medical Relation Extraction

Medical relation extraction discovers relations between entity mentions in unstructured text, such as biomedical literature. Dependency structures have proven to be useful for this task. However, how to effectively make use of structural information from dependency forests remains a challenging research question. Existing approaches directly employing weighted graphs or variable graphs, where the graph can be viewed as a dependency forest, may not always yield optimal results. In this work, we propose a novel model, the auto-learning convolution-based graph convolutional network (AC-GCN), which learns weighted graphs using a 2D convolutional network. The convolution operation is performed over dependency forests to obtain highly informative features useful for medical relation extraction. Results obtained on three biomedical benchmarks show that our model is able to better learn the structural information of the dependency forests, providing significantly better results than those of previous approaches.
Two new preconditioners for mean curvature-based image deblurring problem

&lt;abstract&gt;&lt;p&gt;The mean curvature-based image deblurring model is widely used to enhance the quality of the deblurred images. However, the discretization of the associated Euler-Lagrange equations produce a nonlinear ill-conditioned system which affect the convergence of the numerical algorithms like Krylov subspace methods. To overcome this difficulty, in this paper, we present two new symmetric positive definite (SPD) preconditioners. An efficient algorithm is presented for the mean curvature-based image deblurring problem which combines a fixed point iteration (FPI) with new preconditioned matrices to handle the nonlinearity and ill-conditioned nature of the large system. The eigenvalues analysis is also presented in the paper. Fast convergence has shown in the numerical results by using the proposed new preconditioners.&lt;/p&gt;&lt;/abstract&gt;
Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.
Solar Energy Potential in Horn of Africa: A Comparative Study Using Matlab/Simulink

Geographically, the Horn of Africa is one of the regions that the Equator passes over, providing the opportunity for vigorous solar energy. However, despite the enormous solar energy resources, more than half of the region, 132 million populations have not yet access to electricity. This study presents an overview of the potential of solar energy in the region. Then review the current solar energy status and policies related to it. A Matlab/Simulink model is developed to show the comparative analyses of the solar radiation graphically on the photovoltaic modules and electricity outputs for the Horn of African countries' capitals. As a result of the simulation model, Ethiopia-Addis Ababa receives the highest annual solar radiation of 2915.03 kWh/m2-year while Eritrea-Asmara with the lowest annual solar radiation of 2198.47 kWh/m2-year. The annual electrical output of the photovoltaic modules in Ethiopia-Addis Ababa is the highest with 286.685 kWh/year and Eritrea-Asmara 216.214 kWh/year as the lowest in the region. The number of photovoltaic modules and the optimum tilt angles are calculated. For Ethiopia-Addis Ababa, the lowest photovoltaic module number is 10, and the optimum PV tilt angle is 11.163°. For Eritrea-Asmara, the highest photovoltaic module number is 14, with a tilt angle of 15.397°.
Communicating Sequential Processes

chapter Share on Communicating Sequential Processes Author: C.A.R. Hoare Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 157–186https://doi.org/10.1145/3477355.3477364Online:02 October 2021Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling

Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.
An Exploratory Study on Dead Methods in Open-source Java Desktop Applications

Background. Dead code is a code smell. It can refer to code blocks, fields, methods, etc. that are unused and/or unreachable. Empirical evidence shows that dead code harms source code comprehensibility and maintainability in software applications. Researchers have gathered little empirical evidence on the spread of dead code in software applications. Moreover, we know little about the role of this code smell during software evolution.
Study of effect of Agile software development Methodology on Software Development Process

As per recent discussions, Agile is a software development framework, which has developed software by using 1-4 week iterations, which will produce better quality software as per customer requirements. Agile methodology mostly deals with the frequent changing requirement in the software project. Instead of traditional method, it is always beneficial to use Agile as a stakeholder because traditional software methodologies are less prone to changes. In this research paper, authors have studied the effect of agile approach in the software development process in terms of quality, business worth and architectural framework.
ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.
Chapter 1: Gold Deposit Types: An Overview

Abstract Gold is either the only economically important metal or a major by-product in 11 well-characterized deposit types—paleoplacer, orogenic, porphyry, epithermal, Carlin, placer, reduced intrusion related, volcanogenic massive sulfide (VMS), skarn, carbonate replacement, and iron oxide-copper-gold (IOCG), arguably more than for those of any other metal; it also dominates a number of deposits of uncertain or unknown origin. Major gold concentrations formed worldwide from the Mesoarchean to the Pleistocene, from Earth’s surface to midcrustal paleodepths, alone or in association with silver, base metals, and/or uranium, and from hydrothermal fluids of predominantly metamorphic, magmatic, meteoric, seawater, or, uncommonly, basinal origins, as well as from mafic magma or ambient surface water. Most of the Neoproterozoic and Phanerozoic deposits unequivocally formed in accretionary orogens. As an introduction to this compilation of the world’s major gold deposits and provinces, this paper provides a thumbnail sketch of each gold deposit type, including geologic and economic characteristics and widely accepted genetic models, as well as briefly discusses aspects of their spatial and temporal associations and distributions.