Reviewerly/exHarmony huggingface dataset:
Three-dimensional Reconstruction of Complex Spatial Surface Based on Line Structured Light

In three-dimensional reconstruction technology, it mainly includes the active time of flight method and the passive binocular stereo vision method. However, the time of flight method is easy to be affected by the external environment illumination and the corresponding device is expensive. The matching speed of the binocular stereo vision method is slow. Therefore, this paper chooses the line structured light method with good real-time performance, high accuracy and strong stability. The complex free-form surface is taken as the research object. Due to the serious mutual occlusion and high order derivability at any point, it is difficult to achieve complete 3D reconstruction on this surface. In this paper, a complete algorithm is designed for free-form surface reconstruction. Firstly, in order to solve the problem of beam discontinuity and sensitivity to strong light in the process of laser fringe center extraction, the gray gravity center method combining R-channel is proposed. Secondly, to solve the problems of the traditional optical plane calibration with few points and complex calibration process, a freely moving chessboard calibration method is proposed to improve the flexibility and accuracy of calibration. Then, the 3D point cloud is obtained and the surface is reconstructed using the measurement platform.
Enabling Cross-Jurisdiction Digital Asset Transfer

Enabling blockchain-based digital asset exchanges requires blockchain interoperability capabilities. Although some solutions have been proposed in recent years, asset and crypto-currency transfers across legal jurisdictions are still an unsolved problem. To realize this vision, we propose Hermes, a fault-tolerant middleware that connects blockchain networks, enabling the transfer of data and value across legal jurisdictions. Hermes is based on the Open Digital Asset Protocol (ODAP), an asset transfer protocol. Hermes utilizes a novel mechanism called ODAP-2PC and decentralized logging that can solve disputes regarding asset exchange. We find Hermes to fill an existing gap: the technical infrastructure that can constitute the basis for legislating and regulating cross-chain transfers, enabling the future of finance.
Characterization and Tomography of a Hidden Qubit

In circuit-based quantum computing, the available gate set typically consists of single-qubit gates acting on each individual qubit and at least one entangling gate between pairs of qubits. In certain physical architectures, however, some qubits may be 'hidden' and lacking direct addressability through dedicated control and readout lines, for instance because of limited on-chip routing capabilities, or because the number of control lines becomes a limiting factor for many-qubit systems. In this case, no single-qubit operations can be applied to the hidden qubits and their state cannot be measured directly. Instead, they may be controlled and read out only via single-qubit operations on connected 'control' qubits and a suitable set of two-qubit gates. We first discuss the impact of such restricted control capabilities on the quantum volume of specific qubit coupling networks. We then experimentally demonstrate full control and measurement capabilities in a superconducting two-qubit device with local single-qubit control and iSWAP and controlled-phase two-qubit interactions enabled by a tunable coupler. We further introduce an iterative tune-up process required to completely characterize the gate set used for quantum process tomography and evaluate the resulting gate fidelities.
State Estimation With Heading Constraints for On-Road Vehicle Tracking

In transportation networks, vehicle motions are usually subject to the constraints arising from the roads, preset sea-routes, or preset flight routes. Taking advantage of prior known information of such constraints generally produces better surveillance performance. The problem of state estimation with heading constraints, in which only the direction of the target trajectory is prior known, is considered. When only part of the trajectory information is available, the conventional constrained estimation methods cannot be used to produce constrained estimates. The heading constraints in two typical situations are investigated, one is with a straight line, the other is a circular arc. For the straight trajectory, two augmentation approaches are proposed to formulate the constraints. One, parameter augmentation, augments the base state by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> -intercept of the constraint straight line. In the other approach, state augmentation, the states at the past time step are used to augment the base state. Two corresponding heading constraints are formulated by the augmented state’s elements and the prior known information about road direction. For a circular trajectory, the heading constraint is formulated using position and velocity components in the base state. Pseudo-measurements are constructed to incorporate the heading constraint into the estimators and result in three heading constraint Kalman filters (HCKFs). They are parameter augmentation HCKF and state augmentation HCKF in case of straight trajectory, and HCKF for circular trajectory. The discrimination of road segments at junctions is also discussed. Furthermore, the proposed constraint filters are integrated into the interacting multiple model estimator to handle on-road maneuvers with possible accelerations. Numerical simulations are conducted to evaluate the performances of the three HCKFs compared with existing methods.
LEAP: Leakage-Abuse Attack on Efficiently Deployable, Efficiently Searchable Encryption with Partially Known Dataset

Searchable Encryption (SE) enables private queries on encrypted documents. Most existing SE schemes focus on constructing industrial-ready, practical solutions at the expense of information leakages that are considered acceptable. In particular, ShadowCrypt utilizes a cryptographic approach named ''efficiently deployable, efficiently searchable encryption'' (EDESE) that reveals the encrypted dataset and the query tokens among other information. However, recent attacks showed that such leakages can be exploited to (partially) recover the underlying keywords of query tokens under certain assumptions on the attacker's background knowledge.
The impact of task description linguistic style on task performance: a text mining of crowdsourcing contests

Purpose Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform. Design/methodology/approach An empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance. Findings The study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task. Originality/value This study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.
Method for assessing the impact of rainfall depth on the stormwater volume in a sanitary sewage network

Abstract Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants.
A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.
An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs

Graph Convolutional Networks (GCNs) play a vital role in graph learning tasks such as semi-supervised learning. However, a GCN model requires a large amount of labeled data for verification and model selection, and learning on sparse labeled graphs is still a challenging issue. In order to solve this problem, this paper propose an auxiliary learning task enhanced graph convolutional network (A-GCN), which combines the target supervised learning task of the GCN model with the auxiliary unsupervised learning task to correct its network’s learning. The experimental results demonstrate that A-GCN can achieve a significant performance improvement compared with state-of-the-art methods on a weakly supervised graph.
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.
On Reverse Elastic Channels and the Asymmetry of Commitment Capacity Under Channel Elasticity

Commitment is an important cryptographic primitive. It is well known that noisy channels are a promising resource to realize commitment in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information-theoretically</i> secure manner. However, oftentimes, channel behaviour may be poorly characterized thereby limiting the commitment throughput and/or degrading the security guarantees; a particularly problematic situation arises when a dishonest party, unbeknown to the honest one, maliciously alters the channel characteristics. Reverse elastic channels (RECs) are an interesting class of such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable</i> channels where only a dishonest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">committer</i> , say Alice, can maliciously alter the channel. RECs have attracted recent interest in the study of several cryptographic primitives. Our principal contribution is the REC commitment capacity characterization; this proves a recent related conjecture. A key result is our tight converse which analyses a specific cheating strategy by Alice. Along with elastic channels (ECs), where only a dishonest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receiver</i> Bob can alter the channel, RECs are also closely related to the classic unfair noisy channels (UNCs). In stark contrast to UNCs, both RECs and ECs always exhibit positive commitment throughput for all non-trivial channel parameters. Interestingly, our results show that channels with exclusive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-sided elasticity</i> for dishonest parties, exhibit a fundamental <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">asymmetry</i> where, a committer with one-sided elasticity has a significantly more debilitating effect on the commitment throughput than a similarly capable receiver.
Top 100 Most-Cited Publications on Breast Cancer and Machine Learning Research: A Bibliometric Analysis

Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer.This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies.Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications.The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany, and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning.Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation settings, i.e. usually a bicubic downscaling of low-resolution (LR) image. However, in real-world settings, the LR degradation process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR, or real LR. Therefore, most SR methods are ineffective and inefficient in handling more than one degradation settings within a single network. To handle the multiple degradation, i.e. refers to multi-domain image super-resolution, we propose a deep Super-Resolution Residual StarGAN (SR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2*</sup> GAN), a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. The proposed scheme is trained in a StarGAN like network topology with a single generator and discriminator networks. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.
A computational model of argumentation schemes for multi-agent systems

There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes (reasoning patterns for argumentation) are useful in addressing the requirements of the application domain in regards to argumentation (e.g., defining the scope in which argumentation will be used by agents in that particular application). In this work, we propose an argumentation framework that takes into account the particular structure of argumentation schemes at its core. This paper formally defines such a framework and experimentally evaluates its implementation for both argumentation-based reasoning and dialogues.
Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation

A co-location pattern is a subset of spatial features whose instances are frequently located together in proximate areas. Mining co-location patterns can discover spatial dependencies in spatial datasets and have particular value in many applications. However, it is challengeable to discover co-location patterns from massive spatial datasets, due to the expensive computational cost. In this paper, we present a novel parallel co-location pattern mining approach. First, dividing spatial neighbor relationships into some neighbor-dependency partitions enables to perform mining task on each partition independently in parallel. Then, a column-based calculation approach is proposed to replace the time-consuming generation of table instances for calculating the prevalence of patterns. To further reduce the search space of patterns on each partition, two pruning strategies are suggested. We implement the parallel co-location pattern mining algorithm based on neighbor-dependency partition and column calculation via MapReduce, named PCPM-NDPCC. Substantial experiments are conducted on real and synthetic datasets to examine the performance of PCPM-NDPCC. Experimental results reveal that PCPM-NDPCC has a significant improvement in efficiency than baseline algorithms and shows better scalability for massive spatial data processing.
Examining Narrative Sonification: Using First-Person Retrospection Methods to Translate Radio Production to Interaction Design

We present a first-person, retrospective exploration of two radio sonification pieces that employ narrative scaffolding to teach audiences how to listen to data. To decelerate and articulate design processes that occurred at the rapid pace of radio production, the sound designer and producer wrote retrospective design accounts. We then revisited the radio pieces through principles drawn from guidance design, data storytelling, visualization literacy, and sound studies. Finally, we speculated how these principles might be applied through interactive, voice-based technologies. First-person methods enabled us to access the implicit knowledge embedded in radio production and translate it to technologies of interest to the human–computer-interaction community, such as voice user interfaces that rely on auditory display. Traditionally, sonification practitioners have focused more on generating sounds than on teaching people how to listen; our process, however, treated sound and narrative as a holistic, sonic-narrative experience. Our first-person retrospection illuminated the role of narrative in designing to support people as they learn to listen to data.
New York City Street Cleanliness: Applying Text Mining Techniques to Social Media Information

This study examines social media information within the context of social network theory by utilizing text mining techniques and machine learning algorithms to analyze the tweets and use the information as a proxy to measure the performance of the municipal services. The objective of this study is to provide the government an alternative measure by examining user-generated content, identifying temporal trends and patterns of street cleanliness, evaluating whether people’s opinions are consistent with NYC cleanliness ratings, and assessing the performance of municipal services via sentiment analysis. The results indicate that the analytical approach presented in this study can systematically analyze social media information, and the information can be used as an alternative measure or supplementary evaluation tool to validate the government’s cleanliness ratings and evaluate the performance of the municipal services. This study extends the research on accessing social media feeds to gain valuable insights regarding municipal services’ performance. The analytical approach can be generalized to many other areas where social media information can be utilized.