Reviewerly/exHarmony huggingface dataset:
Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of involved service providers. The focus of the work presented in this paper is on fraud discovery and steps to fully automate it. Fraud discovery in financial transactions has become an important priority for banks. Fraud is increasing significantly with the expansion of modern technology and global communication, which results in substantial damages for the banks. Instant payment (IP) transactions cause new challenges for fraud detection due to the requirement of short processing time. The paper investigates the possibility to use artificial intelligence in IP fraud detection. The main contributions of our work are (a) an analysis of problem relevance from business and literature perspective, (b) a proposal for technological support for using AI in fraud detection of instant payment transactions, and (c) a feasibility study of selected fraud detection approaches.
Asynchronous Distributed Learning From Constraints

In this brief, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied. LfC relies on the generic notion of "constraint" to inject knowledge into the learning problem, and, due to its generality, it deals with possibly nonconvex constraints, enforced either in a hard or soft way. Motivated by recent progresses in the field of distributed and constrained nonconvex optimization, we apply the (distributed) asynchronous method of multipliers (ASYMM) to LfC. The study shows that such a method allows us to support scenarios where selected constraints (i.e., knowledge), data, and outcomes of the learning process can be locally stored in each computational node without being shared with the rest of the network, opening the road to further investigations into privacy-preserving LfC. Constraints act as a bridge between what is shared over the net and what is private to each node, and no central authority is required. We demonstrate the applicability of these ideas in two distributed real-world settings in the context of digit recognition and document classification.
Shorter Pairing-Based Arguments Under Standard Assumptions

This paper constructs efficient non-interactive arguments for correct evaluation of arithmetic and boolean circuits with proof size O(d) group elements, where d is the multiplicative depth of the circuit, under falsifiable assumptions. This is achieved by combining techniques from SNARKs and QA-NIZK arguments of membership in linear spaces. The first construction is very efficient (the proof size is $$\approx 4d$$ group elements and the verification cost is $$\approx 4d$$ pairings and $$O(n+n'+d)$$ exponentiations, where n is the size of the input and $$n'$$ of the output) but one type of attack can only be ruled out assuming the knowledge soundness of QA-NIZK arguments of membership in linear spaces. We give an alternative construction which replaces this assumption with a decisional assumption in bilinear groups at the cost of approximately doubling the proof size. The construction for boolean circuits can be made zero-knowledge with Groth-Sahai proofs, resulting in a NIZK argument for circuit satisfiability based on falsifiable assumptions in bilinear groups of proof size $$O(n+d)$$. Our main technical tool is what we call an "argument of knowledge transfer". Given a commitment $$C_1$$ and an opening x, such an argument allows to prove that some other commitment $$C_2$$ opens to f(x), for some function f, even if $$C_2$$ is not extractable. We construct very short, constant-size, pairing-based arguments of knowledge transfer with constant-time verification for any linear function and also for Hadamard products. These allow to transfer the knowledge of the input to lower levels of the circuit.
Quantum Discord of Certain Two-Qubit States

Quantum discord is an effective measure of quantum correlation introduced by Olliver and Zurek. We evaluate analytically the quantum discord for a large family of non-X-states. Exact solutions of the quantum discord are obtained for four parametric spaces of non-X-states. Dynamic behavior of the quantum discord is also explored under the action of the Kraus operator.
Improved smoothness priors using bilinear transform

Smoothness priors is a well-known and most commonly used method in the analysis of stochastic processes making it very useful in the field of stochastic signal processing. It is particularly suited for smoothing the noisy data and detrending the time-series signals. The method is based on an optimization problem where the n-th order derivative of the signal enters as a constraint. When the method is designed in discrete time domain, the backward difference rule is used to perform differential-to-difference conversion. Moreover, the solution depends on a smoothness trade-off parameter. An efficient algorithm for the trade-off parameter selection remains an important and challenging issue. In this paper, first, we propose a closed-form expression for the trade-off parameter. The closed-form expression resulted from a frequency domain interpretation of the smoothness priors procedure. The trade-off parameter determines the amount of frequency components that the procedure allows to pass. We show that the trade-off parameter is related to the arbitrary choice of cutoff frequency. Second, we introduce a new way to the design and implementation of smoothness priors using bilinear transformation method. Frequency analysis and experiments on both synthetic and real world signals with different levels of noise demonstrate that bilinear transform is indeed more effective for smoothness priors implementation when compared with the traditional ones, i.e., the backward difference rule.
Coverage Path Planning Optimization for Slopes and Dams Inspection

In the last decades, there is a growing in the use of UAVs for inspection applications due to their maneuverability, flexibility, and efficiency. UAVs can be used for regular inspections to verify deformities, reconstruct 3D spaces, and mapping through aerial photogrammetry. An important aspect when inspecting buildings and structures is to cover the entire structure as efficiently as possible. Some applications, such as the inspection of electric power generation facilities, demand security constraints to ensure safety in their big structures like slopes and dams. Those inspections will be repeated regularly to ensure that changes in the structures are not occurring over time. The determination of the optimal path planning for those structures can be quite complex. This is due to the presence of obstacles and safety restrictions, such as high voltage power lines, transformers, and water outlets that will be present and can not be modeled previous to the first inspection. Thus, these initial inspections are performed manually by a skilled operator. However, this first path is performed without considering mission time and optimal trajectory. Therefore, this research work proposes a methodology to maximize coverage inspection trajectories generated manually. The results proved the algorithm capacity of optimizing trajectories performed manually by an operator.
Remote Implementation of a Fredkin Gate via Virtual Excitation of an Atom‐Cavity‐Fiber System

Abstract Simple operations and robust results are always of interest for any quantum tasks. Herein, a novel scheme is proposed for implementing a Fredkin gate via the virtual excitation of an atom‐cavity‐fiber system. The scheme is to control the nonlocal state‐swap of two spatially separated target atoms according to the state of the control atom at hand. In the scheme, only the control atom at hand needs the laser to drive and the virtual excitation of the atom‐cavity‐fiber system effectively suppresses the decoherence. By numerical simulations, appreciated parameters are chosen and it is shown that the Fredkin gate can be implemented with high fidelity. Although the operation time error has slightly stronger influence on the fidelity than atom‐cavity coupling strength error, the robustness of the scheme can be effectively improved against the operation time error by adopting Gaussian pulse to replace the constant pulse. In addition, the scheme can be generalized to implement alternative Fredkin gates by controlling the non‐local state‐swap of two remote atoms or of two remote and spatially separated atoms, which will be undoubtedly of benefit to the distributed quantum computation and remote quantum information processing.
3D modified wavelet block tree coding for hyperspectral images

&lt;p&gt;&lt;span&gt;A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors has been proposed. The proposed scheme uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients. By doing the reconstruction of the transform image cube, taking the difference between the frames, it increases the coding efficiency, reduces the memory requirement and complexity of the hyperspectral compression schemes in comparison with other state-of-the-art compression schemes.&lt;/span&gt;&lt;/p&gt;
The Design of a TLD and Fuzzy-PID Controller Based on the Autonomous Tracking System for Quadrotor Drones

The objective of this paper is to design a new Quadrotor Autonomous Following System, and the main three contents are as follows: Object tracking, quadrotor attitude determination and the controller. The image tracking po... | Find, read and cite all the research you need on Tech Science Press
Deep Point-Wise Prediction for Action Temporal Proposal

Detecting actions in videos is an important yet challenging task. Previous works usually utilize (a) sliding window paradigms, or (b) per-frame action scoring and grouping to enumerate the possible temporal locations. Their performances are also limited to the designs of sliding windows or grouping strategies. In this paper, we present a simple and effective method for temporal action proposal generation, named Deep Point-wise Prediction (DPP). DPP simultaneously predicts the action existing possibility and the corresponding temporal locations, without the utilization of any handcrafted sliding window or grouping. The whole system is end-to-end trained with joint loss of temporal action proposal classification and location prediction. We conduct extensive experiments to verify its effectiveness, generality and robustness on standard THUMOS14 dataset. DPP runs more than 1000 frames per second, which largely satisfies the real-time requirement. The code is available at https://github.com/liluxuan1997/DPP .
A Survey on Agile Practices and Challenges of a Global Software Development Team

The Agile Manifesto describes that the most efficient and effective method of conveying information to and within a development team is through face-to-face conversation. However that is not always possible when teams are working in a Global Software Development (GSD) environment. Based on this scenario, this study presents an exploratory data analysis using survey results to explore agile practices and challenges of a global software development team that uses Scaled Agile Framework (SAFe), which is designed for the need of larger organizations. The goal of this study is to understand the team’s level of knowledge in some agile practices and which types of communication are usually prioritized. As in GSD environments team members are geographically spread across multiple regions and time zones, we aim to identify challenges this environment can present. As a result of this exploratory analysis, it has been identified that communication is one of main challenges in GSD environment and that phone calls are considered to be the most efficient type of communication. Additionally, we have also identified that professionals have different levels of confidence in Agile practices and concluded that knowledge transfers among the professionals could help those team members that are not confident in some agile practices to increase their overall confidence and knowledge.
Detecting Extrapolation with Local Ensembles

We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.
Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching

Energy-based models (EBMs) assign an unnormalized log probability to data samples. This functionality has a variety of applications, such as sample synthesis, data denoising, sample restoration, outlier detection, Bayesian reasoning and many more. But, the training of EBMs using standard maximum likelihood is extremely slow because it requires sampling from the model distribution. Score matching potentially alleviates this problem. In particular, denoising-score matching has been successfully used to train EBMs. Using noisy data samples with one fixed noise level, these models learn fast and yield good results in data denoising. However, demonstrations of such models in the high-quality sample synthesis of high-dimensional data were lacking. Recently, a paper showed that a generative model trained by denoising-score matching accomplishes excellent sample synthesis when trained with data samples corrupted with multiple levels of noise. Here we provide an analysis and empirical evidence showing that training with multiple noise levels is necessary when the data dimension is high. Leveraging this insight, we propose a novel EBM trained with multiscale denoising-score matching. Our model exhibits a data-generation performance comparable to state-of-the-art techniques such as GANs and sets a new baseline for EBMs. The proposed model also provides density information and performs well on an image-inpainting task.
Continuous quantum measurement for general Gaussian unravelings can exist

Quantum measurements and the associated state changes are properly described in the language of instruments. We investigate the properties of a time continuous family of instruments associated with the recently introduced class of general Gaussian non-Markovian stochastic Schrödinger equations. In this article we find that when the covariance matrix for the Gaussian noise satisfies a particular δ-function constraint, the measurement interpretation is possible for a class of models with self-adjoint coupling operator. This class contains, for example the spin-boson and quantum Brownian motion models with colored bath correlation functions. Remarkably, due to quantum memory effects the reduced state, in general, does not obey a closed form master equation while the unraveling has a time continuous measurement interpretation.Received 18 December 2019Accepted 13 November 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043376Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDiffusionFluctuations & noiseOpen quantum systemsQuantum channelsQuantum measurementsQuantum opticsQuantum statistical mechanicsQuantum stochastic processesStochastic processesQuantum InformationAtomic, Molecular & OpticalStatistical Physics
Session-Based Recommendation with Self-Attention

The goal of session-based recommendation is to predict the next action of a user based on the current session and anonymous sessions before. Recent works on session-based recommendation usually use neural network architectures such as convolution neural networks (CNNs) or recurrent neural networks (RNNs) to extract patterns of sessions. Such features have been shown to give promising results because they can discover the user's sequential behavior and understand the purpose of current session. In this paper, we propose a neural network architecture for session-based recommendation without using convolution or recurrent neural networks. Our model is inspired by the Transformer's design, in which the information of important items is passed directly to the hidden states. Experimental results on two real-world datasets show that our method outperforms several state-of-the-art models.
Large-alphabet quantum key distribution using spatially encoded light

Abstract Most quantum key distribution protocols using a two-dimensional basis, such as HV polarization as first proposed by Bennett and Brassard in 1984, are limited to a key generation density of 1 bit per photon. We increase this key density by encoding information in the transverse spatial displacement of the used photons. Employing this higher-dimensional Hilbert space together with modern single-photon-detecting cameras, we demonstrate a proof-of-principle large-alphabet quantum key distribution experiment with 1024 symbols and a shared information between sender and receiver of 7 bit per photon.
Preserving Anthropology's Digital Record: CoPAR in the Age of Electronic Fieldnotes, Data Curation, and Community Sovereignty

This paper discusses the potential role of the Council for the Preservation of Anthropological Records (CoPAR) in the context of contemporary developments in anthropological research and archival practice. Despite many efforts, there are no discipline-wide, agreed-upon best practices for making or keeping anthropological records, and no central space where such conversations are taking place. Founded in the 1990s, CoPAR aims to convey the value of anthropological records, to encourage anthropology practitioners and institutions to preserve the field's records, to identify and locate primary anthropological materials, and to promote the use of records in the discipline. While CoPAR led efforts to preserve records of anthropologists in the 1990s, it became inactive by the early 2000s. Since then, the shift to digital field records and the increased digital access of archival records has exposed new concerns for the field's archival records. This article explores the outcomes of a 2015 meeting on this topic and identifies new gaps and challenges for anthropological records, joining this work with current archival perspectives. The article makes a case for a revitalized CoPAR that will encourage life-cycle data thinking and more community-driven approaches to archival stewardship.
Exploring knowledge graphs for the identification of concept prerequisites

Abstract Learning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts’ complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept c , the goals are to (a) find candidate concepts that serve as possible prerequisite for c ; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).
DBSCAN-KNN-GA: a multi Density-Level Parameter-Free clustering algorithm

Abstract DBSCAN is a popular tool to analyse datasets which can effectively discover clusters with arbitrary shapes. However, it requires two input parameters which are difficult to be determined, according to the fact that the performance of clustering result depends heavily on user-specified parameters. In addition, it uses global parameters which are not appropriate to those multi-density datasets. Aiming at these problems, we propose a parameter-free algorithm to perform DBSCAN with different density-level parameters. We select some classical datasets and a TLC taxi trip record used for experiments to compared our proposed algorithm with the original DBSCAN to evaluate the performance of our improved DBSCAN. The results show that the proposed algorithm is capable for efficiently and effectively detecting clusters automatically with variable density-levels. Compared with original DBSCAN, the proposed algorithm can discover more noise points and its execution accuracy is higher.
Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol

The Online Action Detection (OAD) problem needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find very few works and no consensus on the evaluation protocols to be used. In this work we propose to rethink the OAD scenario, clearly defining the problem itself and the main characteristics that the models which are considered online must comply with. We also introduce a novel metric: the Instantaneous Accuracy (IA). This new metric exhibits an online nature and solves most of the limitations of the previous metrics. We conduct a thorough experimental evaluation on 3 challenging datasets, where the performance of various baseline methods is compared to that of the state-of-the-art. Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario. The baselines models and a development kit with the novel evaluation protocol will be made publicly available.