Reviewerly/exHarmony huggingface dataset:
Entanglement dynamics of a dc SQUID interacting with a single-mode radiation field

In this work, we study the coupling between a superconducting device as a dc SQUID, simulated from an artificial atom with two degrees of freedom, and a single-mode radiation field for the information transference process. We demonstrate that the population transfer among the energy levels of the artificial atom yields an entanglement dynamics, which leads to the generation of a pair of photons. Moreover, we show the quantum information transference between the internal modes of the superconducting device, initially in a maximally entangled state, and the radiation field. The artificial atom absorbs the photon, and the radiation field modes become entangled as quantum coherence is transmitted from the superconducting device to the photons. These results strengthen the applicability of superconducting devices for the transference of quantum information, contributing to promising applications in emerging quantum technologies.
Data augmentation of optical time series signals for small samples

It is difficult to obtain a large amount of labeled data, which has become a bottleneck for the application of deep learning to analyze one-dimensional optical time series signals. In order to solve this problem, a deep convolutional generative adversarial network model suitable for augmenting optical time series signals is proposed. Based on the acoustic emission (AE) data set obtained by an optical sensor with a small amount, the model can learn the corresponding data features and apply them to generate new data. The analysis results show that our model can generate stable and diverse AE fragments in epoch 500, and there is no model collapse. All the features between the generated data and the original data are not significantly different at the 0.05 level, which confirms that the method in this paper can generate the optical time series signals effectively.
Librarians’ Perceptions of the Challenges for Researchers in Rwanda and the Potential of Open Scholarship

Abstract Open scholarship is a major reform movement within research. This paper seeks to understand how open scholarship might address the challenges faced by research in Africa, through a study based on a participatory collaborative workshop to create a partnership with librarians in Rwanda. The literature review identifies three broad perspectives on the apparent under-performance of Rwandan research: one locating the issue in the unequal scholarly communications system, a second pointing to a country deficit and a third blaming cognitive injustices. The Rwandan librarians see researchers as challenged through the pressures on them to publish, the costs of research, poor infrastructure, lack of skills and limited access to literature. Collectively these challenges constitute a critical barrier to research. These limits fit largely the country deficit perspective. Open scholarship as conceived in the Global North is only a small part of the answer to the challenges faced by Rwandan scholars. To promote equity, notions of open scholarship need to take into account the conditions under which research is conducted in less privileged contexts such as Rwanda.
An HHL-based algorithm for computing hitting probabilities of quantum walks

We present a novel application of the HHL (Harrow-Hassidim-Lloyd) algorithm -- a quantum algorithm solving systems of linear equations -- in solving an open problem about quantum random walks, namely computing hitting (or absorption) probabilities of a general (not only Hadamard) one-dimensional quantum random walks with two absorbing boundaries. This is achieved by a simple observation that the problem of computing hitting probabilities of quantum random walks can be reduced to inverting a matrix. Then a quantum algorithm with the HHL algorithm as a subroutine is developed for solving the problem, which is faster than the known classical algorithms by numerical experiments.
LegIoT: Ledgered Trust Management Platform for IoT

We investigate and address the currently unsolved problem of trust establishment in large-scale Internet of Things (IoT) networks where heterogeneous devices and mutually mistrusting stakeholders are involved. We design, prototype and evaluate LegIoT, a novel, probabilistic trust management system that enables secure, dynamic and flexible (yet inexpensive) trust relationships in large IoT networks. The core component of LegIoT is a novel graph-based scheme that allows network devices (graph nodes) to re-use the already existing trust associations (graph edges) very efficiently; thus, significantly reducing the number of individually conducted trust assessments. Since no central trusted third party exists, LegIoT leverages Distributed Ledger Technology (DLT) to create and manage the trust relation graph in a decentralized manner. The trust assessment among devices can be instantiated by any appropriate assessment technique, for which we focus on remote attestation (integrity verification) in this paper. We prototyped LegIoT for Hyperledger Sawtooth and demonstrated through evaluation that the number of trust assessments in the network can be significantly reduced – e.g., by a factor of 20 for a network of 400 nodes and factor 5 for 1000 nodes.
Application of SVM in anomaly detection based on sampling and feature extraction

Abstract In order to identify the four types of attacks in the KDD99 data set, an anomaly detection model based on supporting vector machines is proposed. The model uses supporting vector machines to train five classifiers and detects the attacks by integrating the classification results of the five classifiers. Aiming at the problem that the detection model has a low recall rate when detecting some categories, a feature extraction method based on abnormal proportion analysis is proposed to extract effective feature combinations for each classifier, and a sampling technique is used to preprocess the training set of each classifier. Experimental results show that the improved anomaly detection model can not only improve the performance of each classifier, but also improve the comprehensive discrimination performance of the five classifiers.
Dynamic Exploration of Multi-agent Systems with Periodic Timed Tasks

We formalise and study multi-agent timed models MAPTs (Multi-Agent with Periodic timed Tasks), where each agent is associated with a regular timed schema upon which all possible actions of the agent rely. MAPTs allow for an accelerated semantics and a layered structure of the state space, so that it is possible to explore the latter dynamically and use heuristics to greatly reduce the computation time needed to address reachability problems. We use an available tool for the Petri net implementation of MAPTs, to explore the state space of autonomous vehicle systems. Then, we compare this exploration with timed automata-based approaches in terms of expressiveness of available queries and computation time.
Necessary criteria for Markovian divisibility of linear maps

Characterizing those quantum channels that correspond to Markovian time evolutions is an open problem in quantum information theory, even different notions of quantum Markovianity exist. One such notion is that of infinitesimal Markovian divisibility for quantum channels introduced in arXiv:math-ph/0611057. Whereas there is a complete characterization for infinitesimal Markovian divisible qubit channels, no necessary or sufficient criteria are known for higher dimensions, except for necessity of non-negativity of the determinant. We describe how to extend the notion of infinitesimal Markovian divsibility to general linear maps and closed and convex sets of generators. We give a general approach towards proving necessary criteria for (infinitesimal) Markovian divisibility that involve singular values of the linear map. With this approach, we prove two necessary criteria for infinitesimal divisibility of quantum channels that work in any finite dimension $d$: an upper bound on the determinant in terms of a $\Theta(d)$-power of the smallest singular value, and in terms of a product of $\Theta(d)$ smallest singular values. Our criteria allow us to analytically construct, in any given dimension, a set of channels that contains provably non infinitesimal Markovian divisible ones. We also discuss the classical counterpart of this scenario, i.e., stochastic matrices with the generators given by transition rate matrices. Here, we show that no necessary criteria for infinitesimal Markovian divisibility of the form proved for quantum channels can hold in general. However, we describe subsets of all transition rate matrices for which our reasoning can be applied to obtain necessary conditions for Markovian divisibility.
FinTech and the law and economics of disintermediation

As FinTech promises to increase competition for both banks and investment firms, we consider the market failures that emerge from its existence, particularly as they relate to issues of financial stability and investor protection. This chapter discusses the wave of technology-enabled disintermediation of financial services asking how regulation should cope with the risks associated with disintermediating finance. While regulation of financial intermediaries has been embraced because the industry is particularly prone to market failures, disintermediation has the potential to make current frameworks obsolete. The law and economics problem is twofold: (1) potential market failures, and (2) the issue of enforcement. This chapter discusses the foundations of financial intermediation and the traditional regulatory approaches to both banks and other providers of financial services. Our analysis establishes a distinction between FinTechs working outside and inside the blockchain. For the former, the crucial regulatory trade-off is between efficiency gains from innovation and regulatory arbitrage. For the latter, our analysis suggests that regulating the convertibility of cryptocurrencies into fiat money is a promising strategy not only to safeguard financial stability, but also to attract financial services to the regulatory perimeter, whenever it is efficient to do so.
CROWD CONGESTION DETECTION IN VIDEOS

For tracking multiple targets in a scene, the most common approach is to represent the target in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. In this paper, we argue that instead of tracking the whole body of a target, if we focus on a relatively rigid body organ, better tracking results can be achieved. Based on this assumption, we followed the tracking-by-detection paradigm and generated the target hypothesis of only the spatial locations of heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. For associating the targets in the consecutive frames, combinatorial optimization is used that associates the corresponding targets in a greedy fashion. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.
Reducing Invertible Embedding Distortion Using Graph Matching Model

For a required payload, the existing reversible data hiding (RDH) methods always expect to reduce the embedding distortion as much as possible, such as by utilizing a well-designed predictor, taking into account the carrier-content characteristics, and/or improving modification efficiency etc. However, due to the diversity of natural images, it is actually very hard to accurately model the statistical characteristics of natural images, which has limited the practical use of traditional RDH methods that rely heavily on the content characteristics. Based on this perspective, instead of directly exploiting the content characteristics, in this paper, we model the embedding operation on a weighted bipartite graph to reduce the introduced distortion due to data embedding, which is proved to be equivalent to a graph problem called as \emph{minimum weight maximum matching (MWMM)}. By solving the MWMM problem, we can find the optimal histogram shifting strategy under the given condition. Since the proposed method is essentially a general embedding model for the RDH, it can be utilized for designing an RDH scheme. In our experiments, we incorporate the proposed method into some related works, and, our experimental results have shown that the proposed method can significantly improve the payload-distortion performance, indicating that the proposed method could be desirable and promising for practical use and the design of RDH schemes.
Components of the Preliminary Conceptual Model for Process Capability in LGPD (Brazilian Data Protection Regulation) Context

A capability model describes the complete set of features that an organization requires to execute its business model or fulfill its mission; the user’s environment must be increasingly included in the design and development of necessary and desired solutions. For this, the development of the model and its application are central issues. An account should be taken of legislation involving the protection of individuals’ personal data in any relationship involving the processing of information classified as personal data; as well as its impact on public and private companies across the country, considering any size and market segment, and taking into account the need to comply with legal requirements efficiently and sustainably, mitigating risk factors. Transdisciplinarity characterizes this research, as the digital transformation process integrates legal, technological aspects, risks, business analysis, good practices and standards of information technology management and digital compliance. This paper addresses this problem by analyzing the main areas of contribution to the assessment of process capability for digital transformation concerning cybersecurity in the context of personal data protection legislation. Finally, the main components of the future capability model are presented.
Privacy, Ethics, and Contact-tracing Apps[enter Paper Title]

Data and analytics are being enlisted to play a role in understanding and preventing the spread of COVID-19. This chapter focuses on digital “apps,” which are being deployed by governments around the world to supplement the manual contact-tracing efforts typically performed by public health officials. Contact-tracing apps have been developed rapidly, with little time for user testing, and their adoption raises important privacy and ethical concerns. In this chapter, we outline some of these potential concerns. We begin by tracing the history of contact-tracing as a pre-digital, or manual, method and then detail the current contract-tracing efforts, distinguishing among different types of apps and data use approaches. We then draw from our complementary expertise in law, ethics, and sociology to outline potential risks of contract-tracing apps along these dimensions. Risks include misuse of personal data for surveillance and insufficient uptake leading to inaccurate information for individuals, which could lead to increased infection. Risks also include differential access and thus the reproduction of vulnerability among marginalized communities. Overall, the chapter identifies issues relevant to the responsible development and use of big data and AI for COVID-19 mitigation efforts.
An Experimental Comparison of Algebraic Crossover Operators for Permutation Problems

Crossover operators are very important components in Evolutionary Computation. Here we are interested in crossovers for the permutation representation that find applications in combinatorial optimization problems such as the permutation flowshop scheduling and the traveling salesman problem. We introduce three families of permutation crossovers based on algebraic properties of the permutation space. In particular, we exploit the group and lattice structures of the space. A total of 34 new crossovers is provided. Algebraic and semantic properties of the operators are discussed, while their performances are investigated by experimentally comparing them with known permutation crossovers on standard benchmarks from four popular permutation problems. Three different experimental scenarios are considered and the results clearly validate our proposals.
Verification of Neural Networks: Enhancing Scalability Through Pruning

Verification of deep neural networks has witnessed a recent surge of interest, fueled by success stories in diverse domains and by abreast concerns about safety and security in envisaged applications. Complexity and sheer size of such networks are challenging for automated formal verification techniques which, on the other hand, could ease the adoption of deep networks in safety- and security-critical contexts. In this paper we focus on enabling state-of-the-art verification tools to deal with neural networks of some practical interest. We propose a new training pipeline based on network pruning with the goal of striking a balance between maintaining accuracy and robustness while making the resulting networks amenable to formal analysis. The results of our experiments with a portfolio of pruning algorithms and verification tools show that our approach is successful for the kind of networks we consider and for some combinations of pruning and verification techniques, thus bringing deep neural networks closer to the reach of formally-grounded methods.
Atomic Appends: Selling Cars and Coordinating Armies with Multiple Distributed Ledgers

The various applications using Distributed Ledger Technologies (DLT) or blockchains, have led to the introduction of a new `marketplace' where multiple types of digital assets may be exchanged. As each blockchain is designed to support specific types of assets and transactions, and no blockchain will prevail, the need to perform interblockchain transactions is already pressing.In this work we examine the fundamental problem of interoperable and interconnected blockchains. In particular, we begin by introducing the Multi-Distributed Ledger Objects (MDLO), which is the result of aggregating multiple Distributed Ledger Objects -- DLO (a DLO is a formalization of the blockchain) and that supports append and get operations of records (e.g., transactions) in them from multiple clients concurrently. Next, we define the AtomicAppends problem, which emerges when the exchange of digital assets between multiple clients may involve appending records in more than one DLO. Specifically, AtomicAppend requires that either all records will be appended on the involved DLOs or none. We examine the solvability of this problem assuming rational and risk-averse clients that may fail by crashing, and under different client utility and append models, timing models, and client failure scenarios. We show that for some cases the existence of an intermediary is necessary for the problem solution. We propose the implementation of such intermediary over a specialized blockchain, we term Smart DLO (SDLO), and we show how this can be used to solve the AtomicAppends problem even in an asynchronous, client competitive environment, where all the clients may crash.
Case-based reasoning with precedent models : Preliminary report

Formalizing case-based reasoning is an important topic in AI and Law, which has been discussed using various approaches, such as formal dialogue games, abstract dialectical frameworks. In this paper we model case-based reasoning by using the formal argument semantics of case models. With the precedent models we present, the validity of legal arguments in the case-based reasoning process can be shown formally. We also present a case study of precedent models in a real legal domain and evaluate the validity of arguments in case-based reasoning.
Optimization of a Silicon Micro-Ring Resonator for Photonic Reservoir Computing

A silicon micro-ring resonator used for providing both nonlinearity and delay for photonic reservoir computing (RC) is optimized. We have evaluated how different design parameters affect RC equalization performances of optical transmission systems.
Optical quantum communication complexity in the simultaneous-message-passing model

The communication cost of a classical protocol is typically measured in terms of the number of bits communicated for this determines the time required for communication during the protocol. Similarly, for quantum communication protocols, which use finite-dimensional quantum states, the communication cost is measured in terms of the number of qubits communicated. However, in quantum physics, one can also use infinite-dimensional states, like optical quantum states, for communication protocols. Communication cost measures based on counting the (equivalent) number of qubits transmitted during communication cannot be directly used to measure the cost of such protocols, which use infinite-dimensional states. Moreover, one cannot infer any physical property of infinite-dimensional protocols using such qubit based communication costs. In this paper, we provide a framework to understand the growth of physical resources in infinite-dimensional protocols. We focus on optical protocols for the sake of concreteness. The time required for communication and the energy expended during communication are identified as the important physical resources of such protocols. In an optical protocol, the time required for communication is determined by the number of time-bin modes that are transmitted from one party to another. The mean photon number of the messages sent determines the energy required during communication in the protocol. We prove a lower bound on the tradeoff between the growth of these two quantities with the growth of the problem size. We call such tradeoff relations optical quantum communication complexity relations.
A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction

Publications are an integral part in a scientific community. Bibliographic reference extraction from scientific publication is a challenging task due to diversity in referencing styles and document layout. Existing methods perform sufficiently on one dataset however, applying these solutions to a different dataset proves to be challenging. Therefore, a generic solution was anticipated which could overcome the limitations of the previous approaches. The contribution of this paper is three-fold. First, it presents a novel approach called DeepBiRD which is inspired by human visual perception and exploits layout features to identify individual references in a scientific publication. Second, we release a large dataset for image-based reference detection with 2401 scans containing 38863 references, all manually annotated for individual reference. Third, we present a unified and highly configurable end-to-end automatic bibliographic reference extraction framework called BRExSys which employs DeepBiRD along with state-of-the-art text-based models to detect and visualize references from a bibliographic document. Our proposed approach pre-processes the images in which a hybrid representation is obtained by processing the given image using different computer vision techniques. Then, it performs layout driven reference detection using Mask R-CNN on a given scientific publication. DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach. The proposed system achieved an AP50 of 98.56% on our dataset. DeepBiRD significantly outperformed the current state-of-the-art approach on their dataset. Therefore, suggesting that DeepBiRD is significantly superior in performance, generalized, and independent of any domain or referencing style.