Reviewerly/exHarmony huggingface dataset:
Trapped-ion entangling gates robust against qubit frequency errors

Entangling operations are a necessary tool for large-scale quantum information processing, but experimental imperfections can prevent current schemes from reaching sufficient fidelities as the number of qubits is increased. Here it is shown numerically how multitoned generalizations of standard trapped-ion entangling gates can simultaneously be made robust against noise and mis-sets of the frequencies of the individual qubits. This relaxes the degree of homogeneity required in the trapping field, making physically larger systems more practical.Received 15 April 2020Accepted 22 June 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033117Published 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 AreasQuantum computationQuantum controlQuantum gatesQuantum information with trapped ionsQuantum Information
Enhanced Bell state measurement for efficient measurement-device-independent quantum key distribution using 3-dimensional quantum states

We propose an enhanced discrimination measurement for tripartite 3-dimensional entangled states in order to improve the discernible number of orthogonal entangled states. The scheme suggests 3-dimensional Bell state measurement by exploiting composite two 3-dimensional state measurement setups. The setup relies on state-of-the-art techniques, a multi-port interferometer and nondestructive photon number measurements that are used for the post-selection of suitable ensembles. With this scheme, the sifted signal rate of measurement-device-independent quantum key distribution using 3-dimensional quantum states is improved by up to a factor of three compared with that of the best existing setup.
Learning the Designer’s Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user’s design style to better assess the tool’s procedurally generated content with respect to that user’s preferences. Through this approach, we aim for increasing the user’s agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.
Improved Subsampled Randomized Hadamard Transform for Linear SVM

Subsampled Randomized Hadamard Transform (SRHT), a popular random projection method that can efficiently project a d-dimensional data into r-dimensional space (r ≪ d) in O(dlog(d)) time, has been widely used to address the challenge of high-dimensionality in machine learning. SRHT works by rotating the input data matrix X ∈ ℝn × d by Randomized Walsh-Hadamard Transform followed with a subsequent uniform column sampling on the rotated matrix. Despite the advantages of SRHT, one limitation of SRHT is that it generates the new low-dimensional embedding without considering any specific properties of a given dataset. Therefore, this data-independent random projection method may result in inferior and unstable performance when used for a particular machine learning task, e.g., classification. To overcome this limitation, we analyze the effect of using SRHT for random projection in the context of linear SVM classification. Based on our analysis, we propose importance sampling and deterministic top-r sampling to produce effective low-dimensional embedding instead of uniform sampling SRHT. In addition, we also proposed a new supervised non-uniform sampling method. Our experimental results have demonstrated that our proposed methods can achieve higher classification accuracies than SRHT and other random projection methods on six real-life datasets.
Concurrent Typestate-Oriented Programming in Java

We describe a generative approach that enables concurrent typestate-oriented programming in Java and other mainstream languages. The approach allows programmers to implement objects exposing a state-sensitive interface using a high-level synchronization abstraction that synchronizes methods with the states of the receiver object in which those methods have an effect. An external tool takes care of generating all the boilerplate code that implements the synchronization logic. Behavioral types are used to specify object protocols. The tool integrates protocol conformance verification with the synchronization logic so that protocol violations are promptly detected at runtime.
Gaussian Asymptotic Limits for the α-transformation in the Analysis of Compositional Data

Compositional data consists of vectors of proportions whose components sum to 1. Such vectors lie in the standard simplex, which is a manifold with boundary. One issue that has been rather controversial within the field of compositional data analysis is the choice of metric on the simplex. One popular possibility has been to use the metric implied by logtransforming the data, as proposed by Aitchison [1, 2]; and another popular approach has been to use the standard Euclidean metric inherited from the ambient space. Tsagris et al. [21] proposed a one-parameter family of power transformations, the $\alpha$-transformations, which include both the metric implied by Aitchison's transformation and the Euclidean metric as particular cases. Our underlying philosophy is that, with many datasets, it may make sense to use the data to help us determine a suitable metric. A related possibility is to apply the $\alpha$-transformations to a parametric family of distributions, and then estimate a along with the other parameters. However, as we shall see, when one follows this last approach with the Dirichlet family, some care is needed in a certain limiting case which arises $(\alpha \neq 0)$, as we found out when fitting this model to real and simulated data. Specifically, when the maximum likelihood estimator of a is close to 0, the other parameters tend to be large. The main purpose of the paper is to study this limiting case both theoretically and numerically and to provide insight into these numerical findings.
Divergence-Preserving Branching Bisimilarity

This note considers the notion of divergence-preserving branching bisimilarity.It briefly surveys results pertaining to the notion that have been obtained in the past one-and-a-half decade, discusses its role in the study of expressiveness of process calculi, and concludes with some suggestions for future work.1 For stylistic reasons we prefer the term "divergence-preserving branching bisimilarity" over "branching bisimilarity with explicit divergence", which is used in earlier articles on the topic.
Shining a light on Spotlight: Leveraging Apple's desktop search utility to recover deleted file metadata on macOS

Spotlight is a proprietary desktop search technology released by Apple in 2004 for its Macintosh operating system Mac OS X 10.4 (Tiger) and remains as a feature in current releases of macOS. Spotlight allows users to search for files or information by querying databases populated with filesystem attributes, metadata, and indexed textual content. Existing forensic research into Spotlight has provided an understanding of the metadata attributes stored within the metadata store database. Current approaches in the literature have also enabled the extraction of metadata records for extant files, but not for deleted files. The objective of this paper is to research the persistence of records for deleted files within Spotlight's metadata store, identify if deleted database pages are recoverable from unallocated space on the volume, and to present a strategy for the processing of discovered records. In this paper, the structure of the metadata store database is outlined, and experimentation reveals that records persist for a period of time within the database but once deleted, are no longer recoverable. The experimentation also demonstrates that deleted pages from the database (containing metadata records) are recoverable from unused space on the filesystem.
Proceedings Book - Luso-Brazilian Meeting of Physiology, 2020

55th Annual Congress of SBFIS Proceedings’ Book Livro de Resumos do 55º Congresso Anual da SBFIS
Blockchain-Based Smart Contract Design for Crowdfunding of Electrical Vehicle Charging Station Setup

Electrical vehicle (EV) technology is envisioned as a state of art technology in time to come. One main reason for paramount research interest in this field is that EVs are considered best alternative to counter environmental pollution caused by conventional vehicles. Although many renowned companies like Tesla, Nissan have launched their EVs, still there is hesitation among common masses in buying it. It has been found that in countries specially like India, lack of charging stations is the main reason for people not transiting to EVs as a prime mode of transportation. In this direction, government of India has taken visionary steps by launching schemes like FAME (Faster Adoption and Manufacturing of Hybrid and Electric Vehicles). Through such scheme, government is funding and giving subsidies to set up charging stations. In order to give pace to collect enough funds, the new emerging concept of crowdfunding can be very useful. In presented research work, a blockchain-based smart contract has been designed on Ethereum platform for EV charging station setup crowdfunding. The designed smart contract has been coded in solidity language. It has been implemented and tested on REMIX IDE Ethereum platform.
Brief Overview of DevOps

New ways of working, or new methodologies, begin to unearth because of a problem—yes, it all starts with a problem. DevOps too had its own reasons. Businesses craved fast turnarounds of their solutions. And often businesses found out in the midst of development that they did not have all the information they needed to make the right decisions. They wanted to recommend a few more changes to the requirements and still expected the delivery to happen on time. DevOps was born to solve this problem.
Blockchain-Enabled Supply Chain Transparency, Supply Chain Structural Dynamics, and Sustainability of Complex Global Supply Chains — A Text Mining Analysis

Blockchain technology has been hailed as the technology of the future, not only for banking and finance but also for supply chain management and logistics. As lack of transparency in global supply chains is a major risk for sustainability, blockchain offers an attractive solution in the form of a reliable platform to create transparency and risk management. Not considering the nascent stage of the technology, companies are investing millions of dollars into blockchain solutions for many business problems including that of supply chains. However, blockchain-enabled networkwide transparency and visibility also inject new dynamics into supply chains through introduction of structural changes like redefining what is organizational boundary, creating new resources, and a new transactional economy for supply chain management. The structural changes also create a fundamental need for organizations in a supply network to adapt their supply chain processes to this new and emerging supply chain structural dynamics for organizational and network-level efficiency and sustainability. For efficient restructuring of the supply chain processes, organizations need clarity regarding what should be the focus of their processes for creating sources of competitive advantage.Using topic modeling, a text mining technique, this work finds the focus areas of supply chain processes in organizations with examples of successful application of blockchain technology. Apart from how these organizations have integrated the strengths of blockchain in their supply chain processes, we also provide an exhaustive theoretical explanation about how firms can create sources of competitive advantage from blockchain technology. Identification of the focus areas will also help operations and supply chain managers planning to implement blockchain technology and devise plans for data-centric decision-making for their SCM processes for efficiency.
A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning

Federated learning (FL) has great potential for coalescing isolated data islands. It enables privacy-preserving collaborative model training and addresses security and privacy concerns. Besides booming technological breakthroughs in this field, for better commercialization of FL in the business world, we also need to provide sufficient monetary incentives to data providers. The problem of FL incentive mechanism design is therefore proposed to find out the optimal organization and payment structure for the federation. This problem can be tackled by game theory. In this chapter, we set up a research framework for reasoning about FL incentive mechanism design. We introduce key concepts and their mathematical notations specified under the FML environment, hereby proposing a precise definition of the FML incentive mechanism design problem. Then, we break down the big problem into a demand-side problem and a supply-side problem. Based on different settings and objectives, we provide a checklist for FL practitioners to choose the appropriate FL incentive mechanism without deep knowledge in game theory. As examples, we introduce the Crémer-McLean mechanism to solve the demand-side problem and present a VCG-based mechanism, PVCG, to solve the demand-side problem. These mechanisms both guarantee truthfulness, i.e., they encourage participants to truthfully report their private information and offer all their data to the federation. Crémer-McLean mechanism, together with PVCG, attains allocative efficiency, individual rationality, and weak budget balancedness at the same time, easing the well-known tension between these objectives in the mechanism design literature.
Information Retrieval in Financial Documents

Sumithra, M Kani Sridhar, RajeswariThe improvement in the computation power and decreasing cost of storage has resulted in the exponential growth of day-to-day data which can be processed and information can be retrieved to gain insights and knowledge. There are some information retrieval models like Boolean, vector, and probabilistic models that help achieve this target. Using these models leads to problems such as documentary silence and documentary noise due to approximate, poor, and partial representation of the semantic content of documents. In this paper, we built a system that constructs knowledge graphs from the unstructured data and later queries the graph to retrieve information. A knowledge graph is the type of knowledge representation consisting of a collection of entities, events, real-world objects, or it can be any abstract concepts and they are linked together using some relations. They are preferred as they contain large volumes of factual information with less formal semantics. Our approach is to preprocess the structured data followed by named entity recognition with appropriate finance-related tags. Entity relation formulator extracts entities and matches them to relations forming a triple of a subject, object, and predicate. These set of triples can be queried by a natural language query language and converting it into a knowledge graph query. The evaluation metrics employed in this paper are accuracy, precision, and recall. The system has an accuracy of 0.822, a precision of 0.837, and a recall of 0.9015 for a set of 500 questions and answers.
To trace or not to trace: Saving lives from COVID-19 at the cost of privacy breach in Bangladesh

QScience.com is the innovative and collaborative, peer-reviewed, online publishing platform from Hamad bin Khalifa University Press (HBKU Press). It offers a fast and transparent Open Access scholarly publishing process, which is centered on the author, bringing their research to a global audience.
Students in the digital format of the educational process of the higher educational institution: risks and advantages

Abstract The article presents the goal and tasks of the pilot study and the methodology for processing the results in the aspect of studying the attitude of students to digital formats in education, on the example of implementing the elements of hybrid technologies in the modular structure of studying the course of higher mathematics at a technical university. The obtained results are characterized from the standpoint of identifying the readiness of students to use elements of digital technologies in educational activities using the example of online testing and remote learning in the context of the COVID-19 pandemic. The analysis of the results of a pilot social and pedagogical study using the Likert methodology and their comparison with similar studies is presented. The importance of understanding motivation and taking into account the incentives, risks and problems of modern students in relation to the digital aspects of learning for the further development of pedagogical science in the field of higher education has been updated. The obtained author’s results can serve as a basis for comparison and research in the field of pedagogy and psychology of higher education, as a tool to support pedagogical innovations, improve the educational process and the quality of specialist training.
Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.
Encrypted Key-Value Stores

Distributed key-value stores (KVS) are distributed databases that enable fast access to data distributed across a network of nodes. Prominent examples include Amazon’s Dynamo, Facebook’s Cassandra, Google’s BigTable and LinkedIn’s Voldemort. The design of secure and private key-value stores is an important problem because these systems are being used to store an increasing amount of sensitive data. Encrypting data at rest and decrypting it before use, however, is not enough because each decryption exposes the data and increases its likelihood of being stolen. End-to-end encryption, where data is kept encrypted at all times, is the best way to ensure data confidentiality. In this work, we study end-to-end encryption in distributed KVSs. We introduce the notion of an encrypted KVS and provide formal security definitions that capture the properties one would desire from such a system. We propose and analyze a concrete encrypted KVS construction which can be based on any unencrypted KVS. We first show that this construction leaks at most the operation equality (i.e., if and when two unknown queries are for the same search key) which is standard for similar schemes in the non-distributed setting. However, we also show that if the underlying KVS satisfies read your writes consistency, then the construction only leaks the operation equality of search keys that are handled by adversarially corrupted nodes—effectively showing that a certain level of consistency can improve the security of a system. In addition to providing the first formally analyzed end-to-end encrypted key-value store, our work identifies and leverages new and interesting connections between distributed systems and cryptography.
Effective quantum dynamics induced by a driven two-level-system bath

We derive a Born-Markov master equation describing the dissipation induced by a bath of lossy but coherently driven two-level systems (TLS) coupled to a bosonic system via Jaynes-Cummings interaction. We analytically derive all the master equation rates. We characterize these rates for the particular case of a single-mode system coupled to identical TLS. We study the steady state of the system and its exotic properties stemming from the non-thermal stationary state of the driven TLS bath. These properties include dissipative amplification, bath-induced linear instability, and both coherent and dissipative squeezing. The master equation is valid for arbitrarily strong TLS driving, and it can be generalized to include multi-level systems or other system-bath interaction terms, among others. Our work provides a tool to study and characterize TLS-induced decoherence, a key limiting factor in quantum technological devices based on, for instance, superconducting circuits, magnonic systems, or quantum acoustics.
Patient-centered online platform as a service of digital healthcare ecosystem

Opportunities of healthcare informatization tools are closely related to and limited by requirements of the corresponding model of care. The paper covers specific features of biopsychosocial patient-centered (BPC) model and their implications for informatization. Some approaches to automation of BPC model are discussed: ecosystem architecture and patient-centered online platform that provides communication infrastructure for consumers and providers of health services. Operation principles, functions and services of the platform are described that should help the platform meet the requirements of BPC model. The proposed online platform can supplement the existing healthcare informatization tools in addressing the challenge of chronic diseases epidemic.