Reviewerly/exHarmony huggingface dataset:
Factors influencing innovation performance in higher education institutions

Purpose The purpose of this paper is to explore the relationship between transformational leadership, knowledge management capabilities, organizational learning and innovation performance in the context of higher education institutions. Design/methodology/approach Using a survey research design, data was collected from 219 respondents comprising faculty and administrative staff from two public and five private universities in Ghana. The data were analysed by using the partial least squares approach to structural equation modelling with the use of Smart PLS software. Findings The results revealed that transformational leadership significantly predicts knowledge management capabilities and organizational learning and also has a positive effect on innovation performance. Originality/value Although some studies have covered the theoretical and empirical analyses of links between transformational leadership, innovation performance and some knowledge management capabilities, this study examines the direct links between transformational leadership and knowledge management capabilities, on one hand, and transformational leadership and organizational learning on the other, as well as their overall effect on innovation performance, which has been less discussed in literature, particularly in the tertiary educational sector and in the Ghanaian context.
DenMune: Density peak based clustering using mutual nearest neighbors

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm "DenMune" is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms.
Correlating AGP on a quantum computer

For variational algorithms on the near term quantum computing hardware, it is highly desirable to use very accurate ansatze with low implementation cost. Recent studies have shown that the antisymmetrized geminal power (AGP) wavefunction can be an excellent starting point for ansatze describing systems with strong pairing correlations, as those occurring in superconductors. In this work, we show how AGP can be efficiently implemented on a quantum computer with circuit depth, number of CNOTs, and number of measurements being linear in system size. Using AGP as the initial reference, we propose and implement a unitary correlator on AGP and benchmark it on the ground state of the pairing Hamiltonian. The results show highly accurate ground state energies in all correlation regimes of this model Hamiltonian.
Ethical Framework for Assessing Manual and Digital Contact Tracing for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has challenged the traditional public health balance between benefiting the good of the community through contact tracing and restricting individual liberty. This article first analyzes important technical and ethical issues regarding new smartphone apps that facilitate contact tracing and exposure notification. It then presents a framework for assessing contact tracing, whether manual or digital: the effectiveness at mitigating the pandemic; acceptability of risks, particularly privacy; and equitable distribution of benefits and risks. Both manual and digital contact tracing require public trust, engagement of minority communities, prompt COVID-19 testing and return of results, and high adherence with physical distancing and use of masks.
Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97 %. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.
Performance and security of 5 GHz repetition rate polarization-based quantum key distribution

We present and characterize a source for a 5 GHz clocked polarization-based simplified BB84 protocol. Secret keys are distributed over 151.5 km of standard telecom fiber at a rate of 54.5 kbps. Potentially, an increased clock frequency of the experiment introduces correlations between succeeding pulses. We discuss the impact of these correlations and propose measurements to estimate the relevant parameters.
Meta-Key: A Secure Data-Sharing Protocol Under Blockchain-Based Decentralized Storage Architecture

In this letter we propose Meta-key, a data-sharing mechanism that enables users share their encrypted data under a blockchain-based decentralized storage architecture. All the data-encryption keys are encrypted by the owner's public key and put onto the blockchain for safe and secure storage and easy key-management. Encrypted data are stored in dedicated storage nodes and proxy re-encryption mechanism is used to ensure secure data-sharing in the untrusted environment. Security analysis of our model shows that the proxy re-encryption adopted in our system is naturally free from collusion-attack due to the specific architecture of Meta-key.
QuNetSim: A Software Framework for Quantum Networks

As quantum network technologies develop, the need for teaching and engineering tools such as simulators and emulators rises. QuNetSim addresses this need. QuNetSim is a Python software framework that delivers an easy-to-use interface for simulating quantum networks at the network layer, which can be extended at little effort of the user to implement the corresponding link layer protocols. The goal of QuNetSim is to make it easier to investigate and test quantum networking protocols over various quantum network configurations and parameters. The framework incorporates many known quantum network protocols so that users can quickly build simulations using a quantum-networking toolbox in a few lines of code and so that beginners can easily learn to implement their own quantum networking protocols. Unlike most current tools, QuNetSim simulates with real time and is, therefore, well suited to control laboratory hardware. Here, we present a software design overview of QuNetSim and demonstrate examples of protocols implemented with it. We describe ongoing work, which uses QuNetSim as a library, and describe possible future directions for the development of QuNetSim.
Assessing MITRE ATT&amp;CK Risk Using a Cyber-Security Culture Framework

The MITRE ATT&amp;CK (Adversarial Tactics, Techniques, and Common Knowledge) Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures. Its innovative approach has been broadly welcomed by both vendors and enterprise customers in the industry. Its usage extends from adversary emulation, red teaming, behavioral analytics development to a defensive gap and SOC (Security Operations Center) maturity assessment. While extensive research has been done on analyzing specific attacks or specific organizational culture and human behavior factors leading to such attacks, a holistic view on the association of both is currently missing. In this paper, we present our research results on associating a comprehensive set of organizational and individual culture factors (as described on our developed cyber-security culture framework) with security vulnerabilities mapped to specific adversary behavior and patterns utilizing the MITRE ATT&amp;CK framework. Thus, exploiting MITRE ATT&amp;CK’s possibilities towards a scientific direction that has not yet been explored: security assessment and defensive design, a step prior to its current application domain. The suggested cyber-security culture framework was originally designed to aim at critical infrastructures and, more specifically, the energy sector. Organizations of these domains exhibit a co-existence and strong interaction of the IT (Information Technology) and OT (Operational Technology) networks. As a result, we emphasize our scientific effort on the hybrid MITRE ATT&amp;CK for Enterprise and ICS (Industrial Control Systems) model as a broader and more holistic approach. The results of our research can be utilized in an extensive set of applications, including the efficient organization of security procedures as well as enhancing security readiness evaluation results by providing more insights into imminent threats and security risks.
Experimental quantum conference key agreement

Quantum networks will provide multinode entanglement enabling secure communication on a global scale. Traditional quantum communication protocols consume pair-wise entanglement, which is suboptimal for distributed tasks involving more than two users. Here, we demonstrate quantum conference key agreement, a cryptography protocol leveraging multipartite entanglement to efficiently create identical keys between N users with up to N-1 rate advantage in constrained networks. We distribute four-photon Greenberger-Horne-Zeilinger (GHZ) states, generated by high-brightness telecom photon-pair sources, over optical fiber with combined lengths of up to 50 km and then perform multiuser error correction and privacy amplification. Under finite-key analysis, we establish 1.5 × 106 bits of secure key, which are used to encrypt and securely share an image between four users in a conference transmission. Our work highlights a previously unexplored protocol tailored for multinode networks leveraging low-noise, long-distance transmission of GHZ states that will pave the way for future multiparty quantum information processing applications.
COLA-Net: Collaborative Attention Network for Image Restoration

Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available on https://github.com/MC-E/COLA-Net.
Energy-participation quantization of Josephson circuits

Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints--valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples.
Superconducting Circuit Companion—an Introduction with Worked Examples

This tutorial aims at giving an introductory treatment of the circuit analysis of superconducting qubits, i.e., two-level systems in superconducting circuits. It also touches upon couplings between such qubits and how microwave driving and these couplings can be used for single- and two-qubit gates, as well as how to include noise when calculating the dynamics of the system. We also discuss higher-dimensional superconducting qudits. The tutorial is intended for new researchers with limited or no experience with the field but should be accessible to anyone with a bachelor’s degree in physics. The tutorial introduces the basic methods used in quantum circuit analysis, starting from a circuit diagram and ending with a quantized Hamiltonian, that may be truncated to the lowest levels. We provide examples of all the basic techniques throughout the discussion, while in the last part of the tutorial we discuss several of the most commonly used circuits for quantum-information applications. This includes both worked examples of single qubits and examples of how to analyze the coupling methods that allow multiqubit operations. In several detailed appendices, we provide the interested reader with an introduction to more advanced techniques for handling larger circuit designs.23 MoreReceived 10 March 2021Revised 2 August 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040204Published 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 information architectures & platformsPhysical SystemsJosephson junctionsTechniquesQuantum master equationCondensed Matter, Materials & Applied PhysicsQuantum Information
Conversational Agents: Goals, Technologies, Vision and Challenges

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.
Hardware-Efficient Microwave-Activated Tunable Coupling between Superconducting Qubits

Generating high-fidelity, tunable entanglement between qubits is crucial for realizing gate-based quantum computation. In superconducting circuits, tunable interactions are often implemented using flux-tunable qubits or coupling elements, adding control complexity and noise sources. Here, we realize a tunable ZZ interaction between two transmon qubits with fixed frequencies and fixed coupling, induced by driving both transmons off resonantly. We show tunable coupling over 1 order of magnitude larger than the static coupling, and change the sign of the interaction, enabling cancellation of the idle coupling. Further, this interaction is amenable to large quantum processors: the drive frequency can be flexibly chosen to avoid spurious transitions, and because both transmons are driven, it is resilient to microwave cross talk. We apply this interaction to implement a controlled phase (CZ) gate with a gate fidelity of 99.43(1)% as measured by cycle benchmarking, and we find the fidelity is limited by incoherent errors.
A Global Optimization Approach to Nonzero Sum Six-Person Game

The nonzero sum six-person game has been examined. It is well known that nonzero sum n-person game reduces to a nonconvex optimization problem (Enkhbat et al, IGU Ser Mat 20:109121, 2017). Based on Mills’ result (Mills, J Soc Ind Appl Math 8(2):397–402, 1960), we derive a sufficient condition for a Nash equilibrium. To find a Nash equilibrium numerically, we apply the curvilinear multistart algorithm (Gornov and Zarodnyuk, Mach Learn Data Anal 10(1):1345–1353, 2014) developed for nonconvex optimization. The algorithm was tested numerically on six-person game. The game data was generated by “Gamut” (website: http://gamut.stanford.edu/db/generators.html ). The number of variables of the reduced optimization problems was varied from 29 to 33. In all cases, Nash equilibriums were found.
Use of Virtual Reality in Teaching Fractions at Elementary Level

The learning difficulties of the mathematical concept of fraction is a topic that has been studied by different mathematical educators, but still is a relevant topic today, because despite the proposals that have been made to improve learning, they have not been used for all teachers besides those few that are involved in the use of the technology. Besides that there is little educational software that is based on research and is validated to be taken to the classroom. This article shows a virtual system that has been created for the student to interact with it in order to connect his knowledge with his play skills, skills of discovery, reasoning, communication among others and may have a significant learning of fraction
Discover Diamonds-in-the-Rough Using Interactive Visual Analytics System-Tweets as a Collective Diary of the Occupy Movement

The phenomenally wide-adoption of social media has stimulated a new means in organizing and carrying-out modern social movements. Exemplified by the Occupy Movement (OM), rich information, including protest-related events and people's responses to those events, is posted and shared through social media sites such as Twitter. However, it is quite challenging to make sense of such valuable information in a collective manner, as it is often submerged by all the other content on Twitter. In this case study, we demonstrate the combination of computational methods (e.g., topic modeling and event detection) and interactive visual analytics in facilitating users to examine how relevant tweets can reflect a collective view of a social movement. In particular, we focus on discovering and associating key events throughout the OM. Based on the event frequencies, our system helps users to divide the movement into three distinct stages. Information regarding "what" the events were about, "when" and "where" the events occurred, and "who" were involved is extracted from the tweets to describe each stage of the movement. The resulting case studies show that we can indeed construct a collective diary of the social movement by analyzing events extracted from the content of the tweets.
Managing MyGRANTS Fragmented Database Using Binary Vote Assignment Grid Quorum with Association Rule (BVAGQ-AR) Replication Model

Replication is one of the mechanisms managing data since it improves data access and reliability. However, in recent years, with widely available, low-cost technology, the amount of various data grows rapidly. The problem is although we are packed with data, but we have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper propose a new algorithm namely Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) in order to handle fragmented database replication. BVAGQ-AR algorithm is capable of partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Handling fragmented database replication becomes challenging issue to administrator since the distributed database is scattered into split replica partitions or fragments. We address how to build reliable system by using the proposed BVAGQ-AR algorithm for distributed database fragmentation by using Malaysian Greater Research Network (MyGRANTS) data as a case study. The result shows that managing fragmented database replication through proposed BVAGQ-AR algorithm able to preserve MyGRANTS data consistency.
Noise-free attribute-oriented induction

Attribute-oriented induction (AOI) was originally developed to facilitate the mining of generalized knowledge in relational databases. Input data for the AOI method comprises a relational table and a concept tree for each attribute. The output is a small relation that contains a number of generalized tuples which summarize the general characteristics of the relational table. Ideally, the generalized tuples shown in the induction table represent the patterns of information that appear in the table. However, if the input data contains a large amount of noise, the generalized tuples may contain too little information to be useful. Existing research into AOI has yet to focus on the elimination of noise. To fill this gap, we developed two noise-free AOI algorithms that filter out noise to enhance the specificity of AOI results.