Reviewerly/exHarmony huggingface dataset:
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system that handles the task of understanding the user by capturing the necessary information in the form of slots and generating an appropriate response in accordance with the extracted information. Recently, dialogue systems integrated with complementary information such as images, audio, or video have gained immense popularity. In this work, we propose an end-to-end framework with the capability to extract necessary slot values from the utterance and generate a coherent response, thereby assisting the user to achieve their desired goals in a multimodal dialogue system having both textual and visual information. The task of extracting the necessary information is dependent not only on the text but also on the visual cues present in the dialogue. Similarly, for the generation, the previous dialog context comprising multimodal information is significant for providing coherent and informative responses. We employ a multimodal hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge base (Kb) to provide a stronger context for both the tasks. Finally, we design a slot attention mechanism to focus on the necessary information in a given utterance. Lastly, a decoder generates the corresponding response for the given dialogue context and the extracted slot values. Experimental results on the Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms the baselines approaches in both the tasks.
Safe Artificial Potential Field - Novel Local Path Planning Algorithm Maintaining Safe Distance From Obstacles

This letter proposes a novel local path planning algorithm called Safe Artificial Potential Field (SAPF). The algorithm uses the attractive potential for goal reaching as the original one. However, the repulsive potential is replaced by a general obstacle potential, equal to repulsive potential, vortex potential, or their superposition. Moreover, the analytical tuning procedure of SAPF is described in this letter. The proposed algorithm has been compared to the original Artificial Potential Field (APF), Vortex APF (VAPF), and Dynamic Window Approach (DWA). The comparison has been provided using both, simulational and experimental validation. The SAPF allows to avoid simple local minima, keep safety distance from obstacles and move through narrow corridors. The obtained results in the form of videos are attached as supplementary files.
The tensions of cyber-resilience: From sensemaking to practice

The growing sophistication, frequency and severity of cyberattacks targeting all sectors highlight their inevitability and the impossibility of completely protecting the integrity of critical computer systems. In this context, cyber-resilience offers an attractive alternative to the existing cybersecurity paradigm. We define cyber-resilience as the capacity to withstand, recover from and adapt to the external shocks caused by cyber risks. This article seeks to provide a broader organizational understanding of cyber-resilience and the tensions associated with its implementation. We apply Weick's (1995) sensemaking framework to examine four foundational tensions of cyber-resilience: a definitional tension, an environmental tension, an internal tension, and a regulatory tension. We then document how these tensions are embedded in cyber-resilience practices at the preparatory, response and adaptive stages. We rely on qualitative data from a sample of 58 cybersecurity professionals to uncover these tensions and how they reverberate across cyber-resilience practices.
High Cooperativity Using a Confocal-Cavity–QED Microscope

Cavity quantum electrodynamics (QED) with cooperativity far greater than unity enables high-fidelity quantum sensing and information processing. The high-cooperativity regime is often reached through the use of short single-mode resonators. More complicated multimode resonators, such as the near-confocal optical Fabry-Prot cavity, can provide intracavity atomic imaging in addition to high cooperativity. This capability has recently proved important for exploring quantum many-body physics in the driven-dissipative setting. In this work, we show that a confocal-cavity–QED microscope can realize cooperativity in excess of 110. This cooperativity is on par with the very best single-mode cavities (which are far shorter) and 21 times greater than single-mode resonators of similar length and mirror radii. The 1.7-μm imaging resolution is naturally identical to the photon-mediated interaction range. We measure these quantities by determining the threshold of cavity superradiance when small optically tweezed Bose-Einstein condensates are pumped at various intracavity locations. Transmission measurements of an ex situ cavity corroborate these results. We provide a theoretical description that shows how cooperativity enhancement arises from the dispersive coupling to the atoms of many near-degenerate modes.Received 15 December 2022Accepted 11 April 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020326Published 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 AreasBose-Einstein condensatesCavity quantum electrodynamicsTechniquesCavity resonatorsOptical microscopyAtomic, Molecular & OpticalQuantum Information
Toward Patient-Centric Healthcare Systems: Key Requirements and Framework for Personal Health Records Based on Blockchain Technology

Healthcare data are considered sensitive and confidential, and storing these sensitive data in traditional (i.e., centralized) databases may expose risks, such as penetration or data leaks. Furthermore, patients may have incomplete health records since they visit various healthcare centers and leave their data scattered in different places. One solution to resolve these problems and permit patients to own their records is a decentralized personal health record (PHR); this can be achieved through decentralization and distribution systems, which are fundamental attributes of blockchain technology. Additionally, the requirements for this solution should be identified to provide practical solutions for stakeholders. This study aims to identify the key requirements for PHRs. A design science methodology was utilized to meet the study objectives, and thirteen healthcare experts were interviewed to elicit the requirements and the previous studies. Thirty-three requirements are defined, and based on these, high- and low-level architectures are developed and explained. The result illustrates that the developed solution-based Hyperledger Fabric framework is a promising method for the achievement of PHRs that guarantee security aspects, such as integrity, confidentiality, privacy, traceability, and access control.
Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being

Following social cognitive theory, the current study investigated the impact of artificial intelligence (AI) on employees' productivity in the healthcare sector. AI significantly facilitates the management of hospitals to vigilantly assess employees’ productivity and accurately analyze employees' characteristics, such as attitude, emotion and behavior. With the underlying mechanism of employee mental health and well‐being, and knowledge sharing, the study has considered beneficial and harmful perspectives of AI in the workplace. The study also hypothesizes the important moderating role of technological leadership. The data was collected from 184 doctors in Pakistan's major hospitals. Partial least squares (PLS) results support a direct relationship between AI and employee productivity. The findings also supported the underlying mechanism of knowledge sharing and mental health and well‐being in the relationship between AI and employee productivity. However, the technological leadership moderating effect was found to be insignificant. It opens an important avenue for this further research and future directions.
DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT

Cross-domain authentication requires that there is no trust gap between different trust domains that can cause cross-domain devices to exceed the security control scope of the original trust domain and further expose cross-domain authentication systems to security threats. In addition, as relying on the traditional cross-domain authentication means built by centralized institutions cannot meet the data security needs in a big data environment. Therefore, it is necessary to design a secure dynamic cross-domain authentication scheme. In this paper, we propose a dynamic cross-domain authentication scheme (DCAGS-IoT) in the Internet of Things environment using the group signature technology and the distributed system architecture of blockchain. Specifically aiming at the problem of increasing and revoking users in dynamic cross-domain authentication, a user update algorithm with the complexity of O (logN) was designed to manage users in the trust domain. Moreover, we used the characteristics that group signature users can sign on behalf of a group to protect the users’ privacy and track suspicious users. Since the size of the signature generated by the scheme is independent of the number of group members N and only depends on the security parameters λ, the efficiency of the protocol implementation is improved, and the security and availability of the authentication scheme are guaranteed.
Robust and Efficient Hamiltonian Learning

With the fast development of quantum technology, the sizes of both digital and analog quantum systems increase drastically. In order to have better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static and dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information on the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a robust and efficient Hamiltonian learning method that circumvents these limitations based only on mild assumptions. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short-time dynamics and local operations without any information on the Hamiltonian or preparing any eigenstates or thermal states. The method has a scalable complexity and a vanishing failure probability regarding the qubit number. Meanwhile, it performs robustly given the presence of state preparation and measurement errors and resiliently against a certain amount of circuit and shot noise. We numerically test the scaling and the estimation accuracy of the method for transverse field Ising Hamiltonian with random interaction strengths and molecular Hamiltonians, both with varying sizes and manually added noise. All these results verify the robustness and efficacy of the method, paving the way for a systematic understanding of the dynamics of large quantum systems.
From principles to reality. FAIR implementation in the nanosafety community

Developing safe and sustainable nanomaterials-based solutions to current global challenges including clean energy, sustainable food production and water security requires access to high quality data and appropriate analysis and modelling approaches. Achieving these challenges requires increased re-use of research data to accelerate progress and support development of new materials that are safe and sustainable for energy capture and storage, nano-agriculture and environmental remediation. The principles of Findability, Accessibility, Interoperability and Reusability (FAIR) provide a roadmap to enhanced data sharing and re-use, but require consensus within the nanosafety community on metadata, ontologies and persistent identifiers (among other things) and guidance to support implementation and achieve machine-readability. Here, we highlight the main focus of the AdvancedNano GO FAIR Implementation Network in supporting the nanosafety community with implementation of FAIR to maximize data-driven safe and sustainable application of nano- and advanced materials.
PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.
Critical Factors and Trends in NFT Technology Innovations

Non-fungible token (NFT) products are important for industrial applications. In recent years, they have rapidly gained importance in the field of blockchain combined with metaverse. The concept of NFTs has developed gradually, as many industries have begun using NFTs creatively to raise new business innovation opportunities in entrepreneurship. However, few studies have been conducted analyzing critical features of NFTs for success, trends, and challenges in NFT products. In this study, group discussions, case analysis methods, and the OpenSea database were used to analyze fashion trends among NFT products. A mixed method was used in this study. Quantitative and qualitative data derived from the questionnaire and group discussions were analyzed using the case study method, and the actual historical trading data of NFT products obtained from the OpenSea platform were analyzed. This study analyzed NFT products, fashion characteristics, and trends in NFT artwork. The opportunities and challenges of NFT applications and sustainable NFTs are discussed in this study. Our research results show that the most attractive NFT product types are collectible digital works and creative artworks. The critical design characteristics are lovely (cute), beautiful, and interesting. We recommend that NFT makers use the above-mentioned characteristics to create NFT artworks with special design characteristics to increase NFT values. The advantage of NFTs is that makers can freely create their works through the NFT platform, which can decrease the limitations of traditional methods such as the need for venues, exhibition setup costs, and intermediaries’ commissions. The major challenges of current NFT applications include usability challenges, security and privacy issues, and governance considerations. We believe that our research results can provide useful directions and strategies for future researchers, makers, and ventures seeking to develop NFT applications. Our research results, such as identifying the critical design factors and current trends in NFTs, can provide guidelines for art design and innovation education. In addition, this study discusses the applications of NFTs in sustainable education, which can provide benefits for sustainable educational development to meet the goal of quality education in SDG4.
A Hybrid Approach for Efficient and Secure Point Multiplication on Binary Edwards Curves

The focus of this article is to present a novel crypto-accelerator architecture for a resource-constrained embedded system that utilizes elliptic curve cryptography (ECC). The architecture is built around Binary Edwards curves (BEC) to provide resistance against simple power analysis (SPA) attacks. Furthermore, the proposed architecture incorporates several optimizations to achieve efficient hardware resource utilization for the point multiplication process over GF(2m). This includes the use of a Montgomery radix-2 multiplier and the projective coordinate hybrid algorithm (combination of Montgomery ladder and double and add algorithm) for scalar multiplication. A two-stage pipelined architecture is employed to enhance throughput. The design is modeled in Verilog HDL and verified using Vivado and ISE design suites from Xilinx. The obtained results demonstrate that the proposed BEC accelerator offers significant performance improvements compared to existing solutions. The obtained throughput over area ratio for GF(2233) on Virtex-4, Virtex-5, Virtex-6, and Virtex-7 Xilinx FPGAs are 9.43, 14.39, 26.14, and 28.79, respectively. The computation time required for a single point multiplication operation on the Virtex-7 device is 19.61 µs. These findings indicate that the proposed architecture has the potential to address the challenges posed by resource-constrained embedded systems that require high throughput and efficient use of available resources.
Performance evaluation of seasonal solar irradiation models—case study: Karapınar town, Turkey

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs) and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapınar town Turkey. These methods, namely ANN, PSO and BT methods are widely used for solar irradiance estimation. The study utilizes input data from 2007 to 2020, including air temperature, precipitation, snow mass, air density, and cloud cover fraction, to estimate solar irradiance as the output value. The performance of these methods is analysed according to error indicators such as mean absolute error (MAE), root mean square error (RMSE) and R2. The results demonstrate that the BT method yields the lowest statistical error metrics. Additionally, the correlation matrix reveals a strong positive linear relationship (correlation coefficient of 0.9045) between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted via the employment of multiple linear regression (MLR) subsets and diverse combinations.
A novel deep learning-based technique for detecting prostate cancer in MRI images

Abstract In the western world,the prostate cancer is major cause of death in males. Magnetic Resonance Imaging (MRI) is widely used for the detection of prostate cancer due to which it is an open area of research. The proposed method uses deep learning framework for the detection of prostate cancer using the concept of Gleason grading of the historical images. A3D convolutional neural network has been used to observe the affected region and predicting the affected region with the help of Epithelial and the Gleason grading network. The proposed model has performed the state-of-art while detecting epithelial and the Gleason score simultaneously. The performance has been measured by considering all the slices of MRI, volumes of MRI with the test fold, and segmenting prostate cancer with help of Endorectal Coil for collecting the images of MRI of the prostate 3D CNN network. Experimentally, it was observed that the proposed deep learning approach has achieved overall specificity of 85% with an accuracy of 87% and sensitivity 89% over the patient-level for the different targeted MRI images of the challenge of the SPIE-AAPM-NCI Prostate dataset.
A bio-inspired algorithm in image-based path planning and localization using visual features and maps

With the growing applications of autonomous robots and vehicles in unknown environments, studies on image-based localization and navigation have attracted a great deal of attention. This study is significantly motivated by the observation that relatively little research has been published on the integration of cutting-edge path planning algorithms for robust, reliable, and effective image-based navigation. To address this gap, a biologically inspired Bat Algorithm (BA) is introduced and adopted for image-based path planning in this paper. The proposed algorithm utilizes visual features as the reference in generating a path for an autonomous vehicle, and these features are extracted from the obtained images by convolutional neural networks (CNNs). The paper proceeds as follows: first, the requirements for image-based localization and navigation are described. Second, the principles of the BA are explained in order to expound on the justifications for its successful incorporation in image-based navigation. Third, in the proposed image-based navigation system, the BA is developed and implemented as a path planning tool for global path planning. Finally, the performance of the BA is analyzed and verified through simulation and comparison studies to demonstrate its effectiveness.
PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.
Asymmetric tridirectional quantum teleportation using seven-qubit cluster states

Abstract This paper proposes an asymmetric tridirectional quantum teleportation (TQT) protocol that uses a seven-qubit cluster states as the quantum channels of each party. In this scheme, any one of the three participants can send their arbitrarily entangled states to the other two concurrently. The tridirectional functionality shows that all three users can act as sender and receiver simultaneously. Based on the mathematical modeling of the scheme, the quantum circuit and the appropriate quantum channel is proposed. For examination and implementation, the proposed scheme and circuit were simulated and validated using IBM quantum experience platform, QASM_simulator and Qiskit library.