Reviewerly/exHarmony huggingface dataset:
Impact of geological background on city development

As geographic environment is an expression of the geological settings, it is the objective of this research to explore the roles of geological background in society development, in particular, city development. Based on field trips, geological and tectonic maps, digital elevation model (DEM) and Google Earth images were utilized for this study. Simple linear and binary logistic regression (BLR) and a set of spatial analysis were applied to explore the impacts of environmental factors on city distribution and its relationship with the geological factors taking Jiangxi, China as an example. Here we show that distribution of all level of cities is surprisingly consistent with the lithospheric faults, deep-and-large faults which constitute the fundamental framework for developing the open valleys, rivers and plains that host in turn cities. This finding may provide scientists with deeper understanding and rethinking on the human-environmental interaction and decision-makers an idea where to urbanize in future.
Disentangling causality: assumptions in causal discovery and inference

Abstract Causality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality. For researchers, it has been increasingly difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods. This paper aims to disambiguate the different causal concepts that have emerged in causal inference and causal discovery from observational data by attributing them to different levels of Pearl’s Causal Hierarchy. We will provide the reader with a comprehensive arrangement of assumptions necessary to engage in causal reasoning at the desired level of the hierarchy. Therefore, the assumptions underlying each of these causal concepts will be emphasized and their concomitant graphical components will be examined. We show which assumptions are necessary to bridge the gaps between causal discovery, causal identification and causal inference from a parametric and a non-parametric perspective. Finally, this paper points to further research areas related to the strong assumptions that researchers have glibly adopted to take part in causal discovery, causal identification and causal inference.
Tell me who your contacts are, or what can we learn from standard setting in the context of COVID-19 tracing apps

At the beginning of the pandemic, digital contact tracing was a much-hoped-for initiative that spurred a myriad of apps. Despite a great theoretical promise, however, the tool fell short of significant impact and, essentially, came to nothing. The technological development effort has attracted much scholarly and media attention and coverage. This article seeks to contribute to this growing body of knowledge by approaching the topic from a largely unexplored perspective. It examines the emergence of digital contact tracing as a standard setting exercise, focusing on key actors, processes of technical specification development and data protection assessment of technological choices. It also explores the governance attributes of standard settings from the perspective of data protection law. Given a potential of a technical standard to act as a regulatory means, it is proposed that the governance and legitimacy issues should receive much more consideration. It is believed that for a technical solution to stand the competition for a regulatory share and succeed in the future, the values of inclusiveness, transparency, accountability and openness should be meaningfully internalised in the very process of its development.
An Approach towards Goal-Oriented Requirements Ontology: Consistency and Completeness Based Requirements Analysis

The paper presents a new approach to managing software requirement elicitation techniques with a high level of analyses based on domain ontology techniques, where we established a mapping between user scenario, structured requirement, and domain ontology techniques to improve many attributes such as requirement consistency, completeness and eliminating duplicate requirements to reduce risk of overrun time and budgets.One of the main targets of requirement engineering is to develop a requirement document with high quality.So, we proposed a user interface to collect all vital information about the project directly from the regular user and requirement engineering; After that, the proposal will generate an ontology based on semantic relations and rules.Requirements Engineering tries to keep requirements throughout a project's life cycle consistent necessities clear, and up to date.This prototype allows mapping requirement scenarios into ontology elements for semantically interrupted.The general points of our prototype are to guarantee the identification requirements and improved nature of the Software Requirements Specification (SRS) by solving incomplete and conflicting information in the requirements specification.
Visual Paraphrase Generation with Key Information Retained

Visual paraphrase generation task aims to rewrite a given image-related original sentence into a new paraphrase, where the paraphrase needs to have the same expressed meaning as the original sentence but have a difference in expression form. Existing studies mainly extract two semantic vectors to represent the entire image and the entire original sentence, respectively, for paraphrase generation. However, these semantic vectors for an image or a sentence may lead to the model failing to focus on some key objects in the original sentence, which may generate semantically inconsistent sentences by changing key object information. In this article, we propose an object-level paraphrase generation model, which generates paraphrases by adjusting the permutation of key objects and modifying their associated descriptions. To adjust the permutation of key objects, an object-sorting module aims to obtain new object sequences based on the key object information and original sentences. Then, a sequence generation module sequentially generates paraphrases based on the permutation of the newly object sequences. Each generation step focuses on different image features associated with different key objects to generate descriptions with differences. Furthermore, we use a semantic discriminator module to promote the generated paraphrase to be semantically close to the original sentence. Specifically, the loss function of the discriminator penalizes the excessive distance between the paraphrase and the original sentence. Extensive experiments on the MS COCO dataset show that the proposed model outperforms the baselines.
Blockchain technology’s overview: Consensus, architecture and future trends

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Rohaila Naaz, Ashendra Kumar Saxena, Pradeep Kr. Shah; Blockchain technology’s overview: Consensus, architecture and future trends. AIP Conf. Proc. 27 February 2023; 2427 (1): 020018. https://doi.org/10.1063/5.0125073 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
On extendibility and interoperability properties in knowledge-based engineering

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Andrei Lobov; On extendibility and interoperability properties in knowledge-based engineering. AIP Conf. Proc. 27 February 2023; 2605 (1): 020017. https://doi.org/10.1063/5.0110380 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A 0.8 mW TinyML-Based PDM-to-PCM Conversion for In-Sensor KWS Applications

This paper proposes an ultra-low-power hardware architecture of a tiny machine learning (tinyML)-based conversion from Pulse Density Modulation (PDM) to Pulse Code Modulation (PCM). A hardware-aware efficient design of this conversion is essential to interface digital MEMS microphones, which outputs PDM signals, with audio processing systems, which takes PCM signals, in scenarios of in-sensor computing Keyword Spotting (KWS) applications. Neural network methods are used in a view to effectively combine the proposed converter with tinyML KWS systems, realizing an end-to-end KWS application. The proposed converter consists of a 1-D Convolutional Neural Network, which has been 8-bit quantized to reduce the computational complexity while preserving a 48 dB of Signal-to-Noise Ratio. The hardware accelerator has been implemented on a Xilinx Artix-7 FPGA, achieving a dynamic power consumption (DynP) of 182 µW, a utilization of 917 LUTs and 361 FFs. When the proposed converter is used in the KWS pipeline, the classification accuracy is 89% over 12 classes. Synthesis results in TSMC 0.13 µm CMOS report an area of 0.086 mm2 and a DynP of 837 µW, making it possible to integrate the converter into the sensor integrated circuit.
An Optimal Smooth-Path Motion Planning Method for a Car-like Mobile Robot

This paper proposes an optimal motion planning method consisting of a genetic algorithm (GA), potential field (PF), and Dubins curve for a Car-like mobile robot to solve the problem of finding the shortest and most feasible path in the global environment. Firstly, the GA finds the shortest path by evaluating, selecting, crossing over, and mutating from the initial population and finally provides the strongest individual evolution. Then the result from the GA is further applied with the PF algorithm to improve the ability of obstacle avoidance in the environment. Finally, the Dubins curve method is combined to smooth the path and helps the Car-like mobile robot solve the nonholonomic constraints problem. The major advantages of this method include finding the shortest path, improving avoidance obstacle ability, and smoothing the output path in an environment effectively. The simulation of the proposed method is executed on MATLAB to verify the ability to solve motion planning problems for a Car-like mobile robot.
The relationship between dissolved radon and other geochemical parameters in Campi Flegrei volcanic aquifer (Southern Italy): A follow-up study

Campi Flegrei is one of the most active volcanic areas in the world and assessing the potential proxies of volcanic-related phenomena is critical. Therefore, the spatial distribution of radon and carbon dioxide in groundwater and the statistical relationships between the dissolved gases and other variables deserve further attention. Compositional data analysis (CoDA) was proposed at the end of the last century and further developed in the last decades for reliable data mining, but its potential has not been fully explored for characterization of the groundwater aquifers affected by hydrothermal activity. Based on a prospecting campaign mainly aimed at the determination of both radon and carbon dioxide in Campi Flegrei groundwater, this article explores the spatial patterns of these gases in the local aquifer system and uses a CoDA approach to extract the relevant information and to determine the meaningful geochemical associations. The results show that the spatial distribution of both dissolved gases corresponds to the hydrothermal system. The logratio transformed CO2 (aq) distinguishes bicarbonate-rich groundwater better than the raw values. Principal component analysis reveals two associations: A1) Ca2+, Mg2+, K+, SO42−, HCO3− + CO2 and pH; and A2) Na+, Cl−, As, B, Li, Rn, TDS and T. It highlights that the groundwater composition is generally influenced by two main factors: (1) meteoric water, which is modified by CO2‒rich magmatic gases in some cases; and (2) hydrothermal fluid and/or seawater. The results are in agreement with the literature and application of CoDA is recommended in future investigations because the study area is highly populated and considering the compositional nature of geochemical data might help mitigate the volcanic hazard at Campi Flegrei.
Flourishing digital technology in professional services firms: multidisciplinary perspectives in India

Purpose The purpose of this paper is to identify the role of digitalization on the key characteristics of professional service firms (PSFs) that are part of the service sector and inherently oriented with intense knowledge, capital and professionalized workforces.xD; xA. Design/methodology/approach This study adopted a qualitative, exploratory and inductive research methodology based on in-depth interviews with 49 entrepreneurs/professionals of PSFs focusing on the role of digitalization including capital intensity, knowledge intensity and professionalized workforce. Findings The result reflected that digitalization facilitates at lower levels of knowledge intensity, whereas it increases the capital intensity for most of the firms and decreases the professionalization of the workforce among PSFs. Originality/value The study provides empirical validations where digitalization has changed the distinctive characteristics of PSFs, which promotes new practices, allows for variation and transforms their competitive contexts. In light of these findings, the authors illuminated the application of digitalization on the Indian law firms, retail, education, healthcare and manufacturing industry.
Instantaneous cross-correlation function-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>τ</mml:mi></mml:math>-Wigner distribution: Theory and application

To raise the noisy linear frequency-modulated (LFM) signal detection threshold without sacrificing the computing performance, this study employs the well-known τ-Wigner distribution (τ-WD) in extending further the notion of the instantaneous cross-correlation function WD (ICFWD) to the so-called ICF-τ-WD. We extend some essential properties of the ICFWD to those of the ICF-τ-WD. We analyze the computational complexities of the ICFWD and τ-WD, based on which we derive the computational complexity of the ICF-τ-WD. We address an important concern regarding the ICF-τ-WD's optimal parameters selection for the noisy single component LFM signal processing, in accordance with the output signal-to-noise ratio (SNR) optimization models including the single-objective (expectation-SNR) optimization model and the double-objective (expectation-SNR and variance-SNR) optimization model. We also provide numerical simulations for verifying the advantage of the double-objective optimization model over the single-objective one and the ICF-τ-WD over the ICFWD in the improvement of detection performance.
Saturable Purcell filter for circuit quantum electrodynamics

We consider a typical circuit QED setup where an artificial atom encodes a qubit and is dispersively coupled to a measurement resonator that in turn is coupled to a transmission line. We show theoretically that by placing another artificial atom in this transmission line to act as a filter, the Purcell decay of the qubit into the transmission line is suppressed. When strong control fields are applied in the transmission line, the filter is saturated and effectively switched off. Such a Purcell filtering capability permits both the control and measurement of the qubit using the single transmission line, while maintaining the long coherence time of the qubit in the absence of the control pulses. We show that high fidelity Pauli $\sigma_x$ gates on the qubit can be realized using simple pulse shapes. For devices that already use one transmission line both for control and measurement of the qubit, our work provides a way to completely filter out the qubit frequency without removing the possibility of controlling the system. Further, combining the proposed filter with frequency multiplexing potentially enables both control and measurement of several qubits using a single Purcell-filtered transmission line. This will enhance the scalability of superconducting quantum processors by decreasing the number of the required transmission lines.
An Entity Registry: A Model for a Repository of Entities Found in a Document Set

This paper proposes a conceptual structure for a repository of entities that can be found by usual procedures of Natural Language Processing, that is the search for entities mentioned in text, their identification, possibly through the link to entries in Background Knowledge Basis (BKG) and theconstruction of a Knowledge Basis or Graph to host the information found in this process. We address applications where a BKG is of little help, because the involved entities are not so relevant to be included in any, being for instance ordinary people or small companies. Therefore, we rely on the entities’ attributes and relationships for unique identification, disambiguation, knowledge checking and any other relevant operation. One of the final goals achieved by the proposed method is the ability to merge knowledge collected in separate bases, once they are referred to the same Entity Registry.
Whom do we include and when? participatory design with vulnerable groups

This article makes three contributions to participatory design (PD) research and practice with vulnerable groups: 1) a framework for understanding stakeholder engagement over the course of a PD project; 2) approaches to making user engagement and PD activities more inclusive; and 3) an analysis of how the design and power dynamics of PD projects affect vulnerable groups’ participation. A map of engagement is developed to evaluate stakeholder involvement from initial problem definition to design outcome. The map is applied to three projects aimed at increasing inclusion of vulnerable groups in the planning of public sector services. The first looks at codesign activities to support decision-making in the context of intellectual disabilities; the second looks at culturally diverse youth navigating crisis without adequate assistance from public services; and the third examines nursing students adapting to work in the health sector without accommodations for learning disabilities. Comparing the projects reveals patterns in project planning and execution, and in stakeholder relationships. The article analyses how users are defined, engaged and supported in PD; how proxies shape vulnerable groups’ involvement and PD projects as a whole; and opportunities for greater inclusion when the entire PD project is taken into account.
Integrating a Holistic Enterprise Architecture Pattern

This chapter provides recommendations for the integration of various types of patterns. Complex layers of patterns can be assembled in a holistic enterprise architecture pattern (HEAP), which is integrated by using the integration of HEAP (IoHEAP), which can support agile transformation projects. The IoHEAP is based on a layered concept of patterns and an optimized deployment process. An integration patterns model can be used as a template to integrate solution building blocks (SBB), which can be used to implement a variety of types of transformation projects. In this chapter, the focus is on the integration of various pattern types that can be used to deliver adaptable SBBs. SBBs are the backbone of Enterprise architecture (EA)-based transformation projects.
FARNet: Fragmented affinity reasoning network of text instances for arbitrary shape text detection

Abstract Arbitrary shape text detection is a challenging task in scene text recognition. Driven by deep learning and large‐scale data sets, the detection method based on connected component (CC) has increasingly gained popularity. However, there are still problems of unclear separation of text instances and incorrect component links. Thus, in this paper, the authors propose a novel component connection method, that is, Fragmented Affinity Reasoning Network of Text Instances (FARNet), for arbitrary shape text detection. The network consists of a Weighted Feature Fusion Pyramid Network (WFFPN), Text Fragments Subgraph (TFS), and Dense Graph Attention Network (DGAT), which can be trained end‐to‐end. The WFFPN is used to generate text fragments, TFS and DGAT jointly construct an affinity reasoning network. Since the neighbouring boundaries between text instances may blend them into a single instance, the core idea is to use the WFFPN to divide the text instance into a series of rectangular fragments, the affinity reasoning network infers the affinity between fragments and then links them to rebuild text instances. Extensive experiments on seven challenging datasets (ICDAR2015, MSRA‐TD500, Totaltext, CTW‐1500, ICDAR 2019MLT, ICDAR2019 ArT, and DAST‐1500) demonstrate that the proposed text detector achieves state‐of‐the‐art performance in both on polygon datasets and quadrilateral datasets. The code is available at https://github.com/giganticpower/FARNet .
Emergence of super-Poissonian light from indistinguishable single-photon emitters

Photon indistinguishability constitutes an essential resource in modern physics. At the scale of individual atoms and photons, it is a diverse concept that causes different coherent phenomena. We present the experimental characterization of indistinguishable photons detected in a single mode from a finite and stable ensemble of single-photon emitters: trapped ions. We conclusively observe an increase of second-order correlation and super-Poissonian statistics of these photons with a number of contributing phase-incoherent independent emissions from the atoms, ranging from a single to up to several hundred. This optical emission regime provides insight into the emergence of super-Poissonian light at the atomic scale by photon indistinguishability. It constitutes a unique toolbox for its generation and control at the most microscopic level.
A Real-Time Seismic Intensity Prediction Framework Based on Interpretable Ensemble Learning

Abstract Real-time seismic intensity estimation aims to predict the maximum possible damage caused by an earthquake based on primary waves (P wave), so that the earthquake early warning (EEW) targets can take measures to reduce the potential damage according to the predicted seismic intensity. The peak P-wave displacement amplitude (Pd) is often used as an effective characteristic parameter to predict ground-motion peaks; however, it is difficult to accurately predict the complex nonlinearity between P wave and the peak ground motion using a single parameter. To address this problem, we propose a reliable and efficient real-time seismic intensity prediction framework by investigating and comparing the performance of multiple ensemble learning algorithms using the Kyoshin network (K-NET) dataset, with 52,560 sets of three-component records from 2010 to 2018 as training and test sets, and 9166 sets obtained from 2019 to 2021 as a case study. The proposed framework optimizes the ensemble learning models according to the correlation between characteristic parameters to eliminate redundant and irrelevant parameters. An optimal model with 14 characteristic parameters is determined. In addition, we apply interpretable approaches to explain the effects of different parameters on the results in response to the fact that the poor interpretation of machine learning methods leads to low credibility. We verify the efficiency and prove the generalizability of the model using case sets. The results show that the optimized model can predict the maximum intensity with an accuracy rate exceeding 95% within the 1 s time window after the arrival of P wave, and the accuracy stabilizes at more than 97% after 3 s. The framework established in this study can effectively and continuously predict seismic intensity and provide a potential method for EEW.