Reviewerly/exHarmony huggingface dataset:
Tunable non-Markovian dynamics with a three-level atom mediated by the classical laser in a semi-infinite photonic waveguide

In this work, we investigate the non-Markovian (NM) dynamical evolution of a three-level atom coupled to a semi-infinite one-dimensional photonic waveguide and driven by a classical driving field. The single-end of waveguide behaves as a perfect mirror while light can pass through the opposite end with no backreflection. We derive the exact analytical expressions for the wave function of the driven three-level atom by solving a set of delay differential equations and give the conditions of atom-photon bound state formation that can inhibit the dissipation, which appears in the atom-mirror interspace in order to trap a considerable amount of initial atomic excitation. We show that when the evolution time of the system is shorter than the time delay taken by a photon to perform a round trip between the atom and mirror, the atomic dynamics exhibits Markovian properties, which depend on the driving strength and rescaled parameters. If the evolution time of the system exceeds the time delay, the photon can be reabsorbed by the atom with the photon emitted by the atom having completed the round trip, which exhibits NM memory effects, where the population eventually convergences to the finite-steady values. We also study the influence of the loss and dephasing on the formation of a bound state. Finally, the above results are extended to a more general quantum system involving single-end photonic waveguide coupled to an arbitrary number of noninteracting three-level atoms driven by the driving fields. The presented formalism might open a way to better understand exactly the NM dynamics of driven multilevel atoms coupled to a semi-infinite waveguide.
Alternatives for testing of context-aware software systems in non-academic settings: results from a Rapid Review

Context-awareness challenges the engineering of contemporary software systems and jeopardizes their testing. The variation of context represents a relevant behavior that deepens the limitations of available software testing practices and technologies. However, such software systems are mainstream. Therefore, researchers in non-academic settings also face challenges when developing and testing contemporary software systems. To understand how researchers deal with the variation of context when testing context-aware software systems developed in non-academic settings. To undertake a secondary study ( Rapid Review ) to uncover the necessary evidence from primary sources describing the testing of context-aware software systems outside academia. The current testing initiatives in non-academic settings aim to generate or improve test suites that can deal with the context variation and the sheer volume of test input possibilities. They mostly rely on modeling the systems' dynamic behavior and increasing computing resources to generate test inputs to achieve this. We found no evidence of test results aiming at managing context variation through the testing lifecycle process. So far, the identified testing initiatives and strategies are not ready for mainstream adoption. They are all domain-specific, and while the ideas and approaches can be reproduced in distinct settings, the technologies are to be re-engineered and tailored to the context-awareness of contemporary software systems in different problem domains. Further and joint investigations in academia and experiences in non-academic settings can evolve the body of knowledge regarding the testing of contemporary software systems in the field.
Relational learning with hierarchical attention encoder and recoding validator for few-shot knowledge graph completion

Few-shot knowledge graph complementation (FKGC) has gained broad interest, where each task aims to complete missing facts of the long tail relations by few-shot support instances in the knowledge graph (KG). Most previous FKGC models encode few-shot relations by aggregating the neighbor information of each entity but do not make fine-grained distinctions between neighbor relations and entities. Moreover, many of those models neglect the valuable information interaction between relations, which may reduce the representational performance of the model. In this paper, we propose HARV, a novel FKGC framework based on hierarchical attention encoder and relation recoding validator. Specifically, through the hierarchical attention encoder, we can aggregate fine-grained information at the relation-level and entity-level in the neighbors respectively, thus obtaining more accurate representations. Besides, through the relation recoding validator, we are able to capture the interaction between different relations, which can decrease the over-dependence on specific entities in the few-shot relation encoding stage. Experimental results on two datasets, NELL and Wiki, demonstrate that our model performs better than the state-of-the-art baselines in different few-shot sizes. We also demonstrate the interpretability of our approach through a case study of different levels of attention for heterogeneous neighbors when encoding the central entity in the experiment.
Perspective view of autonomous control in unknown environment: Dual control for exploitation and exploration vs reinforcement learning

This paper overviews and discusses the relationship between Reinforcement Learning (RL) and the recently developed Dual Control for Exploitation and Exploration (DCEE). It is argued that there are two related but quite distinctive approaches, namely, control and machine learning, in tackling intractability arising in optimal decision making/control problems. In the control approach, the original problems (of an infinite horizon) are approximated by finite horizon problems and solved online by taking advantage of the availability of computing power. In the machine learning approach, the optimal solutions are approximated through iterations, or (offline) training through trials when models are not available. When dealing with unknown environments, DCEE as a technique developed from the control approach could potentially solve similar problems as RL while offering a number of advantages, most notably, coping with uncertainty in environment/tasks, high efficiency in learning through balancing exploitation and exploration, and potential in establishing its formal properties like stability. The links between DCEE and other relevant methods like dual control, Model Predictive Control and particularly Active Inference in neuroscience are discussed. The latter provides a strong biological endorsement for DCEE. The methods and discussions are illustrated by autonomous source search using a robot. It is concluded that DCEE provides a promising, complementary approach to RL, and more research is required to develop it as a generic theory and fully realise its potential. The relationships revealed in this paper provide insights into these relevant methods and facilitate cross fertilisation between control, machine learning and neuroscience for developing autonomous control under uncertain environments.
Automating the measurement of heterogeneous chatbot designs

Chatbots are being increasingly used to provide a natural language interface to all kinds of software services. However, while there are many platforms and tools for chatbot development, they typically lack support to statically measure properties of the designed chatbots, as indicators of their size, complexity, quality or usability, and facilitating comparison.
Machine learning and social action in markets: From first- to second-generation automated trading

Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) automated trading systems are different to previous (‘first-generation’) automated trading systems. Where first-generation systems are based on human-defined rules, second-generation systems develop their trading rules independently. We further argue that the use of such second-generation systems prompts a rethinking of established concepts in economic sociology. In particular, a Weberian notion of social action in markets is incompatible with such systems, but we also argue that second-generation automated trading calls for a reconsideration of the notion of the performativity of financial models.
EFL Teachers’ Sources of Remote Teaching Anxiety: Insights and Implications for EFL Teacher Education

Abstract Introduction: This study aims to explore an under-researched issue; namely, remote teaching anxiety. Methods: This study employed a sequential mixed-methods exploratory design where participants initially reported their remote teaching anxiety sources and then rated each. For the analysis, inductive content analysis and statistical tests were employed. Results: The content analysis revealed two major themes: digitalisation-related concerns and online pedagogy-related concerns. Statistically significant difference was only found between anxiety sources and online teaching experience but not between gender, age, teaching experience, work setting and anxiety sources. Discussion: Several studies (Çoklar, Efilti, Şahin, &amp; Akçay, 2016; Hassan et al., 2019) found digitalisation-related concerns causing stress among teachers; however, remote teaching anxiety remains an underexplored construct (Russell, 2020). Although online pedagogy-related concerns were found to cause teaching anxiety in our study, a recent study (Lazarevic &amp; Bentz, 2020) found using technology helpful to decrease anxiety. Limitations: The data were limited to the views of 96 EFL teachers in the Turkish context. Conclusion: We can conclude that limited experience with remote teaching can contribute to higher anxiety among EFL teachers. Additionally, this research can contribute to the relevant literature with several implications on the future of language education.
Question answering system for chemistry—A semantic agent extension

This paper introduces an extension of a previously developed question answering (QA) system for chemistry, operating on a knowledge graph (KG) called Marie. This extension enables the automatic invocation of semantic agents to answer questions when static data is absent from the KG. The agents are semantically described using the agent ontology, OntoAgent, to enable automated agent discovery and invocation. The natural language processing (NLP) models of the QA system need to be trained in order to interpret questions to be answered by new agents. For this purpose, we extend OntoAgent so that it becomes possible to automatically create training material for the NLP models. We evaluate the extended QA system with two example chemistry-related agents and an evaluation question set. The evaluation result shows that the extension allows the QA system to discover the suitable agent and to invoke the agent by automatically constructing requests from the semantic agent description, thereby increasing the range of questions the QA system can answer.
Evolving Optimised Convolutional Neural Networks for Lung Cancer Classification

Detecting pulmonary nodules early significantly contributes to the treatment success of lung cancer. Several deep learning models for medical image analysis have been developed to help classify pulmonary nodules. The design of convolutional neural network (CNN) architectures, however, is still heavily reliant on human domain knowledge. Manually designing CNN design solutions has been shown to limit the data’s utility by creating a co-dependency on the creator’s cognitive bias, which urges the development of smart CNN architecture design solutions. In this paper, an evolutionary algorithm is used to optimise the classification of pulmonary nodules with CNNs. The implementation of a genetic algorithm (GA) for CNN architectures design and hyperparameter optimisation is proposed, which approximates optimal solutions by implementing a range of bio-inspired mechanisms of natural selection and Darwinism. For comparison purposes, two manually designed deep learning models, FractalNet and Deep Local-Global Network, were trained. The results show an outstanding classification accuracy of the fittest GA-CNN (91.3%), which outperformed both manually designed models. The findings indicate that GAs pose advantageous solutions for diagnostic challenges, the development of which may to be fully automated in the future using GAs to design and optimise CNN architectures for various clinical applications.
Integrating User-Group relationships under interest similarity constraints for social recommendation

Traditional collaborative filtering based recommender systems generally suffer from the interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate the issue and improve recommendation performance by introducing social information. Existing social recommendation studies primarily focus on the direct connections between users, such as friendship or users’ correlation. Unfortunately, there often is a severe data sparsity issue in above social data as well, which limits the performance of these models. In contrast, user-group relationships, another valuable social information that is formed by users joining the groups they are interested in, have received insufficient attention. In this paper, we focus on this relationship, demonstrate its excellent effectiveness in alleviating the problem of data sparsity, and integrate it into our recommendation model IGRec (Integrating user-Group relationships for social Recommendation) in a reasonable way. Specifically, to address the problem that existing group-information-enhanced methods have not modeled users’ collaborative interests and social influence in depth, we reformulate the available data into two bipartite graphs: user-item graph and user-group graph. And then employ more robust high-order GCN-based model combining a multi-layer attention mechanism to learn user and item representation from two graphs. Furthermore, we notice that due to the high complexity of user-group networks, the interests of some users in the same group may be far different, especially in those large-scale groups. The indiscriminate use of high-order neighbors’ information in user-group graph may result in the introduction of negative information during the embedding propagation. Thus, to obtain a more precise representation for user and item, we propose to constrain the graph convolution operations at the social side inside subgraphs composed of users with similar interests and the groups they have joined in our model. Finally, experimental results on three real-world datasets clearly show the effectiveness of our proposed model.
Obtaining Ground States of the XXZ Model Using the Quantum Annealing with Inductively Coupled Superconducting Flux Qubits

Preparing ground states of Hamiltonians is important in the condensed matter physics and the quantum chemistry. The interaction Hamiltonians typically contain not only diagonal but also off-diagonal elements. Although quantum annealing provides a way to prepare a ground state of a Hamiltonian, we can only use the Hamiltonian with Ising interaction by using currently available commercial quantum annealing devices. In this work, we propose a quantum annealing for the XXZ model, which contains both Ising interaction and energy-exchange interaction, by using inductively coupled superconducting flux qubits. The key idea is to use a recently proposed spin-lock quantum annealing where the qubits are driven by microwave fields. As long as the rotating wave approximation is valid, the inductive coupling between the superconducting flux qubits produces the desired Hamiltonian in the rotating frame, and we can use such an interaction for the quantum annealing while the microwave fields driving play a role of the transverse fields. To quantify the performance of our scheme, we implement numerical simulations, and show that we can prepare ground states of the two-dimensional Heisenberg model with a high fidelity.
Design of Personalised English Distance Teaching Platform Based on Artificial Intelligence

In the application of the existing distance education platform, the server load is often unbalanced, which increases the response time of the platform. This paper designs a personalised English distance teaching platform based on artificial intelligence. Put forward the overall architecture of the platform, and design the platform on this basis. In order to realise the personalised course resource recommendation function, a collaborative filtering personalised recommendation algorithm is designed to get the best course recommendation results. Using artificial intelligence technology to set up multipoint control unit, the task balance is allocated to multiple operation units, and the reasonable allocation of curriculum resources is realised. According to the functional requirements of the platform design, the composition of functional modules is further set to realise the scheduling and management of curriculum resources. The experimental results show that, under the condition of the same number of concurrent users, the average response time of this design platform is less than that of the existing distance teaching platform, which shows that it has certain advantages in server load balancing and resource scheduling, which can improve the rapid response ability of the platform and enhance the stability of the platform.
Geoscience language models and their intrinsic evaluation

Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in natural language processing (NLP) has the potential to parse through and extract knowledge from unstructured text, but there has, so far, been only limited work on the concepts and vocabularies that are specific to geoscience. Herein we harvest and process public geoscientific reports (i.e., Canadian federal and provincial geological survey publications databases) and a subset of open access and peer-reviewed publications to train new, geoscience-specific language models to address that knowledge gap. Language model performance is validated using a series of new geoscience-specific NLP tasks (i.e., analogies, clustering, relatedness, and nearest neighbour analysis) that were developed as part of the current study. The raw and processed national geological survey corpora, language models, and evaluation criteria are all made public for the first time. We demonstrate that non-contextual (i.e., Global Vectors for Word Representation, GloVe) and contextual (i.e., Bidirectional Encoder Representations from Transformers, BERT) language models updated using the geoscientific corpora outperform the generic versions of these models for each of the evaluation criteria. Principal component analysis further demonstrates that word embeddings trained on geoscientific text capture meaningful semantic relationships, including rock classifications, mineral properties and compositions, and the geochemical behaviour of elements. Semantic relationships that emerge from the vector space have the potential to unlock latent knowledge within unstructured text, and perhaps more importantly, also highlight the potential for other downstream geoscience-focused NLP tasks (e.g., keyword prediction, document similarity, recommender systems, rock and mineral classification).
Knowledge graphs: Introduction, history, and perspectives

Abstract Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.
Alternated superior chaotic variants of gravitational search algorithm for optimization problems

Nature inspired algorithms like Gravitational Search Algorithm (GSA) has been able to solve complex optimization problems with a reasonable performance. But still there is problem of premature convergence to the local optimal solutions due to lack of appropriate balancing between exploration and exploitation procedures. In the literature, chaotic maps have been successful in diversifying the population to a greater level to avoid the entrapment of the solutions in local region(s), and provide better convergence speed with high precision. Recent developments in nonlinear dynamical systems, especially discrete alternated systems, have proved their worth in enhancing the functionality of the metaheuristic algorithms to a great extent (Kumar and Rani, 2019). We have integrated alternated discrete dynamical systems in superior orbit with GSA for more diversification of the population with the aim of increasing the chances for the candidate solutions to arrive at global solutions which otherwise remain stagnant in local optimal points. From the obtained results, we have verified that the proposed methods have outperformed the competing algorithms significantly as they have been able to arrive at global optimal points with much improved optimization rates. In some cases, the improvement in optimization values (mean and standard deviation) have reduced to less than twice the optimization values of the chaotic GSA.
Smart Contract Generation Assisted by AI-Based Word Segmentation

In the last decade, blockchain smart contracts emerged as an automated, decentralized, traceable, and immutable medium of value exchange. Nevertheless, existing blockchain smart contracts are not compatible with legal contracts. The automatic execution of a legal contract written in natural language is an open research question that can extend the blockchain ecosystem and inspire next-era business paradigms. In this paper, we propose an AI-assisted Smart Contract Generation (AIASCG) framework that allows contracting parties in heterogeneous contexts and different languages to collaboratively negotiate and draft the contract clauses. AIASCG provides a universal representation of contracts through the machine natural language (MNL) as the common understanding of the contract obligations. We compare the design of AIASCG with existing smart contract generation approaches to present its novelty. The main contribution of AIASCG is to address the issue in our previous proposed smart contract generation framework. For sentences written in natural language, existing framework requires editors to manually split sentences into words with semantic meaning. We propose an AI-based automatic word segmentation technique called Separation Inference (SpIn) to fulfill automatic split of the sentence. SpIn serves as the core component in AIASCG that accurately recommends the intermediate MNL outputs from a natural language sentence, tremendously reducing the manual effort in contract generation. SpIn is evaluated from a robustness and human satisfaction point of view to demonstrate its effectiveness. In the robustness evaluation, SpIn achieves state-of-the-art F1 scores and Recall of Out-of-Vocabulary (R_OOV) words on multiple word segmentation tasks. In addition, in the human evaluation, participants believe that 88.67% of sentences can be saved 80–100% of the time through automatic word segmentation.