Reviewerly/exHarmony huggingface dataset:
Kernel general loss algorithm based on evolving participatory learning for online time series prediction

In this paper, a dynamic model for online time series prediction is proposed, namely kernel general loss algorithm based on evolving participatory learning (EPL-KGLA). The algorithm can develop its structure and parameters autonomously in response to complex environments, capturing the dynamic changes of time series and achieving accurate prediction. Specifically, EPL based on evolving fuzzy systems is employed in recursive clustering to fully utilize useful information in data streams and generate/prune structures to ensure compactness and reduce computational burden. Then, the general loss function is combined with online kernel learning to propose KGLA for updating consequent parameters in real-time, capturing the dynamic features of data streams and avoiding the negative effects of large anomalies or complex noise on model performance, thereby improving prediction accuracy. Finally, simulation experiments on a benchmark dataset and two real-world datasets are verified the robustness of EPL-KGLA.
Qudit quantum state tomography

In this study, we present a new procedure for quantum state reconstruction of qudit quantum state based on unambiguous state discrimination (USD) measurement. In our recent paper (Karimi et al., 2022), we considered the reconstruction of a single-qubit state via USD measurement. In this paper, we extend this method to the reconstruction of the qudit quantum state.
Towards A Visual Programming Tool to Create Deep Learning Models

Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a visual programming tool that allows DL developers to design, train, and evaluate models without relying on specific programming languages. DeepBlocks works by building on the typical model structure: a sequence of learnable functions whose arrangement defines the specific characteristics of the model. We derived DeepBlocks’ design goals from a 5-participants formative interview, and we validated the first implementation of the tool through a typical use case. Results are promising and show that developers could visually design complex DL architectures.
Site-dependent control of polaritons in the Jaynes–Cummings–Hubbard model with trapped ions

We demonstrate the site-dependent control of polaritons in the Jaynes Cummings Hubbard (JCH) model with trapped ions. In a linear ion crystal under illumination by optical beams nearly resonant to the red-sideband (RSB) transition for the radial vibrational direction, quasiparticles called polaritonic excitations or polaritons, each being a superposition of one internal excitation and one vibrational quantum (phonon), can exist as conserved particles. Polaritons can freely hop between ion sites in a homogeneous configuration, while their motion can be externally controlled by modifying the parameters for the optical beams site-dependently. We demonstrate the blockade of polariton hopping in a system of two ions by the individual control of the frequency of the optical beams illuminating each ion. A JCH system consisting of polaritons in a large number of ion sites can be considered an artificial many-body system of interacting particles and the technique introduced here can be used to exert fine local control over such a system, enabling detailed studies of both its quasi-static and dynamic properties.
Applied orientation of school natural science education: the curriculum of the discipline for higher education applicants of the degree of Doctor of Philosophy in the specialty " 014 Secondary education "

The content of the course substantiates the didactic principles of implementation of the applied orientation of school science education, based on the idea of ensuring compliance of the content, methods and technologies of teaching natural subjects to the requirements of applying the acquired knowledge in practice; the content, essence and formation of the principle of applied orientation and features of its application in school natural education are revealed; the didactic model of teaching school natural subjects on the basis of the principle of applied orientation is described and the didactic conditions of its realization in the educational process are substantiated; proposed a method of forming a system of applied exercises and tasks in the natural cycle in secondary schools II III degrees, aimed at developing students' values, correct behavior in relation to energy, personal health, environment, human relations; developed technology for the formation of skills to solve problems of applied content.
Deliberative Qualities of Online Abortion Discourse: Incivility and Intolerance in the American and Irish Abortion Discussions on Twitter

This paper provides a big-data-scale assessment of the deliberative qualities of online abortion discussions on Twitter in the United States (2020) and Ireland (2018) by specifically focusing on two standards: civility and tolerance for constructive disagreements. Using diverse computational methods and classification, our regression analysis provides mixed evaluations. We find that incivility and intolerance are uncommon behaviours in American and Irish abortion discourse on Twitter, but we also find that these anti-deliberative behaviours are (a) generating more engagements and thereby distorting the overall discussion atmosphere; (b) largely coming from the pro-life tweets; (c) dominated by a small set of hyperactive participants; and that (d) intolerant users tend to communicate within homogeneous echo chambers. Our results indicate that it is crucial for online deliberation to curtail the capabilities of these superparticipants distorting and radicalising the overall online political discourse. By studying two national contexts, our results provide comparability of our findings and insights that can improve our understanding of other contentious and polarised issues more broadly.
Crafting Interactive Experiences with Non-programmers

The Mixed Reality Lab has a long history of creating public interactive experiences in collaboration with creative practitioners. Looking across four such experiences, this keynote explores the role of code (i.e., bespoke software) in making them possible, the practicalities of non-programmers “authoring” key parts of the experience, the relationship between coding and knowledge production, and the changing nature of technical responsibilities. As well as being personally and inherently satisfying, the practical realization of novel interactive systems manifests new creative “materials”, which open the door to new experiences and understandings of people and the world.
Post-Quantum Zero-Knowledge Proofs and Applications

Lattice-based cryptography is one of the most promising candidates for designing post-quantum cryptographic algorithms that resist emerging quantum computing attacks. The recent NIST PQC standardization process is nearing its completion, with practical lattice-based algorithms for basic cryptographic functionalities (namely digital signature and public-key encryption) selected for standardization in the near future. However, practical lattice-based solutions for more advanced privacy-preserving protocols, in particular, Zero-Knowledge Proofs (ZKPs), have only emerged recently and are an active area of research. We discuss some recent developments in design and analysis of practical lattice-based post-quantum ZKPs and their applications. In particular, we review some challenges that arise in designing ZKPs in the lattice setting and some recent progress on efficient lattice-based Schnorr-like proofs for important relations, such as binary/range proofs, one-out-of-many proofs and rounding proofs [1, 2, 4]. We discuss applications and optimization of such proof systems as building blocks for practical advanced cryptographic protocols such as ring signatures and balance proofs for privacy-preserving cryptocurrency payment protocols [2, 3]. We also discuss our recent work on succinct designated-verifier ZKPs (DV-ZKSNARKS) for verifying correctness of general delegated computations [5].
Investigation of Internal Model for Unmanned Vehicle Control in Case of Its Aggressive Motion along a Spatial Trajectory

The work is devoted to the study of methods that are used to control the movement of an object along a given trajectory. A control method involving an accurate internal model is proposed. This internal model was built on the basis of the object’s mathematical model and real object, performed by artificial neural networks. For a limited period of time the model is able to determine the object state without surveillance system usage. The dynamic model of an unmanned vehicle was obtained by method developed at the Robotics Center of the FRC CSC RAS. This method acquires experimental data and performs model identification by means of a neural network. The trajectory is a set of spatial points generated by the developed real unmanned vehicle simulator. The control was carried out on the basis of PID-controller and model predictive control method. The comparison of control methods for a real and virtual unmanned vehicles was conducted in the simulator developed. The results of field experiments, during which control by internal model was applied, are presented.
SoK: On Efficacy of the BGF Decoder for QC-MDPC-based Quantum-Safe Cryptosystems

Bit Flipping Key Encapsulation (BIKE), a shortlisted scheme that proceeded to the fourth round of NIST’s standardization project for post-quantum cryptosystems, is conducive to implementation on embedded devices due to its small key size. However, prior research has indicated the possibility of reaction attacks on this scheme with the potential of compromising private keys through decoder failures. To ensure protection against such reaction attacks, the Decoder Failure Rate (DFR) needs to be sufficiently low. Since these attacks belong to a category of chosen-ciphertext attacks (CCA), a low DFR is essential for ensuring IND-CCA security. The Black Gray Flip (BGF) decoder adopted in the BIKE offers sufficient security. However, the size of the keys needed for the required security level may still be precarious for resource-constrained devices. Therefore, in this work, we formulate and analyze the potential variants of the BGF decoder and compare their performance with the original BGF decoder. To accomplish this, we generate a large set of ciphertexts, and utilize them to compute the DFR of the various variants of the BGF decoder. Our analysis confirms that the BGF decoder with parameters adopted in the original BIKE submission to NIST performs optimally with larger block sizes, which are essential for ensuring higher security levels.
Research on Construction and Application of Ocean Circulation Spatial–Temporal Ontology

Due to the absence of a comprehensive knowledge system for modeling ocean circulation, there is ambiguity and diversity in the semantic expression of ocean circulation. This makes it difficult to organize and share relevant spatiotemporal data effectively. This paper addresses the issue of ocean circulation by introducing ontological theory and methodology based on a comprehensive analysis of domain knowledge. Through a comprehensive analysis of the conceptual and relational characteristics of different elements, we define classes, properties, spatiotemporal relationships, and inference conditions with which to formally express concepts and relationships in ocean circulation, and finally complete the construction of ocean circulation ontology. The formal expression of the Equatorial Counter Current is presented as an example with which to validate the effectiveness of ontological construction. Additionally, an ontology-based knowledge base of ocean circulation is proposed. The construction framework is described, and several examples of knowledge base queries are also illustrated. The results demonstrate that this ontology can effectively represent the relevant knowledge within ocean circulation and provide a meaningful reference for investigating knowledge sharing and semantic integration within this field.
Engineering User Interfaces with Beat Gestures

Beat gestures are biphasic up-and-down or back-and-forth movements of hand(s) that are associated with a specific meaning, such as in speech, or without, such as in rhythmic commands. Incorporating beat gesture recognition into user interface engineering involves dynamic recognition of hand pose, identification of movement direction, and calculation of beat number and frequency. We demonstrate a game that uses beat gestures for musical rhythm learning. We aim to understand the impact of real-time embodiment and guidance visualizations synthesizing user hands and gestures and considering the distance between virtual and real worlds.
Building a framework for open research skills at the University of York

This case study describes the development of an open research skills framework at the University of York. The framework responds to a need for more comprehensive training, clarity and understanding around open research practices across disciplines at York, in line with the University&rsquo;s commitment to the long-term development of an open research culture. The framework was developed by Library, Archives and Learning Services (LALS) in partnership with practitioners from different disciplines across the University&rsquo;s research community. We summarize the background of open research activities at York since 2020, describe how the project was initiated and progressed during the summer of 2022, then provide an overview of the framework itself including areas for future development and consideration. We conclude with some early indicators of usage and reflections on the project, and we hope that this case study will prove useful for research support staff who may be considering developing a similar framework for their own institution.
Directed Acyclic Graph-Type Distributed Ledgers via Young-Age Preferential Attachment

Distributed ledger technologies provide a mechanism to achieve ordering among transactions that are scattered on multiple participants with no prerequisite trust relations. This mechanism is essentially based on the idea of new transactions referencing older ones in a chain structure. Recently, directed acyclic graph (DAG)-type distributed ledgers that are based on DAGs were proposed to increase the system scalability through sacrificing the total order of transactions. In this paper, we develop a mathematical model to study the process that governs the addition of new transactions to the DAG-type distributed ledger. We propose a simple model for DAG-type distributed ledgers that are obtained from a recursive young-age preferential attachment scheme (i.e., new connections are made preferably to transactions that have not been in the system for very long). We determine the asymptotic degree structure of the resulting graph and show that a forward component of linear size arises if the edge density is chosen sufficiently large in relation to the “young-age preference” that tunes how quickly old transactions become unattractive. Funding: The research of C. Mönch is supported by the Deutsche Forschungsgemeinschaft [Grant 443916008].
The Guizmo Framework for Generating Final User Interfaces from Wireframes

We demonstrate Guizmo, a model-driven engineering framework aimed at generating final graphical user interfaces from wireframes according to the following process: (1) design a concrete user interface in a wireframe tool, (2) export/import the wireframe definition into Guizmo for generation of a wireframe model, (3) for segmenting the wireframe into regions by region detection, (4) for inferring positions and dimensions of individual interface elements, (5) in order to obtain a complete layout model that is used for generating the code of a final user interface by model-to-code generation.
A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics

<ns3:p><ns3:bold>Background: </ns3:bold>Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking.</ns3:p><ns3:p> <ns3:bold>Methods: </ns3:bold>In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples.</ns3:p><ns3:p> <ns3:bold>Results: </ns3:bold>Data were visualized with Epistemic Network Analysis to analyze code co-occurrences in the three aligned data streams, and with Qualitative/Unified Exploration of State Transitions to examine the order in which participants in our two subsamples encountered online content.</ns3:p><ns3:p> <ns3:bold>Conclusions: </ns3:bold> This paper describes our methods and planned analyses elaborated with mock figures. Quantifying qualitative data, aligning data streams, and representing all information in a tabularized dataset allows us to group data according to various participant attributes and employ data visualization techniques to pinpoint patterns therein.</ns3:p>
A User Preference and Intent Extraction Framework for Explainable Conversational Recommender Systems

Conversational recommender systems (CRS) communicate with a user through natural language understanding to support the user finding necessary information. While the importance of user information extraction from a dialog is growing, previous systems rely on named-entity recognition to find out user preference based on deep learning methods. However, there is still scope for such recognition modules to perform better in terms of accuracy and richness of the elicited user preference information. Also, extracting user information solely depending on entities mentioned in user utterances might ignore contextual semantics. Besides, black-box recommender systems are widely used in previous CRSs whereas such methods undermine transparency and interpretability of recommended results. To alleviate these problems, we propose a novel framework to extract user preference and user intent and apply it to a recommender system. User preference is extracted from sets of an item feature entity detected by our item feature entity detection module and an estimated rating about each entity. Utilizing graph representation of user utterances, user intent is also elicited to consider the contextual semantic of each element word. Based on both outcomes, we implement recommendation by candidate selection and ranking, then provide explanation of the recommendation result to enhance interpretability and manipulability of the system. We illustrate how our framework works in practice by a sample conversation. Experiments present improvement and effectiveness of user information elicitation in recommendation.
Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation Study

Background: Extractive question-answering (EQA) is a useful natural language processing (NLP) application for answering patient-specific questions by locating answers in their clinical notes. Realistic clinical EQA can have multiple answers to a single question and multiple focus points in one question, which are lacking in the existing datasets for development of artificial intelligence solutions. Objective: Create a dataset for developing and evaluating clinical EQA systems that can handle natural multi-answer and multi-focus questions. Methods: We leveraged the annotated relations from the 2018 National NLP Clinical Challenges (n2c2) corpus to generate an EQA dataset. Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were included to form the multi-answer and multi-focus QA entries, which represent more complex and natural challenges in addition to the basic one-drug-one-reason cases. A baseline solution was developed and tested on the dataset. Results: The derived RxWhyQA dataset contains 96,939 QA entries. Among the answerable questions, 25% require multiple answers, and 2% ask about multiple drugs within one question. There are frequent cues observed around the answers in the text, and 90% of the drug and reason terms occur within the same or an adjacent sentence. The baseline EQA solution achieved a best f1-measure of 0.72 on the entire dataset, and on specific subsets, it was: 0.93 on the unanswerable questions, 0.48 on single-drug questions versus 0.60 on multi-drug questions, 0.54 on the single-answer questions versus 0.43 on multi-answer questions. Discussion: The RxWhyQA dataset can be used to train and evaluate systems that need to handle multi-answer and multi-focus questions. Specifically, multi-answer EQA appears to be challenging and therefore warrants more investment in research.
UAV Tunnel Formation Based on Bearing-only Passive Location Optimization Algorithm

Abstract In the actual bearing-only passive UAV positioning system, both the controller and receiver are in non-standard positions. Three-point positioning cannot be carried out simply by a geometric algorithm. This study introduces positioning only by bearing-only information transmission without considering increasing the complexity of system sensors. When the circular formation and conical formation of UAVs are in abnormal positions to locate the unit and adjust it to the standard position, the error distance between the original position and its accurate position is taken as the target, and an iterative optimization model is established. The positions of other UAVs are revised according to the transmitter position information. Finally, the ideal error position information is obtained, which is regarded as the adjustment of the whole unit distance to the standard distance.