Reviewerly/exHarmony huggingface dataset:
SQuARE: Semantics-based Question Answering and Reasoning Engine

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) and from its early days, it has received significant attention through question answering (QA) tasks.We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework.The framework is based on the denotational semantics approach widely used in programming language research.In our framework, valuation function maps syntax tree of the text to its commonsense meaning represented using basic knowledge primitives (the semantic algebra) coded using answer set programming (ASP).We illustrate an application of this framework by using VerbNet primitives as our semantic algebra and a novel algorithm based on partial tree matching that generates an answer set program that represents the knowledge in the text.A question posed against that text is converted into an ASP query using the same framework and executed using the s(CASP) goal-directed ASP system.Our approach is based purely on (commonsense) reasoning.SQuARE achieves 100% accuracy on all the five datasets of bAbI QA tasks that we have tested.The significance of our work is that, unlike other machine learning based approaches, ours is based on "understanding" the text and does not require any training.SQuARE can also generate an explanation for an answer while maintaining high accuracy.
A novel path planning method of mobile robots based on an improved bat algorithm

In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.
Quantum sensing of intermittent stochastic signals

Realistic quantum sensors face a trade-off between the number of sensors measured in parallel and the control and readout fidelity $(F)$ across the ensemble. We investigate how the number of sensors and fidelity affect sensitivity to continuous and intermittent signals. For continuous signals, we find that increasing the number of sensors by $1/{F}^{2}$ for $F&lt;1$ always recovers the sensitivity achieved when $F=1$. However, when the signal is intermittent, more sensors are needed to recover the sensitivity achievable with one perfect quantum sensor. We also demonstrate the importance of near-unity control fidelity and readout at the quantum projection noise limit by estimating the frequency components of a stochastic, intermittent signal with a single trapped ion sensor. Quantum sensing has historically focused on large ensembles of sensors operated far from the standard quantum limit. The results presented in this paper show that this is insufficient for quantum sensing of intermittent signals and reemphasizes the importance of the unique scaling of quantum projection noise near an eigenstate.
Multiple Aperture Shear-Interferometry (MArS): a solution to the aperture problem for the form measurement of aspheric surfaces

Multiple Aperture Shear-Interferometry (MArS) is a shape measurement technique that uses multi-spot illumination to overcome the problem of a limited observation aperture of conventional interferometric techniques and thus considerably simplifies the measurement of optical aspheres and freeform surfaces. Using a shear interferometry setup, MArS measures the coherence function in order to obtain wave vector distributions created from multi-spot LED illumination reflected by the specimen. Based on the wave vectors we reconstruct the surface topography of aspheric lenses using an inverse ray tracing approach and prior knowledge about the individual source locations. We present the topographic measurement of two aspheric lenses with different global curvature radii measured with the same identical reflection setup. In addition, we examine the achievable accuracy of the wave vector measurement using a single light source to find physical limits of MArS.
Mapping the case analysis genre continuum in an information systems program

The case analysis, a prominent genre within business and information systems (IS) programs, is challenging for students because there are competing demands to perform both academic/learner and professional/mock-consultant roles. Drawing on design-based research data from four different IS courses at our institution, in this paper we aim to map a pedagogical to professional case analysis genre continuum. We examine the expectations for student roles in these four different courses, where and how the case analysis assignments in these courses fit into the continuum, and whether they facilitate an effective developmental trajectory as a whole. Our analysis shows that our institution's trajectory is mostly effective in moving students from pedagogical to professional roles, but could be enhanced with extra support for case analysis writing that falls in the middle of the continuum. We offer recommendations for how academic programs could leverage the value of the developmental trajectory to create a cohesive sequence across the four-year student experience.
Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies

In real-world scenarios, the data are widespread that are annotated with a set of candidate labels but a single ground-truth label per-instance. The learning paradigm with such data, formally referred to as Partial Label (PL) learning, has recently drawn much attention. The traditional PL methods estimate the confidences being the ground-truth label of candidate labels with various regularizations and constraints, however, they only consider the local information, resulting in potentially less accurate estimations as well as worse classification performance. To alleviate this problem, we propose a novel PL method, namely PArtial label learNing by simultaneously leveraging GlObal and Local consIsteNcies (Pangolin). Specifically, we design a global consistency regularization term to pull instances associated with similar labeling confidences together by minimizing the distances between instances and label prototypes, and a local consistency term to push instances marked with no same candidate labels away by maximizing their distances. We further propose a nonlinear kernel extension of Pangolin, and employ the Taylor approximation trick for efficient optimization. Empirical results demonstrate that Pangolin significantly outperforms the existing PL baseline methods.
Quantum proportional-integral (PI) control

Abstract Feedback control is an essential component of many modern technologies and provides a key capability for emergent quantum technologies. We extend existing approaches of direct feedback control in which the controller applies a function directly proportional to the output signal (P feedback), to strategies in which feedback determined by an integrated output signal (I feedback), and to strategies in which feedback consists of a combination of P and I terms. The latter quantum PI feedback constitutes the analog of the widely used proportional-integral feedback of classical control. All of these strategies are experimentally feasible and require no complex state estimation. We apply the resulting formalism to two canonical quantum feedback control problems, namely, generation of an entangled state of two remote qubits, and stabilization of a harmonic oscillator under thermal noise under conditions of arbitrary measurement efficiency. These two problems allow analysis of the relative benefits of P, I, and PI feedback control. We find that for the two-qubit remote entanglement generation the best strategy can be a combined PI strategy when the measurement efficiency is less than one. In contrast, for harmonic state stabilization we find that P feedback shows the best performance when actuation of both position and momentum feedback is possible, while when only actuation of position is available, I feedback consistently shows the best performance, although feedback delay is shown to improve the performance of a P strategy here.
PV Hosting Capacity Assessment of Solar Road Considering Dynamic Shading and Spatial Correlation

The rising of energy demand has accelerated the research on renewable energy, especially photovoltaic (PV). Solar road is an emerging PV technology that can not only harvest the dispersed and vast solar energy but also maximize the land utilization. This paper proposed a solar road hosting capacity assessment model considering dynamic shading and spatial correlation. The dynamic shading caused by traffic flow on solar road is considered as an effect factor which will impact the PV output. In order to quantify the traffic influence on PV output, a traffic flow assignment model is used for simulation. Meanwhile, the spatial correlation between dispersed PV stations is computed by copula model. The proposed method is examined on a test system with coupled transportation and distribution network. The numerical results substantiate that the proposed method is an effective tool for assessing the PV hosting capacity for solar road.
Text Sentiment Classification Based on Feature Fusion

The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.
Empfehlungen für eine nationale Open Science Strategie in Österreich / Recommendations for a National Open Science Strategy in Austria

Der Blick auf internationale Aktivitaten zu Science zeigt ein breites Spektrum von einzelnen institutionellen Policies bis hin zu nationalen Aktionsplanen. Die vorliegenden Empfehlungen fur eine nationale Science in Osterreich orientieren sich an diesen internationalen Initiativen und stellen praktische Uberlegungen fur ihre koordinierte Implementierung im Hinblick auf strategische Entwicklungen in Forschung, Technologie und Innovation (FTI) bis 2030 in Osterreich dar. Dabei richten sie sich an alle relevanten Akteur*innen im FTI System, im Besonderen an Forschungsstatten, Forschungsforderer, Forschungspolitik, Gedachtnisinstitutionen wie Bibliotheken und Wissenschafter*innen. Das Empfehlungspapier wurde von 2018 bis 2020 von der OANA-Arbeitsgruppe Open Science Strategie entwickelt und im Fruhling 2020 das erste Mal fur eine offentliche Konsultation veroffentlicht. Die nun vorliegende finale Version des Empfehlungsdokuments, die Feedback und Kommentare aus der Konsultation enthalt, soll ein Anstos fur die weitere Diskussion und Umsetzung von Science in Osterreich sein und als Beitrag und Grundlage einer potentiellen nationalen Science in Osterreich dienen. Das Dokument baut auf der vielfaltigen Expertise der Autor*innen auf (Wissenschaft, Administration, Bibliothek und Archiv, Informationstechnologie, Wissenschaftspolitik, Forderwesen etc.) und spiegelt deren personliche Erfahrungen und Meinung wider.A look at international activities on Science reveals a broad spectrum from individual institutional policies to national action plans. The present Recommendations for a National Science in Austria are based on these international initiatives and present practical considerations for their coordinated implementation with regard to strategic developments in research, technology and innovation (RTI) in Austria until 2030. They are addressed to all relevant actors in the RTI system, in particular to Research Performing Organisations, Research Funding Organisations, Research Policy, memory institutions such as Libraries and Researchers. The recommendation paper was developed from 2018 to 2020 by the OANA working group Open Science Strategy and published for the first time in spring 2020 for a public consultation. The now available final version of the recommendation document, which contains feedback and comments from the consultation, is intended to provide an impetus for further discussion and implementation of Science in Austria and serves as a contribution and basis for a potential national Science in Austria. The document builds on the diverse expertise of the authors (academia, administration, library and archive, information technology, science policy, funding system, etc.) and reflects their personal experiences and opinions.
USE OF SCIENTIFIC PROFILES TO PRESENT THE RESULTS OF SCIENTIFIC RESEARCH OF UNIVERSITIES

The article substantiates that the main problem of modern universities is the search for effective methods and tools to present the results of their activities in the Internet space in order to attract more applicants and, in turn, increase the position in the rankings. The study found that an equally important component of the vast majority of rankings are research results, which are calculated as the number of scientific articles published in journals indexed in Scopus / Web of Science, as well as the h-index. A study of the positions of Ukrainian universities in international rankings showed that, despite significant advances in science, they can not get into such rankings. The study found that one of the reasons for this situation is the low level of transparency of both the research conducted at universities and the results of such research. To solve this problem, it is proposed to use scientific profiles of university staff, which contribute to the dissemination of information about scientific achievements, as they contain general information about authors that can be used for grants, participation in conferences, peer review, international rankings and others. The aim of the article is to manage the information reflected in the scientific profiles of researchers to increase the position of universities in international rankings using the results of its comparison with the information used to compile these rankings. The research methodology includes: methods of analysis and synthesis, induction and deduction, method of comparing results. It is established that the main scientific profiles that universities can use to publish research results are: Author ID in Scopus, ResearcherID, ORCID, Google Scholar. Thus, the study found that the international rankings of universities, in addition to the information posted on their websites, also use the information contained in the scientific profiles of employees of these institutions. The lack of completed profiles of scientists leads to a decrease in the position of universities in international and domestic rankings.
An integrated classification model for incremental learning

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts.
Agile clinical research: A data science approach to scrumban in clinical medicine

The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.
Variational certification of quantum devices

Abstract One of the requirements imposed on the realistic quantum computers is to provide computation results which can be repeated and reproduced. In the situation when one needs to repeat the quantum computation procedure several times, it is crucial that the copies of the quantum devices are similar in the sense of the produced results. In this work, we describe a simple procedure based on variational quantum eigensolver which can be utilized to compare quantum devices. The procedure is developed by combining Choi–Jamiołkowski isomorphism with the variational hybrid quantum–classical procedure for matrix diagonalization. We compare the introduced procedure with the scheme based on the standard bounds for the similarity between quantum operations by analysing its action on random quantum channels. We also discuss the sensitivity of the described procedure to the noise, and we provide numerical results demonstrating its feasibility in realistic scenarios by running the procedure on IBM quantum computer.
Application of Blockchain Technology to the Credit Management of Supply Chain

Blockchain technology has been extensively applied to Supply Chain Management (SCM). Based on technological innovations such as distributed ledgers, symmetric encryption, authorization, consensus mechanisms, and smart contracts, blockchain technology overcomes the traditional limitations of information gap, traceability, transaction transparency, and credit problems in SCM. In this paper, a platform for SCM is established and analyzed from the perspective of blockchain technology. This paper provides important reference for the construction of supply chain logistics information systems and the design of supply chain application systems for credit management.
Reliability analysis of the internet of things using Space Fault Network

The Internet of Things (IoT) is a network topology structure based on the interconnection of many nodes. It realizes the basic functions of IoT through the transmission of information, data, and energy between the nodes. To study the reliability of Internet of Things Network Topology (IoTNT) structure, we must abstract IoT as network topology and study the reliability of the network itself from the topology structure. This paper attempts to apply the Space Fault Network (SFN) to the study the reliability of IoTNT. To achieve this goal, the nodes and edges of IoTNT are equivalent to events and connections of SFN respectively. A structure analysis method based on SFN is proposed and used to study the reliability of IoTNT. At the same time, the influence of possible logical relationship between nodes on the reliability of IoTNT is studied. According to the SFN structure representation methods (SFNSRMs), considering different network structures and induced modes, the analysis methods and calculation methods of the evolution process of target event are given. An example is given to illustrate the analysis and calculation process. The research provides the new methods for the reliability study of IoT and the development of SFN.
Stochastic optimal control formalism for an open quantum system

A stochastic procedure is developed which allows one to express Pontryagin's maximum principle for dissipative quantum system solely in terms of stochastic wave functions. Time-optimal controls can be efficiently computed without computing the density matrix. Specifically, the proper dynamical update rules are presented for the stochastic costate variables introduced by Pontryagin's maximum principle and restrictions on the form of the terminal cost function are discussed. The proposed procedure is confirmed by comparing the results to those obtained from optimal control on Lindbladian dynamics. Numerically, the proposed formalism becomes time and memory efficient for large systems, and it can be generalized to describe non-Markovian dynamics.