Reviewerly/exHarmony huggingface dataset:
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g. change of image appearances or contrasts caused by different scanners, unexpected imaging artifacts etc. In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples. Both contributions improve model generalization and robustness with limited data. The cooperative training framework consists of a fast-thinking network (FTN) and a slow-thinking network (STN). The FTN learns decoupled image features and shape features for image reconstruction and segmentation tasks. The STN learns shape priors for segmentation correction and refinement. The two networks are trained in a cooperative manner. The latent space augmentation generates challenging examples for training by masking the decoupled latent space in both channel-wise and spatial-wise manners. We performed extensive experiments on public cardiac imaging datasets. Using only 10 subjects from a single site for training, we demonstrated improved cross-site segmentation performance, and increased robustness against various unforeseen imaging artifacts compared to strong baseline methods. Particularly, cooperative training with latent space data augmentation yields 15% improvement in terms of average Dice score when compared to a standard training method.
Local passivity and entanglement in shared quantum batteries

A battery is an energy storage device having utilities in a broad spectrum of fields. We consider batteries where quantum effects are inevitable, and compare the amount of energy extractable using local and global operations. We introduce the concept of local passivity for shared quantum batteries with local Hamiltonians, and characterize the set of all locally passive battery states. We find that the relation between energy extractions by local and global operations is closely connected with the entanglement content, for generic battery states.
People’s Attitudes Towards the Use of Robots in the Social Services: A Multilevel Analysis Using Eurobarometer Data

Abstract Robots have been employed in the industrial sectors for over half a century; however, their appearance in the domestic sphere is a modern phenomenon, occurring in just the last decade. These so-called social robots are carrying out a variety of tasks traditionally carried out by humans, and in contexts in which they must interact with human beings. These kinds of social robots are now being used in the welfare services, providing assistive services and companionship for the infirm or elderly, and even children. Thus, the use of social robots in everyday life has triggered an animated debate about the acceptance of these devices by their end users. In this paper, multilevel analysis is applied using data from the Eurobarometer survey (sample size 27,901, covering 28 countries) to investigate how socio-demographic characteristics and country-level indicators of technological and economic development (the rate of high-technology manufactured exports, the cellular phone subscriptions rate and GDP) influence how robots are accepted in the realm of the social services. The results show that only individual (socio-demographic) and technology acceptance model (TAM) factors influence attitudes towards social robots. The effects of the contextual variables considered were not statistically strong enough to explain the attitudes towards social robots for social services.
ModelSet: a dataset for machine learning in model-driven engineering

Abstract The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet , a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io .
ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU. ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks. The source code, pre-trained models with deploy versions and tutorials of applying to other trackers are released at https://github.com/ifzhang/ByteTrack.
IMPACT OF ARTIFICIAL INTELLIGENCE AND SERVICE ROBOTS IN TOURISM AND HOSPITALITY SECTOR: CURRENT USE &amp; FUTURE TRENDS

Every aspect of the tourist experience has been transformed and automated due to the recent development and application of advanced technology like Artificial Intelligence and robotic services. Tourism and hospitality have seen tremendous changes due to the transformation brought about by the growth of information and communication technologies (ICT). This article will review current research and literature on Artificial Intelligence (AI) and robotics use in hotels and hospitality. The study will majorly centre on three filed of AI and automation applications. First, it explores the present position and current application of artificial intelligence and automation like robotics research in daily modern contemporary society, showcasing their use in each sector of the tourism and hospitality sectors; secondly, it analysed the Artificial intelligence and robotics impact on employment and job opportunities, consumers, and as well as businesses and society at large. Lastly, it examines the possible future trends of AI and robots in the tourism and hospitality sector to see what advantages or dangers may be derived from their use.
Practical distributed quantum information processing with LOCCNet

Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC’s intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.
Students’ Acceptance of Discord as an Alternative Online Learning Media

Since the public release of Discord in May 2015, gamers have widely used it as an additional team speak application to communicate with their teammates while playing games. However, with substantial features, this application is also used as an alternative media of communication in teaching and learning activities. Therefore, this study aims to determine students’ acceptance of Discord as alternative teaching and learning media. Data were obtained using a simple questionnaire. Furthermore, their experience and acceptance towards this application are explored using the technology acceptance model (TAM) framework. The result showed that most participants confirmed that Discord is a favorable alternative media due to the attractive user interface, completeness of features, and its ease of use. Therefore, this application, which was originally intended for the gaming team-speak and team-chat experiences, can surprisingly be used as an alternative online learning media, especially during the Covid-19 pandemic, and it is well accepted by most students.
What innovations impact agricultural productivity in Sub-Saharan Africa?

This study explores innovation characteristics that impact agricultural productivity in Sub-Saharan Africa. The two-ways fixed effects approach is applied to a sample of 22 Sub-Saharan African countries from the 1996 to 2014 period. The results show that, among yield-increasing innovations, fertilizer's constituents have a mitigated impact on agricultural productivity. Whereas, a one-point increase in the application of pesticides and irrigation practice will increase agricultural productivity by 0.08 and 0.0005, respectively. The profit-enhancing innovation includes crop diversification, and one point increase of this factor will augment agricultural productivity by 0.017 and 0.03, with respect to country specific effects. As regard cost-reducing innovations (labor-saving), only tractors and harvesting machines are found to have a significant and positive effect on agricultural productivity. So then, implications for public policies are discussed accordingly.
Concepts for Modeling Smart Cities

The rapid increase and adoption of new Information Technologies (IT) in Smart Cities make the provision of public services more efficient. However, various municipalities and cities deal with challenges to transform and digitize city services. Smart Cities have a high degree of complexity where offered city services must respond to the concerns and goals of multiple stakeholders. These city services must also involve diverse data sources, multi-domain applications, and heterogeneous systems and technologies. Enterprise Architecture (EA) is an instrument to deal with complexity in both private and public organizations. The paper defines the concepts for modeling Smart Cities in ArchiMate, guided by a design-oriented research approach. Particularly, the focus of this paper is on the concepts for modeling city services and underlying information systems which are added to the EA metamodel. The metamodel is demonstrated in a real-world case and validated by Smart City domain experts. The findings suggest that these concepts are essential to achieve the Smart City strategy (e.g., city goals and objectives), as well as to meet the needs of different city stakeholders. Furthermore, an extension mechanism allows addressing the alignment of business and IT in complex environments such as Smart Cities, by adjusting EA metamodels and notations. This can help cities to design, visualize, and communicate architecture decisions when managing the transformation and digitalization of public services.
Optimal Control for Quantum Optimization of Closed and Open Systems

Optimization is one of the key applications of quantum computing where a quantum speedup has been an eagerly anticipated outcome. A promising approach to optimization using quantum dynamics is to consider a linear combination $s(t)B+[1\ensuremath{-}s(t)]C$ of two noncommuting Hamiltonians $B$ and $C$, where $C$ encodes the solution to the optimization problem in its ground state, $B$ is a Hamiltonian whose ground state is easy to prepare, and $s(t)\ensuremath{\in}[0,1]$ is the bounded ``switching schedule'' or ``path,'' with $t\ensuremath{\in}[0,{t}_{f}]$. This approach encompasses two of the most widely studied quantum-optimization algorithms: quantum annealing [QA; continuous $s(t)$] and the quantum approximate optimization algorithm [QAOA; piecewise constant $s(t)$]. While it is notoriously difficult to prove a quantum advantage for either algorithm, it is possible to compare and contrast them by finding the optimal $s(t)$. Here we provide a rigorous analysis of this quantum optimal control problem, entirely within the geometric framework of Pontryagin's maximum principle of optimal control theory. We extend earlier results, derived in a purely closed-system setting, to open systems. This is the natural setting for experimental realizations of QA and QAOA. In the closed-system setting it was shown that the optimal solution is a ``bang-anneal-bang'' schedule, with the bangs characterized by $s(t)=0$ and $s(t)=1$ in finite subintervals of $[0,{t}_{f}]$, in particular, $s(0)=0$ and $s({t}_{f})=1$, in contrast to the standard prescription $s(0)=1$ and $s({t}_{f})=0$ of QA. As an example, we prove that for a single spin-$1/2$, the optimal solution in the closed-system setting is the bang-bang schedule, switching midway from $s\ensuremath{\equiv}0$ to $s\ensuremath{\equiv}1$. For finite-dimensional environments and without any approximations we identify sufficient conditions ensuring that either the bang-anneal, anneal-bang, or bang-anneal-bang schedules are optimal, and recover the optimality of $s(0)=0$ and $s({t}_{f})=1$. However, for infinite-dimensional environments and a system described by an adiabatic Redfield master equation we do not recover the bang-type optimal solution. In fact we can only identify conditions under which $s({t}_{f})=1$, and even this result is not recovered in the fully Markovian limit, suggesting that the pure anneal-type schedule is optimal. Our open-system results have implications for the use of experimental quantum-information processors, which are by necessity noisy, and suggest that in this practical sense the optimal schedules for quantum optimization are likely to be continuous.
An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2

We present an analysis of the eye tracking signal quality of the HoloLens 2's integrated eye tracker. Signal quality was measured from eye movement data captured during a random saccades task from a new eye movement dataset collected on 30 healthy adults. We characterize the eye tracking signal quality of the device in terms of spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. Most notably, our evaluation of spatial accuracy reveals that the eye movement data in our dataset appears to be uncalibrated. Recalibrating the data using a subset of our dataset task produces notably better eye tracking signal quality.
The use of photo comics media: Changing reading interest and learning outcomes in elementary social studies subjects

Reading is useful for gaining understanding, knowledge, and information. This study aimed to determine the effect of using photo comics media on reading interest and learning outcomes in elementary social studies subjects. This research is a quasi-experimental study with a Non-equivalent Pretest-Posttest Control Group Design. The subject of this study is elementary students at the fifth grade level. Data obtained through the implementation of direct learning to school by assessment using questionnaires and tests. Analysis of the data using the t-test. The results showed that the use of photo comics media has a significant positive effect on reading interest and student learning outcomes. This was evidenced after a positive change in asking students to read after being treated with photo comics media. In addition, student learning outcomes are satisfactory after integrating photo comic media in learning activities. &#x0D; &#x0D; Keywords: learning outcomes, photo comics, reading interest, social studies;&#x0D;
Application of Weighted Cross-Entropy Loss Function in Intrusion Detection

The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem.Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network; Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection.In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters.The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model.Experiments prove that the weighted cross-entropy loss function can enhance the model's ability to discriminate samples.
Cloud detection using convolutional neural networks on remote sensing images

Cloud detection is an important task for remote sensing and solar resource modeling, and an initial step toward more complex tasks like solar forecasting. Recent advances in remote sensing have increased the spectral, spatial, and temporal resolution of observations. This study demonstrates the image analysis capacity of convolutional neural networks (CNNs) to identify the presence of clouds from remote sensing directly, without ancillary data and at relatively high temporal resolution. Cloud detection in images from the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI) is validated against ground telemetry from a set of 12 locations of diverse geographic and climatic conditions across the continental U.S. (CONUS). As spatial coverage through ground station networks is typically sparse, transfer learning of baseline models is studied in order to assess the robustness and portability of baseline models through transfer learning scenarios. Performance of the CNN-based cloud mask (CCM) is discussed in three parts: (1) baseline training in comparison to a deterministic model, the ABI cloud mask (ACM), (2) transfer learning of baseline models, and (3) transfer learning of models trained on combined location pairs. In baseline models, the CCM showed average percent improvement over the ACM of 11% in accuracy (ACC) and 30% in Matthews correlation coefficient (MCC) score. In transfer learning scenarios, adding a second location to training samples improved aggregate performance by 18.8%. Results show that CCM transfer learning is highly asymmetric. A framework is proposed to characterize and predict transfer learning performance with respect to this asymmetry.
The human cognitive system corrects traces of error commission on the fly.

Human perception and action rely on a fundamental binding mechanism that forges integrated event representations from distributed features. Encountering any one of these features later on can retrieve the whole event, thus expediting cognitive processing. The traditional view on binding confines it to successful action episodes, holding that the human cognitive system does not leverage errors for optimizing corresponding event representations. Here we use sequential analyses of erroneous action episodes to explore whether binding promotes future successful behavior even when actions go awry. Results indicate that the processes leading to binding integrate different aspects of the action episode in a highly efficient and flexible manner to privilege future correct actions and prepare the ground for error-based learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Quantum homotopy perturbation method for nonlinear dissipative ordinary differential equations

Abstract While quantum computing provides an exponential advantage in solving linear differential equations, there are relatively few quantum algorithms for solving nonlinear differential equations. In our work, based on the homotopy perturbation method, we propose a quantum algorithm for solving n -dimensional nonlinear dissipative ordinary differential equations (ODEs). Our algorithm first converts the original nonlinear ODEs into the other nonlinear ODEs which can be embedded into finite-dimensional linear ODEs. Then we solve the embedded linear ODEs with quantum linear ODEs algorithm and obtain a state ϵ -close to the normalized exact solution of the original nonlinear ODEs with success probability Ω(1). The complexity of our algorithm is O ( gηT poly(log( nT / ϵ ))), where η , g measure the decay of the solution. Our algorithm provides exponential improvement over the best classical algorithms or previous quantum algorithms in n or ϵ .
Multipartite quantum correlations in a two-mode Dicke model

We analyze multipartite correlations in a generalized Dicke model involving two optical modes interacting with an ensemble of two-level atoms. In particular, we examine correlations beyond the standard bipartite entanglement and derive exact results in the thermodynamic limit. The model presents two superradiant phases involving the spontaneous breaking of either a ${\mathbb{Z}}_{2}$ or a U(1) symmetry. The latter is characterized by the emergence of a Goldstone excitation, found to significantly affect the correlation profiles. Focusing on the correlations between macroscopic subsystems, we analyze both the mutual information and the entanglement of formation for all possible bipartitions among the optical and matter degrees of freedom. It is found that while each mode entangles with the atoms, the bipartite entanglement between the modes is zero, and they share only classical correlations and quantum discord. We also study the monogamy of multipartite entanglement and show that there exists genuine tripartite entanglement, i.e., quantum correlations that the atoms share with the two modes but that are not shared with them individually, only in the vicinity of the critical lines. Our results elucidate the intricate correlation structures underlying superradiant phase transitions in multimode systems.