Reviewerly/exHarmony huggingface dataset:
Uncertainty of Artificial Intelligence Assistant: The Effect of Assistant Type on Variety Seeking

In service marketing, AI assistants and self-service technology have become popular. As a result, it is critical to enrich the understanding of whether consumers react differently in the artificial intelligence (AI) service context in comparison with the human service context. This study examines the effect of assistant type (AI vs. human) on consumers' decision-making. Through three experiments, this research finds that variety seeking will be higher when consumers are making decision in AI (vs. human) service environment. Furthermore, we tested uncertainty as the underlying mechanism. Moreover, we demonstrated that this pattern is moderated by situational involvement. Specifically, in consumption contexts of high involvement, the consumers are less likely to seek variety, and in consumption contexts of low involvement, they prefer more variety (study 3). This research offers service providers new insights by revealing how, why, and when the interaction of AI technology influences consumers' decision-making in service marketing.
Impact of the COVID-19 Confinement on the Physics and Chemistry Didactic in High Schools

Online education due to COVID-19 confinement impacted the use of the Information and Communication Technology (ICT) in Spain, where it was poorly implemented. The aim of this paper was to inspect the methodological changes in Physics and Chemistry teaching during the confinement as well as in the ICT use and the lockdown impact afterwards. For this purpose, an online survey was administered by email to the Physics and Chemistry teachers of three provinces of Spain. Based on the analysis, the most widely used methodology was the traditional one. Still, during the lockdown, its use decreased, and others, such as the flipped classroom, increased significantly. Other adaptations included increasing the use of virtual simulations and self-learning by the student. It can be outlined the incorporation of new tools such as WebQuests, the smartphone, or online education platforms, whose use has continued. The ICT was used for new functionalities such as evaluation or answering student questions. According to the respondents, the lockdown had entailed that they strengthen implementation of ICT. In conclusion, there have been changes that have remained in the Physics and Chemistry didactic and in the ICT use due to the lockdown situation.
Pre-trained transformer-based citation context-aware citation network embeddings

Academic papers form citation networks wherein each paper is a node and citation relationships between papers are edges. The embeddings of each paper obtained by projecting the citation network into a vector space are called citation network embeddings. Thus far, only a limited number of studies have focused on incorporating information regarding the intent of one paper to cite another paper. We consider citation context, i.e., the text to cite a paper, as a source of information for citation intent, and propose a new method for generating citation context-aware citation network embeddings. We trained SciBERT with our proposed masked paper prediction task in which the model predicts the cited paper from the citing paper and the citation context. In addition, we propose a new loss function that considers not only the citation context but also the neighboring nodes in the citation network. We conducted experiments involving citation-recommendation and paper-classification tasks which we formulated on two existing datasets: FullTextPeer-Read and AASC. For both tasks, the proposed method outperformed hyperdoc2vec, an existing method for citation context-aware citation network embedding; further, it achieved a comparable performance to a state-of-the-art citation network embedding that do not utilize any citation context for paper classification.
Local Generation of Entanglement with Redfield Dynamics

In phenomenological applications, time evolutions of Bloch-Redfield type are widely adopted for modelling open system dynamics, despite their nonpositive preserving character: this physical inconsistency, that in general shows up at small times, is usually cured by suitably restricting the space of allowed initial states. Nevertheless, additional problems may arise in relation to entanglement: specifically, we show that Redfield dynamics, though being a semigroup, can generate entanglement through a purely local action; moreover, this unphysical effect can persist on long time-scales.
Research on the correlation between solar radiation and sky luminance based on the principle of photothermal integration

It is difficult to obtain both sky luminance and solar radiation data, which greatly restrict the development and utilization of solar resources. In this paper, the functional relationship between sky luminance and solar radiation is established. Firstly, linear and polynomial functions were established for 145 regions of the whole sky. The results show that there is a nearly linear correlation between sky luminance and solar radiation. In 145 regions, 68.3% of the regions have close accuracy between linear and polynomial functions. Secondly, the linear and polynomial equations functions are established for the four typical orientations. The accuracy of the polynomial function in the eastern and northern regions is higher than that of the linear function, but in the southern and western regions they are equivalent. Finally, a unified equation is established for the whole sky, and the result shows the accuracy of the polynomial function is more accurate. This research can realize the mutual conversion between sky luminance and solar radiation. It can not only make up for the missing sky luminance data and solar radiation data, but also provide a theoretical basis for solar thermal, photovoltaic, building energy conservation and agricultural production.
Key graph properties affecting transport efficiency of flip-flop Grover percolated quantum walks

Quantum walks exhibit properties without classical analogues. One of those is the phenomenon of asymptotic trapping -- there can be non-zero probability of the quantum walker being localised in a finite part of the underlying graph indefinitely even though locally all directions of movement are assigned non-zero amplitudes at each step. We study quantum walks with the flip-flop shift operator and the Grover coin, where this effect has been identified previously. For the version of the walk further modified by a random dynamical disruption of the graph (percolated quantum walks) we provide a recipe for the construction of a complete basis of the subspace of trapped states allowing to determine the asymptotic probability of trapping for arbitrary finite connected simple graphs, thus significantly generalizing the previously known result restricted to planar 3-regular graphs. We show how the position of the source and sink together with the graph geometry and its modifications affect the excitation transport. This gives us a deep insight into processes where elongation or addition of dead-end subgraphs may surprisingly result in enhanced transport and we design graphs exhibiting this pronounced behavior. In some cases this even provides closed-form formulas for the asymptotic transport probability in dependence on some structure parameters of the graphs.
Exploring the Limitations of Current Graph Neural Networks for Network Modeling

Graph neural networks (GNN) have recently been proposed as a technique for accurate and cost-efficient network modeling. As an example, the GNN-based model RouteNet has shown potential for network performance evaluation, being the first-of-its-kind Machine-Learning-based model with generalization capabilities to other networks and configurations unseen during training.In this paper we assess the generalization limits of RouteNet, by analyzing how different network parameters affect the accuracy of this model. To this end, we systematically evaluate the accuracy of RouteNet under modifications of properties of the network and the traffic, such as the topology size, link capacities, the packet size distribution, and the network congestion level. We determine that, while this GNN model is robust to changes in the structure of its input graph, the quality of the estimates degrades considerably, when the distributions of the predicted values of the evaluation data differ from the training (e.g., end-to-end delays). As a result, we argue that to achieve practical GNN-based solutions for network modeling, new methods are needed that can, for example, cope with traffic loads and network sizes that are significantly different than those seen during training.
Heavyweight Statistical Alignment to Guide Neural Translation

Transformer neural models with multihead attentions outperform all existing translation models. Nevertheless, some features of traditional statistical models, such as prior alignment between source and target words, prove useful in training the state-of-the-art Transformer models. It has been reported that lightweight prior alignment can effectively guide a head in the multihead cross-attention sublayer responsible for the alignment of Transformer models. In this work, we make a step further by applying heavyweight prior alignments to guide all heads. Specifically, we use the weight of 0.5 for the alignment cost added to the token cost in formulating the overall cost of training a Transformer model, where the alignment cost is defined as the deviation of the attention probability from the prior alignments. Moreover, we increase the role of prior alignment, computing the attention probability by averaging all heads of the multihead attention sublayer within the penultimate layer of Transformer model. Experimental results on an English-Vietnamese translation task show that our proposed approach helps train superior Transformer-based translation models. Our Transformer model (25.71) outperforms the baseline model (21.34) by the large 4.37 BLEU. Case studies by native speakers on some translation results validate the machine judgment. The results so far encourage the use of heavyweight prior alignments to improve Transformer-based translation models. This work contributes to the literature on the machine translation, especially, for unpopular language pairs. Since the proposal in this work is language-independent, it can be applied to different language pairs, including Slavic languages.
RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components’ impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung–Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.
Exploring Engineering Students’ Perception on using Augmented Reality to Improve Learning Performance in the Context of COVID-19 Pandemic

The study evaluates the energy engineering students’ perception and insights concerning the use and usefulness of AR technology in online engineering education during COVID-19 pandemic. The assessment is based on the following criteria: usefulness, suitability, advantages for different activities, types of overlays. Results show an increased interest for AR applications in all types of teaching activities, and its usefulness for a better understanding of studied topics. Among the most important advantages of using AR are considered closer connection to practice, speeding and deepening learning. Overall, these results indicate an improved user perception of AR from acceptance to high interest.
Estimating parameterized entanglement measure

The parameterized entanglement monotone, the q-concurrence, is also a reasonable parameterized entanglement measure. By exploring the properties of the q-concurrence with respect to the positive partial transposition and realignment of density matrices, we present tight lower bounds of the q-concurrence for arbitrary $$q\ge 2$$ . Detailed examples are given to show that the bounds are better than the previous ones.
Capacities of the covariant Pauli channel

We study four well-known capacities of a two-parameter family of qubit Pauli channels. These are the channels that are covariant under the $\text{SO}(2)$ group and contain the depolarizing channel as a special case. We find exact expressions for the classical capacity and entanglement-assisted capacities, and analytically determine the regions where the quantum capacity of the channel vanishes. We then use a flag extension to find upper bound for the quantum capacity and private capacity of these channels in the entire region of parameter space and also obtain the lower bound for the quantum capacity by calculating the single-shot quantum capacity numerically. In conjunction with previous results on depolarizing channels, our result is one step forward for determining the capacities of the full Pauli channel.
Improved Kalman-Particle Kernel Filter on Lie Groups Applied to Angles-Only UAV Navigation

Kalman-Particle Kernel Filter (KPKF) is a sub-class of Particle Filter (PF) that uses Gaussian kernels as particles, which enables a local Kalman update for each measurement in addition to the usual weight update. Besides, recent research about filtering on Lie groups brought powerful theoretical results, and showed the superiority of this approach. Hence, this paper extends the Euclidean KPKF to a new formulation on Lie groups and introduces substantial improvements based on Lie groups Kalman filters theory and Laplace Particle Filters on Lie groups (LG-LPF) for improved resampling. The proposed algorithm is tested on an angles-only UAV navigation scenario with challenging initial errors. It shows superior robustness and accuracy compared to Lie group Extended Kalman Filter (LG-EKF), with near-to optimal performance, even with a limited amount of particles.
Beyond the body: Moving past the metricised bodily goal in self-tracking

Currently self-tracking systems, that sense and infer data about an individual or 'the self', focus on gathering quantitative data about the body.The social features present in these systems measure data about the body against other bodies or 'the other'.However, focus on these metrics is causing harm.In this paper we discuss relations between the self and the other and more-than-human perspectives to pose questions for moving beyond the body and acknowledging potential harm in self-tracking systems.Throughout we draw on work from across Design Research, Human-Computer Interaction (HCI), Philosophy and Sociology, to highlight challenges and opportunities for Designers in the self-tracking space and discuss how the future of these systems needs to change.
Build Your Own Arcade Machine with Tinycade

Tinycade is a platform designed to help game designers build their own mini arcade games by hand. With this platform, one can craft functioning game controllers out of everyday materials such as cardboard and toothpicks. By utilizing computer vision markers, we can create a variety of inputs without a single wire. In this pictorial, we discuss the functionality of Tinycade and showcase three games that demonstrate the variety of controls possible with this platform.
Joint UAVs Position Optimization and Offloading Decision for Blockchain-enabled Intelligent Transportation

With the assistance of the fifth generation (5G) and the internet of things (IoT), intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient. To well assist the ITSs, advanced network architecture and reasonable offloading decision should be specially designed while ensuring data security. In this paper, we consider a blockchain-enabled intelligent transportation uplink scenario, which can collect and encrypt the aggregated data from smart vehicles (SVs) with blockchain. Considering the coupling of unmanned aerial vehicles (UAVs) position and offloading decision, we formulate a joint UAVs position optimization and data offloading decision problem in order to reduce the total time and energy consumption of data processing. We first divide UAVs into two categories and form neighborhood UAVs according to their data load. Then, we adapt the non-dominated sorting genetic algorithm II (NSGA-II) iteratively. Simulation results indicate that the algorithm can reduce energy consumption while ensuring the required time and achieving a good balance.
Experimental Evaluation of Robustness of Panel-Method-Based Path Planning for Urban Air Mobility

In this study, robustness of panel method based path planning algorithm under wind disturbances is evaluated experimentally.Panel method, borrowed from fluid dynamics domain, is a numeric tool for calculating the potential field around arbitrarily shaped objects.Resultant potential field can be used for generating collision free trajectories for uncrewed aerial vehicles with convergence guarantee.Robustness of the proposed method is demonstrated during indoor experiments with a wind generator creating wind speed up to 7 m/s and also during outdoor experiments with wind speeds ranging between 3 -5 m/s.Experiment results suggest that panel method based path planning scheme maintains its obstacle avoidance property under wind disturbances.
Immersive Music Therapy for Elderly Patients

The preliminary results of immersive virtual reality music therapy experiences are presented, carried out with people between ages of 72 and 96, residents of a nursing home, who suffer from mild mental health problems, potential dementia, moderate cognitive disorders, depressive symptoms, or severe dependency. The experiences are carried out in 15-minute sessions, streaming 180° and 360° music videos of genres that the elderly like, recorded for this purpose and performed by musical groups that collaborate voluntarily. The design of the developed application (the web interface, the application logic and the HMD player) is simple so that it is easy to use during music therapy sessions. Preliminary results show that the elderly experience similar levels of sense of presence as reported by other studies in younger population (< 55), and only 19% of them suffer from simulator sickness. This validates the usage of immersive media for music therapy in elderly patients.
Differential Equation Based Path Integral for Open Quantum Systems

We propose the differential equation based path integral method to simulate the real-time evolution of open quantum systems. In this method, a system of partial differential equations is derived based on the continuation of a classical numerical method called the iterative quasi-adiabatic propagator path integral (i-QuAPI). While the resulting system has infinite equations, we introduce a reasonable closure to obtain a series of finite systems. New numerical schemes can be derived by discretizing these differential equations. It is numerically verified that in certain cases, by selecting appropriate systems and applying suitable numerical schemes, the memory cost required in the i-QuAPI method can be significantly reduced.
A Double-Graph Based Framework for Frame Semantic Parsing

Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification.Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge defined in FrameNet.In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID).We first introduce Frame Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs (Frame Elements) built on the frame knowledge so that we can derive knowledgeenhanced representations for frames and FEs.Besides, we propose Frame Semantic Graph (FSG) to represent frame semantic structures extracted from the text with graph structures.In this way, we can transform frame semantic parsing into an incremental graph construction problem to strengthen interactions between subtasks and relations between arguments.Our experiments show that KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets.
GSCINet: Gradual Shrinkage and Cyclic Interaction Network for Salient Object Detection

Feature Pyramid Network (FPN) has been widely applied in the task of salient object detection (SOD), which has achieved great performance. However, most existing FPN-based SOD methods still have some limitations, such as insufficient guidance due to gradual dilution of semantic information, excessive computation leading to slow inference speed, and low efficiency of training models. In this paper, we design a novel Gradual Shrinkage and Cyclic Interaction Network (GSCINet) for efficient and accurate SOD, consisting of a Multi-Scale Contextual Attention Module (MSCAM) and an Adjacent Feature Shrinkage and Interaction Module (AFSIM). Specifically, the MSCAM aims at efficiently capturing multi-scale and multi-receptive-field contextual attention information through a series of well-designed convolutions and attention weight matrices of different scales to enhance the performance of initial input features. Subsequently, in AFSIM, we propose a gradual shrinkage structure and introduce a circular interaction mechanism to optimize the compressed features with less calculation cost, thereby enabling fast and accurate inference of salient objects. Extensive experimental results demonstrate the high efficiency and superiority of GSCINet against 17 state-of-the-art (SOTA) saliency detection methods under multiple evaluation metrics.
A Nearest Neighbor Under-sampling Strategy for Vertical Federated Learning in Financial Domain

Machine learning techniques have been widely applied in modern financial activities. Participants in the field are aware of the importance of data privacy. Vertical federated learning (VFL) was proposed as a solution to multi-party secure computation for machine learning to obtain the huge data required by the models as well as keep the privacy of the data holders. However, previous research majorly analyzed the algorithms under ideal conditions. Data imbalance in VFL is still an open problem. In this paper, we propose a privacy-preserving sampling strategy for imbalanced VFL based on federated graph embedding of the samples, without leaking any distribution information. The participants of the federation provide partial neighbor information for each sample during the intersection stage and the controversial negative sample will be filtered out. Experiments were conducted on commonly used financial datasets and one real-world dataset. Our proposed approach obtained the leading F1 score on all tested datasets on comparing with the baseline under sampling strategies for VFL.