Reviewerly/exHarmony huggingface dataset:
A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge

Abstract In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval framework for household environments which was constructed by extracting knowledge from the VirtualHome dataset ( http://virtual-home.org ). The framework provides knowledge about sequences of actions on how to perform human scaled tasks in a household environment, answers queries about household objects, and performs semantic matching between entities from the web knowledge graphs DBpedia, ConceptNet, and WordNet, with the ones existing in our knowledge graph. We offer a set of predefined SPARQL templates that directly address the ontology on which our knowledge retrieval framework is built, and querying capabilities through SPARQL. We evaluated our framework via two different user evaluations.
Mechanism to capture learner’s interaction in VR-based learning environment: design and application

Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners.
Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling

Abstract The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.
Virtual Reality Genres

Even though virtual reality (VR) shares features with video games, it offers a wider range of experiences. There is currently no cohesive classification for commercial VR offerings. As a first step to account for this deficiency, the work in progress considers the relationship between game genres and users? ratings and downloads of VR experiences. We found Action, Shooter, and Simulation to be the most frequently downloaded genres; Action and Music/Rhythm the most highly rated; and Simulation and Music/Rhythm to occur at a statistically higher rate in VR compared to non-VR. Finally, we learned that VR experiences are less likely to receive positive ratings than 2D games. The findings can inform developers? marketing decisions based on demand.
Leakage Assessment Through Neural Estimation of the Mutual Information

A large variety of side-channel attacks have been developed to extract secrets from electronic devices through their physical leakages. Whatever the utilized strategy, the amount of information one could gain from a side-channel trace is always bounded by the Mutual Information (MI) between the secret and the trace. This makes it, all punning aside, a key quantity for leakage evaluation. Unfortunately, traces are usually of too high dimension for existing statistical estimators to stay sound when computing the MI over full traces. However, recent works from the machine learning community have shown that it is possible to evaluate the MI in high dimensional space thanks to newest deep learning techniques. This paper explores how this new estimator could impact the side channel domain. It presents an analysis which aim is to derive the best way of using this estimator in practice. Then, it shows how such a tool can be used to assess the leakage of any device.
Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss

Speaker identification is gaining popularity, with notable applications in security, automation, and authentication. For speaker identification, deep-convolutional-network-based approaches, such as SincNet, are used as an alternative to i-vectors. Convolution performed by parameterized sinc functions in SincNet demonstrated superior results in this area. This system optimizes softmax loss, which is integrated in the classification layer that is responsible for making predictions. Since the nature of this loss is only to increase interclass distance, it is not always an optimal design choice for biometric-authentication tasks such as face and speaker recognition. To overcome the aforementioned issues, this study proposes a family of models that improve upon the state-of-the-art SincNet model. Proposed models AF-SincNet, Ensemble-SincNet, and ALL-SincNet serve as a potential successor to the successful SincNet model. The proposed models are compared on a number of speaker-recognition datasets, such as TIMIT and LibriSpeech, with their own unique challenges. Performance improvements are demonstrated compared to competitive baselines. In interdataset evaluation, the best reported model not only consistently outperformed the baselines and current prior models, but also generalized well on unseen and diverse tasks such as Bengali speaker recognition.
Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification

Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.
Bed-Fall Detection and Prediction: A Generic Classification and Review of Bed-Fall Related Systems

Along with the rapid growth of the elderly population, the hospital quality and the safety of patients have become one of the priority concerns in the past few decades. The majority of injuries in the elderly result from bed -falls since the bed is a major part in his daily life. A single fall can cause severe physical and emotional injuries. Therefore, bed-falls have become a very active area of research in order to minimize their serious consequences. Mainly, there are two research tracks: bed-fall detection and bed-fall prediction. Both of them handle the fall issues with several sensing techniques and analysis models. In fact, there is a lack of reviews on bed-fall related technologies. Therefore, a generic classification of bed-fall related technologies including fall detection and prediction systems based on their sensor apparatus is proposed in this paper. Data processing techniques have been also highlighted. The objective of this work is to provide researchers in this field a good standpoint regarding bed-fall related systems.
Continuous measurements for control of superconducting quantum circuits

Developments over the last two decades have opened the path towards quantum technologies in many quantum systems, such as cold atoms, trapped ions, cavity-quantum electrodynamics (QED), and circuit-QED. However, the fragility of quantum states to the effects of measurement and decoherence still poses one of the greatest challenges in quantum technology. An imperative capability in this path is quantum feedback, as it enhances the control possibilities and allows for prolonging coherence times through quantum error correction. While changing parameters from shot to shot of an experiment or procedure can be considered feedback, quantum mechanics also allows for the intriguing possibility of performing feedback operations during the measurement process itself. This broader approach to measurements leads to the concepts of weak measurement, quantum trajectories, and numerous types of feedback with no classical analogs. These types of processes are the primary focus of this review. We introduce the concept of quantum feedback in the context of circuit-QED, an experimental platform with significant potential in quantum feedback and technology. We then discuss several experiments and see how they elucidate the concepts of continuous measurements and feedback. We conclude with an overview of coherent feedback, with application to fault-tolerant error correction.
Causal Imitation Learning with Unobserved Confounders

One of the common ways children learn is by mimicking adults. Imitation learning focuses on learning policies with suitable performance from demonstrations generated by an expert, with an unspecified performance measure, and unobserved reward signal. Popular methods for imitation learning start by either directly mimicking the behavior policy of an expert (behavior cloning) or by learning a reward function that prioritizes observed expert trajectories (inverse reinforcement learning). However, these methods rely on the assumption that covariates used by the expert to determine her/his actions are fully observed. In this paper, we relax this assumption and study imitation learning when sensory inputs of the learner and the expert differ. First, we provide a non-parametric, graphical criterion that is complete (both necessary and sufficient) for determining the feasibility of imitation from the combinations of demonstration data and qualitative assumptions about the underlying environment, represented in the form of a causal model. We then show that when such a criterion does not hold, imitation could still be feasible by exploiting quantitative knowledge of the expert trajectories. Finally, we develop an efficient procedure for learning the imitating policy from experts' trajectories.
Development of transmon qubits solely from optical lithography on 300 mm wafers

Qubit information processors are increasing in footprint but currently rely on e-beam lithography for patterning the required Josephson junctions (JJs). Advanced optical lithography is an alternative patterning method, and we report on the development of transmon qubits patterned solely with optical lithography. The lithography uses 193 nm wavelength exposure and 300 mm large silicon wafers. Qubits and arrays of evaluation JJs were patterned with process control which resulted in narrow feature distributions: a standard deviation of 0.78% for a 220 nm linewidth pattern realized across over half the width of the wafers. Room temperature evaluation found a 2.8%–3.6% standard deviation in JJ resistance in completed chips. The qubits used aluminum and titanium nitride films on silicon substrates without substantial silicon etching. T1 times of the qubits were extracted at 26–27 μs, indicating a low level of material-based qubit defects. This study shows that large wafer optical lithography on silicon is adequate for high-quality transmon qubits, and shows a promising path for improving many-qubit processors.
Exploring Logically Dependent Multi-task Learning with Causal Inference

Previous studies have shown that hierarchical multi-task learning (MTL) can utilize task dependencies by stacking encoders and outperform democratic MTL.However, stacking encoders only considers the dependencies of feature representations and ignores the label dependencies in logically dependent tasks.Furthermore, how to properly utilize the labels remains an issue due to the cascading errors between tasks.In this paper, we view logically dependent MTL from the perspective of causal inference and suggest a mediation assumption instead of the confounding assumption in conventional MTL models.We propose a model including two key mechanisms: label transfer (LT) for each task to utilize the labels of all its lower-level tasks, and Gumbel sampling (GS) to deal with cascading errors.In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL.We conduct experiments on two English datasets and one Chinese dataset.Experiment results show that our model achieves state-of-the-art on six out of seven subtasks and improves predictions' consistency.
Efficient fiber in-line single photon source based on colloidal single quantum dots on an optical nanofiber

We demonstrate a fiber in-line single photon source based on a hybrid system of colloidal single quantum dots deposited on an optical nanofiber and cooled down to cryogenic temperature (3.7 K). We show that a charged state (trion) of the single quantum dot exhibits a photo-stable emission of single photons with high quantum efficiency, narrow linewidth (3 meV FWHM) and fast decay time ($$10.0\pm 0.5$$ ns). The single photons are efficiently coupled to the guided modes of the nanofiber and eventually to a single mode optical fiber. The brightness (efficiency) of the single photon source is estimated to be $$16\pm 2\%$$ with a maximum photon count rate of $$1.6\pm \,0.2$$ MHz and a high single photon purity ($$g^2(0)=0.11\pm 0.02$$). The device can be easily integrated to the fiber networks paving the way for potential applications in quantum networks.
Selfish Behavior in the Tezos Proof-of-Stake Protocol

Proof-of-Stake consensus protocols give rise to complex modeling challenges.We analyze the Babylon update (October 2019) to the Proof-of-Stake protocol on the Tezos blockchain, and demonstrate that, under certain conditions, rational participants are incentivized to behave dishonestly.In doing so, we provide a theoretical analysis of the feasibility and profitability of a block stealing attack that we call selfish endorsing, a concrete instance of an attack previously only theoretically considered.We propose and analyze a simple change to the Tezos protocol which significantly reduces the (already small) profitability of this dishonest behavior, and introduce a new delay and reward scheme that is provably secure against length-1 and length-2 selfish endorsing attacks.Our framework provides a template for analyzing other Proof-of-Stake protocols for the possibility of selfish behavior.
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets

To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs. Different from the network enlarging, we observe that resolution and depth are more important than width for tiny networks. Therefore, the original method, i.e., the compound scaling in EfficientNet is no longer suitable. To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet benchmark illustrate that our TinyNet performs much better than the smaller version of EfficientNets using the inversed giant formula. For instance, our TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about 1.9% higher than that of the previous best MobileNetV3 with similar computational cost. Code will be available at this https URL, and this https URL.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function. In this paper, we demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a form of independent learning in which each agent simply estimates its local value function, can perform just as well as or better than state-of-the-art joint learning approaches on popular multi-agent benchmark suite SMAC with little hyperparameter tuning. We also compare IPPO to several variants; the results suggest that IPPO's strong performance may be due to its robustness to some forms of environment non-stationarity.
Natural Deduction Assistant (NaDeA)

We present the Natural Deduction Assistant (NaDeA) and discuss its advantages and disadvantages as a tool for teaching logic.NaDeA is available online and is based on a formalization of natural deduction in the Isabelle proof assistant.We first provide concise formulations of the main formalization results.We then elaborate on the prerequisites for NaDeA, in particular we describe a formalization in Isabelle of "Hilbert's Axioms" that we use as a starting point in our bachelor course on mathematical logic.We discuss a recent evaluation of NaDeA and also give an overview of the exercises in NaDeA.
General-purpose incremental covariance update and efficient belief space planning via a factor-graph propagation action tree

Fast covariance calculation is required both for SLAM (e.g.~in order to solve data association) and for evaluating the information-theoretic term for different candidate actions in belief space planning (BSP). In this paper we make two primary contributions. First, we develop a novel general-purpose incremental covariance update technique, which efficiently recovers specific covariance entries after any change in the inference problem, such as introduction of new observations/variables or re-linearization of the state vector. Our approach is shown to recover them faster than other state-of-the-art methods. Second, we present a computationally efficient approach for BSP in high-dimensional state spaces, leveraging our incremental covariance update method. State of the art BSP approaches perform belief propagation for each candidate action and then evaluate an objective function that typically includes an information-theoretic term, such as entropy or information gain. Yet, candidate actions often have similar parts (e.g. common trajectory parts), which are however evaluated separately for each candidate. Moreover, calculating the information-theoretic term involves a costly determinant computation of the entire information (covariance) matrix which is O(n^3) with n being dimension of the state or costly Schur complement operations if only marginal posterior covariance of certain variables is of interest. Our approach, rAMDL-Tree, extends our previous BSP method rAMDL, by exploiting incremental covariance calculation and performing calculation re-use between common parts of non-myopic candidate actions, such that these parts are evaluated only once, in contrast to existing approaches.
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks

Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.
Better Highlighting: Creating Sub-Sentence Summary Highlights

Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.