Reviewerly/exHarmony huggingface dataset:
Intrinsically Motivated Autonomy in Human-Robot Interaction: Human Perception of Predictive Information in Robots

In this paper we present a fully autonomous and intrinsically motivated robot usable for HRI experiments. We argue that an intrinsically motivated approach based on the Predictive Information formalism, like the one presented here, could provide us with a pathway towards autonomous robot behaviour generation, that is capable of producing behaviour interesting enough for sustaining the interaction with humans and without the need for a human operator in the loop. We present a possible reactive baseline behaviour for comparison for future research. Participants perceive the baseline and the adaptive, intrinsically motivated behaviour differently. In our exploratory study we see evidence that participants perceive an intrinsically motivated robot as less intelligent than the reactive baseline behaviour. We argue that is mostly due to the high adaptation rate chosen and the design of the environment. However, we also see that the adaptive robot is perceived as more warm, a factor which carries more weight in interpersonal interaction than competence.
Verified Decision Procedures for Modal Logics.

We describe a formalization of modal tableaux with histories for the modal logics K, KT and S4 in Lean. We describe how we formalized the static and transitional rules, the non-trivial termination and the correctness of loop-checks. The formalized tableaux are essentially executable decision procedures with soundness and completeness proved. Termination is also proved in order to define them as functions in Lean. All of these decision procedures return a concrete Kripke model in cases where the input set of formulas is satisfiable, and a proof constructed via the tableau rules witnessing unsatisfiability otherwise. We also describe an extensible formalization of backjumping and its verified implementation for the modal logic K. As far as we know, these are the first verified decision procedures for these modal logics.
Stereoscopic Image Generation From Light Field With Disparity Scaling and Super-Resolution

In this paper, we propose a novel method to generate stereoscopic images from light-field images with the intended depth range and simultaneously perform image super-resolution. Subject to the small baseline of neighboring subaperture views and low spatial resolution of light-field images captured using compact commercial light-field cameras, the disparity range of any two subaperture views is usually very small. We propose a method to control the disparity range of the target stereoscopic images with linear or nonlinear disparity scaling and properly resolve the disocclusion problem with the aid of a smooth energy term previously used for texture synthesis. The left and right views of the target stereoscopic image are simultaneously generated by a unified optimization framework, which preserves content coherence between the left and right views by a coherence energy term. The disparity range of the target stereoscopic image can be larger than that of the input light field image. This benefits many light field image-based applications, e.g., displaying light field images on various stereo display devices and generating stereoscopic panoramic images from a light field image montage. An extensive experimental evaluation demonstrates the effectiveness of our method.
Optimising Optimisers with Push GP

This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same problem, and on other continuous-valued problems. In most cases, the optimisers generalise well to the larger problems. Surprisingly, some of them also generalise very well to previously unseen problems, outperforming existing general purpose optimisers such as CMA-ES. Analysis of the behaviour of the evolved optimisers indicates a range of interesting optimisation strategies that are not found within conventional optimisers, suggesting that this approach could be useful for discovering novel and effective forms of optimisation in an automated manner.
Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

Lossless compression of dynamic 2-D+t and 3-D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block-and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block-and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53 dB and 0.28 dB for CT data, as well as 1.04 dB and 1.90 dB for MR data, respectively, while the bit rate is reduced at the same time.
The One: An Interactive Installation for Visualizing the Cognition of Mind State by Capturing Face Expression, Body Shape, Wearing Cloth and Talking Voice

In the artwork, the topic of One has been discussed. Broadly speaking, it implies the whole beings in the universe, and it also narrows down to individuals. Human being was considered and it can be described in physical layer and mental layer. The appearance of people as the protective coloration in the society, and that creates the self-identity of themselves and the impressions of the others. The artwork tried to arouse people to aware the influences of cognition from their minds by visualizing the cognition. The physical properties of human being contain face expression, body shape, wearing cloth and talking voice are transformed into five elements of Chinese Taoist philosophy: wood, fire, earth, metal and water. After the transformation, the interactive installation is performed as an improvisational colorful Chinese brush painting with mimic echoing sounds. Multimedia input contains gray image analysis to form the black block on the painting, color image subtraction to compose the color block on the painting, and a buffer to capture and replay sounds continuously. In the indoor exhibition, the camera was set to capture the audiences coming from the door. In the interactive installation, the dynamic improvisational painting with echoing sounds as evidence remind audiences the way they exist, and that arouse them to rethink the cognition of themselves from the many clones who used known to be "me". In a collaborative interaction, the clones from the crowd also arouse audiences to aware of their impressions on the others. An improvisational painting as colorful Chinese brush painting was exhibited, in which reveals countenance, contour, apparel and utterance in physical point, and the awareness of the cognition of selves and of the others in mental aspect.
Accelerated scale bridging with sparsely approximated Gaussian learning

Multiscale modeling is a systematic approach to describe the behavior of complex systems by coupling models from different scales. The approach has been demonstrated to be very effective in areas of science as diverse as materials science, climate modeling and chemistry. However, routine use of multiscale simulations is often hindered by the very high cost of individual at-scale models. Approaches aiming to alleviate that cost by means of Gaussian process regression based surrogate models have been proposed. Yet, many of these surrogate models are expensive to construct, especially when the number of data needed is large. In this article, we employ a hierarchical sparse Cholesky decomposition to develop a sparse Gaussian process regression method and apply the method to approximate the equation of state of a material in a multiscale model of dynamic deformation. We demonstrate that the method provides a substantial reduction both in computational cost and solution error as compared with previous methods.
Video Imprint

A new unified video analytics framework (ER3) is proposed for complex event retrieval, recognition and recounting, based on the proposed video imprint representation, which exploits temporal correlations among image features across video frames. With the video imprint representation, it is convenient to reverse map back to both temporal and spatial locations in video frames, allowing for both key frame identification and key areas localization within each frame. In the proposed framework, a dedicated feature alignment module is incorporated for redundancy removal across frames to produce the tensor representation, i.e., the video imprint. Subsequently, the video imprint is individually fed into both a reasoning network and a feature aggregation module, for event recognition/recounting and event retrieval tasks, respectively. Thanks to its attention mechanism inspired by the memory networks used in language modeling, the proposed reasoning network is capable of simultaneous event category recognition and localization of the key pieces of evidence for event recounting. In addition, the latent structure in our reasoning network highlights the areas of the video imprint, which can be directly used for event recounting. With the event retrieval task, the compact video representation aggregated from the video imprint contributes to better retrieval results than existing state-of-the-art methods.
First Approach to Semantics of Silence in Testimonies

The right to silence is considered in several legal systems. However, this phenomenon has not been sufficiently studied from a logic perspective. After reviewing some previous studies of intentional silence, Grice’s conversational implicature, and omissive implicature, we formulated three interpretations of silence. Once the semantics are stated, we explore the consequences of such interpretations in puzzles as a case study involving testimonies, expressing them in answer set programming. Several conclusions are derived from the different possibilities that were opened for the analysis. Finally, we propose a strategy to generalize the use of semantics in contexts related to testimonies or interviews.
Counting statistics of microwave photons in circuit QED

In superconducting circuit architectures for quantum computing, microwave resonators are often used both to isolate qubits from the electromagnetic environment and to facilitate qubit state readout. We analyze the full counting statistics of photons emitted from such driven readout resonators both in and beyond the dispersive approximation. We calculate the overlap between emitted-photon distributions for the two qubit states and explore strategies for its minimization with the purpose of increasing fidelity of intensity-sensitive readout techniques. In the dispersive approximation and at negligible qubit relaxation, both distributions are Poissonian, and the overlap between them can be easily made arbitrarily small. Nondispersive terms of the Hamiltonian generate squeezing and the Purcell decay with the latter effect giving the dominant contribution to the overlap between two distributions.
A Deep Hybrid Model for Recommendation Systems

Recommendation has been a long-standing problem in many areas ranging from e-commerce to social websites. Most current studies focus only on traditional approaches such as content-based or collaborative filtering while there are relatively fewer studies in hybrid recommendation systems. With the emergence of deep learning techniques in different fields including computer vision and natural language processing, Recommendation Systems (RSs) have also become an active area of for these techniques. There are several studies that utilize ID embeddings of users and items to implement collaborative filtering with deep neural networks. However, such studies do not take advantage of other categorical or continuous features of inputs. In this paper, we propose a new deep neural network architecture which uses ID embeddings, and also auxiliary information such as features of job postings and candidates. Experimental results on a real world dataset from a job website show that the proposed method improves recommendation results over deep learning models utilizing only ID embeddings.
ABC-based stacking method for multilabel classification

Multilabel classification is a supervised learning problem wherein each individual instance is associated with multiple labels. Ensemble methods are effective in managing multilabel classification problems by creating a set of accurate, diverse classifiers and then combining their outputs to produce classifications. This paper presents a novel stacking-based ensemble algorithm, ABC-based stacking, for multilabel classification. The artificial bee colony algorithm, along with a single-layer artificial neural network, is used to find suitable meta-level classifier configurations. The optimization goal of the meta-level classifier is to maximize the average accuracy of classification of all the instances involved. We run an experiment on 10 benchmark datasets of varying domains and compare the proposed approach to five other ensemble algorithms to demonstrate the feasibility and effectiveness of ABC-based stacking.
Towards machine-assisted meta-studies: the Hubble constant

We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model -- a classical technique in natural language processing -- has successfully extracted 298 measurements of the Hubble constant, with uncertainties, from the 208,541 available arXiv astrophysics papers. We have also created an artificial neural network classifier to identify papers in arXiv which report novel measurements. From the analysis of our results we find that reporting measurements with uncertainties and the correct units is critical information when distinguishing novel measurements in free text. Our results correctly highlight the current tension for measurements of the Hubble constant and recover the $3.5\sigma$ discrepancy -- demonstrating that the tool presented in this paper is useful for meta-studies of astrophysical measurements from a large number of publications.
Control as a Service Architecture to Support Context-aware Control Application Development

Taking advantage of IoT, services and Cloud technologies, the development of Cloud of Things (CoT) changes the way control application are engineered and developed moving from a dedicated design and development of control applications to a Control as a Service vision. This vision requires developing a new architecture to connect physical and logical objects as well as integrating basic control patterns to organize a consistent control service orchestration. To fit this challenge, we propose a multi-layer Control as a Service architecture to describe control systems in a holistic way. The integration of a context manager, analysing continuously the system environment as well as the control system behaviour is used to monitor and orchestrate on the fly the deployment of context-aware control services. Thanks to this context dependent orchestrator, adaptive control system can be implemented.
Quantum Walk and Dressed Photon

A physical model called dressed photon, a composite system of photons and excitation of electrons in the nano-particles, is effectively used in the realm of near-field optics.Many interesting behaviors of dressed photons are known, especially the rapid energy transfer and the accumulation to singular points, e.g., points with strong dissipation.We propose a new modelling of dressed photons based on quantum walks, especially Grover walks on semi-infinite graphs which we call jellyfish graphs, and show a universal accumulation phenomena around the point with strong dissipation.
The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

This study focused on utilizing the Kinect depth sensor to track double-hand gestures and control a real-time robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the real-time images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and D-H link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the real-time robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the real-time imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the real-time robotic arm but also repeat the recorded actions.
No-Reference Stereoscopic Image Quality Assessment Based on Dilation Convolution

Over the years, with the popularization of 3D technology, the demands of accurate and efficient 3D image quality evaluation (SIQA) methods are increasing constantly. Due to the wide application of CNN, CNN-based SIQA methods emerge one after another. However, current methods only consider a single scale or resolution, and some CNN-based methods directly take left and right views as an input of the network ignoring the visual fusion mechanism. In this work, a multi-scale no-reference SIQA method is proposed based on dilation convolution neural network (DCNN). Different from other CNN-based SIQA methods, the proposed one uses dilation convolution to imitate different scale of information processing fields in the human brain. Instead of left or right image, the cyclopean image generated by a new method is used as the input of the network. Moreover, the proposed multi-scale unit significantly can reduce computational parameters and computational complexity. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art no-reference SIQA methods.
Importance conditional sampling for Pitman–Yor mixtures

Abstract Nonparametric mixture models based on the Pitman–Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the number of components characterizing the distribution of the data. We propose a new sampling strategy for such models, named importance conditional sampling (ICS), which combines appealing properties of existing methods, including easy interpretability and a within-iteration parallelizable structure. An extensive simulation study highlights the efficiency of the proposed method which, unlike other conditional samplers, shows stable performances for different specifications of the parameters characterizing the Pitman–Yor process. We further show that the ICS approach can be naturally extended to other classes of computationally demanding models, such as nonparametric mixture models for partially exchangeable data.
Sensor anomaly detection in the industrial internet of things based on edge computing

In the industrial internet of things (IIoT), because thousands of pieces of hardware, instruments, and various controllers are involved, the core problem is the sensors. Detection using sensors is the bottom line of the IIoT, directly affecting the detection accuracy and control indicators of the IIoT system. However, when a large number of real-time data generated by IIoT devices are transferred to cloud computing centers, large-scale data will inevitably bring computing load, which will affect the computing speed of cloud computing centers and increase the computing load of cloud computing data centers. These factors directly lead to instability and delay in sensor data collected in real time in the IIoT. In this paper, a sensor outlier detection algorithm based on edge calculation is proposed. Firstly, focusing on the problem of the large amount of data in terminal equipment of the IIoT, the edge technology method of data compression is used to optimize the compression of sensor data, and different thresholds are set according to different industrial process requirements, so as to ensure the real-time aspect and authenticity of the data. Then, using the K-means clustering algorithm, the compressed test data sets are analyzed and the abnormal sensor detection values and labels are obtained. Finally, the effectiveness of such an approach is evaluated through a sample case study involving a temperature control system.
Topological transition of a non-Markovian dissipative quantum walk

We extend non-Hermitian topological quantum walks on a Su-Schrieffer-Heeger (SSH) lattice [Phys. Rev. Lett. 102, 065703 (2009)] to the case of non-Markovian evolution. This non-Markovian model is established by coupling each unit cell in the SSH lattice to a reservoir formed by a quasicontinuum of levels. We find a topological transition in this model even in the case of non-Markovian evolution where the walker may visit the reservoir and return to the SSH lattice at a later time. The existence of a topological transition does, however, depend on the low-frequency properties of the reservoir, characterized by a spectral density $J(\ensuremath{\epsilon})\ensuremath{\propto}{|\ensuremath{\epsilon}|}^{\ensuremath{\alpha}}$. In particular, we find a robust topological transition for a sub-Ohmic ($\ensuremath{\alpha}&lt;1$) and Ohmic ($\ensuremath{\alpha}=1$) reservoir, but no topological transition for a super-Ohmic ($\ensuremath{\alpha}&gt;1$) reservoir. This behavior is directly related to the well-known localization transition for the spin-boson model. We confirm the presence of non-Markovian dynamics by explicitly evaluating a measure of Markovianity for this model.