Reviewerly/exHarmony huggingface dataset:
Craft-Inspired Digital Fabrication: A Study of Interactive Robotic Clay Carving

Digital fabrication workflow is typically linear that starts from ideating a design form via 3d-modelling or programming, later ends fabricating this predetermined design form. In this linear workflow, design ideation must finish before making/fabrication begins. As a result, designers often miss the opportunity to design with material affordances, such as discovering and incorporating intricate material expressions that can only emerge through an enactive and embodied process of making. Knowing that such process of making forms the cornerstone of craft, this study investigates how digital fabrication can gain craft-like qualities by allowing material expressions to be gradually discovered and mastered. We thus developed an interactive fabrication system by integrating force feedback into a robotic clay carving process. Real-time force feedback allows improvisational control of carving actions by manually interacting with the robotic end effector on-the-fly. Accordingly, carved clay expressions are continuously modified during fabrication. Our results demonstrate that this approach allows intricate material expressions to serendipitously appear, which in turn inspire new design ideas to form and gradually develop. Our contributions include: 1) a new enactive and embodied interaction modality that continuously regenerates a self-similar fabrication action sequence so that its design space can be gradually learned; 2) a craft-inspired digital fabrication workflow that supports on-the-fly design ideation by sequentially sketching, isolating, and composing an intricate material expression; and 3) a collection of critical considerations that propose how our findings could help integrate more improvisation, serendipity, and reflection in digital fabrication.
SCN: Self-Calibration Network for fast and accurate image super-resolution

Deep and wide Convolutional neural networks (CNNs) achieve remarkable performance on image super-resolution (SR), while it is hard to be directly applied on edge devices due to the strict requirements on data transmission and processing. Therefore, numerous lightweight SR methods have been proposed. However, these methods often come with compromises on the reconstruction performance or speed due to the weak reconstruction supervision provided by shallow networks. Aiming to alleviate the above limitation, we introduce a self-calibration network (SCN) equipped with a hierarchical self-calibration module for fast and accurate image SR. Specially, we design a lightweight internal calibration module (ICM) to enable the network to economically focus on the key information by collaboratively refining the weights in both channel and spatial dimensions; and an efficient external calibration module (ECM) to focus on calibrating the prominent error of knowledge learned from the network on Low-Resolution (LR) data for saving computing resources. We experimentally demonstrate the synergy among the two modules guides to a superior SR performance against the state-of-the-art lightweight methods while requiring much fewer parameters and operations.
Single Image Super-resolution with Self-similarity

Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.
Decentralized Neural Block Control for a Robot Manipulator based in UKF training

This work presents an alternative solution of the trajectory tracking problem for a two degrees of freedom (DOF) robot manipulator using a discrete-time decentralized control strategy. The local controller for each joint uses only local angular position and velocity measurements. A recurrent high order neural network (RHONN) structure is used to aproximate the manipulator dynamics, and based on this model, a discrete-time control law is computed, which combines block control and the sliding mode techniques. The neural network learning is performed on-line by Unscented Kalman filter (UKF) algorithm, a variation of nonlinear Kalman Filter.
Towards Uniform Online Spherical Tessellations

The problem of uniformly placing N points onto a sphere finds applications in many areas. An online version of this problem was recently studied with respect to the gap ratio as a measure of uniformity. The proposed online algorithm of Chen et al. was upper-bounded by 5.99 and then improved to 3.69, which is achieved by considering a circumscribed dodecahedron followed by a recursive decomposition of each face. We analyse a simple tessellation technique based on the regular icosahedron, which decreases the upper-bound for the online version of this problem to around 2.84. Moreover, we show that the lower bound for the gap ratio of placing up to three points is $$\frac{1+\sqrt{5}}{2} \approx 1.618$$. The uniform distribution of points on a sphere also corresponds to uniform distribution of unit quaternions which represent rotations in 3D space and has numerous applications in many areas.
Toward an Open-source Toolkit for Machine Learning Education

Machine Learning (ML) has become one of the highly participated courses at the undergraduate level in Computer Science. Open-source ML libraries make it easy for students to implement papers, share ideas, and conduct experiments on large scale datasets. With the emergence of public dataset portals (such as Kaggle, Amazon Datasets, and Google Datasets Search), the open-source community has produced many useful, high-quality libraries (such as Scikit-Learn, PyTorch, Keras, and Tensorflow among others). These open-source tools aim to make state-of-the-art ML algorithms and large-scale datasets accessible to all. While these ML libraries and datasets can benefit many undergraduate students in their pursuit of data-related careers, the task of choosing them for instructional purposes can be daunting for two reasons. First, all of these tools have advantages, drawbacks, and many overlapping issues. There is no single tool or dataset that covers all of the ML instructional needs. Second, due to the rapid advancements in the field, instructors often find a lack of comprehensive guidelines or standards on evaluating the instructional usability and real-world performance of open-source tools. How can these libraries and tools be integrated to aid the instructional activities of both classical machine learning as well as deep learning? This BOF will provide a platform for the discussion of the development of an open-source toolkit to support the teaching and learning of ML at the undergraduate level.
Scalable Multi-Agent Inverse Reinforcement Learning via  Actor-Attention-Critic

Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior. While MA-AIRL has promising results on cooperative and competitive tasks, it is sample-inefficient and has only been validated empirically for small numbers of agents -- its ability to scale to many agents remains an open question. We propose a multi-agent inverse RL algorithm that is more sample-efficient and scalable than previous works. Specifically, we employ multi-agent actor-attention-critic (MAAC) -- an off-policy multi-agent RL (MARL) method -- for the RL inner loop of the inverse RL procedure. In doing so, we are able to increase sample efficiency compared to state-of-the-art baselines, across both small- and large-scale tasks. Moreover, the RL agents trained on the rewards recovered by our method better match the experts than those trained on the rewards derived from the baselines. Finally, our method requires far fewer agent-environment interactions, particularly as the number of agents increases.
Towards Modular Algorithm Induction.

We present a modular neural network architecture Main that learns algorithms given a set of input-output examples. Main consists of a neural controller that interacts with a variable-length input tape and learns to compose modules together with their corresponding argument choices. Unlike previous approaches, Main uses a general domain-agnostic mechanism for selection of modules and their arguments. It uses a general input tape layout together with a parallel history tape to indicate most recently used locations. Finally, it uses a memoryless controller with a length-invariant self-attention based input tape encoding to allow for random access to tape locations. The Main architecture is trained end-to-end using reinforcement learning from a set of input-output examples. We evaluate Main on five algorithmic tasks and show that it can learn policies that generalizes perfectly to inputs of much longer lengths than the ones used for training.
Convolutional Neural Network with Multi-Valued Neurons

In this paper, a convolutional neural network (CNN) based on multi-valued neurons (MVNs) with complex-valued weights is presented. Convolutional neural networks are known as one of the best tools for solving such problems as image and speech recognition. The vast majority of researches and users so far employ a classical CNN, which operates with real-valued inputs/outputs and is based on classical neurons with a sigmoidal activation function. Recently complex-valued CNNs were introduced, but their neurons employ an activation function, which is a complex-valued generalization of a sigmoidal one. While such complex-valued neurons are more flexible when they learn and make it possible to process complex-valued data, their generalization capability is basically not higher than the one of real-valued neurons. At the same time there exists a complex-valued neuron (a multi-valued neuron - MVN) with a phase dependent activation function whose functionality is higher than the one of neurons with a sigmoidal activation function. It is also known that a multilayer neural network with multi-valued neurons (MLMVN) outperforms a classical multilayer peceptron in terms of speed of learning and generalization capability. Hence our goal was to develop a convolutional neural network based on multi-valued neurons (CNNMVN). We consider in detail a learning algorithm for CNNMVN, which is derivative free as well as the one for MLMVN. We also suggested the max pool operation for complex numbers. These results are illustrated by simulations showing that CNNMVN with even a minimal convolutional topology is able to solve image recognition problems with pretty high accuracy.
The 18th Top Chess Engine Championship, TCEC18

TCEC18 was the 18th season of the Top Chess Engine Championship and ran from May 3rd to 2nd July, 2020. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for a comparative analysis of the Shannon-AB and the new Neural-Network engines’ styles of play. STOCKFISH took the Grand Champion title from LEELA CHESS ZERO with +7 in another exciting Superfinal. These two engines have dominated the field for the last seven TCEC seasons now. The attached files at http://centaur.reading.ac.uk/91839 provide the 628 games with engine PVs, the detail on them, some summary statistics and the completion to mate of the wins.
Activity-Recognition Model for Violence Behavior Using LSTM

Among many dangerous situations, the number of cases of violence has been growing recently. However, there is currently no research to recognize conditions such as assault. Therefore, this paper presents a VR (Violence-Recognition) model for recognition activity using LSTM. The VR model develops algorithms that can detect dangerous situations through processing and analysis of sensing data. Also, to improve accuracy by using the FFT algorithm for processing digital signals in combination with LSTM.
Characterizing Stage-aware Writing Assistance for Collaborative Document Authoring

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.
A Way To Increase Student Activity In The Organization Of Lectures

At the heart of the great work carried out in the field of education in our country is the important task of training highly qualified specialists in the national economy, educated in accordance with modern requirements. Solving this problem requires a number of important tasks for university professors. The organizers of this task are to educate students on the basis of modern pedagogical technologies, to form the ideological immunity of young people, to ensure the formation of a harmonious and mature person. Special attention should be paid to these aspects, especially in the training of engineers who are planned to work in the leading plants of the country after graduation. Therefore, increasing the effectiveness of training future engineers in all aspects is an urgent task today.
A Dialectical Approach to Search Engine Evaluation

Abstract Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides’ philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus’ flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato’s theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato’s dialectical method is an appropriate composite method for evaluating information retrieval systems.
Solar irradiance components estimation based on a low-cost sky-imager

The design and location of solar energy technologies are based chiefly on the availability of solar resource. Specialized measurement equipment and solar resource assessment techniques are mandatory in order to identify appropriate locations for deploying solar systems while minimizing risk and increasing the return rate of the site. These needs have given the scientific community a motivation to design highly reliable and cost-competitive sensors. This work presents a novel methodology to estimate solar irradiance components using a low-cost sky-imager system. Captured images are processed to correct the spherical aberration of the dome and to achieve a pixel-radiance model, allowing each pixel of the image to be considered as an individual source of irradiation. The performance of the proposed model is analyzed and compared in four different weather scenarios against a secondary standard solarimetric station, obtaining a normalized mean bias error for direct normal irradiance, global horizontal irradiance and diffuse horizontal irradiance of 4.53%, −6.55% and 19.62% for a sunny day, 0.79%, −6.13% and 7.00% for a partially sunny day, 1.96%, −6.58% and −0.73% for a partially cloudy day, 100%, −10.48% and −0.65% for a cloudy day, respectively. According to the experimental results, the system stands out for its performance, easy setup, low-cost components and a robust estimation in all possible weather scenarios.
Reducing Image Compression Artifacts for Deep Neural Networks

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improve the human visual experience. However, in many applications, large-scale images are collected not for visual examination by humans. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects. Therefore, inspired by the teacher-student network framework, we propose a compression artifacts reduction framework (ARF) for DNN. In addition, we generalize our method to the unsupervised setting (U-ARF) where the corresponding original images are unavailable in training. Extensive experiments indicate the proposed methods can help DNNs improve performance on the highly compressed images significantly.
Haze Removal Method Based on Joint Transmission Map Estimation and Atmospheric-Light Extraction

This paper proposes a new computationally effective method of haze removal based on joint transmission map estimation and atmospheric-light extraction using the probabilistic gamma-normal model. The enhanced version of universal atmospheric light extractor allows reducing the influence of localized light sources to result of processing. Experimental results show that the proposed method has comparable quality results and lower computation time than other haze removal methods.
Neuromorphic processing at 11 Tera-OPs with soliton crystal Kerr microcombs

We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds. We demonstrate a single neuron perceptron at 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We then demonstrate a convolutional accelerator operating beyond 11 TeraOPs/s (TOPs). We test the perceptron on handwritten-digit recognition and cancer-cell detection - achieving over 90% and 85% accuracy, respectively.
Video‐Driven 2D Character Animation

Video-driven animation has always been a hot and challenging topic in the field of computer animation. We propose a method of mapping a sequence of human skeletal keypoints in a video onto a two-dimensional character to generate 2D character animation. For a given two-dimensional character picture, we extract the motion of real human in video data, driving the character deformation. We analyze common two-dimensional human body movements, classify the basic posture of the human body, realize the recognition of skeleton posture based on back propagation network, capture human body motion by automatically tracking the position of the human skeleton keypoints coordinates in the video and redirect the motion data to a 2D character. Compared with the traditional method, our work is less affected by video data illumination and background complexity. We calibrate human body motion in videos to a 2D character according to the skeleton topology to avoid motion distortion caused by the difference in skeleton size and ratio. The experimental results show that the proposed algorithm can generate the motion of two-dimensional characters based on the motion of human characters in video data. The animation is natural and smooth, and the algorithm has strong robustness.
Comment on “Spin-Momentum-Locked Edge Mode for Topological Vortex Lasing”

Received 17 July 2020Accepted 9 September 2021DOI:https://doi.org/10.1103/PhysRevLett.127.209401© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptics & lasersTopological effects in photonic systemsPhysical SystemsSemiconductor lasersTopological materialsNonlinear Dynamics
Question answering over logic puzzles using theorem proving

We developed a tool to automatically solve logical puzzles in natural language. The solution is composed by a parser and an inference engine. The parser translates the text into First Order Logic (FOL), while the Mace4 model finder computes the interpretation models of the given FOL theory. The solver uses the Prover9 theorem prover to compute Yes/No answers to natural language questions related to each puzzle. Each answer is backup by a graphical representation of its proof, which is in line with Explainable Artificial Intelligence (XAI). The advantage of using reasoning for Natural Language Understanding (NLU) instead of learned models is that the user can obtain an explanation of the reasoning chain. We illustrate how the system performs on 382 knights and knaves puzzles. These features together with the overall performance rate of 80.89% makes the proposed solution an improvement upon similar solvers for natural language understanding in the puzzles domain.
Enhancing the fidelity of joint remote state preparation by weak measurement

In this paper, the technique of weak measurement is employed in order to enhance the fidelity of joint remote state preparation (JRSP) protocol under decoherence. We design a quantum circuit of joint remote preparation of a single-qubit state through a GHZ state. Then, we analytically derive the average fidelity of the JRSP protocol under four types of noise usually encountered in real-world implementations of quantum communication protocols, i.e. the phase-flip, depolarizing, amplitude-damping and bit-flip noise. Our study shows that the application of weak measurement and measurement reversal could enhance the average fidelity of the JRSP process under the influence of phase-flip, depolarizing, amplitude-damping noise for most values of the decoherence parameters. If the JRSP process suffers from the bit-flip noise, the weak measurement technique is not useful for increasing the average fidelity.