Reviewerly/exHarmony huggingface dataset:
A High-Throughput Depth Estimation Processor for Accurate Semiglobal Stereo Matching Using Pipelined Inter-Pixel Aggregation

Semiglobal matching is an accurate stereo depth estimation algorithm, whereas implementing the high-throughput architecture has been challenging due to the inherent recursion on inter-pixel cost aggregation. Especially, the computation on horizontal scan pass is the critical path causing the throughput bottleneck. In this paper, we propose a new cluster-wise cost aggregation algorithm and its optimized architecture that enables to pipeline the inter-pixel aggregation and parallelize the scanline-level disparity computation. The proposed approach is performed not on every pixel but on each group of pixels, which significantly alleviates the timing constraint for the recursion. The disparity values at shifted multiple pixel positions are concurrently computed within a single clock period. We also propose the memory reduction scheme selecting a tiny number of informative values, which achieves 96% memory reduction compared to the straightforward approach storing overall values. The system-on-chip-based tiled processing scheme is employed, which allows the implementation without an external memory. The proposed architecture computes a depth map with 128 disparity levels at 103 frames per second on a full HD image on the Zynq ultrascale+ MPSoC platform, thus providing 2.6 times faster performance with a comparable accuracy compared to the state-of-the-art 8-path semiglobal matching implementation.
Understanding Atomic Hand-Object Interaction With Human Intention

Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , hand, object and reference. Specifically, we design a pattern of < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hand-object, object-reference, hand, object, reference</i> > (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.
Adaptive Dynamic Window Approach for Local Navigation

Local navigation is an essential ability of any mobile robot working in a real-world environment. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which heavily depends on the settings of the parameters in its cost function. Since the optimal choice of the parameters depends on the environment that may significantly vary and change at any time, the parameters should be chosen dynamically in a data-driven way. To cope with this problem, we propose a novel deep convolutional neural network, which dynamically predicts these parameters considering the sensor readings. The network is trained using a state-of-the art reinforcement learning algorithm. In this way, we combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. The experimental results show that the proposed method outperforms the DWA method as well as its recent extension.
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning

Behavioral decision making is an important aspect of autonomous vehicles (AV). In this work, we propose a behavior planning structure based on hierarchical reinforcement learning (HRL) which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals. In this hierarchical structure, the network is capable of 1) learning one task with multiple sub-goals simultaneously; 2) extracting attentions of states according to changing sub-goals during the learning process; 3) reusing the well-trained network of sub-goals for other tasks with the same sub-goals. A hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure. Compared to traditional RL methods, our algorithm is more sample-efficient, since its modular design allows reusing the policies of sub-goals across similar tasks for various transportation scenarios. The results show that the proposed method converges to an optimal policy faster than traditional RL methods.
A quantum algorithm for string matching

Abstract Algorithms that search for a pattern within a larger data-set appear ubiquitously in text and image processing. Here, we present an explicit, circuit-level implementation of a quantum pattern-matching algorithm that matches a search string (pattern) of length M inside a longer text of length N . Our algorithm has a time complexity of $$\tilde{O}(\sqrt{N})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>̃</mml:mo> </mml:mrow> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msqrt> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , while the space complexity remains modest at O ( N + M ). We report the quantum gate counts relevant for both pre-fault-tolerant and fault-tolerant regimes.
Inferring Spatial Uncertainty in Object Detection

The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-prone annotation process or sensor observation noises, current object detection datasets only provide deterministic annotations without considering their uncertainty. This precludes an in-depth evaluation among different object detection methods, especially for those that explicitly model predictive probability. In this work, we propose a generative model to estimate bounding box label uncertainties from LiDAR point clouds, and define a new representation of the probabilistic bounding box through spatial distribution. Comprehensive experiments show that the proposed model represents uncertainties commonly seen in driving scenarios. Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty. Experiments on the KITTI and the Waymo Open Datasets show that JIoU is superior to IoU when evaluating probabilistic object detectors.
Machine learning pipeline for quantum state estimation with incomplete measurements

Abstract Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness of the estimation procedure to missing measurements. In this paper, we explore the resilience of machine-learning-based quantum state estimation techniques to missing measurements by creating a pipeline of stacked machine learning models for imputation, denoising, and state estimation. When applied to simulated noiseless and noisy projective measurement data for both pure and mixed states, we demonstrate quantum state estimation from partial measurement results that outperforms previously developed machine-learning-based methods in reconstruction fidelity and several conventional methods in terms of resource scaling. Notably, our developed model does not require training a separate model for each missing measurement, making it potentially applicable to quantum state estimation of large quantum systems where preprocessing is computationally infeasible due to the exponential scaling of quantum system dimension.
Novel automatic group identification approaches for group recommendation

Group recommender systems are specialized in suggesting preferable products or services to a group of users rather than an individual by aggregating personal preferences of group members. In such expert systems, the initial task is to identify groups of similar users via clustering approaches as user groups are usually not predefined. However, clustering users into groups commonly suffer from sparsity, scalability, and complexity problems as the content in the domain proliferate. Moreover, group homogeneity and size are the critical parameters for organizing group members and enhancing their satisfaction. In this study, we propose novel automatic user grouping approaches by constructing a binary decision tree via bisecting k-means clustering for enhanced group formation and group size restriction. Furthermore, we propose adopting a genre-based mapping of user ratings into a tiny and dense vector to represent users, which both improves computation time for constructing the binary decision tree and enables eliminating adverse effects of sparsity. Finally, since the quality of group formation is not only dependent on conforming preferences but also to the demographic harmony among members, we further introduce utilizing similarities based on demographic characteristics along with the genre-based similarities. We propose applying two distinct strategies for small and large groups by decorating the genre-based similarities with demographic properties, which leads to a more homogeneous automatic group formation. Experiments performed on real-world benchmark datasets demonstrate that each proposed method outperforms its traditional rival significantly, and the final proposed method achieves significantly more qualified ranked recommendation lists than the state-of-the-art algorithm.
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. Thus, we propose a method of performing online, real-time anomaly detection for predictive maintenance using Hierarchical Temporal Memory (HTM). Inspired by the human neocortex, HTMs learn and adapt continuously and are robust to noise. Using the Numenta Anomaly Benchmark, we empirically demonstrate that our approach outperforms state-of-the-art algorithms at preemptively detecting real-world cases of bearing failures and simulated 3D printer failures. Our approach achieves an average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and statistical (61.06) methods.
Hand gesture recognition on python and opencv

Abstract Hand gesture recognition is one of the system that can detect the gesture of hand in a real time video. The gesture of hand is classify within a certain area of interest. In this study, designing of the hand gesture recognition is one of the complicated job that involves two major problem. Firstly is the detection of hand. Another problem is to create the sign that is suitable to be used for one hand in a time. This project concentrates on how a system could detect, recognize and interpret the hand gesture recognition through computer vision with the challenging factors which variability in pose, orientation, location and scale. To perform well for developing this project, different types of gestures such as numbers and sign languages need to be created in this system. The image taken from the realtime video is analysed via Haar-cascaded Classifier to detect the gesture of hand before the image processing is done or in the other word to detect the appearance of hand in a frame. In this project, the detection of hand will be done using the theories of Region of Interest (ROI) via Python programming. The explanation of the results will be focused on the simulation part since the different for the hardware implementation is the source code to read the real-time input video. The developing of hand gesture recognition using Python and OpenCV can be implemented by applying the theories of hand segmentation and the hand detection system which use the Haar-cascade classifier.
A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter

Social networks have generated immense amounts of data that have been successfully utilized for research and business purposes. The approachability and immediacy of social media have also allowed ill-intentioned users to perform several harmful activities that include spamming, promoting, and phishing. These activities generate massive amounts of low-quality content that often exhibits duplicate, automated, inappropriate, or irrelevant content that subsequently affects users' satisfaction and imposes a significant challenge for other social media-based systems. Several real-time systems were developed to tackle this problem by focusing on filtering a specific kind of low-quality content. In this paper, we present a fine-grained real-time classification approach to identify several types of low-quality tweets (i.e., phishing, promoting, and spam tweets) written in Arabic. The system automatically extracts textual features using deep learning techniques without relying on hand-crafted features that are often time-consuming to be obtained and are tailored for a single type of low-quality content. This paper also proposes a lightweight model that utilizes a subset of the textual features to identify spamming Twitter accounts in a real-time setting. The proposed methods are evaluated on a real-world dataset (40, 000 tweets and 1, 000 accounts), showing superior performance in both models with accuracy and F1-scores of 0.98. The proposed system classifies a tweet in less than five milliseconds and an account in less than a second.
Efficient Style-Corpus Constrained Learning for Photorealistic Style Transfer

Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Experimental investigation on ducted hot aisle containment system for racks cooling of data center

A new type of ducted hot aisle containment system for racks cooling of data center has been proposed and put into practice gradually. However, the related academic research has not been carried out, especially for the improvement of cooling performance and influencing factors for the cooling system. In this paper, one experiment platform with 8 racks in 2 rows with multiple-heat-sources was established and tested under various working scenarios. In terms of improving the cooling performance, airtight treatment on the basis of ducted hot aisle containment system is better than itself and is also better than only collar, which is better than no collar. Besdides, doors closing is better than that doors opening, "V" diffuser is better than "T" diffuser, and “T” diffuser is better than no diffuser. Properly increasing installation ratio of blanking panels and air volume have also favorable impact. The experimental data and results in this paper will be of great significance to the design and application of ducted hot aisle containment system for racks cooling.
Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multiqubit systems. As system size increases, multiplexed qubit readout becomes a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multiqubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25% due to the compensation of system-dependent nonidealities such as readout crosstalk, which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems.
Introducing Gamification in the AR-Enhanced Order Picking Process: A Proposed Approach

In the last decade, the Industry 4.0 concept has introduced automation and cyber-physical systems as the core elements of future logistics, supported by an array of technologies, such as augmented reality (AR) providing the necessary support for the digital transformation of manufacturing and logistics and the smartification and digital refinement of traditional pre-Industry 4.0 processes. This paper studies the influence and the potential of gamification techniques in supporting innovative Industry 4.0-enhanced processes in the contemporary warehouse work ecosystem. Gamification in the workplace aims to motivate the employees and increase their involvement in an activity, while at the same time creating a sense of an everyday different experience rather than a set of repetitive and monotonous tasks. Since the design of such a system is a complex process, the most widespread design frameworks are studied, and the emphasis is on the principal game elements and their connection to mobilization mechanisms. Finally, an initial proposal of a gamification framework to support the AR-enhanced order picking process in contemporary logistics centers is provided with an emphasis on the mechanics of a fair and functional reward system. The proposed approach aims to showcase the potential alignment of business processes to human motivation, respecting the differences between tasks and the workers’ cognitive workload.
Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation

Although photovoltaic generation has been proposed as a solution for the world’s energy challenges, it depends to a large extent on solar irradiation and air temperature. Therefore, small variations in these meteorological parameters produce sudden changes in power generation, which makes it difficult to integrate photovoltaic generators into the electrical grid. The aim of this study is to develop a very short-term temperature forecaster that makes photovoltaic generation more reliable in order to provide not only power but also ancillary services. To predict ambient temperature in a specific area (Vitoria-Gasteiz, Basque Country) in the next 10 min, this forecaster combines a multilayer perceptron and the optimal nearest number of meteorological. In addition, the distance and relative location between each station and the target station were taken into account. The accumulated deviation between actual and forecasted temperature was lower than 1% in 96.60% of the examined days from the validation database. Moreover, the root mean square error was 0.2557 °C, which represents an improvement of 13.20% as compared with the benchmark result. The results indicated that the forecaster can be considered for implementation in photovoltaic generators to compute key control parameters and improve their integration into the electrical grid.
Inclusion, Dyslexia, Emotional State and Learning: Perceptions of Ibero-American Children with Dyslexia and Their Parents during the COVID-19 Lockdown

The COVID-19 pandemic has transformed educational processes. This has had major consequences for students and, in particular, for those with special education needs. Dyslexic students suffer from widespread educational and legal invisibility, and information on their situation and that of their families during this health crisis is lacking. This article presents the results of an exploratory study based on two online surveys taken by parents (n = 327) and children with dyslexia (n = 203) through the Spanish Dyslexia Federation (acronym in Spanish “FEDIS”), the Dyslexia and Family Association (acronym in Spanish “DISFAM”), and the Ibero-American Organisation for Specific Learning Difficulties (acronym in Spanish “OIDEA”). Data were collected in May–July 2020. The results offer a comprehensive viewpoint (family and children) on the aspects that have helped and hindered learning, such as teacher and family support, emotional state, use of ICT, and the importance of the voluntary/association network. The study provides evidence of how lockdown and school closures have created additional difficulties for learning but also how certain educational processes have been bolstered with the support of technological resources that should serve as benchmarks for education policy and classroom practice.
Robotic Path Planning Strategy Based on Improved Artificial Potential Field

The artificial potential field (APF) algorithm, proposed by Khatib, is frequently utilized in the pathfinding and obstacle avoidance projection of moveable robots with known destination. Aiming at the confinements of the traditional APF technique, which are defined as local minimum and unreachable problem respectively, based on the thread of disturbing the mechanical model of force balance to break the equilibrium state, the following improved algorithm is proposed. By sensing the variation of the distance between the robot and the target in certain time unit, the algorithm can quantitate the tendency of robot to be trapped in the mechanical equilibrium state in the artificial potential field. An optimization and improvement of the repulsive force pointing angle and the gravitational field function has been done so that the robot can take the initiative to detach from the state of mechanical equilibrium induced by obstacles. In the MATLAB simulation, the algorithm demonstrates its efficiency and universality compared with traditional algorithm.