Reviewerly/exHarmony huggingface dataset:
Working memory load dissociates contingency learning and item-specific proportion-congruent effects.

A consistent finding in the Stroop literature is that congruency effects (i.e., the color-naming latency difference between words presented in incongruent vs. congruent colors) are larger for mostly-congruent items (e.g., the word RED presented most often in red) than for mostly-incongruent items (e.g., the word GREEN presented most often in yellow). This proportion-congruent might be produced by a conflict-adaptation process (e.g., fully focus attention to the color when the word GREEN appears) and/or by a more general learning mechanism of stimulus-response contingencies (e.g., respond yellow when the word GREEN appears). Under the assumption that limited-capacity resources are necessary for learning stimulus-response contingencies, we examined the contingency-learning account using both Stroop and nonconflict (i.e., noncolor words written in colors) versions of a color identification task while participants maintained a working memory (WM) load. Consistent with the contingency-learning account, WM load modulated people's ability to learn contingencies in the nonconflict task. In contrast, across 3 experiments, WM load did not affect the item-specific proportion-congruent effect in the Stroop task even though we employed a design (the 2-item set design) in which contingency learning should be the dominant process. These results imply that the item-specific proportion-congruent effect is not merely a byproduct of contingency learning but a manifestation of reactive control, a mode of control engagement that may be especially useful when WM resources are scarce. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
A Reversible Secret Image Sharing Scheme Based on Stick Insect Matrix

This paper first proposes a generation algorithm of a stick insect matrix.Then, a novel reversible secret image sharing scheme based on the stick insect matrix is presented.With the help of our matrix, secret data can be embedded in a cover image to generate two meaningful shadows.It is worth mentioning that the stick insect structure of the proposed matrix can be extended according to the embedding rate of the secret data, meanwhile the generating rules of the stick insect matrix can guarantee optimal visual quality of the generated shadows.Moreover, our scheme also provides an authentication mechanism.The embedded secret data can be retrieved and the cover image can be recovered exactly after successful authentication of two shadows.Experimental results show that our proposed scheme provides excellent visual quality and good authentication ability.
Dual Distribution Alignment Network for Generalizable Person Re-Identification

Domain generalization (DG) offers a preferable real-world setting for Person Re-Identification (Re-ID), which trains a model using multiple source domain datasets and expects it to perform well in an unseen target domain without any model updating. Unfortunately, most DG approaches are designed explicitly for classification tasks, which fundamentally differs from the retrieval task Re-ID. Moreover, existing applications of DG in Re-ID cannot correctly handle the massive variation among Re-ID datasets. In this paper, we identify two fundamental challenges in DG for Person Re-ID: domain-wise variations and identity-wise similarities. To this end, we propose an end-to-end Dual Distribution Alignment Network (DDAN) to learn domain-invariant features with dual-level constraints: the domain-wise adversarial feature learning and the identity-wise similarity enhancement. These constraints effectively reduce the domain-shift among multiple source domains further while agreeing to real-world scenarios. We evaluate our method in a large-scale DG Re-ID benchmark and compare it with various cutting-edge DG approaches. Quantitative results show that DDAN achieves state-of-the-art performance.
Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set

Multi-label attribute reduction as a common dimensionality reduction technique has obtained widely research in recent years. Most existing multi-label attribute reduction methods adopt discretization to deal with mixed data and have strict requirements on the condition of sample classification. However, the process of discretization may lead to information loss, moreover, strict conditions will increase the possibility of a sample classified into a wrong class. Based on this, we construct a multi-label attribute reduction method based on variable precision fuzzy neighborhood rough set. The main motivation is that the variable precision fuzzy neighborhood rough set can process multiple types of data without discretization and tolerate noisy data. Specifically, we first use the parameterized fuzzy neighborhood granule to define the fuzzy decision and decision class of each sample under different labels. Then, the fuzzy decision and decision classes under different labels are fused into the entire multi-label learning space. Finally, a multi-label attribute reduction algorithm is designed according to the defined maximum attribute significance criterion. Our experiments are conducted on a series of multi-label datasets, and the experimental results verify that the proposed algorithm achieves better classification performance than other state-of-the-art comparison algorithms.
Community enhanced graph convolutional networks

Graph representation learning is a key technology for processing graph-structured data. Graph convolutional networks (GCNs), as a type of currently emerging and commonly used model for graph representation learning, have achieved significant performance improvement. However, GCNs acquire node representations mainly through aggregating their neighbor information, largely ignoring the community structure which is one of the most important feature of the graph. In this paper, we propose a novel method called Community Enhanced Graph Convolutional Networks (CE-GCN), which integrates both neighborhood and community information to learn node representations. Specifically, the neighborhood information of nodes is aggregated by a graph convolutional network. The community information of nodes is calculated by a modularity constraint. Finally, we incorporate the modularity constraint into the graph convolutional network, and then form a unified model framework. Experimental results on five real-world network datasets demonstrate that CE-GCN significantly outperforms state-of-the-art methods.
Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation

Progress in autonomous mobile robotics has seen significant advances in the development of many algorithms for motion control and path planning. However, robust performance from these algorithms can often only be expected if the parameters controlling them are tuned specifically for the respective robot model, and optimised for specific scenarios in the environment the robot is working in. Such parameter tuning can, depending on the underlying algorithm, amount to a substantial combinatorial challenge, often rendering extensive manual tuning of these parameters intractable. In this paper, we present a framework that permits the use of different navigation actions and/or parameters depending on the spatial context of the navigation task. We consider the respective navigation algorithms themselves mostly as a "black box", and find suitable parameters by means of an iterative optimisation, improving for performance metrics in simulated environments. We present a genetic algorithm incorporated into the framework, and empirically show that the resulting parameter sets lead to substantial performance improvements in both simulated and real-world environments in the domain of agricultural robots.
Beyond privacy vs. health: a justification analysis of the contact-tracing apps debate in the Netherlands

Abstract In the Netherlands, as in many other nations, the government has proposed the use of a contact-tracing app as a means of helping to contain the spread of the corona virus. The discussion about the use of such an app has mostly been framed in terms of a tradeoff between privacy and public health. This research statement presents an analysis of the Dutch public debate on Corona-apps by using the framework of Orders of Worth by Boltanski and Thévenot (1991). It argues that this framework can help us to move beyond the dichotomy of privacy vs. public health by recognizing a plurality of conceptions of the common good in the debate about contact-tracing apps. This statement presents six orders of worth present in the Dutch debate: civic, domestic, vitality, market, industrial and project, and argues that the identification of which common goods are at stake will contribute to discussions about the use of this technology from a standpoint with a richer ethical perspective.
Study on Comprehensive Calibration and Image Sieving for Coal-Gangue Separation Parallel Robot

Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factorc of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots.
Colocalized Sensing and Intelligent Computing in Micro-Sensors

This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.
Cyclic deterministic bidirectional quantum controlled teleportation with maximally seven-qubit entangled state

Abstract In a local quantum network, multi-qubits in various entangled states might be distributed in different remote nodes due to some peculiar demands. In this paper we consider a seven-qubit entangled state in a given qubit distribution among three participants and study its capacity in quantum teleportation (QT). To be specific, we put forward three controlled QT schemes utilizing the state. In each scheme QT can be deterministically and bidirectionally realized between two participants with another as the controller.
Measuring Systematic Generalization in Neural Proof Generation with Transformers

We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We investigate their systematic generalization abilities on a logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate natural language proofs. We test the generated proofs for logical consistency, along with the accuracy of the final inference. We observe length-generalization issues when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This suggests that Transformers have efficient internal reasoning strategies that are harder to interpret. These results highlight the systematic generalization behavior of TLMs in the context of logical reasoning, and we believe this work motivates deeper inspection of their underlying reasoning strategies.
Please Mind the Root: Decoding Arborescences for Dependency Parsing

The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers.However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree.We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly degrades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24%.Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime.We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime. 1
The Effectiveness of a Personalized Virtual Fitness Trainer in Teaching Physical Education by Applying the Artificial Intelligent Algorithm

Inactivity has been the main cause of obesity which has affected many people worldwide.Studies show that fitness is an important goal for a healthy lifestyle and has been used as a measurement for health-related quality of life.A fitness trainer can motivate and teach users to do fitness activities.However, to use a human fitness trainer may involve high cost and is not suitable for a certain school setting.A personalized virtual fitness trainer has the potential to replace a human trainer whenever possible.It can help physical education teachers in schools to motivate students to exercise.This study discussed the design and development of an AI-based application coined as an Intelligent Virtual Fitness Trainer (IVFIT) as an attempt to provide a personalized physical learning experience for school students.This app is developed to (1) demonstrate five workout activities for 30 minutes; (2) teach the user three types of dances for 22 minutes, and (3) demonstrate five basic self-defense moves.These training scenarios were administered to a physical education class consisting of 23 students in which the recommendation of the fitness trainer is done using a calculation of artificial intelligence algorithms.The percentage of prediction error was calculated after each activity to measure the effectiveness of the app in giving recommendations.Subsequently, a teacher observation test was conducted to determine students' perceptions of the effectiveness of the proposed VFT application.Overall, students found the VFT application to be effective in stimulating higher levels of involvement in physical activity without teacher supervision.It can be concluded from the study that a personalized virtual trainer can assist in assigning a suitable virtual trainer for specific users and can be used for physical education activities.
A reduced-precision network for image reconstruction

Neural networks are often quantized to use reduced-precision arithmetic, as it greatly improves their storage and computational costs. This approach is commonly used in image classification and natural language processing applications. However, using a quantized network for the reconstruction of HDR images can lead to a significant loss in image quality. In this paper, we introduce QW-Net , a neural network for image reconstruction, in which close to 95% of the computations can be implemented with 4-bit integers. This is achieved using a combination of two U-shaped networks that are specialized for different tasks, a feature extraction network based on the U-Net architecture, coupled to a filtering network that reconstructs the output image. The feature extraction network has more computational complexity but is more resilient to quantization errors. The filtering network, on the other hand, has significantly fewer computations but requires higher precision. Our network recurrently warps and accumulates previous frames using motion vectors, producing temporally stable results with significantly better quality than TAA, a widely used technique in current games.
Confidence-aware Non-repetitive Multimodal Transformers for TextCaps

When describing an image, reading text in the visual scene is crucial to understand the key information. Recent work explores the TextCaps task, i.e. image captioning with reading Optical Character Recognition (OCR) tokens, which requires models to read text and cover them in generated captions. Existing approaches fail to generate accurate descriptions because of their (1) poor reading ability; (2) inability to choose the crucial words among all extracted OCR tokens; (3) repetition of words in predicted captions. To this end, we propose a Confidence-aware Non-repetitive Multimodal Transformers (CNMT) to tackle the above challenges. Our CNMT consists of a reading, a reasoning and a generation modules, in which Reading Module employs better OCR systems to enhance text reading ability and a confidence embedding to select the most noteworthy tokens. To address the issue of word redundancy in captions, our Generation Module includes a repetition mask to avoid predicting repeated word in captions. Our model outperforms state-of-the-art models on TextCaps dataset, improving from 81.0 to 93.0 in CIDEr. Our source code is publicly available.
The design of tourism product CAD three-dimensional modeling system using VR technology

In view of the high homogeneity of tourism products all over the country, an attempt is made to design virtual visit tourism products with cultural experience background, which can reflect the characteristics of culture + tourism in different scenic spots, so that tourists can deeply experience the local culture. Combined with computer aided design (CAD), the virtual three-dimensional (3D) modeling system of scenic spots is designed, and VR real scene visit interactive tourism products suitable for different scenic spots are designed. 360° VR panoramic display technology is used for 360° VR panoramic video shooting and visiting system display production of Elephant Trunk Hill park scenery. A total of 157 images are collected and 720 cloud panoramic interactive H5 tool is selected to produce a display system suitable for 360° VR panoramic display of scenic spots. Meanwhile, based on single view RGB-D image, the latest convolutional neural network (CNN) algorithm and point cloud processing algorithm are used to design the indoor 3D scene reconstruction algorithm based on semantic understanding. Experiments show that the pixel accuracy and mean intersection over union of the indoor scene layout segmentation network segmentation results are 89.5% and 60.9%, respectively, that is, it has high accuracy. The VR real scene visit interactive tourism product can make tourists have a more immersive sense of interaction and experience before, during and after the tour.
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction

A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction.Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where labeled data is lacking.Non pre-trained unsupervised domain adaptation methods that incorporate external linguistic knowledge have proven effective in transferring aspect and opinion knowledge from a labeled source domain to unlabeled target domains; however, pre-trained transformer-based models like BERT and RoBERTa already exhibit substantial syntactic knowledge.In this paper, we propose a method for incorporating external linguistic information into a self-attention mechanism coupled with the BERT model.This enables leveraging the intrinsic knowledge existing within BERT together with externally introduced syntactic information, to bridge the gap across domains.Finally, we demonstrate enhanced results on three benchmark datasets.
End-to-End 6DoF Pose Estimation From Monocular RGB Images

We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenarios.Our approach can efficiently detect the traffic participants from a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors.The proposed method 6D-VNet, extends the Mask R-CNN by adding customised heads for predicting vehicle's finer class, rotation and translation.It is trained end-to-end compared to previous methods.Furthermore, we show that the inclusion of translational regression in the joint losses is crucial for the 6DoF pose estimation task, where object translation distance along longitudinal axis varies significantly, e.g. in autonomous driving scenarios.Additionally, we incorporate the mutual information between traffic participants via a modified non-local block to capture the spatial dependencies among the detected objects.As opposed to the original non-local block implementation, the proposed weighting modification takes the spatial neighbouring information into consideration whilst counteracting the effect of extreme gradient values.We evaluate our method on the challenging real-world Pascal3D+ dataset and our 6D-VNet reaches the 1st place in ApolloScape challenge 3D Car Instance task [1], [2].