Reviewerly/exHarmony huggingface dataset:
A survey of learning‐based robot motion planning

Abstract A fundamental task in robotics is to plan collision‐free motions among a set of obstacles. Recently, learning‐based motion‐planning methods have shown significant advantages in solving different planning problems in high‐dimensional spaces and complex environments. This article serves as a survey of various different learning‐based methods that have been applied to robot motion‐planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning‐based methods either rely on a human‐crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning‐related definition of motion‐planning problem are provided in this article. Different learning‐based motion‐planning algorithms are introduced, and the combination of classical motion‐planning and learning techniques is discussed in detail.
A Novel S-Box Dynamic Design Based on Nonlinear-Transform of 1D Chaotic Maps

In this paper, a method to enhance the dynamic characteristics of one-dimension (1D) chaotic maps is first presented. Linear combinations and nonlinear transform based on existing chaotic systems (LNECS) are introduced. Then, a numerical chaotic map (LCLS), based on Logistic map and Sine map, is given. Through the analysis of a bifurcation diagram, Lyapunov exponent (LE), and Sample entropy (SE), we can see that CLS has overcome the shortcomings of a low-dimensional chaotic system and can be used in the field of cryptology. In addition, the construction of eight functions is designed to obtain an S-box. Finally, five security criteria of the S-box are shown, which indicate the S-box based on the proposed in this paper has strong encryption characteristics. The research of this paper is helpful for the development of cryptography study such as dynamic construction methods based on chaotic systems.
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

Transformer based architectures are recently used for the task of answering questions over tables.In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables.In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques.The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells.While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ∼98% Hit@1 accuracy on WikiSQL lookup questions).Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TABERT), achieving ∼3.4% and ∼18.86% additional precision improvement on the standard WikiSQL benchmark 1 .
Hand-Model-Aware Sign Language Recognition

Hand gestures play a dominant role in the expression of sign language. Current deep-learning based video sign language recognition (SLR) methods usually follow a data-driven paradigm under the supervision of the category label. However, those methods suffer limited interpretability and may encounter the overfitting issue due to limited sign data sources. In this paper, we introduce the hand prior and propose a new hand-model-aware framework for isolated SLR with the modeling hand as the intermediate representation. We first transform the cropped hand sequence into the latent semantic feature. Then the hand model introduces the hand prior and provides a mapping from the semantic feature to the compact hand pose representation. Finally, the inference module enhances the spatio-temporal pose representation and performs the final recognition. Due to the lack of annotation on the hand pose under current sign language datasets, we further guide its learning by utilizing multiple weakly-supervised losses to constrain its spatial and temporal consistency. To validate the effectiveness of our method, we perform extensive experiments on four benchmark datasets, including NMFs-CSL, SLR500, MSASL and WLASL. Experimental results demonstrate that our method achieves state-of-the-art performance on all four popular benchmarks with a notable margin.
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

User interest modeling is critical for personalized news recommendation.Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest.However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding.In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec.Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news.We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football.Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting.Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.
NeRP: Neural Rearrangement Planning for Unknown Objects

Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of objects has been noted to be an important benchmark for AI capabilities in recent years. We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world. We compare NeRP to several naive and model-based baselines, demonstrating that our approach is measurably better and can efficiently arrange unseen objects in fewer steps and with less planning time. Finally, we demonstrate it on several challenging rearrangement problems in the real world.
Provably Secure Generative Linguistic Steganography

Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext).However, previous methods generally lead to statistical differences between the conditional probability distributions of stegotext and natural text, which brings about security risks.In this paper, to further ensure security, we present a novel provably secure generative linguistic steganographic method ADG, which recursively embeds secret information by Adaptive Dynamic Grouping of tokens according to their probability given by an offthe-shelf language model.We not only prove the security of ADG mathematically, but also conduct extensive experiments on three public corpora to further verify its imperceptibility.The experimental results reveal that the proposed method is able to generate stegotext with nearly perfect security.
Learning Visual Context for Group Activity Recognition

Group activity recognition aims to recognize an overall activity in a multi-person scene. Previous methods strive to reason on individual features. However, they under-explore the person-specific contextual information, which is significant and informative in computer vision tasks. In this paper, we propose a new reasoning paradigm to incorporate global contextual information. Specifically, we propose two modules to bridge the gap between group activity and visual context. The first is Transformer based Context Encoding (TCE) module, which enhances individual representation by encoding global contextual information to individual features and refining the aggregated information. The second is Spatial-Temporal Bilinear Pooling (STBiP) module. It firstly further explores pairwise relationships for the context encoded individual representation, then generates semantic representations via gated message passing on a constructed spatial-temporal graph. On their basis, we further design a two-branch model that integrates the designed modules into a pipeline. Systematic experiments demonstrate each module's effectiveness on either branch. Visualizations indicate that visual contextual cues can be aggregated globally by TCE. Moreover, our method achieves state-of-the-art results on two widely used benchmarks using only RGB images as input and 2D backbones.
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction

Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN.
Research on QR image code recognition system based on artificial intelligence algorithm

Abstract The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.
Efficient computation of the Nagaoka–Hayashi bound for multiparameter estimation with separable measurements

Abstract Finding the optimal attainable precisions in quantum multiparameter metrology is a non-trivial problem. One approach to tackling this problem involves the computation of bounds which impose limits on how accurately we can estimate certain physical quantities. One such bound is the Holevo Cramér–Rao bound on the trace of the mean squared error matrix. The Holevo bound is an asymptotically achievable bound when one allows for any measurement strategy, including collective measurements on many copies of the probe. In this work, we introduce a tighter bound for estimating multiple parameters simultaneously when performing separable measurements on a finite number of copies of the probe. This makes it more relevant in terms of experimental accessibility. We show that this bound can be efficiently computed by casting it as a semidefinite programme. We illustrate our bound with several examples of collective measurements on finite copies of the probe. These results have implications for the necessary requirements to saturate the Holevo bound.
Detecting Deepfake Videos with Temporal Dropout 3DCNN

While the abuse of deepfake technology has brought about a serious impact on human society, the detection of deepfake videos is still very challenging due to their highly photorealistic synthesis on each frame. To address that, this paper aims to leverage the possible inconsistent cues among video frames and proposes a Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) to detect deepfake videos. In the approach, the fixed-length frame volumes sampled from a video are fed into a 3-Dimensional Convolutional Neural Network (3DCNN) to extract features across different scales and identified whether they are real or fake. Especially, a temporal dropout operation is introduced to randomly sample frames in each batch. It serves as a simple yet effective data augmentation and can enhance the representation and generalization ability, avoiding model overfitting and improving detecting accuracy. In this way, the resulting video-level classifier is accurate and effective to identify deepfake videos. Extensive experiments on benchmarks including Celeb-DF(v2) and DFDC clearly demonstrate the effectiveness and generalization capacity of our approach.
DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification via Indirect Effect Analysis

We propose a novel, complete algorithm for the verification and analysis of feed-forward, ReLU-based neural networks. The algorithm, based on symbolic interval propagation, introduces a new method for determining split-nodes which evaluates the indirect effect that splitting has on the relaxations of successor nodes. We combine this with a new efficient linear-programming encoding of the splitting constraints to further improve the algorithm’s performance. The resulting implementation, DeepSplit, achieved speedups of 1–2 orders of magnitude and 21-34% fewer timeouts when compared to the current SoA toolkits.
Data Vaults for Blockchain-Empowered Accounting Information Systems

When designed, technologies and frameworks are not created to be as dynamic and flexible as to cater to the requirements of other domains, and so is the case with Blockchain technology.Specifically designed for cryptocurrency, Blockchain was not intended to be used in other domains.However, during the past few years, critics argued that Blockchain has the potential to deal with some unique requirements like confidentiality and immutability and can therefore be deployed in several areas other than cryptocurrency.The use of Blockchain to support Accounting Information Systems (AIS) through enterprise resource planning (ERP) is another motivating domain to investigate in this research.ERP is another promising technology that has gained significant attention across the globe.In this research, a hybrid solution is proposed to ensure AIS data integrity against any deliberate attempt or mala-fide intention for alteration or deletion from the database that can be verified at any later stage.Since Blockchain can be used to prevent any mutability in the stored data, the proposed solution presents a concept of Data Vaults backed by the Blockchain.To this end, we apply cryptographic primitives like SHA256 on the data inside the block and then chain that block to secure data vaults.So far, Blockchain has not yet proven itself as an alternative to any traditional database system.However, it can be applied in conjunction with the Relational Database Management Systems (RDBMS) to provide cost-effective yet robust solutions.This research demonstrates the application of a simple and lean version of Blockchain to assist enterprises in storing their financial and accounting data into data vaults, ensuring their data integrity against any alterations.The suggested cost-effective framework can be easily integrated into AIS and ERP systems to identify data breaches.
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into crossattention when training from scratch.We conduct a series of experiments through finetuning a translation model on data where either the source or target language has changed.These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model).We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields crosslingually aligned embeddings.The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.
FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack

Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle’s surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the nonplanar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.
Intelligent and adaptive web data extraction system using convolutional and long short-term memory deep learning networks

Data are crucial to the growth of e-commerce in today's world of highly demanding hyper-personalized consumer experiences, which are collected using advanced web scraping technologies. However, core data extraction engines fail because they cannot adapt to the dynamic changes in website content. This study investigates an intelligent and adaptive web data extraction system with convolutional and Long Short-Term Memory (LSTM) networks to enable automated web page detection using the You only look once (Yolo) algorithm and Tesseract LSTM to extract product details, which are detected as images from web pages. This state-of-the-art system does not need a core data extraction engine, and thus can adapt to dynamic changes in website layout. Experiments conducted on real-world retail cases demonstrate an image detection (precision) and character extraction accuracy (precision) of 97% and 99%, respectively. In addition, a mean average precision of 74%, with an input dataset of 45 objects or images, is obtained.
Attention-aware heterogeneous graph neural network

As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks. It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network (HIN). The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between nodes. HIN contains rich semantic and structural information, which requires a specially designed graph neural network. However, the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the HIN. In this paper, we propose an Attention-aware Heterogeneous graph Neural Network (AHNN) model to effectively extract useful information from HIN and use it to learn the embedding representation of nodes. Specifically, we first use node-level attention to aggregate and update the embedding representation of nodes, and then concatenate the embedding representation of the nodes on different meta-paths. Finally, the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of nodes. Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness.In this paper, we propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings.Our approach (Phrase-BERT) relies on a dataset of diverse phrasal paraphrases, which is automatically generated using a paraphrase generation model, as well as a large-scale dataset of phrases in context mined from the Books3 corpus.Phrase-BERT outperforms baselines across a variety of phrase-level similarity tasks, while also demonstrating increased lexical diversity between nearest neighbors in the vector space.Finally, as a case study, we show that Phrase-BERT embeddings can be easily integrated with a simple autoencoder to build a phrase-based neural topic model that interprets topics as mixtures of words and phrases by performing a nearest neighbor search in the embedding space.Crowdsourced evaluations demonstrate that this phrase-based topic model produces more coherent and meaningful topics than baseline word and phrase-level topic models, further validating the utility of Phrase-BERT.
Computable and Operationally Meaningful Multipartite Entanglement Measures

Multipartite entanglement is an essential resource for quantum communication, quantum computing, quantum sensing, and quantum networks. The utility of a quantum state |ψ⟩ for these applications is often directly related to the degree or type of entanglement present in |ψ⟩. Therefore, efficiently quantifying and characterizing multipartite entanglement is of paramount importance. In this work, we introduce a family of multipartite entanglement measures, called concentratable entanglements. Several well-known entanglement measures are recovered as special cases of our family of measures, and hence we provide a general framework for quantifying multipartite entanglement. We prove that the entire family does not increase, on average, under local operations and classical communications. We also provide an operational meaning for these measures in terms of probabilistic concentration of entanglement into Bell pairs. Finally, we show that these quantities can be efficiently estimated on a quantum computer by implementing a parallelized SWAP test, opening up a research direction for measuring multipartite entanglement on quantum devices.