Reviewerly/exHarmony huggingface dataset:
Long future frame prediction using optical flow‐informed deep neural networks for enhancement of robotic teleoperation in high latency environments

Abstract High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image‐to‐image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical flow components reflecting real‐time control inputs are added to the standard RGB channels of the input image. We have experimented with three data sets of 20,000 input images each that were generated using our custom‐designed teleoperation simulator with a 500‐ms delay added between the input and target frames. Structural Similarity Index Measures (SSIMs) of 0.60 and Multi‐SSIMs of 0.68 were achieved when training the cGAN with three‐channel RGB image data. With the five‐channel input data (incorporating optical flow) these values improved to 0.67 and 0.74, respectively. Applying Fleiss' κ gave a score of 0.40 for three‐channel RGB data, and 0.55 for five‐channel optical flow‐added data. We are confident the predicted synthetic frames are of sufficient quality and reliability to be presented to teleoperators as a video feed that will enhance teleoperation. To the best of our knowledge, we are the first to attempt to reduce the impacts of latency through future frame prediction using deep neural networks.
Construction of bias-preserving operations for pair-cat codes

Fault-tolerant quantum computation with depolarization error often requires demanding error threshold and resource overhead. If the operations can maintain high noise bias---dominated by dephasing error with small bit-flip error---we can achieve hardware-efficient fault-tolerant quantum computation with a more favorable error threshold. Distinct from two-level physical systems, multilevel systems (such as harmonic oscillators) can achieve a desirable set of bias-preserving quantum operations while using continuous engineered dissipation or Hamiltonian protection to stabilize to the encoding subspace. For example, cat codes stabilized with driven-dissipation or Kerr nonlinearity can possess a set of bias-preserving gates while continuously correcting bosonic dephasing error. However, cat codes are not compatible with continuous quantum error correction against excitation loss error, because it is challenging to continuously monitor the parity to correct photon loss errors. In this work, we generalize the bias-preserving operations to pair-cat codes, which can be regarded as a multimode generalization of cat codes, to be compatible with continuous quantum error correction against both bosonic loss and dephasing errors. Our results open the door towards hardware-efficient robust quantum information processing with both bias-preserving operations and continuous quantum error correction simultaneously correcting bosonic loss and dephasing errors.
Daily solar radiation prediction using NARX and MLP-NNs networks: A Case study of Kenitra City, Morocco

The aim of this paper is to predict daily solar radiation by two artificial neural network models using weather data. The first model is a multilayer perceptron neural network (MLP-NNs)while the second is the nonlinear autoregressive network with exogenous inputs (NARX). In order to investigate the influence of different meteorological variables on daily solar radiation forecast, there are four input feature combinations to evaluate. To reduce the error, we used three training algorithms to develop the two models MLP-NNs and NARX, adjusting the number of neurons in each and the delay in the latter. These models were compared using four evaluation metrics: Mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and the regression coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor

The convolution operator is a crucial kernel for many computer vision and signal processing applications that rely on deep learning (DL) technologies. As such, the efficient implementation of this operator has received considerable attention in the past few years for a fair range of processor architectures. In this paper, we follow the technology trend toward integrating long SIMD (single instruction, multiple data) arithmetic units into high performance multicore processors to analyse the benefits of this type of hardware acceleration for latency-constrained DL workloads. For this purpose, we implement and optimise for the Fujitsu processor A64FX, three distinct methods for the calculation of the convolution, namely, the lowering approach, a blocked variant of the direct convolution algorithm, and the Winograd minimal filtering algorithm. Our experimental results include an extensive evaluation of the parallel scalability of these three methods and a comparison of their global performance using three popular DL models and a representative dataset.
The moderating effect of psychological trust on knowledge spillovers and firms’ open innovation

Psychological trust is an important link in building interpersonal relationships and has a significant impact on the attitude and behavior of knowledge subjects. Based on the characteristics of knowledge attributes, this paper analyzed the data of 180 high-tech firms in China from 2014 to 2020 to deeply explore the effects of explicit knowledge spillover and tacit knowledge spillover on firms' open innovation, and the moderating effect of psychological trust on the relationship between the two. It is found that: first, explicit knowledge spillover and tacit knowledge spillover have an inverted U-shaped relationship with firms' open innovation, i.e., the effect of open innovation increases and then decreases as the degree of knowledge spillover increases; second, psychological trust positively moderates the non-linear relationship between knowledge spillover and firms' open innovation. This paper provides a rational explanation of firms' management behavior from a psychological perspective, and enriches and expands the research related to knowledge spillover, firms' open innovation and psychological trust. It is suggested that firms should pay more attention to inter-organizational trust relationships and pay attention to the psychological growth and development of knowledge employees to improve open innovation in firms.
Part-aware attention correctness for video salient object detection

Video salient object detection (VSOD), aiming to detect the most conspicuous objects or regions in a video, has become an important research topic over the past few years. Preliminary studies mainly focus on spatial–temporal architecture that heavily relies on implicit attention model to aggregate complementary information from adjacent video frames. Despite the remarkable improvements, existing approaches pay little attention to cross-video affinities, which is important to build explicit attention schema for VSOD. To this end, we propose a novel attention correctness strategy to supervise the aggregation process. Specifically, different from previous works, we employ pairwise training schema, leveraging both positive and negative aggregation supervision to explore inter-video affinities for VSOD. The proposed mechanism successfully suppresses negative correspondence for video frames and reinforces discriminative feature mining for conspicuous objects. To enhance intra-video correspondence, we propose part-aware similarity aggregation module that helps intra-video affinities to segment the salient objects with video-level context. Extensive experiments are conducted on six popular benchmarks, including FBMS, DAVIS, DAVSOD, SegTrack-V2, VOS and ViS. Experimental results on challenging scenes (i.e., for DAVSOD-T, we achieve an improvement of 0.4% for MAE, 1.1% for maximum F-measure and 0.5% for S-measure compared with other competitive models) demonstrate the effectiveness of our proposed method.
Oblivious-Transfer Complexity of Noisy Coin-Toss via Secure Zero Communication Reductions

In p-noisy coin-tossing, Alice and Bob obtain fair coins which are of opposite values with probability p. Its Oblivious-Transfer (OT) complexity refers to the least number of OTs required by a semi-honest perfectly secure 2-party protocol for this task. We show a tight bound of $$\varTheta (\log 1/p)$$ for the OT complexity of p-noisy coin-tossing. This is the first instance of a lower bound for OT complexity that is independent of the input/output length of the function. We obtain our result by providing a general connection between the OT complexity of randomized functions and the complexity of Secure Zero Communication Reductions (SZCR), as recently defined by Narayanan et al. (TCC 2020), and then showing a lower bound for the complexity of an SZCR from noisy coin-tossing to (a predicate corresponding to) OT.
Affective Knowledge Augmented Interactive Graph Convolutional Network for Chinese-Oriented Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis(ABSA) aims to identify the sentiment polarity of specific aspects in sentences, which can more accurately mine the sentiment polarity of users towards different aspects.Most of the existing works derive the sentiment features of specific aspects by interactively learning the dependencies between different aspects of the context.However, the above work has neglected to use the external affective commonsense knowledge to augment the ability of the Graph Convolutional Networks(GCNs) to interactively capture sentiment dependencies of the inter-aspect words in different contexts.In addition, compared to the ABSA research in English, the existing research pays less attention to the Chinese-oriented research.Meanwhile, multi-head self-sttention(MHSA) is applied to extract richer context syntax and semantic interaction features.In this paper, we propose a novel knowledge-aware model in which affective knowledge augments interactive GCN for Chinese-oriented ABSA, namely AKM-IGCN.Moreover, this model can be applied to effectively analyze both Chinese and English comments simultaneously.Hence, we conducted experiments on four Chinese datasets(Camera, Phone, Notebook and Car) and six English benchmark datasets(Restaurant14, Restaurant15, Restaurant16, Twitter, MAMS, Tshirt).Experimental results illustrate that our proposed model outperforms or approaches state-of-the-art models.
Imitation Learning over Heterogeneous Agents with Restraining Bolts

A common problem in Reinforcement Learning (RL) is that the reward function is hard to express. This can be overcome by resorting to Inverse Reinforcement Learning (IRL), which consists in first obtaining a reward function from a set of execution traces generated by an expert agent, and then making the learning agent learn the expert's behavior –this is known as Imitation Learning (IL). Typical IRL solutions rely on a numerical representation of the reward function, which raises problems related to the adopted optimization procedures.We describe an IL method where the execution traces generated by the expert agent, possibly via planning, are used to produce a logical (as opposed to numerical) specification of the reward function, to be incorporated into a device known as Restraining Bolt (RB). The RB can be attached to the learning agent to drive the learning process and ultimately make it imitate the expert. We show that IL can be applied to heterogeneous agents, with the expert, the learner and the RB using different representations of the environment's actions and states, without specifying mappings among their representations.
Joint Representation Learning for Anomaly Detection in Surveillance Videos

Video anomaly detection in the unconstrained environment is challenging due to various background scenes, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve remarkable performance on video anomaly detection. In this paper, we propose a joint representation learning structure for video anomaly detection. The proposed architecture extracts features from the object appearance and their associate motion features via different encoders based on ResNet network architecture. Our network architecture is designed to combine spatial and temporal features, which share the same decoder. Using a joint representation learning approach, the proposed architecture effectively learn both appearance and motion features to detect anomalies in various scene scenarios. The experiments on three benchmark datasets demonstrate the remarkable detection accuracy with respect to existing state-of-the-art methods, which achieve 96.5%, 86.9%, and 73.4% in UCSD Pedestrian, CHUK Avenue, and ShanghaiTech datasets, respectively.
Split-GCN: Effective Interactive Annotation for Segmentation of Disconnected Instance

Annotating object boundaries by humans demands high costs. Recently, polygon-based annotation methods with human interaction have shown successful performance. However, given the connected vertex topology, these methods exhibit difficulty predicting the disconnected components in an object. This article introduces Split-GCN, a novel architecture based on the polygon approach and self-attention mechanism. By offering the direction information, Split-GCN enables the polygon's vertices to move more precisely to the object boundary. Our model successfully predicts disconnected components of an object by transforming the initial topology using the context exchange about the dependencies of vertices. Split-GCN demonstrates competitive performance with the state-of-the-art models on Cityscapes and even higher performance with the baseline models. On four cross-domain datasets, we confirm our model's generalization ability.
Human resource management practices and technological innovation: an empirical study in an emerging market economy

This research examines the relationship between human resource management practices (HRMPs), i.e., staffing, compensation, performance appraisal, training, participation in decision-making, and technological innovation in the Kingdom of Bahrain's banking sector. The primary data were collected via a questionnaire using a simple random sampling method. The results showed that HRMPs positively correlated with technological innovation. The principal implications for future research in this field concern paving the pa for developing theoretical approaches to studying HRM orientations in Bahraini banks. Moreover, this research would assist decision-makers in Bahrain's banking sector in adopting HRMPs based on the relevance of HRM and its beneficial association with technological innovation.
Traffic Load Learning Towards Early Detection of Intrusion in Industrial mMTC Networks

The fifth generation network offers a powerful infrastructure for Industrial internet of things (IoT) in that its support on massive machine-type communications (mMTC). Recently, grant-free access has been recognized as the promising new access approach for mMTC; however, it also introduces the potential risk. To timely discover intrusion, in this article, we propose a learning network by extracting traffic load information from the states (success, collision, and idle) of access resources observed at media access control and physical layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic load estimation, traffic load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion, i.e., false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.
Exploiting Partial Observability for Optimal Deception

Deception is a useful tool in situations where an agent operates in the presence of its adversaries. We consider a setting where a supervisor provides a reference policy to an agent, expects the agent to operate in an environment by following the reference policy, and partially observes the agent's behavior. The agent instead follows a different, deceptive policy to achieve a different task. We model the environment with a Markov decision process and study the synthesis of optimal deceptive policies under partial observability. We formalize the notion of deception as a hypothesis testing problem and show that the synthesis of optimal deceptive policies is NP-hard. As an approximation, we consider the class of mixture policies, which provides a convex optimization formulation of the deception problem. We give an algorithm that converges to the optimal mixture policy. We also consider a special class of Markov decision processes where the transition and observation functions are deterministic. For this case, we give a randomized algorithm for path planning that generates a path for the agent in polynomial time and achieves the optimal value for the considered objective function.
Hyperbolic Neural Collaborative Recommender

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in Euclidean geometry. Since recent studies have shown that the bipartite graph structure has the non-Euclidean latent anatomy, Euclidean embeddings may suffer from a certain degree of distortion. In this work, we present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relationships among users/items for collaborative filtering tasks. HNCR first introduces a neighbor construction strategy to build user and item semantic neighborhoods. Then HNCR develops a framework based on deep learning and hyperbolic geometry to integrate constructed neighborhoods into recommendation. To evaluate our method, we conduct experiments on the four datasets. Experimental results show the superiority of HNCR compared with its Euclidean counterpart and state-of-the-art recommendation baselines. The results also indicate that hyperbolic representations can reflect meaningful data insights.
An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML

Model accuracy is the traditional metric employed in machine learning (ML) applications. However, privacy, fairness, and robustness guarantees are crucial as ML algorithms increasingly pervade our lives and play central roles in socially important systems. These four desiderata constitute the pillars of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trustworthy ML</i> (TML) and may mutually inhibit or reinforce each other. It is necessary to understand and clearly delineate the trade-offs among these desiderata in the presence of adversarial attacks. However, threat models for the desiderata are different and the defenses introduced for each leads to further trade-offs in a multilateral adversarial setting (i.e., a setting attacking several pillars simultaneously). The first half of the paper reviews the state of the art in TML research, articulates known multilateral trade-offs, and identifies open problems and challenges in the presence of an adversary that may take advantage of such multilateral trade-offs. The fundamental shortcomings of statistical association-based TML are discussed, to motivate the use of causal methods to achieve TML. The second half of the paper, in turn, advocates the use of causal modeling in TML. Evidence is collected from across the literature that causal ML is well-suited to provide a unified approach to TML. Causal discovery and causal representation learning are introduced as essential stages of causal modeling, and a new threat model for causal ML is introduced to quantify the vulnerabilities introduced through the use of causal methods. The paper concludes with pointers to possible next steps in the development of a causal TML pipeline.
Design and application of intelligent monitoring and management system for campus weak current pipeline network based on IoT+GIS technology

In view of the characteristics and problems in the process of information construction and operation of the campus weak current pipeline network, combined with the actual needs of the development of smart campus, an intelligent monitoring and management system of the weak current pipeline network based on IoT+GIS technology is designed and the campus geospatial information database as well as the weak current pipeline network database are established at the same time. The three-dimensional visual integration management of weak current communication resources, such as underground pipeline laying, optical fiber routing analysis, fiber-optic fusion relationship, integrated wiring in the building, off-motion monitoring of well cover, and so on, is realized, which greatly improves the campus weak current pipeline network resources management and maintenance efficiency, and provides scientific decision-making basis for rational and effective use of these resources.
An Efficient Approach for Solving Robotic Task Sequencing Problems Considering Spatial Constraint

In many industrial applications, the robot is required to perform a set of repetitive tasks without collision as quickly as possible to maximize productivity. It is essential to find an optimal sequence of collision-free motions to visit a set of repetitive tasks and determine the optimal robot configuration used to complete each task, which is formulated as the Robotic Task Sequencing Problem (RTSP). In this paper, we propose an approach based on a typical decoupling strategy to solve RTSP efficiently. Firstly, the task execution sequence is obtained by solving a TSP in task space and candidates of the optimal configuration for each task are selected from the collision-free configuration space according to the self-designed optimality value derived from the similarity to the initial configuration in configuration space. Then the optimal configuration for each task is determined by finding the shortest path in a graph that is constructed according to the task execution sequence and optimal configuration candidates. Finally, collision-free motion trajectories required for the robot to complete each task with the optimal configuration are generated by running a motion planning algorithm. Through a series of experiments, we show that our approach outperforms the state-of-the-art approaches when applied to the RTSP instances in a cluttered 3D environment, with up to 29.6% reduction in computation time while providing comparable performance.