Reviewerly/exHarmony huggingface dataset:
Regulated Photon Transport in Chaotic Microcavities by Tailoring Phase Space

Manipulating light dynamics in optical microcavities has been made mainly either in real or momentum space. Here we report a phase-space tailoring scheme, simultaneously incorporating spatial and momentum dimensions, to enable deterministic and in situ regulation of photon transport in a chaotic microcavity. In the time domain, the chaotic photon transport to the leaky region can be suppressed, and the cavity resonant modes show stronger temporal confinement with quality factors being improved by more than 1 order of magnitude. In the spatial domain, the emission direction of the cavity field is controlled on demand through rerouting chaotic photons to a desired channel, which is verified experimentally by the far-field pattern of a quantum-dot microlaser. This work paves a way to in situ study of chaotic physics and promoting advanced applications such as arbitrary light routing, ultrafast random bit generation, and multifunctional on-chip lasers.
An Analysis of COVID-19 Knowledge Graph Construction and Applications

The construction and application of knowledge graphs have seen a rapid increase across many disciplines in re-cent years. Additionally, the problem of uncovering relationships between developments in the COVID-19 pandemic and social me-dia behavior is of great interest to researchers hoping to curb the spread of the disease. In this paper we present a knowledge graph constructed from COVID-19 related tweets in the Los Angeles area, supplemented with federal and state policy announcements and disease spread statistics. By incorporating dates, topics, and events as entities, we construct a knowledge graph that describes the connections between these useful information. We use natural language processing and change point analysis to extract tweet-topic, tweet-date, and event-date relations. Further analysis on the constructed knowledge graph provides insight into how tweets reflect public sentiments towards COVID-19 related topics and how changes in these sentiments correlate with real-world events.
Lightweight Multidimensional Encrypted Data Aggregation Scheme With Fault Tolerance for Fog-Assisted Smart Grids

Encrypted data aggregation is an effective approach to preserve power consumption data confidentiality as well as reduce communication overhead in smart grids. As the core component of smart grids, smart meters periodically report encrypted multidimensional data to an aggregator gateway. However, heavy computation and communication overhead may hinder the wide applications of smart meters. Besides, once the secret key is leaked, encrypted data will be broken, thereby violating users’ privacy. In this article, we present a lightweight multidimensional encryption data aggregation (MD-EDA) scheme, enabling a grid control center to retrieve fine-grain aggregated data in a confidential way. In particular, each fog node is installed at the edge of networks, playing the role of aggregator gateway for data verification and aggregation in the same grid area. MD-EDA prevents adversaries from extracting valuable data even if the secret key is compromised. MD-EDA exploits Shamir secret-sharing technique to achieve transmission fault tolerance mechanism from smart meters to a corresponding fog node. The performance evaluation demonstrates the lightweight computation advantages of MD-EDA in the deployment of fog-assisted smart grids.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentation is one of the primary approaches for this. However, prior works utilize simple image transformations such as resizing, which limits input diversity. To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class. Our motivation comes from the humans' superior perception of an image printed on a 3D object. If the image is clear enough, humans can recognize the image content in a variety of viewing conditions. Likewise, if an adversarial example looks like the target class to the model, the model should also classify the rendered image of the 3D object as the target class. The ODI method effectively diversifies the input by leveraging an ensemble of multiple source objects and randomizing viewing conditions. In our experimental results on the ImageNet-Compatible dataset, this method boosts the average targeted attack success rate from 28.3% to 47.0% compared to the state-of-the-art methods. We also demonstrate the applicability of the ODI method to adversarial examples on the face verification task and its superior performance improvement. Our code is available at https://github.com/dreamflake/ODI.
CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup. The obtained experimental results revealed that the proposed optimization accelerates the inference time of the neural network in the cases with up to 5 out of 6 cameras containing target objects.
Optimal survival trees

Abstract Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.
Privacy-Preserved Cyberattack Detection in Industrial Edge of Things (IEoT): A Blockchain-Orchestrated Federated Learning Approach

The Industrial Internet ofThings (IIoT) plays an essential role in the digital renovation of conventional industries to Industry 4.0. With the connectivity of sensors, actuators, appliances, and other industrial objects, IIoT enables data availability, improved analytics, and automatic control. Thanks to the complex distributed nature, a wide range of stealthy and evolving cyberattacks become a major threat to the trustworthiness and security of IIoT systems. This makes the standard security procedures unable to assure the trustworthiness of IIoT that protect against cyberattacks. As a remedy, this article presents a blockchain-orchestrated edge intelligence (BoEI) framework that integrates an innovative decentralized federated learning (called Fed-Trust) for cyberattack detection in IIoT. In the Fed-Trust, a temporal convolutional generative network is introduced to enable semi-supervised learning from semi-labeled data. BoEI includes reputation-based blockchain to enable decentralized recording and verification of the transactions for guaranteeing the security and privacy of data and gradients. Fog computing is exploited to offload the block mining operation from the edge side thereby improving the overall computation and communication performance of Fed-Trust. Proof of concept simulations using two public datasets validate the robustness and efficiency of the Fed-Trust over the cutting-edge cyberattack detection approaches.
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion

Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heuristically cluster views for local fusion, thus restricting their ability to consider all views jointly. Our key insight is that greater detail can be retained without restricting view diversity by learning a view-fusion function conditioned on camera pose and image content. We propose to learn this multi-view fusion using a transformer. To this end, we introduce VoRTX, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion. Our model is occlusion-aware, leveraging the transformer architecture to predict an initial, projective scene geometry estimate. This estimate is used to avoid back-projecting image features through surfaces into occluded regions. We train our model on ScanNet and show that it produces better reconstructions than state-of-the-art methods. We also demonstrate generalization without any fine-tuning, outperforming the same state-of-the-art methods on two other datasets, TUM-RGBD and ICL-NUIM.
Cross-Speaker Emotion Disentangling and Transfer for End-to-End Speech Synthesis

The cross-speaker emotion transfer task in text-to-speech (TTS) synthesis particularly aims to synthesize speech for a target speaker with the emotion transferred from reference speech recorded by another (source) speaker. During the emotion transfer process, the identity information of the source speaker could also affect the synthesized results, resulting in the issue of speaker leakage, i.e., synthetic speech may have the voice identity of the source speaker rather than the target speaker. This paper proposes a new method with the aim to synthesize controllable emotional expressive speech and meanwhile maintain the target speaker’s identity in the cross-speaker emotion TTS task. The proposed method is a Tacotron2-based framework with emotion embedding as the conditioning variable to provide emotion information. Two emotion disentangling modules are contained in our method to 1) get speaker-irrelevant and emotion-discriminative embedding, and 2) explicitly constrain the emotion and speaker identity of synthetic speech to be that as expected. Moreover, we present an intuitive method to control the emotion strength in the synthetic speech for the target speaker. Specifically, the learned emotion embedding is adjusted with a flexible scalar value, which allows controlling the emotion strength conveyed by the embedding. Extensive experiments have been conducted on a Mandarin disjoint corpus, and the results demonstrate that the proposed method is able to synthesize reasonable emotional speech for the target speaker. Compared to the state-of-the-art reference embedding learned methods, our method gets the best performance on the cross-speaker emotion transfer task, indicating that our method achieves the new state-of-the-art performance on learning the speaker-irrelevant emotion embedding. Furthermore, the strength ranking test and pitch trajectories plots demonstrate that the proposed method can effectively control the emotion strength, leading to prosody-diverse synthetic speech.
Quantum distributed deep learning architectures: Models, discussions, and applications

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) has emerged to complement existing DL methods. Furthermore, a quantum distributed deep learning (QDDL) technique that combines and maximizes these advantages is getting attention. This paper compares several model structures for QDDL and discusses their possibilities and limitations to leverage QDDL for some representative application scenarios.
Cyber-Attacks Against Critical Infrastructure

In the cyber world, the most important threat focuses on critical infrastructure (CI). CI encompasses the structures and functions that are vital to society’s uninterrupted functioning. It comprises physical facilities and structures as well as electronic functions and services. Critical infrastructure systems comprise a heterogeneous mixture of dynamic, interactive, and non-linear elements. In recent years, attacks against critical infrastructures, critical information infrastructures and the Internet have become ever more frequent, complex and targeted because perpetrators have become more professional. Attackers can inflict damage or disrupt on physical infrastructure by infiltrating the digital systems that control physical processes, damaging specialized equipment and disrupting vital services without a physical attack. Those threats continue to evolve in complexity and sophistication.
An Adaptive Tunicate Swarm Algorithm for Optimization of Shallow Foundation

This paper aims to introduce an adaptive metaheuristic algorithm based on tunicate swarm optimization (TSA) for effectively solving global optimization problems and the optimum design of a shallow spread foundation. The proposed adaptive tunicate swarm optimization (ATSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm’s exploration ability while also preventing premature convergence. The suggested algorithm’s performance is confirmed using a set of 23 mathematical test functions of well-known CEC 2017 and the outcomes are compared with TSA as well as some effective optimization algorithms. In addition, the new method automates the optimum design of shallow spread foundations while taking two objectives into account: cost and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions. The analysis and design procedures are based on both geotechnical and structural limit states. A case study of a spread foundation has been solved using the proposed methodology, and a sensitivity analysis has been conducted to investigate the effect of soil parameters on the total cost and embedded CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions of the foundation. The simulation results demonstrate that, when compared to other competing algorithms, ATSA is superior and may produce better optimal solutions.
Mobile-assisted language learning in Chinese higher education context: a systematic review from the perspective of the situated learning theory

Abstract Recent years have witnessed numerous systematic investigations on mobile-assisted language learning (MALL). However, very few research synthesis studies focused on the higher education context in mainland China. This paper provides a systematic review of the findings of 23 studies published between 2015 and 2020 in mainland China. The aim of this review is twofold: 1) to examine prevalent researched elements in extant MALL studies; and 2) to investigate ways in which the two original components of the Situated Learning Theory (SLT), including Legitimate Peripheral Participation and Communities of Practice (Lave &amp; Wenger, 1991), have a bearing on MALL. This systematic review resulted in two major findings. First, five key researched elements were revealed, e.g., target language teaching areas, and adopted applications/software. Second, guided by the two original elements of SLT, four derived elements were identified, namely authenticity, social interactions and collaborations, apprenticeship learning, and beliefs and behaviours. The findings suggest the need to develop sound MALL pedagogies associated with sociocultural aspects of language learning in relevant contexts. This review study also provides insights into how Chinese language professionals and practitioners can improve curriculum design and resource development to adapt to future trends in MALL.
Reversible data hiding based on adaptive IPVO and two-segment pairwise PEE

• A new prediction method AIPVO can adaptively determine predicted values. • Defined a new evaluation index of the prediction error sequence. • Designing novel 2D mapping rules according to different distributions of prediction error. Improved pixel value order (IPVO) and pairwise prediction error extension (PEE) strategies can effectively improve the hidden capacity and invisibility of reversible data hiding (RDH) scheme since they can increase the effective prediction error rate (EPER). To further improve EPER, this paper proposes an improved RDH scheme by combining adaptive IPVO (AIPVO) with two-segment pairwise PEE. Different from the fixed predicted value (such as the highest value) for all blocks in the existing IPVO, AIPVO adaptively determine the predicted value of each block according to its own complexity level. To obtain the complexity level of each block, the block complexity level division model is constructed and optimized with EPER as the objective function. AIPVO can effectively improve EPER due to the fact that the pixels with large fluctuate in a block are eliminated by adaptively selecting the predicted value. In the embedding stage, the two-segment pairwise PEE is designed according to the distribution characteristics of the prediction error sequence obtained by AIPVO. Specifically, the prediction error sequence is firstly divided into two segments, and the 2D mappings of the front and back parts use small-magnitude and slightly large-magnitude prediction errors to embed data, respectively. Experimental results show that this paper scheme is better than the latest research results. For example, PSNR of the Lena image reaches 61.55 dB under capacity of 10,000 bit, and the gain is 0.39 dB against the best result in the literature.
Lw-Count: An Effective Lightweight Encoding-Decoding Crowd Counting Network

Crowd counting is a task of intelligent applications, and its operation efficiency is very important. However, in order to obtain a better counting performance, most of the existing works often design larger and more complex network structures, which will result in them occupying considerable memory, time and other resources at runtime, seriously limiting their deployment scope and making it difficult to be widely used in practical scenarios. In this paper, in order to overcome the above problems, we propose an effective lightweight encoding-decoding crowd counting network, named Lw-Count. Specifically, in the encoding process, we design an efficient and lightweight convolution module (ELCM), which extracts the crowd features in the network through a refined ghost block to reduce the network parameters and computing cost, and solves the problem of inaccurate counting caused by uneven crowd distribution through spatial group normalization (SGN). In the decoding process, we design a scale regression module (SRM) to reduce the error details and chessboard effect caused by linear interpolation and deconvolution. In addition, we design a new loss function, which enhances the spatial correlation of the density map and the sensitivity of the crowd through a regional normalized cross-correlation loss and counting loss, to ensure the counting accuracy. Extensive experiments on five mainstream datasets demonstrate that Lw-Count achieves an optimal trade-off between counting performance and running speed compared with other methods.
On the impact of VR assessment on the quality of experience of highly realistic digital humans

Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud encoding solutions handle visually-lossless compression, are discussed.
Automatic collective motion tuning using actor-critic deep reinforcement learning

Collective behaviours such as swarm formation of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, tuning the behaviour of a group of agents so that they swarm, is difficult. Behaviour-bootstrapping algorithms permit agents to self-tune behaviour adapted for their physical form and associated movement constraints. This paper proposes a reinforcement learning framework to tune collective motion behaviours from random behaviours. The learning process is guided by a novel reward function capable of autonomously detecting generic collective motion behaviours from sensor data about the relative velocity and position of neighbouring agents. Our reward function is designed using a meta-learner trained on a human-labelled collective motion dataset. We demonstrate that our reinforcement learner can tune the behaviour of randomly moving groups so that structured collective motion emerges. We compare our framework to an existing developmental evolutionary framework for this purpose. Our results demonstrate that the proposed learning framework can generate behaviours with different collective motion characteristics more quickly than existing approaches. In addition, the trained reinforcement learner can tune the behaviour of robots with movement characteristics that it has not been trained on.