Reviewerly/exHarmony huggingface dataset:
Squeezing of intensity noise in nanolasers and nanoLEDs with extreme dielectric confinement

Low-noise light sources are important for on-chip interconnects, sensors, and quantum technology. We show that, using novel cavity designs featuring deep sub-wavelength confinement, it is possible to strongly reduce quantum fluctuations over a large bandwidth. The results could enable integrated sources with extremely low amplitude noise.
Cross-lingual Retrieval for Iterative Self-Supervised Training

Recent studies have demonstrated the cross-lingual alignment ability of multilingual pretrained language models. In this work, we found that the cross-lingual alignment can be further improved by training seq2seq models on sentence pairs mined using their own encoder outputs. We utilized these findings to develop a new approach -- cross-lingual retrieval for iterative self-supervised training (CRISS), where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. Using this method, we achieved state-of-the-art unsupervised machine translation results on 9 language directions with an average improvement of 2.4 BLEU, and on the Tatoeba sentence retrieval task in the XTREME benchmark on 16 languages with an average improvement of 21.5% in absolute accuracy. Furthermore, CRISS also brings an additional 1.8 BLEU improvement on average compared to mBART, when finetuned on supervised machine translation downstream tasks.
A Novel Dynamic Clustering Method by Integrating Marine Predators Algorithm and Particle Swarm Optimization Algorithm

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data.Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed.The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching.The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers.The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases.Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results.The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.
Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting

Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman performance in visual recognition tasks. However, CNNs can easily be fooled by adversarial examples (AEs), i.e., maliciously crafted images that force the networks to predict an incorrect output while being extremely similar to those for which a correct output is predicted. Regular AEs are not robust to input image transformations, which can then be used to detect whether an AE is presented to the network. Nevertheless, it is still possible to generate AEs that are robust to such transformations. This article extensively explores the detection of AEs via image transformations and proposes a novel methodology, called defense perturbation, to detect robust AEs with the same input transformations the AEs are robust to. Such a defense perturbation is shown to be an effective counter-measure to robust AEs. Furthermore, multinetwork AEs are introduced. This kind of AEs can be used to simultaneously fool multiple networks, which is critical in systems that use network redundancy, such as those based on architectures with majority voting over multiple CNNs. An extensive set of experiments based on state-of-the-art CNNs trained on the Imagenet dataset is finally reported.
Self-Learning for Weakly Supervised Gleason Grading of Local Patterns

Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Motivated by this, we propose a novel weakly-supervised deep-learning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin, as well as state-of-the-art methods on global biopsy-level scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.
BSSPD: A Blockchain-Based Security Sharing Scheme for Personal Data with Fine-Grained Access Control

Privacy protection and open sharing are the core of data governance in the AI-driven era. A common data-sharing management platform is indispensable in the existing data-sharing solutions, and users upload their data to the cloud server for storage and dissemination. However, from the moment users upload the data to the server, they will lose absolute ownership of their data, and security and privacy will become a critical issue. Although data encryption and access control are considered up-and-coming technologies in protecting personal data security on the cloud server, they alleviate this problem to a certain extent. However, it still depends too much on a third-party organization’s credibility, the Cloud Service Provider (CSP). In this paper, we combined blockchain, ciphertext-policy attribute-based encryption (CP-ABE), and InterPlanetary File System (IPFS) to address this problem to propose a blockchain-based security sharing scheme for personal data named BSSPD. In this user-centric scheme, the data owner encrypts the sharing data and stores it on IPFS, which maximizes the scheme’s decentralization. The address and the decryption key of the shared data will be encrypted with CP-ABE according to the specific access policy, and the data owner uses blockchain to publish his data-related information and distribute keys for data users. Only the data user whose attributes meet the access policy can download and decrypt the data. The data owner has fine-grained access control over his data, and BSSPD supports an attribute-level revocation of a specific data user without affecting others. To further protect the data user’s privacy, the ciphertext keyword search is used when retrieving data. We analyzed the security of the BBSPD and simulated our scheme on the EOS blockchain, which proved that our scheme is feasible. Meanwhile, we provided a thorough analysis of the storage and computing overhead, which proved that BSSPD has a good performance.
Multimodal Marketing Intent Analysis for Effective Targeted Advertising

People&#x2019;s daily information sharing and acquisition through the Internet has become more and more popular. The comprehensive multimodal marketing advertorial generated by &#x2018;We Media&#x2019; accounts besides the normal social news is gaining its importance on social media platforms. In order to achieve effective advertising, the marketing intent understanding is a key step towards generating targeted advertising strategies (push advertorials to specific people at a specific time). However, advertorials in real are usually designed to pretend as normal social news with a wide range of contents. This poses big challenges to the platforms on accurately recognizing and analyzing the marketing intents behind the advertorials. As a pioneering study, we address this new problem of multimodal-based marketing intent analysis and answer three core questions: (1) does a piece of social news contain marketing intent? (2) what is the topic of marketing intent? (3) what is the extent of marketing intent? Towards this end, we propose a novel Multimodal-based Marketing Intent Analysis scheme (MMIA) to estimate the marketing intent embedded in the multimodal contents. Specifically, a novel supervised neural autoregressive model (SmiDocNADE) is proposed to enhance the discriminative capacity of the learned hidden features so that a single system is capable of solving the three questions. In order to effectively model inter-correlations between images and text in advertorials, we fuse multimodal data and extract features by Graph Convolution Networks as an enhancement to SmiDocNADE. The extensive evaluations demonstrate the advantages of our proposed system in multimodal-based marketing intent analysis from multiple aspects.
VSync: push-button verification and optimization for synchronization primitives on weak memory models

Implementing highly efficient and correct synchronization primitives on modern Weak Memory Model (WMM) architectures, such as ARM and RISC-V, is very difficult even for human experts. We introduce VSync, a framework to assist in optimizing and verifying synchronization primitives on WMM architectures. VSync automatically detects missing and overly-constrained barriers, while ensuring essential safety and liveness properties. VSync relies on two novel techniques: 1) Adaptive Linear Relaxation (ALR), which utilizes barrier monotonicity and speculation to quickly find a correct maximally-relaxed barrier combination; and 2) Await Model Checking (AMC), which for the first time makes it possible to check termination of await loops on WMMs.
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark

Abstract: In the last few years, Automated Machine Learning (AutoML) has gained much attention. With that said, the question arises whether AutoML can outperform results achieved by human data scientists. This paper compares four AutoML frameworks on 12 different popular datasets from OpenML; six of them supervised classification tasks and the other six supervised regression ones. Additionally, we consider a real-life dataset from one of our recent projects. The results show that the automated frameworks perform better or equal than the machine learning community in 7 out of 12 OpenML tasks.
Blind Stereoscopic Image Quality Evaluator Based on Binocular Semantic and Quality Channels

Human beings always evaluate the perceptual quality of an image coupled with identifying the semantic content of images. This paper addresses the correlation issue between stereoscopic image quality assessment (SIQA) and semantic recognition. In contrast to the previous SIQA methods that relied on binocular quality-aware features of a stereoscopic image, our approach also extracts binocular semantic features using a pre-trained deep convolutional neural network (DCNN) on a large dataset like ImageNet dataset, as well as the manually designed binocular quality-aware features. It can solve the problem of limited SIQA dataset size and facilitate better prediction on the quality. Experimental results demonstrate that the binocular semantic features are a good predictor for the stereoscopic image quality. The proposed method outperforms the state-of-the-art SIQA methods on four benchmark SIQA datasets. Significantly, all Spearman rank-order correlation coefficients (SROCCs) between the predicted scores and the subjective scores on the four datasets exceed 0.95. The MATLAB source code of the proposed method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kyohoonsim/Blind-Stereoscopic-Image-Quality-Evaluator-based-on-Binocular-Semantic-and-Quality-Channels</uri> <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kyohoonsim</uri> .
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation

Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing.Prior research focuses on exploring sequential information but ignores the discourse structures of conversations.In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC.To this end, we propose a discourse-aware graph neural network (ERMC-DisGCN) for ERMC.In particular, we design a relational convolution to lever the self-speaker dependency of interlocutors to propagate contextual information.Furthermore, we exploit a gated convolution to select more informative cues for ERMC from dependent utterances.The experimental results show our method outperforms multiple baselines, illustrating that discourse structures are of great value to ERMC.
Towards Balancing VR Immersion and Bystander Awareness

Head-mounted displays (HMDs) increase immersion into virtual worlds. The problem is that this limits headset users' awareness of bystanders: headset users cannot attend to bystanders' presence and activities. We call this the HMD boundary. We explore how to make the HMD boundary permeable by comparing different ways of providing informal awareness cues to the headset user about bystanders. We adapted and implemented three visualization techniques (Avatar View, Radar and Presence++) that share bystanders' location and orientation with headset users. We conducted a hybrid user and simulation study with three different types of VR content (high, medium, low interactivity) with twenty participants to compare how these visualization techniques allow people to maintain an awareness of bystanders, and how they affect immersion (compared to a baseline condition). Our study reveals that a see-through avatar representation of bystanders was effective, but led to slightly reduced immersion in the VR content. Based on our findings, we discuss how future awareness visualization techniques can be designed to mitigate the reduction of immersion for the headset user.
Blockchain announcements and stock value: a technology management perspective

Purpose This study aims to explore the impact of blockchain announcements on enterprises' stock market value. Design/methodology/approach Based on resource-based theory, this study constructs a complete framework of the impact mechanism of blockchain announcements on the stock price of the announcing firm using the data of 143 blockchain announcements. An event study methodology is used in this research, and the market model, market-adjusted model and Carhart four-factor model are used to estimate stock abnormal returns after the blockchain announcement; and the cross-sectional regression model is used to test the influencing factors. Findings Blockchain announcements elicit a significantly positive market reaction on the release day. Compared to announcements not pertaining to technical innovation, blockchain technical innovation announcements exhibit a more positive market reaction towards the announcing companies. Strategic-level announcements exhibit a more positive market reaction than operational-level announcements. Enterprise characteristics, such as enterprise-scale and enterprise innovation ability, do not affect stock market reactions to blockchain announcements. Practical implications The findings reveal the economic value of conducting blockchain activities in the Chinese stock market. Findings of this study can help managers understand the value of implementing blockchain activities in a different market environment and guide them on how to improve the market value of their enterprises through the active implementation of blockchain activities. Originality/value To the best of the authors’ knowledge, this is the first event study to focus solely on the value of pure blockchain announcements in an emerging market. This study considers multiple resource and capability factors that would influence blockchain technology adoption, improve the current understanding of how blockchain announcements affect corporate stock prices and provide directions for future comparative studies of market reactions to blockchain announcements in different stock markets.
WebFormer: The Web-page Transformer for Structure Information Extraction

Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price. It is an important research topic which has been widely studied in document understanding and web search. Recent natural language models with sequence modeling have demonstrated state-of-the-art performance on web information extraction. However, effectively serializing tokens from unstructured web pages is challenging in practice due to a variety of web layout patterns. Limited work has focused on modeling the web layout for extracting the text fields. In this paper, we introduce WebFormer, a Web-page transFormer model for structure information extraction from web documents. First, we design HTML tokens for each DOM node in the HTML by embedding representations from their neighboring tokens through graph attention. Second, we construct rich attention patterns between HTML tokens and text tokens, which leverages the web layout for effective attention weight computation. We conduct an extensive set of experiments on SWDE and Common Crawl benchmarks. Experimental results demonstrate the superior performance of the proposed approach over several state-of-the-art methods.
Reinforcement Learning for Mobile Robotics Exploration: A Survey

Efficient exploration of unknown environments is a fundamental precondition for modern autonomous mobile robot applications. Aiming to design robust and effective robotic exploration strategies, suitable to complex real-world scenarios, the academic community has increasingly investigated the integration of robotics with reinforcement learning (RL) techniques. This survey provides a comprehensive review of recent research works that use RL to design unknown environment exploration strategies for single and multirobots. The primary purpose of this study is to facilitate future research by compiling and analyzing the current state of works that link these two knowledge domains. This survey summarizes: what are the employed RL algorithms and how they compose the so far proposed mobile robot exploration strategies; how robotic exploration solutions are addressing typical RL problems like the exploration-exploitation dilemma, the curse of dimensionality, reward shaping, and slow learning convergence; and what are the performed experiments and software tools used for learning and testing. Achieved progress is described, and a discussion about remaining limitations and future perspectives is presented.
Computational model for the recognition of lower limb movement using wearable gyroscope sensor

Human activity recognition (HAR) using inertial sensors has enabled many applications in different fields, especially healthcare and biomedical engineering. In this regard, an activity recognition system is proposed using the signals of a single gyroscope sensor placed at the shank. Principal component analysis method was utilised to exclude the redundant features from the feature set. Furthermore, different classifiers such as probabilistic neural network, k-nearest neighbour (KNN) and support vector machine (SVM) were used for recognition of walking activities. K-fold cross validation and four performance parameters namely accuracy, sensitivity, specificity, and Matthew's correlation coefficient were used to inspect the performance of the recognition model. The proposed model yielded encouraging recognition accuracy of 98.7% compared to the existing activity recognition systems. It is realised that the proposed system will potentially be utilised in the control of lower limb prosthesis and be useful tool for the gait analysis applications.
Simple Unsupervised Graph Representation Learning

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https://github.com/YujieMo/SUGRL.
Ground-State Preparation and Energy Estimation on Early Fault-Tolerant Quantum Computers via Quantum Eigenvalue Transformation of Unitary Matrices

Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground state energy and prepare the ground state of a quantum Hamiltonian with near-optimal query complexities. However, this is based on a block encoding input model of the Hamiltonian, whose implementation is known to require a large resource overhead. We develop a tool called quantum eigenvalue transformation of unitary matrices with real polynomials (QET-U), which uses a controlled Hamiltonian evolution as the input model, a single ancilla qubit and no multi-qubit control operations, and is thus suitable for early fault-tolerant quantum devices. This leads to a simple quantum algorithm that outperforms all previous algorithms with a comparable circuit structure for estimating the ground state energy. For a class of quantum spin Hamiltonians, we propose a new method that exploits certain anti-commutation relations and further removes the need of implementing the controlled Hamiltonian evolution. Coupled with Trotter based approximation of the Hamiltonian evolution, the resulting algorithm can be very suitable for early fault-tolerant quantum devices. We demonstrate the performance of the algorithm using IBM Qiskit for the transverse field Ising model. If we are further allowed to use multi-qubit Toffoli gates, we can then implement amplitude amplification and a new binary amplitude estimation algorithm, which increases the circuit depth but decreases the total query complexity. The resulting algorithm saturates the near-optimal complexity for ground state preparation and energy estimating using a constant number of ancilla qubits (no more than 3).