Reviewerly/exHarmony huggingface dataset:
Robust multipartite entanglement generation via a collision model

We examine a simple scheme to generate genuine multipartite entangled states across disjoint qubit registers. We employ a shuttle qubit that is sequentially coupled, in an energy preserving manner, to the constituents within each register through rounds of interactions. We establish that stable $W$-type entanglement can be generated among all qubits within the registers. Furthermore, we find that the entanglement is sensitive to how the shuttle is treated, showing that a significantly larger degree is achieved by performing projective measurements on it. Finally, we assess the resilience of this entanglement generation protocol to several types of noise and imperfections, showing that it is remarkably robust.
Twitter Benchmark Dataset for Arabic Sentiment Analysis

Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media.Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets.There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis.An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects.This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative.Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.
Configuration-Driven Software Debloating

With legitimate code becoming an attack surface due to the proliferation of code reuse attacks, software debloating is an effective mitigation that reduces the amount of instruction sequences that may be useful for an attacker, in addition to eliminating potentially exploitable bugs in the removed code. Existing debloating approaches either statically remove code that is guaranteed to not run (e.g., non-imported functions from shared libraries), or rely on profiling with realistic workloads to pinpoint and keep only the subset of code that was executed.
Cybersecurity in accounting research

Purpose This paper aims to update the cybersecurity-related accounting literature by synthesizing 39 recent theoretical and empirical studies on the topic. Furthermore, the paper provides a set of categories into which the studies fit. Design/methodology/approach This is a synthesis paper that summarizes the research literature on cybersecurity, introducing knowledge from the extant research and revealing areas requiring further examination. Findings This synthesis identifies a research framework that consists of the following research themes: cybersecurity and information sharing, cybersecurity investments, internal auditing and controls related to cybersecurity, disclosure of cybersecurity activities and security threats and security breaches. Practical implications Academics, practitioners and the public would benefit from a research framework that categorizes the research topics related to cybersecurity in the accounting field. This type of analysis is vital to enhance the understanding of the academic research on cybersecurity and can be used to support the identification of new lines for future research. Originality/value This is the first literature analysis of cybersecurity in the accounting field, and it has significant implications for research and practice by detailing, for example, the benefits of and obstacles to information sharing. This synthesis also highlights the importance of the model for cybersecurity investments. Further, the review emphasizes the role of internal auditing and controls to improve cybersecurity.
Multi-Prototype Networks for Unconstrained Set-based Face Recognition

In this paper, we address the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image. Naively aggregating information from all the media within a set would suffer from the large intra-set variance caused by heterogeneous factors (e.g., varying media modalities, poses and illumination) and fail to learn discriminative face representations. A novel Multi-Prototype Network (MP- Net) model is thus proposed to learn multiple prototype face representations adaptively from the media sets. Each learned prototype is representative for the subject face under certain condition in terms of pose, illumination and media modality. Instead of handcrafting the set partition for prototype learn- ing, MPNet introduces a Dense SubGraph (DSG) learning sub-net that implicitly untangles inconsistent media and learns a number of representative prototypes. Qualitative and quantitative experiments clearly demonstrate the superiority of the proposed model over state-of-the-arts.
Variational Quantum Singular Value Decomposition

Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algorithms are promising, the required quantum subroutines and resources are too costly on near-term quantum devices. In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD). By exploiting the variational principles for singular values and the Ky Fan Theorem, we design a novel loss function such that two quantum neural networks (or parameterized quantum circuits) could be trained to learn the singular vectors and output the corresponding singular values. Furthermore, we conduct numerical simulations of VQSVD for random matrices as well as its applications in image compression of handwritten digits. Finally, we discuss the applications of our algorithm in recommendation systems and polar decomposition. Our work explores new avenues for quantum information processing beyond the conventional protocols that only works for Hermitian data, and reveals the capability of matrix decomposition on near-term quantum devices.
Observation of dressed states of distant atoms with delocalized photons in coupled-cavities quantum electrodynamics

In a cavity quantum electrodynamics (QED) system, where atoms coherently interact with photons in a cavity, the eigenstates of the system are the superposition states of atoms and cavity photons, the so-called dressed states of atoms. When two cavities are connected by an optical fiber with negligible loss, the coherent coupling between the cavities gives rise to photonic normal modes. One of these normal modes is the fiber-dark mode, in which photons are delocalized in the two distant cavities. Here we demonstrate the setting of coupled-cavities QED, where two nanofiber cavity-QED systems are coherently connected by a meter-long low-loss channel in an all-fiber fashion. Specifically, we observe dressed states of distant atoms with delocalized photons of the fiber-dark normal mode. Our system will provide a platform for the study of delocalized atomic and photonic states, photonic many-body physics, and distributed quantum computation.
Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies

Blockchain, as well as Internet of Things (IoT), is considered as two major disruptive emerging technologies. However, both of them suffer from innate technological limitations to some extent. IoT requires strengthening its security features while Blockchain inherently possesses them due to its extensive use of cryptographic mechanisms and Blockchain, in an inverted manner, needs contributions from the distributed nodes for its P2P (Peer-to-Peer) consensus model while IoT rudimentarily embodies them within its architecture. This chapter, therefore, acutely dissects the viability, along with prospective challenges, of incorporating Blockchain with IoT technologies,inducing the notion of Blockchain of Things (BCoT), as well as the benefits such consolidation can offer.
Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.
Simulating the dynamics of time-dependent Hamiltonians with a truncated Dyson series

We provide a general method for efficiently simulating time-dependent Hamiltonian dynamics on a circuit-model based quantum computer. Our approach is based on approximating the truncated Dyson series of the evolution operator, extending the earlier proposal by Berry to evolution generated by explicitly time-dependent Hamiltonians. Two alternative strategies are proposed to implement time ordering while exploiting the superposition principle for sampling the Hamiltonian at different times. The resource cost of our simulation algorithm retains the optimal logarithmic dependence on the inverse of the desired precision.
Adversarial Vision Challenge

This competition was meant to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. It encouraged researchers to develop query-efficient adversarial attacks that can successfully operate against a wide range of defenses while just observing the final model decision to generate adversarial examples. Conversely, the competition encouraged the development of new defenses that can resist a wide range of strong decision-based attacks. In this chapter we describe the organisation and structure of the challenge as well as the solutions developed by the top-ranking teams.
Improved two-stream model for human action recognition

Abstract This paper addresses the recognitions of human actions in videos. Human action recognition can be seen as the automatic labeling of a video according to the actions occurring in it. It has become one of the most challenging and attractive problems in the pattern recognition and video classification fields. The problem itself is difficult to solve by traditional video processing methods because of several challenges such as the background noise, sizes of subjects in different videos, and the speed of actions. Derived from the progress of deep learning methods, several directions are developed to recognize a human action from a video, such as the long-short-term memory (LSTM)-based model, two-stream convolutional neural network (CNN) model, and the convolutional 3D model.In this paper, we focus on the two-stream structure. The traditional two-stream CNN network solves the problem that CNNs do not have satisfactory performance on temporal features. By training a temporal stream, which uses the optical flow as the input, a CNN can have the ability to extract temporal features. However, the optical flow only contains limited temporal information because it only records the movements of pixels on the x -axis and the y -axis. Therefore, we attempt to design and implement a new two-stream model by using an LSTM-based model in its spatial stream to extract both spatial and temporal features in RGB frames. In addition, we implement a DenseNet in the temporal stream to improve the recognition accuracy. This is in-contrast to traditional approaches which typically utilize the spatial stream for extracting only spatial features. The quantitative evaluation and experiments are conducted on the UCF-101 dataset, which is a well-developed public video dataset. For the temporal stream, we choose the optical flow of UCF-101. Images in the optical flow are provided by the Graz University of Technology. The experimental result shows that the proposed method outperforms the traditional two-stream CNN method with an accuracy of at least 3%. For both spatial and temporal streams, the proposed model also achieves higher recognition accuracies. In addition, compared with the state of the art methods, the new model can still have the best recognition performance.
Improving Subject-Area Question Answering with External Knowledge

We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus.In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area QA.The first enriches the original subject-area reference corpus with relevant text snippets extracted from an open-domain resource (i.e., Wikipedia) that cover potentially ambiguous concepts in the question and answer options.As in other QA research, the second method simply increases the amount of training data by appending additional indomain subject-area instances.Experiments on three challenging multiplechoice science QA tasks (i.e., ARC-Easy, ARC-Challenge, and OpenBookQA) demonstrate the effectiveness of our methods: in comparison to the previous state-of-the-art, we obtain absolute gains in accuracy of up to 8.1%, 13.0%, and 12.8%, respectively.While we observe consistent gains when we introduce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original training data.As one of the first studies on exploiting unstructured external knowledge for subject-area QA, we hope our methods, observations, and discussion of the exposed limitations may shed light on further developments in the area.
A pure hardware implementation of CRYSTALS-KYBER PQC algorithm through resource reuse

This paper presents a pure hardware implementation of CRYSTALS-KYBER algorithm on Xilinx FPGAs. CRYSTALS-KYBER is one of 26 candidate algorithms in Round 2 of NIST Post-Quantum Cryptography (PQC) standardization process. The proposed design focuses on maximizing resource utilization by reusing most of the functional modules in the encapsulation and decapsulation processes of the algorithm. For instance, the hash module integrates several different hash functions in one module. Efficient parallel and pipelined computations are applied in the NTT module. Through the analysis of simulation and synthesis results, it is found that the proposed work has the advantages of higher frequencies and lower execution times. The scheme operates at 155 MHz and 192 MHz frequencies on Xilinx Artix-7 and Virtex-7 FPGAs, respectively. Compared with the performance of an embedded Cortex-M4 processor, the hardware implementation can achieve a maximum speedup of 129 times for encryption/decryption.
Summarizing Medical Conversations via Identifying Important Utterances

Summarization is an important natural language processing (NLP) task in identifying key information from text.For conversations, the summarization systems need to extract salient contents from spontaneous utterances by multiple speakers.In a special task-oriented scenario, namely medical conversations between patients and doctors, the symptoms, diagnoses, and treatments could be highly important because the nature of such conversation is to find a medical solution to the problem proposed by the patients.Especially consider that current online medical platforms provide millions of public available conversations between real patients and doctors, where the patients propose their medical problems and the registered doctors offer diagnosis and treatment, a conversation in most cases could be too long and the key information is hard to be located.Therefore, summarizations to the patients' problems and the doctors' treatments in the conversations can be highly useful, in terms of helping other patients with similar problems have a precise reference for potential medical solutions.In this paper, we focus on medical conversation summarization, using a dataset of medical conversations and corresponding summaries which were crawled from a well-known online healthcare service provider in China.We propose a hierarchical encoder-tagger model (HET) to generate summaries by identifying important utterances (with respect to problem proposing and solving) in the conversations.For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations.Experimental results demonstrate that HET outperforms strong baselines and models from previous studies, and adding conversation-related features can further improve system performance. 1* Equal contribution. 1 Our code, models, and the dataset are released at https://github.com/cuhksz-nlp/HET-MC. 2 E.g., in China, the number of outpatient visits exceeded 7 billions and inpatient visits over 200 millions in a recent year.
Semantics-Consistent Representation Learning for Remote Sensing Image–Voice Retrieval

With the development of earth observation technology, massive amounts of remote sensing (RS) images are acquired. To find useful information from these images, cross-modal RS image-voice retrieval provides a new insight. This paper aims to study the task of RS image-voice retrieval so as to search effective information from massive amounts of RS data. Existing methods for RS image-voice retrieval rely primarily on the pairwise relationship to narrow the heterogeneous semantic gap between images and voices. However, apart from the pairwise relationship included in the datasets, the intra-modality and non-paired inter-modality relationships should also be taken into account simultaneously, since the semantic consistency among non-paired representations plays an important role in the RS image-voice retrieval task. Inspired by this, a semantics-consistent representation learning (SCRL) method is proposed for RS image-voice retrieval. The main novelty is that the proposed method takes the pairwise, intra-modality, and non-paired inter-modality relationships into account simultaneously, thereby improving the semantic consistency of the learned representations for the RS image-voice retrieval. The proposed SCRL method consists of two main steps: 1) semantics encoding and 2) semantics-consistent representation learning. Firstly, an image encoding network is adopted to extract high-level image features with a transfer learning strategy, and a voice encoding network with dilated convolution is devised to obtain high-level voice features. Secondly, a consistent representation space is conducted by modeling the three kinds of relationships to narrow the heterogeneous semantic gap and learn semantics-consistent representations across two modalities. Extensive experimental results on three challenging RS image-voice datasets show the effectiveness of the proposed method.
Gigahertz measurement-device-independent quantum key distribution using directly modulated lasers

Measurement-device-independent quantum key distribution (MDI-QKD) is a technique for quantum-secured communication that eliminates all detector side-channels, although is currently limited by implementation complexity and low secure key rates. Here, we introduce a simple and compact MDI-QKD system design at gigahertz clock rates with enhanced resilience to laser fluctuations - thus enabling free-running semiconductor laser sources to be employed without spectral or phase feedback. This is achieved using direct laser modulation, carefully exploiting gain-switching and injection-locking laser dynamics to encode phase-modulated time-bin bits. Our design enables secure key rates that improve upon the state of the art by an order of magnitude, up to 8 bps at 54 dB channel loss and 2 kbps in the finite-size regime for 30 dB channel loss. This greatly simplified MDI-QKD system design and proof-of-principle demonstration shows that MDI-QKD is a practical, high-performance solution for future quantum communication networks.
Auto-MSFNet: Search Multi-scale Fusion Network for Salient Object Detection

Multi-scale features fusion plays a critical role in salient object detection. Most of existing methods have achieved remarkable performance by exploiting various multi-scale features fusion strategies. However, an elegant fusion framework requires expert knowledge and experience, heavily relying on laborious trial and error. In this paper, we propose a multi-scale features fusion framework based on Neural Architecture Search (NAS), named Auto-MSFNet. First, we design a novel search cell, named FusionCell to automatically decide multi-scale features aggregation. Rather than searching one repeatable cell stacked, we allow different FusionCells to flexibly integrate multi-level features. Simultaneously, considering features generated from CNNs are naturally spatial and channel-wise, we propose a new search space for efficiently focusing on the most relevant information. The search space mitigates incomplete object structures or over-predicted foreground regions caused by progressive fusion. Second, we propose a progressive polishing loss to further obtain exquisite boundaries by penalizing misalignment of salient object boundaries. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics. The code and results of our method are available at https://github.com/OIPLab-DUT/Auto-MSFNet.
Quantum Fisher Information from Randomized Measurements

The quantum Fisher information (QFI) is a fundamental quantity of interest in many areas from quantum metrology to quantum information theory. It can in particular be used as a witness to establish the degree of multiparticle entanglement in quantum many-body systems. In this work, we use polynomials of the density matrix to construct monotonically increasing lower bounds that converge to the QFI. Using randomized measurements we propose a protocol to accurately estimate these lower bounds in state-of-the-art quantum technological platforms. We estimate the number of measurements needed to achieve a given accuracy and confidence level in the bounds, and present two examples of applications of the method in quantum systems made of coupled qubits and collective spins.