Reviewerly/exHarmony huggingface dataset:
Blockchain and Smart Contracts to Secure Property Transactions in Smart Cities

Cities have been growing at an impressive rate, and with the advancement of technology and digitization, new ways have been proposed to provide cities with a certain level of autonomy and intelligence so that they can self-regulate tasks that currently take a lot of time for both citizens and authorities, and which also have a negative impact on the environment. We have recently experienced one of the most abrupt changes in history as a result of the COVID-19 pandemic, where millions of people, businesses, schools, companies, governments, etc., have had to modify their ways of working, in addition to dealing with many other changes in the present moment, the most important of which is the transition to electronic transactions. This is a chance to progress toward smart city transformation and the implementation of models that assist citizens in their ordinary activities. One of the most common activities is the purchase of real estate. In this study, we propose a model to make the purchase and sale of real estate more equitable and fairer, and not just based on the amount of money. A smart home should be able to identify who its current owner is by functioning as an IoT badge, and, after the subject’s identity has been verified, it should be granted full access to all services, or else it should be blocked and/or alerted to the authorities. The use of smart contracts in a blockchain to transfer ownership of a smart home that includes, as inputs, those interested in acquiring a specific house and, as outputs, a person who chose the system based on the requirements previously established by the government of the Smart City. As a result, susceptibility to fraud or interference by a third person is reduced, and an individual’s security of living in a smart home, and therefore, in a smart city, is strengthened.
A privacy protection scheme based on linkable ring signature for user payment of Peer-to-Peer uniform-price double auction transaction in the microgrid day-ahead market

In the microgrid day-ahead market Peer-to-Peer (P2P) transaction, the uniform-price double auction mechanism has advantages in the microgrid day-ahead market because it can prevent power price fluctuations during the one hour trading period, and give priority to trading Distributed Energy Resources(DERs) based on trading volume, which is conducive to the consumption of new energy. In this kind of transaction, aiming at the problem that the attacker can obtain the privacy information such as the collection and payment address and transaction amount of the producer and consumer through the account book information on the blockchain, so that the relationship between the producer and consumer and the payment behavior is subject to inference attacks, and thus the identity of the participants is inferred, a privacy protection scheme based on linkable ring signature for user payment of P2P uniform-price double auction transaction in the microgrid day-ahead market is proposed. This scheme has the following novelty and contribution: Firstly, the linkable ring signature is used to sign the one-time account address and payment information of the producer and consumer, which ensures the untraceability of transaction payment in the case of anonymity of the producer and consumer, and it has low computational cost; Secondly, in order to hide its real output address, destroy the association between the input address and the real output address, and provides the unlinkability of transactions, the one-time address is proposed; Finally, a one-time address generation method based on elliptic curve is designed, and point multiplication is used to improve the efficiency of address generation. Theoretical analysis proves that the scheme is untraceable and unlinkable while ensuring privacy. Experiments show that this scheme has lower computational cost.
RFI-GAN: A reference-guided fuzzy integral network for ultrasound image augmentation

The Generative Adversarial Network (GAN) is commonly used for medical image augmentation, a method to alleviate the data shortage for downstream tasks. However, traditional GANs is not effective in mining the intrinsic association between samples and data space. Also, the multiple sections and variable scaling ratios of ultrasound images lead to inconsistency among images. The current GAN-based image generation methods cannot obtain the overall anatomical semantic constraints of inconsistent images, thus illogical images are generated. Therefore, a Reference-guided Fuzzy Integral GAN (RFI-GAN) is proposed. First, RFI-GAN implicitly constrains the logicality of the generated images through feature fusion between samples and reference set. These references, a kind of prior knowledge, cover most of the anatomical structures in the dataset. Second, as the general linear fusion takes features as additive and cannot fit the inconsistency of features, we generalize the additivity of features to non-additivity through the Fuzzy Integral, which is used to fusion non-additive measures. Two Fuzzy Integral Modules are proposed to nonlinearly fusion the texture and structure features of ultrasound images under the guidance of references. Finally, we perform experiments on the thyroid ultrasound image dataset, demonstrating the effectiveness of RFI-GAN in terms of generation and downstream classification tasks.
Identification of offensive language in Urdu using semantic and embedding models

Automatic identification of offensive/abusive language is very necessary to get rid of unwanted behavior. However, it is more challenging to generalize the solution due to the different grammatical structures and vocabulary of each language. Most of the prior work targeted western languages, however, one study targeted a low-resource language (Urdu). The prior study used basic linguistic features and a small dataset. This study designed a new dataset (collected from popular Pakistani Facebook pages) containing 7,500 posts for offensive language detection in Urdu. The proposed methodology used four types of feature engineering models: three are frequency-based and the fourth one is the embedding model. Frequency-based are either determined by the term frequency-inverse document frequency (TF-IDF) or bag-of-words or word n-gram feature vectors. The fourth is generated by the word2vec model, trained on the Urdu embeddings using a corpus of 196,226 Facebook posts. The experiments demonstrate that the stacking-based ensemble model with word2vec shows the best performance as a standalone model by achieving 88.27% accuracy. In addition, the wrapper-based feature selection method further improves performance. The hybrid combination of TF-IDF, bag-of-words, and word2vec feature models achieved 90% accuracy and 97% AUC. In addition, it outperformed the baseline with an improvement of 3.55% in accuracy, 3.68% in the recall, 3.60% in f1-measure, 3.67% in precision, and 2.71% in AUC. The findings of this research provide practical implications for commercial applications and future research.
Ultra-long quantum walks via spin–orbit photonics

The possibility of fine-tuning the couplings between optical modes is a key requirement in photonic circuits for quantum simulations. In these architectures, emulating the long-time evolution of particles across large lattices requires sophisticated setups, that are often intrinsically lossy. Here we report ultra-long photonic quantum walks across several hundred optical modes, obtained by propagating a light beam through very few closely-stacked liquid-crystal metasurfaces. By exploiting spin-orbit effects, these implement space-dependent polarization transformations that mix circularly polarized optical modes carrying quantized transverse momentum. As each metasurface implements long-range couplings between distant modes, by using only a few of them we simulate quantum walks up to 320 discrete steps without any optical amplification, far beyond state-of-the-art experiments. To showcase the potential of this method, we experimentally demonstrate that in the long-time limit a quantum walk affected by dynamical disorder generates maximal entanglement between two system partitions. Our platform grants experimental access to large-scale unitary evolutions while keeping optical losses at a minimum, thereby paving the way to massive multi-photon multi-mode quantum simulations.
Is quantum computing green? An estimate for an energy-efficiency quantum advantage

The quantum advantage threshold determines when a quantum processing unit (QPU) is more efficient with respect to classical computing hardware in terms of algorithmic complexity. The "green" quantum advantage threshold $-$ based on a comparison of energetic efficiency between the two $-$ is going to play a fundamental role in the comparison between quantum and classical hardware. Indeed, its characterization would enable better decisions on energy-saving strategies, e.g. for distributing the workload in hybrid quantum-classical algorithms. Here, we show that the green quantum advantage threshold crucially depends on (i) the quality of the experimental quantum gates and (ii) the entanglement generated in the QPU. Indeed, for NISQ hardware and algorithms requiring a moderate amount of entanglement, a classical tensor network emulation can be more energy-efficient at equal final state fidelity than quantum computation. We compute the green quantum advantage threshold for a few paradigmatic examples in terms of algorithms and hardware platforms, and identify algorithms with a power-law decay of singular values of bipartitions $-$ with power-law exponent $\alpha \lesssim 1$ $-$ as the green quantum advantage threshold in the near future.
Medical knowledge-based network for Patient-oriented Visual Question Answering

Visual Question Answering (VQA) systems have achieved great success in general scenarios. In medical domain, VQA systems are still in their infancy as the datasets are limited by scale and application scenarios. Current medical VQA datasets are designed to conduct basic analyses of medical imaging such as modalities, planes, organ systems, abnormalities, etc., aiming to provide constructive medical suggestions for doctors, containing a large number of professional terms with limited help for patients. In this paper, we introduce a new Patient-oriented Visual Question Answering (P-VQA) dataset, which builds a VQA system for patients by covering an entire treatment process including medical consultation, imaging diagnosis, clinical diagnosis, treatment advice, review, etc. P-VQA covers 20 common diseases with 2,169 medical images, 24,800 question-answering pairs, and a medical knowledge graph containing 419 entities. In terms of methodology, we propose a Medical Knowledge-based VQA Network (MKBN) to answer questions according to the images and a medical knowledge graph in our P-VQA. MKBN learns two cluster embeddings (disease-related and relation-related embeddings) according to structural characteristics of the medical knowledge graph and learns three different interactive features (image-question, image-disease, and question-relation) according to characteristics of diagnosis. For comparisons, we evaluate several state-of-the-art baselines on the P-VQA dataset as benchmarks. Experimental results on P-VQA demonstrate that MKBN achieves the state-of-the-art performance compared with baseline methods. The dataset is available at https://github.com/cs-jerhuang/P-VQA.
Quantum Methods for Neural Networks and Application to Medical Image Classification

Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms. The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks. We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.
Accelerating Deep Learning Inference via Model Parallelism and Partial Computation Offloading

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile Edge Computing (MEC). To address the computation limitation of IoT devices in processing complex Deep Neural Networks (DNNs), computation offloading is proposed as a promising approach. Recently, partial computation offloading is developed to dynamically adjust task assignment strategy in different channel conditions for better performance. In this paper, we take advantage of intrinsic DNN computation characteristics and propose a novel Fused-Layer-based (FL-based) DNN model parallelism method to accelerate inference. The key idea is that a DNN layer can be converted to several smaller layers in order to increase partial computation offloading flexibility, and thus further create the better computation offloading solution. However, there is a trade-off between computation offloading flexibility as well as model parallelism overhead. Then, we investigate the optimal DNN model parallelism and the corresponding scheduling and offloading strategies in partial computation offloading. In particular, we propose a Particle Swarm Optimization with Minimizing Waiting (PSOMW) method, which explores and updates the FL strategy, path scheduling strategy, and path offloading strategy to reduce time complexity and avoid invalid solutions. Finally, we validate the effectiveness of the proposed method in commonly used DNNs. The results show that the proposed method can reduce the DNN inference time by an average of 12.75 times compared to the legacy No FL (NFL) algorithm, and is very close to the optimal solution achieved by the Brute Force (BF) algorithm with the difference of less than 0.04%.
Verifiable, Fair and Privacy-Preserving Broadcast Authorization for Flexible Data Sharing in Clouds

The cloud-based data sharing technology with cryptographic primitives enables data owners to outsource data into paradigms and privately share information with arbitrary recipients without geographic barriers. However, we argue that most of existing efforts for outsourced data sharing are either inefficient, inflexible, or incompletely secure due to the following problems: (1) lack of efficient strategies for dynamically designating target ciphertexts to multiple recipients; (2) how to hide the identity of the recipient and (3) how to verify the correctness of outsourced ciphertext transformation without any denial. To the best of our knowledge, no previous work has thoroughly explored the above three issues, motivating us to design such an efficient and comprehensively secure outsourced data sharing mechanism. We design VF-PPBA, the first Verifiable, Fair and Privacy-preserving Broadcast Authorization framework for flexible data sharing in clouds. In more detail, we first invent a new primitive, privacy-preserving multi-recipient broadcast proxy re-encryption (PPMR-BPRE), which enables the authorization of a given ciphertext to different recipients with efficient ciphertext transformation, and further guarantees that any malicious adversary deduces nothing about the identity of the recipient. Then, we present VF-PPBA for flexible data sharing with PPMR-BPRE as the underlying structure, which in addition to inheriting all the functionalities of PPMR-BPRE, is capable of supporting the verifiability of the outcome correctness of the outsourced conversion task, and being immune to the malicious accusation if the outsourcing outcome is correctly completed. We formalize the adversarial models and render comprehensively strict security proofs to prove the security of our proposed solutions. Its performance is also validated via experimental simulations to showcase the practicability and effectiveness.
Hybrid-scale contextual fusion network for medical image segmentation

Medical image segmentation result is an essential reference for disease diagnosis. Recently, with the development and application of convolutional neural networks, medical image processing has significantly developed. However, most existing automatic segmentation tasks are still challenging due to various positions, sizes, and shapes, resulting in poor segmentation performance. In addition, most of the current methods use the encoder-decoder architecture for feature extraction, focusing on the acquisition of semantic information but ignoring the specific target and global context information. In this work, we propose a hybrid-scale contextual fusion network to capture the richer spatial and semantic information. First, a hybrid-scale embedding layer (HEL) is employed before the transformer. By mixing each embedding with multiple patches, the object information of different scales can be captured availably. Further, we present a standard transformer to model long-range dependencies in the first two skip connections. Meanwhile, the pooling transformer (PTrans) is employed to handle long input sequences in the following two skip connections. By leveraging the global average pooling operation and the corresponding transformer block, the spatial structure information of the target will be learned effectively. In the last, dual-branch channel attention module (DCA) is proposed to focus on crucial channel features and conduct multi-level features fusion simultaneously. By utilizing the fusion scheme, richer context and fine-grained features are captured and encoded efficiently. Extensive experiments on three public datasets demonstrate that the proposed method outperforms state-of-the-art methods.
RUAD: Unsupervised anomaly detection in HPC systems

The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrators' effort towards increasing the system's availability. Anomaly detection is an integral part of improving the availability as it eases the system administrator's burden and reduces the time between an anomaly and its resolution. However, current state-of-the-art (SOTA) approaches to anomaly detection are supervised and semi-supervised, so they require a human-labelled dataset with anomalies — this is often impractical to collect in production HPC systems. Unsupervised anomaly detection approaches based on clustering, aimed at alleviating the need for accurate anomaly data, have so far shown poor performance. In this work, we overcome these limitations by proposing RUAD, a novel Recurrent Unsupervised Anomaly Detection model. RUAD achieves better results than the current semi-supervised and unsupervised SOTA approaches. This is achieved by considering temporal dependencies in the data and including long-short term memory cells in the model architecture. The proposed approach is assessed on a complete ten-month history of a Tier-0 system (Marconi100 from CINECA with 980 nodes). RUAD achieves an area under the curve (AUC) of 0.763 in semi-supervised training and an AUC of 0.767 in unsupervised training, which improves upon the SOTA approach that achieves an AUC of 0.747 in semi-supervised training and an AUC of 0.734 in unsupervised training. It also vastly outperforms the current SOTA unsupervised anomaly detection approach based on clustering, achieving the AUC of 0.548.
Supply chain finance: What are the challenges in the adoption of blockchain technology?

As an emerging information technology, blockchain has aroused extensive discussions around the world and been suggested as a solution to address current issues in supply chain finance (SCF). The Chinese government also attaches great importance to this technology, and many Chinese state-owned enterprises have invested in establishing their own blockchain research and development centres. However, there is a lack of studies on identifying challenges when deploying this technology; theoretical framework and conceptual exposition are also scarcely seen. Therefore, the aim of this study is to investigate the challenges and obstacles in the adoption of blockchain technology in SCF. An exploratory case study of a Chinese state-owned enterprise was conducted to build up an initial conceptual framework. Semi-structured interview was applied to collect data from the case firm's employees, top management, and technical specialists. The results of the analysis indicate that in the adoption of blockchain technology, there are technological, operational, and other challenges. From a technological perspective, framework identification, cross-chain interoperability, and data governance are major barriers; whereas, from an operational perspective, the new business process and transformation in the entire supply chain are identified as challenges. Besides, other obstacles such as the elimination of jobs and regulatory issues are also not neglectable. This study contributes to research on blockchain and supply chains by shedding light on the challenges of blockchain adoption through an exploratory case study of a Chinese state-owned enterprise. A conceptual framework was generated as a basis for future research, and the findings also provide insights for companies that may or are planning to adopt blockchain technology.
Displacement measurement of large structures using nonoverlapping field of view multi‐camera systems under six degrees of freedom ego‐motion

Abstract In this work, a non‐overlapping field of view (FOV) multi‐camera system (MCS) has been proposed to realize the motion self‐compensation, which can be flexibly arranged according to the field situation. The challenges of ensuring a complete fixed measurement platform owing to the self‐weight, unexpected collisions, and adjacent engineering constructions have been addressed in this study. MCS can be fixed on an unstable platform and employ two or more correcting cameras, without an overlapping FOV, to measure and compensate the six degrees of freedom (6‐DOF) ego‐motion of the platform, with high accuracy. The numerical simulation results show that, under the same configuration conditions, the 6‐DOF motion estimation accuracy of the proposed method is better than that of the Perspective‐n‐Points methods, especially for the estimation accuracy of the out‐of‐plane motion and the roll motion of the camera. The field demonstration has been performed on a suspension bridge, and the results demonstrate that the deflection measurements provided by the proposed MCS are consistent with those provided by the Robotic Total Station. The proposed MCS, which is not restrained by a strictly stable platform, proves to be an effective method to compensate for the errors induced by the platform ego‐motion, and it can meet the requirements of the displacement measurement of large structures needing high precision.