Reviewerly/exHarmony huggingface dataset:
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SO) algorithm. It is subsequently adopted to implement a P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate, and it can achieve even higher prediction accuracy for missing data of an HiDS matrix. On the other hand, compared with state-of-the-art adaptive LFA models, a PLFA model's prediction accuracy and computational efficiency are highly competitive. Hence, it has high potential in addressing real industrial issues.
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. Existing methods typically conduct adaptation on the representation space that contains both id-related and id-unrelated factors, thus inevitably undermining the adaptation efficacy of id-related features. In this paper, we seek to improve adaptation by purifying the representation space to be adapted. To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively. Our model involves a disentangling module that encodes cross-domain images into a shared appearance space and two separate structure spaces, and an adaptation module that performs adversarial alignment and self-training on the shared appearance space. The two modules are co-designed to be mutually beneficial. Extensive experiments demonstrate that the proposed joint learning framework outperforms the state-of-the-art methods by clear margins.
Minimum Class Confusion for Versatile Domain Adaptation

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios they are not tailored to. To this end, this paper studies Versatile Domain Adaptation (VDA), where one method can handle several different DA scenarios without any modification. Towards this goal, a more general inductive bias other than the domain alignment should be explored. We delve into a missing piece of existing methods: class confusion, the tendency that a classifier confuses the predictions between the correct and ambiguous classes for target examples, which is common in different DA scenarios. We uncover that reducing such pairwise class confusion leads to significant transfer gains. With this insight, we propose a general loss function: Minimum Class Confusion (MCC). It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date ( $$7.3\%$$ on DomainNet). Its versatility is further justified by two scenarios proposed in this paper: Multi-Source Partial DA and Multi-Target Partial DA. In addition, it can also be used as a general regularizer that is orthogonal and complementary to a variety of existing DA methods, accelerating convergence and pushing these readily competitive methods to stronger ones. Code is available at https://github.com/thuml/Versatile-Domain-Adaptation .
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset

Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark. Simply adjusting the brightness of a low-light image will inevitably amplify those artifacts. To address this difficult problem, this paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network. To this end, we first construct a synthetic dataset with carefully designed low-light simulation strategies. The dataset is much larger and more diverse than existing ones. With the new dataset for training, our method learns two attention maps to guide the brightness enhancement and denoising tasks respectively. The first attention map distinguishes underexposed regions from well lit regions, and the second attention map distinguishes noises from real textures. With their guidance, the proposed multi-branch decomposition-and-fusion enhancement network works in an input adaptive way. Moreover, a reinforcement-net further enhances color and contrast of the output image. Extensive experiments on multiple datasets demonstrate that our method can produce high fidelity enhancement results for low-light images and outperforms the current state-of-the-art methods by a large margin both quantitatively and visually.
Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher Level Markov Jump Systems With Uncertain Transition Probabilities

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.
Bayesian Analysis Reporting Guidelines

Previous surveys of the literature have shown that reports of statistical analyses often lack important information, causing lack of transparency and failure of reproducibility. Editors and authors agree that guidelines for reporting should be encouraged. This Review presents a set of Bayesian analysis reporting guidelines (BARG). The BARG encompass the features of previous guidelines, while including many additional details for contemporary Bayesian analyses, with explanations. An extensive example of applying the BARG is presented. The BARG should be useful to researchers, authors, reviewers, editors, educators and students. Utilization, endorsement and promotion of the BARG may improve the quality, transparency and reproducibility of Bayesian analyses.
Model complexity of deep learning: a survey

Model complexity is a fundamental problem in deep learning. In this paper, we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity. We review the existing studies on those two categories along four important factors, including model framework, model size, optimization process, and data complexity. We also discuss the applications of deep learning model complexity including understanding model generalization, model optimization, and model selection and design. We conclude by proposing several interesting future directions.
Is blockchain able to enhance environmental sustainability? A systematic review and research agenda from the perspective of Sustainable Development Goals (SDGs)

Abstract Blockchain is a disruptive technology that is revolutionizing information technology and represents a change of cultural paradigm for the way in which information is shared. Companies are rushing to understand how they can use blockchain distributed ledger technology to innovate processes, products and transactions. In a globalized world where environmental sustainability is a critical success factor, what is the role of the blockchain? By using a systematic review approach and the Preferred Reporting Items for Systematic Review and Meta‐Analyses (PRISMA) protocol, this study attempts to identify whether and how blockchain technology is considered able to affect environmental sustainability. Findings from 195 studies from 2015 to 2020 were analysed after the search protocol was applied. The results indicate that blockchain technology could contribute to environmentally sustainable development goals (SDGs) from different points of view, such as supporting the realization of a sustainable supply chain, improving energy efficiency and promoting the creation of secure and reliable smart cities. Furthermore, the investigation highlights the sectors where to focus research investments, providing a way to reward sustainable behaviour and increasing environmental sustainability. On the other hand, blockchain has no negligible negative effects on the environment that need to be considered before adoption.
Breaking the Rate-Loss Bound of Quantum Key Distribution with Asynchronous Two-Photon Interference

Twin-field quantum key distribution can overcome the secret key capacity of repeaterless quantum key distribution via single-photon interference. However, to compensate for the channel fluctuations and lock the laser fluctuations, the techniques of phase tracking and phase locking are indispensable in experiment, which drastically increase experimental complexity and hinder free-space realization. Inspired by the duality in entanglement, we herein present an asynchronous measurement-device-independent quantum key distribution protocol that can surpass the secret key capacity even without phase tracking and phase locking. Leveraging the concept of time multiplexing, asynchronous two-photon Bell-state measurement is realized by postmatching two interference detection events. For a 1 GHz system, the new protocol reaches a transmission distance of 450 km without phase tracking. After further removing phase locking, our protocol is still capable of breaking the capacity at 270 km. Intriguingly, when using the same experimental techniques, our protocol has a higher key rate than the phase-matching-type twin-field protocol. In the presence of imperfect intensity modulation, it also has a significant advantage in terms of the transmission distance over the sending-or-not-sending type twin-field protocol. With high key rates and accessible technology, our work provides a promising candidate for practical scalable quantum communication networks.
TransMorph: Transformer for unsupervised medical image registration

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.
TrustChain: A Sybil-resistant scalable blockchain

TrustChain is capable of creating trusted transactions among strangers without central control. This enables new areas of blockchain use with a focus on building trust between individuals. Our innovative approach offers scalability, openness and Sybil-resistance while replacing proof-of-work with a mechanism to establish the validity and integrity of transactions. TrustChain is a permission-less tamper-proof data structure for storing transaction records of agents. We create an immutable chain of temporally ordered interactions for each agent. It is inherently parallel and every agent creates his own genesis block. TrustChain includes a novel Sybil-resistant algorithm named NetFlow to determine trustworthiness of agents in an online community. NetFlow ensures that agents who take resources from the community also contribute back. We demonstrate that irrefutable historical transaction records offer security and seamless scalability, without requiring global consensus. Experimentation shows that the transaction throughput of TrustChain surpasses that of traditional blockchain architectures like Bitcoin. We show by using extracted data from a live network that TrustChain has sufficient informativeness to identify freeriders, leading to refusal of service.
Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Similar to a fingerprint search system, face recognition technology can assist law enforcement agencies in identifying suspects or finding missing persons. Face recognition technology lets the police detect a suspect’s face and compare it with image databases of known criminals and provides investigators with a match list of the most similar faces. Face recognition is a highly efficient and accurate tool in investigation processes. However, in some sensitive scenarios covert methods are required for the detection of suspects or missing persons without risking the lives of police. With the availability of the nano devices such as Raspberry Pi, law enforcement agencies such as police can be equipped with a concealed and secure face recognition system. In this paper, a Raspberry Pi and cloud assisted face recognition framework is proposed. A small-sized portable wireless camera is mounted on a police officer’s uniform to capture a video stream, which is passed to Raspberry Pi for face detection and recognition. The proposed method uses Bag of Words for extraction of oriented FAST and rotated BRIEF points from the detected face, followed by support vector machine for identification of suspects. Raspberry Pi has limited resources such as storage space, memory, and processing power, and therefore the proposed classifier is stored and trained on the cloud. The proposed method is implemented on Raspberry Pi 3 model B in Python 2.7 and is tested on various standard datasets. Experimental results validate the efficiency of the proposed method in accurate detection of faces compared to state-of-the-art face detection and recognition methods, and verify its effectiveness for enhancing law-enforcement services in smart cities.
Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological, syntactic and semantic features

This research presents an enhanced approach for Aspect-Based Sentiment Analysis (ABSA) of Hotels’ Arabic reviews using supervised machine learning. The proposed approach employs a state-of-the-art research of training a set of classifiers with morphological, syntactic, and semantic features to address the research tasks namely: (a) T1:Aspect Category Identification, (b) T2:Opinion Target Expression (OTE) Extraction, and (c) T3: Sentiment Polarity Identification. Employed classifiers include Naïve Bayes, Bayes Networks, Decision Tree, K-Nearest Neighbor (K-NN), and Support-Vector Machine (SVM).The approach was evaluated using a reference dataset based on Semantic Evaluation 2016 workshop (SemEval-2016: Task-5). Results show that the supervised learning approach outperforms related work evaluated using the same dataset. More precisely, evaluation results show that all classifiers in the proposed approach outperform the baseline approach, and the overall enhancement for the best performing classifier (SVM) is around 53% for T1, around 59% for T2, and around 19% in T3.
ADMM Based Privacy-Preserving Decentralized Optimization

Privacy preservation is addressed for decentralized optimization, where $N$ agents cooperatively minimize the sum of $N$ convex functions private to these individual agents. In most existing decentralized optimization approaches, participating agents exchange and disclose states explicitly, which may not be desirable when the states contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on alternating direction method of multipliers (ADMM) and partially homomorphic cryptography. To the best of our knowledge, this is the first time that cryptographic techniques are incorporated in a fully decentralized setting to enable privacy preservation in decentralized optimization in the absence of any third party or aggregator. To facilitate the incorporation of encryption in a fully decentralized manner, we introduce a new ADMM, which allows time-varying penalty matrices and rigorously prove that it has a convergence rate of $O(1/t)$ . Numerical and experimental results confirm the effectiveness and low-computational complexity of the proposed approach.
Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios

Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal. Due to inherent non-linearities of the power amplifiers of emitters, these fingerprints provide distinguish features for emitter identification. In this paper, we develop an emitter identification based on variational mode decomposition and spectral features (VMD-SF). As VMD decomposes the received signal simultaneously into various temporal and spectral modes, we choose to explore different spectral features, including spectral flatness, spectral brightness, and spectral roll-off for improving the identification accuracy contrary to existing temporal features-based methods. For demonstrating the robustness of VMD in decomposing the received signal into emitter-specific modes, we also develop a VMD-entropy and moments (EM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) method based on existing temporal features extracted from the Hilbert Huang transform of the emitter-specific temporal modes. Our proposed method has three major steps: received signal decomposition using VMD, feature extraction, and emitter identification. We evaluate the performance of the proposed methods using the probability of correct classification (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> ) both in single hop and in relaying scenario by varying the number of emitters. To demonstrate the superior performance of our proposed methods, we compared our methods with the existing empirical mode decomposition-(entropy-, first-, and second-order moments) (EMD-EM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) method both in terms of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> and computational complexity. Results depict that the proposed VMD-SF emitter identification method outperforms the proposed VMD-EM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> method and the existing EMD-EM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> method both in single hop and relaying scenarios for a varying number of emitters. In addition, the proposed VMD-SF method has lowest computational cost as compared with the aforementioned methods.
RDF2Vec: RDF graph embeddings and their applications

Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Li nked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler–Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.