Reviewerly/exHarmony huggingface dataset:
An Improved Moth-Flame Optimization algorithm with hybrid search phase

In order to solve real-life problems, several metaheuristic optimization algorithms have been developed. The Moth-Flame Optimization (MFO) algorithm is a search algorithm based on a mechanism called transverse orientation. In this mechanism, the moths tend to maintain a fixed angle with respect to the moon. MFO suffers from the degeneration of the global search capability and convergence speed. To overcome these imperfections, an Improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main novelty of the proposed approach is the definition of a hybrid phase between exploration and exploitation. This phase is characterized by a fitness depended weight factor for updating the moths positions. IMFO is tested on selected benchmark functions, CEC2014 test functions and 6 design problems, and compared with recent well-known optimization algorithms. The results show that IMFO achieves the best results with respect to the comparison algorithms in terms of search capability and convergence performances.
Deep Metric Learning With Tuplet Margin Loss

Deep metric learning, in which the loss function plays a key role, has proven to be extremely useful in visual recognition tasks. However, existing deep metric learning loss functions such as contrastive loss and triplet loss usually rely on delicately selected samples (pairs or triplets) for fast convergence. In this paper, we propose a new deep metric learning loss function, tuplet margin loss, using randomly selected samples from each mini-batch. Specifically, the proposed tuplet margin loss implicitly up-weights hard samples and down-weights easy samples, while a slack margin in angular space is introduced to mitigate the problem of overfitting on the hardest sample. Furthermore, we address the problem of intra-pair variation by disentangling class-specific information to improve the generalizability of tuplet margin loss. Experimental results on three widely used deep metric learning datasets, CARS196, CUB200-2011, and Stanford Online Products, demonstrate significant improvements over existing deep metric learning methods.
A Blockchain Tokenizer for Industrial IOT trustless applications

The Blockchain is a novel technology with a wide range of potential industrial applications. Despite a vast range of tests, prototypes, and proof of concepts implemented in the last years, the industrial use of Blockchain technology is still in the early stages. Enabling the interaction of industrial Internet of Things (IOT) platforms with Blockchain might be challenging because standards are still missing in both these technologies. Moreover, integrating productive assets with distributed data exchange and storage technologies is a kind of activity that needs to take into account various aspects, in particular: interoperability, portability, scalability, and security that need to be guaranteed by design. This paper describes the implementation of a portable, platform-agnostic and secure Blockchain Tokenizer for Industrial IOT trustless applications. The Industrial Blockchain Tokenizer (IBT) is based on an industrial data acquisition unit able to gather data from both modern and legacy machines while also interfacing directly with sensors. Acquired data can be processed locally enabling an edge filtering paradigm and then sent to any Blockchain platform. The system has been designed, implemented and then tested on two supply chain scenarios. Tests demonstrated the system capability to act as a bridge between industrial assets and Blockchain platforms enabling the generation of immutable and trust-less “digital twins” for industrial IOT applications.
Challenges and Opportunities of Near-Term Quantum Computing Systems

The concept of quantum computing has inspired a whole new generation of scientists, including physicists, engineers, and computer scientists, to fundamentally change the landscape of information technology. With experimental demonstrations stretching back more than two decades, the quantum computing community has achieved a major milestone over the past few years: the ability to build systems that are stretching the limits of what can be classically simulated, and which enable cloud-based research for a wide range of scientists, thus increasing the pool of talent exploring early quantum systems. While such noisy near-term quantum computing systems fall far short of the requirements for fault-tolerant systems, they provide unique testbeds for exploring the opportunities for quantum applications. Here we highlight the facets associated with these systems, including quantum software, cloud access, benchmarking quantum systems, error correction and mitigation in such systems, and understanding the complexity of quantum circuits and how early quantum applications can run on near term quantum computers.
Aspect-based sentiment classification with multi-attention network

Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence instead of the sentiment polarity of the entire sentence. Neural networks have been used for this task, and most existing methods have adopted sequence models, which require more training time than other models. When an aspect term comprises several words, most methods involve a coarse-level attention mechanism to model the aspect, and this may result in information loss. In this paper, we propose a multi-attention network (MAN) to address the above problems. The proposed model uses intra- and inter-level attention mechanisms. In the former, the MAN employs a transformer encoder instead of a sequence model to reduce training time. The transformer encoder encodes the input sentence in parallel and preserves long-distance sentiment relations. In the latter, the MAN uses a global and a local attention module to capture differently grained interactive information between aspect and context. The global attention module focuses on the entire relation, whereas the local attention module considers interactions at word level; this was often neglected in previous studies. Experiments demonstrate that the proposed model achieves superior results when compared to the baseline models.
LayoutVAE: Stochastic Scene Layout Generation From a Label Set

Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignore plausible visual variations within the structure dictated by the text. We propose LayoutVAE, a variational autoencoder based framework for generating stochastic scene layouts. LayoutVAE is a versatile modeling framework that allows for generating full image layouts given a label set, or per label layouts for an existing image given a new label. In addition, it is also capable of detecting unusual layouts, potentially providing a way to evaluate layout generation problem. Extensive experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset verifies the effectiveness of our proposed framework.
Occluded Face Recognition in the Wild by Identity-Diversity Inpainting

Face recognition has achieved advanced development by using convolutional neural network (CNN) based recognizers. Existing recognizers typically demonstrate powerful capacity in recognizing un-occluded faces, but often suffer from accuracy degradation when directly identifying occluded faces. This is mainly due to insufficient visual and identity cues caused by occlusions. On the other hand, generative adversarial network (GAN) is particularly suitable when it needs to reconstruct visually plausible occlusions by face inpainting. Motivated by these observations, this paper proposes identity-diversity inpainting to facilitate occluded face recognition. The core idea is integrating GAN with an optimized pre-trained CNN recognizer which serves as the third player to compete with the generator by distinguishing diversity within the same identity class. To this end, a collect of identity-centered features is applied in the recognizer as supervision to enable the inpainted faces clustering towards their identity centers. In this way, our approach can benefit from GAN for reconstruction and CNN for representation, and simultaneously addresses two challenging tasks, face inpainting and face recognition. Experimental results compared with 4 state-of-the-arts prove the efficacy of the proposed approach.
An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

We perform an in-depth, systematic benchmarking study and evaluation of phishing features on diverse and extensive datasets. We propose a new taxonomy of features based on the interpretation and purpose of each feature. Next, we propose a benchmarking framework called ‘PhishBench,’ which enables us to evaluate and compare the existing features for phishing detection systematically and thoroughly under identical experimental conditions, i.e., unified system specification, datasets, classifiers, and evaluation metrics. PhishBench is a first in the field of benchmarking phishing related research and incorporates thorough and systematic evaluation and feature comparison. We use PhishBench to test methods published in the phishing literature on new and diverse datasets to check their robustness and scalability. We study how dataset characteristics, e.g., varying legitimate to phishing ratios and increasing the size of imbalanced datasets, affect classification performance. Our results show that the imbalanced nature of phishing attacks affects the detection systems’ performance and researchers should take this into account when proposing a new method. We also found that retraining alone is not enough to defeat new attacks. New features and techniques are required to stop attackers from fooling detection systems.
Avatar and Sense of Embodiment: Studying the Relative Preference Between Appearance, Control and Point of View

In Virtual Reality, a number of studies have been conducted to assess the influence of avatar appearance, avatar control and user point of view on the Sense of Embodiment (SoE) towards a virtual avatar. However, such studies tend to explore each factor in isolation. This paper aims to better understand the inter-relations among these three factors by conducting a subjective matching experiment. In the presented experiment ( n=40), participants had to match a given "optimal" SoE avatar configuration (realistic avatar, full-body motion capture, first-person point of view), starting by a "minimal" SoE configuration (minimal avatar, no control, third-person point of view), by iteratively increasing the level of each factor. The choices of the participants provide insights about their preferences and perception over the three factors considered. Moreover, the subjective matching procedure was conducted in the context of four different interaction tasks with the goal of covering a wide range of actions an avatar can do in a VE. The paper also describes a baseline experiment ( n=20) which was used to define the number and order of the different levels for each factor, prior to the subjective matching experiment (e.g. different degrees of realism ranging from abstract to personalised avatars for the visual appearance). The results of the subjective matching experiment show that point of view and control levels were consistently increased by users before appearance levels when it comes to enhancing the SoE. Second, several configurations were identified with equivalent SoE as the one felt in the optimal configuration, but vary between the tasks. Taken together, our results provide valuable insights about which factors to prioritize in order to enhance the SoE towards an avatar in different tasks, and about configurations which lead to fulfilling SoE in VE.
Fault-Attention Generative Probabilistic Adversarial Autoencoder for Machine Anomaly Detection

Anomaly detection is one of the most fundamental and indispensable components in predictive maintenance. In this article, anomaly detection is modeled as a one-class classification problem. Based on the scenario that the training data only include healthy state data, a fault-attention generative probabilistic adversarial autoencoder (FGPAA) is proposed to automatically find low-dimensional manifold embedded in high-dimensional space of the signal. Benefited from the characteristics of autoencoder, the signal information loss in feature extraction is reduced. Then, the fault-attention abnormal state indictor can be constructed with the distribution probability of low-dimensional feature and reconstruction error. Effectiveness of the model is verified with fault classification datasets and run-to-failure experimental datasets. The results show that FGPAA outperforms both GPAA and other traditional methods and can be processed in real time. It not only can obtain high accuracy for both classification data and run-to-failure data, but also achieve a certain trend index for run-to-failure data.
EB-RRT: Optimal Motion Planning for Mobile Robots

In a human-robot coexisting environment, it is pivotal for a mobile service robot to arrive at the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory. Inspired by the EB method, we propose a hierarchical framework consisting of two planners. In the global planner, a time-based RRT algorithm is used to generate a feasible heuristic trajectory for a specific task in the dynamic environment. However, this heuristic trajectory is nonoptimal. In the dynamic replanner, the time-based nodes on the heuristic trajectory are updated due to the internal contraction force and the repulsive force from the obstacles. In this way, the heuristic trajectory is optimized continuously, and the final trajectory can be proved to be optimal in the homotopy class of the heuristic trajectory. Simulation experiments reveal that compared with two stateof-the-art algorithms, our proposed method can achieve better performance in dynamic environments.
BC-SABE: Blockchain-Aided Searchable Attribute-Based Encryption for Cloud-IoT

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.
VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification

Much progress has been made recently on text classification with methods based on neural networks. In particular, models using attention mechanism such as BERT have shown to have the capability of capturing the contextual information within a sentence or document. However, their ability of capturing the global information about the vocabulary of a language is more limited. This latter is the strength of Graph Convolutional Networks (GCN). In this paper, we propose VGCN-BERT model which combines the capability of BERT with a Vocabulary Graph Convolutional Network (VGCN). Local information and global information interact through different layers of BERT, allowing them to influence mutually and to build together a final representation for classification. In our experiments on several text classification datasets, our approach outperforms BERT and GCN alone, and achieve higher effectiveness than that reported in previous studies.
Dehazing Evaluation: Real-World Benchmark Datasets, Criteria, and Baselines

On benchmark images, modern dehazing methods are able to achieve very comparable results whose differences are too subtle for people to qualitatively judge. Thus, it is imperative to adopt quantitative evaluation on a vast number of hazy images. However, existing quantitative evaluation schemes are not convincing due to a lack of appropriate datasets and poor correlations between metrics and human perceptions. In this work, we attempt to address these issues, and we make two contributions. First, we establish two benchmark datasets, i.e., the BEnchmark Dataset for Dehazing Evaluation (BeDDE) and the EXtension of the BeDDE (exBeDDE), which had been lacking for a long period of time. The BeDDE is used to evaluate dehazing methods via full reference image quality assessment (FR-IQA) metrics. It provides hazy images, clear references, haze level labels, and manually labeled masks that indicate the regions of interest (ROIs) in image pairs. The exBeDDE is used to assess the performance of dehazing evaluation metrics. It provides extra dehazed images and subjective scores from people. To the best of our knowledge, the BeDDE is the first dehazing dataset whose image pairs were collected in natural outdoor scenes without any simulation. Second, we provide a new insight that dehazing involves two separate aspects, i.e., visibility restoration and realness restoration, which should be evaluated independently; thus, to characterize them, we establish two criteria, i.e., the visibility index (VI) and the realness index (RI), respectively. The effectiveness of the criteria is verified through extensive experiments. Furthermore, 14 representative dehazing methods are evaluated as baselines using our criteria on BeDDE. Our datasets and relevant code are available at https://github.com/xiaofeng94/BeDDE-for-defogging.
Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection

Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.
LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, with skewed positive and negative ranges. Typical learnable quantization schemes [5], [7] assume unsigned quantization for activations and quantize all negative activations to zero which leads to significant loss in performance. Naively using signed quantization to accommodate these negative values requires an extra sign bit which is expensive for low-bit (2-, 3-, 4-bit) quantization. To solve this problem, we propose LSQ+, a natural extension of LSQ [7], wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations. Gradient-based learnable quantization schemes also commonly suffer from high instability or variance in the final training performance, hence requiring a great deal of hyper-parameter tuning to reach a satisfactory performance. LSQ+ alleviates this problem by using an MSE-based initialization scheme for the quantization parameters. We show that this initialization leads to significantly lower variance in final performance across multiple training runs. Overall, LSQ+ shows state-of-the-art results for EfficientNet and MixNet and also significantly outperforms LSQ for low-bit quantization of neural nets with Swish activations (e.g.: 1.8% gain with W4A4 quantization and upto 5.6% gain with W2A2 quantization of EfficientNet-B0 on ImageNet dataset). To the best of our knowledge, ours is the first work to quantize such architectures to extremely low bit-widths.
Combining Detection and Tracking for Human Pose Estimation in Videos

We propose a novel top-down approach that tackles the problem of multi-person human pose estimation and tracking in videos. In contrast to existing top-down approaches, our method is not limited by the performance of its person detector and can predict the poses of person instances not localized. It achieves this capability by propagating known person locations forward and backward in time and searching for poses in those regions. Our approach consists of three components: (i) a Clip Tracking Network that performs body joint detection and tracking simultaneously on small video clips; (ii) a Video Tracking Pipeline that merges the fixed-length tracklets produced by the Clip Tracking Network to arbitrary length tracks; and (iii) a Spatial-Temporal Merging procedure that refines the joint locations based on spatial and temporal smoothing terms. Thanks to the precision of our Clip Tracking Network and our merging procedure, our approach produces very accurate joint predictions and can fix common mistakes on hard scenarios like heavily entangled people. Our approach achieves state-of-the-art results on both joint detection and tracking, on both the PoseTrack 2017 and 2018 datasets, and against all top-down and bottom-down approaches.