Reviewerly/exHarmony huggingface dataset:
A novel spatio-temporal Siamese network for 3D signature recognition

Signature forgery is at the centre of several fraudulent activities and legal battles. The introduction of 3D signatures, the virtual signing of ones name in the air, has the potential to restrict forgers due to the absence of visual cues that can be easily copied. Existing 3D signature recognition approaches, however, have not leveraged the inherent spatial and temporal information, making it difficult to handle the diminished separability and reproducibility of these signatures. In this paper, we propose a novel spatio-temporal adaptation of the Siamese Neural Network, wherein one branch extracts spatial features using a 1D Convolutional Neural Network (CNN) while the other processes the input in the temporal domain using Long Short-Term Memory networks (LSTMs). Unlike conventional deep learning networks, Siamese networks are an application of One-Shot Learning so as to learn from a small amount of data as is often the case in real life problems. They employ a distance metric that is forced to be small for like samples (signatures from the same person), and large for different samples (from different persons). The proposed approach, termed ST-SNN, is compared to other baseline classification architectures, and demonstrated using a publicly available biometric 3D signature benchmark dataset, yielding True Positive Rate (TPR) of 94.63% with 4.1% False Acceptance Rate (FAR).
Graph-Based Multi-Interaction Network for Video Question Answering

Video question answering is an important task combining both Natural Language Processing and Computer Vision, which requires a machine to obtain a thorough understanding of the video. Most existing approaches simply capture spatio-temporal information in videos by using a combination of recurrent and convolutional neural networks. Nonetheless, most previous work focus on only salient frames or regions, which normally lacks some significant details, such as potential location and action relations. In this paper, we propose a new method called Graph-based Multi-interaction Network for video question answering. In our model, a new attention mechanism named multi-interaction is designed to capture both element-wise and segment-wise sequence interactions simultaneously, which can be found between and inside the multi-modal inputs. Moreover, we propose a graph-based relation-aware neural network to explore a more fine-grained visual representation, which could explore the relationships and dependencies between objects spatially and temporally. We evaluate our method on TGIF-QA and other two video QA datasets. The qualitative and quantitative experimental results show the effectiveness of our model, which achieves state-of-the-art performance.
EACNet: Enhanced Asymmetric Convolution for Real-Time Semantic Segmentation

Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can't meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise asymmetric convolution and dilated convolution to extract short-range and long-range features, which are efficient and powerful. Additionally, we apply a bilateral structure in which the detail branch preserves low-level spatial details while the semantic branch captures high-level context information. The two branches are merged at different stages of the network to strengthen information propagation between different levels. The experiments on the Cityscapes dataset show that our method achieves high accuracy and speed with relatively small parameters. Compared with other real-time semantic segmentation methods, our network attains a good trade-off among parameters, speed, and accuracy.
Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images

image can be represented with different formats, such as the equirectangular projection (ERP) image, viewport images or spherical image, for its different processing procedures and applications. Accordingly, the 360-degree image quality assessment (360-IQA) can be performed on these different formats. However, the performance of 360-IQA with the ERP image is not equivalent with those with the viewport images or spherical image due to the over-sampling and the resulted obvious geometric distortion of ERP image. This imbalance problem brings challenge to ERP image based applications, such as 360-degree image/video compression and assessment. In this paper, we propose a new blind 360-IQA framework to handle this imbalance problem. In the proposed framework, cubemap projection (CMP) with six inter-related faces is used to realize the omnidirectional viewing of 360-degree image. A multi-distortions visual attention quality dataset for 360-degree images is firstly established as the benchmark to analyze the performance of objective 360-IQA methods. Then, the perception-driven blind 360-IQA framework is proposed based on six cubemap faces of CMP for 360-degree image, in which human attention behavior is taken into account to improve the effectiveness of the proposed framework. The cubemap quality feature subset of CMP image is first obtained, and additionally, attention feature matrices and subsets are also calculated to describe the human visual behavior. Experimental results show that the proposed framework achieves superior performances compared with state-of-the-art IQA methods, and the cross dataset validation also verifies the effectiveness of the proposed framework. In addition, the proposed framework can also be combined with new quality feature extraction method to further improve the performance of 360-IQA. All of these demonstrate that the proposed framework is effective in 360-IQA and has a good potential for future applications.
A new EM algorithm for flexibly tied GMMs with large number of components

Gaussian mixture models (GMMs) are a family of generative models used extensively in many machine learning applications. The modeling power of GMMs is directly linked to the number of components. Memory, computational load and lack of enough data hinders using GMMs with large number of components. To tackle this problem, GMMs with a tying scheme that we call flexibly tied GMM was proposed in the literature of the speech recognition community. In the literature, a coordinate-descent EM algorithm was proposed for estimating the parameters of flexibly tied GMMs. In this paper, we aim at reintroducing flexibly tied GMMs to the pattern recognition community. We rigorously investigate various optimization methods and see none of the out-of-the-box optimization methods can solve the parameter estimation problem due to the complexity of the cost function. To this end, we develop a fast Newton EM algorithm that combined with the coordinate descent EM algorithm, it significantly outperforms pure coordinate descent EM and all other optimization algorithms. Furthermore, we propose a computation factorization technique to increase the speed and decrease memory requirement of both Newton and coordinate descent EM algorithms in the case of large number of components. Experimental results on many datasets verifies the efficacy of the proposed algorithm. It also verifies that flexibly tied GMM outperforms both basic GMM and other types of tied GMMs on the datasets in terms of the log-likelihood. We also evaluate the performance of flexibly tied GMM on a clustering problem, and show that it can outperform basic GMM and kmeans algorithm.
Effect of pitch enhancement in Punjabi children's speech recognition system under disparate acoustic conditions

In this work, a Punjabi children speech recognition system is developed under different acoustic matched and mismatched conditions. One major problem in children's speech recognition is the differences in the acoustic attributes of the children and adult speech signals, which leads to the poor recognition rate for the children's speech. This paper shows how pitch enhanced features extracted from the front-end feature extraction process plays an important role under mismatched acoustic conditions. After enhancing the pitch using the Cepstral analysis in the feature extraction process, the recognition rate of the children's speech recognition system using different age group datasets increases as compared to the normal acoustics features extracted using Mel Frequency Cepstral Coefficient (MFCC) feature extraction process. Kaldi toolkit is used for building the children's speech recognition models at different phoneme levels. Results show the improvement of 0.03% to 16.47% WER under different acoustic conditions using pitch enhanced features.
Attention-aware metapath-based network embedding for HIN based recommendation

Heterogeneous information network (HIN) attracts increasing attention from the communities of recommender systems. HIN based recommendation methods can help overcome the difficulties of data sparsity and cold start. The majority of the existing HIN based recommendation methods use path-based semantic similarity between users and/or between items on HINs. However, the existing HIN based recommendation methods using metapath disregard the semantic differences among multiple metapaths (i.e., inter-metapaths) and the influence differences among neighbor pairs in each individual metapath (i.e., intra-metapaths). To solve these problems, we propose an attention-aware metapath-based network embedding for HIN based recommendation. To obtain additional semantic information, our method generates multiple metapath-based weighted homogeneous networks to model the auxiliary information of users and items of HIN. Thereafter, we design a novel self-attention integration to integrate multiple semantic information from multiple weighted homogenous information networks. Lastly, we utilize three deep neural network methods to model the implicit relations between users and items for the rating prediction task. Experimental results of three real-world datasets demonstrate that the proposed model outperforms existing state-of-the-art recommendation methods, solves the data sparsity problem, and models the multiple semantic information of users and items.
Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows

Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments.
Invariances of JPEG-quantized DCT coefficients and their application in robust image steganography

The JPEG compression is employed in many image-related fields, and the characteristics of DCT coefficients before and after JPEG compression are always one of the research hotspots. In this paper, we discover a characteristic by analyzing the image compression and decompression process, and we name this characteristic the “nonzero invariance of JPEG-quantized DCT coefficients.” Based on this, we deduce another characteristic, “position invariance of the last nonzero JPEG-quantized DCT coefficients.” This characteristic is then successfully applied to robust image steganography, a research field dedicated to achieving covert communication on lossy channels. A method resisting JPEG compression and statistical detection is proposed. This method is robust to JPEG compression attacks by skillfully utilizing the “position invariance of the last nonzero JPEG-quantized DCT coefficients.” Moreover, a novel distortion function is proposed. It is designed by searching for the most suitable embedding position using particular rules. The searching scope is defined between the last two nonzero coefficients to balance good antistatistical detection performance and image details retention. Particular rules are constructed with position parity of coefficients to reduce limitation on embedding positions. Experimental results on three open image databases show the high reliability of two characteristics and the robustness of the proposed method in JPEG compression and statistical detection.
Security proof of practical quantum key distribution with detection-efficiency mismatch

Quantum key distribution (QKD) protocols with threshold detectors are driving high-performance QKD demonstrations. The corresponding security proofs usually assume that all physical detectors have the same detection efficiency. However, the efficiencies of the detectors used in practice might show a mismatch depending on the manufacturing and setup of these detectors. A mismatch can also be induced as the different spatial-temporal modes of an incoming signal might couple differently to a detector. Here we develop a method that allows to provide security proofs without the usual assumption. Our method can take the detection-efficiency mismatch into account without having to restrict the attack strategy of the adversary. Especially, we do not rely on any photon-number cutoff of incoming signals such that our security proof is directly applicable to practical situations. We illustrate our method for a receiver that is designed for polarization encoding and is sensitive to a number of spatial-temporal modes. In our detector model, the absence of quantum interference between any pair of spatial-temporal modes is assumed. For a QKD protocol with this detector model, we can perform a security proof with characterized efficiency mismatch and without photon-number cutoff assumption. Our method also shows that in the absence of efficiency mismatch in our detector model, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control, as compared to the view where for a security proof this loss is attributed to the action of the adversary.3 MoreReceived 16 April 2020Accepted 7 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013076Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum cryptographyQuantum Information
Security analysis and performance evaluation of a new lightweight cryptographic algorithm for cloud computing

Nowadays, cloud computing is rapid growth in the IT industry, which provides a new way to manage the different information systems. The rapid development of technology makes it essential to use this technology and to benefit from its advantages. With raising awareness and worries about cloud services and information security, understanding and knowledge are growing for the usage of algorithms security into the processes and data systems. The primary purpose of this research work is to undertake a security analysis and performance assessment of a new lightweight cryptographic algorithm for enhancing data security in cloud computing. It focuses on studying the performance and security analysis algorithm architecture base on computational time and key sensitivity, statistical analysis, image histogram, and entropy change analysis in cloud computing environment. It also, provides a performance comparison of general symmetric algorithms used to secure the cloud services: DES, AES, Blowfish, RC4, HIGH, SF, and SIT.
R2-RRT*: Reliability-Based Robust Mission Planning of Off-Road Autonomous Ground Vehicle Under Uncertain Terrain Environment

This article presents a reliable and robust rapidly exploring random tree (R2-RRT&#x002A;) algorithm to tackle challenges in mission planning of off-road autonomous ground vehicles (AGVs) under uncertain terrain environment. Two types of mobility reliability metrics, namely state mobility reliability (SMR) and mission mobility reliability (MMR), are first defined to quantify the mobility reliability of an AGV and to incorporate mobility reliability into mission planning. SMR measures the probability that a vehicle can pass through a specific location on a map of interest, whereas MMR quantifies mobility reliability of a mission path with the consideration of dependence of soil properties and slope over space. Based on the defined SMR and MMR metrics, two reliability-based robust mission planning models are developed to identify optimal paths that have robust travel time and satisfy specific reliability requirements. Moreover, a reliability-based path smoothing algorithm is developed to address the suboptimality of R2-RRT&#x002A;. Results of a case study demonstrate the efficacy of the proposed models and algorithms. <i>Note to Practitioners</i>&#x2014;This article was motivated to explicitly account for the uncertain terrain environment in mission planning of off-road autonomous ground vehicles (AGVs). Existing approaches, e.g., RRT and its variants, in general, oversimplify the uncertainty sources and overlook the reliability of vehicle mobility. These simplifications could lead to failure (i.e., immobility) of off-road AGVs on the obtained paths. This article suggests a reliability-based mission planning model by incorporating the proposed SMR and MMR metrics into mission planning considering the spatial-dependent uncertainty sources. To identify a reliable and robust path, we extend RRT&#x002A; to R2-RRT&#x002A; that achieves a tradeoff between mission cost and reliability. The proposed reliability-based mission planning model, however, is not limited to RRT&#x002A; and can also be integrated with other path planning algorithms. It can be also applied to other unmanned vehicles and robots, such as the motion planning of unmanned aerial vehicles (UAVs) in adverse weather conditions.
Multiple perspective attention based on double BiLSTM for aspect and sentiment pair extract

Aspect category sentiment analysis has attracted increasing attention because of its outstanding performance in mining the fine-grained sentiment expression of users. In recent years, a new aspect category and sentiment pair extraction (ASPE) task has been proposed to simultaneously extract aspect categories and sentiment pairs. Most existing research works are designed in a two step pipeline, that is, they first perform aspect category detection, and subsequently conduct aspect category sentiment analysis. However, the pipeline method can clearly lead to error propagation from previous step. In this work, we propose a new framework for multiple perspective attention based on double BiLSTM with a novel joint strategy for ASPE to alleviate the accumulation of errors in the pipeline method. The experimental results on benchmark datasets SemEval and BDCI-2018 demonstrate the effectiveness of the proposed approach in terms of both accuracy and explainability for the ASPE task.
The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin

Abstract In recent years, there is a widespread belief among researchers and academicians that Bitcoin usage is imposing an additional burden on the environment inducing climate change. Although several studies have focussed on issues related to the energy consumption of the basic cryptocurrencies, an open question remains regarding the environmental depiction of Bitcoin. By resorting to Bayesian analysis and quantile cointegrated vector autoregression (CQVAR), this study seeks to disentangle the driving forces that shape the carbon footprint of Bitcoin. The sample used in the empirical analysis consists of a daily panel dataset covering 50 countries over the period 2016–2018. The empirical findings corroborate a causal effect between the use of Bitcoin and its underlying carbon dioxide emissions generated by the increasing energy load. The CQVAR is associated with positive marginal posterior means for most of the covariates of the model across all the estimated quantiles. In contrast, there is a negative and statistically significant relationship between Bitcoin miner's revenue and carbon emissions, uncovering a multimodal distribution pattern of the marginal posterior densities which is stronger at higher than in lower quantiles. This finding suggests that the lower (higher) miner's Bitcoin revenues, the more abrupt (gradual) the effect on environmental degradation. Therefore, a sustainable energy strategy focussing on the penetration of renewable energy sources along with the use of energy‐efficient mining hardware will alleviate the carbon footprint of Bitcoin.
Stochastic configuration network ensembles with selective base models

Studies have demonstrated that stochastic configuration networks (SCNs) have good potential for rapid data modeling because of their sufficient adequate learning power, which is theoretically guaranteed. Empirical studies have verified that the learner models produced by SCNs can usually achieve favorable test performance in practice but more in-depth theoretical analysis of their generalization power would be useful for constructing SCN-based ensemble models with enhanced generalization capacities. In particular, given a collection of independently developed SCN-based learner models, it is useful to select certain base learners that can potentially obtain preferable test results rather than considering all of the base models together, before simply taking their average in order to build an effective ensemble model. In this study, we propose a novel framework for building SCN ensembles by exploring key factors that might potentially affect the generalization performance of the base model. Under a mild assumption, we provide a comprehensive theoretical framework for examining a learner model’s generalization error, as well as formulating a novel indicator that contains measurement information for the training errors, output weights, and a hidden layer output matrix, which can be used by our proposed algorithm to find a subset of appropriate base models from a pool of randomized learner models. A toy example of one-dimensional function approximation, a case study for developing a predictive model for forecasting student learning performance, and two large-scale data sets were used in our experiments. The experimental results indicate that our proposed method has some remarkable advantages for building ensemble models.