Reviewerly/exHarmony huggingface dataset:
Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer

The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages.For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios.Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets.However, AK-LS-SVM still faces two main challenges: (1) improving the classification performance of AK-LS-SVM, and (2) saving time when performing a grid search for model selection.Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this study.This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges.Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM.In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer.Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers.Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-oneout error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer.We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-SVM and SVM using additive kernels on seven public UCI datasets and one real world dataset.The experimental results show that both DTA-LS-SVM and iDTA-LS-SVM exhibit better generalization performance and faster learning speed.
Minimizing Latency for Secure Coded Computing Using Secret Sharing via Staircase Codes

We consider the setting of a Master server, M, who possesses confidential data (e.g., personal, genomic or medical data) and wants to run intensive computations on it, as part of a machine learning algorithm for example. The Master wants to distribute these computations to untrusted workers who have volunteered or are incentivized to help with this task. However, the data must be kept private and not revealed to the individual workers. Some of the workers may be stragglers, e.g., slow or busy, and will take a random time to finish the task assigned to them. We are interested in reducing the delays experienced by the Master. We focus on linear computations as an essential operation in many iterative algorithms such as principal component analysis, support vector machines and other gradient-descent based algorithms. A classical solution is to use a linear secret sharing scheme, such as Shamir's scheme, to divide the data into secret shares on which the workers can perform linear computations. However, classical codes can provide straggler mitigation assuming a worst-case scenario of a fixed number of stragglers. We propose a solution based on new secure codes, called Staircase codes, introduced previously by two of the authors. Staircase codes allow flexibility in the number of stragglers up to a given maximum, and universally achieve the information theoretic limit on the download cost by the Master, leading to latency reduction. Under the shifted exponential model, we find upper and lower bounds on the Master's mean waiting time. We derive the distribution of the Master's waiting time, and its mean, for systems with up to two stragglers. For systems with any number of stragglers, we derive an expression that can give the exact distribution, and the mean, of the waiting time of the Master. We show that Staircase codes always outperform classical secret sharing codes.
Detection of People With Camouflage Pattern Via Dense Deconvolution Network

In this letter, we explore the detection of people with camouflage pattern in cluttered natural scenes. First, considering the lack of open evaluation and training data on camouflaged people detection, a specific dataset of camouflaged people in natural scenes is constructed by us for the first time to the best of our knowledge. Secon, due to the serious corruption of the discrimination of low-level features by the camouflage patterns and cluttered background, we extract the high-level semantic features in deep convolution network and introduce short connections in deconvolution phase, to construct the dense deconvolution network. In training procedure, we augment and shift the images of camouflaged people to generate the proper training data. Attributing to the usage and fusion of semantic information, the proposed network effectively labels the camouflaged people regions as a whole. Finally, we use the superpixel segmentation and spatial smoothness constraint for further improvement of the detection result. Experimental results demonstrate that the proposed method outperforms the classical camouflaged object detection method and typical CNN-based detection methods.
Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm

In this paper a novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem. The optimum threshold values are determined by the maximization of Kapur's or Otsu's objective function using Krill Herd Optimization technique. The proposed method reduces the computational time for computing the optimum thresholds for multilevel thresholding. The applicability and computational efficiency of the Krill Herd Optimization based multilevel thresholding is demonstrated using various benchmark images. A detailed comparative analysis with other existing bio-inspired techniques based multilevel thresholding techniques such as Bacterial Foraging (BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Moth-Flame Optimization (MFO) has been performed to prove the superior performance of the proposed method.
A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance

Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multi-scenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for the conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multi-scenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multi-scenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than the other state-of-the-art methods.
Trust-Based Collaborative Privacy Management in Online Social Networks

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals' privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users' privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users' opinions, and the values are updated according to users' privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others' privacy, and the proposed bandit approach can bring the user a high payoff.
Wave2Vec: Deep representation learning for clinical temporal data

Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs). However, existing models cannot deal with continuous physiological records, which are often included in EHRs. The major challenges for this task are to model non-obvious representations from observed high-resolution biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec, an end-to-end deep representation learning model, to bridge the gap between biosignal processing and semantic learning. Wave2Vec not only jointly learns both inherent and temporal representations of biosignals, but also allows us to interpret the learned representations reasonably over time. We propose two embedding mechanisms to capture the temporal knowledge within signals, and discover latent knowledge from signals in time-frequency domain, namely component-based motifs. To validate the effectiveness of our model in clinical task, we carry out experiments on two real-world benchmark biosignal datasets. Experimental results demonstrate that the proposed Wave2Vec model outperforms six feature learning baselines in biosignal processing. Analytical results show that the proposed model can incorporate both motif co-occurrence information and time series information of biosignals, and hence provides clinically meaningful interpretation.
A Channel-Dependent Statistical Watermark Detector for Color Images

Data security is a main concern in everyday data transmissions over the Internet. A possible solution to guarantee secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, that is, watermarking. In this paper, we propose a new color image watermarking scheme and its corresponding detector in the sparse domain. The watermark detector aims at verifying the ownership and circumventing any unauthorized duplication of the digital data. Most of the existing color image watermarking schemes disregard the inter-channel dependencies. In view of this, we take into account the interchannel dependencies between RGB channels and interscale dependencies of the sparse coefficients of color images by employing the hidden Markov model. An efficient detector is designed by establishing a binary hypothesis test through which the existence of the hidden watermark is examined. Experiments are conducted to evaluate the performance of the proposed watermark detector for color images. The results show that the proposed detector provides detection rates higher than those provided by the other detectors, even in the presence of attacks. It is also shown that the proposed detector exhibits better performance in terms of the robustness of the embedded watermark.
Multiple-Model State Estimation Based on Variational Bayesian Inference

In this paper, we propose a new approach to state estimation of multiple state-space models. Unlike the traditional methods (including the interacting multiple-model algorithm) that approximate a Gaussian mixture distribution with a single Gaussian distribution, the proposed method approximates the joint probability density functions of the state and model identity through Bayesian inference. It is shown that the proposed method reduces the approximation error considerably, and improves estimation accuracy without increasing computational cost. Analysis of its specific features as well as a potential extension is also presented. Numerical examples with a practically oriented simulation are employed to illustrate the effectiveness of the proposed method.
SM-DTW: Stability Modulated Dynamic Time Warping for signature verification

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.
Efficient public key encryption with equality test in the standard model

Public key encryption with equality test (PKEET) is a special kind of public encryption scheme (PKE) that allows a tester to perform equality tests on ciphertexts generated by different public keys as well as the same public key. This feature enables us to apply PKEET to various scenarios in practice, such as efficient data management on encrypted databases and spam filtering in encrypted email systems. From these reasons, since Yang et al. [1] first proposed the concept of PKEET, there have been proposed many PKEET schemes to improve efficiency or to enhance functionalities. However, to the best of our knowledge, almost all existing schemes were presented under assuming the existence of random oracles, except for generic construction proposed by Lee et al. On the other hand, their generic approach for PKEET employs a 2-level hierarchical identity-based encryption and a strongly unforgeable one-time signature, which suffers from low efficiency. In this paper, we propose an efficient PKEET scheme under a specific cryptographic assumption in the standard model. To this end, we first encrypt a message and its hash value in a parallel way by following the recently proposed strategy. Then, to prevent adaptive chosen ciphertext attacks (CCA2), we give a link between them by adapting the technique which was originally proposed for identity-based encryption and previously exploited to design efficient CCA2-secure PKE schemes. We show that our proposed construction satisfies formal security requirements for PKEET under the decisional bilinear Diffie–Hellman (DBDH) assumption in the standard model. As a result, we obtain a new PKEET scheme which has shorter ciphertext and trapdoor sizes, and improves computational costs for encryption, decryption, and test algorithms, by about 60%, 77%, and 66%, respectively, compared to a PKEET instantiation obtained by the prior generic framework.
Duality of Quantum and Classical Error Correction Codes: Design Principles and Examples

Quantum error correction codes (QECCs) can be constructed from the known classical coding paradigm by exploiting the inherent isomorphism between the classical and quantum regimes, while also addressing the challenges imposed by the strange laws of quantum physics. In this spirit, this paper provides deep insights into the duality of quantum and classical coding theory, hence aiming for bridging the gap between them. Explicitly, we survey the rich history of both classical as well as quantum codes. We then provide a comprehensive slow-paced tutorial for constructing stabilizer-based QECCs from arbitrary binary as well as quaternary codes, as exemplified by the dual-containing and non-dual-containing Calderbank-Shor-Steane (CSS) codes, non-CSS codes and entanglement-assisted codes. Finally, we apply our discussions to two popular code families, namely to the family of Bose-Chaudhuri-Hocquenghem as well as of convolutional codes and provide detailed design examples for both their classical as well as their quantum versions.
Improving Bitcoin Ownership Identification Using Transaction Patterns Analysis

Bitcoin is a cryptocurrency and a financial transaction network implemented using blockchain technology. Users in the Bitcoin network use pseudonymous Bitcoin addresses and conduct transactions with others without revealing their real identities. In order to further enhance their privacy and convenience, users often use a large number of different addresses. In this paper, we analyze different patterns of transactions occurring in the Bitcoin network in order to cluster addresses that share the same ownership. In order to evaluate the proposed clustering approach, Bitcoin addresses belonging to known entities are tagged and these are used in conjunction with the Gini impurity index to test the accuracy of the recovered identity-based clusters. The results show that our heuristic was able to detect relationships between Bitcoin addresses that were missed by the existing heuristics.
Semi-supervised feature learning for improving writer identification

Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we proposed a weighted label smoothing regularization (WLSR) method for data augmentation, which assigned the weighted uniform label distribution to the extra unlabeled data. The WLSR method could regularize the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach could significantly improve the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline write identification.
A semantics-based approach to sensor data segmentation in real-time Activity Recognition

Activity Recognition (AR) is key in context-aware assistive living systems. One challenge in AR is the segmentation of observed sensor events when interleaved or concurrent activities of daily living (ADLs) are performed. Several studies have proposed methods of separating and organising sensor observations and recognise generic ADLs performed in a simple or composite manner. However, little has been explored in semantically distinguishing individual sensor events directly and passing it to the relevant ongoing/new atomic activities. This paper proposes Semiotic theory inspired ontological model, capturing generic knowledge and inhabitant-specific preferences for conducting ADLs to support the segmentation process. A multithreaded decision algorithm and system prototype were developed and evaluated against 30 use case scenarios where each event was simulated at 10sec interval on a machine with i7 2.60GHz CPU, 2 cores and 8GB RAM. The result suggests that all sensor events were adequately segmented with 100% accuracy for single ADL scenarios and minor improvement of 97.8% accuracy for composite ADL scenario. However, the performance has suffered to segment each event with the average classification time of 3971ms and 62183ms for single and composite ADL scenarios, respectively.
A secure versatile light payment system based on blockchain

Ever-increasing transaction costs, serious network congestion, and low transaction rates in the current blockchain systems restrict their extensive use. To relieve from this situation, we present a secure versatile light payment (SVLP) scheme. The SVLP merely employs a digital signature algorithm and a one-way function and has similar security comparing to existing blockchain systems, such as Bitcoin and Ethereum. The proposed scheme is of ultra-low power consumption, since the payers and payees only need one-way functions to achieve multiple transactions, instead of the costly digital signature algorithms. Furthermore, the processes of payment and refunding are flexible. This is due to the fact that the denomination in our scheme possesses divisibility and the users need not to verify the preimages on the long chain one-by-one. Finally, as the transaction can be taken off-chain and offline, it can be even used in remote areas or geological disasters areas where communication infrastructures are lacked or destroyed. All these features indicate that our scheme is practical and versatile.
A new data characterization for selecting clustering algorithms using meta-learning

Meta-learning has been successfully used for algorithm recommendation tasks. It uses machine learning to induce meta-models able to predict the best algorithms for a new dataset. In this paper, meta-models are applied to a set of meta-features, describing a dataset, to predict the performance of clustering algorithms applied to this dataset. The paper also proposes a new set of meta-features, based on correlation and dissimilarity measures. Experimental results show that these meta-features improve the recommendation. Additionally, this paper evaluates the importance of each meta-feature for the recommendation.