Reviewerly/exHarmony huggingface dataset:
Adaptive Hashing With Sparse Matrix Factorization

Hashing offers a desirable and effective solution for efficiently retrieving the nearest neighbors from large-scale data because of its low storage and computation costs. One of the most appealing techniques for hashing learning is matrix factorization. However, most hashing methods focus only on building the mapping relationships between the Euclidean and Hamming spaces and, unfortunately, underestimate the naturally sparse structures of the data. In addition, parameter tuning is always a challenging and head-scratching problem for sparse hashing learning. To address these problems, in this article, we propose a novel hashing method termed adaptively sparse matrix factorization hashing (SMFH), which exploits sparse matrix factorization to explore the parsimonious structures of the data. Moreover, SMFH adopts an orthogonal transformation to minimize the quantization loss while deriving the binary codes. The most distinguished property of SMFH is that it is adaptive and parameter-free, that is, SMFH can automatically generate sparse representations and does not require human involvement to tune the regularization parameters for the sparse models. Empirical studies on four publicly available benchmark data sets show that the proposed method can achieve promising performance and is competitive with a variety of state-of-the-art hashing methods.
Trust Enhancement Scheme for Cross Domain Authentication of PKI System

Public Key Infrastructure (PKI) has been popularized in many scenarios such as e-government applications, enterprises, etc. Due to the construction of PKI system of various regions and departments, there formed a lot of isolated PKI management domains, cross-domain authentication has become a problem that cannot ignored, which also has some traditional solutions such as cross-authentication, trust list, etc. However, some issues still exist, which hinder the popularity of unified trust services. For example, lack of unified cross domain standard, the update period of Certificate Revocation List (CRL) is too long, which affects the security of cross-domain authentication. In this paper, we proposed a trust transferring model by using blockchain consensus instead of traditional trusted third party for e-government applications. We exploit how to solve the unified trust service problem of PKI at the national level through consensus and transfer some CA management functions to the blockchain. And we prove the scheme's feasibility from engineering perspective. Besides, the scheme has enough scalability to satisfy trust transfer requirements of multiple PKI systems. Meanwhile, the security and efficiency are also guaranteed compared with traditional solutions.
Locally Private High-Dimensional Crowdsourced Data Release Based on Copula Functions

With the increasing popularity of crowdsourcing services, high-dimensional crowdsourced data provides a wealth of knowledge. Nonetheless, unprecedented privacy threats to participants have emerged, due to complex correlations among multiple attributes and the vulnerabilities of untrusted crowdsourcing servers. Differential privacy-based paradigms have been proposed to release privacy-preserving datasets with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted crowdsourcing servers. To address this issue, we propose two novel solutions, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoCop</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR_LoCop</i> , which guarantee local differential privacy based on the randomized response technique while synthesizing and releasing high-dimensional crowdsourced data with high data utility. Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoCop</i> leverages copula theory to synthesize high-dimensional crowdsourced data via univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from aggregated privacy-preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian copula. Based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoCop</i> , the enhanced solution <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR_LoCop</i> not only takes advantage of C-vine copula to reflect conditional dependencies among high-dimensional attributes, but also achieves dimension reduction. Extensive experiments on real-world datasets demonstrate that our solutions substantially outperform the state-of-the-art techniques in terms of both data utility and computational overhead.
Double-Channel Object Tracking With Position Deviation Suppression

The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem. The discriminative correlation filter (DCF), which has strong discriminative power of low-level features, is employed for the position deviation suppress of the samples generated from MDNet. Firstly the pre-trained deep network is used to learn and classify the target and background in the video frames. If the tracked position of the DCF is judged to be correct, we delete the target candidate samples with high position deviation from MDNet. The position deviation is measured by the distance between the tracked positions of the DCF and MDNet. Finally, MDNet and DCF are updated with a robust update strategy. The experiments are performed on OTB-100 and VOT-2016. The overlap precision and distance precision of DCOT on OTB-100 are 92.2% and 69.5%, respectively, which are higher than those of MDNet by 1.3% and 1.7%. The results of DCOT in background clutter are higher than those of SANet by 0.2% and 2.8%, respectively. DCOT is also superior to other state-of-the-art trackers on VOT-2016.
Classroom Attention Analysis Based on Multiple Euler Angles Constraint and Head Pose Estimation

Classroom attention analysis aims to capture rich semantic information to analyze how the students are reacting to the lecture. However, there are some challenges for constructing a uniform attention model of students in the classroom. Each student is an individual and it is hard to make a unified judgment. The orientation of the head reflects the direction of attention, but changes in posture and space can interfere with the direction of attention. Aiming to solve these, this paper proposes a scoring module for converting the head Euler angle and attention in the classroom. This module takes the head Euler angle in three directions as input, and introduces spatial information to correct the angle. The key idea of the proposed method lies in introducing the mutual constraint of multiple Euler angles with the head spatial information, aiming to make attention model less susceptible to the difference of head information. The mutual constraint of multiple Euler angles can provide more accurate head information, while the head spatial information can be utilized to correct the angle. Extensive experiments using classroom video data demonstrate that the proposed method can achieve more accurate results.
Discrimination of topsoil environments in a karst landscape: an outcome of a geochemical mapping campaign

The study presented in this work emerged as a result of a multiyear regional geochemical survey based on low-density topsoil sampling and the ensuing geochemical atlas of Croatia. This study focuses on the Dinaric part of Croatia to expound the underlying mechanisms controlling the mobilities and variations in distribution of potentially harmful elements as observed from different environmental angles. Although serious environmental degradation of the vulnerable karst soil landscapes was expected to occur chiefly through the accumulation of various heavy metals, the most acute threat materialized through the soil acidification (Al-toxicity) affecting the entire Dinaric karst area. This picture surfaced from the analysis of all three investigated discriminant function models employing the abovementioned environmental criteria selected autonomously with respect to the evaluated soil geochemistry, namely, geologic setting, regional placement and land use. These models are presented by not only the characteristic discriminant-function diagrams but also a set of appropriate mathematically derived geochemical maps disclosing the allocations of potential threats to the karst soil landscapes posed by soil acidity.
Onboarding and Software Update Architecture for IoT Devices

The vast number of in-use Internet of Things (IoT) devices is by consensus, expected to continue rapid growth. These devices are subject to an expanding list of attacks that exploit both software vulnerabilities and design choices. This highlights the importance of architectural design of management for cryptographic keys involved in both initial configuration (onboarding) and secure, automatic update of device software and firmware. Low-level IoT devices with constrained processors and smaller registers and caches are computationally challenged to carry out desktop- and server-type public-key cryptographic operations, e.g., as needed for key establishment and authentication of software updates. To this end, we design and prototype an architecture for onboarding and secure software update of low-level IoT devices (8-bit). It uses elliptic curve cryptography (Curve25519), authenticated key establishment, and a known continuity-based key-locking mechanism that uses a public key embedded in a current software image to verify the signature on a software update. We also provide an informal security analysis. The design addresses the scenario of a transfer of update authority, e.g., when a manufacturer ceases to provide ongoing software updates upon going out of business.
Photo-voltaic power daily predictions using expanding PDE sum models of polynomial networks based on Operational Calculus

Photo-Voltaic (PV) power production is subject to the current local weather situation which result in the amount of solar radiation components possible to convert by PV modules. Numerical Weather Prediction (NWP) systems are usually run every 6 h to provide course local 24–48-hour forecasts. Statistical models, developed with spatial historical data, can convert or post-process these NWP data to predict PV power for a plant specific situation. Statistical predictions are more precise if rely on the latest weather observations and power measurements as the accuracy of NWP cloudiness is mostly inadequate for PV plant operation and the forecast errors are only magnified. Differential Polynomial Neural Network (D-PNN) is a novel neuro-computing technique based on analogies with brain pulse signal processing. It can model complex patterns without reducing significantly the data dimensionality as regression and soft-computing methods do. D-PNN decomposes the general Partial Differential Equation (PDE), being able to describe the local atmospheric dynamics, into node specific 2nd order sub-PDEs. These are converted using adapted procedures of Operational Calculus to obtain the Laplace images of unknown node functions, which are inverse transformed to obtain the originals. D-PNN can select from dozens of input variables to produce applicable sum PDE components which can extend, step by step, its composite models towards the optima. The PDE models are developed with historical spatial data from the estimated optimal lengths of daily training periods to process the last day input data and predict Clear Sky Index 24-hours ahead. They obtain a better prediction accuracy than simplified statistical solutions which allow to predict in horizon of a few hours only.
Application of statistical analyses for lapis lazuli stone provenance determination by XRL and XRF

Lapis lazuli use stretches back more than 6500 years; ancient civilizations of Mesopotamia, Egypt, China, Greece, and Rome treasured and prized it. Afghanistan has been the oldest source for this stone, while Chile, Canada, Russia and a few other countries have been reported as sources for raw material in more recent times; the rarity of historical mines surely represents a positive aspect for the provenance clue of artefacts. Lapis lazuli is a rock consisting mainly of lazurite, to which it owes the blue colour, calcite and pyrite. Other constituents may be present, related to the different mines. In the present work, we apply the principles of Radio-luminescence (RL) exploiting as radiation source the X-ray tube of a portable commercial X-Ray Fluorescence spectrometer; in this way, X-Ray Fluorescence spectra (XRF) can be simultaneously acquired to have a larger set of data. To highlight the instrumental experimental differences, we refer to the portable set up as X-Ray Luminescence (XRL), as suggested by recent literature. We thus looked for the possibility of applying a wieldy, low cost and non-destructive method that could fit also to precious objects, based on the join use of XRF and XRL. We performed analyses on raw lapis lazuli stones from five different provenances, both historical and modern, and on four sets of unknown origin carved polished stones, to test our methods on real artefacts. We focalised on a limited number of samples to concentrate on the statistical treatment of spectra obtained, so to get a synergic response of the two applied techniques. We were able to obtain a clear distinction for the different classified provenances and could speculate those of unknown samples.
3D Dense Separated Convolution Module for Volumetric Medical Image Analysis

With the thriving of deep learning, 3D convolutional neural networks have become a popular choice in volumetric image analysis due to their impressive 3D context mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data are often limited in biomedical tasks, a trade-off has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of overfitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network’s representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric medical image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.
Improved Teaching Learning-Based QoS-Aware Services Composition for Internet of Things

The emergence of Internet of Things (IoT) paradigm has led to a continuous increase in the number of services that have similar functionalities but different quality of service (QoS). The IoT services composition, therefore, becomes an important challenge that aims at creating added-value services by combining several services offered by thousands of smart devices in the IoT environment to meet the user's QoS requirements. In this context, an improved teaching learning-based QoS-aware services composition algorithm (ITL-QCA) is proposed to find a very close to the optimal composition in a reasonable amount of time. Unlike the evolutionary computation-based and swarm intelligence-based approaches, the proposed services composition algorithm is characterized by a few tuning parameters and a high exploration capability of the search space of compositions. This allows obtaining compositions with a high optimality in terms of QoS and without requiring some hard tuning parameters. The simulation results show clearly that the ITL-QCA algorithm performs better in terms of the composition optimality and has a reasonable execution time in a large-scale environment compared to other services composition approaches proposed in the literature.
A New Similarity Computing Model of Collaborative Filtering

Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations.The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects.However, most similarity calculation methods only use the user common score and provide bad recommendations.This paper proposes a new similarity measure method, which effectively utilizes the user context information.The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits.It can improve the accuracy of the prediction.The new method has been tested on the dataset and compared with other algorithms.The results show that the proposed method can improve the recommendation quality.
Quantum algorithm for the simulation of open-system dynamics and thermalization

The quantum open-system simulation is an important category of quantum simulation. By simulating the thermalization process at zero temperature, we can solve the ground-state problem of quantum systems. To realize the open-system evolution on the quantum computer, we need to encode the environment using qubits. However, usually the environment is much larger than the system, i.e., numerous qubits are required if the environment is directly encoded. In this paper, we propose a way to simulate open-system dynamics by reproducing reservoir correlation functions using a minimized Hilbert space. In this way, we only need a small number of qubits to represent the environment. To simulate the $n\mathrm{th}\ensuremath{-}\mathrm{order}$ expansion of the time-convolutionless master equation by reproducing up to $n$-time correlation functions, the number of qubits representing the environment is $\ensuremath{\sim}\ensuremath{\lfloor}\frac{n}{2}\ensuremath{\rfloor}{log}_{2}({N}_{\ensuremath{\omega}}{N}_{\ensuremath{\beta}})$. Here, ${N}_{\ensuremath{\omega}}$ is the number of frequencies in the discretized environment spectrum, and ${N}_{\ensuremath{\beta}}$ is the number of terms in the system-environment interaction. By reproducing two-time correlation functions, i.e., taking $n=2$, we can simulate the Markovian quantum master equation. In our algorithm, the environment on the quantum computer could be even smaller than the system.
Building Deep Random Ferns Without Backpropagation

Randomized sampling-based ensemble learning is emerging as a new alternative to deep neural networks (DNNs) because it supports diversity and locality and does not require backpropagation in the learning process. By connecting randomized weak classifiers in a layer-by-layer manner, it is possible to create models having a performance similar to that of DNNs, but with less overfitting and better generalizability than before. Therefore, in this paper, we propose a deep random ferns (d-RFs) model, in which extremely randomized ferns are connected to multilayers, allowing a high classification performance and a lightweight and fast structure. The input vector is first encoded as a transformed feature vector in the feature encoder layer and then is input to the cascade layers. The feature encoding process is similar to the DNN convolution and helps improve the performance of the final output layer. Unlike in the backpropagation paradigm, the cascade layer adjusts the number of ferns and layers required for the d-RFs adaptively, using only a small scale of data. RFs ensemble approaches have considerably fewer hyper-parameters than a DNN or deep forest model, and the complexity can be determined automatically in a data-dependent manner. In addition, experimental results show that an ensemble of multiple weak classifiers reduces the bias between models through an averaging of the weakly correlated classifiers, resulting in a better overall model. The proposed lightweight d-RFs was successfully applied to benchmark datasets and yielded a similar or better accuracy level and a smaller number of parameters and operations as compared with state-of-the-art methods.
Bag-of-Concepts representation for document classification based on automatic knowledge acquisition from probabilistic knowledge base

Text representation, a crucial step for text mining and natural language processing, concerns about transforming unstructured textual data into structured numerical vectors to support various machine learning and data mining algorithms. For document classification, one classical and commonly adopted text representation method is Bag-of-Words (BoW) model. BoW represents document as a fixed-length vector of terms, where each term dimension is a numerical value such as term frequency or tf-idf weight. However, BoW simply looks at surface form of words. It ignores the semantic, conceptual and contextual information of texts, and also suffers from high dimensionality and sparsity issues. To address the aforementioned issues, we propose a novel document representation scheme called Bag-of-Concepts (BoC), which automatically acquires useful conceptual knowledge from external knowledge base, then conceptualizes words and phrases in the document into higher level semantics (i.e. concepts) in a probabilistic manner, and eventually represents a document as a distributed vector in the learned concept space. By utilizing background knowledge from knowledge base, BoC representation is able to provide more semantic and conceptual information of texts, as well as better interpretability for human understanding. We also propose Bag-of-Concept-Clusters (BoCCl) model which clusters semantically similar concepts together and performs entity sense disambiguation to further improve BoC representation. In addition, we combine BoCCl and BoW representations using an attention mechanism to effectively utilize both concept-level and word-level information and achieve optimal performance for document classification.
End-to-End Video Captioning

Building correspondences across different modalities, such as video and language, has recently become critical in many visual recognition applications, such as video captioning. Inspired by machine translation, recent models tackle this task using an encoder-decoder strategy. The (video) encoder is traditionally a Convolutional Neural Network (CNN), while the decoding (for language generation) is done using a Recurrent Neural Network (RNN). Current state-of-the-art methods, however, train encoder and decoder separately. CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features. The decoder is then optimised on such static features to generate the video's description. This disjoint setup is arguably sub-optimal for input (video) to output (description) mapping. In this work, we propose to optimise both encoder and decoder simultaneously in an end-to-end fashion. In a two-stage training setting, we first initialise our architecture using pre-trained encoders and decoders - then, the entire network is trained end-to-end in a fine-tuning stage to learn the most relevant features for video caption generation. In our experiments, we use GoogLeNet and Inception-ResNet-v2 as encoders and an original Soft-Attention (SA-) LSTM as a decoder. Analogously to gains observed in other computer vision problems, we show that end-to-end training significantly improves over the traditional, disjoint training process. We evaluate our End-to-End (EtENet) Networks on the Microsoft Research Video Description (MSVD) and the MSR Video to Text (MSR-VTT) benchmark datasets, showing how EtENet achieves state-of-the-art performance across the board.