Reviewerly/exHarmony huggingface dataset:
Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval

Cross-modal hashing has been widely investigated recently for its efficiency in large-scale cross-media retrieval. However, most existing cross-modal hashing methods learn hash functions in a batch-based learning mode. Such mode is not suitable for large-scale data sets due to the large memory consumption and loses its efficiency when training streaming data. Online cross-modal hashing can deal with the above problems by learning hash model in an online learning process. However, existing online cross-modal hashing methods cannot update hash codes of old data by the newly learned model. In this paper, we propose Online Collective Matrix Factorization Hashing (OCMFH) based on collective matrix factorization hashing (CMFH), which can adaptively update hash codes of old data according to dynamic changes of hash model without accessing to old data. Specifically, it learns discriminative hash codes for streaming data by collective matrix factorization in an online optimization scheme. Unlike conventional CMFH which needs to load the entire data points into memory, the proposed OCMFH retrains hash functions only by newly arriving data points. Meanwhile, it generates hash codes of new data and updates hash codes of old data by the latest updated hash model. In such way, hash codes of new data and old data are well-matched. Furthermore, a zero mean strategy is developed to solve the mean-varying problem in the online hash learning process. Extensive experiments on three benchmark data sets demonstrate the effectiveness and efficiency of OCMFH on online cross-media retrieval.
READ: Recursive Autoencoders for Document Layout Generation

Layout is a fundamental component of any graphic design. Creating large varieties of plausible document layouts can be a tedious task, requiring numerous constraints to be satisfied, including local ones relating different semantic elements and global constraints on the general appearance and spacing. In this paper, we present a novel framework, coined READ, for REcursive Autoencoders for Document layout generation, to generate plausible 2D layouts of documents in large quantities and varieties. First, we devise an exploratory recursive method to extract a structural decomposition of a single document. Leveraging a dataset of documents annotated with labeled bounding boxes, our recursive neural network learns to map the structural representation, given in the form of a simple hierarchy, to a compact code, the space of which is approximated by a Gaussian distribution. Novel hierarchies can be sampled from this space, obtaining new document layouts. Moreover, we introduce a combinatorial metric to measure structural similarity among document layouts. We deploy it to show that our method is able to generate highly variable and realistic layouts. We further demonstrate the utility of our generated layouts in the context of standard detection tasks on documents, showing that detection performance improves when the training data is augmented with generated documents whose layouts are produced by READ.
Embedding Algorithms for Quantum Annealers with Chimera and Pegasus Connection Topologies

We propose two new algorithms – Spring-Based MinorMiner (SPMM) and Clique-Based MinorMiner (CLMM) – which take as input the connectivity graph of a Quadratic Unconstrained Binary Optimization (QUBO) problem and produce as output an embedding of the input graph on a host graph that models the topology of a quantum computing device. As host graphs, we take the Chimera graph and the Pegasus graph, which are the topology graphs of D-Wave's 2000 qubit (first introduced in 2017) and 5000 qubit (expected 2020) quantum annealer devices, respectively. We evaluate our algorithms on a large set of random graph QUBO inputs (Erdős-Rényi $$G_{n,p}$$ , Barabási-Albert and d-regular graphs) on both host topologies against other embedding algorithms. For the Pegasus topology, we find that CLMM outperforms all other algorithms at edge densities larger than 0.08, while SPMM wins at edge densities smaller than 0.08 for Erdős-Rényi graphs, with very similar transition densities for the other graph classes. Surprisingly, the standard D-Wave MinorMiner embedding algorithm – while also getting slightly outperformed by SPMM for sparse and very dense graphs on Chimera – does not manage to extend its overall good performance on Chimera to Pegasus as it fails to embed even medium-density graphs on 175–180 nodes which are known to have clique embeddings on Pegasus.
CNN-BiLSTM Model for Violence Detection in Smart Surveillance

In this paper, a lightweight computational model has been introduced for the better classification of violent and non-violent activities. Nowadays, the occurrences of violent activities get increased in public places. It has various social and economic reasons behind the growth of violent actions. Therefore, the government agencies and public administrators need to check such incidents using smart surveillance. Deep learning-based an efficient violent activity detection model can help the authorities in detecting a violent activity in real-time. The evaluated results can be henceforth sent to store and analyze the captured video to automate the crime monitoring system. Convolutional Neural Network-based Bidirectional LSTM has been used to detect violent activities and also compared with other existing approaches. Our proposed model gives 99.27%, 100% and 98.64% classification accuracies for the widely used standard Hockey Fights, Movies and Violent-Flows video datasets, respectively.
Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning

Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive samples from the unvoted ones. Existing works, such as Bayesian Personalized Ranking (BPR), sample unvoted items as negative samples uniformly, therefore suffer from a critical noisy-label issue. To address this gap, we design an adaptive sampler based on noisy-label robust learning for implicit feedback data.
Route planning and track keeping control for ships based on the leader-vertex ant colony and nonlinear feedback algorithms

This paper aims at rapid route planning and tracking system design of intelligent ship. For rapid route planning, leader-vertex ant colony optimization (LVACO) algorithm is proposed based on the ant colony optimization (ACO), which ensures a leader of ant colony and optimizes the route by vertex method. For track keeping control, the indirect track keeping controller is designed, which utilizes the nonlinear feedback control and line of sight (LOS) algorithm. The nonlinear feedback control means that the feedback error of controller is decorated by the bipolar sigmoid function. Simulation results show that the route given by LVACO algorithm is less time to use and more suitable for navigation. Besides, compared with back-stepping control, the nonlinear feedback control saves more energy and mitigates abrasion of steering gear, simultaneously, obtains a better control effect.
Deep Learning Approach for Earthquake Parameters Classification in Earthquake Early Warning System

Magnitude determination of earthquakes is a mandatory step before an earthquake early warning (EEW) system sends an alarm. Beneficiary users of EEW systems dependon how far they are located from such strong events. Therefore,determining the locations of these shakes is an important is sue for the tranquility of citizens as well. In light of that, this article proposes a magnitude, location, depth, and origin timecategorization using earthquake Ml magnitudes between 2 and 9.The dataset used is the fore and aftershocks of the great Tohokuearthquake of March 11,2011, recorded by three stations fromthe Japanese Hi-net seismic network. The proposed algorithmdepends on a convolutional neural network (CNN) which hasthe ability to extract significant features from waveforms thatenabled the classifier to reach a robust performance in the required earthquake parameters. The classification accuracies ofthe suggested approach for magnitude, origin time, depth, andlocation are 93.67%,89.55%,92.54%,and 89.50%, respectively.
Latent Feature Extraction for Process Data via Multidimensional Scaling

Computer-based interactive items have become prevalent in recent educational assessments. In such items, detailed human–computer interactive process, known as response process, is recorded in a log file. The recorded response processes provide great opportunities to understand individuals' problem solving processes. However, difficulties exist in analyzing these data as they are high-dimensional sequences in a nonstandard format. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory analysis that extracts latent variables from process data through a multidimensional scaling framework. A dissimilarity measure is described to quantify the discrepancy between two response processes. The proposed method is applied to both simulated data and real process data from 14 PSTRE items in PIAAC 2012. A prediction procedure is used to examine the information contained in the extracted latent variables. We find that the extracted latent variables preserve a substantial amount of information in the process and have reasonable interpretability. We also empirically prove that process data contains more information than classic binary item responses in terms of out-of-sample prediction of many variables.
Detecting non-Markovianity via quantified coherence: theory and experiments

Abstract The dynamics of open quantum systems and manipulation of quantum resources are both of fundamental interest in quantum physics. Here, we investigate the relation between quantum Markovianity and coherence, providing an effective way for detecting non-Markovianity based on the quantum-incoherent relative entropy of coherence ( $${\mathcal{Q}}{\mathcal{I}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Q</mml:mi><mml:mi>I</mml:mi></mml:mrow></mml:math> REC). We theoretically show the relation between completely positive (CP) divisibility and the monotonic behavior of the $${\mathcal{Q}}{\mathcal{I}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Q</mml:mi><mml:mi>I</mml:mi></mml:mrow></mml:math> REC. Also we implement an all-optical experiment to demonstrate that the behavior of the $${\mathcal{Q}}{\mathcal{I}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Q</mml:mi><mml:mi>I</mml:mi></mml:mrow></mml:math> REC is coincident with the entanglement shared between the system and the ancilla for both Markovian and non-Markovian evolution; while other coherence-based non-Markovian information carriers violate monotonicity, even in Markovian processes. Moreover, both theoretically and experimentally, we show that non-Markovianity enhances the ability of creating coherence on an ancilla. This is the first experimental study of the relation between dynamical behavior of the $${\mathcal{Q}}{\mathcal{I}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Q</mml:mi><mml:mi>I</mml:mi></mml:mrow></mml:math> REC and the phenomenon of information backflow. Our methods for detecting non-Markovianity are applicable to general quantum evolutions.
The people's smart city dashboard (PSCD): Delivering on community-led governance with blockchain

Smart City dashboards, also known as Mayor's dashboards, are to visualise city's performance on strategic policy areas such as sustainable mobility, energy efficiency, air pollution, etc. Therefore, they use indicators and indices as quantitative means to display performance. This research studies the current delivery of strategic smart city agendas as well as current means to measure and present their performance and proposes a method that uses blockchain to reimagine both in a more empowering and collaborative manner, the so-called People's Smart City Dashboard (PSCD). The thesis of this research is that smart city agendas have failed in their promise of being citizen-centric instruments and this is due to their top-down approach. This can be reversed by using community-led technologies such as blockchain. However, this bottom-up and blockchain-based implementation of smart city agendas will require from new, people-centric measurement tools and thus the so-called Activators and Aggregators are designed as well.
HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression

Recent developments in machine learning algorithms have enabled models to exhibit impressive performance in healthcare tasks using electronic health record (EHR) data. However, the heterogeneous nature and sparsity of EHR data remains challenging. In this work, we present a model that utilizes heterogeneous data and addresses sparsity by representing diagnoses, procedures, and medication codes with temporal Hierarchical Clinical Embeddings combined with Topic modeling (HCET) on clinical notes. HCET aggregates various categories of EHR data and learns inherent structure based on hospital visits for an individual patient. We demonstrate the potential of the approach in the task of predicting depression at various time points prior to a clinical diagnosis. We found that HCET outperformed all baseline methods with a highest improvement of 0.07 in precision-recall area under the curve (PRAUC). Furthermore, applying attention weights across EHR data modalities significantly improved the performance as well as the model's interpretability by revealing the relative weight for each data modality. Our results demonstrate the model's ability to utilize heterogeneous EHR information to predict depression, which may have future implications for screening and early detection.
Drone Detection and Classification using Deep Learning

This paper presents a systematic approach to drone detection and classification using deep learning with different modalities. The YOLOv3 object detector is used to detect the moving or still objects. It uses a computer vision-based approach as a reliable solution. The convolutional neural network helps to extract features from images and to detect the object with maximum accuracy. The model is trained with a proper dataset and trained for 150 epoch only to detect various types of drones. A convolutional neural network with modern object detection methods shows an excellent approach for real-time detection of drones.
Detecting Malicious Accounts on the Ethereum Blockchain with Supervised Learning

Ethereum is a blockchain platform where users can transact cryptocurrency as well as build and deploy decentralized applications using smart contracts. The participants in the Ethereum platform are 'pseudo-anonymous' and same user can have multiple accounts under multiple cryptographic identities. As a result, detecting malicious users engaged in fraudulent activities as well as attribution are quite difficult. In the recent past, multiple such activities came to light. In the famous Ethereum DAO attack, hackers exploited bug in smart contracts stole large amount of cryptocurrency using fraudulent transactions. However, activities such as ponzi-scheme, tax evasion by transacting in cryptocurrency, using pseudo-anonymous accounts for receiving ransom payment, consolidation of funds accumulated under multiple identities etc. should be monitored and detected in order to keep legitimate users safe on the platform. In this work, we detect malicious nodes by using supervised machine learning based anomaly detection in the transactional behavior of the accounts. Depending on the two prevalent account types – Externally Owned Account (EOA) and smart contract accounts, we apply two distinct machine learning models. Our models achieve a detection accuracy of 96.54% with 0.92% false-positive ratio and 96.82% with 0.78% false-positive ratio for EOA and smart contract account analysis, respectively. We also find the listing of 85 new malicious EOA and 1 smart contract addresses between 20 January 2020 and 24 February 2020. We evaluate our model on these, and the accuracy of that evaluation is 96.21% with 3% false positive.
Quantum-like mutation-induced dragonfly-inspired optimization approach

This study proposed an improved dragonfly algorithm (DA). This algorithm is a recently proposed metaheuristic optimizer inspired by swarming behaviors of dragonflies, which has reasonably achieved satisfactory results in dealing with engineering, education, and other fields. However, the original method will show some shortcomings in convergence speed or falling into local optimum. Given these shortcomings, this paper proposes an improved optimizer to balance the relationship between exploitation and exploration and mitigate any deficiency. First, by implementing the idea of the quantum rotation gate, the swarm of agents can be shifted to a position more conducive to the optimal value. Then, Gaussian mutation is adopted to improve the swarm's ability to mutate and realize its diversity, which enables the primary method to have a strong local search capability. The proposed method was compared against six other common meta-heuristics and five state-of-the-art algorithms on a comprehensive set of nineteen functions selected from twenty-three classic benchmark problems and thirty IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) 2014 benchmark tasks. To verify the effectiveness of the approach, the non-parametric statistical Wilcoxon signed-rank and Friedman tests were performed to validate the significance of the proposed method against other counterparts. The results of experimental simulations demonstrate that two introduced strategies can significantly improve the exploitative and exploratory tendencies of the original algorithm. Furthermore, the convergence speed of the conventional approach has been improved to a large extent. Additionally, quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA) is utilized as a searching core in a wrapper feature selection technique, and it is compared with other advanced feature selection methods. The results show that QGDA achieves substantial superiority in feature selection through optimum fitness and minimum error rate. Also, the results of QGDA on the three classical engineering design problems have demonstrated that the proposed method can effectively solve these constraints problems. It is encouraging that the proposed method can be used as a useful, auxiliary tool for solving complex optimization problems.
Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos

The ability of artificial intelligence techniques to build synthesized brand new videos or to alter the facial expression of already existing ones has been efficiently demonstrated in the literature. The identification of such new threat generally known as Deepfake, but consisting of different techniques, is fundamental in multimedia forensics. In fact this kind of manipulated information could undermine and easily distort the public opinion on a certain person or about a specific event. Thus, in this paper, a new technique able to distinguish synthetic generated portrait videos from natural ones is introduced by exploiting inconsistencies due to the prediction error in the re-encoding phase. In particular, features based on inter-frame prediction error have been investigated jointly with a Long Short-Term Memory (LSTM) model network able to learn the temporal correlation among consecutive frames. Preliminary results have demonstrated that such sequence-based approach, used to distinguish between original and manipulated videos, highlights promising performances.
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to unsupervised learning to obtain monocular depth. However, most unsupervised learning methods capable of achieving high depth prediction accuracy will require a deep network architecture which will be too heavy and complex to run on embedded devices with limited storage and memory spaces. To address this issue, we propose a new powerful network with a recurrent module to achieve the capability of a deep network while at the same time maintaining an extremely lightweight size for real-time high performance unsupervised monocular depth prediction from video sequences. Besides, a novel efficient upsample block is proposed to fuse the features from the associated encoder layer and recover the spatial size of features with the small number of model parameters. We validate the effectiveness of our approach via extensive experiments on the KITTI dataset. Our new model can run at a speed of about 110 frames per second (fps) on a single GPU, 37 fps on a single CPU, and 2 fps on a Raspberry Pi 3. Moreover, it achieves higher depth accuracy with nearly 33 times fewer model parameters than state-of-the-art models. To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices.
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions.