Reviewerly/exHarmony huggingface dataset:
Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving

In this work, a simple yet effective deep neural network is proposed to generate the dense depth map of the scene by exploiting both LiDAR sparse point cloud and the monocular camera image. Specifically, a feature pyramid network is firstly employed to extract feature maps from images across time. Then the relative pose is calculated by minimizing the feature distance between aligned pixels from inter-frame feature maps. Finally, the feature maps and the relative pose are further applied to compute the feature-metric loss for training the depth completion network. The key novelty of this work lies in that a self-supervised mechanism is presented to train the depth completion network by directly using visual-LiDAR odometry between consecutive frames. Comprehensive experiments and ablation studies on benchmark dataset KITTI demonstrate the superior performance over other state-of-the-art methods in terms of pose estimation and depth completion. The detailed performance of the proposed approach (referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SelfCompDVLO</i> ) can be found on the KITTI depth completion benchmark. The source code, models, and data have been made available at GitHub.
WEClustering: word embeddings based text clustering technique for large datasets

A massive amount of textual data now exists in digital repositories in the form of research articles, news articles, reviews, Wikipedia articles, and books, etc. Text clustering is a fundamental data mining technique to perform categorization, topic extraction, and information retrieval. Textual datasets, especially which contain a large number of documents are sparse and have high dimensionality. Hence, traditional clustering techniques such as K-means, Agglomerative clustering, and DBSCAN cannot perform well. In this paper, a clustering technique especially suitable to large text datasets is proposed that overcome these limitations. The proposed technique is based on word embeddings derived from a recent deep learning model named "Bidirectional Encoders Representations using Transformers". The proposed technique is named as WEClustering. The proposed technique deals with the problem of high dimensionality in an effective manner, hence, more accurate clusters are formed. The technique is validated on several datasets of varying sizes and its performance is compared with other widely used and state of the art clustering techniques. The experimental comparison shows that the proposed clustering technique gives a significant improvement over other techniques as measured by metrics such Purity and Adjusted Rand Index.
Diminishing returns of information quality: Untangling the determinants of best answer selection

Online question and answer (Q&A) platforms are ideal live repositories for knowledge sharing and exchange. On Q&A platforms, a seeker may receive one answer to a question, none, or multiple answers. In the case of many answers, it is important to understand which answer will be selected as the best to inform how seekers retrieve knowledge-based solutions to inquiries. This study develops a best answer selection model that integrates the characteristics of the information exchanged as perceived by the knowledge community and those of the individuals exchanging such information (i.e., answers contributors and answers seekers). Using data from StackOverflow.com, we built conditional logit models to examine the determinants of the best answer selection. Results show that contributors' reputation and the quality of the information (measured as the net vote score received by an answer) are important determinants of best answer selection. This research theorizes on and uncovers a non-linear relationship between the net vote score received by an answer (a proxy for information quality) and its likelihood of being selected as the best answer by the seeker. We also find that the seekers' reputation and the contributors’ reputation influence how information quality affects the selection of the best answer.
Implementation of measurement reduction for the variational quantum eigensolver

One limitation of the variational quantum eigensolver algorithm is the large number of measurement steps required to estimate different terms in the Hamiltonian of interest. Unitary partitioning reduces this overhead by transforming the problem Hamiltonian into one containing fewer terms. We explore two different circuit constructions of the transformation required - one built by a sequence of rotations and the other a linear combination of unitaries (LCU). To assess performance, we simulated chemical Hamiltonians and studied the ground states of H2 and LiH. Both implementations are successful even in the presence of noise. The sequence of rotations realization offers the greatest benefit to calculations, whereas the probabilistic nature of LCU reduces its effectiveness. To our knowledge, this work also demonstrates the first experimental implementation of LCU on quantum hardware.
An Ising Machine Solving Max-Cut Problems based on the Circuit Synthesis of the Phase Dynamics of a Modified Kuramoto Model

A promising approach for efficiently solving NP-hard optimization problems is based on mapping the problems onto Ising machines. Oscillator-based Ising machines can be implemented by utilizing sub-harmonic injection locking, which enables binary phase shifts between the oscillators and leads to an improved reliability of Ising machines. Based on a modified Kuramoto model with sub-harmonic injection locking, we synthesize an ideal electrical circuit displaying the phase dynamics of an Ising machine. The ideal circuit can be utilized to specifically take non-ideal effects into account, serving as a starting point for designing Ising machines with increased performance when considering existing electrical components. We furthermore derive a corresponding wave digital model, which is utilized for emulating the synthesized electrical circuit. The emulation results show that the synthesized circuit indeed models the phase dynamics of an Ising machine capable of solving Max-Cut problems with an accuracy of 88% for a 5-node problem and weights between 0 and 31.
Testing Deep Learning-based Visual Perception for Automated Driving

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.
DNN Model Compression for IoT Domain-Specific Hardware Accelerators

Machine learning techniques, particularly those based on neural networks, are always more often used at the edge of the network by Internet of Things (IoT) nodes. Unfortunately, the computation capabilities demanded by those applications, together with their energy efficiency-related constraints, exceed those exposed by embedded general-purpose processors. For this reason, the use of domain-specific hardware accelerators (DSAs) is considered the most viable solution to the unsustainable &#x201C;Turing tariff&#x201D; of general-purpose hardware. Starting from the observation that memory and communication traffic account for a large fraction of the overall latency and energy in deep neural network (DNN) inferences, this article proposes a new compression technique aimed at: 1) reducing the memory footprint for storing the model parameters of a DNN and 2) improving DNN inference latency and energy on resource-constrained IoT devices. The proposed compression technique, namely, LineCompress, is applied on a set of representative convolutional neural networks (CNNs) for object recognition mapped on a state-of-the-art DSA targeted for resource-constrained IoT devices. We show that on average, <inline-formula> <tex-math notation="LaTeX">$7.4\times $ </tex-math></inline-formula> memory footprint reduction can be obtained, thus reducing the memory and communication traffic that result to 77&#x0025; and 87&#x0025; inference latency and energy reduction, respectively, trading-off efficiency versus accuracy.
The Effects of Online Learning on EFL Students’ Academic Achievement during Coronavirus Disease Pandemic

&lt;p style="text-align: justify;"&gt;Coronavirus disease (COVID-19) Pandemic changed education conditions worldwide forcing all the parties involved to adapt to a new system. This study aimed to collect information related to the effects of teaching English online on English as a Foreign Language (EFL) students’ achievement. Data were collected from EFL teachers and students enrolled in three different Ecuadorian Universities (Technical University of Ambato, Higher Polytechnic School of Chimborazo, and University of Cuenca) from five different levels: A1, A2, B1, B1+, and B2. This preliminary paper reports the results of 480 students regarding four major sections: pedagogical practice and assessment, learning outcomes, affective factors and perceptions of students about the advantages and disadvantages of online learning during the pandemic COVID-19; considering the Hierarchy of online learning needs of Justin Shewell. An online survey questionnaire with 17 questions and a 5-point Likert scale was applied. The Cronbach's Alpha test presented 0.84 and 0.73 level of reliability. The Kolmogorov Smirnov’s statistic and, the Kendall's Tau_b tests, and the Levene's test for homogeneity of variances were performed with the SPSS statistical program. The results made evident that online learning affects academic achievement in EFL students during the COVID-19 pandemic, which was confirmed after analyzing four main areas: pedagogical practices and assessment, learning outcomes, affective factors and students’ perceptions about the advantages and disadvantages of online learning. The importance of online learning was highlighted since it has been understood as a tool to face the emergency produced by the COVID-19 pandemic.&lt;/p&gt;
Bridging the Gap Between Semantic Segmentation and Instance Segmentation

Fine-grained instance segmentation is considerably more complicated and challenging than semantic segmentation. Most existing instance segmentation methods only focus on accuracy without paying much attention to inference latency, which, is critical to real-time applications, such as autonomous driving. In this paper, we aim to bridge the gap between semantic segmentation and instance segmentation by presenting a novel real-time model for instance segmentation, Sem2Ins, which effectively generates instance boundaries according to a semantic segmentation by leveraging conditional generative adversarial networks (cGANs) coupled with deep supervision and a weighted fusion layer. Specifically, supervision is imposed on each output layer, and features from different levels are fused to produce a well-generated boundary map. Sem2Ins has the following desirable features: 1) Combined with some fast semantic segmentation methods, Sem2Ins runs at a real-time speed that is fairly well-balanced against accuracy; 2) Sem2Ins works flexibly with any semantic segmentation model for instance segmentation, and if the given semantic segmentation is sufficiently good, Sem2Ins even achieves state-of-the-art in terms of accuracy; 3) deep supervision and weighted fusion can be leveraged to generate high-quality boundaries; and 4) Sem2Ins can be easily extended to panoptic segmentation. Extensive experiments performed on the Cityscapes, WildDash, KITTI and COCO benchmarks have demonstrated that 1) Sem2Ins, when combined with PSPNet and DDRNet-23-Slim, consistently outperforms the state-of-the-art real-time solution (Box2Pix) in terms of both speed and accuracy; and 2) Sem2Ins combined with DPC performs comparably to some powerful detect-and-segment approaches.
Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients

Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time.
Few-Shot Website Fingerprinting Attack with Data Augmentation

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).
Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.
MoCapaci: Posture and gesture detection in loose garments using textile cables as capacitive antennas

We present a wearable system to detect body postures and gestures that does not require sensors to be firmly fixed to the body or integrated into a tight-fitting garment. The sensing system can be used in a loose piece of clothing such as a coat/blazer. It is based on the well-known theremin musical instrument, which we have unobtrusively integrated into a standard men's blazer using conductive textile antennas and OpenTheremin hardware as a prototype, the "MoCaBlazer." Fourteen participants with diverse body sizes and balanced gender distribution mimicked 20 arm/torso movements with the unbuttoned, single-sized blazer. State-of-the-art deep learning approaches were used to achieve average recognition accuracy results of 97.18% for leave one recording out and 86.25% for user independent recognition.
Multi-Modal Model Predictive Control Through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this letter, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.
An Improved Equilibrium Optimizer Algorithm and Its Application in LSTM Neural Network

An improved equilibrium optimizer (EO) algorithm is proposed in this paper to address premature and slow convergence. Firstly, a highly stochastic chaotic mechanism is adopted to initialize the population for range expansion. Secondly, the capability to conduct global search to jump out of local optima is enhanced by assigning adaptive weights and setting adaptive convergence factors. In addition 25 classical benchmark functions are used to validate the algorithm. As revealed by the analysis of the accuracy, speed, and stability of convergence, the IEO algorithm proposed in this paper significantly outperforms other meta-heuristic algorithms. In practice, the distribution is asymmetric because most logging data are unlabeled. Traditional classification models have difficulty in accurately predicting the location of oil layer. In this paper, the oil layers related to oil exploration are predicted using long short-term memory (LSTM) networks. Due to the large amount of data used, however, it is difficult to adjust the parameters. For this reason, an improved equilibrium optimizer algorithm (IEO) is applied to optimize the parameters of LSTM for improved performance, while the effective IEO-LSTM is applied for oil layer prediction. As indicated by the results, the proposed model outperforms the current popular optimization algorithms including particle swarm algorithm PSO and genetic algorithm GA in terms of accuracy, absolute error, root mean square error and mean absolute error.
Monitoring and management of high-end tourism in protected areas based on 3D sensor image collection

The expansion of tourism demand and the rapid growth of tourism industry objectively promote the tourism development and utilization of nature reserves. At present, there are various difficulties in the practical application of tourism environmental capacity assessment technology in nature reserves. This article attempts to carry out tourism monitoring and management in protected areas by combining 3D sensor image acquisition and environmental capacity assessment technology. First of all, this paper proposes a dynamic monitoring plan for the ecological environment of the reserve using 3D sensor image acquisition technology, and realizes the monitoring of tourists in the reserve and the collection of natural environment data. Secondly, this article proposes a new natural reserve tourism environment capacity assessment and pricing plan, and takes the Changtang National Park high-end tourism project as an example, and discusses its application in the form of a case study. This program provides a new solution for balancing the economic and environmental benefits of the reserve. Through simulation tests and questionnaire surveys, we found that the program can effectively improve the nature protection and tourism inspection work in the reserve.